Convert dataframe column to 1 or 0 for "true"/"false" values and assign to dataframe

@chappers solution (in the comments) works as.integer(as.logical(data.frame$column.name))

Even when you asked finally for the opposite, to reform 0s and 1s into Trues and Falses, however, I post an answer about how to transform falses and trues into ones and zeros (1s and 0s), for a whole dataframe, in a single line.

Example given

df <- structure(list(p1_1 = c(TRUE, FALSE, FALSE, NA, TRUE, FALSE, 
                NA), p1_2 = c(FALSE, TRUE, FALSE, NA, FALSE, NA, 
                TRUE), p1_3 = c(TRUE, 
                TRUE, FALSE, NA, NA, FALSE, TRUE), p1_4 = c(FALSE, NA, 
                FALSE,  FALSE, TRUE, FALSE, NA), p1_5 = c(TRUE, NA, 
                FALSE, TRUE, FALSE, NA, TRUE), p1_6 = c(TRUE, NA, 
                FALSE, TRUE, FALSE, NA, TRUE), p1_7 = c(TRUE, NA, 
                FALSE, TRUE, NA, FALSE, TRUE), p1_8 = c(FALSE, 
                FALSE, NA, FALSE, TRUE, FALSE, NA), p1_9 = c(TRUE, 
                FALSE,  NA, FALSE, FALSE, NA, TRUE), p1_10 = c(TRUE, 
                FALSE, NA, FALSE, FALSE, NA, TRUE), p1_11 = c(FALSE, 
                FALSE, NA, FALSE, NA, FALSE, TRUE)), .Names = 
                c("p1_1", "p1_2", "p1_3", "p1_4", "p1_5", "p1_6", 
                "p1_7", "p1_8", "p1_9", "p1_10", "p1_11"), row.names = 
                 c(NA, -7L), class = "data.frame")

   p1_1  p1_2  p1_3  p1_4  p1_5  p1_6  p1_7  p1_8  p1_9 p1_10 p1_11
1  TRUE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE
2 FALSE  TRUE  TRUE    NA    NA    NA    NA FALSE FALSE FALSE FALSE
3 FALSE FALSE FALSE FALSE FALSE FALSE FALSE    NA    NA    NA    NA
4    NA    NA    NA FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
5  TRUE FALSE    NA  TRUE FALSE FALSE    NA  TRUE FALSE FALSE    NA
6 FALSE    NA FALSE FALSE    NA    NA FALSE FALSE    NA    NA FALSE
7    NA  TRUE  TRUE    NA  TRUE  TRUE  TRUE    NA  TRUE  TRUE  TRUE

Then by running that: df * 1 all Falses and Trues are trasnformed into 1s and 0s. At least, this was happen in the R version that I have (R version 3.4.4 (2018-03-15) ).

> df*1
  p1_1 p1_2 p1_3 p1_4 p1_5 p1_6 p1_7 p1_8 p1_9 p1_10 p1_11
1    1    0    1    0    1    1    1    0    1     1     0
2    0    1    1   NA   NA   NA   NA    0    0     0     0
3    0    0    0    0    0    0    0   NA   NA    NA    NA
4   NA   NA   NA    0    1    1    1    0    0     0     0
5    1    0   NA    1    0    0   NA    1    0     0    NA
6    0   NA    0    0   NA   NA    0    0   NA    NA     0
7   NA    1    1   NA    1    1    1   NA    1     1     1

I do not know if it a total "safe" command, under all different conditions / dfs.

can you try if.else

> col2=ifelse(df1$col=="true",1,0)
> df1
$col
[1] "true"  "false"

> cbind(df1$col)
     [,1]   
[1,] "true" 
[2,] "false"
> cbind(df1$col,col2)
             col2
[1,] "true"  "1" 
[2,] "false" "0"