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bd81e37dfcf6b3cdb4c0bd715854b39667cedc7d | d6ff1e6257582f785915e3a0fad3d4896ebd9acb | /R_old/OVERALL_TRANSPIRATION.R | dd4c315e6edbe8f2886bcf7adad85997b5a0dd40 | [] | no_license | RemkoDuursma/Kelly2015NewPhyt | 355084d7d719c30b87200b75887f5521c270b1b5 | 447f263f726e68298ee47746b4de438fbc8fdebf | refs/heads/master | 2021-01-15T13:02:00.392000 | 2015-09-08T04:56:15 | 2015-09-08T04:56:15 | 42,089,956 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,247 | r | OVERALL_TRANSPIRATION.R | setwd("C:/Documents and Settings/Jeffrey Kelly/Desktop/EUC DATA/EUC OVERALL BIOMASS")
PILBIOMASS<-read.csv("PILTRANSAA.csv",sep=",", header=TRUE)
names(PILBIOMASS)
str(PILBIOMASS)
windows(width=8, height=4) #, pointsize=18)
par(xaxs="i",yaxs="i")
par(las=2)
par(mar=c(4.5,4.5,1,1))
par(xaxs="i",yaxs="i")
par(mfrow=c(1,2), cex.lab=1)
#PIL
par(las=1)
with(PILBIOMASS,plot(E[ST=="PILAD"]~D[ST=="PILAD"],col="blue",pch=1,
ylim=range(0,1.1*max(E)),xlim=range(0,1.05*max(D)),
ylab="",xlab=expression(bold(Day))))
title(main="Eucalyptus pilularis", font.main=4,cex.main=1)
with(PILBIOMASS,arrows(D[ST=="PILAD"],
ESE[ST=="PILAD"], D[ST=="PILAD"], LSE[ST=="PILAD"]
, length = .035, angle = 90, code = 3,col="blue"))
#or
with(PILBIOMASS,points(E[ST=="PILAND"]~D[ST=="PILAND"],col="blue",pch=16))
with(PILBIOMASS,arrows(D[ST=="PILAND"],
ESE[ST=="PILAND"], D[ST=="PILAND"], LSE[ST=="PILAND"]
, length = .035, angle = 90, code = 3,col="blue"))
with(PILBIOMASS,points(E[ST=="PILED"]~D[ST=="PILED"],col="red",pch=1))
with(PILBIOMASS,arrows(D[ST=="PILED"],
ESE[ST=="PILED"], D[ST=="PILED"], LSE[ST=="PILED"]
, length = .035, angle = 90, code = 3,col="red"))
with(PILBIOMASS,points(E[ST=="PILEND"]~D[ST=="PILEND"],col="red",pch=16))
with(PILBIOMASS,arrows(D[ST=="PILEND"],
ESE[ST=="PILEND"], D[ST=="PILEND"], LSE[ST=="PILEND"]
, length = .035, angle = 90, code = 3,col="red"))
par(las=3)
mtext(side = 2, text =expression(bold(Transpiration~~(l~week^-1))), line = 2.5,font=2, cex=1.0)
legend("topleft", expression(aC[a]~-~W, aC[a]~-~D,eC[a]~-~W ,eC[a]~-~D),
cex=0.75,bty="n",
pch = c(16,1,16,1), col=c("blue","blue","red","red"), #xjust = .5, yjust = .5,
)
par(las=1)
setwd("C:/Documents and Settings/Jeffrey Kelly/Desktop/EUC DATA/EUC OVERALL BIOMASS")
POPBIOMASS<-read.csv("POPTRANSAA.csv",sep=",", header=TRUE)
names(POPBIOMASS)
str(POPBIOMASS)
#POP
#bottom,left,top,right
par(xaxs="i",yaxs="i")
par(las=2)
par(mar=c(4.5,1,1,4.5))
par(las=1)
with(POPBIOMASS,plot(E[ST=="POPAD"]~D[ST=="POPAD"],col="blue",pch=1,yaxt="n",
ylim=c(0, 1.1*max(E)),xlim=c(0,1.05*max(D)),
ylab="",xlab=expression(bold(Day))))
title(main="Eucalyptus populnea", font.main=4,cex.main=1)
with(POPBIOMASS,arrows(D[ST=="POPAD"],
ESE[ST=="POPAD"], D[ST=="POPAD"], LSE[ST=="POPAD"]
, length = .035, angle = 90, code = 3,col="blue"))
axis(4,labels=TRUE,tcl=-0.5,cex.axis=1)
#or
with(POPBIOMASS,points(E[ST=="POPAND"]~D[ST=="POPAND"],col="blue",pch=16))
with(POPBIOMASS,arrows(D[ST=="POPAND"],
ESE[ST=="POPAND"], D[ST=="POPAND"], LSE[ST=="POPAND"]
, length = .035, angle = 90, code = 3,col="blue"))
with(POPBIOMASS,points(E[ST=="POPED"]~D[ST=="POPED"],col="red",pch=1))
with(POPBIOMASS,arrows(D[ST=="POPED"],
ESE[ST=="POPED"], D[ST=="POPED"], LSE[ST=="POPED"]
, length = .035, angle = 90, code = 3,col="red"))
with(POPBIOMASS,points(E[ST=="POPEND"]~D[ST=="POPEND"],col="red",pch=16))
with(POPBIOMASS,arrows(D[ST=="POPEND"],
ESE[ST=="POPEND"], D[ST=="POPEND"], LSE[ST=="POPEND"]
, length = .035, angle = 90, code = 3,col="red"))
# looks great on screeen / printer
dev.copy2pdf(file="somname.pdf")
# looks great printed, or after printing MS to PDF
# this is the one you paste in word
dev.copy2eps(file="fig19.eps")
|
7e8c94c982763d3b9a74d47bf81ecba200e74f3e | a47ce30f5112b01d5ab3e790a1b51c910f3cf1c3 | /A_github/sources/authors/2774/plotly/coord.R | c489eb9d4c358419e3fd6f91a129c297999fc8aa | [] | no_license | Irbis3/crantasticScrapper | 6b6d7596344115343cfd934d3902b85fbfdd7295 | 7ec91721565ae7c9e2d0e098598ed86e29375567 | refs/heads/master | 2020-03-09T04:03:51.955000 | 2018-04-16T09:41:39 | 2018-04-16T09:41:39 | 128,578,890 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,359 | r | coord.R | #' *** This won't be possible until plotly.js implements aspect ratios... ***
#'
#' #' Force the aspect ratio according to x and y scales
#' #'
#' #' When x and y are numeric variables measured on the same scale,
#' #' or are related in some meaningful way, forcing the aspect ratio of the
#' #' plot to be proportional to the ratio of a unit change in x versus y improves
#' #' our ability to correctly perceive the data.
#' #'
#' #' @param p a plotly object
#' #' @param ratio aspect ratio, expressed as y / x
#' #' @export
#' #' @examples
#' #'
#' #' canada <- map_data("world", "canada")
#' #'
#' #' canada %>%
#' #' group_by(group) %>%
#' #' plot_ly(x = ~long, y = ~lat, alpha = 0.2) %>%
#' #' add_polygons(hoverinfo = "none", color = I("black")) %>%
#' #' coord_fix()
#' #'
#' #' # works on (non-faceted) ggplot2 plots, too
#' #' gg <- ggplot(canada, aes(long, lat, group = group)) +
#' #' geom_polygon() + coord_fixed()
#' #'
#' #' gg %>%
#' #' ggplotly() %>%
#' #' coord_fix()
#' #'
#'
#' coord_fix <- function(p, ratio = 1) {
#' p <- plotly_build(p)
#' # this won't work for subplots, or categorical data
#' x <- grepl("^xaxis", names(p$x$layout))
#' y <- grepl("^yaxis", names(p$x$layout))
#' if (sum(x) > 1 || sum(y) > 1) {
#' stop("Can not impose aspect ratio a plot with more than one x/y axis", call. = FALSE)
#' }
#' xDat <- unlist(lapply(p$x$data, "[[", "x"))
#' yDat <- unlist(lapply(p$x$data, "[[", "y"))
#' if (!is.numeric(xDat) || !is.numeric(yDat)) {
#' stop("Must have numeric data on both x and y axes to enforce aspect ratios", call. = FALSE)
#' }
#'
#' # warn about any pre-populated domains, they will get squashed
#' xDom <- p$x$layout[["xaxis"]]$domain %||% c(0, 1)
#' yDom <- p$x$layout[["yaxis"]]$domain %||% c(0, 1)
#' if (!identical(yDom, c(0, 1)) || !identical(xDom, c(0, 1))) {
#' warning(
#' "coord_fix() won't respect prespecified axis domains (other than the default)",
#' call. = FALSE
#' )
#' }
#'
#' xRng <- range(xDat, na.rm = TRUE)
#' yRng <- range(yDat, na.rm = TRUE)
#' asp <- ratio * diff(yRng) / diff(xRng)
#' if (asp < 1) {
#' p$x$layout[["yaxis"]]$domain <- c(0 + asp / 2, 1 - asp / 2)
#' } else {
#' asp <- 1 / asp
#' p$x$layout[["xaxis"]]$domain <- c(0 + asp / 2, 1 - asp / 2)
#' }
#' p
#' }
|
87612036fd5fa980712ac1e05cfc398425c50685 | 86c0b4c6c1746ebf0441c62421748190d057067d | /plot/mass.R | 20769a023b830ddef7c35ab7c099b5ac260e9f87 | [
"MIT"
] | permissive | yufree/democode | 372f0684c49505965b0ba5abe0675c2b6f7fb3da | 0a332ac34a95677ce859b49033bdd2be3dfbe3c4 | refs/heads/master | 2022-09-13T11:08:55.152000 | 2022-08-28T23:09:00 | 2022-08-28T23:09:00 | 20,328,810 | 5 | 14 | null | 2017-01-06T16:07:25 | 2014-05-30T12:41:28 | HTML | UTF-8 | R | false | false | 1,185 | r | mass.R | source("http://bioconductor.org/biocLite.R")
biocLite("mzR")
library(mzR)
all <- openMSfile('./FULL200.CDF')
df <- header(all)
bb <- peaks(all)
aaaa <- sapply(bb,as.data.frame)
oddvals <- seq(1, ncol(aaaa), by=2)
aaaaa <- unlist(aaaa[oddvals])
ccc <- unique(c(aaaaa))
ccc <- ccc[order(ccc)]
# bbb <- sapply(bb, "[",250:700)
# ddd <- unique(c(bbb))
# dddd <- ddd[ddd<700]
time <- df$retentionTime
df2 <- matrix(0, nrow = length(ccc), ncol = length(time))
rownames(df2) <- ccc
colnames(df2) <- time
rm(aaaa)
rm(aaaaa)
rm(oddvals)
rm(df)
rm(all)
gc()
for(i in 1:length(time)){
temp <- bb[[i]]
index <- which(ccc%in%temp[,1])
df2[index,i] <- temp[,2]
}
ddd <- as.integer(ccc)
library(data.table)
dt = data.table(df2)
dt$fac <- ddd
df3 <- dt[,lapply(.SD, sum), by=ddd ]
df3 <- as.matrix(df3)
df7 <- df3[,2000:3000]
heatmap(df7)
library(rARPACK)
df4 <- svds(df3,2)
df5 <- df4$u %*% diag(df4$d) %*% t(df4$v)
rownames(df5) <- ddd
colnames(df5) <- time
df6 <- df5[,2000:3000]
heatmap(df6)
df8 <- as.data.frame(df5)
df9 <- as.data.frame(t(df8))
rownames(df8) <- ddd
colnames(df9) <- time
write.table(df3,'df3.txt')
|
99a524e8baa9751bbd5db7787f3567c66a6e8bee | 4450235f92ae60899df1749dc2fed83101582318 | /ThesisRpackage/R/3Article_old/GSE42861_function.R | 4e60f4de4028e99df28eb3e6e687f0b5409e866e | [
"MIT"
] | permissive | cayek/Thesis | c2f5048e793d33cc40c8576257d2c9016bc84c96 | 14d7c3fd03aac0ee940e883e37114420aa614b41 | refs/heads/master | 2021-03-27T20:35:08.500000 | 2017-11-18T10:50:58 | 2017-11-18T10:50:58 | 84,567,700 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,617 | r | GSE42861_function.R | #' main experiment
#'
#' @export
GSE42861_experiment <- function(s, save = TRUE) {
# glm
glm <- Method(name = "glm",
hypothesis.testing.method = phenotypeWayReg_glm_score(family = binomial,
factorized.X1 = TRUE),
impute.genotype.method = imputeByMean(),
nickname = "glm")
# glm + 2 PCs
glm_2PC <- PCAClassicLinearMethod(K = 2,
center = TRUE,
hypothesis.testing.method = phenotypeWayReg_glm_score(family = binomial,
factorized.X1 = TRUE),
nickname = "glm+2PCs",
assumingStructure = FALSE)
# glm + 6 pcs
glm_6PC <- PCAClassicLinearMethod(K = 6,
center = TRUE,
hypothesis.testing.method = phenotypeWayReg_glm_score(family = binomial,
factorized.X1 = TRUE),
nickname = "glm+6PCs",
assumingStructure = FALSE)
# glm + 6 refactor
glm_6refractor <- refractorMethod(K = 6,
verbose = FALSE,
t = 500,
nickname = "glm+6refractor")
# glm + 6 lfmm ridge
glm_6lfmm.ridge <- RidgeLFMMMethod(K = 6,
hypothesis.testing.method = phenotypeWayReg_glm_score(),
lambda = 1e6,
nickname = "glm+6lfmm")
# lfmm ridge
lfmm.ridge <- RidgeLFMMMethod(K = 6,
hypothesis.testing.method = lm_zscore(gif = FALSE),
lambda = 1e6,
nickname = "lfmm ridge")
# run exp
exp <- ComparisonExperiment(s,
glm,
glm_2PC,
glm_6PC,
glm_6refractor,
glm_6lfmm.ridge,
lfmm.ridge)
exp <- runExperiment(exp)
# save exp
if (save) {
dumpExperiment(exp)
}
exp
}
#' @export
GSE42861_plot <- function(exp) {
# Rmk: i am only interested in pvalue1 and score1 other pavalue was not computed
# with glm of lm
# qqplot
ggplot(exp$df.res %>% dplyr::filter(variable.name == "pvalue1")) +
stat_qq(aes(sample = -log10(estimate)),
distribution = stats::qexp, dparams = list(rate = log(10))) +
geom_abline(slope = 1, intercept = 0) +
facet_grid(method.name~.) +
ggtitle("-log10(pvalue) qqplot")
}
#' @export
GSE42861_get_RahmaniLoci <- function() {
table <- tabulizer::extract_tables("~/Projects/Biblio/org-ref-pdfs/SF_Rahmani_2016.pdf",
pages = 19, method = "data.frame")[[1]]
table
}
################################################################################
# Long running
#' Run of PCA
#'
#'
#' @export
long_GSE42861_PCA <- function() {
library(Article3Package)
G.file <- "~/Projects/Data2016_2017/GSE42861/betanormalized_metylationlvl.filtered.rds"
X.file <- "~/Projects/Data2016_2017/GSE42861/X.rds"
s <- TrueSampler(G.file = G.file,
X.file = X.file,
outlier.file = NULL,
n = NULL,
L = NULL)
exp <- HGDP_PCA(s, save = TRUE)
}
#' Run of LFMM
#'
#'
#' @export
long_GSE42861_LFMM <- function() {
cl <- parallel::makeCluster(2)
doParallel::registerDoParallel(cl)
library(Article3Package)
G.file <- "~/Projects/Data2016_2017/GSE42861/betanormalized_metylationlvl.filtered.rds"
X.file <- "~/Projects/Data2016_2017/GSE42861/X.rds"
s <- TrueSampler(G.file = G.file,
X.file = X.file,
outlier.file = NULL,
n = NULL,
L = NULL)
lambdas <- c(1e-10, 1e0, 1e2, 1e10)
Ks <- c(1,6,8,20)
HGDB_runs(s, Ks = Ks, lambdas = lambdas, save = TRUE)
}
#' Run of GSE42861_experiment
#'
#'
#' @export
long_GSE42861_exp <- function() {
library(Article3Package)
G.file <- "~/Projects/Data2016_2017/GSE42861/betanormalized_metylationlvl.rds"
X.file <- "~/Projects/Data2016_2017/GSE42861/X.rds"
s <- TrueSampler(G.file = G.file,
X.file = X.file,
outlier.file = NULL,
n = NULL,
L = NULL)
cl <- parallel::makeCluster(6)
doParallel::registerDoParallel(cl)
exp <- GSE42861_experiment(s, save = TRUE)
exp
}
#' cross validation
#'
#'
#' @export
long_GSE42861_CrossVal <- function(cluster.nb = NULL,
K = 6,
G.file = "~/Projects/Data2016_2017/GSE42861/betanormalized_metylationlvl.filtered.rds",
X.file = "~/Projects/Data2016_2017/GSE42861/X.rds",
lambdas = c(1e-10, 1e0, 1e2, 1e10),
rep = 5,
missing.prop = 0.5,
save = TRUE,
bypass = FALSE) {
KrakTest(bypass)
if (!is.null(cluster.nb)) {
cl <- parallel::makeCluster(cluster.nb)
doParallel::registerDoParallel(cl)
}
s <- TrueSampler(G.file = G.file,
X.file = X.file,
outlier.file = NULL,
n = NULL,
L = NULL)
dat <- sampl(s)
m <- finalLfmmRdigeMethod(K = K,
lambda = NULL)
description <- paste0("long_GSE42861_CrossVal with K=", K,
"and lambdas = ",paste(lambdas,collapse = '|'))
exp <- Experiment(name = "long_GSE42861_CrossVal", description = description)
exp$crossvalidation.res <- crossvalidation_kfold_missingvalue(m = m,
dat = dat,
rep = rep,
missing.prop = missing.prop,
lambdas = lambdas)
# save exp
if (save) {
dumpExperiment(exp)
}
exp
}
#' cross validation
#'
#'
#' @export
long_GSE42861_lfmm_glm <- function(K.lfmm = 6,
K.refactor = 6,
G.file = "~/Projects/Data2016_2017/GSE42861/betanormalized_metylationlvl.filtered.rds",
X.file = "~/Projects/Data2016_2017/GSE42861/X.rds",
lambda = 1e-10,
save = TRUE,
bypass = FALSE,
refactor = FALSE) {
KrakTest(bypass)
s <- TrueSampler(G.file = G.file,
X.file = X.file,
outlier.file = NULL,
n = NULL,
L = NULL)
dat <- sampl(s)
G <- dat$G
X <- dat$X
## other co.var correction
dat$G <- G
dat$X <- X[,-1]
m.lm <- finalLm()
m.lm <- fit(m.lm, dat)
m.lfmm <- finalLfmmRdigeMethod(K = K.lfmm,
lambda = lambda)
m.refactor <- finalRefactorMethod(K = K.refactor)
description <- paste0("long_GSE42861_lfmm_glm with K=", K.lfmm,
"and lambdas = ", lambda)
exp <- Experiment(name = "long_GSE42861_lfmm_glm", description = description)
# run of the method
dat$G <- m.lm$epsilon
dat$X <- X[,1, drop = FALSE]
exp$m.lfmm <- fit(m.lfmm, dat)
exp$m.refactor <- fit(m.refactor, dat)
# hypothesis testing
glm.aux <- function(m, name) {
glm.func <- phenotypeWayReg_glm_score(family = binomial,
factorized.X1 = TRUE)
glm.res <- glm.func$fun(m, dat)
df <- tibble(index = 1:length(glm.res$score), method.name = name,
estimate = glm.res$score[1,], variable.name = "score")
df <- tibble(index = 1:length(glm.res$pvalue), method.name = name,
estimate = glm.res$pvalue[1,], variable.name = "pvalue") %>%
rbind(df)
df
}
exp$df.res <- rbind(glm.aux(exp$m.refactor, "refactor"),
glm.aux(exp$m.lfmm, "lfmm"))
# save exp
if (save) {
dumpExperiment(exp)
}
exp
}
|
ac457a941d93eb56777aeb1bda10707ce8907e13 | c54d1c0a3d81bddb25f3f55078f305ad6c15997b | /R/get_internal_tree.R | ffd29651c660f728d71fcc716d3ca033637fb637 | [] | no_license | cran/genpathmox | dc065d3b5ea1c8632068fe3d9bfa7b063045bb2c | 517be94b39d8742cd3d39aedc152e026d865afd6 | refs/heads/master | 2023-01-12T03:39:55.183000 | 2022-12-22T10:00:12 | 2022-12-22T10:00:12 | 25,984,875 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,114 | r | get_internal_tree.R | #' ############################################################################################
#' @title Calculating size (numeber of individual of a node)
#' @details
#' Internal function
#' @param x matrix or dataframe with data.
#' @param size value indicating the minimun threshold of number of observations for a node
#' @return the number of observations in a node
#' @keywords internal
#' @export
#'
percent.node <- function(x,size)
{
indiv = nrow(x)
min.n.ind = trunc(indiv*size)
list(min.n.ind=min.n.ind)
}
#' ############################################################################################
#' @title Calculating Deepth stop criterion
#' @details
#' Internal function
#' @param node id that identifies a specicif node
#' @return deepth of the tree
#' @keywords internal
#' @export
#'
showDeepth=function(node)
{
return (trunc(log2(node@id)))
}
#' ############################################################################################
#' @title Observations belonging to the root node
#' @details
#' Internal function
#' @param moxtree class containing the moxtree elements
#' @return the observations belonging to the root node
#' @keywords internal
#' @export
#'
root.tree <- function(moxtree)
{
root = NULL
for (n in moxtree@nodes)
{
if (n@id == 1)
{
root=n@elements
}
}
root
}
#' ############################################################################################
#' @title Observations belonging to the terminal nodes
#' @details
#' Internal function
#' @param moxtree class containing the moxtree element.
