B is for bind_rows

April 2, 2020
By

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Moving on to the letter B, today we’ll talk about merging datasets that contain the same variables but add new cases. This is easily done with bind_rows. Let’s say I realized I forgot to log some of the books I read last year, and I wanted to merge those in to my existing dataset. I selected a handful of books from my to-read list, generated some read time and rating data, and saved the results in a csv file (which you can find here). Now I want to load my existing dataset and the new one:

library(tidyverse)

## -- Attaching packages ------------------------------------------- tidyverse 1.3.0 --
##  ggplot2 3.2.1      purrr   0.3.3
## tibble 2.1.3 dplyr 0.8.3
## tidyr 1.0.0 stringr 1.4.0
## readr 1.3.1 forcats 0.4.0
## -- Conflicts ---------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()

reads2019 <- read_csv("~/Downloads/Blogging A to Z/SarasReads2019.csv", col_names = TRUE)

## Parsed with column specification:
## cols(
## Title = col_character(),
## Pages = col_double(),
## date_started = col_character(),
## date_read = col_character(),
## Book.ID = col_double(),
## Author = col_character(),
## AdditionalAuthors = col_character(),
## AverageRating = col_double(),
## OriginalPublicationYear = col_double(),
## read_time = col_double(),
## MyRating = col_double(),
## Gender = col_double(),
## Fiction = col_double(),
## Childrens = col_double(),
## Fantasy = col_double(),
## SciFi = col_double(),
## Mystery = col_double(),
## SelfHelp = col_double()
## )

addreads <- read_csv("~/Downloads/Blogging A to Z/SarasAdds.csv")

## Parsed with column specification:
## cols(
## Title = col_character(),
## Pages = col_double(),
## date_started = col_character(),
## date_read = col_character(),
## Book.ID = col_double(),
## Author = col_character(),
## AdditionalAuthors = col_character(),
## AverageRating = col_double(),
## OriginalPublicationYear = col_double(),
## read_time = col_double(),
## MyRating = col_double(),
## Gender = col_double(),
## Fiction = col_double(),
## Childrens = col_double(),
## Fantasy = col_double(),
## SciFi = col_double(),
## Mystery = col_double(),
## SelfHelp = col_double()
## )

Now we just bind the two datasets together:


reads2019 <- reads2019 %>%
bind_rows(addreads)

Did these additions change the ordering by page length?


reads2019 <- reads2019 %>%
arrange(desc(Pages), Author)

head(reads2019)

## # A tibble: 6 x 18
## Title Pages date_started date_read Book.ID Author AdditionalAutho…
##
## 1 The … 1216 6/12/2019 6/18/2019 3.30e1 Tolki…
## 2 The … 1181 6/12/2019 6/17/2019 1.86e7 Atwoo…
## 3 It 1156 8/14/2019 8/21/2019 2.79e7 King,…
## 4 1Q84 925 9/3/2019 9/10/2019 1.04e7 Murak… Jay Rubin, Phil…
## 5 Inso… 890 8/10/2019 8/13/2019 1.06e4 King,… Bettina Blanch …
## 6 The … 592 8/18/2019 8/23/2019 1.16e4 King,…
## # … with 11 more variables: AverageRating , OriginalPublicationYear ,
## # read_time , MyRating , Gender , Fiction ,
## # Childrens , Fantasy , SciFi , Mystery , SelfHelp

It did! The longest book is now The Lord of the Rings, at 1216 pages, and number two is The MaddAddam Trilogy, 1181 pages.

This is a pretty easy trick. Later on in this series, we’ll talk about combining datasets that share cases but add new variables – joins – which is one of the times the tidy data mindset becomes very important.

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