5 Assistance
In this section, we harmonize WFP intervention variables.
library(haven)
library(labelled) # for general functions to work with labelled data
library(tidyverse) # general wrangling
library(dplyr)
library(Hmisc)
library(gtsummary) # to demonstrate automatic use of variable labels in summary tables
library(readxl)
library(foreign)
library(sjPlot)
library(sjmisc)
library(sjlabelled) # for example efc data set with variable labels
library(stringr)dir_input_data = "C:/Users/AHema/OneDrive - CGIAR/Desktop/WFP Resilience dataset/data/input_data/Chad"
dir_output_data = "C:/Users/AHema/OneDrive - CGIAR/Desktop/WFP Resilience dataset/data/output_data/Chad"Chad_Harmonization_variables <- read_excel(paste0(dir_input_data,"/Chad_Harmonization.xlsx"),
sheet = "variables_harmonization")
#View(Chad_Harmonization_variables)
Chad_Harmonization_description <- read_excel(paste0(dir_input_data,"/Chad_Harmonization.xlsx"),
sheet = "description")
#View(Chad_Harmonization_description)lst_data = Chad_Harmonization_description$Data
lst_test = Chad_Harmonization_description$Name
for(i in 1:length(lst_data)) { # Head of for-loop
assign(lst_test[i], # Read and store data frames
read_sav(paste0(dir_input_data,"/",lst_data[i])))
}for (j in 1:length(lst_test)){
df= get(lst_test[j], envir = .GlobalEnv)
for (i in 1:nrow(Chad_Harmonization_variables)){
df[,Chad_Harmonization_variables$NewVariable_Name[i]]=ifelse(is.na(Chad_Harmonization_variables[i,lst_test[j]]),NA,df[,Chad_Harmonization_variables[i,lst_test[j]][[1]]])
}
df<-df %>% select(Chad_Harmonization_variables$NewVariable_Name)
assign(lst_test[j], # Read and store data frames
df)
#write_sav(df, paste0(dir_output_data,"/",lst_test[j],".sav"))
#write_dta(df, paste0(dir_output_data,"/",lst_test[j],".dta"))
}5.1 Date assistance check
# Chad_baseline_2018$DebutAssistance<-as.Date(Chad_baseline_2018$DebutAssistance) # No assistance
# Chad_ea_2019$DebutAssistance<-as.Date(Chad_ea_2019$DebutAssistance, origin = "1899-12-30")
# Chad_ea_2020 = Chad_ea_2020 %>%
# dplyr::mutate(DateDerniereAssist_other = ifelse(DebutAssistance==1,"Bénéficiaire de l'assistance saisonnière de l'année passée (2020)","Non bénéficiaire de l'assistance saisonnière de l'année passée (2020)"))
Chad_ea_2021$DebutAssistance<-as.Date(Chad_ea_2021$DebutAssistance, origin = "1970-01-01")
Chad_ea_2022$DebutAssistance<-as.Date(Chad_ea_2022$DebutAssistance, origin = "1970-01-01")
Chad_pdm_2020$DebutAssistance<-as.Date(Chad_pdm_2020$DebutAssistance)
Chad_pdm_2022$DebutAssistance<-as.Date(Chad_pdm_2022$DebutAssistance, origin = "1970-01-01")
#Chad_ea_2020$DebutAssistance<-as.Date(Chad_ea_2020$DebutAssistance)
#Chad_pdm_2021$DebutAssistance<-as.Date(Chad_pdm_2021$DebutAssistance)
# Chad_ea_2023$DebutAssistance
# Chad_pdm_2023$DebutAssistance5.2 Date last assistance check
#DateDerniereAssist_other
expss::val_lab(Chad_baseline_2018$DateDerniereAssist)
expss::val_lab(Chad_ea_2019$DateDerniereAssist)
expss::val_lab(Chad_ea_2020$DateDerniereAssist)
expss::val_lab(Chad_ea_2021$DateDerniereAssist)
Chad_ea_2021 %>%
plot_frq(coord.flip =T,DateDerniereAssist,show.na =T)
Chad_ea_2021 <-
Chad_ea_2021 %>% dplyr::mutate_at(c("DateDerniereAssist"),recode,"1"=1,"2"=1,"3"=2,"4"=3)
Chad_ea_2021$DateDerniereAssist <- labelled::labelled(Chad_ea_2021$DateDerniereAssist, c(`moins d’une semaine` = 1, `entre 1 et 3 semaines` = 2,`plus de 3 semaines`=3, `other`=4))
expss::val_lab(Chad_ea_2021$DateDerniereAssist)
Chad_ea_2021 %>%
plot_frq(coord.flip =T,DateDerniereAssist,show.na =T)
expss::val_lab(Chad_ea_2022$DateDerniereAssist)
Chad_ea_2022 %>%
plot_frq(coord.flip =T,DateDerniereAssist,show.na =T)
Chad_ea_2022 <-
Chad_ea_2022 %>% dplyr::mutate_at(c("DateDerniereAssist"),recode,"1"=1,"2"=1,"3"=2,"4"=3)
Chad_ea_2022$DateDerniereAssist <- labelled::labelled(Chad_ea_2022$DateDerniereAssist, c(`moins d’une semaine` = 1, `entre 1 et 3 semaines` = 2,`plus de 3 semaines`=3, `other`=4))
expss::val_lab(Chad_ea_2022$DateDerniereAssist)
Chad_ea_2022 %>%
plot_frq(coord.flip =T,DateDerniereAssist,show.na =T)
expss::val_lab(Chad_pdm_2020$DateDerniereAssist)
Chad_pdm_2020 %>%
plot_frq(coord.flip =T,DateDerniereAssist,show.na =T)
expss::val_lab(Chad_pdm_2021$DateDerniereAssist)
expss::val_lab(Chad_pdm_2022$DateDerniereAssist)
Chad_pdm_2022 %>%
plot_frq(coord.flip =T,DateDerniereAssist,show.na =T)
Chad_pdm_2022 <-
Chad_pdm_2022 %>% dplyr::mutate_at(c("DateDerniereAssist"),recode,"1"=1,"2"=1,"3"=2,"4"=3)
Chad_pdm_2022$DateDerniereAssist <- labelled::labelled(Chad_pdm_2022$DateDerniereAssist, c(`moins d’une semaine` = 1, `entre 1 et 3 semaines` = 2,`plus de 3 semaines`=3, `other`=4))
expss::val_lab(Chad_pdm_2022$DateDerniereAssist)
Chad_pdm_2022 %>%
plot_frq(coord.flip =T,DateDerniereAssist,show.na =T)
expss::val_lab(Chad_ea_2023$DateDerniereAssist)
Chad_ea_2023 %>%
plot_frq(coord.flip =T,DateDerniereAssist,show.na =T)
Chad_ea_2023 <-
Chad_ea_2023 %>% dplyr::mutate_at(c("DateDerniereAssist"),recode,"1"=1,"2"=2,"3"=3,"other"=4)
Chad_ea_2023$DateDerniereAssist <- labelled::labelled(Chad_ea_2023$DateDerniereAssist, c(`moins d’une semaine` = 1, `entre 1 et 3 semaines` = 2,`plus de 3 semaines`=3,`Other`=4 ))
expss::val_lab(Chad_ea_2023$DateDerniereAssist)
Chad_ea_2023 %>%
plot_frq(coord.flip =T,DateDerniereAssist,show.na =T)
expss::val_lab(Chad_pdm_2023$DateDerniereAssist)
Chad_pdm_2023 <-
Chad_pdm_2023 %>% dplyr::mutate_at(c("DateDerniereAssist"),recode,"1"=1,"2"=2,"3"=3,"other"=4)
Chad_pdm_2023$DateDerniereAssist <- labelled::labelled(Chad_pdm_2023$DateDerniereAssist, c(`moins d’une semaine` = 1, `entre 1 et 3 semaines` = 2,`plus de 3 semaines`=3,`Other`=4 ))
expss::val_lab(Chad_pdm_2023$DateDerniereAssist)
Chad_pdm_2023 %>%
plot_frq(coord.flip =T,DateDerniereAssist,show.na =T)
5.4 Type d’assistance
var_type_assistance = c("TransfBenef",
"BanqueCerealiere",
"VivreContreTravail",
"ArgentContreTravail",
"DistribVivresSoudure",
"DistribArgentSoudure",
"DistribVivresArgentSoudure",
"BlanketFeedingChildrenWomen",
"BoursesAdo",
"BlanketFeedingChildren",
"BlanketFeedingWomen",
"MAMMASChildrenMAMPLWomen",
"MAMChildren",
"MASChildren",
"MAMPLWomen",
"FARNcommunaut",
"FormationRenfCapacite",
"CashTransfert",
"CantineScolaire",
"CantineScolaireBoursesAdo",
"AutreTransferts"
)5.4.2 EA 2019
#Chad_ea_2019$ArgentContreTravail
# Chad_ea_2019=Chad_ea_2019 %>% labelled::to_factor() %>%
# mutate_at(var_type_assistance, as.character)
Chad_ea_2019 <- Chad_ea_2019 %>%
dplyr::mutate(ArgentContreTravail = case_when(
ArgentContreTravail == "Oui" ~ "1",
ArgentContreTravail == "Non" ~ "3",
.default = NA
))
Chad_ea_2019 <-
Chad_ea_2019 %>% dplyr::mutate(ArgentContreTravail = dplyr::recode(ArgentContreTravail,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_ea_2019$ArgentContreTravail <- labelled::labelled(Chad_ea_2019$ArgentContreTravail, c(`Oui PAM` = 1, `Oui Autre` = 2, Non=3, `Ne Sait Pas`= 4))
#Chad_ea_2019$DistribVivresArgentSoudure
# Chad_ea_2019=Chad_ea_2019 %>% labelled::to_factor() %>%
# mutate_at(var_type_assistance, as.character)
Chad_ea_2019 <- Chad_ea_2019 %>%
dplyr::mutate(DistribVivresArgentSoudure = case_when(
DistribVivresArgentSoudure == "Oui" ~ "1",
DistribVivresArgentSoudure == "Non" ~ "3",
.default = NA
))
Chad_ea_2019 <-
Chad_ea_2019 %>% dplyr::mutate(DistribVivresArgentSoudure = dplyr::recode(DistribVivresArgentSoudure,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_ea_2019$DistribVivresArgentSoudure <- labelled::labelled(Chad_ea_2019$DistribVivresArgentSoudure, c(`Oui PAM` = 1, `Oui Autre` = 2, Non=3, `Ne Sait Pas`= 4))
#Chad_ea_2019$BlanketFeedingChildrenWomen
# Chad_ea_2019=Chad_ea_2019 %>% labelled::to_factor() %>%
# mutate_at(var_type_assistance, as.character)
Chad_ea_2019 <- Chad_ea_2019 %>%
dplyr::mutate(BlanketFeedingChildrenWomen = case_when(
BlanketFeedingChildrenWomen == "Oui" ~ "1",
BlanketFeedingChildrenWomen == "Non" ~ "3",
.default = NA
))
Chad_ea_2019 <-