#' @return the observations belonging to the terminal nodes
#' @keywords internal
#' @export
#'
terminal.tree <- function(moxtree)
{
terminal = list()
id = list()
if (length(moxtree@nodes) > 1)
{
for (n in moxtree@nodes)
{
if (n@id == 1)
{
terminal[[length(terminal)+1]] = n@elements
id[[length(id)+1]] = "Root"
}
if (length(n@childs) == 0)
{
terminal[[length(terminal)+1]] = n@elements
id[[length(id)+1]] = n@id
}
}
for (i in 1:length(terminal)){names(terminal) = paste("node",id)}
terminal
}
else
{
terminal = NULL
}
terminal
}
#' ############################################################################################
#' @title Observations belonging to the nodes
#' @details
#' Internal function
#' @param moxtree class containing the moxtree elements
#' @return the observations belonging to the nodes
#' @keywords internal
#' @export
#'
nodes.tree <- function(moxtree)
{
nodes = list()
id = list()
if (length(moxtree@nodes) > 1)
{
for (n in moxtree@nodes)
{
nodes[[length(nodes)+1]] = n@elements
id[[length(id)+1]] = n@id
}
for (i in 1:length(nodes)) {names(nodes) = paste("node",id)}
nodes
}
else
{
nodes = NULL
}
nodes
}
#' ############################################################################################
#' @title Posibble partions for each node of the tree
#' @details
#' Internal function
#' @param moxtree class containing the moxtree elements
#' @return the Posibble partions for each node of the tree
#' @keywords internal
#' @export
#'
candidates.tree <- function(moxtree)
{
candidates = list()
id = list()
if (length(moxtree@nodes) > 1)
{
for (n in moxtree@nodes)
{
if (length(n@childs)>0)
{
candidates[[length(candidates)+1]] = n@info@candidates
id[[length(id)+1]] = n@id
}
}
for (i in 1:length(candidates)) {names(candidates) = paste("node",id)}
candidates
}
else
{
candidates = NULL
}
candidates
}
#' ############################################################################################
#' @title F-global test results for each tree partition
#' @details
#' Internal function
#' @param moxtree class containing the moxtree elements
#' @return the F-global test results for each tree partition
#' @keywords internal
#' @export
#'
fglobal.tree <- function(moxtree)
{
fglobal = list()
fgtable = NULL
if (length(moxtree@nodes) > 1)
{
for (n in moxtree@nodes)
{
if (length(n@childs) > 0)
{
fglobal[[length(fglobal)+1]] = data.frame(n@id,n@info@fgstatistic,n@info@fpvalg,n@info@variable,t(n@info@level))
}
}
for (i in 1:length(fglobal)) {fgtable = rbind(fgtable,fglobal[[i]])}
colnames(fgtable) = c("node","F value","Pr(>F)","variable","g1.mod","g2.mod")
Fg.r = fgtable
}
else
{
Fg.r = NULL
}
Fg.r
}
#' ############################################################################################
#' @title F-coefficients test results for each tree partition
#' @details
#' Internal function
#' @param moxtree class containing the moxtree elements
#' @return the F-coefficients test results for each tree partition
#' @keywords internal
#' @export
fcoef.tree <- function(moxtree)
{
fc = list()
id = list()
fctable = NULL
if (length(moxtree@nodes) > 1)
{
for (n in moxtree@nodes)
{
if (length(n@childs) > 0)
{
id[[length(id)+1]] = n@id
fctable = data.frame(as.matrix(n@info@fcstatistic),as.matrix(n@info@fpvalc))
colnames(fctable) = c("F value","Pr(>F)")
fc[[length(fc)+1]] = fctable
}
}
names(fc) = paste("node",id,sep="")
Fc.r = fc
}
else
{
Fc.r=list(fc=NULL,Signif=NULL)
}
Fc.r
}
#' ############################################################################################
#' @title General information about the tree
#' @details
#' Internal function
#' @param moxtree class containing the tree elements
#' @return a dataframe containing information about the tree and its nodes
#' @keywords internal
#' @export
#'
mox.tree <- function(moxtree)
{
info.node = list()
type = NULL
terminal = NULL
perc = NULL
var = NULL
mox = NULL
if (length(moxtree@nodes)>1)
{
for (n in moxtree@nodes)
{
if (n@id == 1)
{
length.root = length(n@elements)
}
if (length(n@childs) > 0)
{
info.node[[length(info.node)+1]] = data.frame(n@info@variable,n@id,n@childs,n@info@level)
}
if (length(n@childs) == 0)
{
type = "least"
terminal = "yes"
}
if (n@father == 0)
{
type = "root"
terminal = "no"
}
if (n@father!=0 && length(n@childs) != 0)
{
type = "node"
terminal = "no"
}
perc = round((length(n@elements)/length.root)*100,2)
data = data.frame(n@id,n@father,showDeepth(n),type,terminal,length(n@elements),perc)
mox = rbind(mox,data)
}
data.info.node = NULL
for (i in 1:length(info.node)) {data.info.node = rbind(data.info.node,info.node[[i]])}
names(data.info.node)[2] = "n.father"
names(data.info.node)[3] = "n.id"
MOX =merge (mox, data.info.node,by="n.id",all.x=TRUE)[,-9]
names(MOX) = c("node","parent","depth","type","terminal","size","%","variable","category")
MOX
}
else
{
MOX = NULL
}
MOX
}
#' ############################################################################################
#' @title General information about the pathmox algorithm
#' @details
#' Internal function
#' @param signif stop condition 1: significance of the p-value
#' @param size stop condition 2: minimum number of individuals in a node
#' @param deep stop condition 3: maximum tree depth level
#' @param y: set of segmentation variables
#' @keywords internal
#' @export
info.mox <- function(signif,size,deep,y)
{
cat("\n")
cat("PLS-SEM PATHMOX ANALYSIS","\n")
cat("\n")
cat("---------------------------------------------")
cat("\n")
cat("Info parameters algorithm","\n")
info.value = rbind(signif,size,deep)
dimnames(info.value) = NULL
info.name = c("threshold signif.","node size limit(%)","tree depth level")
info.tree = data.frame(info.name,info.value)
names(info.tree) = c("parameters algorithm", "value")
print(info.tree)
cat("\n")
cat("---------------------------------------------")
cat("\n")
cat("Info segmentation variables","\n")
type.y = rep(0, ncol(y))
treat.y = rep("binary", ncol(y))
for (i in 1:length(type.y))
{
type.y[i] = ifelse(is.ordered(y[, i]), "ord","nom")
if (nlevels(y[, i]) > 2)
if (is.ordered(y[, i]))
treat.y[i] = "ordinal"
else treat.y[i] = "nominal"
}
df.y = data.frame(nlevels = unlist(lapply(y, nlevels)),ordered = unlist(lapply(y, is.ordered)),
treatment = treat.y)
if (y[1,1] == 1){
df.y = df.y[-1,]
}
else
{
df.y
}
print(df.y)
}
#' ############################################################################################
#' @title printing the tree structure
#' @details
#' Internal function.
#' @param moxtree moxtree object
#' @return the tree structure
#' @keywords internal
#' @export
#'
printTree <- function(moxtree)
{
for (n in moxtree@nodes){
print (n)
}
}
|
84afd0009d68337cd59225335f8ca45ec7753b3d | c2061964216f76ad0f440c76dbfe1119e0279a22 | /R/API-methods.R | 65f3d6cff7f46778421a4f00c57d3ebfa0b38824 | [] | no_license | cran/antaresRead | 046829e05e411adfb55fc652ad49ea84f2610264 | f6a182b21854e12c5c470afcd38c26f44fb2b8d5 | refs/heads/master | 2023-04-16T10:45:23.521000 | 2023-04-06T16:20:02 | 2023-04-06T16:20:02 | 87,090,660 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,663 | r | API-methods.R |
#' API methods
#'
#' @param endpoint API endpoint to interrogate, it will be added after `default_endpoint`.
#' Can be a full URL (by wrapping ìn [I()]), in that case `default_endpoint` is ignored.
#' @param ... Additional arguments passed to API method.
#' @param default_endpoint Default endpoint to use.
#' @param opts Antares simulation options or a `list` with an `host = ` slot.
#'
#' @return Response from the API.
#' @export
#'
#' @name API-methods
#'
#' @importFrom httr GET accept_json stop_for_status content add_headers timeout
#'
#' @examples
#' \dontrun{
#'
#' # List studies with local API
#' api_get(
#' opts = list(host = "http://0.0.0.0:8080"),
#' endpoint = NULL
#' )
#'
#' }
api_get <- function(opts, endpoint, ..., default_endpoint = "v1/studies") {
if (inherits(endpoint, "AsIs")) {
opts$host <- endpoint
endpoint <- NULL
default_endpoint <- NULL
}
if (is.null(opts$host))
stop("No host provided in `opts`: use a valid simulation options object or explicitly provide a host with opts = list(host = ...)")
config <- c(
opts$httr_config,
list(
accept_json()
)
)
if (!is.null(opts$token) && opts$token != "") {
config <- c(
config,
add_headers(Authorization = paste("Bearer ", opts$token))
)
}
if (is.null(opts$timeout))
opts$timeout <- 60
result <- GET(
url = URLencode(paste(c(opts$host, default_endpoint, endpoint), collapse = "/")),
config = config,
timeout(opts$timeout),
...
)
#fix for skipping 404 when some output is missing
url_elements <- strsplit(result$url, "%2F")[[1]]
condition_status_check <- !(!is.na(url_elements[4]) & url_elements[4] %in% c("economy","adequacy") & result$status_code == 404)
if (condition_status_check) stop_for_status(result) else warn_for_status(result)
content(result)
}
#' @export
#'
#' @rdname API-methods
#'
#' @importFrom httr POST accept_json content_type_json stop_for_status content add_headers
api_post <- function(opts, endpoint, ..., default_endpoint = "v1/studies") {
if (inherits(endpoint, "AsIs")) {
opts$host <- endpoint
endpoint <- NULL
default_endpoint <- NULL
}
if (is.null(opts$host))
stop("No host provided in `opts`: use a valid simulation options object or explicitly provide a host with opts = list(host = ...)")
config <- c(
opts$httr_config,
list(
accept_json(),
content_type_json()
)
)
if (!is.null(opts$token) && opts$token != "") {
config <- c(
config,
add_headers(Authorization = paste("Bearer ", opts$token))
)
}
result <- POST(
url = URLencode(paste(c(opts$host, default_endpoint, endpoint), collapse = "/")),
config = config,
...
)
stop_for_status(result)
content(result)
}
#' @export
#'
#' @rdname API-methods
#'
#' @importFrom httr PUT accept_json stop_for_status content add_headers
api_put <- function(opts, endpoint, ..., default_endpoint = "v1/studies") {
if (inherits(endpoint, "AsIs")) {
opts$host <- endpoint
endpoint <- NULL
default_endpoint <- NULL
}
if (is.null(opts$host))
stop("No host provided in `opts`: use a valid simulation options object or explicitly provide a host with opts = list(host = ...)")
if (!is.null(opts$token) && opts$token != "") {
config <- add_headers(Authorization = paste("Bearer ", opts$token), Accept = "application/json")
} else {
config <- add_headers(Accept = "application/json")
}
result <- PUT(
url = URLencode(paste(c(opts$host, default_endpoint, endpoint), collapse = "/")),
config,
...
)
stop_for_status(result)
content(result)
}
#' @export
#'
#' @rdname API-methods
#'
#' @importFrom httr DELETE accept_json stop_for_status content
api_delete <- function(opts, endpoint, ..., default_endpoint = "v1/studies") {
if (inherits(endpoint, "AsIs")) {
opts$host <- endpoint
endpoint <- NULL
default_endpoint <- NULL
}
if (is.null(opts$host))
stop("No host provided in `opts`: use a valid simulation options object or explicitly provide a host with opts = list(host = ...)")
config <- c(
opts$httr_config,
list(
accept_json()
)
)
if (!is.null(opts$token) && opts$token != "") {
config <- c(
config,
add_headers(Authorization = paste("Bearer ", opts$token))
)
}
result <- DELETE(
url = URLencode(paste(c(opts$host, default_endpoint, endpoint), collapse = "/")),
config = config,
...
)
stop_for_status(result)
content(result)
}
|
d29addc45ad1540ad95c8544e8002562baf29435 | d8affab3b21ca33c2b6397e28171c4ad69b03d98 | /regression.R | 471e4414ef889e20c3e50e5acbebf24faa2d7f99 | [] | no_license | nupurkok/analytics | 3e69e9eb88d9eb6cc4f33ae105b7993c46a69fce | b0b76dd306e443aae010cac55ffcda484c39ad42 | refs/heads/master | 2020-03-28T15:22:27.782000 | 2018-09-16T13:03:53 | 2018-09-16T13:03:53 | 148,586,169 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 546 | r | regression.R | women
str(women)
cor(women$height, women$weight)
cov(women$height, women$weight)
plot(women)
#create linear model
fit1 = lm (formula=weight ~ height,data = women)
summary(fit1)
fitted(fit1)
cbind(women, fitted(fit1), residuals(fit1))
ndata1 = data.frame(height = c(62.5, 63.5))
predict(fit1, newdata = ndata1)
#multiple linear regression
#predict mpg vs wt, hp
mtcars
fit2 = lm(mpg ~ wt + hp, data = mtcars)
summary(fit2)
range(mtcars$wt) ; range(mtcars$hp)
ndata2=data.frame(wt=c(2.5,3.4), hp=c(100,250))
predict(fit2, newdata = ndata2)
|
46c4e6309d7e779524b8b1a79263f38885577650 | ebb09f52b1ee12d8ae8d4c493e6f1079ee57868c | /ExploratoryDataAnalysis/Project2/plot1.R | 344f1ab64d1fa16fc56bc45754d6205e3ffc4c86 | [] | no_license | r6brian/datasciencecoursera | a1723f812a34eee7094dfaa0bfde6c618b349d6c | 548944d3ba68d302160f05158fb90859bc4c8bae | refs/heads/master | 2021-01-19T10:29:54.605000 | 2015-08-23T20:00:04 | 2015-08-23T20:00:04 | 26,268,379 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 634 | r | plot1.R | # 1. Have total emissions from PM2.5 decreased in the United States from 1999 to 2008?
# Read data files
NEI <- readRDS("data/exdata-data-NEI_data/summarySCC_PM25.rds")
SCC <- readRDS("data/exdata-data-NEI_data/Source_Classification_Code.rds")
# aggregrate based upon Emissions and Years
totalEmissions <- aggregate(Emissions ~ year, NEI, sum)
# plot a bar graph
png('plot1.png')
barplot(height=totalEmissions$Emissions/10^6,
names.arg=totalEmissions$year,
xlab="years",
ylab=expression('total PM'[2]*' emission(10^6 Tons)'),
main=expression('Total PM'[2]*' emissions at various years'))
dev.off() |
d016bf7c1cea2be45570d0826610230b375be3ce | 9bc17a169325375bc993b540d2ad0f0810ca0e76 | /R/twoway.plots.R | a98edb8797477c8f6316b7dfb57853a3015db298 | [] | no_license | alanarnholt/PASWR | 335b960db32232a19d08560938d26f168e43b0d6 | f11b56cff44d32c3683e29e15988b6a37ba8bfd4 | refs/heads/master | 2022-06-16T11:34:24.098000 | 2022-05-14T22:56:11 | 2022-05-14T22:56:11 | 52,523,116 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,375 | r | twoway.plots.R | #' @title Exploratory Graphs for Two Factor Designs
#'
#' @description Function creates side-by-side boxplots for each factor, a design plot (means), and an interaction plot.
#'
#' @param Y response variable
#' @param fac1 factor one
#' @param fac2 factor two
#' @param COL a vector with two colors
#'
#' @author Alan T. Arnholt <arnholtat@@appstate.edu>
#'
#' @seealso \code{\link{oneway.plots}}, \code{\link{checking.plots}}
#'
#' @export
#'
#' @examples
#' with(data = TireWear, twoway.plots(Wear, Treat, Block))
#'
#' @keywords hplot
####################################################################
twoway.plots<-function(Y, fac1, fac2, COL=c("#A9E2FF", "#0080FF")){
opar <- par(no.readonly = TRUE)
par(mfrow=c(2, 2), mar = c(5.1, 4.1, 1.1, 1.1))
YL <- range(Y)
plot(Y ~ fac1, col = COL[1], xlab = deparse(substitute(fac1)),
ylab = deparse(substitute(Y)), ylim = YL)
plot(Y ~ fac2, col = COL[2], xlab = deparse(substitute(fac2)),
ylab = deparse(substitute(Y)), ylim = YL)
plot.design(Y ~ fac1 + fac2, fun = "mean",
ylab = deparse(substitute(Y)), ylim = YL)
interaction.plot(fac1, fac2, Y, xlab = deparse(substitute(fac1)),
trace.label = deparse(substitute(fac2)),
type = "b", legend = FALSE,
ylab = deparse(substitute(Y)), ylim = YL)
on.exit(par(opar))
}
|
b4e93e3bcccb0eb0d1014bd355bcfff5a5be6187 | 280019f481fe09da00296f45e5fa530051780756 | /ui.R | 10b50c14611abb664f6dbfc7ea4c164e2ac58b15 | [] | no_license | linareja/2017_Buenos_Aires_Elections | 1effb2b1d39bf660e9fa678a6a78ac3000f2122c | d500aaedb233fe541fe00dc63f0d488043467111 | refs/heads/master | 2021-09-12T16:33:33.715000 | 2018-04-18T18:19:56 | 2018-04-18T18:19:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,802 | r | ui.R |
library(shiny)
dashboardPage(
dashboardHeader(title = "2017 Elections in Buenos Aires Province"),
dashboardSidebar( sidebarMenu(
menuItem("Overview", tabName = "overview", icon = icon("globe")),
menuItem("Analysis", tabName = "analysis", icon = icon("bar-chart"))
)),
dashboardBody(
tabItems(
tabItem("overview",
fluidRow(
column(6,selectInput("charge", "Select charge to visualize", levels(raw_data$variable))
)),
fluidRow(
column(6, h3("Overview"))
),
fluidRow(
column(6,drawMapUI("map1")
),
column(6,drawTreeMapUI("treemap1")
)
),
fluidRow(
column(6, h3("Comparison"))
),
fluidRow(
column(6, selectInput("comparison_plot", "Select Comparison Plot", choices = c("Map", "Treemap")))
),
#Aca va a ir un selector para comparar por treemap o por mapa
conditionalPanel("input.comparison_plot == 'Map'",
fluidRow(
column(6,drawMapUI("map_compare1",is.multiple = F)),
column(6,drawMapUI("map_compare2",is.multiple = F))
)
),
conditionalPanel("input.comparison_plot == 'Treemap'",
fluidRow(
column(6,drawTreeMapUI("treemap_compare1",is.multiple = F)),
column(6,drawTreeMapUI("treemap_compare2",is.multiple = F))
)
)
),
tabItem("analysis",
drawHeatmapUI("heatmap"))
)
)
)
|
564a95d83be7184c25e4953fc74f13401f3970ba | b6ed5857732c3261abab33a6665e7193d6862aef | /tests/testthat/test-read-oneshot-eav.R | d2b16cc13808d4cf760c57f846c793651568b48e | [
"MIT"
] | permissive | cran/REDCapR | 5ac1ebdb03fbf7dfa1aab23a2c23f711adcd4847 | a1aa09eb27fb627207255018fa41e30fa5d4b0fc | refs/heads/master | 2022-08-27T14:49:33.798000 | 2022-08-10T15:10:18 | 2022-08-10T15:10:18 | 24,255,971 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,860 | r | test-read-oneshot-eav.R | library(testthat)
credential <- retrieve_credential_testing()
update_expectation <- FALSE
test_that("smoke test", {
testthat::skip_on_cran()
expect_message(
returned_object <- REDCapR:::redcap_read_oneshot_eav(redcap_uri=credential$redcap_uri, token=credential$token)
)
})
test_that("default", {
testthat::skip_on_cran()
path_expected <- "test-data/specific-redcapr/read-oneshot-eav/default.R"
expected_outcome_message <- "\\d+ records and \\d+ columns were read from REDCap in \\d+(\\.\\d+\\W|\\W)seconds\\."
expect_message(
regexp = expected_outcome_message,
returned_object <- REDCapR:::redcap_read_oneshot_eav(
redcap_uri = credential$redcap_uri,
token = credential$token
)
)
if (update_expectation) save_expected(returned_object$data, path_expected)
expected_data_frame <- retrieve_expected(path_expected)
expect_equal(returned_object$data, expected=expected_data_frame) # dput(returned_object$data)
expect_equal(returned_object$status_code, expected=200L)
expect_equal(returned_object$raw_text, expected="", ignore_attr = TRUE) # dput(returned_object$raw_text)
expect_true(returned_object$records_collapsed=="", "A subset of records was not requested.")
expect_true(returned_object$fields_collapsed=="", "A subset of fields was not requested.")
expect_true(returned_object$filter_logic=="", "A filter was not specified.")
expect_match(returned_object$outcome_message, regexp=expected_outcome_message, perl=TRUE)
expect_true(returned_object$success)
})
test_that("specify-forms", {
testthat::skip_on_cran()
path_expected <- "test-data/specific-redcapr/read-oneshot-eav/specify-forms.R"
desired_forms <- c("demographics", "race_and_ethnicity")
expected_outcome_message <- "\\d+ records and \\d+ columns were read from REDCap in \\d+(\\.\\d+\\W|\\W)seconds\\."
expect_message(
regexp = expected_outcome_message,
returned_object <- REDCapR:::redcap_read_oneshot_eav(redcap_uri=credential$redcap_uri, token=credential$token, forms=desired_forms)
)
if (update_expectation) save_expected(returned_object$data, path_expected)
expected_data_frame <- retrieve_expected(path_expected)
expect_equal(returned_object$data, expected=expected_data_frame) # dput(returned_object$data)
expect_equal(returned_object$status_code, expected=200L)
expect_equal(returned_object$raw_text, expected="", ignore_attr = TRUE) # dput(returned_object$raw_text)
expect_true(returned_object$records_collapsed=="", "A subset of records was not requested.")
expect_true(returned_object$fields_collapsed=="", "A subset of fields was not requested.")
expect_true(returned_object$filter_logic=="", "A filter was not specified.")
expect_match(returned_object$outcome_message, regexp=expected_outcome_message, perl=TRUE)
expect_true(returned_object$success)
})
test_that("raw", {
testthat::skip_on_cran()
path_expected <- "test-data/specific-redcapr/read-oneshot-eav/raw.R"
expected_outcome_message <- "\\d+ records and \\d+ columns were read from REDCap in \\d+(\\.\\d+\\W|\\W)seconds\\."
expect_message(
regexp = expected_outcome_message,
returned_object <- REDCapR:::redcap_read_oneshot_eav(redcap_uri=credential$redcap_uri, token=credential$token, raw_or_label="raw")
)
if (update_expectation) save_expected(returned_object$data, path_expected)
expected_data_frame <- retrieve_expected(path_expected)
expect_equal(returned_object$data, expected=expected_data_frame, label="The returned data.frame should be correct") # dput(returned_object$data)
expect_equal(returned_object$status_code, expected=200L)
expect_equal(returned_object$raw_text, expected="", ignore_attr = TRUE) # dput(returned_object$raw_text)
expect_true(returned_object$records_collapsed=="", "A subset of records was not requested.")
expect_true(returned_object$fields_collapsed=="", "A subset of fields was not requested.")
expect_true(returned_object$filter_logic=="", "A filter was not specified.")
expect_match(returned_object$outcome_message, regexp=expected_outcome_message, perl=TRUE)
expect_true(returned_object$success)
})
test_that("raw-and-dag", {
testthat::skip_on_cran()
path_expected <- "test-data/specific-redcapr/read-oneshot-eav/raw-and-dag.R"
expected_outcome_message <- "\\d+ records and \\d+ columns were read from REDCap in \\d+(\\.\\d+\\W|\\W)seconds\\."
expect_message(
regexp = expected_outcome_message,
returned_object <- REDCapR:::redcap_read_oneshot_eav(redcap_uri=credential$redcap_uri, token=credential$token, raw_or_label="raw", export_data_access_groups=TRUE)
)
if (update_expectation) save_expected(returned_object$data, path_expected)
expected_data_frame <- retrieve_expected(path_expected)
expect_equal(returned_object$data, expected=expected_data_frame, label="The returned data.frame should be correct") # dput(returned_object$data)
expect_equal(returned_object$status_code, expected=200L)
expect_equal(returned_object$raw_text, expected="", ignore_attr = TRUE) # dput(returned_object$raw_text)
expect_true(returned_object$records_collapsed=="", "A subset of records was not requested.")
expect_true(returned_object$fields_collapsed=="", "A subset of fields was not requested.")
expect_true(returned_object$filter_logic=="", "A filter was not specified.")
expect_match(returned_object$outcome_message, regexp=expected_outcome_message, perl=TRUE)
expect_true(returned_object$success)
})
test_that("label-and-dag", {
testthat::skip_on_cran()
path_expected <- "test-data/specific-redcapr/read-oneshot-eav/label-and-dag.R"
expected_outcome_message <- "\\d+ records and \\d+ columns were read from REDCap in \\d+(\\.\\d+\\W|\\W)seconds\\."
expect_message(
regexp = expected_outcome_message,
returned_object <- REDCapR:::redcap_read_oneshot_eav(redcap_uri=credential$redcap_uri, token=credential$token, raw_or_label="label", export_data_access_groups=TRUE)
)
if (update_expectation) save_expected(returned_object$data, path_expected)
expected_data_frame <- retrieve_expected(path_expected)
expect_equal(returned_object$data, expected=expected_data_frame, label="The returned data.frame should be correct") # dput(returned_object$data)
expect_equal(returned_object$status_code, expected=200L)
expect_equal(returned_object$raw_text, expected="", ignore_attr = TRUE) # dput(returned_object$raw_text)
expect_true(returned_object$records_collapsed=="", "A subset of records was not requested.")
expect_true(returned_object$fields_collapsed=="", "A subset of fields was not requested.")
expect_true(returned_object$filter_logic=="", "A filter was not specified.")
expect_match(returned_object$outcome_message, regexp=expected_outcome_message, perl=TRUE)
expect_true(returned_object$success)
})
test_that("label-header", {
testthat::skip_on_cran()
path_expected <- "test-data/specific-redcapr/read-oneshot-eav/label-header.R"
expected_outcome_message <- "\\d+ records and \\d+ columns were read from REDCap in \\d+(\\.\\d+\\W|\\W)seconds\\."
