Merging binary variables in R to a new variable-CodePudding

I have three categorical variables i.e. stroke, MI and BP with values of 0 = yes and 1= No. I want to merge them to make a new variable "cvd" out of these three variables where each row with 0 gets 0 values in new cardiovascular variable. For example:

Stroke  MI  BP  CVD
0       1    1   0
1       1    1   1
1       1    0   0

I tried the following code but this is not what i want

transform(koratest, cvd=paste(stroke,MI, BP))

Can someone please help what could be the script for this?

Best,

CodePudding user response：

Try,

(rowSums(df) == ncol(df)) * 1
#[1] 0 1 0

CodePudding user response：

Don't know how you arrange your variables. If they are separted vectors, this should work:

Stroke = c(0,1,1)
MI = c(1,1,1)
BP = c(1,1,0)
CVD = as.numeric(Stroke & MI & BP)

If a data.frame:

df$CVD = with(df, as.numeric(Stroke & MI & BP)

Or the solutions mentioned by others.

CodePudding user response：

Try this using dplyr rowwise function

library(dplyr)

df |> rowwise() |> mutate(CVD = if(all(c_across() == 1)) 1 else 0) |> ungroup()

output

# A tibble: 3 × 4
# Rowwise: 
  Stroke    MI    BP   CVD
   <int> <int> <int> <dbl>
1      0     1     1     0
2      1     1     1     1
3      1     1     0     0

CodePudding user response：

Maybe this:

library(tidyverse)

Data <- data.frame(Stroke = c(0,1,1),
                   MI = c(1,1,1),
                   BP = c(1,1,0))

Data <- Data %>% 
  mutate(CVD = if_else(Stroke == 1 &MI == 1 & BP == 1, 1, 0))

CodePudding user response：

Another way:

library(dplyr)

df <- data.frame(Stroke = c(0,1,1),
                   MI = c(1,1,1),
                   BP = c(1,1,0))

df %>% 
  rowwise() %>% 
  mutate(
    CVD = min(Stroke, MI, BP) 
  ) %>% 
  ungroup()

#> # A tibble: 3 × 4
#>   Stroke    MI    BP   CVD
#>    <dbl> <dbl> <dbl> <dbl>
#> 1      0     1     1     0
#> 2      1     1     1     1
#> 3      1     1     0     0

^{Created on 2022-07-11 by the reprex package (v2.0.1)}

CodePudding user response：

base R option:

df$CVD <- apply(df,2, function(x) !any(0 %in% x))   0
df

Output:

  Stroke MI BP CVD
1      0  1  1   0
2      1  1  1   1
3      1  1  0   0

CodePudding user response：

Using rowSums in cbind detects that dat is a data frame and creates such.

cbind(dat, CVD= (rowSums(dat[c('Stroke', 'MI', 'BP')]) == 3))
#   Stroke MI BP CVD
# 1      0  1  1   0
# 2      1  1  1   1
# 3      1  1  0   0

If you only have these columns, it simplifies to:

cbind(dat, CVD= (rowSums(dat) == 3))

Data:

dat <- structure(list(Stroke = c(0L, 1L, 1L), MI = c(1L, 1L, 1L), BP = c(1L, 
1L, 0L)), class = "data.frame", row.names = c(NA, -3L))