B is for bind_rows
This article is originally published at http://www.deeplytrivial.com/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 --
## <U+2713> ggplot2 3.2.1 <U+2713> purrr 0.3.3
## <U+2713> tibble 2.1.3 <U+2713> dplyr 0.8.3
## <U+2713> tidyr 1.0.0 <U+2713> stringr 1.4.0
## <U+2713> readr 1.3.1 <U+2713> 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…
## <chr> <dbl> <chr> <chr> <dbl> <chr> <chr>
## 1 The … 1216 6/12/2019 6/18/2019 3.30e1 Tolki… <NA>
## 2 The … 1181 6/12/2019 6/17/2019 1.86e7 Atwoo… <NA>
## 3 It 1156 8/14/2019 8/21/2019 2.79e7 King,… <NA>
## 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,… <NA>
## # … with 11 more variables: AverageRating <dbl>, OriginalPublicationYear <dbl>,
## # read_time <dbl>, MyRating <dbl>, Gender <dbl>, Fiction <dbl>,
## # Childrens <dbl>, Fantasy <dbl>, SciFi <dbl>, Mystery <dbl>, SelfHelp <dbl>
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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This article is originally published at http://www.deeplytrivial.com/
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