expect_message(
regexp = expected_outcome_message,
returned_object <- REDCapR:::redcap_read_oneshot_eav(redcap_uri=credential$redcap_uri, token=credential$token, raw_or_label_headers="label")
)
if (update_expectation) save_expected(returned_object$data, path_expected)
expected_data_frame <- retrieve_expected(path_expected)
expect_equal(returned_object$data, expected=expected_data_frame, label="The returned data.frame should be correct", ignore_attr = TRUE) # dput(returned_object$data)
expect_equal(returned_object$status_code, expected=200L)
expect_equal(returned_object$raw_text, expected="", ignore_attr = TRUE) # dput(returned_object$raw_text)
expect_true(returned_object$records_collapsed=="", "A subset of records was not requested.")
expect_true(returned_object$fields_collapsed=="", "A subset of fields was not requested.")
expect_true(returned_object$filter_logic=="", "A filter was not specified.")
expect_match(returned_object$outcome_message, regexp=expected_outcome_message, perl=TRUE)
expect_true(returned_object$success)
})
test_that("filter-numeric", {
testthat::skip_on_cran()
path_expected <- "test-data/specific-redcapr/read-oneshot-eav/filter-numeric.R"
expected_outcome_message <- "\\d+ records and \\d+ columns were read from REDCap in \\d+(\\.\\d+\\W|\\W)seconds\\."
filter <- "[age] >= 61"
expect_message(
regexp = expected_outcome_message,
returned_object <- REDCapR:::redcap_read_oneshot_eav(redcap_uri=credential$redcap_uri, token=credential$token, filter_logic=filter)
)
if (update_expectation) save_expected(returned_object$data, path_expected)
expected_data_frame <- retrieve_expected(path_expected)
expect_equal(returned_object$data, expected=expected_data_frame, label="The returned data.frame should be correct") # dput(returned_object$data)
expect_equal(returned_object$status_code, expected=200L)
expect_equal(returned_object$raw_text, expected="", ignore_attr = TRUE) # dput(returned_object$raw_text)
expect_true(returned_object$records_collapsed=="", "A subset of records was not requested.")
expect_true(returned_object$fields_collapsed=="", "A subset of fields was not requested.")
expect_equal(returned_object$filter_logic, filter)
expect_match(returned_object$outcome_message, regexp=expected_outcome_message, perl=TRUE)
expect_true(returned_object$success)
})
test_that("filter-character", {
testthat::skip_on_cran()
path_expected <- "test-data/specific-redcapr/read-oneshot-eav/filter-character.R"
if (update_expectation) save_expected(returned_object$data, path_expected)
expected_data_frame <- retrieve_expected(path_expected)
expected_outcome_message <- "\\d+ records and \\d+ columns were read from REDCap in \\d+(\\.\\d+\\W|\\W)seconds\\."
filter <- "[name_first] = 'John Lee'"
expect_message(
regexp = expected_outcome_message,
returned_object <- REDCapR:::redcap_read_oneshot_eav(redcap_uri=credential$redcap_uri, token=credential$token, filter_logic=filter)
)
if (update_expectation) save_expected(returned_object$data, path_expected)
expected_data_frame <- retrieve_expected(path_expected)
expect_equal(returned_object$data, expected=expected_data_frame, label="The returned data.frame should be correct") # dput(returned_object$data)
expect_equal(returned_object$status_code, expected=200L)
expect_equal(returned_object$raw_text, expected="", ignore_attr = TRUE) # dput(returned_object$raw_text)
expect_true(returned_object$records_collapsed=="", "A subset of records was not requested.")
expect_true(returned_object$fields_collapsed=="", "A subset of fields was not requested.")
expect_equal(returned_object$filter_logic, filter)
expect_match(returned_object$outcome_message, regexp=expected_outcome_message, perl=TRUE)
expect_true(returned_object$success)
})
test_that("date-range", {
testthat::skip_on_cran()
path_expected <- "test-data/specific-redcapr/read-oneshot-eav/default.R"
expected_outcome_message <- "\\d+ records and \\d+ columns were read from REDCap in \\d+(\\.\\d+\\W|\\W)seconds\\."
start <- as.POSIXct(strptime("2018-08-01 03:00", "%Y-%m-%d %H:%M"))
stop <- Sys.time()
expect_message(
regexp = expected_outcome_message,
returned_object <-
REDCapR:::redcap_read_oneshot_eav(
redcap_uri = credential$redcap_uri,
token = credential$token,
datetime_range_begin = start,
datetime_range_end = stop
)
)
if (update_expectation) save_expected(returned_object$data, path_expected)
expected_data_frame <- retrieve_expected(path_expected)
expect_equal(returned_object$data, expected=expected_data_frame, label="The returned data.frame should be correct", ignore_attr = TRUE) # dput(returned_object$data)
expect_equal(returned_object$status_code, expected=200L)
expect_equal(returned_object$raw_text, expected="", ignore_attr = TRUE) # dput(returned_object$raw_text)
expect_true(returned_object$records_collapsed=="", "A subset of records was not requested.")
expect_true(returned_object$fields_collapsed=="", "A subset of fields was not requested.")
expect_equal(returned_object$filter_logic, "")
expect_match(returned_object$outcome_message, regexp=expected_outcome_message, perl=TRUE)
expect_true(returned_object$success)
})
test_that("bad token -Error", {
testthat::skip_on_cran()
expected_outcome_message <- "The REDCapR record export operation was not successful\\."
expect_error(
regexp = expected_outcome_message,
REDCapR:::redcap_read_oneshot_eav(
redcap_uri = credential$redcap_uri,
token = "BAD00000000000000000000000000000"
)
)
})
rm(credential)
|
ae049e4f7dded0c1877205b17e89aab67356d759 | cf4263e82b2c118bc3ecea5dc62d561e7487cbd3 | /tests/testthat/test_flatten_data.R | 327e274c4b13ccbaaa4edf5a2d6be774fcc94394 | [
"MIT"
] | permissive | EDIorg/ecocomDP | 151a2d519ff740d466fafab74df5171a6ef196bf | 0554d64ce81f35ed59985d9d991203d88fe1621f | refs/heads/main | 2023-08-14T02:07:19.274000 | 2023-06-19T22:27:30 | 2023-06-19T22:27:30 | 94,339,321 | 26 | 10 | NOASSERTION | 2023-07-26T22:21:00 | 2017-06-14T14:22:43 | R | UTF-8 | R | false | false | 7,103 | r | test_flatten_data.R | context("flatten_data()")
# Compare L0 flat and L1 flat - The column names and values of the L0 flat and L1 flattened tables should match, with an exception:
# 1.) Primary keys, row identifiers, of the ancillary tables are now present.
# Column presence -------------------------------------------------------------
testthat::test_that("Column presence", {
for (i in c("df", "tbbl")) {
# Parameterize
if (i == "df") { # test w/data.frame
L0_flat <- as.data.frame(ants_L0_flat)
for (tbl in names(ants_L1$tables)) {
ants_L1$tables[[tbl]] <- as.data.frame(ants_L1$tables[[tbl]])
}
} else { # test w/tibble
L0_flat <- ants_L0_flat
}
crit <- read_criteria()
L1_flat <- flatten_data(ants_L1$tables)
# Adjust L0 flat to our expectations
L0_flat$location_name <- NA_character_ # Add exception
# TEST: All L0 flat columns (with above exceptions) should be in L1 flat
cols_missing_from_L1 <- base::setdiff(colnames(L0_flat), colnames(L1_flat))
expect_true(length(cols_missing_from_L1) == 0)
# TEST: All L1 flat columns should be in L0 flat
cols_missing_from_L0 <- base::setdiff(colnames(L1_flat), colnames(L0_flat))
expect_true(length(cols_missing_from_L0) == 0)
}
})
# Column classes --------------------------------------------------------------
testthat::test_that("Column classes", {
for (i in c("df", "tbbl")) {
# Parameterize
if (i == "df") { # test w/data.frame
L0_flat <- as.data.frame(ants_L0_flat)
for (tbl in names(ants_L1$tables)) {
ants_L1$tables[[tbl]] <- as.data.frame(ants_L1$tables[[tbl]])
}
} else { # test w/tibble
L0_flat <- ants_L0_flat
}
crit <- read_criteria()
L1_flat <- flatten_data(ants_L1$tables)
# TEST: flatten_data() applies a set of "smart" class coercions to return numeric values stored in the L1 as character back to their original numeric class. The following code tests that column classifications in L1 should be "similar" to those in L0.
L0_classes <- unlist(lapply(L0_flat, class))
L1_classes <- unlist(lapply(L1_flat, class))
# Harmonize classes (because there is some variation) before comparing
L0_classes[stringr::str_detect(names(L0_classes), "id")] <- "character" # identifiers should be character
L1_classes[stringr::str_detect(names(L1_classes), "id")] <- "character"
L0_classes[stringr::str_detect(L0_classes, "integer")] <- "numeric" # integer ~= numeric
L1_classes[stringr::str_detect(L1_classes, "integer")] <- "numeric"
# TEST: Compare col classes
for (c in seq(L1_classes)) {
col <- L1_classes[c]
if (names(col) %in% names(L0_classes)) {
use_i <- names(L0_classes) %in% names(col)
if (any(use_i)) {
expect_equal(L0_classes[use_i], col)
}
}
}
}
})
# Observations (rows) match ---------------------------------------------------
# TODO Implement this test?
# testthat::test_that("Observations (rows) match", {
# # Parameterize
# crit <- read_criteria()
# L0_flat <- ants_L0_flat
# L1_flat <- ecocomDP::flatten_data(ants_L1$tables)
# # Adjust L0 flat to our expectations
# L0_flat <- L0_flat %>%
# dplyr::select(-block) %>% # A higher level location lost when flattened
# dplyr::select(-author) %>% # Columns of NA are dropped when flattened
# dplyr::rename(location_name = plot) # Becomes "location_name" when flattened
# # TEST: Observation "A" in L0 flat has the same values in observation "A" of L1 flat
# # TODO observation_id are identical
# # TODO match cols and sort, then compare (some subset?)
# })
# Non-required columns --------------------------------------------------------
# Non-required columns of ecocomDP aren't required by flatten_data()
testthat::test_that("Non-required columns", {
for (i in c("df", "tbbl")) {
# Parameterize
if (i == "df") { # test w/data.frame
for (tbl in names(ants_L1$tables)) {
ants_L1$tables[[tbl]] <- as.data.frame(ants_L1$tables[[tbl]])
}
}
# Parameterize
crit <- read_criteria() %>%
dplyr::filter(required == TRUE, !is.na(column)) %>%
dplyr::select(table, column)
tbls <- ants_L1$tables
# Throw out all non-required columns
for (tname in names(tbls)) {
rqd <- crit$column[crit$table %in% tname]
tbls[[tname]] <- tbls[[tname]] %>% dplyr::select(dplyr::any_of(rqd))
}
# TEST: Missing non-required columns isn't an issue
L1_flat <- ecocomDP::flatten_data(tbls)
cols_in <- unname(unlist(lapply(tbls, colnames)))
cols_out <- colnames(L1_flat)
dif <- base::setdiff(cols_in, cols_out)
expect_equal(dif, # Difference is a set of cols that shouldn't be returned by anyway
c("location_ancillary_id", "taxon_ancillary_id", "observation_ancillary_id",
"variable_mapping_id", "table_name"))
}
})
# flatten_location() ----------------------------------------------------------
# location_name values are parsed into the original L0 column representation
testthat::test_that("flatten_location(): No nesting", {
loc <- tidyr::as_tibble( # A table demonstrating this use case
data.frame(
location_id = c("H1"),
location_name = c("Highest__1"),
latitude = 45,
longitude = 123,
elevation = 200,
parent_location_id = NA_character_,
stringsAsFactors = FALSE))
for (i in c("df", "tbbl")) {
# Parameterize
if (i == "df") { # test w/data.frame
loc <- as.data.frame(loc)
}
# Parameterize
res <- flatten_location(loc)
loc_flat <- res$location_flat
# TEST: Original columns of data are returned
expect_true(all(c("Highest") %in% colnames(loc_flat))) # column names
expect_equal(loc_flat$Highest, "1") # values
}
})
testthat::test_that("flatten_location(): 3 nested sites", {
loc <- tidyr::as_tibble( # A table demonstrating this use case
data.frame(
location_id = c("H1", "M2", "L3"),
location_name = c("Highest__1", "Middle__2", "Lowest__3"),
latitude = c(NA, NA, 45),
longitude = c(NA, NA, 123),
elevation = c(NA, NA, 200),
parent_location_id = c(NA_character_, "H1", "M2"),
stringsAsFactors = FALSE))
for (i in c("df", "tbbl")) {
# Parameterize
if (i == "df") { # test w/data.frame
loc <- as.data.frame(loc)
}
# Parameterize
res <- flatten_location(loc)
loc_flat <- res$location_flat
# TEST: Original columns of data are returned
expect_true(all(c("Highest", "Middle", "Lowest") %in% colnames(loc_flat))) # column names
expect_equal(loc_flat$Highest, "1") # values
expect_equal(loc_flat$Middle, "2")
expect_equal(loc_flat$Lowest, "3")
# TEST: Original columns are returned in the order of nesting
expect_equal(which(colnames(loc_flat) %in% "Highest"), 3)
expect_equal(which(colnames(loc_flat) %in% "Middle"), 4)
expect_equal(which(colnames(loc_flat) %in% "Lowest"), 5)
}
})
|
8ce7a9d3e16bf2b520b938c008850a5ca1577fb8 | 92456ce1d280dd99f0df1cc2a2567c5021286f03 | /R/prepare_data.R | 5c8fabf25b3ad3505598af1c3c14f7a6948f57d1 | [] | no_license | nzfarhad/AFG_MSNA_19_Analysis | 41643620a065ff3eaba40779624101b55562efe4 | 66b4cfe032b7665475606dcab5eae4fcacba0e9c | refs/heads/master | 2020-07-28T17:27:34.829000 | 2020-01-28T10:01:02 | 2020-01-28T10:01:02 | 209,478,936 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 100,120 | r | prepare_data.R | # Title: Preparation of data for woa survey
# Authors: Sayed Nabizada, Jarod Lapp, Christopher Jarvis,
# Date created: 20/09/2019
# Date last changed: 25/09/2019
# Purpose: This script is for recoding variables in the whole of
# of Afghanistan survey data
# indicators and composite scores are created.
# setup analysis environment
source("./R/source.R")
library(msni19)
# character operation
ch<-as.character
chr<-as.character
coerc<-function(x){as.numeric(chr(x))}
# load data
# data <- read_excel(master_data, sheet = "MSNA_AFG_19_parent_sheet", na = c("","NA"), guess_max = 3000)
# overall_muac_data <- read_excel(master_data, sheet = "MSNA_AFG_19_muac" , na = c("","NA"))
# overall_hh_roster <- read_excel(master_data, sheet = "MSNA_AFG_19_hh_roster" , na = c("","NA"))
# overall_death_roster <- read_excel(master_data, sheet = "MSNA_AFG_19_hh_death_roster" , na = c("","NA"))
# overall_left_roster <- read_excel( master_data, sheet = "MSNA_AFG_19_hh_left_roster" , na = c("","NA"))
# data <- read.csv("input/data/clean/MSNA_AFG_19_parent_sheet.csv",stringsAsFactors=F,na.strings = c("", "NA"), check.names = F)
# overall_muac_data <- read.csv("input/data/clean/MSNA_AFG_19_muac.csv",stringsAsFactors=F,na.strings = c("", "NA"), check.names = F)
# overall_hh_roster <- read.csv("input/data/clean/MSNA_AFG_19_hh_roster.csv",stringsAsFactors=F,na.strings = c("", "NA"), check.names = F)
# overall_death_roster <- read.csv("input/data/clean/MSNA_AFG_19_hh_death_roster.csv",stringsAsFactors=F,na.strings = c("", "NA"), check.names = F)
# overall_left_roster <- read.csv("input/data/clean/MSNA_AFG_19_hh_left_roster.csv",stringsAsFactors=F,na.strings = c("", "NA"), check.names = F)
#
data <- read.csv("input/data/clean/complete_with_farah/MSNA_AFG_19_parent_sheet.csv",stringsAsFactors=F,na.strings = c("", "NA"), check.names = F)
overall_muac_data <- read.csv("input/data/clean/complete_with_farah/MSNA_AFG_19_muac.csv",stringsAsFactors=F,na.strings = c("", "NA"), check.names = F)
overall_hh_roster <- read.csv("input/data/clean/complete_with_farah/MSNA_AFG_19_hh_roster.csv",stringsAsFactors=F,na.strings = c("", "NA"), check.names = F)
overall_death_roster <- read.csv("input/data/clean/complete_with_farah/MSNA_AFG_19_hh_death_roster.csv",stringsAsFactors=F,na.strings = c("", "NA"), check.names = F)
overall_left_roster <- read.csv("input/data/clean/complete_with_farah/MSNA_AFG_19_hh_left_roster.csv",stringsAsFactors=F,na.strings = c("", "NA"), check.names = F)
# Temp for the data is exported out of kobo incorrectly.
rename1 <- function(d1) {
sub("/", ".", names(d1))
}
data$uuid <- data$`_uuid`
names(data) <- rename1(data)
names(overall_muac_data ) <- rename1(overall_muac_data )
names(overall_hh_roster ) <- rename1(overall_hh_roster )
names(overall_death_roster ) <- rename1(overall_death_roster)
names(overall_left_roster ) <- rename1(overall_left_roster)
# composite indicators #
# The composite indicators are a combination of different variables
# each value within a variable has a score and these need to be
# coded for the different categories.
# Then the variables can be summed in order to get the score
# This will be done for multiple sectors.
#### Composite indicators ############
### Food Security & Agriculture ####
# FCS
data <- data %>%
mutate(
# FCS
fcs_category_class = recode(
fcs_category,
"poor" = 4,
"borderline" = 2,
"acceptable" = 0
),
# HHS
hhs_category_class = recode(
hhs_category,
"severe_hunger" = 4,
"moderate_hunger" = 2,
"little_hunger" = 0
),
# Food Source
food_source_class = case_when(
food_source %in% c('gift', 'assistance') ~ 2,
food_source == 'borrowed' ~1,
TRUE ~ 0
),
# ag impact
ag_impact_class = case_when(
agricultural_impact_how == '76_100' ~ 3,
agricultural_impact_how == '51_75' ~ 1,
agricultural_impact %in% c('no', 'not_applicable') ~ 0,
agricultural_impact_how %in% c('0_25', '26_50' ) ~ 0
),
# livestock impact
ls_impact_class = case_when(
livestock_impact_how.livestock_died == 1 |
livestock_impact_how.left_unattended == 1 ~ 2,
livestock_impact_how.livestock_ill == 1 |
livestock_impact_how.less_milk == 1 ~ 1,
livestock_impact == 0 ~ 0,
TRUE ~ 0,
is.na(livestock_impact) ~ NA_real_
)
)
fsac_vars <- c("fcs_category_class", "hhs_category_class", "food_source_class", "ag_impact_class", "ls_impact_class")
data$fsac_score <- comp_score(data, fsac_vars)
data <- data %>%
mutate(
fsac_severity = case_when(
fsac_score <= 2 ~ 1,
fsac_score <= 5 ~ 2,
fsac_score <= 8 ~ 3,
fsac_score <= 16 ~ 4
),
fsac_sev_high = case_when(
fsac_severity <= 2 ~ 0,
fsac_severity <= 4 ~ 1
)
)
