如何使用 group_by 将每个变量中的数据从长转换为宽？ R

Question

I have a dataframe with id variable name . I'm trying to figure out a way to transpose each variable in the dataframe by name.

My current df is below:

name   jobtitle companyname datesemployed empduration joblocation jobdescrip 

1 David… Project… EOS IT Man… Aug 2018 – P… 1 yr 9 mos  San Franci… Coordinati…
2 David… Technic… Options Te… Sep 2017 – J… 5 mos       Belfast, U… Working wi…
3 David… Data An… NA          Jan 2018 – J… 6 mos       Belfast, U… Working wi…

However, I'd like a dataframe in which there is only one row for name, and every observation for name becomes its own column, like below:

name   jobtitle_1 companyname_1 datesemployed_1 empduration_1 joblocation_1 jobdescrip_1 job_title2 companyname_2 datesemployed_2 empduration_2 joblocation_2 jobdescrip_2

1 David… Project… EOS IT Man… Aug 2018 – P… 1 yr 9 mos  San Franci… Coordinati… Technic… Options Te… Sep 2017 – J… 5 mos       Belfast, U… Working wi…

I have used commands like gather_by and melt in the past to reshape from long to wide, but in this case, I'm not sure how to apply it, since every observation for the id variable will need to become its own column.

Answer 1

It sounds like you are looking for gather and pivot_wider .

I used my own sample data with two names:

df <- tibble(name = c('David', 'David', 'David', 'Bill', 'Bill'),
             jobtitle = c('PM', 'TPM', 'Analyst', 'Dev', 'Eng'),
             companyname = c('EOS', 'Options', NA, 'Microsoft', 'Nintendo'))

First add an index column to distinguish the different positions for each name.

indexed <- df %>%
  group_by(name) %>%
  mutate(.index = row_number())
indexed
#   name  jobtitle companyname .index
#   <chr> <chr>    <chr>        <int>
# 1 David PM       EOS              1
# 2 David TPM      Options          2
# 3 David Analyst  NA               3
# 4 Bill  Dev      Microsoft        1
# 5 Bill  Eng      Nintendo         2

Then it is possible to use gather to get a long form, with one value per row.

gathered <- indexed %>% gather('var', 'val', -c(name, .index))
gathered
#    name  .index var         val      
#    <chr>  <int> <chr>       <chr>    
#  1 David      1 jobtitle    PM       
#  2 David      2 jobtitle    TPM      
#  3 David      3 jobtitle    Analyst  
#  4 Bill       1 jobtitle    Dev      
#  5 Bill       2 jobtitle    Eng      
#  6 David      1 companyname EOS      
#  7 David      2 companyname Options  
#  8 David      3 companyname NA       
#  9 Bill       1 companyname Microsoft
# 10 Bill       2 companyname Nintendo 

Now pivot_wider can be used to create a column for each variable and index.

gathered %>% pivot_wider(names_from = c(var, .index), values_from = val)
#   name  jobtitle_1 jobtitle_2 jobtitle_3 companyname_1 companyname_2 companyname_3
#   <chr> <chr>      <chr>      <chr>      <chr>         <chr>         <chr>        
# 1 David PM         TPM        Analyst    EOS           Options       NA           
# 2 Bill  Dev        Eng        NA         Microsoft     Nintendo      NA    

Answer 2

Get the data in long format, create a unique column identifier and get it back to wide format.

library(dplyr)
library(tidyr)

df %>%
  pivot_longer(cols = -name, names_to = 'col') %>%
  group_by(name, col) %>%
  mutate(row = row_number()) %>%
  pivot_wider(names_from = c(col, row), values_from = value)