Chad_ea_2019 %>% dplyr::mutate(BlanketFeedingChildrenWomen = dplyr::recode(BlanketFeedingChildrenWomen,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_ea_2019$BlanketFeedingChildrenWomen <- labelled::labelled(Chad_ea_2019$BlanketFeedingChildrenWomen, c(`Oui PAM` = 1, `Oui Autre` = 2, Non=3, `Ne Sait Pas`= 4))
#Chad_ea_2019$MAMMASChildrenMAMPLWomen
# Chad_ea_2019=Chad_ea_2019 %>% labelled::to_factor() %>%
# mutate_at(var_type_assistance, as.character)
Chad_ea_2019 <- Chad_ea_2019 %>%
dplyr::mutate(MAMMASChildrenMAMPLWomen = case_when(
MAMMASChildrenMAMPLWomen == "Oui" ~ "1",
MAMMASChildrenMAMPLWomen == "Non" ~ "3",
.default = NA
))
Chad_ea_2019 <-
Chad_ea_2019 %>% dplyr::mutate(MAMMASChildrenMAMPLWomen = dplyr::recode(MAMMASChildrenMAMPLWomen,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_ea_2019$MAMMASChildrenMAMPLWomen <- labelled::labelled(Chad_ea_2019$MAMMASChildrenMAMPLWomen, c(`Oui PAM` = 1, `Oui Autre` = 2, Non=3, `Ne Sait Pas`= 4))
#Chad_ea_2019$FormationRenfCapacite
# Chad_ea_2019=Chad_ea_2019 %>% labelled::to_factor() %>%
# mutate_at(var_type_assistance, as.character)
Chad_ea_2019 <- Chad_ea_2019 %>%
dplyr::mutate(FormationRenfCapacite = case_when(
FormationRenfCapacite == "Oui" ~ "1",
FormationRenfCapacite == "Non" ~ "3",
.default = NA
))
Chad_ea_2019 <-
Chad_ea_2019 %>% dplyr::mutate(FormationRenfCapacite = dplyr::recode(FormationRenfCapacite,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_ea_2019$FormationRenfCapacite <- labelled::labelled(Chad_ea_2019$FormationRenfCapacite, c(`Oui PAM` = 1, `Oui Autre` = 2, Non=3, `Ne Sait Pas`= 4))
#Chad_ea_2019$CantineScolaire
# Chad_ea_2019=Chad_ea_2019 %>% labelled::to_factor() %>%
# mutate_at(var_type_assistance, as.character)
Chad_ea_2019 <- Chad_ea_2019 %>%
dplyr::mutate(CantineScolaire = case_when(
CantineScolaire == "Oui" ~ "1",
CantineScolaire == "Non" ~ "3",
.default = NA
))
Chad_ea_2019 <-
Chad_ea_2019 %>% dplyr::mutate(CantineScolaire = dplyr::recode(CantineScolaire,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_ea_2019$CantineScolaire <- labelled::labelled(Chad_ea_2019$CantineScolaire, c(`Oui PAM` = 1, `Oui Autre` = 2, Non=3, `Ne Sait Pas`= 4))5.4.3 EA 2020
Chad_ea_2020=Chad_ea_2020 %>% labelled::to_factor() %>%
mutate_at(var_type_assistance, as.character)
Chad_ea_2020 <- Chad_ea_2020 %>%
dplyr::mutate(ArgentContreTravail = case_when(
ArgentContreTravail == "Oui" ~ "1",
ArgentContreTravail == "Non" ~ "3",
ArgentContreTravail == "Ne Sait Pas" ~ "4",
ArgentContreTravail == "4" ~ "3",
ArgentContreTravail == "5" ~ "3",
.default = NA
))
Chad_ea_2020 <-
Chad_ea_2020 %>% dplyr::mutate(ArgentContreTravail = dplyr::recode(ArgentContreTravail,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2020$ArgentContreTravail <- labelled::labelled(Chad_ea_2020$ArgentContreTravail, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2020 <- Chad_ea_2020 %>%
dplyr::mutate(DistribVivresArgentSoudure = case_when(
DistribVivresArgentSoudure == "Oui" ~ "1",
DistribVivresArgentSoudure == "Non" ~ "3",
DistribVivresArgentSoudure == "Ne Sait Pas" ~ "4",
DistribVivresArgentSoudure == "4" ~ "3",
DistribVivresArgentSoudure == "5" ~ "3",
.default = NA
))
Chad_ea_2020 <-
Chad_ea_2020 %>% dplyr::mutate(DistribVivresArgentSoudure = dplyr::recode(DistribVivresArgentSoudure,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2020$DistribVivresArgentSoudure <- labelled::labelled(Chad_ea_2020$DistribVivresArgentSoudure, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2020 <- Chad_ea_2020 %>%
dplyr::mutate(BlanketFeedingChildrenWomen = case_when(
BlanketFeedingChildrenWomen == "Oui" ~ "1",
BlanketFeedingChildrenWomen == "Non" ~ "3",
BlanketFeedingChildrenWomen == "Ne Sait Pas" ~ "4",
BlanketFeedingChildrenWomen == "4" ~ "3",
BlanketFeedingChildrenWomen == "5" ~ "3",
.default = NA
))
Chad_ea_2020 <-
Chad_ea_2020 %>% dplyr::mutate(BlanketFeedingChildrenWomen = dplyr::recode(BlanketFeedingChildrenWomen,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2020$BlanketFeedingChildrenWomenWomen <- labelled::labelled(Chad_ea_2020$BlanketFeedingChildrenWomen, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2020 <- Chad_ea_2020 %>%
dplyr::mutate(MAMMASChildrenMAMPLWomen = case_when(
MAMMASChildrenMAMPLWomen == "Oui" ~ "1",
MAMMASChildrenMAMPLWomen == "Non" ~ "3",
MAMMASChildrenMAMPLWomen == "Ne Sait Pas" ~ "4",
MAMMASChildrenMAMPLWomen == "4" ~ "3",
MAMMASChildrenMAMPLWomen == "5" ~ "3",
.default = NA
))
Chad_ea_2020 <-
Chad_ea_2020 %>% dplyr::mutate(MAMMASChildrenMAMPLWomen = dplyr::recode(MAMMASChildrenMAMPLWomen,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2020$MAMMASChildrenMAMPLWomen <- labelled::labelled(Chad_ea_2020$MAMMASChildrenMAMPLWomen, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2020 <- Chad_ea_2020 %>%
dplyr::mutate(FormationRenfCapacite = case_when(
FormationRenfCapacite == "Oui" ~ "1",
FormationRenfCapacite == "Non" ~ "3",
FormationRenfCapacite == "Ne Sait Pas" ~ "4",
FormationRenfCapacite == "4" ~ "3",
FormationRenfCapacite == "5" ~ "3",
.default = NA
))
Chad_ea_2020 <-
Chad_ea_2020 %>% dplyr::mutate(FormationRenfCapacite = dplyr::recode(FormationRenfCapacite,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2020$FormationRenfCapacite <- labelled::labelled(Chad_ea_2020$FormationRenfCapacite, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2020 <- Chad_ea_2020 %>%
dplyr::mutate(CantineScolaire = case_when(
CantineScolaire == "Oui" ~ "1",
CantineScolaire == "Non" ~ "3",
CantineScolaire == "Ne Sait Pas" ~ "4",
CantineScolaire == "4" ~ "3",
CantineScolaire == "5" ~ "3",
.default = NA
))
Chad_ea_2020 <-
Chad_ea_2020 %>% dplyr::mutate(CantineScolaire = dplyr::recode(CantineScolaire,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2020$CantineScolaire <- labelled::labelled(Chad_ea_2020$CantineScolaire, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))5.4.4 EA 2021
# df <-Chad_ea_2021 %>%
# dplyr::select(var_type_assistance) # ok
# Labels:
# value label
# 1 Oui
# 2 Non
# 3 Ne Sait Pas
# df <-Chad_ea_2021 %>%
# dplyr::select(var_type_assistance)
Chad_ea_2021=Chad_ea_2021 %>% labelled::to_factor() %>%
mutate_at(var_type_assistance, as.character)
Chad_ea_2021 <- Chad_ea_2021 %>%
dplyr::mutate(TransfBenef = case_when(
TransfBenef == "Oui" ~ "1",
TransfBenef == "Non" ~ "3",
TransfBenef == "Ne Sait Pas" ~ "4",
TransfBenef == "4" ~ "3",
TransfBenef == "5" ~ "3",
.default = NA
))
Chad_ea_2021 <-
Chad_ea_2021 %>% dplyr::mutate(TransfBenef = dplyr::recode(TransfBenef,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2021$TransfBenef <- labelled::labelled(Chad_ea_2021$TransfBenef, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2021 <- Chad_ea_2021 %>%
dplyr::mutate(BanqueCerealiere = case_when(
BanqueCerealiere == "Oui" ~ "1",
BanqueCerealiere == "Non" ~ "3",
BanqueCerealiere == "Ne Sait Pas" ~ "4",
BanqueCerealiere == "4" ~ "3",
BanqueCerealiere == "5" ~ "3",
.default = NA
))
Chad_ea_2021 <-
Chad_ea_2021 %>% dplyr::mutate(BanqueCerealiere = dplyr::recode(BanqueCerealiere,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2021$BanqueCerealiere <- labelled::labelled(Chad_ea_2021$BanqueCerealiere, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2021 <- Chad_ea_2021 %>%
dplyr::mutate(VivreContreTravail = case_when(
VivreContreTravail == "Oui" ~ "1",
VivreContreTravail == "Non" ~ "3",
VivreContreTravail == "Ne Sait Pas" ~ "4",
VivreContreTravail == "4" ~ "3",
VivreContreTravail == "5" ~ "3",
.default = NA
))
Chad_ea_2021 <-
Chad_ea_2021 %>% dplyr::mutate(VivreContreTravail = dplyr::recode(VivreContreTravail,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2021$VivreContreTravail <- labelled::labelled(Chad_ea_2021$VivreContreTravail, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2021 <- Chad_ea_2021 %>%
dplyr::mutate(ArgentContreTravail = case_when(
ArgentContreTravail == "Oui" ~ "1",
ArgentContreTravail == "Non" ~ "3",
ArgentContreTravail == "Ne Sait Pas" ~ "4",
ArgentContreTravail == "4" ~ "3",
ArgentContreTravail == "5" ~ "3",
.default = NA
))
Chad_ea_2021 <-
