Find a percentage based on multiple columns of cri

问题:

I have multiple columns and I would like to find the percentage of a one column in the other columns are the same. For example;

ST  cd  variable
1   1   23432
1   1   2345
1   2   908890
1   2   350435
1   2   2343432
2   1   9999
2   1   23432 

so what I'd like to do is:

if ST and cd are the same, then find the percentage of variable for that row over all with the same ST and cd. So in the end it would look like:

ST  cd  variable  percentage
1   1   23432     90.90%
1   1   2345      9.10%
1   2   908890    25.30%
1   2   350435    9.48%
1   2   2343432   65.23%
2   1   9999      29.91%
2   1   23432     70.09%

How can I do this in R?

Thanks for all the help.

回答1:

You can create your proportion format function:

prop_format <- 
function (x, digits=4) 
{
  x <- round(x/sum(x), digits)*100
  paste0(x,'%')
}

Then using ave :

ave(dt$variable,list(dt$ST,dt$cd),FUN=prop_format)

[1] "90.9%"  "9.1%"   "25.23%" "9.73%"  "65.05%" "29.91%" "70.09%"

回答2:

library(data.table)

DT <- data.table(read.table(text = "ST  cd  variable
1   1   23432
1   1   2345
1   2   908890
1   2   350435
1   2   2343432
2   1   9999
2   1   23432 ", header = TRUE))

DT[, percentage := variable / sum(variable) , by = list(ST, cd)]

##    ST cd variable percentage
## 1:  1  1    23432 0.90902743
## 2:  1  1     2345 0.09097257
## 3:  1  2   908890 0.25227624
## 4:  1  2   350435 0.09726856
## 5:  1  2  2343432 0.65045519
## 6:  2  1     9999 0.29909366
## 7:  2  1    23432 0.70090634

回答3:

Using dplyr:

require(dplyr)

df %>% group_by(ST, cd) %>% mutate(percentage = variable/sum(variable))

#  ST cd variable percentage
#1  1  1    23432 0.90902743
#2  1  1     2345 0.09097257
#3  1  2   908890 0.25227624
#4  1  2   350435 0.09726856
#5  1  2  2343432 0.65045519
#6  2  1     9999 0.29909366
#7  2  1    23432 0.70090634

You can modify this if you want:

dd %>% group_by(ST, cd) %>% mutate(percentage = round(variable/sum(variable)*100, 2))

#  ST cd variable percentage
#1  1  1    23432      90.90
#2  1  1     2345       9.10
#3  1  2   908890      25.23
#4  1  2   350435       9.73
#5  1  2  2343432      65.05
#6  2  1     9999      29.91
#7  2  1    23432      70.09