Row-wise operation always feel a bit strange and awkward to me. At the same time they are really fascinating as well because we mostly deal with column-wise operations.

In this blog post, we will be going through a #tidytuesday data set that is about plastic and we will be doing row-wise operations the column-wise way with pivot_longer().

What we’ll be covering:

• Row-wise operation the column-wise way with pivot_longer
• Row-wise data operation with transpose
• Row-wise operations with rowSums
• Row-wise operations with pmap and apply
• ID columns for doing row-wise operations the column-wise way

Get the #tidytuesday Data

library(tidyverse) 
library(tidytuesdayR) 

plastic <- tidytuesdayR::tt_download(tt_load_gh("2021-01-26"))
plastic[[1]]

## # A tibble: 13,380 x 14
##    country  year parent_company empty  hdpe  ldpe     o   pet    pp    ps   pvc
##    <chr>   <dbl> <chr>          <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1 Argent…  2019 Grand Total        0   215    55   607  1376   281   116    18
##  2 Argent…  2019 Unbranded          0   155    50   532   848   122   114    17
##  3 Argent…  2019 The Coca-Cola…     0     0     0     0   222    35     0     0
##  4 Argent…  2019 Secco              0     0     0     0    39     4     0     0
##  5 Argent…  2019 Doble Cola         0     0     0     0    38     0     0     0
##  6 Argent…  2019 Pritty             0     0     0     0    22     7     0     0
##  7 Argent…  2019 PepsiCo            0     0     0     0    21     6     0     0
##  8 Argent…  2019 Casoni             0     0     0     0    26     0     0     0
##  9 Argent…  2019 Villa Del Sur…     0     0     0     0    19     1     0     0
## 10 Argent…  2019 Manaos             0     0     0     0    14     4     0     0
## # … with 13,370 more rows, and 3 more variables: grand_total <dbl>,
## #   num_events <dbl>, volunteers <dbl>

We can see that there are certain names for different plastic types in the columns. The columns that count the different types of plastic are from empty until pvc. There is also a grand_total column that sums up all the plastic. Let’s do a little quality check and see if the grand_total column has all the plastic from column empty until column pvc summed up.

For the approach, we will be using the pivot_longer function from the tidyr package. We will demonstrate some other approaches later that perform some row-wise operations. However, I have a better mental model of the data and what I want to accomplish when I am thinking column-wise.

Row-wise operation the column-wise way with pivot_longer()

plastic[[1]] %>% 
  tidyr::pivot_longer(empty:pvc) %>% 
  dplyr::group_by(country, year, parent_company) %>% 
  dplyr::mutate(grand_total_check = sum(value, na.rm = T)) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider() %>% 
  dplyr::filter(grand_total_check != grand_total)

## # A tibble: 2 x 15
##   country  year parent_company grand_total num_events volunteers
##   <chr>   <dbl> <chr>                <dbl>      <dbl>      <dbl>
## 1 Korea    2020 Dongsuh                 44         26         NA
## 2 United…  2020 null                  4037        134        511
## # … with 9 more variables: grand_total_check <dbl>, empty <dbl>, hdpe <dbl>,
## #   ldpe <dbl>, o <dbl>, pet <dbl>, pp <dbl>, ps <dbl>, pvc <dbl>

With the code above, we are pivoting longer on all the plastic categories and then are identifying the row by grouping by country, year, and parent_company. The we can do any operation we wish to do in the summarise function.

And we find some rows where the sum in the plastic columns does not match the total.

Row-wise Data Operations With transpose

plastic[[1]] %>%
  dplyr::mutate(
    grand_total_check = plastic[[1]] %>% 
      dplyr::select(empty:pvc) %>% 
      purrr::transpose() %>% 
      purrr::map_dbl(~ flatten_dbl(.) %>% 
                       sum(na.rm = T))
  ) %>% 
  dplyr::filter(grand_total_check != grand_total)

## # A tibble: 2 x 15
##   country  year parent_company empty  hdpe  ldpe     o   pet    pp    ps   pvc
##   <chr>   <dbl> <chr>          <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Korea    2020 Dongsuh            0     0    32     4     2     0     0     0
## 2 United…  2020 null             102   165   275   752   788  1668   243    41
## # … with 4 more variables: grand_total <dbl>, num_events <dbl>,
## #   volunteers <dbl>, grand_total_check <dbl>

It basically turns the data frame “inside-out” and then we can add up the rows with map_dbl.