Chad_ea_2021 %>% dplyr::mutate(ArgentContreTravail = dplyr::recode(ArgentContreTravail,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2021$ArgentContreTravail <- labelled::labelled(Chad_ea_2021$ArgentContreTravail, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2021 <- Chad_ea_2021 %>%
dplyr::mutate(DistribVivresSoudure = case_when(
DistribVivresSoudure == "Oui" ~ "1",
DistribVivresSoudure == "Non" ~ "3",
DistribVivresSoudure == "Ne Sait Pas" ~ "4",
DistribVivresSoudure == "4" ~ "3",
DistribVivresSoudure == "5" ~ "3",
.default = NA
))
Chad_ea_2021 <-
Chad_ea_2021 %>% dplyr::mutate(DistribVivresSoudure = dplyr::recode(DistribVivresSoudure,"1"=1,"2"=2,"3"=4,"4"=3,"4"=4))
Chad_ea_2021$DistribVivresSoudure <- labelled::labelled(Chad_ea_2021$DistribVivresSoudure, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2021 <- Chad_ea_2021 %>%
dplyr::mutate(DistribArgentSoudure = case_when(
DistribArgentSoudure == "Oui" ~ "1",
DistribArgentSoudure == "Non" ~ "3",
DistribArgentSoudure == "Ne Sait Pas" ~ "4",
DistribArgentSoudure == "4" ~ "3",
DistribArgentSoudure == "5" ~ "3",
.default = NA
))
Chad_ea_2021 <-
Chad_ea_2021 %>% dplyr::mutate(DistribArgentSoudure = dplyr::recode(DistribArgentSoudure,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2021$DistribArgentSoudure <- labelled::labelled(Chad_ea_2021$DistribArgentSoudure, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2021 <- Chad_ea_2021 %>%
dplyr::mutate(BoursesAdo = case_when(
BoursesAdo == "Oui" ~ "1",
BoursesAdo == "Non" ~ "3",
BoursesAdo == "Ne Sait Pas" ~ "4",
BoursesAdo == "4" ~ "3",
BoursesAdo == "5" ~ "3",
.default = NA
))
Chad_ea_2021 <-
Chad_ea_2021 %>% dplyr::mutate(BoursesAdo = dplyr::recode(BoursesAdo,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2021$BoursesAdo <- labelled::labelled(Chad_ea_2021$BoursesAdo, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2021 <- Chad_ea_2021 %>%
dplyr::mutate(BlanketFeedingChildren = case_when(
BlanketFeedingChildren == "Oui" ~ "1",
BlanketFeedingChildren == "Non" ~ "3",
BlanketFeedingChildren == "Ne Sait Pas" ~ "4",
BlanketFeedingChildren == "4" ~ "3",
BlanketFeedingChildren == "5" ~ "3",
.default = NA
))
Chad_ea_2021 <-
Chad_ea_2021 %>% dplyr::mutate(BlanketFeedingChildren = dplyr::recode(BlanketFeedingChildren,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2021$BlanketFeedingChildren <- labelled::labelled(Chad_ea_2021$BlanketFeedingChildren, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2021 <- Chad_ea_2021 %>%
dplyr::mutate(BlanketFeedingWomen = case_when(
BlanketFeedingWomen == "Oui" ~ "1",
BlanketFeedingWomen == "Non" ~ "3",
BlanketFeedingWomen == "Ne Sait Pas" ~ "4",
BlanketFeedingWomen == "4" ~ "3",
BlanketFeedingWomen == "5" ~ "3",
.default = NA
))
Chad_ea_2021 <-
Chad_ea_2021 %>% dplyr::mutate(BlanketFeedingWomen = dplyr::recode(BlanketFeedingWomen,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2021$BlanketFeedingWomen <- labelled::labelled(Chad_ea_2021$BlanketFeedingWomen, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2021 <- Chad_ea_2021 %>%
dplyr::mutate(MAMChildren = case_when(
MAMChildren == "Oui" ~ "1",
MAMChildren == "Non" ~ "3",
MAMChildren == "Ne Sait Pas" ~ "4",
MAMChildren == "4" ~ "3",
MAMChildren == "5" ~ "3",
.default = NA
))
Chad_ea_2021 <-
Chad_ea_2021 %>% dplyr::mutate(MAMChildren = dplyr::recode(MAMChildren,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2021$MAMChildren <- labelled::labelled(Chad_ea_2021$MAMChildren, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2021 <- Chad_ea_2021 %>%
dplyr::mutate(MAMPLWomen = case_when(
MAMPLWomen == "Oui" ~ "1",
MAMPLWomen == "Non" ~ "3",
MAMPLWomen == "Ne Sait Pas" ~ "4",
MAMPLWomen == "4" ~ "3",
MAMPLWomen == "5" ~ "3",
.default = NA
))
Chad_ea_2021 <-
Chad_ea_2021 %>% dplyr::mutate(MAMPLWomen = dplyr::recode(MAMPLWomen,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2021$MAMPLWomen <- labelled::labelled(Chad_ea_2021$MAMPLWomen, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2021 <- Chad_ea_2021 %>%
dplyr::mutate(FARNcommunaut = case_when(
FARNcommunaut == "Oui" ~ "1",
FARNcommunaut == "Non" ~ "3",
FARNcommunaut == "Ne Sait Pas" ~ "4",
FARNcommunaut == "4" ~ "3",
FARNcommunaut == "5" ~ "3",
.default = NA
))
Chad_ea_2021 <-
Chad_ea_2021 %>% dplyr::mutate(FARNcommunaut = dplyr::recode(FARNcommunaut,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2021$FARNcommunaut <- labelled::labelled(Chad_ea_2021$FARNcommunaut, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2021 <- Chad_ea_2021 %>%
dplyr::mutate(FormationRenfCapacite = case_when(
FormationRenfCapacite == "Oui" ~ "1",
FormationRenfCapacite == "Non" ~ "3",
FormationRenfCapacite == "Ne Sait Pas" ~ "4",
FormationRenfCapacite == "4" ~ "3",
FormationRenfCapacite == "5" ~ "3",
.default = NA
))
Chad_ea_2021 <-
Chad_ea_2021 %>% dplyr::mutate(FormationRenfCapacite = dplyr::recode(FormationRenfCapacite,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2021$FormationRenfCapacite <- labelled::labelled(Chad_ea_2021$FormationRenfCapacite, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2021 <- Chad_ea_2021 %>%
dplyr::mutate(CashTransfert = case_when(
CashTransfert == "Oui" ~ "1",
CashTransfert == "Non" ~ "3",
CashTransfert == "Ne Sait Pas" ~ "4",
CashTransfert == "4" ~ "3",
CashTransfert == "5" ~ "3",
.default = NA
))
Chad_ea_2021 <-
Chad_ea_2021 %>% dplyr::mutate(CashTransfert = dplyr::recode(CashTransfert,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2021$CashTransfert <- labelled::labelled(Chad_ea_2021$CashTransfert, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2021 <- Chad_ea_2021 %>%
dplyr::mutate(CantineScolaire = case_when(
CantineScolaire == "Oui" ~ "1",
CantineScolaire == "Non" ~ "3",
CantineScolaire == "Ne Sait Pas" ~ "4",
CantineScolaire == "4" ~ "3",
CantineScolaire == "5" ~ "3",
.default = NA
))
Chad_ea_2021 <-
Chad_ea_2021 %>% dplyr::mutate(CantineScolaire = dplyr::recode(CantineScolaire,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2021$CantineScolaire <- labelled::labelled(Chad_ea_2021$CantineScolaire, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2021 <- Chad_ea_2021 %>%
dplyr::mutate(MASChildren = case_when(
MASChildren == "Oui" ~ "1",
MASChildren == "Non" ~ "3",
MASChildren == "Ne Sait Pas" ~ "4",
MASChildren == "4" ~ "3",
MASChildren == "5" ~ "3",
.default = NA
))
Chad_ea_2021 <-
Chad_ea_2021 %>% dplyr::mutate(MASChildren = dplyr::recode(MASChildren,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2021$MASChildren <- labelled::labelled(Chad_ea_2021$MASChildren, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2021 <- Chad_ea_2021 %>%
dplyr::mutate(AutreTransferts = case_when(
AutreTransferts == "Oui" ~ "1",
AutreTransferts == "Non" ~ "3",
AutreTransferts == "Ne Sait Pas" ~ "4",
AutreTransferts == "4" ~ "3",
AutreTransferts == "5" ~ "3",
.default = NA
))
Chad_ea_2021 <-
Chad_ea_2021 %>% dplyr::mutate(AutreTransferts = dplyr::recode(AutreTransferts,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2021$AutreTransferts <- labelled::labelled(Chad_ea_2021$AutreTransferts, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))5.4.5 EA 2022
# df <-Chad_ea_2022 %>%
# dplyr::select(var_type_assistance) #ok
# Labels:
# value label
# 1 Oui
# 2 Non
# 3 Ne Sait Pas
# df <-Chad_ea_2022 %>%
# dplyr::select(var_type_assistance)
Chad_ea_2022=Chad_ea_2022 %>% mutate_at(var_type_assistance, as.character)
Chad_ea_2022 <- Chad_ea_2022 %>%
dplyr::mutate(TransfBenef = case_when(
TransfBenef == "1" ~ "1",
TransfBenef == "2" ~ "4",
TransfBenef == "3" ~ "3",
.default = NA
))
Chad_ea_2022 <-
Chad_ea_2022 %>% dplyr::mutate(TransfBenef = dplyr::recode(TransfBenef,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2022$TransfBenef <- labelled::labelled(Chad_ea_2022$TransfBenef, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2022 <- Chad_ea_2022 %>%
dplyr::mutate(BanqueCerealiere = case_when(
BanqueCerealiere == "1" ~ "1",
BanqueCerealiere == "2" ~ "4",
BanqueCerealiere == "3" ~ "3",
.default = NA
))
Chad_ea_2022 <-
Chad_ea_2022 %>% dplyr::mutate(BanqueCerealiere = dplyr::recode(BanqueCerealiere,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_ea_2022$BanqueCerealiere <- labelled::labelled(Chad_ea_2022$BanqueCerealiere, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2022 <- Chad_ea_2022 %>%