##################################################################
### Protection ####
# First setup the variables required to calculate the indicators and then calculate them
# This way around if the weights are changed then it's all in one place.
# protection incidents
severe_prot_incidents_vars <- c(
"adult_prot_incidents.assaulted_with_weapon",
"child_prot_incidents.assaulted_with_weapon",
"adult_prot_incidents.forced_work",
"child_prot_incidents.forced_work",
"adult_prot_incidents.forcibly_detained",
"child_prot_incidents.forcibly_detained",
"adult_prot_incidents.hindered_leave_settlement",
"child_prot_incidents.hindered_leave_settlement",
#### added from less_severe_prot_incidents
"adult_prot_incidents.verbally_threatened",
"child_prot_incidents.verbally_threatened",
"adult_prot_incidents.assaulted_without_weapon",
"child_prot_incidents.assaulted_without_weapon",
"adult_prot_incidents.hindered_leave_district",
"child_prot_incidents.hindered_leave_district"
)
# less_severe_prot_incidents_vars <-c(
# "adult_prot_incidents.verbally_threatened",
# "child_prot_incidents.verbally_threatened",
# "adult_prot_incidents.assaulted_without_weapon",
# "child_prot_incidents.assaulted_without_weapon",
# "adult_prot_incidents.hindered_leave_district",
# "child_prot_incidents.hindered_leave_district"
# )
data$severe_prot_incidents <- comp_score(data, severe_prot_incidents_vars)
# data$less_severe_prot_incidents <- comp_score(data, less_severe_prot_incidents_vars)
# protection concerns
severe_prot_concerns_vars <- c(
"prot_concerns.violence_maiming",
"prot_concerns.abduction",
"prot_concerns.explosive_hazards",
"prot_concerns.psych_wellbeing",
# added from less_severe_prot_concern
"prot_concerns.violence_injuries",
"prot_concerns.early_marriage",
"prot_concerns.destruction_property",
"prot_concerns.theft"
)
# less_severe_prot_concerns_vars <- c(
# "prot_concerns.violence_injuries",
# "prot_concerns.early_marriage",
# "prot_concerns.destruction_property",
# "prot_concerns.theft"
# )
data$severe_prot_concerns <- comp_score(data, severe_prot_concerns_vars)
# data$less_severe_prot_concerns <- comp_score(data, less_severe_prot_concerns_vars)
# explosive hazards
severe_explosive_hazards_vars <- c(
"explosive_impact.injury_death",
"explosive_impact.access_services",
"explosive_impact.relocation",
"explosive_impact.livelihoods_impact",
"explosive_impact.psych_impact"
)
less_severe_explosive_hazards_vars <- c(
"explosive_impact.restrict_recreation"
)
data$severe_explosive_hazards <- comp_score(data, severe_explosive_hazards_vars)
data$less_severe_explosive_hazards <- comp_score(data, less_severe_explosive_hazards_vars)
# tazkira
tazkira_total_vars <- c(
"adult_tazkira",
"child_tazkira")
data$tazkira_total <- comp_score(data, tazkira_total_vars)
children_working_yes_no_2 = case_when(
data$children_working == 0 ~ "0",
data$children_working >= 1 ~ "1 or more",
TRUE ~ NA_character_
)
# Protection Severity Score
## Weights
data <- data %>%
mutate(
prot_incident_class = case_when(
severe_prot_incidents >= 1 ~ 3,
# severe_prot_incidents == 0 & data$less_severe_prot_incidents >= 1 ~ 2,
TRUE ~ 0),
# violence targeting women, girls, boys
sgbv_incidents_class = case_when(
other_incidents.sgbv == 1 | other_concerns.sgbv == 1 ~ 2,
TRUE ~ 0
),
# children working unsafe conditions
children_work_safety_class = case_when(
children_working_yes_no_2 =='1 or more' ~ 1,
TRUE ~ 0
),
prot_concerns_class = case_when(
severe_prot_concerns >= 1 ~ 3,
# severe_prot_concerns == 0 & data$less_severe_prot_concerns >= 1 ~ 2,
TRUE ~ 0
),
# hh members injured conflict or nat disaster
injuries_class = case_when(
adult_injuries_cause %in% c('conflict', 'natural_disaster') |
child_injuries_cause %in% c('conflict', 'natural_disaster') ~ 3,
TRUE ~ 0
),
prot_explosive_hazards_class = case_when(
severe_explosive_hazards >= 1 ~ 3,
severe_explosive_hazards == 0 & less_severe_explosive_hazards >=1 ~ 2,
TRUE ~ 0
),
tazkira_class = case_when(
tazkira_total == 0 ~ 2,
tazkira_total > 0 & tazkira_total < hh_size ~ 1
)
)
# Score
prot_score_vars <- c(
"prot_incident_class",
"sgbv_incidents_class",
"children_work_safety_class",
"prot_concerns_class",
"injuries_class",
"prot_explosive_hazards_class",
"tazkira_class")
data$prot_score <- comp_score(data, prot_score_vars)
data <- data %>%
mutate(
prot_severity = case_when(
prot_score <= 2 ~ 1,
prot_score <= 5 ~ 2,
prot_score <= 8 ~ 3,
prot_score <= 18 ~ 4
),
prot_sev_high = case_when(
prot_severity >= 3 ~ 1,
TRUE ~ 0
)
)
################## protection new indicator 1 ######################
prot_all_indictors <- c(
"adult_prot_incidents.verbally_threatened",
"adult_prot_incidents.assaulted_without_weapon",
"adult_prot_incidents.assaulted_with_weapon",
"adult_prot_incidents.hindered_leave_settlement",
"adult_prot_incidents.hindered_leave_district",
"adult_prot_incidents.forced_work",
"adult_prot_incidents.forcibly_detained",
"child_prot_incidents.verbally_threatened",
"child_prot_incidents.assaulted_without_weapon",
"child_prot_incidents.assaulted_with_weapon",
"child_prot_incidents.hindered_leave_settlement",
"child_prot_incidents.hindered_leave_district",
"child_prot_incidents.forced_work",
"child_prot_incidents.forcibly_detained",
"other_incidents.sgbv",
"other_incidents.other",
"prot_concerns.violence_maiming",
"prot_concerns.violence_injuries",
"prot_concerns.psych_wellbeing",
"prot_concerns.abduction",
"prot_concerns.theft",
"prot_concerns.explosive_hazards",
"prot_concerns.destruction_property",
"prot_concerns.early_marriage",
"prot_concerns.other",
"other_concerns.sgbv",
"other_concerns.other"
)
data$prot_all_indictors_score <- comp_score(data, prot_all_indictors)
data <- data %>%
mutate(
prot_new_indicator_1 = case_when(
prot_all_indictors_score >= 1 ~ ">=1",
prot_all_indictors_score == 0 ~ "0",
TRUE ~ NA_character_
)
)
################## protection new indicator 2 ######################
data <- data %>%
mutate( displ_explosive_presence_na_to_0 = case_when(
displ_explosive_presence == "both" ~ 1,
displ_explosive_presence == "current" ~ 1,
displ_explosive_presence == "previous" ~ 1,
displ_explosive_presence == "no" ~ 0,
TRUE ~ 0
)
)
prot_all_indictors_2 <- c(
"adult_prot_incidents.verbally_threatened",
"adult_prot_incidents.assaulted_without_weapon",
"adult_prot_incidents.assaulted_with_weapon",
"adult_prot_incidents.hindered_leave_settlement",
"adult_prot_incidents.hindered_leave_district",
"adult_prot_incidents.forced_work",
"adult_prot_incidents.forcibly_detained",
"child_prot_incidents.verbally_threatened",
"child_prot_incidents.assaulted_without_weapon",
"child_prot_incidents.assaulted_with_weapon",
"child_prot_incidents.hindered_leave_settlement",
"child_prot_incidents.hindered_leave_district",
"child_prot_incidents.forced_work",
"child_prot_incidents.forcibly_detained",
"other_incidents.sgbv",
"other_incidents.other",
"prot_concerns.violence_maiming",
"prot_concerns.violence_injuries",
"prot_concerns.psych_wellbeing",
"prot_concerns.abduction",
"prot_concerns.theft",
"prot_concerns.explosive_hazards",
"prot_concerns.destruction_property",
"prot_concerns.early_marriage",
"prot_concerns.other",
"other_concerns.sgbv",
"other_concerns.other",
"displ_explosive_presence_na_to_0"
)
data$prot_all_indictors_score_2 <- comp_score(data, prot_all_indictors_2)
data <- data %>%
mutate(
prot_new_indicator_2 = case_when(
prot_all_indictors_score_2 >= 1 ~ ">=1",
prot_all_indictors_score_2 == 0 ~ "0",
TRUE ~ NA_character_
)
)
################## protection new indicator 3 ######################
data <- data %>%
mutate( nondispl_explosive_presence_na_to_0 = case_when(
nondispl_explosive_presence == "yes" ~ 1,
nondispl_explosive_presence == "no" ~ 0,
TRUE ~ 0
)
)
prot_all_indictors_3 <- c(
"adult_prot_incidents.verbally_threatened",
"adult_prot_incidents.assaulted_without_weapon",
"adult_prot_incidents.assaulted_with_weapon",
"adult_prot_incidents.hindered_leave_settlement",
"adult_prot_incidents.hindered_leave_district",
"adult_prot_incidents.forced_work",
"adult_prot_incidents.forcibly_detained",
"child_prot_incidents.verbally_threatened",
"child_prot_incidents.assaulted_without_weapon",
"child_prot_incidents.assaulted_with_weapon",
"child_prot_incidents.hindered_leave_settlement",
"child_prot_incidents.hindered_leave_district",
"child_prot_incidents.forced_work",
"child_prot_incidents.forcibly_detained",
"other_incidents.sgbv",
"other_incidents.other",
"prot_concerns.violence_maiming",
"prot_concerns.violence_injuries",
"prot_concerns.psych_wellbeing",
"prot_concerns.abduction",
"prot_concerns.theft",
"prot_concerns.explosive_hazards",
"prot_concerns.destruction_property",
"prot_concerns.early_marriage",
"prot_concerns.other",
"other_concerns.sgbv",
"other_concerns.other",
"displ_explosive_presence_na_to_0",
"nondispl_explosive_presence_na_to_0"
)
data$prot_all_indictors_score_3 <- comp_score(data, prot_all_indictors_3)
data <- data %>%
mutate(
prot_new_indicator_3 = case_when(
prot_all_indictors_score_3 >= 1 ~ ">=1",
prot_all_indictors_score_3 == 0 ~ "0",
TRUE ~ NA_character_
)
)
################## protection new indicator 4 ######################
data <- data %>%
mutate(
lcsi_category_class2 = case_when(
lcsi_category == "food_secure" | lcsi_category == "marginally_insecure" ~ 0,
lcsi_category == "moderately_insecure" | lcsi_category == "severely_insecure" ~ 1,
TRUE ~ 0
)
)
prot_all_indictors_4 <- c(
"adult_prot_incidents.verbally_threatened",
"adult_prot_incidents.assaulted_without_weapon",
"adult_prot_incidents.assaulted_with_weapon",
"adult_prot_incidents.hindered_leave_settlement",
"adult_prot_incidents.hindered_leave_district",
"adult_prot_incidents.forced_work",
"adult_prot_incidents.forcibly_detained",
"child_prot_incidents.verbally_threatened",
"child_prot_incidents.assaulted_without_weapon",
"child_prot_incidents.assaulted_with_weapon",
"child_prot_incidents.hindered_leave_settlement",
"child_prot_incidents.hindered_leave_district",
"child_prot_incidents.forced_work",
"child_prot_incidents.forcibly_detained",
"other_incidents.sgbv",
"other_incidents.other",
"prot_concerns.violence_maiming",
"prot_concerns.violence_injuries",
"prot_concerns.psych_wellbeing",
"prot_concerns.abduction",
"prot_concerns.theft",
"prot_concerns.explosive_hazards",
"prot_concerns.destruction_property",
"prot_concerns.early_marriage",
"prot_concerns.other",
"other_concerns.sgbv",
"other_concerns.other",
"displ_explosive_presence_na_to_0",
"nondispl_explosive_presence_na_to_0",
"lcsi_category_class2"
)
data$prot_all_indictors_score_4 <- comp_score(data, prot_all_indictors_4)
data <- data %>%
mutate(
prot_new_indicator_4 = case_when(
prot_all_indictors_score_4 >= 1 ~ ">=1",
prot_all_indictors_score_4 == 0 ~ "0",
TRUE ~ NA_character_
)
)
#################################################################
################## protection new indicator 5 ######################
prot_all_indictors_5 <- c(
"adult_prot_incidents.verbally_threatened",
"adult_prot_incidents.assaulted_without_weapon",
"adult_prot_incidents.assaulted_with_weapon",
"adult_prot_incidents.hindered_leave_settlement",
"adult_prot_incidents.hindered_leave_district",
"adult_prot_incidents.forced_work",
"adult_prot_incidents.forcibly_detained",
"child_prot_incidents.verbally_threatened",
"child_prot_incidents.assaulted_without_weapon",
"child_prot_incidents.assaulted_with_weapon",
"child_prot_incidents.hindered_leave_settlement",
"child_prot_incidents.hindered_leave_district",
"child_prot_incidents.forced_work",
"child_prot_incidents.forcibly_detained",
"other_incidents.sgbv",
"other_incidents.other",
"prot_concerns.violence_maiming",
"prot_concerns.violence_injuries",
"prot_concerns.psych_wellbeing",
"prot_concerns.abduction",
"prot_concerns.theft",
"prot_concerns.explosive_hazards",
"prot_concerns.destruction_property",
"prot_concerns.early_marriage",
"prot_concerns.other",
"other_concerns.sgbv",
"other_concerns.other",
"displ_explosive_presence_na_to_0",
"nondispl_explosive_presence_na_to_0",
"lcsi_category_class2",
"children_work_safety_class"
)
data$prot_all_indictors_score_5 <- comp_score(data, prot_all_indictors_5)
data <- data %>%
mutate(
prot_new_indicator_5 = case_when(
prot_all_indictors_score_5 >= 1 ~ ">=1",
prot_all_indictors_score_5 == 0 ~ "0",
TRUE ~ NA_character_
)
)
################## protection new indicator 6 ######################
data <- data %>%
mutate(
other_impact_class = case_when(
other_impact.injury_death == 1 | other_impact.new_mines == 1 ~ 1,
TRUE ~ 0
)
)
prot_all_indictors_6 <- c(
"adult_prot_incidents.verbally_threatened",
"adult_prot_incidents.assaulted_without_weapon",
"adult_prot_incidents.assaulted_with_weapon",
"adult_prot_incidents.hindered_leave_settlement",
"adult_prot_incidents.hindered_leave_district",
"adult_prot_incidents.forced_work",
"adult_prot_incidents.forcibly_detained",
"child_prot_incidents.verbally_threatened",
"child_prot_incidents.assaulted_without_weapon",
"child_prot_incidents.assaulted_with_weapon",
"child_prot_incidents.hindered_leave_settlement",
"child_prot_incidents.hindered_leave_district",
"child_prot_incidents.forced_work",
"child_prot_incidents.forcibly_detained",
"other_incidents.sgbv",
"other_incidents.other",
"prot_concerns.violence_maiming",
"prot_concerns.violence_injuries",
"prot_concerns.psych_wellbeing",
"prot_concerns.abduction",
"prot_concerns.theft",
"prot_concerns.explosive_hazards",
"prot_concerns.destruction_property",
"prot_concerns.early_marriage",
"prot_concerns.other",
"other_concerns.sgbv",
"other_concerns.other",
"displ_explosive_presence_na_to_0",
"nondispl_explosive_presence_na_to_0",
"lcsi_category_class2",
"other_impact_class"
)
data$prot_all_indictors_score_6 <- comp_score(data, prot_all_indictors_6)
data <- data %>%
mutate(
prot_new_indicator_6 = case_when(
prot_all_indictors_score_6 >= 1 ~ ">=1",
prot_all_indictors_score_6 == 0 ~ "0",
TRUE ~ NA_character_
)
)
###################################################end
### ESNFI ####
# shelter type
data$shelter_class<-ifelse(data$shelter == 'open_space',3,ifelse(data$shelter == 'tent' | data$shelter == 'makeshift_shelter' | data$shelter == 'collective_centre' | data$shelter == 'transitional',2,0))
# shelter damage
data$shelter_damage_class<-ifelse(data$shelter_damage_extent== 'fully_destroyed' & data$shelter_damage_repair == 'no',3,
ifelse(data$shelter_damage_extent== 'significant_damage' & data$shelter_damage_repair == 'no',2,
ifelse(data$shelter_damage_extent== 'partial_damage' & data$shelter_damage_repair == 'no',1,0)))
data$shelter_damage_class[is.na(data$shelter_damage_class)] <- 0
# TENENCY AGREEMENT
data$tenancy_class<-ifelse(data$tenancy == 'unofficial',3,ifelse(data$tenancy == 'own_home_without_doc' | data$tenancy == 'rental_verbal' | data$shelter_hosted == 'yes',2,0))
data$tenancy_class[is.na(data$tenancy_class)] <- 0
# blankets
data$blankets_class<-ifelse(data$blankets_number > data$hh_size,3,0)
data$blankets_class[is.na(data$blankets_class)] <- 0
# basic needs
data$sleeping_mats <- car::recode(data$sleeping_mats, " 'yes' = 1; 'no' = 0")
data$tarpaulin <- car::recode(data$tarpaulin, " 'yes' = 1; 'no' = 0")
data$cooking_pots <- car::recode(data$cooking_pots, " 'yes' = 1; 'no' = 0")
data$stainless_steel <- car::recode(data$stainless_steel, " 'yes' = 1; 'no' = 0")
data$water_storage <- car::recode(data$water_storage, " 'yes' = 1; 'no' = 0")
data$hygiene_sanitation <- car::recode(data$hygiene_sanitation, " 'yes' = 1; 'no' = 0")
data$basic_needs_total<-coerc(data[["sleeping_mats"]])+coerc(data[["tarpaulin"]])+coerc(data[["cooking_pots"]])+coerc(data[["stainless_steel"]])+coerc(data[["water_storage"]])+coerc(data[["hygiene_sanitation"]])
data$basic_needs_score<-car::recode(data$basic_needs_total,
"0:2=3;
3:5=2;
6=0")
# ESNFI Severity Score
data$esnfi_score<-coerc(data[["shelter_class"]])+coerc(data[["shelter_damage_class"]])+coerc(data[["tenancy_class"]])+coerc(data[["blankets_class"]])+coerc(data[["basic_needs_score"]])
data$esnfi_severity<-car::recode(data$esnfi_score,
"0:2='1';
3:6='2';
7:9='3';
10:16='4'")
data$esnfi_sev_high<-ifelse(data$esnfi_severity==3|data$esnfi_severity==4,1,0)
###################################################end
### <3 <3 <3 <3 <3 ESNFI 4 ARI <3 <3 <3 <3 <3 ####
# shelter type
data$shelter_class_4_ari<-case_when(data$shelter == 'open_space'| data$shelter == 'tent' | data$shelter == 'makeshift_shelter'| data$shelter == 'collective_centre' ~3, data$shelter == 'transitional'~2,
data$shelter=='permanent' & (data$shelter_hosted_why =='cash_rent'| data$shelter_hosted_why =='trans_shelter_host_family'| data$shelter_hosted_why =='materials_tools_extend') ~2, TRUE~ 0)
# shelter damage
data$shelter_damage_class_4_ari<-ifelse(data$shelter_damage_extent== 'fully_destroyed' & data$shelter_damage_repair == 'no',3,
ifelse(data$shelter_damage_extent== 'significant_damage' & data$shelter_damage_repair == 'no',2,
ifelse(data$shelter_damage_extent== 'partial_damage' & data$shelter_damage_repair == 'no',1,0)))
data$shelter_damage_class_4_ari[is.na(data$shelter_damage_class_4_ari)] <- 0
# TENENCY AGREEMENT
data$tenancy_class_4_ari<-ifelse(data$tenancy == 'unofficial',3,ifelse(data$tenancy == 'own_home_without_doc' | data$tenancy == 'rental_verbal' | data$shelter_hosted == 'yes',2,0))
data$tenancy_class_4_ari[is.na(data$tenancy_class_4_ari)] <- 0
# ESNFI Severity Score
data$esnfi_score_4_ari<-coerc(data[["shelter_class_4_ari"]])+coerc(data[["shelter_damage_class_4_ari"]])+coerc(data[["tenancy_class_4_ari"]])
data$esnfi_severity_4_ari<-car::recode(data$esnfi_score_4_ari,
"0:2='1';
3:4='2';
5:6='3';
7:10='4'")
data$esnfi_sev_high_4_ari<-ifelse(data$esnfi_severity_4_ari==3|data$esnfi_severity_4_ari==4,1,0)
#################################################################
### WASH ####
# water source #
data$water_source_class<-car::recode(data$water_source,
"'surface_water'=3;
'water_trucking'=2;
'spring_unprotected'=2;
'spring_protected'=0;
'handpump_private'=0;
'handpump_public'=0;
'piped_public'=0;
'other'=0")
# water barriers
data$water_barriers_class<-ifelse(data$water_sufficiency== 'insufficient' &
(data$water_barriers== 'too_far' |
data$water_barriers== 'high_risk' |
data$water_barriers== 'social_restrictions'),
3,ifelse(data$water_sufficiency== 'insufficient',2,
ifelse(data$water_sufficiency== 'barely_sufficient',1,0)))
data$water_barriers_class[is.na(data$water_barriers)] <- 0
# soap
data$soap_class<-ifelse(data$soap == 'yes_didnt_see' | data$soap == 'no', 1,0)
# latrines #
data$latrine_class<-ifelse(data$latrine == 'open' | data$latrine == 'public_latrine', 3,
ifelse(data$latrine == 'pit_latrine_uncovered',2,0))
# primary waste dispopsal #
data$waste_disposal_class<-ifelse(data$waste_disposal == 'open_space' | data$waste_disposal == 'burning', 2,0)
#distance to primary water source
data$water_distance_class<-ifelse(data$water_distance == 'over_1km'| data$water_distance == '500m_to_1km',3,0)
# WASH Severity Score
data$wash_score<-coerc(data[["water_source_class"]])+coerc(data[["water_barriers_class"]])+coerc(data[["soap_class"]])+coerc(data[["latrine_class"]])+coerc(data[["waste_disposal_class"]])+coerc(data[["water_distance_class"]])
data$wash_severity<-car::recode(data$wash_score,
"0:2='1';
3:5='2';
6:8='3';
9:16='4'")
data$wash_sev_high<-ifelse(data$wash_severity==3|data$wash_severity==4,1,0)
#################################################################
### Nutrition ####
muac_presence_analysis<-overall_muac_data %>%
group_by(`_submission__uuid`) %>%
filter(person_muac>=1) %>%
summarize(number_muac_person=sum(person_muac),
number_muac_mod_mal=sum(moderate_malnutrition),
number_muac_sev_mal=sum(severe_malnutrition),
number_muac_above_125 = sum(muac_measurement>=125, na.rm = T),
min_muac=min(muac_measurement),
ruft_reception_num = sum(rutf_reception== "yes"),
ruft_reception = sum(rutf_reception== "yes")>=1)
# Malnutrition present = 1, not present = 0
muac_presence_analysis$malnutrition_present<-ifelse(muac_presence_analysis$number_muac_mod_mal>=1 | muac_presence_analysis$number_muac_sev_mal>=1,1,0)
# join with parent table
data<-full_join(data, muac_presence_analysis, by = c("uuid"="_submission__uuid"))
# reported malnourishment (mod & sev muac)
data$muac_score<-ifelse(data$number_muac_sev_mal>1,7,ifelse(data$number_muac_sev_mal==1,6,ifelse(data$number_muac_sev_mal==0 & data$number_muac_mod_mal>1,4,ifelse(data$number_muac_sev_mal==0 & data$number_muac_mod_mal==1,3,0))))
