从宽到长的变量组重塑

Question

This question is very similar to an already existing question .

However I am unable to extend this to multiple groups of variables. This is the dataset I am dealing with

A tibble: 12 x 9
   Month Cabo_BU_PCT Acapulco_BU_PCT Cabo_LOS_AVG Acapulco_LOS_AVG BED_BUGS_Cabo BED_BUGS_Acapulco TOTAL_OCCUPIED_Cabo TOTAL_OCCUPIED_Acapulco

       1   0.6470034       0.6260116     5.223000         4.307667             5                 3               19216                    6498
       2   0.6167027       0.6777457     5.893571         4.247500             3                 0               17095                    6566
       3   0.6372108       0.6348126     5.229677         4.327742             5                 1               19556                    6809
       4   0.6357912       0.6548170     5.356667         4.220000             4                 6               18883                    6797
       5   0.6449006       0.6409659     5.344194         4.162903             2                 5               19792                    6875
       6   0.6747811       0.6935453     5.812667         4.362000             4                 3               20041                    7199
       7   0.6697947       0.6932687     5.544516         4.462903             5                 6               20556                    7436
       8   0.6595960       0.6777923     5.260323         4.135806             0                 7               20243                    7270
       9   0.6792256       0.6863198     5.424333         4.133333             5                 0               20173                    7124
      10   0.6976214       0.7370875     5.419677         4.350000             3                 3               21410                    7906
      11   0.6600337       0.6615607     5.450000         4.184333             3                 2               19603                    6867
      12   0.6761812       0.6773261     5.347097         4.318710             2                 2               20752                    7265

My goal is to reshape this into a long format like this below, where the columns, Cabo_BU_PCT Acapulco_BU_PCT are transformed to long format under column name BU_PCT , similarly columns, Cabo_LOS_AVG Acapulco_LOS_AVG are transformed to long format under column name LOS_AVG so on.

  Month    Location    BU_PCT      LOS_AVG     BED_BUGS       TOTAL_OCCUPIED
  1        Cabo        0.6470034   5.223000    5              19216
  1        Acapulco    0.6260116   4.307667    3              6498
  2        Cabo        0.6167027   5.893571    3              17095
  2        Acapulco    0.6777457   4.247500    0              6566
  .
  .
  .
  12       Cabo        0.6761812   5.347097    2              20752
  12       Acapulco    0.6773261   4.318710    2              7265  

Any help in reshaping this dataframe is much appreciated. Thanks.

======== dataset ===========

df_wide <- structure(list(Month = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
), Cabo_BU_PCT = c(0.647003367003367, 0.616702741702742, 0.637210817855979, 
0.635791245791246, 0.644900619094168, 0.674781144781145, 0.669794721407625, 
0.65959595959596, 0.679225589225589, 0.69762137504073, 0.66003367003367, 
0.676181166503747), Acapulco_BU_PCT = c(0.626011560693642, 0.677745664739884, 
0.634812604885325, 0.654816955684008, 0.640965877307477, 0.69354527938343, 
0.693268692895767, 0.677792280440052, 0.686319845857418, 0.737087451053515, 
0.661560693641619, 0.677326123438374), Cabo_LOS_AVG = c(5.223, 
5.89357142857143, 5.22967741935484, 5.35666666666667, 5.3441935483871, 
5.81266666666667, 5.54451612903226, 5.26032258064516, 5.42433333333333, 
5.41967741935484, 5.45, 5.34709677419355), Acapulco_LOS_AVG = c(4.30766666666667, 
4.2475, 4.32774193548387, 4.22, 4.16290322580645, 4.362, 4.46290322580645, 
4.1358064516129, 4.13333333333333, 4.35, 4.18433333333333, 4.31870967741935
), BED_BUGS_Cabo = c(5, 3, 5, 4, 2, 4, 5, 0, 5, 3, 3, 2), BED_BUGS_Acapulco = c(3, 
0, 1, 6, 5, 3, 6, 7, 0, 3, 2, 2), TOTAL_OCCUPIED_Cabo = c(19216, 
17095, 19556, 18883, 19792, 20041, 20556, 20243, 20173, 21410, 
19603, 20752), TOTAL_OCCUPIED_Acapulco = c(6498, 6566, 6809, 
6797, 6875, 7199, 7436, 7270, 7124, 7906, 6867, 7265)), class = c("tbl_df", 
"tbl", "data.frame"), .Names = c("Month", "Cabo_BU_PCT", "Acapulco_BU_PCT", 
"Cabo_LOS_AVG", "Acapulco_LOS_AVG", "BED_BUGS_Cabo", "BED_BUGS_Acapulco", 
"TOTAL_OCCUPIED_Cabo", "TOTAL_OCCUPIED_Acapulco"), row.names = c(NA, 
-12L))