dplyr::mutate(VivreContreTravail = case_when(
VivreContreTravail == "1" ~ "1",
VivreContreTravail == "2" ~ "4",
VivreContreTravail == "3" ~ "3",
.default = NA
))
Chad_ea_2022 <-
Chad_ea_2022 %>% dplyr::mutate(VivreContreTravail = dplyr::recode(VivreContreTravail,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_ea_2022$VivreContreTravail <- labelled::labelled(Chad_ea_2022$VivreContreTravail, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2022 <- Chad_ea_2022 %>%
dplyr::mutate(ArgentContreTravail = case_when(
ArgentContreTravail == "1" ~ "1",
ArgentContreTravail == "2" ~ "4",
ArgentContreTravail == "3" ~ "3",
.default = NA
))
Chad_ea_2022 <-
Chad_ea_2022 %>% dplyr::mutate(ArgentContreTravail = dplyr::recode(ArgentContreTravail,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_ea_2022$ArgentContreTravail <- labelled::labelled(Chad_ea_2022$ArgentContreTravail, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2022 <- Chad_ea_2022 %>%
dplyr::mutate(DistribVivresSoudure = case_when(
DistribVivresSoudure == "1" ~ "1",
DistribVivresSoudure == "2" ~ "4",
DistribVivresSoudure == "3" ~ "3",
.default = NA
))
Chad_ea_2022 <-
Chad_ea_2022 %>% dplyr::mutate(DistribVivresSoudure = dplyr::recode(DistribVivresSoudure,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_ea_2022$DistribVivresSoudure <- labelled::labelled(Chad_ea_2022$DistribVivresSoudure, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2022 <- Chad_ea_2022 %>%
dplyr::mutate(DistribArgentSoudure = case_when(
DistribArgentSoudure == "1" ~ "1",
DistribArgentSoudure == "2" ~ "4",
DistribArgentSoudure == "3" ~ "3",
.default = NA
))
Chad_ea_2022 <-
Chad_ea_2022 %>% dplyr::mutate(DistribArgentSoudure = dplyr::recode(DistribArgentSoudure,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_ea_2022$DistribArgentSoudure <- labelled::labelled(Chad_ea_2022$DistribArgentSoudure, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2022 <- Chad_ea_2022 %>%
dplyr::mutate(BoursesAdo = case_when(
BoursesAdo == "1" ~ "1",
BoursesAdo == "2" ~ "4",
BoursesAdo == "3" ~ "3",
.default = NA
))
Chad_ea_2022 <-
Chad_ea_2022 %>% dplyr::mutate(BoursesAdo = dplyr::recode(BoursesAdo,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_ea_2022$BoursesAdo <- labelled::labelled(Chad_ea_2022$BoursesAdo, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2022 <- Chad_ea_2022 %>%
dplyr::mutate(BlanketFeedingChildren = case_when(
BlanketFeedingChildren == "1" ~ "1",
BlanketFeedingChildren == "2" ~ "4",
BlanketFeedingChildren == "3" ~ "3",
.default = NA
))
Chad_ea_2022 <-
Chad_ea_2022 %>% dplyr::mutate(BlanketFeedingChildren = dplyr::recode(BlanketFeedingChildren,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_ea_2022$BlanketFeedingChildren <- labelled::labelled(Chad_ea_2022$BlanketFeedingChildren, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2022 <- Chad_ea_2022 %>%
dplyr::mutate(BlanketFeedingWomen = case_when(
BlanketFeedingWomen == "1" ~ "1",
BlanketFeedingWomen == "2" ~ "4",
BlanketFeedingWomen == "3" ~ "3",
.default = NA
))
Chad_ea_2022 <-
Chad_ea_2022 %>% dplyr::mutate(BlanketFeedingWomen = dplyr::recode(BlanketFeedingWomen,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_ea_2022$BlanketFeedingWomen <- labelled::labelled(Chad_ea_2022$BlanketFeedingWomen, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2022 <- Chad_ea_2022 %>%
dplyr::mutate(MAMChildren = case_when(
MAMChildren == "1" ~ "1",
MAMChildren == "2" ~ "4",
MAMChildren == "3" ~ "3",
.default = NA
))
Chad_ea_2022 <-
Chad_ea_2022 %>% dplyr::mutate(MAMChildren = dplyr::recode(MAMChildren,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_ea_2022$MAMChildren <- labelled::labelled(Chad_ea_2022$MAMChildren, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2022 <- Chad_ea_2022 %>%
dplyr::mutate(MAMPLWomen = case_when(
MAMPLWomen == "1" ~ "1",
MAMPLWomen == "2" ~ "4",
MAMPLWomen == "3" ~ "3",
.default = NA
))
Chad_ea_2022 <-
Chad_ea_2022 %>% dplyr::mutate(MAMPLWomen = dplyr::recode(MAMPLWomen,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_ea_2022$MAMPLWomen <- labelled::labelled(Chad_ea_2022$MAMPLWomen, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2022 <- Chad_ea_2022 %>%
dplyr::mutate(FARNcommunaut = case_when(
FARNcommunaut == "1" ~ "1",
FARNcommunaut == "2" ~ "4",
FARNcommunaut == "3" ~ "3",
.default = NA
))
Chad_ea_2022 <-
Chad_ea_2022 %>% dplyr::mutate(FARNcommunaut = dplyr::recode(FARNcommunaut,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_ea_2022$FARNcommunaut <- labelled::labelled(Chad_ea_2022$FARNcommunaut, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2022 <- Chad_ea_2022 %>%
dplyr::mutate(FormationRenfCapacite = case_when(
FormationRenfCapacite == "1" ~ "1",
FormationRenfCapacite == "2" ~ "4",
FormationRenfCapacite == "3" ~ "3",
.default = NA
))
Chad_ea_2022 <-
Chad_ea_2022 %>% dplyr::mutate(FormationRenfCapacite = dplyr::recode(FormationRenfCapacite,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_ea_2022$FormationRenfCapacite <- labelled::labelled(Chad_ea_2022$FormationRenfCapacite, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2022 <- Chad_ea_2022 %>%
dplyr::mutate(CashTransfert = case_when(
CashTransfert == "1" ~ "1",
CashTransfert == "2" ~ "4",
CashTransfert == "3" ~ "3",
.default = NA
))
Chad_ea_2022 <-
Chad_ea_2022 %>% dplyr::mutate(CashTransfert = dplyr::recode(CashTransfert,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_ea_2022$CashTransfert <- labelled::labelled(Chad_ea_2022$CashTransfert, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2022 <- Chad_ea_2022 %>%
dplyr::mutate(CantineScolaire = case_when(
CantineScolaire == "1" ~ "1",
CantineScolaire == "2" ~ "4",
CantineScolaire == "3" ~ "3",
.default = NA
))
Chad_ea_2022 <-
Chad_ea_2022 %>% dplyr::mutate(CantineScolaire = dplyr::recode(CantineScolaire,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_ea_2022$CantineScolaire <- labelled::labelled(Chad_ea_2022$CantineScolaire, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2022 <- Chad_ea_2022 %>%
dplyr::mutate(MASChildren = case_when(
MASChildren == "1" ~ "1",
MASChildren == "2" ~ "4",
MASChildren == "3" ~ "3",
.default = NA
))
Chad_ea_2022 <-
Chad_ea_2022 %>% dplyr::mutate(MASChildren = dplyr::recode(MASChildren,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_ea_2022$MASChildren <- labelled::labelled(Chad_ea_2022$MASChildren, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2022 <- Chad_ea_2022 %>%
dplyr::mutate(AutreTransferts = case_when(
AutreTransferts == "1" ~ "1",
AutreTransferts == "2" ~ "4",
AutreTransferts == "3" ~ "3",
.default = NA
))
Chad_ea_2022 <-
Chad_ea_2022 %>% dplyr::mutate(AutreTransferts = dplyr::recode(AutreTransferts,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_ea_2022$AutreTransferts <- labelled::labelled(Chad_ea_2022$AutreTransferts, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))5.4.6 EA 2023
Chad_ea_2023=Chad_ea_2023 %>% mutate_at(var_type_assistance, as.character)
Chad_ea_2023 <- Chad_ea_2023 %>%
dplyr::mutate(TransfBenef = case_when(
TransfBenef == "1" ~ "1",
TransfBenef == "2" ~ "3",
TransfBenef == "3" ~ "4",
.default = NA
))
Chad_ea_2023 <-
Chad_ea_2023 %>% dplyr::mutate(TransfBenef = dplyr::recode(TransfBenef,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2023$TransfBenef <- labelled::labelled(Chad_ea_2023$TransfBenef, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2023 <- Chad_ea_2023 %>%
dplyr::mutate(BanqueCerealiere = case_when(
BanqueCerealiere == "1" ~ "1",
BanqueCerealiere == "2" ~ "3",
BanqueCerealiere == "3" ~ "4",
.default = NA
))
Chad_ea_2023 <-
Chad_ea_2023 %>% dplyr::mutate(BanqueCerealiere = dplyr::recode(BanqueCerealiere,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2023$BanqueCerealiere <- labelled::labelled(Chad_ea_2023$BanqueCerealiere, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2023 <- Chad_ea_2023 %>%
dplyr::mutate(VivreContreTravail = case_when(