# dietary diversity ---
# therefore nutrition compotite indicator will exclude hhs with children aged 2-5, since this they are not asked this question ###
data$dietary_div_count<-coerc(data[["minimum_dietary_diversity.staples"]])+coerc(data[["minimum_dietary_diversity.legumes"]])+coerc(data[["minimum_dietary_diversity.dairy"]])+coerc(data[["minimum_dietary_diversity.meat"]])+coerc(data[["minimum_dietary_diversity.eggs"]])+coerc(data[["minimum_dietary_diversity.vitamin_a_veg"]])+coerc(data[["minimum_dietary_diversity.other_veg"]])
data$dietary_div_score<-ifelse(data$dietary_div_count==0,4,ifelse(data$dietary_div_count==1,3,ifelse(data$dietary_div_count==2,2,ifelse(data$dietary_div_count==3,1,0))))
data$dietary_div_score[is.na(data$dietary_div_score)] <- 0
# Nutrition Severity Score
data$nut_score_hh_w_muac<-coerc(data[["muac_score"]])+coerc(data[["dietary_div_score"]])
data$nut_score<-data$nut_score_hh_w_muac
data$nut_score[is.na(data$nut_score)] <- 0
data$nut_severity<-car::recode(data$nut_score,
"0:2='1';
3:5='2';
6:8='3';
9:16='4'")
# data$nut_sev_high<-ifelse(data$nut_severity==3|data$nut_severity==4,1,0)
data$nut_sev_high<-ifelse(data$nut_severity==3|data$nut_severity==4 | data$nut_severity==2 ,1,0)
#################################################################
### Education EiE ####
education_analysis<-overall_hh_roster %>%
filter(!is.na(current_year_enrolled))
education_analysis$enrolled_and_attending<-ifelse(education_analysis$current_year_enrolled=='no',0,
ifelse(education_analysis$current_year_enrolled=='yes' & education_analysis$current_year_attending=='no',0,1))
education_analysis$total_schoolage_child<-1
#removal from school due to shock
education_analysis$shock_presence<-coerc(education_analysis[["edu_removal_shock.displacement"]])+coerc(education_analysis[["edu_removal_shock.conflict"]])+coerc(education_analysis[["edu_removal_shock.natural_disaster"]])
education_analysis$shock_presence[is.na(education_analysis$shock_presence)] <- 0
################## not part of composite #################################################
education_analysis$enrolled_1 <- if_else(education_analysis$current_year_enrolled=='no',0,1)
education_analysis$attending_1 <- if_else(education_analysis$current_year_attending=='no',0,1)
education_analysis <- education_analysis %>%
mutate(
attending_male = case_when(
current_year_attending == "yes" & hh_member_sex == "male" ~ 1,
TRUE ~ 0
),
attending_female = case_when(
current_year_attending == "yes" & hh_member_sex == "female" ~ 1,
TRUE ~ 0
),
enrolled_male = case_when(
current_year_enrolled == "yes" & hh_member_sex == "male" ~ 1,
TRUE ~ 0
),
enrolled_female = case_when(
current_year_enrolled == "yes" & hh_member_sex == "female" ~ 1,
TRUE ~ 0
),
shock_presence_male = case_when(
shock_presence > 0 & hh_member_sex == "male" ~ 1,
TRUE ~ 0
),
shock_presence_female = case_when(
shock_presence > 0 & hh_member_sex == "female" ~ 1,
TRUE ~ 0
)
)
####################################################################################
# group dataset into hh
education_analysis_hh<-education_analysis %>%
group_by(`_submission__uuid`) %>%
summarize(count_school_child=sum(total_schoolage_child),
count_enrolled_attending=sum(enrolled_and_attending),
count_current_enrolled = sum(enrolled_1, na.rm = T),
count_current_enrolled_male = sum(enrolled_male, na.rm = T),
count_current_enrolled_female = sum(enrolled_female, na.rm = T),
count_current_attending = sum(attending_1, na.rm = T),
count_current_attending_male = sum(attending_male, na.rm = T),
count_current_attending_female = sum(attending_female, na.rm = T),
count_shock=sum(shock_presence),
count_shock_male = sum(shock_presence_male, na.rm = T),
count_shock_female = sum(shock_presence_female, na.rm = T)
)
# shock weight
education_analysis_hh$shock_class<-ifelse(education_analysis_hh$count_shock >= 1, 5,0)
# percent children enrolled or attending
education_analysis_hh$percent_enrolled= coerc(education_analysis_hh[["count_enrolled_attending"]])/coerc(education_analysis_hh[["count_school_child"]])
education_analysis_hh$enroll_perc_class<-car::recode(education_analysis_hh$percent_enrolled,
"0:0.249=1;
0.25:0.499=2;
0.5:0.749=3;
0.75:1=4")
# greater than 3 children not attending
education_analysis_hh$count_not_enrolled<-coerc(education_analysis_hh[["count_school_child"]])-coerc(education_analysis_hh[["count_enrolled_attending"]])
education_analysis_hh$count_not_enrolled_class<-ifelse(education_analysis_hh$count_not_enrolled>=3,3,0)
# join with parent table
data<-full_join(data, education_analysis_hh,by = c("uuid"="_submission__uuid"))
# reasons not attending
data$severe_not_attending<-coerc(data[["boy_unattendance_reason.insecurity"]])+coerc(data[["boy_unattendance_reason.child_works_instead"]])+coerc(data[["girl_unattendance_reason.insecurity"]])+coerc(data[["girl_unattendance_reason.child_works_instead"]])
data$severe_not_attending[is.na(data$severe_not_attending)] <- 0
data$less_severe_not_attending<-coerc(data[["boy_unattendance_reason.lack_facilities"]])+coerc(data[["boy_unattendance_reason.lack_documentation"]])+coerc(data[["boy_unattendance_reason.too_expensive"]])+coerc(data[["girl_unattendance_reason.lack_facilities"]])+coerc(data[["girl_unattendance_reason.lack_documentation"]])+coerc(data[["girl_unattendance_reason.too_expensive"]])
data$less_severe_not_attending[is.na(data$less_severe_not_attending)] <- 0
data$not_attending_class<-ifelse(data$severe_not_attending >= 1,3, ifelse(data$severe_not_attending==0 & data$less_severe_not_attending >=1,2,0))
data$not_attending_class[is.na(data$not_attending_class)] <- 0
# Education Severity Score
data$edu_score_hh_w_schoolage<-coerc(data[["enroll_perc_class"]])+coerc(data[["shock_class"]])+coerc(data[["count_not_enrolled_class"]])+coerc(data[["not_attending_class"]])
data$edu_score<-data$edu_score_hh_w_schoolage
data$edu_score[is.na(data$edu_score)] <- 0
data$edu_severity<-car::recode(data$edu_score,
"0:2='1';
3:5='2';
6:8='3';
9:16='4'")
data$edu_sev_high<-ifelse(data$edu_severity==3|data$edu_severity==4,1,0)
#################################################################
### Health ####
#deaths under 5 years age
overall_death_roster$deaths_under5<-ifelse(overall_death_roster$hh_died_age<5,1,0)
# deaths >= 5
overall_death_roster$deaths_over5<-ifelse(overall_death_roster$hh_died_age>=5,1,0)
# group by hh
health_analysis<-overall_death_roster %>%
group_by(`_submission__uuid`) %>%
summarize(number_death_under5=sum(deaths_under5),
hh_member_died = sum(hh_member_died),
number_death_over5=sum(deaths_over5))
# join with parent dataset
data<-full_join(data, health_analysis,by = c("uuid"="_submission__uuid"))
data$number_death_under5[is.na(data$number_death_under5)] <- 0
# #deaths under 5 yrs age weight
# data$number_death_under5_class<-ifelse(data$number_death_under5 >= 1, 3,0)
# data$number_death_under5_class[is.na(data$number_death_under5_class)] <- 0
#
# # deaths >= 5 weight
# data$number_death_over5_class<-ifelse(data$number_death_over5 >= 1, 2,0)
# data$number_death_over5_class[is.na(data$number_death_over5_class)] <- 0
# health facility barriers
data$health_barriers_total<-coerc(data[["health_facility_barriers.unsafe"]])+coerc(data[["health_facility_barriers.cost_services"]])+coerc(data[["health_facility_barriers.cost_medicines"]])+coerc(data[["health_facility_barriers.too_far"]])+coerc(data[["health_facility_barriers.documentation_problems"]])+coerc(data[["health_facility_barriers.insufficient_female_staff"]])+coerc(data[["health_facility_barriers.treatment_refused"]])+coerc(data[["health_facility_barriers.other"]])
data$health_barriers_total[is.na(data$health_barriers_total)] <- 0
# data$health_facility_barriers_class<-ifelse(data$health_facility_access == 'no' & data$health_barriers_total>1,3,ifelse(data$health_facility_access == 'no' & data$health_barriers_total==1,2,0))
data$health_facility_barriers_class<-ifelse(data$health_facility_access == 'no' ,3,0)
# health facility distance
data$health_facility_dist_class<-ifelse(data$health_facility_distance == 'none' | data$health_facility_distance=='more_10km',3,ifelse(data$health_facility_distance=='6_10km',2,0))
# health facilities affected
data$health_facility_affected_class<-ifelse(data$health_facility_affected_how == 'forcibly_closed'|data$health_facility_affected_how == 'damaged_conflict'|data$health_facility_affected_how == 'damaged_natural_disasters',3, ifelse(data$health_facility_affected_how=='lack_staff'|data$health_facility_affected_how=='lack_medicine',2,0))
data$health_facility_affected_class[is.na(data$health_facility_affected_class)] <- 0
# health selected as priority need
data$health_priority_need_class<-ifelse(data$priority_needs.healthcare == 1, 3,0)
# behaviour changes as result of conflict
data$behavior_change_cause_class<-case_when(data$adult_behavior_change == 'yes'& data$behavior_change_cause=='yes'~ 3,
data$child_behavior_change == 'yes'& data$behavior_change_cause=='yes'~ 3, TRUE~ 0)
data$behavior_change_cause_class[is.na(data$behavior_change_cause_class)] <- 0
# birth location
# data$birth_location_class<-ifelse(data$birth_location == 'home'|data$birth_location == 'midwife_home'|data$birth_location == 'outside'|data$birth_location == 'other',1,0)
# data$birth_location_class[is.na(data$birth_location_class)] <- 0
# Health Severity Score
# data$health_score<- coerc(data[["health_facility_barriers_class"]])+coerc(data[["health_facility_dist_class"]])+coerc(data[["health_facility_affected_class"]])+coerc(data[["health_priority_need_class"]])+coerc(data[["behavior_change_cause_class"]])+coerc(data[["birth_location_class"]])
data$health_score<- coerc(data[["health_facility_barriers_class"]])+coerc(data[["health_facility_dist_class"]])+coerc(data[["health_facility_affected_class"]])+coerc(data[["health_priority_need_class"]])+coerc(data[["behavior_change_cause_class"]])
data$health_severity<-car::recode(data$health_score,
"0:2='1';
3:5='2';
6:8='3';
9:16='4'")
data$health_sev_high<-ifelse(data$health_severity==3|data$health_severity==4,1,0)
#################################################################
# number sectoral needs ####
data$total_sectoral_needs<-coerc(data[["fsac_sev_high"]])+coerc(data[["prot_sev_high"]])+coerc(data[["esnfi_sev_high"]])+coerc(data[["wash_sev_high"]])+coerc(data[["nut_sev_high"]])+coerc(data[["edu_sev_high"]])+coerc(data[["health_sev_high"]])
#################################################################
### LSCI - coping strategies ####
# coping severity
data$lcsi_severity<-car::recode(data$lcsi_category,
"'food_secure'='minimal';
'marginally_insecure'='stress';
'moderately_insecure'='severe';
'severely_insecure'='extreme'")
## Indicators ####
### Numerators
## Some numerators combine variables calcualte those here
data$edu_age_boys_girls_num <- comp_score(data, c("boys_ed","girls_ed"))
food_water_rent_vars <- c(
"food_exp",
"water_expt",
"rent_exp"
)
data$food_water_rent_num <- comp_score(data, food_water_rent_vars)
all_expenses_vars <- c(
"food_exp",
"water_expt",
"rent_exp",
"fuel_exp",
"debt_exp")
data$all_expenses <- comp_score(data, all_expenses_vars)
min_die_vars <- c(
"minimum_dietary_diversity.staples",
"minimum_dietary_diversity.legumes",
"minimum_dietary_diversity.dairy",
"minimum_dietary_diversity.meat",
"minimum_dietary_diversity.eggs",
"minimum_dietary_diversity.vitamin_a_veg",
"minimum_dietary_diversity.other_veg")
data$min_die_num <- comp_score(data, min_die_vars)
priority_nfi_vars <- c(
"sleeping_mats",
"tarpaulin",
"cooking_pots",
"stainless_steel",
"water_storage",
"hygiene_sanitation"
)
data$priority_nfi_num <- comp_score(data, priority_nfi_vars)
child_vars <- c(
"males_0_2_total",
"males_3_5_total",
"females_0_2_total",
"females_3_5_total")
data$children_under5 <- comp_score(data, child_vars)
comp_ind_vars <- c(
"prot_sev_high",
"fsac_sev_high",
"esnfi_sev_high",
"wash_sev_high",
"edu_sev_high",
"health_sev_high"
)
data$comp_ind_sev <- comp_score(data, comp_ind_vars)
comp_ind_vars_nut <- c(
"prot_sev_high",
"fsac_sev_high",
"esnfi_sev_high",
"wash_sev_high",
"edu_sev_high",
"health_sev_high",
"nut_sev_high"
)
data$comp_ind_sev_nut <- comp_score(data, comp_ind_vars_nut)
## Age categories hh
data <- data %>%
mutate(
age_0_4_hh = case_when(
hoh_age <=4 ~ 1,
TRUE ~ 0
),
age_0_17_hh = case_when(
hoh_age <=17 ~ 1,
TRUE ~ 0
) ,
age_0_14_hh = case_when(
hoh_age <=14 ~ 1,
TRUE ~ 0
) ,
age_10_17_hh = case_when(
hoh_age >= 10 & hoh_age <=17 ~ 1,
TRUE ~ 0
) ,
age_15_64_hh = case_when(
hoh_age >= 14 & hoh_age <=64 ~ 1,
TRUE ~ 0
) ,
age_18_59_hh = case_when(
hoh_age >= 18 & hoh_age <=59 ~ 1,
TRUE ~ 0
) ,
age_18_64_hh = case_when(
hoh_age >= 18 & hoh_age <=64 ~ 1,
TRUE ~ 0
) ,
age_60_and_more_hh = case_when(
hoh_age >= 60 ~ 1,
TRUE ~ 0
) ,
age_65_hh = case_when(
hoh_age >= 65 ~ 1,
TRUE ~ 0
) ,
testt = case_when(
hoh_age < 120 ~ 1,
TRUE ~ 0
)
)
## Age categories roster
hh_group <- overall_hh_roster %>%
mutate(
age_0_4 = hh_member_age <=4,
age_0_17 = hh_member_age <=17,
age_0_14 = hh_member_age <=14,
age_10_17 = hh_member_age >= 10 & hh_member_age <=17,
age_15_64 = hh_member_age >= 14 & hh_member_age <=64,
age_18_59 = hh_member_age >= 18 & hh_member_age <=59,
age_18_64 = hh_member_age >= 18 & hh_member_age <=64,
age_60_and_more = hh_member_age >= 60,
age_65 = hh_member_age >= 65
) %>%
group_by(`_submission__uuid`) %>%
summarise(
age_0_4 = sum(age_0_4, na.rm = TRUE),
age_0_17 = sum(age_0_17, na.rm = TRUE),
age_0_14 = sum(age_0_14, na.rm = TRUE),
age_10_17 = sum(age_10_17, na.rm = TRUE),
age_15_64 = sum(age_15_64, na.rm = TRUE),
age_18_59 = sum(age_18_59, na.rm = TRUE),
age_18_64 = sum(age_18_64, na.rm = TRUE),
age_60_and_more = sum(age_60_and_more, na.rm = T),
age_65 = sum(age_65, na.rm = TRUE)
)
# Age Cat Vars
age_0_4_vars <- c(
'age_0_4',
'age_0_4_hh'
)
age_0_17_vars <- c(
'age_0_17',
'age_0_17_hh'
)
age_0_14_vars <- c(
'age_0_14',
'age_0_14_hh'
)
age_10_17_vars <- c(
'age_10_17',
'age_10_17_hh'
)
age_15_64_vars <- c(
'age_15_64',
'age_15_64_hh'
)
age_18_59_vars <- c(
'age_18_59',
'age_18_59_hh'
)
age_18_64_vars <- c(
'age_18_64',
'age_18_64_hh'
)
age_60_and_more_var <- c(
'age_60_and_more',
'age_60_and_more_hh'
)
age_65_var <- c(
'age_65',
'age_65_hh'
)
data <- full_join(data, hh_group,by = c("uuid"="_submission__uuid"))
# Merge Age cat hh_roster and hh data
data$age_0_4_merged <- comp_score(data,age_0_4_vars)
data$age_0_17_merged <- comp_score(data,age_0_17_vars)
data$age_0_14_merged <- comp_score(data,age_0_14_vars)
data$age_10_17_merged <- comp_score(data,age_10_17_vars)
data$age_15_64_merged <- comp_score(data,age_15_64_vars)
data$age_18_59_merged <- comp_score(data,age_18_59_vars)
data$age_18_64_merged <- comp_score(data,age_18_64_vars)
data$age_60_and_more_merged <- comp_score(data,age_60_and_more_var)
data$age_65_merged <- comp_score(data,age_65_var)
# Adjust displacement status as more information in other data
non_displ_data <- read.csv("input/Non_Displaced_Host_List_v2.csv",stringsAsFactors=F,na.strings = c("", "NA"))
data<-full_join(data, non_displ_data,by = c("district"="district"))
data$final_displacement_status_non_displ<-ifelse(data$final_displacement_status=='non_displaced'|data$final_displacement_status=='host', data$non_displ_class,data$final_displacement_status)
# prev_displacement
data <- data %>%
mutate(
# prev_displacement_num
prev_displacement_num_class = case_when(
prev_displacement_num == 2 ~ "2",
prev_displacement_num == 3 ~ "3",
prev_displacement_num >3 ~ "4+"
),
# refugee_displace_year
refugee_displace_year_class = case_when(
refugee_displace_year == 0 ~ "0",
refugee_displace_year == 1 ~ "1",
refugee_displace_year == 2 ~ "2",
refugee_displace_year == 3 ~ "3",
refugee_displace_year > 3 ~ "4+"
),
# cb_return_displace_year
cb_return_displace_year_class = case_when(
cb_return_displace_year == 0 ~ "0",
cb_return_displace_year == 1 ~ "1",
cb_return_displace_year == 2 ~ "2",
cb_return_displace_year == 3 ~ "3",
cb_return_displace_year > 3 ~ "4+"
),
# cb_return_return_year
cb_return_return_year_call = case_when(
cb_return_return_year == 0 ~ "0",
cb_return_return_year == 1 ~ "1",
cb_return_return_year == 2 ~ "2",
cb_return_return_year == 3 ~ "3",
cb_return_return_year > 3 ~ "4+"
),
# idp_displ_year
idp_displ_year_class = case_when(
idp_displ_year == 0 ~ "0",
idp_displ_year == 1 ~ "1",
idp_displ_year == 2 ~ "2",
idp_displ_year == 3 ~ "3",
idp_displ_year > 3 ~ "4+"
),
# head of household age_group
hoh_age_group = case_when(
hoh_age >= 65 ~ "65+",
hoh_age < 65 ~ "<65"
),
# head of household disabled
hoh_disabled = case_when(
wg_walking == "yes" | wg_selfcare == "yes" ~ "disabled",
wg_walking == "no" | wg_selfcare == "no" ~ "not_disabled",
TRUE ~ NA_character_
),
pregnant_member = case_when(
pregnant > 0 ~ "at_least_one_mem_pregnant",
pregnant == 0 ~ "no_mem_pregnent",
TRUE ~ NA_character_
),
lactating_member = case_when(
lactating > 0 ~ "at_least_one_mem_lactating",
lactating == 0 ~ "no_mem_lactating",
TRUE ~ NA_character_
),
pregnant_lactating_member = case_when(
pregnant > 0 | lactating > 0 ~ "at_least_one_mem_pregnant_lactating",
pregnant == 0 & lactating == 0 ~ "no_mem_pregnent_lactating",
TRUE ~ NA_character_
),
female_literacy_yes_no = case_when(
female_literacy == 0 ~ "0",
female_literacy >= 1 ~ "1 or more",
TRUE ~ NA_character_
),
male_literacy_yes_no = case_when(
male_literacy == 0 ~ "0",
male_literacy >= 1 ~ "1 or more",
TRUE ~ NA_character_
),
# How many adults 18+ years worked outside of the household in the last 30 days?
adults_working_yes_no = case_when(
adults_working == 0 ~ "0",
adults_working >= 1 ~ "1 or more",
TRUE ~ NA_character_
),
children_working_yes_no = case_when(
children_working == 0 ~ "0",
children_working >= 1 ~ "1 or more",
TRUE ~ NA_character_
),
ag_income_cal = case_when(
ag_income == 0 ~ 0,
ag_income > 0 ~ ag_income / hh_size,
TRUE ~ NA_real_
),
livestock_income_cal = case_when(
livestock_income == 0 ~ 0,
livestock_income > 0 ~ livestock_income / hh_size,
TRUE ~ NA_real_
),
rent_income_cal = case_when(
rent_income == 0 ~ 0,
rent_income > 0 ~ rent_income / hh_size,
TRUE ~ NA_real_
),
small_business_income_cal = case_when(
small_business_income == 0 ~ 0,
small_business_income > 0 ~ small_business_income / hh_size,
TRUE ~ NA_real_
),
unskill_labor_income_cal = case_when(
unskill_labor_income == 0 ~ 0,
unskill_labor_income > 0 ~ unskill_labor_income / hh_size,
TRUE ~ NA_real_
),
skill_labor_income_cal = case_when(
skill_labor_income == 0 ~ 0,
skill_labor_income > 0 ~ skill_labor_income / hh_size,
TRUE ~ NA_real_
),
formal_employment_income_cal = case_when(
formal_employment_income == 0 ~ 0,
formal_employment_income > 0 ~ formal_employment_income / hh_size,
TRUE ~ NA_real_
),
gov_benefits_income_cal = case_when(
gov_benefits_income == 0 ~ 0,
gov_benefits_income > 0 ~ gov_benefits_income / hh_size,
TRUE ~ NA_real_
),
hum_assistance_income_cal = case_when(
hum_assistance_income == 0 ~ 0,
hum_assistance_income > 0 ~ hum_assistance_income / hh_size,
TRUE ~ NA_real_
),
remittance_income_cal = case_when(
remittance_income == 0 ~ 0,
remittance_income > 0 ~ remittance_income / hh_size,
TRUE ~ NA_real_
),
loans_income_cal = case_when(
loans_income == 0 ~ 0,
loans_income > 0 ~ loans_income / hh_size,
TRUE ~ NA_real_
),
asset_selling_income_cal = case_when(
asset_selling_income == 0 ~ 0,
asset_selling_income > 0 ~ asset_selling_income / hh_size,
TRUE ~ NA_real_
),
total_income_cal = case_when(
total_income == 0 ~ 0,
total_income > 0 ~ total_income / hh_size,
TRUE ~ NA_real_
),
# Debt level
debt_amount_cal = case_when(
debt_amount == 0 ~ 0,
debt_amount > 0 ~ debt_amount / hh_size,
TRUE ~ NA_real_),
food_exp_cal = case_when(
total_income == 0 ~ 0,
total_income > 0 ~ food_exp / total_income,
TRUE ~ NA_real_
),
water_expt_cal = case_when(
total_income == 0 ~ 0,
total_income > 0 ~ water_expt / total_income,
TRUE ~ NA_real_
),
rent_exp_cal = case_when(
total_income == 0 ~ 0,
total_income > 0 ~ rent_exp / total_income,
TRUE ~ NA_real_
),
fuel_exp_cal = case_when(
total_income == 0 ~ 0,
total_income > 0 ~ fuel_exp / total_income,
TRUE ~ NA_real_
),
debt_exp_cal = case_when(
total_income == 0 ~ 0,
total_income > 0 ~ debt_exp / total_income,
TRUE ~ NA_real_
),
basic_needs_cal = case_when(
