如何在数据帧的每一列中应用功能？

Question

I have the following data frame with 345 rows and 237 columns in R:

snp1 snp2 snp3 ... snp237 
0 1 2 ... 0
0 1 1 ... 1
1 1 2 ... 2
1 0 0 ... 0
... ... ... ...
2 2 1 ... 0

I want to apply the following function in each column:

D=(number of 0)/(number of rows)
H=(number of 1)/(number of rows)
R=(number of 2)/(number of rows)
p=D+(0.5*H)
q=R+(0.5*H)

Lastly, I want to store the "p" and "q" for each snp in a vector. This function have calculate "p" and "q" for each snp in a single command of R. It is possible?

The output is:

snp1 snp2 snp3 ... snp237
p1 p2 p3 ... ... p237
q1 q2 q3 ... ... q237

Thanks in advance.

Answer 1

#DATA
set.seed(42)
d = data.frame(snp1 = sample(0:2, 10, TRUE),
               snp2 = sample(0:2, 10, TRUE),
               snp3 = sample(0:2, 10, TRUE))

#Function    
foo = function(x){
    len = length(x)
    D = sum(x == 0)/len
    H = sum(x == 1)/len
    R = sum(x == 2)/len
    p = D + 0.5 * H
    q = R + 0.5 * H
    return(c(p = p, q = q))
}

#Run foo for each column   
sapply(d, foo)
#  snp1 snp2 snp3
#p 0.35 0.4  0.35
#q 0.65 0.6  0.65

Answer 2

Here is an option with tidyverse . Create a function ( f1 ) based on the logic in OP's code to return a list of length 2, then use that in summarise_all to apply the function on each of the columns of dataset

library(dplyr)
library(tidyr)
f1 <- function(x) {
              H <- 0.5 * mean(x == 1)
              list(list(p = mean(x == 0) + H,
                  q = mean(x == 2) + H))
                  }
df1 %>%
   summarise_all(f1) %>% 
   unnest
#  snp1  snp2  snp3
#1 0.75 0.625 0.375
#2 0.25 0.375 0.625

data

df1 <- structure(list(snp1 = c(0L, 0L, 1L, 1L), snp2 = c(1L, 1L, 1L, 
 0L), snp3 = c(2L, 1L, 2L, 0L)), class = "data.frame", row.names = c(NA, 
  -4L))