Solution to unpivot a table

[zhenghong]

Am trying to convert a table from "wide form" to "long form", but in this case create new categories based on current values.

Basically, am trying to "unpivot" a roster, as my colleague mentioned is the right term. The Rows are dates, and the columns are personnel names. However, the values are "shifts" represented by shift names like (X, O, M1).

Want to move from "before" to "after"

Any advice on a simple solution or how to better describe my question to find preexisting answers ?

[FJCC]

This give you basically what you want. The pivot_longer() function does the important reshaping and the select() is only used to reorder the columns.

DF <- data.frame(Date=c("1-Mar-2021","2-Mar-2021","3-Mar-2021","4-Mar-2021","5-Mar-2021",
                        "6-Mar-2021","7-Mar-2021"),
                 AC3=c("OS","X","C1","NO","M1","M2","O"),
                 C13=c("O","PDL","X","A3f","E2","O","O"))
library(tidyr)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
DF_new <- pivot_longer(DF,cols = c("AC3","C13"),values_to = "Shift") %>% 
  select(Date,Shift,name)
DF_new
#> # A tibble: 14 x 3
#>    Date       Shift name 
#>    <chr>      <chr> <chr>
#>  1 1-Mar-2021 OS    AC3  
#>  2 1-Mar-2021 O     C13  
#>  3 2-Mar-2021 X     AC3  
#>  4 2-Mar-2021 PDL   C13  
#>  5 3-Mar-2021 C1    AC3  
#>  6 3-Mar-2021 X     C13  
#>  7 4-Mar-2021 NO    AC3  
#>  8 4-Mar-2021 A3f   C13  
#>  9 5-Mar-2021 M1    AC3  
#> 10 5-Mar-2021 E2    C13  
#> 11 6-Mar-2021 M2    AC3  
#> 12 6-Mar-2021 O     C13  
#> 13 7-Mar-2021 O     AC3  
#> 14 7-Mar-2021 O     C13

^{Created on 2021-03-14 by the reprex package (v0.3.0)}

[zhenghong]

Exactly what I needed
Working beautifully on the big huge roster!
Thanks!!