total_income == 0 ~ 0,
food_water_rent_num > 0 ~ food_water_rent_num / total_income,
TRUE ~ NA_real_
),
minimum_dietary_diversity_cal = case_when(
min_die_num >= 4 ~ "4 food groups",
min_die_num < 4 ~ "<4 food groups",
TRUE ~ NA_character_
),
rooms_hh_cal = case_when(
rooms > 0 ~ hh_size / rooms,
TRUE ~ 0
),
blankets_people_cal = case_when(
blankets_number == 0 ~ 0,
blankets_number > 0 ~ blankets_number / hh_size,
TRUE ~ NA_real_
),
blankets_suff_cal = case_when(
blankets_people_cal < 1 ~ "<1",
blankets_people_cal >= 1 ~ "1+",
TRUE ~ NA_character_
),
priority_nfi_cal = case_when(
priority_nfi_num <= 1 ~ "0_1",
priority_nfi_num <= 3 ~ "2_3",
priority_nfi_num <= 5 ~ "4_5",
priority_nfi_num <= 6 ~ "6",
TRUE ~ NA_character_
),
imp_energy_source1_cal = case_when(
energy_source %in% c("wood" , "animal_waste" , "paper_waste") ~ 1,
TRUE ~ 0
),
imp_energy_source2_cal = case_when(
energy_source %in% c("coal" , "charcoal" , "lpg" , "electricity") ~ 1,
TRUE ~ 0
),
diarrhea_cases_cal = case_when(
diarrhea_cases == 0 ~ 0,
diarrhea_cases > 0 ~ diarrhea_cases / diarrhea_total,
TRUE ~ NA_real_
),
perc_diarrhea_cases_cal = case_when(
diarrhea_cases == 0 ~ "0",
diarrhea_cases > 0 ~ ">=1",
TRUE ~ NA_character_
),
imp_water_source1_cal = case_when(
water_source %in% c("handpump_private", "handpump_public",
"piped_public", "spring_protected") ~ 1,
TRUE ~ 0
),
imp_water_source2_cal = case_when(
water_source %in% c("spring_unprotected","surface_water"
, "water_trucking", "other") ~ 1,
TRUE ~ 0
),
imp_san_source1_cal = case_when(
water_source %in% c("open", "pit_latrine_uncovered",
"other") ~ 1,
TRUE ~ 0
),
imp_san_source2_cal = case_when(
water_source %in% c("public_latrine", "pit_latrine_covered",
"vip_latrine", "flush_toilet_open_drain",
"flush_toilet_septic") ~ 1,
TRUE ~ 0
),
comp_ind_sev_2_call = case_when(
comp_ind_sev >= 2 ~ ">=2",
comp_ind_sev <2 ~ "<2",
TRUE ~ NA_character_
),
comp_ind_sev_2_nut_call = case_when(
comp_ind_sev_nut >= 2 ~ ">=2",
comp_ind_sev_nut <2 ~ "<2",
TRUE ~ NA_character_
),
comp_ind_sev_3_call = case_when(
comp_ind_sev >= 3 ~ ">=3",
comp_ind_sev < 3 ~ "<3",
TRUE ~ NA_character_
),
dep_ratio_call = case_when(
age_0_4_merged == 0 & age_65_merged == 0 ~ 0,
(age_0_14_merged > 0 | age_65_merged > 0) ~
sum(age_0_14_merged,age_65_merged, na.rm = TRUE)/sum(age_15_64_merged,na.rm = TRUE),
TRUE ~ NA_real_
),
dep_ratio_call_2 = case_when(
age_0_17_merged == 0 & age_60_and_more_merged == 0 ~ 0,
(age_0_17_merged > 0 | age_60_and_more_merged > 0) ~
sum(age_0_17_merged,age_60_and_more_merged, na.rm = TRUE)/sum(age_18_59_merged ,na.rm = TRUE),
TRUE ~ NA_real_
),
female_lit_call = case_when(
female_literacy == 0 ~ 0,
female_literacy == 0 ~
female_literacy/sum(females_11_17_total,females_18_plus_total, na.rm=TRUE),
TRUE ~ NA_real_
),
male_lit_call = case_when(
male_literacy == 0 ~ 0,
male_literacy == 0 ~
male_literacy/sum(males_11_17_total,males_18_plus_total, na.rm=TRUE),
TRUE ~ NA_real_
),
adult_behavior_change_call = case_when(
adult_behavior_change == "yes" ~ 1,
adult_behavior_change == "no" ~ 0,
TRUE ~ NA_real_
),
child_behavior_change_call = case_when(
child_behavior_change == "yes" ~ 1,
child_behavior_change == "no" ~ 0,
TRUE ~ NA_real_
),
atleast_one_behav_change_call = case_when(
child_behavior_change_call == 0 & adult_behavior_change_call == 0 ~ 0,
child_behavior_change_call > 0 | adult_behavior_change_call > 0 ~ 1,
TRUE ~ NA_real_
),
adults_working_call = case_when(
adults_working == 0 ~ 0,
adults_working > 0 & age_18_64 > 0 ~ adults_working/age_18_64,
TRUE ~ NA_real_
),
child_working_call = case_when(
is.na(children_working) ~ 0,
children_working == 0 ~ 0,
children_working > 0 & age_10_17 > 0 ~ children_working/age_10_17,
TRUE ~ NA_real_
),
adult_tazkira_cal = case_when(
adult_tazkira == 0 ~ "0",
adult_tazkira >= 1 ~ ">=1",
TRUE ~ NA_character_
),
child_tazkira_cal = case_when(
child_tazkira == 0 ~ "0",
child_tazkira >= 1 ~ ">=1",
TRUE ~ NA_character_
),
child_tazkira_cal = case_when(
child_tazkira == 0 ~ "0",
child_tazkira >= 1 ~ ">=1",
TRUE ~ NA_character_
),
any_tazkira_cal = case_when(
adult_tazkira == 0 & child_tazkira == 0~ "0",
adult_tazkira >= 1 | child_tazkira >= 1~ ">=1",
TRUE ~ NA_character_
),
insuf_blank_energy = case_when(
blankets_suff_cal == "<1" & energy_source == "wood" | energy_source == "paper_waste" | energy_source == "animal_waste" ~ "yes",
TRUE ~ "no"
),
current_presence_mines = case_when(
displ_explosive_presence == "both" | displ_explosive_presence == "current" |
nondispl_explosive_presence == "yes" ~ "current_explosive_presence",
displ_explosive_presence == "previous" | displ_explosive_presence == "no" |
nondispl_explosive_presence == "no" ~ "no_explosive_presence",
TRUE ~ NA_character_
),
# child_working_call = case_when(
# children_working == 0 ~ 0,
# children_working > 0 ~ children_working/age_10_17,
# TRUE ~ NA_real_
# ),
count_current_enrolled_avg = count_current_enrolled / edu_age_boys_girls_num,
count_current_attending_avg = count_current_attending / edu_age_boys_girls_num
)
# Major events
major_events_vars <- c(
"major_events.avalanche",
"major_events.conflict",
"major_events.drought",
"major_events.earthquake",
"major_events.floods",
"major_events.other"
)
major_events_score <- (rowSums(data[major_events_vars]))
data <- data %>%
mutate(
major_events_cal = case_when(
major_events_score == 0 ~ "none",
major_events_score == 1 ~ "1",
major_events_score == 2 ~ "2",
major_events_score >= 3 ~ ">= 3",
TRUE ~ NA_character_
)
)
# hno_intersectoral analysis
data <- data %>%
mutate(
# GBV incident OR threat
gbv_incidents_threats = case_when(
other_incidents == "sgbv" | other_concerns == "sgbv" ~ ">=1",
(other_incidents == "no" | other_incidents == "other") &
(other_concerns == "no" | other_concerns == "other") ~ "0",
TRUE ~ NA_character_
),
# At least one protection incident for adult OR child
prot_incident_adult_child = case_when(
adult_prot_incidents != "none" | child_prot_incidents != "none" ~ ">=1",
adult_prot_incidents == "none" & child_prot_incidents == "none" ~ "0",
TRUE ~ NA_character_
),
# Total income per day per household member in USD
daily_income_hh_members = case_when(
(((total_income / 30) / hh_size) / 78.36) > 1.90 ~ 1,
(((total_income / 30) / hh_size) / 78.36) <= 1.90 ~ 0,
TRUE ~ NA_real_
),
# health
health_service_access_class = case_when(
health_facility_access == "no" ~ 1,
health_facility_access == "yes" ~ 0,
TRUE ~ NA_real_
),
#ESNFI
shelter_type_access_class = case_when(
shelter == "tent" | shelter == "makeshift_shelter" |
shelter == "collective_centre" | shelter == "open_space" ~ 1,
shelter == "transitional" | shelter == "permanent" ~ 0,
TRUE ~ NA_real_
),
#EiE
hh_level_school_attendance_class = case_when(
count_current_attending > 0 ~ 1,
TRUE ~ 0
),
#FSA No data for Farah paper interviews
market_service_access_class = case_when(
market_access == "no" ~ 1,
market_access == "yes" ~ 0,
TRUE ~ NA_real_
),
#protection
identity_ownership_class = case_when(
child_tazkira == 0 & adult_tazkira == 0 ~ 1,
child_tazkira >=1 | adult_tazkira >=1 ~ 0,
TRUE ~ NA_real_
),
#WASH
access_to_water_class = case_when(
water_source == "handpump_private" | water_source == "handpump_public" |
water_source == "piped_public" | water_source == "spring_protected" ~ 1,
water_source == "spring_unprotected" | water_source == "surface_water" |
water_source == "water_trucking" | water_source == "other" ~ 0,
TRUE ~ NA_real_
)
)
access_services_vars <- c("health_service_access_class", "shelter_type_access_class",
"hh_level_school_attendance_class", "market_service_access_class",
"identity_ownership_class","access_to_water_class")
data$services_score <- comp_score(data, access_services_vars)
data <- data %>%
mutate(
comp_ind_access_services = case_when(
services_score <= 2 ~ 0,
services_score >= 3 ~ 1
)
)
#Recoding new variables
data$hh_no_tazkira <- ifelse(data$tazkira_total < 1, "Tazkira_No", "Tazkira_Yes")
data$muac_yes_no <- ifelse(data$muac_total > 0 & !is.na(data$min_muac) ,"Yes","No")
data$recent_non_recent <- ifelse(data$final_displacement_status_non_displ == "recent_idps", "recent_idps",
ifelse(data$final_displacement_status_non_displ == "non_recent_idps", "non_recent_idps", NA ))
data$edu_removal_shock_cal <- ifelse(data$shock_class == 5, "Yes", "No")
data$enrolled_attending <- ifelse(data$count_enrolled_attending > 0, "Enrolled_and_Attending", "Not" )
data$schoo_age_boys_girls <- coerc(data$boys_ed) + coerc(data$girls_ed)
## source disaggs
source("r/prepare_disagg.R")
################ MEB analysis ###########################################################
# sustainable income vars
sustainable_income_vars <- c(
'ag_income',
'livestock_income',
'rent_income',
'small_business_income',
'unskill_labor_income',
'skill_labor_income',
'formal_employment_income',
'gov_benefits_income'
)
# sustainable income per HH
data$sustainable_income <- comp_score(data, sustainable_income_vars)
# sustainable income per HH member
data$sustainable_income_per_mem <- data$sustainable_income / data$hh_size
# net inocome per hh
data$net_income <- data$sustainable_income - data$all_expenses
# total Exp per hh memeber
data$total_exp_per_mem <- data$all_expenses / data$hh_size
data <- data %>%
mutate(
food_exp_per_mem = food_exp / hh_size,
water_expt_per_mem = water_expt / hh_size,
rent_exp_per_mem = rent_exp / hh_size,
fuel_exp_per_mem = fuel_exp / hh_size,
debt_exp_per_mem = debt_exp / hh_size,
food_exp_spent = case_when(
food_exp > 0 ~ 1,
food_exp == 0 ~ 0,
TRUE ~ NA_real_
),
water_expt_spent = case_when(
water_expt > 0 ~ 1,
water_expt == 0 ~ 0,
TRUE ~ NA_real_
),
rent_exp_spent = case_when(
rent_exp > 0 ~ 1,
rent_exp == 0 ~ 0,
TRUE ~ NA_real_
),
fuel_exp_spent = case_when(
fuel_exp > 0 ~ 1,
fuel_exp == 0 ~ 0,
TRUE ~ NA_real_
),
debt_exp_spent = case_when(
debt_exp > 0 ~ 1,
debt_exp == 0 ~ 0,
TRUE ~ NA_real_
)
)
# sustainable income 2 vars
sustainable_income_2_vars <- c(
'ag_income',
'livestock_income',
'rent_income',
'small_business_income',
'unskill_labor_income',
'skill_labor_income',
'formal_employment_income'
)
# sustainable income per HH
data$sustainable_income_2 <- comp_score(data, sustainable_income_2_vars)
# sustainable income per HH member
data$sustainable_income_2_per_mem <- data$sustainable_income_2 / data$hh_size
# unskilled_labor_income per hh member
data$unskill_labor_income_per_mem <- data$unskill_labor_income / data$hh_size
# unskilled + agriculture + livestock income vars
unskill_ag_live_income_vars <- c(
'ag_income',
'livestock_income',
'unskill_labor_income'
)
# unskilled + agriculture + livestock income
data$unskill_ag_live_income_income <- comp_score(data, unskill_ag_live_income_vars)
# unskilled + agriculture + livestock income per HH member
data$unskill_ag_live_income_income_per_mem <- data$unskill_ag_live_income_income / data$hh_size
########################################################################################
############################# Vulnerablity composites ###################################
data <- data %>%
mutate(
hoh_disabled_vul_class = case_when(
data$hoh_disabled == "disabled" ~ 1,
data$hoh_disabled == "not_disabled" ~ 0,
TRUE ~ 0
),
hoh_debt_disagg_vul_class = case_when(
hoh_debt_disagg == "high_debt" ~ 1,
hoh_debt_disagg == "low_debt" ~ 0,
hoh_debt_disagg == "medium_debt" ~ 0,
hoh_debt_disagg == "no_debt" ~ 0,
TRUE ~ 0
),
tazkira_disagg_vul_class = case_when(
tazkira_disagg == "non_have_tazkira" ~ 1,
tazkira_disagg == "all_have_tazkira" ~ 0,
TRUE ~ 0
),
hoh_age_group_vul_class = case_when(
hoh_age_group == "65+" ~ 1,
hoh_age_group == "<65" ~ 0,
TRUE ~ 0
),
hoh_sex_disagg_vul_class = case_when(
hoh_sex_disagg == "female" ~ 1,
hoh_sex_disagg == "male" ~ 0,
TRUE ~ 0
),
pregnant_lactating_member_vul_class = case_when(
pregnant_lactating_member == "at_least_one_mem_pregnant_lactating" ~ 1,
pregnant_lactating_member == "no_mem_pregnent_lactating" ~ 0,
TRUE ~ 0
),
chronic_illness_vul_class = case_when(
chronic_illness == "yes" ~ 1,
chronic_illness == "no " ~ 0,
chronic_illness == "no_answer" ~ 0,
TRUE ~ 0
),
literacy_vul_class = case_when(
female_literacy_yes_no == "0" & male_literacy_yes_no == "0" ~ 1,
female_literacy_yes_no == "1 or more" ~ 0,
male_literacy_yes_no == "1 or more" ~ 0,
TRUE ~ 0
),
behav_change_disagg_vul_class = case_when(
behav_change_disagg == "yes" ~ 1,
behav_change_disagg == "no" ~ 0,
TRUE ~ 0
),
female_literacy_yes_no_class = case_when(
female_literacy_yes_no == "0" ~ 1,
female_literacy_yes_no == "1 or more" ~ 0,
TRUE ~ 0
),
behavior_change_cause_class2 = case_when(
behavior_change_cause == "yes" ~ 1,
TRUE ~ 0
),
child_behavior_change_class2 = case_when(
child_behavior_change == "yes" ~ 1,
TRUE ~ 0
),
adult_behavior_change_class2 = case_when(
adult_behavior_change == "yes" ~ 1,
TRUE ~ 0
),
adult_behavior_only_by_conflict = case_when(
adult_behavior_change == "yes" & behavior_change_cause == "yes" ~ 1,
TRUE ~ 0
)
)
## Vulnerable_group_1
Vulnerable_group_1_vars <- c(
"hoh_disabled_vul_class",
"hoh_debt_disagg_vul_class",
"tazkira_disagg_vul_class",
"hoh_age_group_vul_class",
"hoh_sex_disagg_vul_class",
"chronic_illness_vul_class",
"literacy_vul_class"
)
data$Vulnerable_group_1_vars_score <- comp_score(data, Vulnerable_group_1_vars)
data <- data %>%
mutate(
vulnerable_group_1 = case_when(
Vulnerable_group_1_vars_score >= 1 ~ "vulnerable",
Vulnerable_group_1_vars_score == 0 ~ "not_vulnerable",
TRUE ~ NA_character_
)
)
## vulnerable_group_4
Vulnerable_group_4_vars <- c(
"hoh_disabled_vul_class",
"hoh_debt_disagg_vul_class",
"tazkira_disagg_vul_class",
"hoh_age_group_vul_class",
"hoh_sex_disagg_vul_class",
"behav_change_disagg_vul_class"
)
data$Vulnerable_group_4_vars_score <- comp_score(data, Vulnerable_group_4_vars)
data <- data %>%
mutate(
vulnerable_group_4 = case_when(
Vulnerable_group_4_vars_score >= 1 ~ "vulnerable",
Vulnerable_group_4_vars_score == 0 ~ "not_vulnerable",
TRUE ~ NA_character_
)
)
## Vulnerable_group_5
Vulnerable_group_5_vars <- c(
"hoh_disabled_vul_class",
"hoh_debt_disagg_vul_class",
"tazkira_disagg_vul_class",
"hoh_age_group_vul_class",
"hoh_sex_disagg_vul_class",
"pregnant_lactating_member_vul_class",
"chronic_illness_vul_class",
"behav_change_disagg_vul_class",
"literacy_vul_class"
)
data$Vulnerable_group_5_vars_score <- comp_score(data, Vulnerable_group_5_vars)
data <- data %>%
mutate(
vulnerable_group_5 = case_when(
Vulnerable_group_5_vars_score >= 1 ~ "vulnerable",
Vulnerable_group_5_vars_score == 0 ~ "not_vulnerable",
TRUE ~ NA_character_
)
)
## Vulnerable_group_6
Vulnerable_group_6_vars <- c(
"hoh_disabled_vul_class",
"hoh_debt_disagg_vul_class",
"tazkira_disagg_vul_class",
"hoh_age_group_vul_class",
"hoh_sex_disagg_vul_class",
"behav_change_disagg_vul_class",
"female_literacy_yes_no_class"
)
data$Vulnerable_group_6_vars_score <- comp_score(data, Vulnerable_group_6_vars)
data <- data %>%
mutate(
vulnerable_group_6 = case_when(
Vulnerable_group_6_vars_score >= 1 ~ "vulnerable",
Vulnerable_group_6_vars_score == 0 ~ "not_vulnerable",
TRUE ~ NA_character_
)
)
## Vulnerable_group_7
## vulnerable_group_7
Vulnerable_group_7_vars <- c(
"hoh_disabled_vul_class",
"hoh_debt_disagg_vul_class",
"tazkira_disagg_vul_class",
"hoh_age_group_vul_class",
"hoh_sex_disagg_vul_class",
"behavior_change_cause_class2"
)
data$Vulnerable_group_7_vars_score <- comp_score(data, Vulnerable_group_7_vars)
data <- data %>%
mutate(
vulnerable_group_7 = case_when(
Vulnerable_group_7_vars_score >= 1 ~ "vulnerable",
Vulnerable_group_7_vars_score == 0 ~ "not_vulnerable",
TRUE ~ NA_character_
)
)
## vulnerable_group_8
Vulnerable_group_8_vars <- c(
"hoh_disabled_vul_class",
"hoh_debt_disagg_vul_class",
"tazkira_disagg_vul_class",
"hoh_age_group_vul_class",
"hoh_sex_disagg_vul_class",
"adult_behavior_only_by_conflict"
)
data$Vulnerable_group_8_vars_score <- comp_score(data, Vulnerable_group_8_vars)
data <- data %>%
mutate(
vulnerable_group_8 = case_when(
Vulnerable_group_8_vars_score >= 1 ~ "vulnerable",
Vulnerable_group_8_vars_score == 0 ~ "not_vulnerable",
TRUE ~ NA_character_
)
)
###############################################end
#########################
## esnfi_new_indicator_1
data <- data %>%
mutate(
shelter_class2 = case_when(
shelter == "tent" | shelter == "collective_centre" | shelter == "makeshift_shelter" |
shelter == "open_space" ~ 1,
shelter == "transitional" | shelter == "permanent" ~ 0,
TRUE ~ 0
),
shelter_damage_and_repair_class = case_when(
(shelter_damage.due_to_conflict == 1 | shelter_damage.due_to_natural_disaster == 1) &
shelter_damage_repair == "no" ~ 1,
shelter_damage.no == 1 | shelter_damage_repair == "yes" ~ 0,
TRUE ~ 0
),
priority_nfi_cal_class = case_when(
priority_nfi_cal == "0_1" | priority_nfi_cal == "2_3" ~ 1,
priority_nfi_cal == "4_5" | priority_nfi_cal == "6" ~ 0,
TRUE ~ 0
)
)
esnfi_new_indicator_1_vars <- c(
"shelter_class2",
"shelter_damage_and_repair_class",
"priority_nfi_cal_class"
)
esnfi_new_indicator_1_vars_score <- comp_score(data, esnfi_new_indicator_1_vars)
data <- data %>%
mutate(
esnfi_new_indicator_1 = case_when(
esnfi_new_indicator_1_vars_score >= 1 ~ 1,
esnfi_new_indicator_1_vars_score == 0 ~ 0,
TRUE ~ 0
)
)
###################################end
######## wash_new_indicator 1#######
data <- data %>%
mutate(
water_source_class2 = case_when(
water_source == "spring_unprotected" | water_source == "surface_water" | water_source == "water_trucking" |
water_source == "other" ~ 1,
water_source == "handpump_private" | water_source == "handpump_public" | water_source == "handpump_public" |
water_source == "spring_protected" ~ 0,
TRUE ~ 0
),
latrine_class2 = case_when(
latrine == "open" | latrine == "pit_latrine_uncovered" | latrine == "other" ~ 1,
latrine == "public_latrine" | latrine == "pit_latrine_covered" | latrine == "vip_latrine" |
latrine == "flush_toilet_open_drain" | latrine == "flush_toilet_septic" ~ 0,
TRUE ~ 0
),
soap_class2 = case_when(
soap == "no" ~ 1,
soap == "yes_didnt_see" | soap == "yes_saw" ~ 0,
TRUE ~ 0
),
diarrhea_cases_class = case_when(
diarrhea_cases >= 1 ~ 1,
diarrhea_cases == 0 ~ 0,
TRUE ~ 0
),
diarrhea_cases_class_children = case_when(
diarrhea_cases >= 1 ~ 1,
diarrhea_cases == 0 ~ 0,
TRUE ~ NA_real_
)
)
wash_new_indicator_1_vars <- c(
"water_source_class2",
"latrine_class2",
"soap_class2"
)
wash_new_indicator_1_vars_score <- comp_score(data, wash_new_indicator_1_vars)
data <- data %>%
mutate(
wash_new_indicator_1 = case_when(
wash_new_indicator_1_vars_score >= 2 ~ 1,
wash_new_indicator_1_vars_score == 0 ~ 0,
TRUE ~ 0
)
)
##############################################end
######## wash_new_indicator 2 #######
wash_new_indicator_2_vars <- c(
"water_source_class2",
"latrine_class2",
"soap_class2",
"diarrhea_cases_class"
)
wash_new_indicator_2_vars_score <- comp_score(data, wash_new_indicator_2_vars)
data <- data %>%
mutate(
wash_new_indicator_2 = case_when(
wash_new_indicator_2_vars_score >= 2 ~ 1,
wash_new_indicator_2_vars_score == 0 ~ 0,
TRUE ~ 0
)
)
############# winterization_indicator ##################
data <- data %>%
mutate(
shelter_class3 = case_when(
shelter == "tent" | shelter == "collective_centre" | shelter == "makeshift_shelter" |
shelter == "open_space" ~ 1,
shelter == "transitional" | shelter == "permanent" ~ 0,
TRUE ~ 0
),
blankets_suff_cal_class = case_when(
blankets_suff_cal == "<1" ~ 1,
blankets_suff_cal == "1+" ~ 0,
TRUE ~ 0
),
energy_source_class = case_when(
energy_source == "animal_waste" | energy_source == "charcoal" | energy_source == "paper_waste" |
energy_source == "wood" ~ 1,
energy_source == "coal" | energy_source == "lpg" | energy_source == "electricity" |
energy_source == "other" ~ 0,
TRUE ~ 0
)
)
winterization_indicator_vars <- c(
"shelter_class3",
"blankets_suff_cal_class",
"energy_source_class"
)
winterization_indicator_vars_score <- comp_score(data, winterization_indicator_vars)
data <- data %>%
mutate(
winterization_indicator = case_when(
winterization_indicator_vars_score >= 2 ~ 1,
winterization_indicator_vars_score == 0 ~ 0,
TRUE ~ 0
)
)
################################################end
#################### dip push factors ############
ipd_push_factors_vars <- c(
'idp_push_factors.active_conflict',
'idp_push_factors.anticipated_conflict',
'idp_push_factors.earthquake',
'idp_push_factors.floods',
'idp_push_factors.avalanche',
'idp_push_factors.drought',
'idp_push_factors.poverty',
