CODEWITHSUNDEEP
rpivottransform
Jascha
Jascha

Reputation: 21

How can I transform multiple repeated measures from wide to long format?

I have a data set that looks like that:

id          <- c(1:3)
gender      <- factor(c("male","female","female"))
age         <- c(51,69,44)
cortisol_1 <- c(23,32,54)
cortisol_2 <- c(34,52,49)
cortisol_3 <- c(34,65,12)
blood_1    <- c(12,64,54)
blood_2    <- c(52,32,75)
blood_3    <- c(12,12,75)
temp_1     <- c(38.5,38.7,37.9) 
temp_3     <- c(36.5,36.4,37.1)
df          <- data.frame(id,gender,age,cortisol_1,cortisol_2,cortisol_3,blood_1,blood_2,blood_3,temp_1,temp_3)
df

    id gender age cortisol_1 cortisol_2 cortisol_3 blood_1 blood_2 blood_3 temp_1 temp_3
1  1   male  51         23         34         34      12      52      12   38.5   36.5
2  2 female  69         32         52         65      64      32      12   38.7   36.4
3  3 female  44         54         49         12      54      75      75   37.9   37.1

So I have cortisol level and blood pressure which were measured annually at three time points. However, body temperature was only assessed at baseline and wave 3.

How can I change the data structure from wide to long? I would hope that the data looks like that:

  id gender wave cortisol blood temp
1  1   male    1       23    12 38.5
2  1   male    2       34    52   NA
3  1   male    3       34    12 36.5
4  2 female    1       32    64 37.7
5  2 female    2       52    32   NA
6  2 female    3       65    12 36.4
7  3 female    1       54    54 37.9
8  3 female    2       49    75   NA
9  3 female    3       12    75 37.1

Best Jascha

Upvotes: 1

Views: 337

Answers (1)

akrun
akrun

Reputation: 887501

We can use pivot_longer

library(dplyr)
library(tidyr)
df %>%
  pivot_longer(cols = -c(id, gender, age), 
   names_to = c('.value', 'grp'), names_sep = "_") %>%
  select(-grp)

-output

# A tibble: 9 x 6
#     id gender   age cortisol blood  temp
#  <int> <fct>  <dbl>    <dbl> <dbl> <dbl>
#1     1 male      51       23    12  38.5
#2     1 male      51       34    52  NA  
#3     1 male      51       34    12  36.5
#4     2 female    69       32    64  38.7
#5     2 female    69       52    32  NA  
#6     2 female    69       65    12  36.4
#7     3 female    44       54    54  37.9
#8     3 female    44       49    75  NA  
#9     3 female    44       12    75  37.1

Upvotes: 2

Related Questions