Extract data from a nested dataframe into the same record-CodePudding

I have this main dataframe:

testdataframe
       id     sensors_data
       <chr>  <list>
    1  AA     <data.frame [6 × 4]>              
    2  BB     <data.frame [6 × 4]>

and every dataframe of sensors_data looks like this:

         id type  value           status
      <chr><chr>  <dbl>           <int>
1   SN01TP  a     25.800          1
2   SN01HU  b     40.000          1
3   SN02VD  c     1.146           1
4   SN02C2  d     1270.000        1
5   SY01DS  e     31.000          1
6   TD01TP  f     22.500          1

I would want my main dataframe to be, instead of only sensors_data, something like this

   a      b       c      d         e       f
1  25.800 40.000  1.146  1270.000  31.000  22.500

I've tried unnesting the main dataframe but that would create a record for each field. What I'm trying is to mutate the main dataframe accessing the data inside sensors_data, but I can't figure out how

CodePudding user response：

Using purrr:map and tidyr::pivot_wider, you can do this. Use bind_rows if you want one dataframe.

sensors_data %>% 
  map(~ tidyr::pivot_wider(.x[,c("type","value")], names_from = type))

[[1]]
      a     b     c     d     e     f
1  25.8    40  1.15  1270    31  22.5

[[2]]
      a     b     c     d     e     f
1  25.8    40  1.15  1270    31  22.5

## With bind_rows:
      a     b     c     d     e     f
1  25.8    40  1.15  1270    31  22.5
2  25.8    40  1.15  1270    31  22.5

Data:

df1 <- read.table(header = T, text="         id type  value           status
1   SN01TP  a     25.800          1
2   SN01HU  b     40.000          1
3   SN02VD  c     1.146           1
4   SN02C2  d     1270.000        1
5   SY01DS  e     31.000          1
6   TD01TP  f     22.500          1")
df2 = df1
sensors_data = list(df1, df2)

CodePudding user response：

My favorite answer is already provided by Maël! Here is an alternative using lapply

library(dplyr)
library(tidyr)

sensors_data_sub <- lapply(sensors_data, function(x)x[,2:3])

sensors_data_sub_wide <- lapply(1:length(sensors_data_sub), 
                  function(x) (pivot_wider(sensors_data_sub[[x]], names_from = type, values_from = value)))

bind_rows(sensors_data_sub_wide)

      a     b     c     d     e     f
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1  25.8    40  1.15  1270    31  22.5
2  25.8    40  1.15  1270    31  22.5