I wanted to restore the iris dataset to original format from longer but getting NA's. Any suggestions?
library(tidyverse)
# pivot_longer
iris_longer <- iris %>%
pivot_longer(cols = -c(Species),
names_to = "feature",
values_to = "value")
# Restoring original structure
iris_longer %>% mutate(row = row_number()) %>%
pivot_wider(names_from = feature,
values_from = value,
id_cols = c(row, Species)) %>%
arrange(row) %>% # Make sure rows are ordered
select(-row)
#> # A tibble: 600 × 5
#> Species Sepal.Length Sepal.Width Petal.Length Petal.Width
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 setosa 5.1 NA NA NA
#> 2 setosa NA 3.5 NA NA
#> 3 setosa NA NA 1.4 NA
#> 4 setosa NA NA NA 0.2
#> 5 setosa 4.9 NA NA NA
#> 6 setosa NA 3 NA NA
#> 7 setosa NA NA 1.4 NA
#> 8 setosa NA NA NA 0.2
#> 9 setosa 4.7 NA NA NA
#> 10 setosa NA 3.2 NA NA
#> # ℹ 590 more rows
Created on 2025-04-08 with reprex v2.1.1
Just create a id column grouped by feature, then pivot_wider.
res <- iris_longer %>%
mutate(id = row_number(), .by = feature) %>%
pivot_wider(names_from = feature, values_from = value) %>%
relocate(-Species)
res
# # A tibble: 150 × 6
# id Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# <int> <dbl> <dbl> <dbl> <dbl> <fct>
# 1 1 5.1 3.5 1.4 0.2 setosa
# 2 2 4.9 3 1.4 0.2 setosa
# 3 3 4.7 3.2 1.3 0.2 setosa
# 4 4 4.6 3.1 1.5 0.2 setosa
# 5 5 5 3.6 1.4 0.2 setosa
# 6 6 5.4 3.9 1.7 0.4 setosa
# 7 7 4.6 3.4 1.4 0.3 setosa
# 8 8 5 3.4 1.5 0.2 setosa
# 9 9 4.4 2.9 1.4 0.2 setosa
# 10 10 4.9 3.1 1.5 0.1 setosa
# ℹ 140 more rows
all.equal(res[-1], iris, check.attributes = FALSE)
# [1] TRUE