Answer 1

If you've only got two locations, you can just chuck them in regex, accounting for the fact that they could be at the beginning or end of the name:

library(tidyverse)

df_wide %>% 
    gather(variable, value, -Month) %>% 
    mutate(location = sub('.*(Cabo|Acapulco).*', '\\1', variable), 
           variable = sub('_?(Cabo|Acapulco)_?', '', variable)) %>% 
    spread(variable, value)
#> # A tibble: 24 x 6
#>    Month location BED_BUGS    BU_PCT  LOS_AVG TOTAL_OCCUPIED
#>  * <dbl>    <chr>    <dbl>     <dbl>    <dbl>          <dbl>
#>  1     1 Acapulco        3 0.6260116 4.307667           6498
#>  2     1     Cabo        5 0.6470034 5.223000          19216
#>  3     2 Acapulco        0 0.6777457 4.247500           6566
#>  4     2     Cabo        3 0.6167027 5.893571          17095
#>  5     3 Acapulco        1 0.6348126 4.327742           6809
#>  6     3     Cabo        5 0.6372108 5.229677          19556
#>  7     4 Acapulco        6 0.6548170 4.220000           6797
#>  8     4     Cabo        4 0.6357912 5.356667          18883
#>  9     5 Acapulco        5 0.6409659 4.162903           6875
#> 10     5     Cabo        2 0.6449006 5.344194          19792
#> # ... with 14 more rows

Answer 2

This uses reshape from base R. No packages are used. varying= specifies that columns 2 and 3 are to be combined, 4 and 5, etc. The new columns are given the names specified in v.names= and the locations are specified in times= .

We could derive the varying= , v.names= and times= arguments from the headings but it involves a messy regex given their irregularity so it is simpler just to write them out (however, we show how to do it further below).

The result is ordered by location and then month within location but could be resorted if desired.

df_long <- reshape(df_wide, dir = "long", 
 varying = list(2:3, 4:5, 6:7, 8:9),
 v.names = c("BU_OCT", "LOS_AVG", "BED_BUGS", "TOTAL_OCCUPIED"),
 times = c("Cabo", "Acupuloc"))[-7]
names(df_long)[2] <- "LOCATION"

Alternately, if we did want to derive varying= , v.names= and times= from names(df_wide) it could be done like this where names1 is names(df_wide) without the location names. We use the fact that the location names consist of lower case letters except for the first letter and start or end each name.

names1 <- names(df_wide)[-1]
pat <- "(.[a-z]+)_(.*)|(.*)_(.[a-z]+)"
varying <- split(names1, sub(pat, "\\2\\3", names1))
v.names <- names(varying)
locations <- unique(sub(pat, "\\1\\4", names1))

df_long <- reshape(df_wide, dir = "long", varying = varying, v.names = v.names, 
     times = locations)[-7]
names(df_long)[2] <- "LOCATION"

Answer 3

As spread and gather are deprecated, I provide an answer based on @alistaire:

library(tidyverse)
df_wide %>%  
  pivot_longer(-Month)  %>%  
  mutate(location = sub('.*(Cabo|Acapulco).*', '\\1', name), 
         name  = sub('_?(Cabo|Acapulco)_?', '', name)) %>% 
  pivot_wider()
# ------ Outputs below ------
# A tibble: 24 × 6
   Month location BU_PCT LOS_AVG BED_BUGS TOTAL_OCCUPIED
   <dbl> <chr>     <dbl>   <dbl>    <dbl>          <dbl>
 1     1 Cabo      0.647    5.22        5          19216
 2     1 Acapulco  0.626    4.31        3           6498
 3     2 Cabo      0.617    5.89        3          17095
 4     2 Acapulco  0.678    4.25        0           6566
 5     3 Cabo      0.637    5.23        5          19556
 6     3 Acapulco  0.635    4.33        1           6809
 7     4 Cabo      0.636    5.36        4          18883
 8     4 Acapulco  0.655    4.22        6           6797
 9     5 Cabo      0.645    5.34        2          19792
10     5 Acapulco  0.641    4.16        5           6875
# … with 14 more rows