VivreContreTravail == "1" ~ "1",
VivreContreTravail == "2" ~ "3",
VivreContreTravail == "3" ~ "4",
.default = NA
))
Chad_ea_2023 <-
Chad_ea_2023 %>% dplyr::mutate(VivreContreTravail = dplyr::recode(VivreContreTravail,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2023$VivreContreTravail <- labelled::labelled(Chad_ea_2023$VivreContreTravail, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2023 <- Chad_ea_2023 %>%
dplyr::mutate(ArgentContreTravail = case_when(
ArgentContreTravail == "1" ~ "1",
ArgentContreTravail == "2" ~ "3",
ArgentContreTravail == "3" ~ "4",
.default = NA
))
Chad_ea_2023 <-
Chad_ea_2023 %>% dplyr::mutate(ArgentContreTravail = dplyr::recode(ArgentContreTravail,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2023$ArgentContreTravail <- labelled::labelled(Chad_ea_2023$ArgentContreTravail, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2023 <- Chad_ea_2023 %>%
dplyr::mutate(DistribVivresSoudure = case_when(
DistribVivresSoudure == "1" ~ "1",
DistribVivresSoudure == "2" ~ "3",
DistribVivresSoudure == "3" ~ "4",
.default = NA
))
Chad_ea_2023 <-
Chad_ea_2023 %>% dplyr::mutate(DistribVivresSoudure = dplyr::recode(DistribVivresSoudure,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2023$DistribVivresSoudure <- labelled::labelled(Chad_ea_2023$DistribVivresSoudure, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2023 <- Chad_ea_2023 %>%
dplyr::mutate(DistribArgentSoudure = case_when(
DistribArgentSoudure == "1" ~ "1",
DistribArgentSoudure == "2" ~ "3",
DistribArgentSoudure == "3" ~ "4",
.default = NA
))
Chad_ea_2023 <-
Chad_ea_2023 %>% dplyr::mutate(DistribArgentSoudure = dplyr::recode(DistribArgentSoudure,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2023$DistribArgentSoudure <- labelled::labelled(Chad_ea_2023$DistribArgentSoudure, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2023 <- Chad_ea_2023 %>%
dplyr::mutate(BoursesAdo = case_when(
BoursesAdo == "1" ~ "1",
BoursesAdo == "2" ~ "3",
BoursesAdo == "3" ~ "4",
.default = NA
))
Chad_ea_2023 <-
Chad_ea_2023 %>% dplyr::mutate(BoursesAdo = dplyr::recode(BoursesAdo,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2023$BoursesAdo <- labelled::labelled(Chad_ea_2023$BoursesAdo, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2023 <- Chad_ea_2023 %>%
dplyr::mutate(BlanketFeedingChildren = case_when(
BlanketFeedingChildren == "1" ~ "1",
BlanketFeedingChildren == "2" ~ "3",
BlanketFeedingChildren == "3" ~ "4",
.default = NA
))
Chad_ea_2023 <-
Chad_ea_2023 %>% dplyr::mutate(BlanketFeedingChildren = dplyr::recode(BlanketFeedingChildren,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2023$BlanketFeedingChildren <- labelled::labelled(Chad_ea_2023$BlanketFeedingChildren, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2023 <- Chad_ea_2023 %>%
dplyr::mutate(BlanketFeedingWomen = case_when(
BlanketFeedingWomen == "1" ~ "1",
BlanketFeedingWomen == "2" ~ "3",
BlanketFeedingWomen == "3" ~ "4",
.default = NA
))
Chad_ea_2023 <-
Chad_ea_2023 %>% dplyr::mutate(BlanketFeedingWomen = dplyr::recode(BlanketFeedingWomen,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2023$BlanketFeedingWomen <- labelled::labelled(Chad_ea_2023$BlanketFeedingWomen, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2023 <- Chad_ea_2023 %>%
dplyr::mutate(MAMChildren = case_when(
MAMChildren == "1" ~ "1",
MAMChildren == "2" ~ "3",
MAMChildren == "3" ~ "4",
.default = NA
))
Chad_ea_2023 <-
Chad_ea_2023 %>% dplyr::mutate(MAMChildren = dplyr::recode(MAMChildren,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2023$MAMChildren <- labelled::labelled(Chad_ea_2023$MAMChildren, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2023 <- Chad_ea_2023 %>%
dplyr::mutate(MAMPLWomen = case_when(
MAMPLWomen == "1" ~ "1",
MAMPLWomen == "2" ~ "3",
MAMPLWomen == "3" ~ "4",
.default = NA
))
Chad_ea_2023 <-
Chad_ea_2023 %>% dplyr::mutate(MAMPLWomen = dplyr::recode(MAMPLWomen,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2023$MAMPLWomen <- labelled::labelled(Chad_ea_2023$MAMPLWomen, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2023 <- Chad_ea_2023 %>%
dplyr::mutate(FARNcommunaut = case_when(
FARNcommunaut == "1" ~ "1",
FARNcommunaut == "2" ~ "3",
FARNcommunaut == "3" ~ "4",
.default = NA
))
Chad_ea_2023 <-
Chad_ea_2023 %>% dplyr::mutate(FARNcommunaut = dplyr::recode(FARNcommunaut,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2023$FARNcommunaut <- labelled::labelled(Chad_ea_2023$FARNcommunaut, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2023 <- Chad_ea_2023 %>%
dplyr::mutate(FormationRenfCapacite = case_when(
FormationRenfCapacite == "1" ~ "1",
FormationRenfCapacite == "2" ~ "3",
FormationRenfCapacite == "3" ~ "4",
.default = NA
))
Chad_ea_2023 <-
Chad_ea_2023 %>% dplyr::mutate(FormationRenfCapacite = dplyr::recode(FormationRenfCapacite,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2023$FormationRenfCapacite <- labelled::labelled(Chad_ea_2023$FormationRenfCapacite, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2023 <- Chad_ea_2023 %>%
dplyr::mutate(CashTransfert = case_when(
CashTransfert == "1" ~ "1",
CashTransfert == "2" ~ "3",
CashTransfert == "3" ~ "4",
.default = NA
))
Chad_ea_2023 <-
Chad_ea_2023 %>% dplyr::mutate(CashTransfert = dplyr::recode(CashTransfert,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2023$CashTransfert <- labelled::labelled(Chad_ea_2023$CashTransfert, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2023 <- Chad_ea_2023 %>%
dplyr::mutate(CantineScolaire = case_when(
CantineScolaire == "1" ~ "1",
CantineScolaire == "2" ~ "3",
CantineScolaire == "3" ~ "4",
.default = NA
))
Chad_ea_2023 <-
Chad_ea_2023 %>% dplyr::mutate(CantineScolaire = dplyr::recode(CantineScolaire,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2023$CantineScolaire <- labelled::labelled(Chad_ea_2023$CantineScolaire, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2023 <- Chad_ea_2023 %>%
dplyr::mutate(MASChildren = case_when(
MASChildren == "1" ~ "1",
MASChildren == "2" ~ "3",
MASChildren == "3" ~ "4",
.default = NA
))
Chad_ea_2023 <-
Chad_ea_2023 %>% dplyr::mutate(MASChildren = dplyr::recode(MASChildren,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2023$MASChildren <- labelled::labelled(Chad_ea_2023$MASChildren, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_ea_2023 <- Chad_ea_2023 %>%
dplyr::mutate(AutreTransferts = case_when(
AutreTransferts == "1" ~ "1",
AutreTransferts == "2" ~ "3",
AutreTransferts == "3" ~ "4",
.default = NA
))
Chad_ea_2023 <-
Chad_ea_2023 %>% dplyr::mutate(AutreTransferts = dplyr::recode(AutreTransferts,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_ea_2023$AutreTransferts <- labelled::labelled(Chad_ea_2023$AutreTransferts, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))5.4.7 PDM 2020
df <-Chad_pdm_2020 %>%
dplyr::select(var_type_assistance)
# Labels:
# value label
# 1 1
# 2 2
# 3 3
# 4 4
# 5 5
Chad_pdm_2020=Chad_pdm_2020 %>% mutate_at(var_type_assistance, as.character)
Chad_pdm_2020 <- Chad_pdm_2020 %>%
dplyr::mutate(ArgentContreTravail = case_when(
ArgentContreTravail == "1" ~ "1",
ArgentContreTravail == "0" ~ "3",
.default = NA
))
Chad_pdm_2020 <-
Chad_pdm_2020 %>% dplyr::mutate(ArgentContreTravail = dplyr::recode(ArgentContreTravail,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2020$ArgentContreTravail <- labelled::labelled(Chad_pdm_2020$ArgentContreTravail, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2020 <- Chad_pdm_2020 %>%
dplyr::mutate(DistribVivresArgentSoudure = case_when(
DistribVivresArgentSoudure == "1" ~ "1",
DistribVivresArgentSoudure == "0" ~ "3",
.default = NA
))
Chad_pdm_2020 <-
Chad_pdm_2020 %>% dplyr::mutate(DistribVivresArgentSoudure = dplyr::recode(DistribVivresArgentSoudure,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2020$DistribVivresArgentSoudure <- labelled::labelled(Chad_pdm_2020$DistribVivresArgentSoudure, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2020 <- Chad_pdm_2020 %>%
dplyr::mutate(CantineScolaireBoursesAdo = case_when(
