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This is a preliminary cleaning file in R which receives the Stata output file in Stata Version 13 format.

Scripts/Script1cleanandmerge.R

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+####August 2023 University of Manchester
+
+####Script for ICC calculations for with both poisson and Logit MLM
+####Authors: Diego Perez Ruiz, Madhu Chauhan, Wendy Olsen
+
+### This script is the starting point for the Spatial Besag-Yorke-Mollie-2 estimation activities.
+
+### This first cleaning script clean data-- 
+
+###--ensure only sexes are male and female 
+###-- keep a very wide set of adult age groups
+###data for individuals aged 14-max age, --double digitizing state and 
+###district codes, --making a combined district code for district name-matching, 
+###adding count of individuals per district as sample size; (this is an unweighted adult case count)
+###adding state names to the dataset, extracting state code and name from 2011 
+###shape file.
+
+setwd("C:/data/SpatialBayesian2023newFiles") 
+#revise the working directory to place saved data in the correct folder.
+
+library(dplyr)
+library(tidyr)
+library(stringr)
+library(withr)
+library(readr)
+library(readxl)
+#install.packages("readstata13")
+library(readstata13)
+#loading the PLFS1718 data from where it was merged using Stata.  All persons. )
+IndiaPLFS201718 <-  read.dta13("C:/data/PLFS20172018/plfs_2018version13.dta")
+
+                           # previously we used IndiaPLFS20178adults1565.csv")
+                                #you might use library(readstata13)
+                                #dat <- read.dta13("path to file.dta")
+             #and if you want to work entirely in stata, you can use this: 
+                                #save.dta13(dat, file="newfile.dta")  
+#but we have stata 18 so we used that to make a file called by the stata v13 in its filename.
+#Filtering for only males and females
+IndiaPLFS201718 <- IndiaPLFS201718 %>% filter( sex != "3")
+
+#Filtering for people less than 25 years of age (14-25)
+#IndiaPLFS201718 <- IndiaPLFS201718 %>% filter( age <= "24")
+
+#code for dist and state concatenation as distcode variable, also making 
+#dist code and state codes 2 digit values
+IndiaPLFS201718 <-IndiaPLFS201718 %>% 
+  mutate(state = str_pad(string = state, pad = '0', width = 2), 
+         district = str_pad(string = district, pad = '0', width = 2)) %>% 
+  unite(distcode, state, district, sep='', remove = FALSE) 
+
+save(IndiaPLFS201718, file="IndiaPLFS201718.rds")
+
+#Checking if the code worked fine and if we have two digit state Id's, district 
+#ID's and a combination of state and district codes as distcode in the table. 
+#This dist code can be used to map the districts for spatial analysis in the future.
+
+table(IndiaPLFS201718$distcode)
+
+summary(unique(IndiaPLFS201718$state))
+
+IndiaPLFS201718$distcode
+
+#Adding state names to the state code from the data layout file
+#Importing data layout state code sheet from PLFS
+
+#first prepare 2 files for the join (move locations)
+Data_LayoutPLFSinfo <- read_excel("C:/data/PLFS20172018/Data_LayoutPLFS.xlsx", sheet = "State code")
+
+colnames(Data_LayoutPLFSinfo) = Data_LayoutPLFSinfo[1, ] # the first row will be the header
+Data_LayoutPLFSinfo = Data_LayoutPLFSinfo[-1, ]          # removing the first row.
+colnames(Data_LayoutPLFSinfo)<- c("state","stateName")
+Data_LayoutPLFSinfo$state<-as.numeric(Data_LayoutPLFSinfo$state)
+
+Data_LayoutPLFSinfo <-Data_LayoutPLFSinfo %>% 
+  mutate(state = str_pad(string = state, pad = '0', width = 2))
+IndiaPLFS201718<- left_join(IndiaPLFS201718, Data_LayoutPLFSinfo, by= 'state')
+
+#Creating the corresponding RDS file with state, dist,
+#distcode, rural, sex, female, medwork, monthly expenditure,  MPerCapitaExp, and log of MPCE for ease in usability in future.
+
+IndiaPLFS201718  <- IndiaPLFS201718  %>% 
+  dplyr::select( psid, state, stateName, district, distcode, age, sex, female, rural, medwork, hhsize, comb_wt , mpce, logRsincpc)
+
+#note we are using the /data folder inside the workshop activity folder. The command below cannot use / key.
+
+#      #      #      The below 'save' places the main all-persons file into two folders      #      #      #  
+saveRDS(IndiaPLFS201718, file = "IndiaPLFS201718.rds")
+setwd("c:/data/SpatialBayesian2023newFiles/data")
+saveRDS(IndiaPLFS201718, file = "IndiaPLFS201718.rds")
+
+
+# Part 2
+# Aim 2:  getting a List of all the district codes from the census2011 shp file and 
+#creating an rds for the same to compare and check the missing districts in the
+#shape file and to use it in future to map with the dist codes in the PLFS dataframe.
+
+setwd("c:/data/SpatialBayesian2023newFiles/maps-master/Districts/Census_2011")
+
+library(sf)
+
+India_Districts<-  st_read("2011_Dist.shp")
+
+India2011Census<-data.frame(district = India_Districts$DISTRICT,
+                censuscode = India_Districts$censuscode,statename= India_Districts$ST_NM)
+
+India2011Census<-unique(India2011Census)
+
+setwd("c:/data/SpatialBayesian2023newFiles/data")
+saveRDS(India2011Census, file = "India2011Census.RDS")
+
+                  #The below not needed 2023.
+
+#add the sample size as a variable to the dataset with total counts of 
+#observations in each district in the data [note, we need to make the count
+#whilst the age-range covered in the data is correct, see above to limit ages.]
+
+#IndiaPLFS201718<-IndiaPLFS201718 %>%
+#  add_count(distcode, name = 'sample_size')
+
+#Create a new RDS file with district code and district sample for future usability.
+
+#distSampleCount<-IndiaPLFS201718%>%distinct(distcode, sample_size)
+#sum(distSampleCount$sample_size)
+#saveRDS(distSampleCount, file = "Sample_Sizes.rds")
+#write.csv(distSampleCount, "Sample_Sizes.csv", row.names=FALSE, quote=FALSE)
+
+
+