Wide to Long Multiple Measures Each Time

wide to long multiple measures each time

This is pretty close and changing the names of columns should be within your skillset:

reshape(DF, 
       varying=c(work= c(3, 7), play= c(4,8), talk= c(5,9), total= c(6,10) ), 
       direction="long")

EDIT: Adding a version that is almost an exact solution:

reshape(DF, varying=list(work= c(3, 7), play= c(4,8), talk= c(5,9), total= c(6,10) ), 
        v.names=c("Work", "Play", "Talk", "Total"), 
          # that was needed after changed 'varying' arg to a list to allow 'times' 
        direction="long",  
        times=1:2,        # substitutes number for T1 and T2
        timevar="times")  # to name the time col

Long to wide w/ two repeated measures

I think this will do it:

library(reshape)
m <- melt(DF)

Simplest, but time and score are in the opposite order from your example (in case it matters)

cast(m,id+sex~...)

Or more explicitly:

cast(m,id+sex~variable+time)

You can cut this down to a one-liner:

recast(DF,id+sex~...)

If you like you can use the newer reshape2 package instead of reshape, replacing cast with dcast (the version of recast included in reshape2 doesn't give the desired result.)

Convert wide data to long format for repeated measures/mixed models

dplyr

tidyr::pivot_longer(wide, -Participant, names_to = "Item", values_to = "Accuracy")
# # A tibble: 9 x 3
#   Participant Item   Accuracy
#   <chr>       <chr>     <int>
# 1 P1          Banana        0
# 2 P1          Apple         0
# 3 P1          Orange        0
# 4 P2          Banana        1
# 5 P2          Apple         1
# 6 P2          Orange        1
# 7 P3          Banana        0
# 8 P3          Apple         0
# 9 P3          Orange        0

Data wrangling from wide to long format with multiple repeating columns of different types

You could do:

library(tidyr)
library(dplyr)

df_wider %>% pivot_longer(-id, 
                          names_pattern = "(.*)_(\\d)", 
                          names_to = c(".value", "cluster"))

# A tibble: 15 x 5
      id cluster fruit        date       number
   <int> <chr>   <fct>        <date>      <int>
 1     1 1       olive        2020-04-21     50
 2     1 2       elderberry   2020-02-23     59
 3     1 3       cherimoya    2020-03-07      9
 4     2 1       jujube       2020-03-22     88
 5     2 2       mandarine    2020-03-06     45
 6     2 3       grape        2020-04-23     78
 7     3 1       nut          2020-01-26     53
 8     3 2       cantaloupe   2020-01-27     70
 9     3 3       durian       2020-02-15     39
10     4 1       chili pepper 2020-03-17     60
11     4 2       raisin       2020-04-14     20
12     4 3       cloudberry   2020-03-11      4
13     5 1       honeydew     2020-01-04     81
14     5 2       lime         2020-03-23     53
15     5 3       ugli fruit   2020-01-13     26

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