CantineScolaireBoursesAdo == "1" ~ "1",
CantineScolaireBoursesAdo == "0" ~ "3",
.default = NA
))
Chad_pdm_2020 <-
Chad_pdm_2020 %>% dplyr::mutate(CantineScolaireBoursesAdo = dplyr::recode(CantineScolaireBoursesAdo,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2020$CantineScolaireBoursesAdo <- labelled::labelled(Chad_pdm_2020$CantineScolaireBoursesAdo, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2020 <- Chad_pdm_2020 %>%
dplyr::mutate(BlanketFeedingChildrenWomen = case_when(
BlanketFeedingChildrenWomen == "1" ~ "1",
BlanketFeedingChildrenWomen == "0" ~ "3",
.default = NA
))
Chad_pdm_2020 <-
Chad_pdm_2020 %>% dplyr::mutate(BlanketFeedingChildrenWomen = dplyr::recode(BlanketFeedingChildrenWomen,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2020$BlanketFeedingChildrenWomen <- labelled::labelled(Chad_pdm_2020$BlanketFeedingChildrenWomen, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2020 <- Chad_pdm_2020 %>%
dplyr::mutate(MAMMASChildrenMAMPLWomen = case_when(
MAMMASChildrenMAMPLWomen == "1" ~ "1",
MAMMASChildrenMAMPLWomen == "0" ~ "3",
.default = NA
))
Chad_pdm_2020 <-
Chad_pdm_2020 %>% dplyr::mutate(MAMMASChildrenMAMPLWomen = dplyr::recode(MAMMASChildrenMAMPLWomen,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2020$MAMMASChildrenMAMPLWomen <- labelled::labelled(Chad_pdm_2020$MAMMASChildrenMAMPLWomen, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2020 <- Chad_pdm_2020 %>%
dplyr::mutate(FARNcommunaut = case_when(
FARNcommunaut == "1" ~ "1",
FARNcommunaut == "0" ~ "3",
.default = NA
))
Chad_pdm_2020 <-
Chad_pdm_2020 %>% dplyr::mutate(FARNcommunaut = dplyr::recode(FARNcommunaut,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2020$FARNcommunaut <- labelled::labelled(Chad_pdm_2020$FARNcommunaut, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))5.4.8 PDM 2021
Chad_pdm_2021=Chad_pdm_2021 %>% mutate_at(var_type_assistance, as.character)
Chad_pdm_2021 <- Chad_pdm_2021 %>%
dplyr::mutate(ArgentContreTravail = case_when(
ArgentContreTravail == "1" ~ "1",
ArgentContreTravail == "0" ~ "3",
.default = NA
))
Chad_pdm_2021 <-
Chad_pdm_2021 %>% dplyr::mutate(ArgentContreTravail = dplyr::recode(ArgentContreTravail,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2021$ArgentContreTravail <- labelled::labelled(Chad_pdm_2021$ArgentContreTravail, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2021 <- Chad_pdm_2021 %>%
dplyr::mutate(DistribVivresArgentSoudure = case_when(
DistribVivresArgentSoudure == "1" ~ "1",
DistribVivresArgentSoudure == "0" ~ "3",
.default = NA
))
Chad_pdm_2021 <-
Chad_pdm_2021 %>% dplyr::mutate(DistribVivresArgentSoudure = dplyr::recode(DistribVivresArgentSoudure,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2021$DistribVivresArgentSoudure <- labelled::labelled(Chad_pdm_2021$DistribVivresArgentSoudure, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2021 <- Chad_pdm_2021 %>%
dplyr::mutate(BlanketFeedingChildrenWomen = case_when(
BlanketFeedingChildrenWomen == "1" ~ "1",
BlanketFeedingChildrenWomen == "0" ~ "3",
.default = NA
))
Chad_pdm_2021 <-
Chad_pdm_2021 %>% dplyr::mutate(BlanketFeedingChildrenWomen = dplyr::recode(BlanketFeedingChildrenWomen,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2021$BlanketFeedingChildrenWomen <- labelled::labelled(Chad_pdm_2021$BlanketFeedingChildrenWomen, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2021 <- Chad_pdm_2021 %>%
dplyr::mutate(MAMMASChildrenMAMPLWomen = case_when(
MAMMASChildrenMAMPLWomen == "1" ~ "1",
MAMMASChildrenMAMPLWomen == "0" ~ "3",
.default = NA
))
Chad_pdm_2021 <-
Chad_pdm_2021 %>% dplyr::mutate(MAMMASChildrenMAMPLWomen = dplyr::recode(MAMMASChildrenMAMPLWomen,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2021$MAMMASChildrenMAMPLWomen <- labelled::labelled(Chad_pdm_2021$MAMMASChildrenMAMPLWomen, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2021 <- Chad_pdm_2021 %>%
dplyr::mutate(FormationRenfCapacite = case_when(
FormationRenfCapacite == "1" ~ "1",
FormationRenfCapacite == "0" ~ "3",
.default = NA
))
Chad_pdm_2021 <-
Chad_pdm_2021 %>% dplyr::mutate(FormationRenfCapacite = dplyr::recode(FormationRenfCapacite,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2021$FormationRenfCapacite <- labelled::labelled(Chad_pdm_2021$FormationRenfCapacite, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2021 <- Chad_pdm_2021 %>%
dplyr::mutate(CantineScolaire = case_when(
CantineScolaire == "1" ~ "1",
CantineScolaire == "0" ~ "3",
.default = NA
))
Chad_pdm_2021 <-
Chad_pdm_2021 %>% dplyr::mutate(CantineScolaire = dplyr::recode(CantineScolaire,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2021$CantineScolaire <- labelled::labelled(Chad_pdm_2021$CantineScolaire, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))5.4.9 PDM 2022
# df <-Chad_pdm_2022 %>%
# dplyr::select(var_type_assistance) #ok
# Labels:
# value label
# 1 Oui
# 2 Non
# 3 Ne Sait Pas
Chad_pdm_2022=Chad_pdm_2022 %>% mutate_at(var_type_assistance, as.character)
Chad_pdm_2022 <- Chad_pdm_2022 %>%
dplyr::mutate(TransfBenef = case_when(
TransfBenef == "1" ~ "1",
TransfBenef == "2" ~ "4",
TransfBenef == "3" ~ "3",
.default = NA
))
Chad_pdm_2022 <-
Chad_pdm_2022 %>% dplyr::mutate(TransfBenef = dplyr::recode(TransfBenef,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_pdm_2022$TransfBenef <- labelled::labelled(Chad_pdm_2022$TransfBenef, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2022 <- Chad_pdm_2022 %>%
dplyr::mutate(BanqueCerealiere = case_when(
BanqueCerealiere == "1" ~ "1",
BanqueCerealiere == "2" ~ "4",
BanqueCerealiere == "3" ~ "3",
.default = NA
))
Chad_pdm_2022 <-
Chad_pdm_2022 %>% dplyr::mutate(BanqueCerealiere = dplyr::recode(BanqueCerealiere,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_pdm_2022$BanqueCerealiere <- labelled::labelled(Chad_pdm_2022$BanqueCerealiere, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2022 <- Chad_pdm_2022 %>%
dplyr::mutate(VivreContreTravail = case_when(
VivreContreTravail == "1" ~ "1",
VivreContreTravail == "2" ~ "4",
VivreContreTravail == "3" ~ "3",
.default = NA
))
Chad_pdm_2022 <-
Chad_pdm_2022 %>% dplyr::mutate(VivreContreTravail = dplyr::recode(VivreContreTravail,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_pdm_2022$VivreContreTravail <- labelled::labelled(Chad_pdm_2022$VivreContreTravail, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2022 <- Chad_pdm_2022 %>%
dplyr::mutate(ArgentContreTravail = case_when(
ArgentContreTravail == "1" ~ "1",
ArgentContreTravail == "2" ~ "4",
ArgentContreTravail == "3" ~ "3",
.default = NA
))
Chad_pdm_2022 <-
Chad_pdm_2022 %>% dplyr::mutate(ArgentContreTravail = dplyr::recode(ArgentContreTravail,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_pdm_2022$ArgentContreTravail <- labelled::labelled(Chad_pdm_2022$ArgentContreTravail, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2022 <- Chad_pdm_2022 %>%
dplyr::mutate(DistribVivresSoudure = case_when(
DistribVivresSoudure == "1" ~ "1",
DistribVivresSoudure == "2" ~ "4",
DistribVivresSoudure == "3" ~ "3",
.default = NA
))
Chad_pdm_2022 <-
Chad_pdm_2022 %>% dplyr::mutate(DistribVivresSoudure = dplyr::recode(DistribVivresSoudure,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_pdm_2022$DistribVivresSoudure <- labelled::labelled(Chad_pdm_2022$DistribVivresSoudure, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2022 <- Chad_pdm_2022 %>%
dplyr::mutate(DistribArgentSoudure = case_when(
DistribArgentSoudure == "1" ~ "1",
DistribArgentSoudure == "2" ~ "4",
DistribArgentSoudure == "3" ~ "3",
.default = NA
))
Chad_pdm_2022 <-
Chad_pdm_2022 %>% dplyr::mutate(DistribArgentSoudure = dplyr::recode(DistribArgentSoudure,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_pdm_2022$DistribArgentSoudure <- labelled::labelled(Chad_pdm_2022$DistribArgentSoudure, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2022 <- Chad_pdm_2022 %>%
dplyr::mutate(BoursesAdo = case_when(
BoursesAdo == "1" ~ "1",
BoursesAdo == "2" ~ "4",
BoursesAdo == "3" ~ "3",
.default = NA
))
Chad_pdm_2022 <-
