Reshape a dataset using gather()

[Martino31]

Hello,

I'm working on a dataset that I want to plot.

# Here is the structure of my dataset:

   ID      var1     var2      t0     t1   ... t100      y_t0      y_t1  ...  y_t100
   A         10        blue       0      1        100         1%        3%    ...    20%
   B         9         green      0     1         100         2%        4%    ...    19%
   C         8         pink         0     1         100         1%        6%    ...    22%

Where ID, var1 and var2 are the variables I want to keep like that, and t0 to t100 are time (always the same) and y_t0 to y_t100 are the measures at that same time.

I want to ggplot (geom_point) Y_time function of time but as this isn't in row i need to reshape that dataset (I thought using gather but I don't really see how to do with this amount of variables).

Does anyone know how to handle such a problem?

Thanks in advance

[vlad_aluas]

Hi Martino,

gather() and spread() are obsolete as they are confusing everyone. I would suggest using tidyr's pivot_longer() and it will give you exactly what you need. You can check the code below:

library(dplyr)
library(tidyr)

df <- tibble(
  ID   = c("A", "B", "C"),
  var1 = c(10, 9, 8),
  var2 = c("blue", "green", "pink"),
  `t0` = c(0, 0, 0),
  `t1` = c(1, 1, 1),
  `t2` = c(2, 2, 2),
  `t100` = c(100, 100, 100)
)

df %>% 
  pivot_longer(`t0`:`t100`, names_to = "measurement_time", values_to = "measurement_value")

P.S. a better data example would help a lot so people will not spend time inputting the values manually. You can check more on how to do that at the link below:
Tidyverse

Hope his helps.

Vlad

[martin.R]

@vlad_aluas

gather() and spread() are obsolete as they are confusing everyone.

That's not quite right. They are merely superseded by by the pivot_*() functions and can still be used:
Lifecycle stages • lifecycle (r-lib.org)

As for the confusion this is entirely subjective. The new functions do offer more functionality though.

[vlad_aluas]

Agree, it was poor choice of words from my part.

[iamericfletcher]

If you're curious what an approach looks like using gather():

Note:

• Thank you @vlad_aluas for the data.
• key and value are the names of the new columns.
• 1:4 indicate the selection of columns that are to be gathered into key-value pairs (from wide to long).
• arrange is used to match the pivot_longer solution provided by @vlad_aluas.

library(dplyr)
library(tidyr)

df <- tibble(
  ID   = c("A", "B", "C"),
  var1 = c(10, 9, 8),
  var2 = c("blue", "green", "pink"),
  `t0` = c(0, 0, 0),
  `t1` = c(1, 1, 1),
  `t2` = c(2, 2, 2),
  `t100` = c(100, 100, 100)
)

df %>% 
  gather(key = 'measurement_time',
         value = 'measurement_value',
         4:7) %>% 
  arrange(ID)

Output

#> # A tibble: 12 x 5
#>    ID     var1 var2  measurement_time measurement_value
#>    <chr> <dbl> <chr> <chr>                        <dbl>
#>  1 A        10 blue  t0                               0
#>  2 A        10 blue  t1                               1
#>  3 A        10 blue  t2                               2
#>  4 A        10 blue  t100                           100
#>  5 B         9 green t0                               0
#>  6 B         9 green t1                               1
#>  7 B         9 green t2                               2
#>  8 B         9 green t100                           100
#>  9 C         8 pink  t0                               0
#> 10 C         8 pink  t1                               1
#> 11 C         8 pink  t2                               2
#> 12 C         8 pink  t100                           100

^{Created on 2021-03-19 by the reprex package (v0.3.0)}

[Martino31]

Hello to all,

I see that a lot of intéresting answers have been written. I will test all your recommendations and tell which one I prefered.

a big thanks!!