问题
Looking for the quickest way to achieve below task using "expss" package.
With a great package of "expss", we can easily do cross tabulation (which has other advantage and useful functions for cross-tabulations.), we can cross-tabulate multiple variables easily like below.
#install.packages("expss")
library("expss")
data(mtcars)
var1 <- "vs, am, gear, carb"
var_names = trimws(unlist(strsplit(var1, split = ",")))
mtcars %>%
tab_prepend_values %>%
tab_cols(total(), ..[(var_names)]) %>%
tab_cells(cyl) %>%
tab_stat_cpct() %>%
tab_pivot()
Above gives an output as: (column %)
#Total vs am gear carb
0 1 0 1 3 4 5 1 2 3 4 6 8
cyl 4 34.4 5.6 71.4 15.8 61.5 6.7 66.7 40 71.4 60
6 21.9 16.7 28.6 21.1 23.1 13.3 33.3 20 28.6 40 100
8 43.8 77.8 63.2 15.4 80.0 40 40 100 60 100
#Total cases 32.0 18.0 14.0 19.0 13.0 15.0 12.0 5 7.0 10 3 10 1 1
However, looking for an approach to create a table like below:
CYL | VS = 0 | AM = 1 | Gear = 4 or Gear = 5 | Carb (All)
4 5.56 61.54 58.82 34.38
6 16.67 23.08 29.41 21.88
8 77.78 15.38 11.76 43.75
Total(col%) 100.00 100.00 100.00 100.00
Though i can achive this using dplyr and join functions but that is too complex incase we have to pass variables in runtime or dynamically.
Any help will be appriciable. Thanks!!
回答1:
You may try this:
1) Making a function which can create proportion out of the sum.
myprop_tbl <- function(x){
return(round(x*100/sum(x),2))
}
2) Using purrr's map, applying the function on your data frame and then binding the result.
library(tidyverse)
tab <- mtcars %>%
group_by(cyl) %>%
summarise(vs_sum = sum(vs==0), am_sum = sum(am==1),
gear_sum = sum(gear == 4|gear==5), carb_sum= n())
finaltab <- bind_cols(tab[,1],map_df(tab[,2:length(tab)], myprop_tbl))
Output:
# * cyl vs_sum am_sum gear_sum carb_sum
# <dbl> <dbl> <dbl> <dbl> <dbl>
#1 4.00 5.56 61.5 58.8 34.4
#2 6.00 16.7 23.1 29.4 21.9
#3 8.00 77.8 15.4 11.8 43.8**
EDIT:
After had a discussion with OP, it seems he also wanted to pass string of functions,
I am using here a package seplyr
tab <- mtcars %>%
group_by(cyl) %>%
summarise_se(c("vs_sum = sum(vs==0)",
"am_sum = sum(am==1)",
"gear_sum = sum(gear == 4|gear==5)",
"carb_sum = n()"))
It works also, but weired names you will get, to fix that you can do this:
This works perfectly as original answer which I have posted:
tab <- mtcars %>%
group_by(cyl) %>%
summarise_se(c("vs_sum" := "sum(vs==0)",
"am_sum" := "sum(am==1)",
"gear_sum" := "sum(gear == 4|gear==5)",
"carb_sum" := "n()"))
You may read this here @ this link
回答2:
Solution with orginal 'tab_*':
library("expss")
data(mtcars)
var_text = "vs_sum = vs==0, am_sum = am==1, gear_sum = gear == 4|gear==5, carb_sum = total(carb)"
var_expr = parse(text = sprintf("data.frame(%s)", var_text)) # parse text string to expression
var_list = calc(mtcars, 1*eval(var_expr)) %>% # caclulate data.frame with zero/one columns
prepend_names() %>% # add names as labels
mis_val(0) %>% # we don't need columns with FALSE condition
set_val_lab(c("|" = 1)) # suppress values in table - we don't want to see TRUE/1
mtcars %>%
tab_prepend_values %>%
tab_cols(total(), var_list) %>%
tab_cells(cyl) %>%
tab_stat_cpct() %>%
tab_pivot()
# | | | #Total | vs_sum | am_sum | gear_sum | carb_sum |
# | --- | ------------ | ------ | ------ | ------ | -------- | --------- |
# | cyl | 4 | 34.4 | 5.6 | 61.5 | 58.8 | 34.4 |
# | | 6 | 21.9 | 16.7 | 23.1 | 29.4 | 21.9 |
# | | 8 | 43.8 | 77.8 | 15.4 | 11.8 | 43.8 |
# | | #Total cases | 32.0 | 18.0 | 13.0 | 17.0 | 32.0 |
来源:https://stackoverflow.com/questions/50055988/conditional-cross-tabulation-in-r