Chad_pdm_2022 %>% dplyr::mutate(BoursesAdo = dplyr::recode(BoursesAdo,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_pdm_2022$BoursesAdo <- labelled::labelled(Chad_pdm_2022$BoursesAdo, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2022 <- Chad_pdm_2022 %>%
dplyr::mutate(BlanketFeedingChildren = case_when(
BlanketFeedingChildren == "1" ~ "1",
BlanketFeedingChildren == "2" ~ "4",
BlanketFeedingChildren == "3" ~ "3",
.default = NA
))
Chad_pdm_2022 <-
Chad_pdm_2022 %>% dplyr::mutate(BlanketFeedingChildren = dplyr::recode(BlanketFeedingChildren,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_pdm_2022$BlanketFeedingChildren <- labelled::labelled(Chad_pdm_2022$BlanketFeedingChildren, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2022 <- Chad_pdm_2022 %>%
dplyr::mutate(BlanketFeedingWomen = case_when(
BlanketFeedingWomen == "1" ~ "1",
BlanketFeedingWomen == "2" ~ "4",
BlanketFeedingWomen == "3" ~ "3",
.default = NA
))
Chad_pdm_2022 <-
Chad_pdm_2022 %>% dplyr::mutate(BlanketFeedingWomen = dplyr::recode(BlanketFeedingWomen,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_pdm_2022$BlanketFeedingWomen <- labelled::labelled(Chad_pdm_2022$BlanketFeedingWomen, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2022 <- Chad_pdm_2022 %>%
dplyr::mutate(MAMChildren = case_when(
MAMChildren == "1" ~ "1",
MAMChildren == "2" ~ "4",
MAMChildren == "3" ~ "3",
.default = NA
))
Chad_pdm_2022 <-
Chad_pdm_2022 %>% dplyr::mutate(MAMChildren = dplyr::recode(MAMChildren,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_pdm_2022$MAMChildren <- labelled::labelled(Chad_pdm_2022$MAMChildren, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2022 <- Chad_pdm_2022 %>%
dplyr::mutate(MAMPLWomen = case_when(
MAMPLWomen == "1" ~ "1",
MAMPLWomen == "2" ~ "4",
MAMPLWomen == "3" ~ "3",
.default = NA
))
Chad_pdm_2022 <-
Chad_pdm_2022 %>% dplyr::mutate(MAMPLWomen = dplyr::recode(MAMPLWomen,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_pdm_2022$MAMPLWomen <- labelled::labelled(Chad_pdm_2022$MAMPLWomen, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2022 <- Chad_pdm_2022 %>%
dplyr::mutate(FARNcommunaut = case_when(
FARNcommunaut == "1" ~ "1",
FARNcommunaut == "2" ~ "4",
FARNcommunaut == "3" ~ "3",
.default = NA
))
Chad_pdm_2022 <-
Chad_pdm_2022 %>% dplyr::mutate(FARNcommunaut = dplyr::recode(FARNcommunaut,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_pdm_2022$FARNcommunaut <- labelled::labelled(Chad_pdm_2022$FARNcommunaut, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2022 <- Chad_pdm_2022 %>%
dplyr::mutate(FormationRenfCapacite = case_when(
FormationRenfCapacite == "1" ~ "1",
FormationRenfCapacite == "2" ~ "4",
FormationRenfCapacite == "3" ~ "3",
.default = NA
))
Chad_pdm_2022 <-
Chad_pdm_2022 %>% dplyr::mutate(FormationRenfCapacite = dplyr::recode(FormationRenfCapacite,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_pdm_2022$FormationRenfCapacite <- labelled::labelled(Chad_pdm_2022$FormationRenfCapacite, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2022 <- Chad_pdm_2022 %>%
dplyr::mutate(CashTransfert = case_when(
CashTransfert == "1" ~ "1",
CashTransfert == "2" ~ "4",
CashTransfert == "3" ~ "3",
.default = NA
))
Chad_pdm_2022 <-
Chad_pdm_2022 %>% dplyr::mutate(CashTransfert = dplyr::recode(CashTransfert,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_pdm_2022$CashTransfert <- labelled::labelled(Chad_pdm_2022$CashTransfert, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2022 <- Chad_pdm_2022 %>%
dplyr::mutate(CantineScolaire = case_when(
CantineScolaire == "1" ~ "1",
CantineScolaire == "2" ~ "4",
CantineScolaire == "3" ~ "3",
.default = NA
))
Chad_pdm_2022 <-
Chad_pdm_2022 %>% dplyr::mutate(CantineScolaire = dplyr::recode(CantineScolaire,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_pdm_2022$CantineScolaire <- labelled::labelled(Chad_pdm_2022$CantineScolaire, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2022 <- Chad_pdm_2022 %>%
dplyr::mutate(MASChildren = case_when(
MASChildren == "1" ~ "1",
MASChildren == "2" ~ "4",
MASChildren == "3" ~ "3",
.default = NA
))
Chad_pdm_2022 <-
Chad_pdm_2022 %>% dplyr::mutate(MASChildren = dplyr::recode(MASChildren,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_pdm_2022$MASChildren <- labelled::labelled(Chad_pdm_2022$MASChildren, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2022 <- Chad_pdm_2022 %>%
dplyr::mutate(AutreTransferts = case_when(
AutreTransferts == "1" ~ "1",
AutreTransferts == "2" ~ "4",
AutreTransferts == "3" ~ "3",
.default = NA
))
Chad_pdm_2022 <-
Chad_pdm_2022 %>% dplyr::mutate(AutreTransferts = dplyr::recode(AutreTransferts,"1"=1,"2"=2,"3"=3,"4"=4))
Chad_pdm_2022$AutreTransferts <- labelled::labelled(Chad_pdm_2022$AutreTransferts, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))5.4.10 PDM 2023
Chad_pdm_2023=Chad_pdm_2023 %>% mutate_at(var_type_assistance, as.character)
Chad_pdm_2023 <- Chad_pdm_2023 %>%
dplyr::mutate(TransfBenef = case_when(
TransfBenef == "1" ~ "1",
TransfBenef == "2" ~ "3",
TransfBenef == "3" ~ "4",
.default = NA
))
Chad_pdm_2023 <-
Chad_pdm_2023 %>% dplyr::mutate(TransfBenef = dplyr::recode(TransfBenef,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2023$TransfBenef <- labelled::labelled(Chad_pdm_2023$TransfBenef, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2023 <- Chad_pdm_2023 %>%
dplyr::mutate(BanqueCerealiere = case_when(
BanqueCerealiere == "1" ~ "1",
BanqueCerealiere == "2" ~ "3",
BanqueCerealiere == "3" ~ "4",
.default = NA
))
Chad_pdm_2023 <-
Chad_pdm_2023 %>% dplyr::mutate(BanqueCerealiere = dplyr::recode(BanqueCerealiere,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2023$BanqueCerealiere <- labelled::labelled(Chad_pdm_2023$BanqueCerealiere, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2023 <- Chad_pdm_2023 %>%
dplyr::mutate(VivreContreTravail = case_when(
VivreContreTravail == "1" ~ "1",
VivreContreTravail == "2" ~ "3",
VivreContreTravail == "3" ~ "4",
.default = NA
))
Chad_pdm_2023 <-
Chad_pdm_2023 %>% dplyr::mutate(VivreContreTravail = dplyr::recode(VivreContreTravail,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2023$VivreContreTravail <- labelled::labelled(Chad_pdm_2023$VivreContreTravail, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2023 <- Chad_pdm_2023 %>%
dplyr::mutate(ArgentContreTravail = case_when(
ArgentContreTravail == "1" ~ "1",
ArgentContreTravail == "2" ~ "3",
ArgentContreTravail == "3" ~ "4",
.default = NA
))
Chad_pdm_2023 <-
Chad_pdm_2023 %>% dplyr::mutate(ArgentContreTravail = dplyr::recode(ArgentContreTravail,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2023$ArgentContreTravail <- labelled::labelled(Chad_pdm_2023$ArgentContreTravail, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2023 <- Chad_pdm_2023 %>%
dplyr::mutate(DistribVivresSoudure = case_when(
DistribVivresSoudure == "1" ~ "1",
DistribVivresSoudure == "2" ~ "3",
DistribVivresSoudure == "3" ~ "4",
.default = NA
))
Chad_pdm_2023 <-
Chad_pdm_2023 %>% dplyr::mutate(DistribVivresSoudure = dplyr::recode(DistribVivresSoudure,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2023$DistribVivresSoudure <- labelled::labelled(Chad_pdm_2023$DistribVivresSoudure, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2023 <- Chad_pdm_2023 %>%
dplyr::mutate(DistribArgentSoudure = case_when(
DistribArgentSoudure == "1" ~ "1",
DistribArgentSoudure == "2" ~ "3",
DistribArgentSoudure == "3" ~ "4",
.default = NA
))
Chad_pdm_2023 <-
Chad_pdm_2023 %>% dplyr::mutate(DistribArgentSoudure = dplyr::recode(DistribArgentSoudure,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2023$DistribArgentSoudure <- labelled::labelled(Chad_pdm_2023$DistribArgentSoudure, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2023 <- Chad_pdm_2023 %>%
dplyr::mutate(BoursesAdo = case_when(
BoursesAdo == "1" ~ "1",
BoursesAdo == "2" ~ "3",
BoursesAdo == "3" ~ "4",
.default = NA
))
Chad_pdm_2023 <-
Chad_pdm_2023 %>% dplyr::mutate(BoursesAdo = dplyr::recode(BoursesAdo,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2023$BoursesAdo <- labelled::labelled(Chad_pdm_2023$BoursesAdo, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2023 <- Chad_pdm_2023 %>%