Row-wise Operations with rowSums

We can also just simply with the base R rowSums function. However, this operation is specific to adding up numbers so if you would like to do other row-wise operations, you have to use another function.

plastic[[1]] %>% 
  dplyr::mutate(
    grand_total_check = dplyr::select(
      ., empty:pvc
    ) %>% base::rowSums(na.rm = T)
  ) %>% 
  dplyr::filter(grand_total_check != grand_total)

plastic[[1]] %>% 
  dplyr::mutate(
    grand_total_check = dplyr::select(
      ., empty:pvc
    ) %>% base::rowSums(na.rm = T)
  ) %>% 
  dplyr::filter(grand_total_check != grand_total)

Row-wise Operations With pmap and apply

Other ways to do row-wise operations are with purrr’s pmap function and the base R apply function.

plastic[[1]] %>% 
  dplyr::mutate(
    grand_total_check = dplyr::select(
      ., empty:pvc
    ) %>% purrr::pmap_dbl(sum, na.rm = T)
  ) %>% 
  dplyr::filter(grand_total_check != grand_total)

## # A tibble: 2 x 15
##   country  year parent_company empty  hdpe  ldpe     o   pet    pp    ps   pvc
##   <chr>   <dbl> <chr>          <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Korea    2020 Dongsuh            0     0    32     4     2     0     0     0
## 2 United…  2020 null             102   165   275   752   788  1668   243    41
## # … with 4 more variables: grand_total <dbl>, num_events <dbl>,
## #   volunteers <dbl>, grand_total_check <dbl>
plastic[[1]] %>% 
  dplyr::mutate(
    grand_total_check = apply(dplyr::select(plastic[[1]], empty:pvc), 1, 
                              function(x) {sum(x, na.rm = TRUE)})
  ) %>% 
  dplyr::filter(grand_total_check != grand_total)

## # A tibble: 2 x 15
##   country  year parent_company empty  hdpe  ldpe     o   pet    pp    ps   pvc
##   <chr>   <dbl> <chr>          <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Korea    2020 Dongsuh            0     0    32     4     2     0     0     0
## 2 United…  2020 null             102   165   275   752   788  1668   243    41
## # … with 4 more variables: grand_total <dbl>, num_events <dbl>,
## #   volunteers <dbl>, grand_total_check <dbl>

All these approaches work. However, lately, I have been finding myself using the pivot_longer and pivot_wider functions a lot from the tidyr package to do row-wise operations.

ID Columns for Doing Row-wise Operations the Column-wise Way

Sometimes, you have to first add an id to do row-wise operations column-wise. For example, when you would like to sum up all the rows where the columns are numeric in the mtcars data set, you can add an id, pivot_wider and then group by id (the row previously) and then sum up the value.

mtcars %>% 
  dplyr::select_if(is.numeric) %>% 
  dplyr::mutate(id = dplyr::row_number()) %>% 
  tidyr::pivot_longer(-id) %>% 
  dplyr::group_by(id) %>% 
  dplyr::summarise(total = sum(value))

## # A tibble: 32 x 2
##       id total
##  * <int> <dbl>
##  1     1  329.
##  2     2  330.
##  3     3  260.
##  4     4  426.
##  5     5  590.
##  6     6  386.
##  7     7  657.
##  8     8  271.
##  9     9  300.
## 10    10  350.
## # … with 22 more rows

Additional Resources

• A tutorial about pivot_wider() and pivot_longer() with a #tidytuesday data set
• Row-wise data operation’s with purrr and pmap
• Another purrr apply example

If you have any feedback or comment, let me know in the comments below. Thank you fro reading.

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