R: Copy values from a column in one dataframe to several selected columns in another dataframe for g-CodePudding

I would like to copy the value from a dataframe to another dataframe. The difficulty for me is that I have four groups in both dataframes and in the dataframe where I would like to copy the values, I have several columns where I want to insert the value. More specifically, the dataframe (df1) from which I would like to copy the values looks like this:

structure(list(Name = c("A", "B", "C", "D"), Value = c(2L, 5L, 
3L, 2L)), class = "data.frame", row.names = c(NA, -4L))

The dataframe (df2) where I want to insert the values looks like this:

structure(list(Name = c("A", "B", "C", "D"), `Rating 2017-06` = c(NA, 
NA, NA, NA), `Rating 2017-07` = c(NA, NA, NA, NA), `Ratin g 2017-08` = c(NA, 
NA, NA, NA), `Rating 2017-09` = c(NA, NA, NA, NA), `Rating 2017-10` = c(NA, 
NA, NA, NA), `Rating 2017-11` = c(NA, NA, NA, NA), `Rating 2017-12` = c(NA, 
NA, NA, NA), `Rating 2018-01` = c(4L, 4L, 3L, 3L), `Rating 2018-02` = c(3L, 
4L, 3L, 2L)), class = "data.frame", row.names = c(NA, -4L))

I would like to copy the data in df1 in column "Value" to df2 to columns 2 to 8 ("Rating 2017-06" to "Rating 2017-12") for each Name.

What I have tried so far:

Merged_Data <- df1 %>% 
  left_join(df1, df2, by="Name")

The problem is with this code that I cannot specifically copy the values form df1 to selected columns as mentioned above in df2. I don't know what I have to right in the code that I would be able to do that.

Could someone help me here? Thank you already.

CodePudding user response：

Try this approach:

library(tidyverse)

df1 <- structure(list(
  Name = c("A", "B", "C", "D"),
  Value = c(2L, 5L,
            3L, 2L)
),
class = "data.frame",
row.names = c(NA,-4L))

df2 <- structure(
  list(
    Name = c("A", "B", "C", "D"),
    `Rating 2016-06` = c(NA,
                         NA, NA, NA),
    `Rating 2017-07` = c(NA, NA, NA, NA),
    `Ratin g 2017-08` = c(NA,
                          NA, NA, NA),
    `Rating 2017-09` = c(NA, NA, NA, NA),
    `Rating 2017-10` = c(NA,
                         NA, NA, NA),
    `Rating 2017-11` = c(NA, NA, NA, NA),
    `Rating 2017-12` = c(NA,
                         NA, NA, NA),
    `Rating 2018-01` = c(4L, 4L, 3L, 3L),
    `Rating 2018-02` = c(3L,
                         4L, 3L, 2L)), class = "data.frame", row.names = c(NA, -4L))

df2 |> 
  left_join(df1) |> 
  mutate(across(contains("2016") | contains("2017"), ~ Value)) |> 
  select(- Value)
#> Joining, by = "Name"
#>   Name Rating 2016-06 Rating 2017-07 Ratin g 2017-08 Rating 2017-09
#> 1    A              2              2               2              2
#> 2    B              5              5               5              5
#> 3    C              3              3               3              3
#> 4    D              2              2               2              2
#>   Rating 2017-10 Rating 2017-11 Rating 2017-12 Rating 2018-01 Rating 2018-02
#> 1              2              2              2              4              3
#> 2              5              5              5              4              4
#> 3              3              3              3              3              3
#> 4              2              2              2              3              2

df2 |> 
  left_join(df1) |> 
  mutate(across(`Rating 2016-06`:`Rating 2017-12`, ~ Value)) |> 
  select(- Value)
#> Joining, by = "Name"
#>   Name Rating 2016-06 Rating 2017-07 Ratin g 2017-08 Rating 2017-09
#> 1    A              2              2               2              2
#> 2    B              5              5               5              5
#> 3    C              3              3               3              3
#> 4    D              2              2               2              2
#>   Rating 2017-10 Rating 2017-11 Rating 2017-12 Rating 2018-01 Rating 2018-02
#> 1              2              2              2              4              3
#> 2              5              5              5              4              4
#> 3              3              3              3              3              3
#> 4              2              2              2              3              2

^{Created on 2022-04-30 by the reprex package (v2.0.1)}