dplyr::mutate(BlanketFeedingChildren = case_when(
BlanketFeedingChildren == "1" ~ "1",
BlanketFeedingChildren == "2" ~ "3",
BlanketFeedingChildren == "3" ~ "4",
.default = NA
))
Chad_pdm_2023 <-
Chad_pdm_2023 %>% dplyr::mutate(BlanketFeedingChildren = dplyr::recode(BlanketFeedingChildren,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2023$BlanketFeedingChildren <- labelled::labelled(Chad_pdm_2023$BlanketFeedingChildren, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2023 <- Chad_pdm_2023 %>%
dplyr::mutate(BlanketFeedingWomen = case_when(
BlanketFeedingWomen == "1" ~ "1",
BlanketFeedingWomen == "2" ~ "3",
BlanketFeedingWomen == "3" ~ "4",
.default = NA
))
Chad_pdm_2023 <-
Chad_pdm_2023 %>% dplyr::mutate(BlanketFeedingWomen = dplyr::recode(BlanketFeedingWomen,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2023$BlanketFeedingWomen <- labelled::labelled(Chad_pdm_2023$BlanketFeedingWomen, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2023 <- Chad_pdm_2023 %>%
dplyr::mutate(MAMChildren = case_when(
MAMChildren == "1" ~ "1",
MAMChildren == "2" ~ "3",
MAMChildren == "3" ~ "4",
.default = NA
))
Chad_pdm_2023 <-
Chad_pdm_2023 %>% dplyr::mutate(MAMChildren = dplyr::recode(MAMChildren,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2023$MAMChildren <- labelled::labelled(Chad_pdm_2023$MAMChildren, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2023 <- Chad_pdm_2023 %>%
dplyr::mutate(MAMPLWomen = case_when(
MAMPLWomen == "1" ~ "1",
MAMPLWomen == "2" ~ "3",
MAMPLWomen == "3" ~ "4",
.default = NA
))
Chad_pdm_2023 <-
Chad_pdm_2023 %>% dplyr::mutate(MAMPLWomen = dplyr::recode(MAMPLWomen,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2023$MAMPLWomen <- labelled::labelled(Chad_pdm_2023$MAMPLWomen, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2023 <- Chad_pdm_2023 %>%
dplyr::mutate(FARNcommunaut = case_when(
FARNcommunaut == "1" ~ "1",
FARNcommunaut == "2" ~ "3",
FARNcommunaut == "3" ~ "4",
.default = NA
))
Chad_pdm_2023 <-
Chad_pdm_2023 %>% dplyr::mutate(FARNcommunaut = dplyr::recode(FARNcommunaut,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2023$FARNcommunaut <- labelled::labelled(Chad_pdm_2023$FARNcommunaut, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2023 <- Chad_pdm_2023 %>%
dplyr::mutate(FormationRenfCapacite = case_when(
FormationRenfCapacite == "1" ~ "1",
FormationRenfCapacite == "2" ~ "3",
FormationRenfCapacite == "3" ~ "4",
.default = NA
))
Chad_pdm_2023 <-
Chad_pdm_2023 %>% dplyr::mutate(FormationRenfCapacite = dplyr::recode(FormationRenfCapacite,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2023$FormationRenfCapacite <- labelled::labelled(Chad_pdm_2023$FormationRenfCapacite, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2023 <- Chad_pdm_2023 %>%
dplyr::mutate(CashTransfert = case_when(
CashTransfert == "1" ~ "1",
CashTransfert == "2" ~ "3",
CashTransfert == "3" ~ "4",
.default = NA
))
Chad_pdm_2023 <-
Chad_pdm_2023 %>% dplyr::mutate(CashTransfert = dplyr::recode(CashTransfert,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2023$CashTransfert <- labelled::labelled(Chad_pdm_2023$CashTransfert, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2023 <- Chad_pdm_2023 %>%
dplyr::mutate(CantineScolaire = case_when(
CantineScolaire == "1" ~ "1",
CantineScolaire == "2" ~ "3",
CantineScolaire == "3" ~ "4",
.default = NA
))
Chad_pdm_2023 <-
Chad_pdm_2023 %>% dplyr::mutate(CantineScolaire = dplyr::recode(CantineScolaire,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2023$CantineScolaire <- labelled::labelled(Chad_pdm_2023$CantineScolaire, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2023 <- Chad_pdm_2023 %>%
dplyr::mutate(MASChildren = case_when(
MASChildren == "1" ~ "1",
MASChildren == "2" ~ "3",
MASChildren == "3" ~ "4",
.default = NA
))
Chad_pdm_2023 <-
Chad_pdm_2023 %>% dplyr::mutate(MASChildren = dplyr::recode(MASChildren,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2023$MASChildren <- labelled::labelled(Chad_pdm_2023$MASChildren, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))
Chad_pdm_2023 <- Chad_pdm_2023 %>%
dplyr::mutate(AutreTransferts = case_when(
AutreTransferts == "1" ~ "1",
AutreTransferts == "2" ~ "3",
AutreTransferts == "3" ~ "4",
.default = NA
))
Chad_pdm_2023 <-
Chad_pdm_2023 %>% dplyr::mutate(AutreTransferts = dplyr::recode(AutreTransferts,"1"=1,"2"=2,"3"=4,"4"=3))
Chad_pdm_2023$AutreTransferts <- labelled::labelled(Chad_pdm_2023$AutreTransferts, c(`Oui PAM` = 1, `Oui Autre` = 2, `Ne Sait Pas`= 4, Non=3))5.5 Merging all data
Chad_baseline_2018 <- labelled::to_factor(Chad_baseline_2018)
Chad_ea_2019 <- labelled::to_factor(Chad_ea_2019)
Chad_ea_2020 <- labelled::to_factor(Chad_ea_2020)
Chad_ea_2021 <- labelled::to_factor(Chad_ea_2021)
Chad_ea_2022 <- labelled::to_factor(Chad_ea_2022)
Chad_ea_2023 <- labelled::to_factor(Chad_ea_2023)
Chad_pdm_2020 <- labelled::to_factor(Chad_pdm_2020)
Chad_pdm_2021 <- labelled::to_factor(Chad_pdm_2021)
Chad_pdm_2022 <- labelled::to_factor(Chad_pdm_2022)
Chad_pdm_2023 <- labelled::to_factor(Chad_pdm_2023)
WFP_Chad<-plyr::rbind.fill(Chad_baseline_2018,
Chad_ea_2019,
Chad_ea_2020,
Chad_ea_2021,
Chad_ea_2022,
Chad_ea_2023,
Chad_pdm_2020,
Chad_pdm_2021,
Chad_pdm_2022,
Chad_pdm_2023)WFP_Chad$DebutAssistance<-as.Date(WFP_Chad$DebutAssistance, origin = "1970-01-01")
WFP_Chad$DateDerniereAssist <- labelled::labelled(WFP_Chad$DateDerniereAssist, c(`moins d’une semaine` = 1, `entre 1 et 3 semaines` = 2,`plus de 3 semaines`=3,`Other`=4 ))WFP_Chad <- WFP_Chad %>%
mutate_at(var_type_assistance, as.numeric)
WFP_Chad <- WFP_Chad %>%
dplyr::mutate(across(var_type_assistance,
~labelled(., labels = c(
`Oui PAM` = 1,
`Oui Autre` = 2,
`Ne Sait Pas`= 4,
`Non`=3
)
)
)
)key_vars = c("ID"
# "YEAR",
# "SURVEY",
# "ADMIN0Name",
# "adm0_ocha",
# "adm1_ocha",
# "adm2_ocha",
# "Key_with_subset_data",
# "SvyDatePDM",
# "RESPConsent",
# "ADMIN1Name",
# "ADMIN2Name",
# "ADMIN3Name",
# "sous_bureau",
# "site",
# "site_autre",
# "village",
# "village_autre",
# "Longitude",
# "Latitude",
# "Longitude_precision",
# "Latitude_precision"
)5.7 Keep assistance original variables
In this section, we just keep WFP interventions original variables.
library(haven)
library(labelled) # for general functions to work with labelled data
library(tidyverse) # general wrangling
library(dplyr)
library(Hmisc)
library(gtsummary) # to demonstrate automatic use of variable labels in summary tables
library(readxl)
library(foreign)
library(sjPlot)
library(sjmisc)
library(sjlabelled) # for example efc data set with variable labels
library(stringr)dir_input_data = "C:/Users/AHema/OneDrive - CGIAR/Desktop/WFP Resilience dataset/data/input_data/Chad"
dir_output_data = "C:/Users/AHema/OneDrive - CGIAR/Desktop/WFP Resilience dataset/data/output_data/Chad"Chad_Harmonization_variables <- read_excel(paste0(dir_input_data,"/Chad_Harmonization_assistance.xlsx"),
sheet = "variables_harmonization")
#View(Chad_Harmonization_variables)
Chad_Harmonization_description <- read_excel(paste0(dir_input_data,"/Chad_Harmonization_assistance.xlsx"),
sheet = "description")
#View(Chad_Harmonization_description)lst_data = Chad_Harmonization_description$Data
lst_test = Chad_Harmonization_description$Name
for(i in 1:length(lst_data)) { # Head of for-loop
assign(lst_test[i], # Read and store data frames
read_sav(paste0(dir_input_data,"/",lst_data[i])))
}for (j in 1:length(lst_test)){
df= get(lst_test[j], envir = .GlobalEnv)
for (i in 1:nrow(Chad_Harmonization_variables)){
df[,Chad_Harmonization_variables$NewVariable_Name[i]]=ifelse(is.na(Chad_Harmonization_variables[i,lst_test[j]]),NA,df[,Chad_Harmonization_variables[i,lst_test[j]][[1]]])
}
df<-df %>% select(Chad_Harmonization_variables$NewVariable_Name)
assign(lst_test[j], # Read and store data frames
df)
#write_sav(df, paste0(dir_output_data,"/",lst_test[j],".sav"))
write_dta(df, paste0(dir_output_data,"/",lst_test[j],"_assistance_originalVars.dta"))
}