'idp_push_factors.service_access',
'idp_push_factors.other'
)
ipd_push_factors_vars_short <- c(
'idp_push_factors.active_conflict',
'idp_push_factors.anticipated_conflict',
'idp_push_factors.earthquake',
'idp_push_factors.floods',
'idp_push_factors.avalanche',
'idp_push_factors.drought'
)
data$ipd_push_factors_vars_score <- comp_score(data, ipd_push_factors_vars)
data$ipd_push_factors_vars_score_short <- comp_score(data, ipd_push_factors_vars_short)
data <- data %>%
mutate(
idp_push_factors_cat = case_when(
ipd_push_factors_vars_score == 1 ~ "1_event",
ipd_push_factors_vars_score < 3 ~ "2_events",
ipd_push_factors_vars_score >=3 ~ "3_or_more_events",
TRUE ~ NA_character_
),
idp_push_factors_cat_short = case_when(
ipd_push_factors_vars_score_short == 1 ~ "1_event",
ipd_push_factors_vars_score_short < 3 ~ "2_events",
ipd_push_factors_vars_score_short >=3 ~ "3_or_more_events",
TRUE ~ NA_character_
)
)
##################### MSNI #######################
#### IMPACT
data <- data %>%
mutate(
major_events_impc = case_when(
major_events_cal == ">= 3" | major_events_cal == "2" ~ 3,
major_events_cal == "1" ~ 1,
TRUE ~ 0
),
agricultural_impact_how_impc = case_when(
agricultural_impact_how == "51_75" ~ 1,
agricultural_impact_how == "76_100" ~ 2,
TRUE ~ 0
),
livestock_impact_how_impc = case_when(
livestock_impact_how.livestock_died == 1 ~ 1,
livestock_impact_how.left_unattended == 1 ~ 1,
TRUE ~ 0
),
explosive_impact_death_impc = case_when(
explosive_impact.injury_death == 1 ~ 3,
TRUE ~ 0
),
explosive_impact_others_impc = case_when(
explosive_impact.psych_impact == 1 | explosive_impact.relocation == 1 |
explosive_impact.access_services == 1 | explosive_impact.restrict_recreation == 1 |
explosive_impact.livelihoods_impact == 1 | explosive_impact.other == 1 &
explosive_impact.injury_death != 1 ~ 2,
TRUE ~ 0
),
adult_injuries_cause_impc = case_when(
adult_injuries_cause == "conflict" | adult_injuries_cause == "natural_disaster" |
child_injuries_cause == "conflict" | child_injuries_cause == "natural_disaster" ~ 3,
TRUE ~ 0
),
shelter_damage_impc = case_when(
shelter_damage == "due_to_conflict" | shelter_damage == "due_to_natural_disaster" ~ 2,
TRUE ~ 0
),
edu_removal_shock_impc = case_when(
count_shock >= 1 ~ 1,
TRUE ~ 0
),
health_facility_reopened_impc = case_when(
health_facility_reopened == "remain_closed" ~ 1,
TRUE ~ 0
),
water_damaged_cause_impc = case_when(
water_damaged_cause == "conflict" | water_damaged_cause == "natural_disaster" |
water_damaged_cause == "drought" ~ 2,
TRUE ~ 0
),
aid_access_issue_2_impc = case_when(
aid_access_issue == "yes" & aid_access_issue_type == "insecurity" |
aid_access_issue_type == "explosive_hazards" ~ 2,
TRUE ~ 0
),
aid_access_issue_1_impc = case_when(
aid_access_issue == "yes" & aid_access_issue_type == "distance" |
aid_access_issue_type == "social_restrictions" ~ 1,
TRUE ~ 0
)
)
# impact class vars
msni_impact_score_vars <- c(
"major_events_impc",
"agricultural_impact_how_impc",
"livestock_impact_how_impc",
"explosive_impact_death_impc",
"explosive_impact_others_impc",
"adult_injuries_cause_impc",
"shelter_damage_impc",
"edu_removal_shock_impc",
"health_facility_reopened_impc",
"water_damaged_cause_impc",
"aid_access_issue_2_impc",
"aid_access_issue_1_impc"
)
# impact score
data$msni_impact_score <- comp_score(data, msni_impact_score_vars)
# impact severity
data <- data %>%
mutate(
impact = case_when(
msni_impact_score < 3 ~ 1,
msni_impact_score > 2 & msni_impact_score < 6 ~ 2,
msni_impact_score > 5 & msni_impact_score < 9 ~ 3,
msni_impact_score >= 9 ~ 4,
TRUE ~ 0
)
)
#### End IMPACT
#### Capacity gaps
data <- data %>%
mutate(
capacity_gaps = case_when(
lcsi_severity == "minimal" ~ 1,
lcsi_severity == "stress" ~ 2,
lcsi_severity == "severe" ~ 3,
lcsi_severity == "extreme" ~ 4,
TRUE ~ NA_real_
)
)
#### End Capacity gaps
# HC-LSG/ESNFI - shelter_lsg
data <- data %>%
mutate(
shelter_type_lsg = case_when(
shelter == "open_space" ~ 3,
shelter == "tent" | shelter == "makeshift_shelter" | shelter == "collective_centre" ~ 2,
# shelter == "transitional" ~ 1,
TRUE ~ 0
),
shelter_damage_lsg = case_when(
shelter_damage_extent == "fully_destroyed" & shelter_damage_repair == "no" ~ 3,
shelter_damage_extent == "significant_damage" & shelter_damage_repair == "no" ~ 2,
TRUE ~ 0
),
winterisation_lsg = case_when(
blankets_suff_cal == "<1" & (energy_source == "animal_waste" | energy_source == "paper_waste" |
energy_source == "wood") ~ 3,
TRUE ~ 0
),
access_nfi_lsg = case_when(
priority_nfi_num < 3 ~ 3,
priority_nfi_num > 2 & priority_nfi_num < 6 ~ 2,
TRUE ~ 0
)
)
# shelter_lsg class vars
msni_shelter_lsg_vars <- c(
"shelter_type_lsg",
"shelter_damage_lsg",
"winterisation_lsg",
"access_nfi_lsg"
)
# shelter_lsg score
data$msni_shelter_lsg_score <- comp_score(data, msni_shelter_lsg_vars)
# shelter_lsg severity
data <- data %>%
mutate(
shelter_lsg = case_when(
msni_shelter_lsg_score < 3 ~ 1,
msni_shelter_lsg_score > 2 & msni_shelter_lsg_score < 6 ~ 2,
msni_shelter_lsg_score > 5 & msni_shelter_lsg_score < 9 ~ 3,
msni_shelter_lsg_score >= 9 ~ 4
)
)
#### end shelter_lsg
# HC-LSG/FSA - fsl_lsg
data <- data %>%
mutate(
fcs_lsg = case_when(
fcs_category == "poor" ~ 3,
fcs_category == "borderline" ~ 2,
TRUE ~ 0
),
hhs_lsg = case_when(
hhs_category == "severe_hunger" ~ 3,
hhs_category == "moderate_hunger" ~ 2,
TRUE ~ 0
),
food_source_lsg = case_when(
food_source == "assistance" | food_source == "gift" | food_source == "borrowed" ~ 3,
# food_source == "borrowed" ~ 2,
TRUE ~ 0
),
market_access_lsg = case_when(
market_access == "no" ~ 3,
TRUE ~ 0
),
market_distance_lsg = case_when(
market_distance == "6_10km" ~ 2,
TRUE ~ 0
)
)
# fsl_lsg class vars
msni_fsl_lsg_vars <- c(
"fcs_lsg",
"hhs_lsg",
"food_source_lsg",
"market_access_lsg",
"market_distance_lsg"
)
# fsl_lsg score
data$msni_fsl_lsg_score <- comp_score(data, msni_fsl_lsg_vars)
# fsl_lsg severity
data <- data %>%
mutate(
fsl_lsg = case_when(
msni_fsl_lsg_score < 3 ~ 1,
msni_fsl_lsg_score > 2 & msni_fsl_lsg_score < 6 ~ 2,
msni_fsl_lsg_score > 5 & msni_fsl_lsg_score < 9 ~ 3,
msni_fsl_lsg_score >= 9 ~ 4
)
)
# fsl_lsg severity 2
data <- data %>%
mutate(
fsl_lsg_2 = case_when(
msni_fsl_lsg_score < 3 ~ 1,
msni_fsl_lsg_score > 2 & msni_fsl_lsg_score < 7 ~ 2,
msni_fsl_lsg_score > 6 & msni_fsl_lsg_score < 9 ~ 3,
msni_fsl_lsg_score >= 9 ~ 4
)
)
# fsl_lsg severity 3
data <- data %>%
mutate(
fsl_lsg_3 = case_when(
msni_fsl_lsg_score < 3 ~ 1,
msni_fsl_lsg_score > 2 & msni_fsl_lsg_score < 8 ~ 2,
msni_fsl_lsg_score > 7 & msni_fsl_lsg_score < 10 ~ 3,
msni_fsl_lsg_score >= 10 ~ 4
)
)
#### end fsl_lsg
# HC-LSG/Health - health_lsg
data <- data %>%
mutate(
access_health_center_lsg = case_when(
health_facility_access == "no" ~ 3,
TRUE ~ 0
),
health_facility_distance_lsg = case_when(
health_facility_distance == "none" | health_facility_distance == "more_10km" ~ 3,
health_facility_distance == "6_10km" ~ 2,
TRUE ~ 0
),
behav_change_lsg = case_when(
behav_change_disagg == "yes" ~ 3,
TRUE ~ 0
),
birth_location_lsg = case_when(
birth_location == "outside" | diarrhea_cases_class == 1 ~ 3,
birth_location == "home" | birth_location == "midwife_home" | birth_location == "other" ~ 2,
TRUE ~ 0
)
)
# health_lsg class vars
msni_health_lsg_vars <- c(
"access_health_center_lsg",
"health_facility_distance_lsg",
"behav_change_lsg",
"birth_location_lsg"
)
# health_lsg score
data$msni_health_lsg_score <- comp_score(data, msni_health_lsg_vars)
# health_lsg severity
data <- data %>%
mutate(
health_lsg = case_when(
msni_health_lsg_score < 3 ~ 1,
msni_health_lsg_score > 2 & msni_health_lsg_score < 6 ~ 2,
msni_health_lsg_score > 5 & msni_health_lsg_score < 9 ~ 3,
msni_health_lsg_score >= 9 ~ 4
)
)
#### end health_lsg
# HC-LSG/Protection - protection_lsg
data <- data %>%
mutate(
prot_incidents_4_lsg = case_when(
adult_prot_incidents.assaulted_with_weapon == 1 | adult_prot_incidents.hindered_leave_settlement == 1 |
adult_prot_incidents.forced_work == 1 | adult_prot_incidents.forcibly_detained == 1 |
child_prot_incidents.assaulted_with_weapon == 1 | child_prot_incidents.hindered_leave_settlement == 1 |
child_prot_incidents.forced_work == 1 | child_prot_incidents.forcibly_detained == 1 ~ 4,
TRUE ~ 0
),
prot_incidents_3_lsg = case_when(
adult_prot_incidents.verbally_threatened == 1 | adult_prot_incidents.assaulted_without_weapon == 1 |
adult_prot_incidents.hindered_leave_district == 1 | child_prot_incidents.verbally_threatened == 1 |
child_prot_incidents.assaulted_without_weapon == 1 | child_prot_incidents.hindered_leave_district == 1 &
(adult_prot_incidents.assaulted_with_weapon == 0 | adult_prot_incidents.hindered_leave_settlement == 0 |
adult_prot_incidents.forced_work == 0 | adult_prot_incidents.forcibly_detained == 0 |
child_prot_incidents.assaulted_with_weapon == 0 | child_prot_incidents.hindered_leave_settlement == 0 |
child_prot_incidents.forced_work == 0 | child_prot_incidents.forcibly_detained == 0) ~ 3,
TRUE ~ 0
),
other_incidents_lsg = case_when(
other_incidents == "sgbv" | other_concerns == "sgbv" | boy_marriage == "yes" | girl_marriage == "yes" ~ 3,
TRUE ~ 0
),
prot_concerns_lsg = case_when(
prot_concerns.violence_maiming == 1 | prot_concerns.violence_injuries == 1 | prot_concerns.psych_wellbeing == 1 |
prot_concerns.abduction == 1 | prot_concerns.theft == 1 | prot_concerns.explosive_hazards == 1 |
prot_concerns.destruction_property == 1 | prot_concerns.early_marriage == 1 | prot_concerns.other == 1 ~ 3,
TRUE ~ 0
),
safety_lsg = case_when(
safety == "poor" | safety == "very_poor" ~ 2,
TRUE ~ 0
)
)
# protection_lsg class vars
msni_protection_lsg_vars <- c(
"prot_incidents_4_lsg",
"prot_incidents_3_lsg",
"other_incidents_lsg",
"prot_concerns_lsg",
"safety_lsg"
)
# protection_lsg score
data$msni_protection_lsg_score <- comp_score(data, msni_protection_lsg_vars)
# protection_lsg severity
data <- data %>%
mutate(
protection_lsg = case_when(
msni_protection_lsg_score < 3 ~ 1,
msni_protection_lsg_score > 2 & msni_protection_lsg_score < 6 ~ 2,
msni_protection_lsg_score > 5 & msni_protection_lsg_score < 9 ~ 3,
msni_protection_lsg_score >= 9 ~ 4
)
)
#### end protection_lsg
# HC-LSG/WASH wash_lsg
data <- data %>%
mutate(
water_source_lsg = case_when(
water_source == "surface_water" ~ 3,
water_source == "water_trucking" | water_source == "spring_unprotected" ~ 2,
TRUE ~ 0
),
soap_lsg = case_when(
soap == "no" ~ 3,
TRUE ~ 0
),
latrine_lsg = case_when(
latrine == "open" | latrine == "public_latrine" | waste_disposal == "open_space" ~ 3,
latrine == "pit_latrine_uncovered" | waste_disposal == "burning" ~ 2,
TRUE ~ 0
),
water_distance_lsg = case_when(
water_distance == "over_1km" ~ 3,
water_distance == "500m_to_1km" ~ 2,
TRUE ~ 0
)
)
# wash_lsg class vars
msni_wash_lsg_vars <- c(
"water_source_lsg",
"soap_lsg",
"latrine_lsg",
"water_distance_lsg"
)
# wash_lsg score
data$msni_wash_lsg_score <- comp_score(data, msni_wash_lsg_vars)
# wash_lsg severity
data <- data %>%
mutate(
wash_lsg = case_when(
msni_wash_lsg_score < 3 ~ 1,
msni_wash_lsg_score > 2 & msni_wash_lsg_score < 6 ~ 2,
msni_wash_lsg_score > 5 & msni_wash_lsg_score < 9 ~ 3,
msni_wash_lsg_score >= 9 ~ 4
)
)
# wash_lsg severity 2
data <- data %>%
mutate(
wash_lsg_2 = case_when(
msni_wash_lsg_score < 3 ~ 1,
msni_wash_lsg_score > 2 & msni_wash_lsg_score < 7 ~ 2,
msni_wash_lsg_score > 6 & msni_wash_lsg_score < 9 ~ 3,
msni_wash_lsg_score >= 9 ~ 4
)
)
# wash_lsg severity 3
data <- data %>%
mutate(
wash_lsg_3 = case_when(
msni_wash_lsg_score < 3 ~ 1,
msni_wash_lsg_score > 2 & msni_wash_lsg_score < 8 ~ 2,
msni_wash_lsg_score > 7 & msni_wash_lsg_score < 10 ~ 3,
msni_wash_lsg_score >= 10 ~ 4
)
)
#### end wash_lsg
# HC-LSG/EiE - education_lsg
data <- data %>%
mutate(
not_attending_lsg = case_when(
percent_enrolled >= 0.75 & percent_enrolled <= 1 ~ 4,
percent_enrolled >= 0.5 & percent_enrolled <= 0.749 ~ 3,
percent_enrolled >= 0.25 & percent_enrolled <= 0.449 ~ 2,
percent_enrolled >= 0 & percent_enrolled <= 0.249 ~ 1,
TRUE ~ 0
),
education_level_lsg = case_when(
highest_edu == "none" ~ 2,
highest_edu == "primary" ~ 1,
TRUE ~ 0
),
unattending_security_lsg = case_when(
boy_unattendance_reason.insecurity == 1 | boy_unattendance_reason.child_works_instead == 1 |
girl_unattendance_reason.insecurity == 1 | girl_unattendance_reason.child_works_instead == 1 ~ 3,
TRUE ~ 0
),
unattending_cultural_lsg = case_when(
boy_unattendance_reason.cultural_reasons == 1 | girl_unattendance_reason == 1 |
boy_unattendance_reason.lack_facilities == 1 | girl_unattendance_reason.lack_facilities == 1 ~ 2,
TRUE ~ 0
),
unattending_finance_doc_lsg = case_when(
boy_unattendance_reason.lack_documentation == 1 | boy_unattendance_reason.too_expensive == 1 |
boy_unattendance_reason.lack_teachers == 1 | girl_unattendance_reason.lack_documentation ==1 |
girl_unattendance_reason.too_expensive == 1 | girl_unattendance_reason.lack_teachers == 1 ~ 1,
TRUE ~ 0
)
)
# education_lsg class vars
msni_education_lsg_vars <- c(
"not_attending_lsg",
"unattending_security_lsg",
"unattending_cultural_lsg",
"unattending_finance_doc_lsg",
"education_level_lsg"
)
# education_lsg score
data$msni_education_lsg_score <- comp_score(data, msni_education_lsg_vars)
# education_lsg severity
data <- data %>%
mutate(
education_lsg = case_when(
msni_education_lsg_score < 3 ~ 1,
msni_education_lsg_score > 2 & msni_education_lsg_score < 6 ~ 2,
msni_education_lsg_score > 5 & msni_education_lsg_score < 9 ~ 3,
msni_education_lsg_score >= 9 ~ 4
)
)
#### end education_lsg
#################################################
data <- data %>% filter(!is.na(province))
# fliter prolematic feilds
uuid_filter <- c("ac3e8430-ba88-497b-9895-c1bd8da7f79e",
"8ac61e9b-8ff8-4e4a-9619-1dc0ab31f396",
"7171e0a8-3a40-4c57-b84d-a65f08115994",
"596c244b-ea20-48ef-8218-023ac3f2831f")
`%notin%` <- Negate(`%in%`)
data <- data %>% filter(uuid %notin% uuid_filter )
# MSNI Indicator
data$msni <- msni(education_lsg = data$education_lsg,
fsl_lsg = data$fsl_lsg,
health_lsg = data$health_lsg,
protection_lsg = data$protection_lsg,
shelter_lsg = data$shelter_lsg,
wash_lsg = data$wash_lsg,
capacity_gaps = data$capacity_gaps,
impact = data$impact)
data$msni2 <- msni(education_lsg = data$education_lsg,
fsl_lsg = data$fsl_lsg_2,
health_lsg = data$health_lsg,
protection_lsg = data$protection_lsg,
shelter_lsg = data$shelter_lsg,
wash_lsg = data$wash_lsg_2,
capacity_gaps = data$capacity_gaps,
impact = data$impact)
data$msni3 <- msni(education_lsg = data$education_lsg,
fsl_lsg = data$fsl_lsg_3,
health_lsg = data$health_lsg,
protection_lsg = data$protection_lsg,
shelter_lsg = data$shelter_lsg,
wash_lsg = data$wash_lsg_3,
capacity_gaps = data$capacity_gaps,
impact = data$impact)
data$msni_sev_high <- ifelse(data$msni==3|data$msni==4,1,0)
# HHs found to have severe or extreme sectoral needs in one or more sectors
# lsg_needs_2_cal
data <- data %>%
mutate(
shelter_lsg_class = case_when(
shelter_lsg == 3 | shelter_lsg == 4 ~ 1,
shelter_lsg == 1 | shelter_lsg == 2 ~ 0,
TRUE ~ NA_real_
),
fsl_lsg_class = case_when(
fsl_lsg == 3 | fsl_lsg == 4 ~ 1,
fsl_lsg == 1 | fsl_lsg == 2 ~ 0,
TRUE ~ NA_real_
),
health_lsg_class = case_when(
health_lsg == 3 | health_lsg == 4 ~ 1,
health_lsg == 1 | health_lsg == 2 ~ 0,
TRUE ~ NA_real_
),
protection_lsg_class = case_when(
protection_lsg == 3 | protection_lsg == 4 ~ 1,
protection_lsg == 1 | protection_lsg == 2 ~ 0,
TRUE ~ NA_real_
),
wash_lsg_class = case_when(
wash_lsg == 3 | wash_lsg == 4 ~ 1,
wash_lsg == 1 | wash_lsg == 2 ~ 0,
TRUE ~ NA_real_
),
education_lsg = case_when(
education_lsg == 3 | education_lsg == 4 ~ 1,
education_lsg == 1 | education_lsg == 2 ~ 0,
TRUE ~ NA_real_
)
)
lsg_needs_2_cal_vars <- c(
"shelter_lsg_class",
"fsl_lsg_class",
"health_lsg_class",
"protection_lsg_class",
"wash_lsg_class",
"education_lsg"
)
# lsg_needs_2_cal score
data$lsg_needs_2_cal_score <- comp_score(data, lsg_needs_2_cal_vars)
# lsg_needs_2_cal
data <- data %>%
mutate(
lsg_needs_2_cal = case_when(
lsg_needs_2_cal_score == 0 ~ "no_need",
lsg_needs_2_cal_score == 1 ~ "one_need",
lsg_needs_2_cal_score > 1 ~ "two_or_more_need"
)
)
# msni drivers
data <- data %>%
mutate(
fsl_wash_driver = case_when(
fsl_lsg == 3 | fsl_lsg == 4 | wash_lsg == 3 | wash_lsg == 4 ~ "sectoral_need",
fsl_lsg == 1 | fsl_lsg == 2 | wash_lsg == 1 | wash_lsg == 2 ~ "no_need",
TRUE ~ NA_character_
),
impact_driver = case_when(
((impact == 3 | impact == 4) & (health_lsg == 3 | health_lsg == 4)) |
((impact == 3 | impact == 4) & (shelter_lsg == 3 | shelter_lsg == 4)) |
((impact == 3 | impact == 4) & (protection_lsg == 3 | protection_lsg == 4)) ~ "sectoral_need",
TRUE ~ "no_need"
),
shelter_driver_class = case_when(
shelter_lsg == 3 | shelter_lsg == 4 ~ 1,
TRUE ~ 0,
),
protection_driver_class = case_when(
protection_lsg == 3 | protection_lsg == 4 ~ 1,
TRUE ~ 0
),
health_driver_class = case_when(
health_lsg == 3 | health_lsg == 4 ~ 1,
TRUE ~ 0
),
capacity_gaps_sev = case_when(
capacity_gaps >=3 ~ "high",
capacity_gaps <=2 ~ "low",
TRUE ~ NA_character_
)
)
# esnfi_prot_health_driver
esnfi_prot_health_driver_vars <- c(
"shelter_driver_class",
"protection_driver_class",
"health_driver_class"
)
# esnfi_prot_health_driver score
data$esnfi_prot_health_driver_score <- comp_score(data, esnfi_prot_health_driver_vars)
# lsg_needs_2_cal
data <- data %>%
mutate(
esnfi_prot_health_driver = case_when(
esnfi_prot_health_driver_score <= 1 ~ "no_need",
esnfi_prot_health_driver_score >= 2 ~ "sectoral_need",
)
)
############### MSNI TEST ###############
data <- data %>%
mutate(
hh_msni_one = case_when(
education_lsg == 1 & fsl_lsg == 1 & health_lsg == 1 & protection_lsg == 1 & shelter_lsg == 1 & wash_lsg == 1 &
capacity_gaps == 1 & impact == 1 ~ "1",
TRUE ~ "1+"
),
hh_msni_one_only_sectors = case_when(
education_lsg == 1 & fsl_lsg == 1 & health_lsg == 1 & protection_lsg == 1 & shelter_lsg == 1 & wash_lsg == 1 ~ "1",
TRUE ~ "1+"
)
)
#########################################
#join main dataset var to hh roster
data_sub <- data %>% select(final_displacement_status_non_displ, region_disagg, urban_disagg,
hoh_sex_disagg, hoh_disabled_disagg, hoh_chronic_illness_disagg, hoh_elderly_disagg,
displacements_disagg, literate_adult_disagg, lcsi_disagg, host_disagg, disp_length_disagg, hoh_sex2_disagg,
behav_change_disagg, child_behav_change_disagg,
tazkira_disagg3, hoh_debt_disagg , vulnerable_group_4, vulnerable_group_7, registered_dissagg, informal_settlement,
child_tazkira_disagg, uuid)
overall_hh_roster <- overall_hh_roster %>%
mutate(
school_age = case_when(
hh_member_age >=6 & hh_member_age <= 18 ~ "school_age",
TRUE ~ "not_school_age"
),
current_year_attending_na_no = case_when(
current_year_attending == "no" ~ "no",
current_year_attending == "yes" ~ "yes",
TRUE & school_age == "school_age" ~ "no"
),
edu_removal_shock.no_sch_age = case_when(
edu_removal_shock.no == 1 ~ 1,
TRUE & school_age == "school_age" ~ 0
),
edu_removal_shock.conflict_sch_age = case_when(
edu_removal_shock.conflict == 1 ~ 1,
TRUE & school_age == "school_age" ~ 0
),
edu_removal_shock.displacement_sch_age = case_when(
edu_removal_shock.displacement == 1 ~ 1,
TRUE & school_age == "school_age" ~ 0
),
edu_removal_shock.natural_disaster_sch_age = case_when(
edu_removal_shock.natural_disaster == 1 ~ 1,
TRUE & school_age == "school_age" ~ 0
),
edu_removal_shock_sch_age = case_when(
edu_removal_shock.no == 1 ~ "yes",
TRUE & school_age == "school_age" ~ "no"
),
hh_member_age_cat = case_when(
hh_member_age >= 0 & hh_member_age < 6 ~ "0_5",
hh_member_age > 5 & hh_member_age < 19 ~ "6_18",
hh_member_age > 18 & hh_member_age < 60 ~ "19_59",
hh_member_age > 59 ~ "60+"
),
# demographic hh roster data
hh_member_age_cat_gender = case_when(
hh_member_age >= 0 & hh_member_age < 6 & hh_member_sex == "female" ~ "female_0_5",
hh_member_age >= 0 & hh_member_age < 6 & hh_member_sex == "male" ~ "male_0_5",
hh_member_age > 5 & hh_member_age < 19 & hh_member_sex == "female" ~ "female_6_18",
hh_member_age > 5 & hh_member_age < 19 & hh_member_sex == "male" ~ "male_6_18",
hh_member_age > 18 & hh_member_age < 60 & hh_member_sex == "female" ~ "female_19_59",
hh_member_age > 18 & hh_member_age < 60 & hh_member_sex == "male" ~ "male_19_59",
hh_member_age > 59 & hh_member_sex == "female" ~ "female_60+",
hh_member_age > 59 & hh_member_sex == "male" ~ "male_60+"
),
male_female_perc = case_when(
hh_member_sex == "female" ~ "female",
