How to use purrr's map function to perform row-wise prop.tests and add results to the dataframe?

Question

I\'m trying to solve the following problem in R: I have a dataframe with two variables (number of successes, and number of total trials).

# A tibble: 4 x 2
          


        

Answer 1

We can use pmap after changing the column names with the arguments of 'prop.test'

pmap(setNames(df, c("x", "n")), prop.test)

---

Or using map2

map2(df$Success, df$N, prop.test)

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The problem with map is that it is looping through each of the columns of the dataset and it is a list of vectors

df %>%
   map(~ .x)
#$Success
#[1] 38 12 27  9

#$N
#[1] 50 50 50 50

So, we cannot do .x$Success or .x$N

Update

As @Steven Beaupre mentioned, if we need to create new columns with p-value and confidence interval

res <- df %>%
        mutate(newcol = map2(Success, N, prop.test), 
            pval = map_dbl(newcol, ~ .x[["p.value"]]), 
            CI = map(newcol, ~ as.numeric(.x[["conf.int"]]))) %>% 
            select(-newcol) 
# A tibble: 4 x 4
#   Success     N      pval CI       
#              
#1   38.0   50.0 0.000407    
#2   12.0   50.0 0.000407  
#3   27.0   50.0 0.671     
#4    9.00  50.0 0.0000116 

The 'CI' column is a list of 2 elements, which can be unnested to make it a 'long' format data

res %>%
   unnest

---

Or create 3 columns

df %>% 
  mutate(newcol = map2(Success, N,  ~ prop.test(.x, n = .y) %>% 
                  {tibble(pvalue = .[["p.value"]],
                         CI_lower = .[["conf.int"]][[1]], 
                         CI_upper = .[["conf.int"]][[2]])})) %>%
  unnest
# A tibble: 4 x 5
#  Success     N    pvalue CI_lower CI_upper
#                  
#1   38.0   50.0 0.000407    0.615     0.865
#2   12.0   50.0 0.000407    0.135     0.385
#3   27.0   50.0 0.671       0.395     0.679
#4    9.00  50.0 0.0000116   0.0905    0.319