通过按行应用函数并创建（可能）更大的列来转换 R 数据框

Question

I'm trying to transform a dataframe (tibble) by using each row as function arguments and create a new column out of it, which is possibly bigger than the number of arguments. Consider the following example, where I have some sample observations:

library(dplyr)
library(stringi)

observations <- c("110", "11011", "1100010")

df <- tibble(obs = observations) %>%
    transmute(
        Failure = stri_count(obs, fixed = "0"),
        Success = stri_count(obs, fixed = "1")
    )

df is then:

# A tibble: 3 x 2
  Failure Success
    <int>  <int>
1       1      2
2       1      4
3       4      3

I would like to take every row and use that for calculating a bunch of values, and save each result vector in a new column. For example I would like to do:

p_values = pgrid <- seq(from = 0, to = 1, length.out = 11)

df %>%
    rowwise() %>%
    transmute(
        p = p_values,
        likelihood = dbinom(Success,
            size = Failure + Success,
            prob = p_values
        )
    )

Error: Column `p` must be length 1 (the group size), not 11

And get something like:

# A tibble: 4 x 11
  p_values likelihood_1 likelihood_2 likelihood_3
     <float>  <float>     <float>      <float>
1       0      ...         ...           ...
2       0.1    ...         ...           ...
...     ...    ...         ...           ...
10      0.9    ...         ...           ...
11      1      ...         ...           ...     

Answer 1

This sort of workflow can be somewhat awkward with a tidyverse approach, as the data is not in a 'tidy' format.

I would come at it from the other angle, starting with the p_values vector:

likelihoods <- 
  tibble(p = p_values) %>%
  mutate(likelihood_1 = dbinom(df[1,]$Success,size = df[1,]$Failure + df[1,]$Success,prob = p),
         likelihood_2 = dbinom(df[2,]$Success,size = df[2,]$Failure + df[2,]$Success,prob = p),
         likelihood_3 = dbinom(df[3,]$Success,size = df[3,]$Failure + df[3,]$Success,prob = p))

Answer 2

The issue is that transmute or mutate expects the number of elements to be same as number of rows (or if it is grouped, then the number of rows for that group). Here, we do rowwise - which is basically grouping each row, so the n() expected is 1, whereas the output is length of 'p_values'. One option is to wrap in a list , unnest , and reshape to 'wide' format with pivot_wider (if needed)

library(dplyr)
library(tidyr)
library(stringr)
df %>%
    mutate(grp = str_c('likelihood_', row_number())) %>%
    rowwise() %>%
         transmute(grp, p = list(p_values),
         likelihood = list(dbinom(Success,
            size = Failure + Success,
          prob = p_values
      ))
    ) %>% 
    unnest(c(p, likelihood)) %>%
    pivot_wider(names_from = grp, values_from = likelihood)
# A tibble: 11 x 4
#       p likelihood_1 likelihood_2 likelihood_3
#   <dbl>        <dbl>        <dbl>        <dbl>
# 1   0          0          0            0      
# 2   0.1        0.027      0.00045      0.0230 
# 3   0.2        0.096      0.0064       0.115  
# 4   0.3        0.189      0.0284       0.227  
# 5   0.4        0.288      0.0768       0.290  
# 6   0.5        0.375      0.156        0.273  
# 7   0.6        0.432      0.259        0.194  
# 8   0.7        0.441      0.360        0.0972 
# 9   0.8        0.384      0.410        0.0287 
#10   0.9        0.243      0.328        0.00255
#11   1          0          0            0      

Answer 3

I would actually switch into purrr for this. The function pmap() will iterate by row. We use ..1 and ..2 to signify the first and second inputs, respectively. Using pmap_dfc() will bind the results by columns (dfc = data frame columns).

library(purrr)
library(tibble)

df %>%
  pmap_dfc(~ dbinom(..2, size = ..1 + ..2, prob = p_values)) %>%
  set_names(paste0("likelihood_", seq_along(.))) %>%
  add_column(p_values = p_values, .before = 1)

# A tibble: 11 x 4
   p_values likelihood_1 likelihood_2 likelihood_3
      <dbl>        <dbl>        <dbl>        <dbl>
 1      0          0          0            0      
 2      0.1        0.027      0.00045      0.0230 
 3      0.2        0.096      0.0064       0.115  
 4      0.3        0.189      0.0284       0.227  
 5      0.4        0.288      0.0768       0.290  
 6      0.5        0.375      0.156        0.273  
 7      0.6        0.432      0.259        0.194  
 8      0.7        0.441      0.360        0.0972 
 9      0.8        0.384      0.410        0.0287 
10      0.9        0.243      0.328        0.00255
11      1          0          0            0