Distributing multiple columns in a function

Question

Often I need spreadcolumns with multiple values, as in this question. But I do it often enough that I want to write a function that does this.

For example, given the data:

set.seed(42)
dat <- data_frame(id = rep(1:2,each = 2),
                  grp = rep(letters[1:2],times = 2),
                  avg = rnorm(4),
                  sd = runif(4))
> dat
# A tibble: 4 x 4
     id   grp        avg        sd
  <int> <chr>      <dbl>     <dbl>
1     1     a  1.3709584 0.6569923
2     1     b -0.5646982 0.7050648
3     2     a  0.3631284 0.4577418
4     2     b  0.6328626 0.7191123

I would like to create a function that returns something like:

# A tibble: 2 x 5
     id     a_avg      b_avg      a_sd      b_sd
  <int>     <dbl>      <dbl>     <dbl>     <dbl>
1     1 1.3709584 -0.5646982 0.6569923 0.7050648
2     2 0.3631284  0.6328626 0.4577418 0.7191123

How can i do this?

Answer 1

We will return to the answer indicated in the question, but for now we start with a more naive approach.

One idea would be to spreadeach column of values ​​separately, and then join the results, i.e.

library(dplyr)
library(tidyr)
library(tibble)

dat_avg <- dat %>% 
    select(-sd) %>%
    spread(key = grp,value = avg) %>%
    rename(a_avg = a,
           b_avg = b)

dat_sd <- dat %>% 
    select(-avg) %>%
    spread(key = grp,value = sd) %>%
    rename(a_sd = a,
           b_sd = b)

> full_join(dat_avg,
          dat_sd,
          by = 'id')

# A tibble: 2 x 5
     id     a_avg      b_avg      a_sd      b_sd
  <int>     <dbl>      <dbl>     <dbl>     <dbl>
1     1 1.3709584 -0.5646982 0.6569923 0.7050648
2     2 0.3631284  0.6328626 0.4577418 0.7191123

( full_join , , .)

, spread, key value :

spread_chr <- function(data, key_col, value_cols, fill = NA, 
                       convert = FALSE,drop = TRUE,sep = NULL){
    n_val <- length(value_cols)
    result <- vector(mode = "list", length = n_val)
    id_cols <- setdiff(names(data), c(key_col,value_cols))

    for (i in seq_along(result)){
        result[[i]] <- spread(data = data[,c(id_cols,key_col,value_cols[i]),drop = FALSE],
                              key = !!key_col,
                              value = !!value_cols[i],
                              fill = fill,
                              convert = convert,
                              drop = drop,
                              sep = paste0(sep,value_cols[i],sep))
    }

    result %>%
        purrr::reduce(.f = full_join, by = id_cols)
}

> dat %>%
  spread_chr(key_col = "grp",
             value_cols = c("avg","sd"),
             sep = "_")

# A tibble: 2 x 5
     id grp_avg_a  grp_avg_b  grp_sd_a  grp_sd_b
  <int>     <dbl>      <dbl>     <dbl>     <dbl>
1     1 1.3709584 -0.5646982 0.6569923 0.7050648
2     2 0.3631284  0.6328626 0.4577418 0.7191123

key_col value_cols[i] !! sep spread .

, , :

spread_nq <- function(data, key_col,..., fill = NA, 
                      convert = FALSE, drop = TRUE, sep = NULL){
    val_quos <- rlang::quos(...)
    key_quo <- rlang::enquo(key_col)
    value_cols <- unname(tidyselect::vars_select(names(data),!!!val_quos))
    key_col <- unname(tidyselect::vars_select(names(data),!!key_quo))

    n_val <- length(value_cols)
    result <- vector(mode = "list",length = n_val)
    id_cols <- setdiff(names(data),c(key_col,value_cols))

    for (i in seq_along(result)){
        result[[i]] <- spread(data = data[,c(id_cols,key_col,value_cols[i]),drop = FALSE],
                              key = !!key_col,
                              value = !!value_cols[i],
                              fill = fill,
                              convert = convert,
                              drop = drop,
                              sep = paste0(sep,value_cols[i],sep))
    }

    result %>%
        purrr::reduce(.f = full_join,by = id_cols)
}

> dat %>%
  spread_nq(key_col = grp,avg,sd,sep = "_")

# A tibble: 2 x 5
     id grp_avg_a  grp_avg_b  grp_sd_a  grp_sd_b
  <int>     <dbl>      <dbl>     <dbl>     <dbl>
1     1 1.3709584 -0.5646982 0.6569923 0.7050648
2     2 0.3631284  0.6328626 0.4577418 0.7191123

, rlang::quos rlang::enquo, tidyselect::vars_select.

, gather, unite spread, , , ​​:

spread_nt <- function(data,key_col,...,fill = NA,
                      convert = TRUE,drop = TRUE,sep = "_"){
  key_quo <- rlang::enquo(key_col)
  val_quos <- rlang::quos(...)
  value_cols <- unname(tidyselect::vars_select(names(data),!!!val_quos))
  key_col <- unname(tidyselect::vars_select(names(data),!!key_quo))

  data %>%
    gather(key = ..var..,value = ..val..,!!!val_quos) %>%
    unite(col = ..grp..,c(key_col,"..var.."),sep = sep) %>%
    spread(key = ..grp..,value = ..val..,fill = fill,
           convert = convert,drop = drop,sep = NULL)
}

> dat %>%
  spread_nt(key_col = grp,avg,sd,sep = "_")

# A tibble: 2 x 5
     id     a_avg      a_sd      b_avg      b_sd
* <int>     <dbl>     <dbl>      <dbl>     <dbl>
1     1 1.3709584 0.6569923 -0.5646982 0.7050648
2     2 0.3631284 0.4577418  0.6328626 0.7191123

rlang . , ..var.. , .

, sep unite , , spread sep = NULL.

Answer 2

, tidyverse:

# helper function that returns an horizontal one lined named tibble wrapped into a list
lhframe <- function(x,nms) list(setNames(as_tibble(t(x)),nms))
dat %>% group_by(id) %>%
  summarize(avg = lhframe(avg,grp),
            sd  = lhframe(sd,grp)) %>%
  unnest(.sep="_")

# # A tibble: 2 x 5
#      id      avg_a     avg_b      sd_a      sd_b
#   <int>      <dbl>     <dbl>     <dbl>     <dbl>
# 1     1 -1.7631631 0.4600974 0.7595443 0.5664884
# 2     2 -0.6399949 0.4554501 0.8496897 0.1894739

, :

dat %>% group_by(id) %>%
  summarize_at(vars(avg,sd),lhframe,grp) %>%
  unnest(.sep="_")

Answer 3

Starting from version 1.0.0

tidyr::pivot_wider(data = dat, id_cols = id, names_from = grp, values_from = avg:sd) 
# # A tibble: 2 x 5
#      id avg_a  avg_b  sd_a  sd_b
#   <int> <dbl>  <dbl> <dbl> <dbl>
# 1     1 1.37  -0.565 0.657 0.705
# 2     2 0.363  0.633 0.458 0.719