CODEWITHSUNDEEP
rreshapetidyr
AMSS
AMSS

Reputation: 23

Combining rows into one row with multiple columns of data R

I have a dataset that measures pre and post weight conditions. I was trying to combine rows with similar ID's to one row and create two additional columns to show the results for pre and post for each ID.

Original table


ID  Sex         Race        TIME    SH           PRE_WEIGHT POST_WEIGHT
1   FEMALE      White       POST    Poor         168            152
1   FEMALE      White       PRE     Good         168            152
2   MALE        Hispanic    POST    Very Poor    157            143
2   MALE        Hispanic    PRE     Poor         157            143
3   FEMALE      Black       POST    Good         129            118
3   FEMALE      Black       PRE     Satisfactory 129            118

Results should look like this:


ID  Sex         Race        TIME    SH          PRE_WEIGHT  POST_WEIGHT TIME_PRE    TIME_POST   SH_PRE      SH_POST
1   FEMALE      White       POST    Poor        168         152         POST        PRE         Poor        Good
2   MALE        Hispanic    POST    Very Poor   157         143         POST        PRE         Very Poor   Poor
3   FEMALE      Black       POST    Good        129         118         POST        PRE         Good        Satisfactor

I tried to do it with reshape which is very slow and didn't work. I also wasn't able to reshape the table with tidyr.

Hope anyone can help me with this.

Upvotes: 0

Views: 1632

Answers (3)

ThomasIsCoding
ThomasIsCoding

Reputation: 102625

A simple base R option might be using reshape, e.g.,

reshape(
  df,
  direction = "wide",
  idvar = c("ID","Sex","Race"),
  timevar = "TIME"
)

which gives

  ID    Sex     Race   SH.POST PRE_WEIGHT.POST POST_WEIGHT.POST       SH.PRE
1  1 FEMALE    White      Poor             168              152         Good
3  2   MALE Hispanic Very Poor             157              143         Poor
5  3 FEMALE    Black      Good             129              118 Satisfactory
  PRE_WEIGHT.PRE POST_WEIGHT.PRE
1            168             152
3            157             143
5            129             118

Data

> dput(df)
structure(list(ID = c(1L, 1L, 2L, 2L, 3L, 3L), Sex = c("FEMALE", 
"FEMALE", "MALE", "MALE", "FEMALE", "FEMALE"), Race = c("White",
"White", "Hispanic", "Hispanic", "Black", "Black"), TIME = c("POST", 
"PRE", "POST", "PRE", "POST", "PRE"), SH = c("Poor", "Good",
"Very Poor", "Poor", "Good", "Satisfactory"), PRE_WEIGHT = c(168L,
168L, 157L, 157L, 129L, 129L), POST_WEIGHT = c(152L, 152L, 143L,
143L, 118L, 118L)), class = "data.frame", row.names = c(NA, -6L
))

Upvotes: 1

akrun
akrun

Reputation: 887851

An option with data.table

library(data.table)
dcast(setDT(df1), ID + Sex + Race  + PRE_WEIGHT + 
           POST_WEIGHT ~ TIME, value.var = 'SH')

-output

#   ID    Sex     Race PRE_WEIGHT POST_WEIGHT      POST          PRE
#1:  1 FEMALE    White        168         152      Poor         Good
2#:  2   MALE Hispanic        157         143 Very Poor         Poor
3#:  3 FEMALE    Black        129         118      Good Satisfactory

Upvotes: 1

Chuck P
Chuck P

Reputation: 3923

As @Duck mentioned you can do this

library(dplyr)
library(tidyr)

df3 %>% 
   group_by(ID) %>% 
   pivot_wider(names_from = TIME, 
               values_from = SH, 
               names_prefix = "SH_")

#> # A tibble: 3 x 7
#> # Groups:   ID [3]
#>      ID Sex    Race     PRE_WEIGHT POST_WEIGHT SH_POST   SH_PRE      
#>   <dbl> <chr>  <chr>         <dbl>       <dbl> <chr>     <chr>       
#> 1     1 FEMALE White           168         152 Poor      Good        
#> 2     2 MALE   Hispanic        157         143 Very Poor Poor        
#> 3     3 FEMALE Black           129         118 Good      Satisfactory

Your data

df3 <- readr::read_table("
ID  Sex         Race        TIME    SH           PRE_WEIGHT POST_WEIGHT
1   FEMALE      White       POST    Poor         168            152
1   FEMALE      White       PRE     Good         168            152
2   MALE        Hispanic    POST    Very Poor    157            143
2   MALE        Hispanic    PRE     Poor         157            143
3   FEMALE      Black       POST    Good         129            118
3   FEMALE      Black       PRE     Satisfactory 129            118")

Upvotes: 1

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