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#' @title levelsCollapser
#'
#' @description This function displays the response rates by the levels of an attribute
#' Levels with similar response rates may be combined
#'
#' @param dset The data frame containing the data set
#'
#' @param resp A character respresenting the name of the binary outcome variable
#' The binary outcome variable may be a factor with two levels or an integer (or numeric ) with two unique values
#'
#' @param bins A number denoting the number of bins.Default value is 10
#'
#' @return A list containing the tables of response rate by levels for every attribute
#'
#' @examples
#'
#' # Load the German_Credit data set supplied with this package
#'
#' data("German_Credit")
#'
#' # Create an empty list
#'
#' l<-list()
#'
#' # Call the function as follows
#'
#' l<-levelsCollapser(German_Credit,resp="Good_Bad",bins=10)
#'
#' # response rate by levels of the Account_Balance in the German_Credit data
#'
#' l$Account_Balance
#'
#' # Collapse levels with similar response percentages.
#'
#' @export
levelsCollapser<-function(dset,resp="y",bins=10)
{
l<-list()
d<-data.frame()
d<-dset
if(class(dset[[resp]])=="factor")
{
d[[resp]]<-as.numeric(d[[resp]])
d[[resp]]<-ifelse(d[[resp]]==max(d[[resp]]),1,0)
}
if(class(dset[[resp]])=="numeric" | class(dset[[resp]])=="integer")
{
d[[resp]]<-ifelse(d[[resp]]==max(d[[resp]]),1,0)
}
for(i in 1:ncol(d))
{
if(names(d)[i]!= resp)
{
naml<-names(d)[i]
df<-data.frame()
df_tot<-data.frame()
df_one<-data.frame()
df_zero<-data.frame()
if(class(d[[i]])=="numeric" | class(d[[i]])=="integer")
{
nr<-length(dset[[i]])
n<-round(nr/bins)
n
GC1<-data.frame()
GC1<-d[order(d[[i]]),]
vec<-GC1[[i]]
br<-numeric(length = bins+1)
lrec<-length(vec)
for(k in 1:bins+1)
{
if(k==1)
{
br[k]<-vec[k]
}
else if (k==(bins+1))
{
br[k]<-vec[lrec]
}
else{
br[k]<-vec[((k-1)*n)+1]
}
}
br<-unique(br)
if(length(br)==2)
{
cbr<-cut(vec,breaks=br,right=FALSE)
}
else
{
cbr<-cut(vec,breaks=br,right=FALSE,include.lowest = TRUE)
}
naml<-gsub(" ","",naml)
varnum<-paste('categorical',naml,sep="")
GC1<-cbind(GC1,cbr)
names(GC1)[ncol(GC1)]<-varnum
naml<-names(GC1)[ncol(GC1)]
d<-data.frame()
d<-GC1
}
df_tot <- d %>% dplyr::group_by_(naml) %>% dplyr::summarise(tot=dplyr::n())
df_tot<-as.data.frame(df_tot)
df_one <- d %>% dplyr::filter(d[[resp]]==1) %>% dplyr::group_by_(naml) %>% dplyr::summarise(bad=dplyr::n())
df_one<-as.data.frame(df_one)
one_rate <- (df_one[,2]/sum(df_one[,2]))*100
df_zero <- d %>% dplyr::filter(d[[resp]]==0) %>% dplyr::group_by_(naml) %>% dplyr::summarise(good=dplyr::n())
df_zero<-as.data.frame(df_zero)
zero_rate<-(df_zero[,2]/sum(df_zero[,2]))*100
if(nrow(df_tot)>nrow(df_zero))
{
zero<-as.numeric()
zero<-df_tot[,2]-df_one[,2]
zero_rate<-as.numeric()
zero_rate<-(zero/sum(zero))*100
df<-cbind(df_tot,response=df_one[,2],non_response=zero,response_pct=one_rate,non_response_pct=zero_rate)
}
else if(nrow(df_tot)>nrow(df_one))
{
one<-as.numeric()
one<-df_tot[,2]-df_zero[,2]
one_rate<-as.numeric()
one_rate<-(one/sum(one))*100
df<-cbind(df_tot,response=one,non_response=df_zero[,2],response_pct=one_rate,non_response_pct=zero_rate)
}
else
{
df<-cbind(df_tot,response=df_one[,2],non_response=df_zero[,2],response_pct=one_rate,non_response_pct=zero_rate)
}
df<-df[order(df$response_pct),]
b=numeric()
for(j in 1:nrow(df))
{
if(j==1)
{
b[j]<-0
}
else
{
b[j]<-((df$response_pct[j]-df$response_pct[j-1])/df$response_pct[j])*100
}
}
df<-cbind(df,response_pct_change=b)
l[[i]]=df
names(l)[[i]]<-names(d)[i]
}
}
return(l)
}
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