### Bijay Lal Pradhan ##################################
### Coding and Decoding ##################################
## download data from my website #### http://bijaylalpradhan.com.np/data/ ### click on CS_Bank #############
#### or download from http://bijaylalpradhan.com.np/wp-content/uploads/2022/04/CS_Bank.csv#########################
## keep your data into your specific fodder and retrive it #####
data1 <- read.csv(“E:/R-chitwan/CS_Bank.csv”)
#### decoding data ######################
data1$gender1[data1$Sex==1]=”Male”
data1$gender1[data1$Sex==2]=”Female”
### similarly coding can be done as #################
data1$gender2[data1$gender1==”Male”]=1
data1$gender2[data1$gender2==”Female”]=2
### decoding the data ###################################### Practice ##############
Now decode the data under the variable Confidence.Score.for.Bank as
1= strongly agree
2= agree
3= somewhat agree
4= neither agree nor disagree
5= somewhat disagree
6= disagree
7= strongly disagree
#################### solution ##############################################################
data1$Confidence.Score.for.Bank1[data1$Confidence.Score.for.Bank==”1″]=”strongly agree”
data1$Confidence.Score.for.Bank1[data1$Confidence.Score.for.Bank==”2″]=”agree”
data1$Confidence.Score.for.Bank1[data1$Confidence.Score.for.Bank==”3″]=”somewhat agree”
data1$Confidence.Score.for.Bank1[data1$Confidence.Score.for.Bank==”4″]=”neither agree nor disagree”
data1$Confidence.Score.for.Bank1[data1$Confidence.Score.for.Bank==”5″]=”somewhat disagree”
data1$Confidence.Score.for.Bank1[data1$Confidence.Score.for.Bank==”6″]=”disagree”
data1$Confidence.Score.for.Bank1[data1$Confidence.Score.for.Bank==”7″]=”strongly disagree”
##################################################################################################
############# the coding decoding can be done alternatively as ##################################
######### using package: plyr::revalue(data1,) #################
data1$gender3=revalue(data1$gender, c(“1″=”Male”,”2″=”Female”)) ## using plyr
#################################################################################################
data1$confidance=revalue(data1$Confidence.Score.for.Bank, c(“1″= “strongly agree”, “2”= “agree”, “3”= “somewhat agree”,
“4”= “neither agree nor disagree”, “5”= “somewhat disagree”, “6”= “disagree”, “7”= “strongly disagree”))
####### renaming variable name in dataframe #################################################
names(data1)[names(data1) == ‘Confidence.Score.for.Bank’] = ‘cs’
###########################################################
#### to remove column from data frame ###############
data1 = data1[ -c(9) ]
#####################################################
###### categories the number data ##################
data1 <- within(data1, {
Income.cat <- NA # need to initialize variable
Income.cat[Income < 10000] <- “Low”
Income.cat[Income >= 10000 & Income < 20000] <- “Middle”
Income.cat[Income >= 20000] <- “High”})
############ converting data structure #######################
data1$Income.cat <- factor(data1$Income.cat) # cat to factor
##############################################################