In the example below, I would like to add column 'value' based on the values of column 'variable' (i.e., 1 and 20).

toy_data <-
  tibble::tribble(
    ~x, ~y, ~variable,
    1,  2,  "x",
    10, 20, "y"
  )

Like this:

However, none of the below works:

toy_data %>%
  dplyr::mutate(
    value = get(variable)
  )

toy_data %>%
  dplyr::mutate(
    value = mget(variable)
  )

toy_data %>%
  dplyr::mutate(
    value = mget(variable, inherits = TRUE)
  )

toy_data %>%
  dplyr::mutate(
    value = !!variable
  )

How can I do this?

CodePudding user response：

Here are a few options that should scale well.

First is a base option that works along both the variable column and its index. (I made a copy of the data frame just so I had the original intact for more programming.)

library(dplyr)

toy2 <- toy_data
toy2$value <- mapply(function(v, i) toy_data[[v]][i], toy_data$variable, seq_along(toy_data$variable))
toy2
#> # A tibble: 2 × 4
#>       x     y variable value
#>   <dbl> <dbl> <chr>    <dbl>
#> 1     1     2 x            1
#> 2    10    20 y           20

Second uses purrr::imap_dbl to iterate along the variable and its index and return a double.

toy_data %>%
  mutate(value = purrr::imap_dbl(variable, function(v, i) toy_data[[v]][i]))
#> # A tibble: 2 × 4
#>       x     y variable value
#>   <dbl> <dbl> <chr>    <dbl>
#> 1     1     2 x            1
#> 2    10    20 y           20

Third is least straightforward, but what I'd most likely use personally, maybe just because it's a process that fits many of my workflows. Pivotting makes a long version of the data, letting you see both values of variable and corresponding values of x and y, which you can then filter for where those 2 columns match. Then self-join back to the data frame.

inner_join(
  toy_data,
  toy_data %>%
    tidyr::pivot_longer(cols = -variable, values_to = "value") %>%
    filter(variable == name),
  by = "variable"
) %>%
  select(-name)
#> # A tibble: 2 × 4
#>       x     y variable value
#>   <dbl> <dbl> <chr>    <dbl>
#> 1     1     2 x            1
#> 2    10    20 y           20

Edit: @jpiversen rightly points out that the self-join won't work if variable has duplicates—in that case, add a row number to the data and use that as an additional joining column. Here I first add an additional observation to illustrate.

toy3 <- toy_data %>%
  add_row(x = 5, y = 4, variable = "x") %>%
  tibble::rowid_to_column()
inner_join(
  toy3,
  toy3 %>%
    pivot_longer(cols = c(-rowid, -variable), values_to = "value") %>%
    filter(variable == name),
  by = c("rowid", "variable")
) %>%
  select(-name, -rowid)

CodePudding user response：

If you know which variables you have in the dataframe in advance: use simple logic like ifelse() or dplyr::case_when() to choose between them.

If not: use functional programming. Under is an example:

library(dplyr)

f <- function(data, variable_col) {
  
  data[[variable_col]] %>% 
    purrr::imap_dbl(~ data[[.y, .x]])
  
}

toy_data$value <- f(toy_data, "variable")