hh_member_sex == "male" ~ "male"
),
## request # 30
school_age_cat_gender = case_when(
hh_member_age > 5 & hh_member_age < 13 & hh_member_sex == "female" ~ "female_6_12",
hh_member_age > 12 & hh_member_age < 19 & hh_member_sex == "female" ~ "female_13_18",
hh_member_age > 5 & hh_member_age < 13 & hh_member_sex == "male" ~ "male_6_12",
hh_member_age > 12 & hh_member_age < 19 & hh_member_sex == "male" ~ "male_13_18",
TRUE ~ NA_character_
)
)
############## demographic hoh data #####################
hoh_data <- data %>%
select(
hh_member_sex = hoh_sex,
hh_member_age = hoh_age,
`_submission__uuid` = uuid,
province,
survey_village) %>%
mutate(
hh_member_age_cat_gender = case_when(
hh_member_age >= 0 & hh_member_age < 6 & hh_member_sex == "female" ~ "female_0_5",
hh_member_age >= 0 & hh_member_age < 6 & hh_member_sex == "male" ~ "male_0_5",
hh_member_age > 5 & hh_member_age < 19 & hh_member_sex == "female" ~ "female_6_18",
hh_member_age > 5 & hh_member_age < 19 & hh_member_sex == "male" ~ "male_6_18",
hh_member_age > 18 & hh_member_age < 60 & hh_member_sex == "female" ~ "female_19_59",
hh_member_age > 18 & hh_member_age < 60 & hh_member_sex == "male" ~ "male_19_59",
hh_member_age > 59 & hh_member_sex == "female" ~ "female_60+",
hh_member_age > 59 & hh_member_sex == "male" ~ "male_60+"
),
male_female_perc = case_when(
hh_member_sex == "female" ~ "female",
hh_member_sex == "male" ~ "male"
),
school_age_cat_gender = case_when(
hh_member_age > 5 & hh_member_age < 13 & hh_member_sex == "female" ~ "female_6_12",
hh_member_age > 12 & hh_member_age < 19 & hh_member_sex == "female" ~ "female_13_18",
hh_member_age > 5 & hh_member_age < 13 & hh_member_sex == "male" ~ "male_6_12",
hh_member_age > 12 & hh_member_age < 19 & hh_member_sex == "male" ~ "male_13_18",
TRUE ~ NA_character_
)
)
hoh_data <- hoh_data %>%
select(
hh_member_sex,
hh_member_age,
hh_member_age_cat_gender,
male_female_perc,
school_age_cat_gender,
province,
survey_village,
`_submission__uuid`
)
roster_data <- overall_hh_roster %>%
select(
hh_member_sex,
hh_member_age,
hh_member_age_cat_gender,
male_female_perc,
school_age_cat_gender,
province,
survey_village,
`_submission__uuid`
)
combined_hoh_and_roster <- rbind(roster_data, hoh_data)
# for demographic
combined_hoh_and_roster_joined <- koboloops::add_parent_to_loop(combined_hoh_and_roster, data_sub, uuid.name.loop = "_submission__uuid", uuid.name.parent = "uuid")
combined_hoh_and_roster_joined <- combined_hoh_and_roster_joined %>%
mutate(
hh_member_under_over_15 = case_when(
hh_member_age <= 15 ~ "15_and_under",
hh_member_age > 15 ~ "over_15",
TRUE ~ NA_character_
)
)
write.csv(combined_hoh_and_roster_joined, "./input/data/recoded/hh_roster_hoh_demographic.csv", row.names = F)
# for education questions
hh_roster_joined <- koboloops::add_parent_to_loop(overall_hh_roster, data_sub, uuid.name.loop = "_submission__uuid", uuid.name.parent = "uuid")
write.csv(hh_roster_joined, "./input/data/recoded/hh_roster.csv", row.names = F)
write.csv(data, "./input/data/recoded/data_with_strata2.csv", row.names = F)
## Test
|
17d0b4508a89eda9690757bbd1a506dc8eba11fb | de83a2d0fef79a480bde5d607937f0d002aa879e | /P2C2M.SNAPP/R/draw.samples2.R | 4afd6ee40fe1adb1f7db29b2654b926047494a2b | [] | no_license | P2C2M/P2C2M_SNAPP | 0565abc0ea93195c9622dc5d4e693ccde17bebc7 | 94cd62285419a79f5d03666ec2ea3e818803d0db | refs/heads/master | 2020-05-07T18:54:40.440000 | 2020-01-10T15:59:45 | 2020-01-10T15:59:45 | 180,788,408 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,099 | r | draw.samples2.R | ##### Randomly sample from posterior #####
draw.samples <- function(num_sims, gens_run, sample_unif){ # num.sims = user input # of simulations to perform; gens_run = # of markov steps saved; sample_unif = if true, sample posterior uniformly. Otherwise sample randomly
burnin <- ceiling(gens_run * 0.10)
non_burnin <- seq(burnin + 1, gens_run, 1) # get sequence of step numbers in non burnin posterior
if (num_sims > length(non_burnin)){ # if # of simulations input is greater than the number of steps in the posterior
post_samples <- non_burnin # use all non_burnin steps
} else{
if (sample_unif == TRUE){
interval <- length(non_burnin) / num_sims # get interval to sample
post_samples <- non_burnin[1]
while (post_samples[length(post_samples)] + interval <= gens_run){
post_samples = c(post_samples, post_samples[length(post_samples)] + interval)
}
post_samples <- sapply(post_samples, floor) # round down
} else{
post_samples <- sort(sample(non_burnin, num_sims)) # randomly sample steps
}
}
return(post_samples)
}
|
5e6123c9c6678ffff155f6d6bb0973954d846370 | 925c515b771a8ea7ca31cc530308d594c30fba07 | /code/TableS3.R | 3591bcb04a019b009fd1c4d141478c8c465a6176 | [] | no_license | melofton/freshwater-forecasting-review | 41ba42f0aee6180d7a731fcf838dccc8f7590588 | c06097cbab6d88c1dc30d0f2c3cf8a3baddaeacc | refs/heads/main | 2023-07-06T21:54:48.183000 | 2023-06-27T20:18:46 | 2023-06-27T20:18:46 | 478,673,588 | 0 | 1 | null | 2022-07-08T19:45:20 | 2022-04-06T18:05:25 | R | UTF-8 | R | false | false | 541 | r | TableS3.R | #Matrix analysis
#Author: Mary Lofton
#Date: 06JUL22
#clear environment
rm(list = ls())
#set-up
pacman::p_load(tidyverse, lubridate, cowplot,ggbeeswarm, viridis)
#read in data
dat5 <- read_csv("./data/cleaned_matrix.csv")
##Table 3 ####
dat10 <- dat5 %>%
mutate(ecosystem_type = ifelse(ecosystem == "river" | grepl("basin",other_ecosystem),"Lotic","Lentic"))
colnames(dat10)
tab3 <- dat10[,c(2,4,3,27,11,12,13,14,18,19,20,21,16,17,23)] %>%
arrange(Year)
tab3[16,"Year"] <- 2022
write.csv(tab3,"Table3.csv",row.names = FALSE)
|
1bc891cc48422875088ad36e2f4ff1053e811f2d | 218aae83a9d0994561991ba8affe528f1e381457 | /R/edgepoints.R | e7cee35ceeef11b83b9b9d0b6abfaa1dc7d09ef5 | [] | no_license | cran/edci | 4efcf830e8cec5d1522397140afd5650655b66b3 | d24ed3f7d6bd543f5b1fa07b8db821d42c8fe795 | refs/heads/master | 2020-12-25T16:56:26.204000 | 2018-05-16T20:49:37 | 2018-05-16T20:49:37 | 17,718,677 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,698 | r | edgepoints.R | edgepoints = function(data, h1n, h2n, asteps = 4, estimator = "kernel", kernel = "mean",
score = "gauss", sigma = 1, kernelfunc = NULL, margin = FALSE) {
epDelta = function(x) {
if (x < 0)
-1
else
1
}
epAt = function(x, y) {
if (x == 0) {
if (y >= 0)
pi/2
else
pi/2
} else {
atan(y/x)
}
}
epR1 = function(theta, x, y) {
sqrt(x^2 + y^2) * epDelta(x) * cos(epAt(x, y) - theta)
}
epR2 = function(theta, x, y) {
sqrt(x^2 + y^2) * epDelta(x) * sin(epAt(x, y) - theta)
}
angle = matrix(double(length(data)), nrow=nrow(data))
value = matrix(double(length(data)), nrow=nrow(data))
es = NULL
sc = NULL
ms = sigma * max(data)
if (estimator == "kernel")
es = 0
else if (estimator == "median")
es = 1
else if (estimator == "M_mean")
es = 2
else if (estimator == "M_median")
es = 3
else if (estimator == "test_mean")
es = 5
else if (estimator == "test_median")
es = 6
else
stop("estimator \"", estimator, "\" unknown.")
if (es==2 || es ==3) {
if (score == "gauss") {
sc = 0
}
if (score == "huber") {
sc = 1
#ms = sigma/2
}
if (score == "mean") {
sc = 9
}
}
env = ceiling(sqrt((h1n * nrow(data))^2 + (h2n * ncol(data))^2))
kernmat = NULL
if (kernel == "mean" || es >= 5) {
kern = 0
} else if (kernel == "linear") {
kern = 1
} else if (kernel == "linear2") {
kern = 2
} else if (kernel == "gauss") {
kern = 3
} else if (kernel == "func") {
kern = 4
kernmat = double(asteps * (2 * env + 1)^2)
for (i in ((-env):env)) {
for(j in ((-env):env)) {
for (k in (0:(asteps - 1))) {
theta = -pi/2 + (k * pi/asteps)
x = epR1(theta, i/nrow(data), j/ncol(data))/h1n
y = epR2(theta, i/nrow(data), j/ncol(data))/h2n
kernmat[k * (2 * env + 1)^2 + (i + env) * (2 * env + 1) + (j + env) + 1] =
kernelfunc(2 * x, y)
}
}
}
} else {
stop("kernel \"",kernel,"\" unknown.")
}
if (es == 1)
kern = 0
if (!is.null(es)) {
result = .C("c_edgepoints",
as.double(data),
nrow(data),
ncol(data),
as.integer(kern), # kernel
as.double(h1n),
as.double(h2n),
as.integer(es),
as.integer(sc), # Typ der Scorefunktion
as.double(sigma), # Sigma
as.double(kernmat), # Gewichtsmatrix
as.double(ms), # Max_Schritt
as.integer(asteps),
angle = angle,
value = value,
PACKAGE = "edci")
}
value = result$value
angle = result$angle
if (es == 5 || es == 6)
value = -value
if (margin == FALSE) {
if (es == 5 || es == 6)
v = 1
else
v = 0
value[c(1:env,(nrow(value) - env + 1):nrow(value)), ] = v
value[, c(1:env, (ncol(value) - env+1):ncol(value))] = v
} else if (margin == "cut") {
value = value[(env + 1):(nrow(value) - env), (env + 1):(ncol(value) - env)]
angle = angle[(env + 1):(nrow(angle) - env), (env + 1):(ncol(angle) - env)]
}
list(value = value, angle = angle)
}
eplist = function(data, maxval, test = FALSE, xc = NULL, yc = NULL) {
if (test == TRUE) {
data[[1]] = -data[[1]]
maxval = -maxval
}
n = sum(data[[1]] > maxval)
if (is.null(xc))
xc = seq(1/nrow(data[[1]]), 1, 1/nrow(data[[1]]))
if (is.null(yc))
yc = seq(1/ncol(data[[1]]), 1, 1/ncol(data[[1]]))
o = order(data[[1]], decreasing = TRUE)[1:n]
result = cbind(xc[(o - 1) %% nrow(data[[1]]) + 1], yc[(o - 1) %/% nrow(data[[1]]) +1 ],
data[[2]][o])
colnames(result) = c("x", "y", "angle")
result
}
|
4e4ff604aaf7b5ff470c8227b043cf073c00c388 | d3fdbf9442b8e0ffbc208ad50087f0ece05f405e | /Modulo 3- Resampling-Bayesianos-Markov/Ejercicio 3.3/Ejercicio3_3_MarianaSilvera.R | c9857fb242ead0825c194c8628a35108f8e2f36e | [] | no_license | msilvera/R-DataAnalysis2021-OTGA | b176f5f48076ce57ed1c7935fbe37ada31f21bda | 1bc03219b4d36c73d2196534c111878476d4373d | refs/heads/main | 2023-06-14T21:55:42.036000 | 2021-07-04T18:38:51 | 2021-07-04T18:38:51 | 380,082,123 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 947 | r | Ejercicio3_3_MarianaSilvera.R | library(FSAdata)
library(MASS)
library(dplyr)
#library(help="FSAdata")
#cargo los datos
#data <- WalleyeErie2
summary(WalleyeErie2)
data <-subset(x=WalleyeErie2, subset = !is.na(w)) #elimino los datos incompletos
summary(data)
set.seed(1) # semilla para el random
data <- data %>% mutate_at(vars("age"), factor) # transformo en factor la comlumna edad
#extraigo el 80% de los datos para entrenamiento
intrain <- sample(1:nrow(data), size = round(0.8*nrow(data)))
#genero modelo para la edad en base a las demas variables
lda.fit <-lda(age~. , data= data, subset= intrain)
lda.fit
#verifico que tan bien se comporta el discriminante lineal generado
lda.pred <- predict(lda.fit, data)
names(lda.pred)
#obtenfo la clase
lda.class <- lda.pred$class
#construyo la matriz
table(lda.class, data$age)
#veo que tan bien se ajusta, utilizando la media
mean(lda.class==data$age)
#resultado, desempeño de : 0.6571231 |
eb3d9c97b02f6f8d4ca16e857d987432473f6d4c | 89d2d6b83bb0fcad3db66b139a617b0cc40bf34a | /R3-Aliona.R | dfc622532aca8311dc0e2430e94dcfdf29a65c9b | [] | no_license | alionahst/R3 | 5e6760cab681ab10149267ed31884ccb16cc6eb5 | d980eddc32efd762b3178bc3933b8ba486929944 | refs/heads/master | 2023-01-03T05:25:17.275000 | 2020-10-20T22:13:42 | 2020-10-20T22:13:42 | 305,684,425 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,799 | r | R3-Aliona.R | #Chapter 3: Basic graphics and data - Aliona Hoste
demo(graphics)
plot(iris)
#1. Plot a cheat-sheet with values of color and point type (col = , and pch = ) from 1 to 25, and export it as a jpeg of 15 cm wide, 6 cm high and resolution 100 points per cm.
plot(0, 0, xlim = c(0, 26), ylim = c(0.5, 1.5)
, ylab = "color", xlab = "color number", yaxt = "n")
for (i in 1:25) {
points(i, 1, pch = i, col = i, cex = 1.5)
}
jpeg(filename = "firstplot.jpeg", width = 15, height = 6, units = "cm", res = 100)
plot(0, 0, xlim = c(0, 26), ylim = c(0.5, 1.5)
, ylab = "colors & sign", xlab = "color number", yaxt = "n")
for (i in 1:25) {
points(i, 1, pch = i, col = i, cex = 1.5)
}
dev.off()
#2. Plot into a graph ten Poisson distributions with lambda ranging from 1 to 10. Put legend and title. Export it as a .tiff file with size of 15x15 cm.
x <- seq(-1, 20, 1) # Sequence
y <- dpois(x, lambda = 1) # densities for x
plot(x, y, type = "n") # Empty plot (type = "n")
for(i in 1:10){
y <- dpois(x, lambda = i)
lines(x, y, col = i)
}
title(main="Poisson distribution, lambda = 1:10")
legend("topright", legend = paste("lambda =", 1:10),lty = 1, col = 1:10)
#export into tiff plot
tiff("Plot1_poisson_1to10.tiff", width = 15, height = 15, units = "cm",
bg = "transparent", res = 150) # Open the device "Plot1.tiff"
x <- seq(-1, 20, 1) # Sequence
y <- dpois(x, lambda = 1) # densities for x
plot(x, y, type = "n") # Empty plot (type = "n")
for(i in 1:10){
y <- dpois(x, lambda = i)
lines(x, y, col = i)
}
title(main="Poisson distribution, lambda = 1:10")
legend("topright", legend = paste("lambda =", 1:10),lty = 1, col = 1:10)
dev.off()
#3. Import data from this article: https://peerj.com/articles/328/
Webcsv <- "https://dfzljdn9uc3pi.cloudfront.net/2014/328/1/Appendix1.csv"
Data <- read.table(Webcsv, header = T, sep = ",", skip = 2)
str(Data)
#Be careful importing the data. Notice that you have to skip two first lines using “skip = 2”13.
#With these data, using for(), plot graphs to represent the effect of all the numerical variables, from “richness” to “mean_quality” on “yield”. Choose the type of graph that you think better represents this effect for the different species. Create only one pdf with all the graphs inside.
#To find the best graph for each type of data, a very helpful web is from Data to Viz https://www.data-to-viz.com/.
plot(Data[-1])
plot(Data$mean_yield ~ Data$richness)
for(i in names(Data[6:12]))
{
plot(Data$mean_yield ~ Data[[i]], ylab = "Mean yields", xlab = as.character(names(Data[i])))
title(main= paste("Mean yield in function of", as.character(names(Data[i]))))
}
|
7015d5870ad5056141a600ab0b532cfd67a48a59 | e56da52eb0eaccad038b8027c0a753d9eb2ff19e | /man-roxygen/tipsForTreeGeneration.R | b3874469bd8aa861c1cbae942f72fce3a7ff9898 | [] | no_license | ms609/TreeTools | fb1b656968aba57ab975ba1b88a3ddf465155235 | 3a2dfdef2e01d98bf1b58c8ee057350238a02b06 | refs/heads/master | 2023-08-31T10:02:01.031000 | 2023-08-18T12:21:10 | 2023-08-18T12:21:10 | 215,972,277 | 16 | 5 | null | 2023-08-16T16:04:19 | 2019-10-18T08:02:40 | R | UTF-8 | R | false | false | 174 | r | tipsForTreeGeneration.R | #' @param tips An integer specifying the number of tips, or a character vector
#' naming the tips, or any other object from which [`TipLabels()`] can
#' extract leaf labels.
|
15f17c33f851b0ab97d37c7507f338f9cc08551e | d30fa10aa7b3837145a1d1f0bcff6a55372ea4eb | /plot_kmer_dist.R | a39c14daba7632aabe23b7da8c1d0a54f095915a | [] | no_license | mborche2/Matts_Satellite_Size_Code | 541bfdada9a61238ecb6c59594dbfd5e60766e97 | 824fcf6e8f4ab555df774baa9cd8caf6dd8200ae | refs/heads/master | 2023-03-28T07:29:31.677000 | 2021-03-23T18:57:52 | 2021-03-23T18:57:52 | 348,837,905 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 689 | r | plot_kmer_dist.R | library(ggplot2)
setwd("/n/core/bigDataAI/Genomics/Gerton/jennifer_gerton/jeg10/")
for (i in 1:21){
filenam <- paste("plots/kmer_frequency_asp/kmer_frequency_",toString(i),"_array.tsv",sep = "")
array_specifics <- read.table(filenam,header = FALSE)
freq_table <- table(array_specifics[,2])
freq_df <- as.data.frame(freq_table)
freq_df[,2] <- log(freq_df[,2])
filenam2 <- paste("kmer_frequency_",toString(i),"_array_standard_axes.png",sep="")
png(filenam2)
plot(freq_df,xlab="Number of Occurences of Kmer in Array",ylab="Log (ln) Frequency of Kmer Occurence Number",axes=FALSE)
axis(side=1, at=seq(0,4000,by=25))
axis(side=2, at=seq(0, 10, by=1))
box()
dev.off()
}
|
393e68c42ae3b36432c1265386c913a44b8e6d7e | c97fa9aadc45c44fad6433ae10c772060bde355c | /MyNotes/03 - Geting and Cleaning Data/01 Class_Data.Table_Package.R | 41cd46ae55c1ae3679c91b186b783fad89090d5a | [] | no_license | vitorefigenio/datasciencecoursera | 9866816242d39fa9fc9520bc4d543efc815afeb5 | 03722d0c7c6d219ec84f48e02065493f6657cc0a | refs/heads/master | 2021-01-17T11:17:58.099000 | 2016-02-28T03:06:37 | 2016-02-28T03:06:37 | 29,034,385 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 943 | r | 01 Class_Data.Table_Package.R | #data.table package
# Create Data.Table
install.packages("data.table")
library(data.table)
DF = data.frame(x=rnorm(9), y=rep(c("a","b","c"), each=3), z=rnorm(9))
head(DF,3)
DT = data.table(x=rnorm(9), y=rep(c("a","b","c"), each=3), z=rnorm(9))
head(DT,3)
# comando ara ver tdas as abelas criadas na memória
tables()
# Subsetting rows
DT[2,]
DT[DT$y=="a"]
DT[c(2,3)]
# Subseting columns
DT[,c(2,3)]
# É comum o uso de expressões
{
x=1
y=2
}
k = {print(10);5}
print(k)
# Calculating values for variables with expressions
DT[,list(mean(x),sum(z))]
DT[,table(y)]
# Adding new column
DT[,w:=z^2]
DT
# Multiple Operations
DT[,m:={tmp <- (x+z); log2(tmp+5)}]
plot(DT[,m])
# plyr like operations
DT[, a:=x>0]
DT[,b:= mean(x+w), by=a]
DT
# Special Variables
set.seed(123)
DT <- data.table(x=sample(letters[1:3], 1E5, TRUE))
DT[, .N, by=x]
# keys
DT = data.table(x=rep(c("a","b","c"), each=100), z=rnorm(300))
setkey(DT,x)
DT['a'] |
12d5a52eb7e5fb10a0b5d87bdc8740c29b7c2a5a | 39315660a0226ae527ec8e0c7e6ae866df675b5f | /exercise1/computeCost.R | 5057e5c02d42dd31073fb2393dff4c7ded690bc3 | [] | no_license | Lemmawool/R-Practice | 28a7ce208f7d012eb4bc886fdb27b72754a171e9 | 0c3bed53e27953e9f19f92fd6e7b595a7e379262 | refs/heads/master | 2021-05-14T13:44:03.051000 | 2018-01-22T02:02:51 | 2018-01-22T02:02:51 | 115,955,944 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 105 | r | computeCost.R | computeCost <- function(X, y, theta){
m = length(y)
return ((1/(2*m)) * sum((X %*% theta - y) ^ 2))
} |
e662f9c90536aa7a7802ef2046cda55ac460d02e | 63227ea5a4085bb789824448502c95a98d8f375f | /cachematrix.R | 4e87ebeb3d25a82fe63b07127301dae99ce920d6 | [] | no_license | lfdelama/ProgrammingAssignment2 | f81f6ae4cf9246cc21a2fce019bc59a04949303d | 417909969f9fdd8c4d23700e1fcf535237a2c2ec | refs/heads/master | 2020-12-24T14:18:50.589000 | 2014-05-22T21:32:07 | 2014-05-22T21:32:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,314 | r | cachematrix.R | ## These two functions below are used to cache the inverse of a square matrix,
## so every time the same inverse is required, it doesn't need to be recomputed.
## This function creates a special matrix which
## contains a list of the following functions:
## - set, to set the value of the matrix
## - get, to get the value of the matrix
## - setinverse, to set the value of the inverse of the matrix
## - getinverse, to get the value of the inverse of the matrix
makeCacheMatrix<- function(x = matrix()) {
inv <- NULL
set <- function(y) {
x <<- y
inv <<- NULL
}
get <- function() x
setinverse <- function(inverse) inv <<- inverse
getinverse <- function() inv
list(set = set, get = get,
setinverse = setinverse,
getinverse = getinverse)
}
## This function calculates and returns the inverse of the special matrix that
## it was created with the above function.
## If the inverse of the matrix has previously calculated, this function will return
## directly the value stored in the cache of the makeCacheMatrix function.
cacheSolve <- function(x, ...) {
inv <- x$getinverse()
if(!is.null(inv)) {
message("getting cached data")
return(inv)
}
data <- x$get()
inv <- solve(data, ...)
x$setinverse(inv)
inv
}
|
48f4c3afd8bf9957f151bbbad760e9b7f9c317fe | 64e7ac1d0437b1d874b4ed070e6bda152decddee | /plot2.R | e2892d66195304ed5b560890e85b152215d7920e | [] | no_license | mooctus/ExData_Plotting1 | 072db8facebd27a8a8aab985be057b9b2c2b8122 | 005cba7dd9d88e94113a57eb6f8d77b9a3618811 | refs/heads/master | 2021-01-12T20:07:17.994000 | 2014-05-09T15:54:17 | 2014-05-09T15:54:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 569 | r | plot2.R | Sys.setlocale(category = "LC_ALL", locale = "C")
df <- read.table(file="household_power_consumption.txt", sep=";", na.strings="?", header=TRUE)
df$Time <- strptime(
paste0(df$Date, " ", df$Time),
format=paste0("%d/%m/%Y %H:%M:%S")
)
df$Date <- as.Date(df$Date,format="%d/%m/%Y")
df1 <- df[df$Date %in% as.Date(c('2007-02-01', '2007-02-02')),]
png(filename="plot2.png",width=480, height=480)
with (df1,
plot(Time, Global_active_power, type="n", xlab="", ylab="Global Active Power (kilowatts)")
)
with (df1,
lines(Time, Global_active_power)
)
dev.off()
|
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