在R中创建百分位类别

Question

I have following data:

len = 1000
vint1 = vint1=sample(1:150,len,replace=TRUE)
vch1=sample(LETTERS[1:5],len,replace=TRUE)
vbin1=sample(letters[1:2],len,replace=TRUE)
mydf = data.frame(vint1, vch1, vbin1)

But I have to make another column 'category' which should have entries according to following rules:

'N' if < 90th percentile 
'cat1' if >=90th and <95th percentile
'cat2' if >=95th and <99th percentile
'cat3' if >99th percentile

Percentile is always checked for that vch1 and vbin1.

I can determine if vint1 value is > 90th percentile for that group of vch1 and vch2 by following code:

with(mydf, ave(vint1, vch1, vbin1, FUN=function(x) x>quantile(x,0.9)))

But how can I make categories?

EDIT:

I tried following code. Want to confirm if it is OK or is there some better method:

with(mydf, ave(vint1, vch1, vbin1, FUN=function(x) 
    ifelse(x<quantile(x,0.9), 'N',
    ifelse(x<quantile(x,0.95),'cat1',
    ifelse(x<(quantile(x,0.99)),'cat2','cat3'
    )))
    )
)

This is a follow up question from: Categorize dataframe by percentile in R

Answer 1

This may be helpful. Using group_by , ntile in dplyr , and your ifelse statement, I came up with the following.

library(dplyr)

group_by(mydf, vch1, vbin1) %>%
mutate(check = ntile(vint1, 100),
       out = ifelse(check > 99, "cat3",
                 ifelse(between(check, 95, 99), "cat2",
                    ifelse(between(check, 90, 95), "cat1", "N")))) %>%
ungroup()

# A part of the outcome
#   vint1 vch1 vbin1 check  out
#1    138    C     b    88    N
#2     66    B     a    39    N
#3     24    D     a    16    N
#4    141    B     a    90 cat1
#5     27    C     a    13    N
#6     29    C     a    16    N
#7     11    D     b     4    N
#8     24    B     b    21    N
#9     72    E     a    46    N
#10    25    C     b    15    N

IDEA

transform(mydf,
          check = ave(vint1, vch1, vbin1, FUN=function(x){
                       ifelse(x<quantile(x,0.9), 'N',
                       ifelse(x<quantile(x,0.95),'cat1',
                       ifelse(x<(quantile(x,0.99)),'cat2','cat3'
                    )))  
                  })
          )

#  vint1 vch1 vbin1 check
#1    90    D     b     N
#2   136    C     b  cat1
#3    55    B     a     N
#4    56    B     b     N
#5    56    D     a     N
#6   100    A     b     N

Answer 2

I hope this can help

group_percentile <- do.call ( rbind, with( mydf, tapply(vint1, interaction(vch1 , vbin1), quantile, probs=seq(0,1,.01 ) ) ))# create quatile for each group of "vch1" and "vbin1"


group_percentile<- data.frame(group_percentile)#Change this to data, X0. is the percentile

group_percentile$group <- rownames(group_percentile)#Create a new column


group_percentile2 <- melt(group_percentile)#Change the data to wide format so we can used this for lookup


mydf["group"] <- NA
mydf$group <- paste(mydf$vch,".",mydf$vbin1,sep='')#Create new column for combination of "vch1""vbin1"

 mydf2 <- merge(mydf,group_percentile2,by.x=c("vint1","group"),by.y=c("value","group"))#Create new column for percentile

 > head(mydf2)#Variable is the percentile
  vint1 group vch1 vbin1 variable
1     1   B.a    B     a      X0.
2     1   D.a    D     a      X0.
3     1   D.a    D     a      X1.
4     1   D.a    D     a      X0.
5     1   D.a    D     a      X1.
6     1   D.b    D     b      X0.