###   Bijay Lal Pradhan   ####
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###  Coding and Decoding  ####
##############################cs
## 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  ####################################
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## 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,) or package:dplyr::recode(data1,) #################
data1$gender3=revalue(data1$gender, c("1"="Male","2"="Female"))  ## using plyr   
data1=data1 %>% mutate(gender4=recode(Sex, "1"="Male","2"="Female")) ## using dplyr
#################################################################################################
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
##############################################################