First we will call the Tidyverse library and create an example dataset.

Let’s get to know the dataset.

head(df1)

## # A tibble: 6 x 3
## # Groups:   Year [2]
##    Year Color  Score
##   <dbl> <fct>  <dbl>
## 1  2000 blue     218
## 2  2000 green    229
## 3  2000 orange   226
## 4  2000 red      219
## 5  2000 yellow   182
## 6  2001 blue     165

names(df1)

## [1] "Year"  "Color" "Score"

n_distinct(df1$Color)

## [1] 5

Now we will take the Color column and make each color its own colomn.

wide <- df1 %>% pivot_wider(names_from = Color, values_from = Score)
print(wide)

## # A tibble: 5 x 6
## # Groups:   Year [5]
##    Year  blue green orange   red yellow
##   <dbl> <dbl> <dbl>  <dbl> <dbl>  <dbl>
## 1  2000   218   229    226   219    182
## 2  2001   165   149    151   273    160
## 3  2002   134   198    187   130    307
## 4  2003   143   194    236   189    216
## 5  2004   153   322    227   182    299

Now we will use pivot longer to change the wide dataset back to its original format

long <- wide %>% pivot_longer(-Year, names_to = "Color", values_to = "Score" )
print(long)

## # A tibble: 25 x 3
## # Groups:   Year [5]
##     Year Color  Score
##    <dbl> <chr>  <dbl>
##  1  2000 blue     218
##  2  2000 green    229
##  3  2000 orange   226
##  4  2000 red      219
##  5  2000 yellow   182
##  6  2001 blue     165
##  7  2001 green    149
##  8  2001 orange   151
##  9  2001 red      273
## 10  2001 yellow   160
## # … with 15 more rows

Let’s take it a step further and widen out both the year and the color.

wide2 <- df1 %>% pivot_wider(names_from = c(Year, Color), values_from = Score)
print(wide2)

## # A tibble: 1 x 25
##   `2000_blue` `2000_green` `2000_orange` `2000_red` `2000_yellow` `2001_blue`
##         <dbl>        <dbl>         <dbl>      <dbl>         <dbl>       <dbl>
## 1         218          229           226        219           182         165
## # … with 19 more variables: `2001_green` <dbl>, `2001_orange` <dbl>,
## #   `2001_red` <dbl>, `2001_yellow` <dbl>, `2002_blue` <dbl>,
## #   `2002_green` <dbl>, `2002_orange` <dbl>, `2002_red` <dbl>,
## #   `2002_yellow` <dbl>, `2003_blue` <dbl>, `2003_green` <dbl>,
## #   `2003_orange` <dbl>, `2003_red` <dbl>, `2003_yellow` <dbl>,
## #   `2004_blue` <dbl>, `2004_green` <dbl>, `2004_orange` <dbl>,
## #   `2004_red` <dbl>, `2004_yellow` <dbl>

Now we will take the previous dataset (wide2) and revert it back to it’s original format

long2 <- wide2 %>% pivot_longer(everything(), names_sep = "_", names_to = c("Year","Color"), values_to = "Score" )
print(long2)

## # A tibble: 25 x 3
##    Year  Color  Score
##    <chr> <chr>  <dbl>
##  1 2000  blue     218
##  2 2000  green    229
##  3 2000  orange   226
##  4 2000  red      219
##  5 2000  yellow   182
##  6 2001  blue     165
##  7 2001  green    149
##  8 2001  orange   151
##  9 2001  red      273
## 10 2001  yellow   160
## # … with 15 more rows