# Tidy Animated Verbs Garrick Aden-Buie – [@grrrck](https://twitter.com/grrrck) – [garrickadenbuie.com](https://www.garrickadenbuie.com). Set operations contributed by [Tyler Grant Smith](https://github.com/TylerGrantSmith). [](https://mybinder.org/v2/gh/gadenbuie/tidy-animated-verbs/master?urlpath=rstudio) [_-CC0-green.svg)](https://creativecommons.org/publicdomain/zero/1.0/) [_-MIT-green.svg)](https://opensource.org/licenses/MIT) - [**Mutating Joins**](#mutating-joins) — [`inner_join()`](#inner-join), [`left_join()`](#left-join), [`right_join()`](#right-join), [`full_join()`](#full-join) - [**Filtering Joins**](#filtering-joins) — [`semi_join()`](#semi-join), [`anti_join()`](#anti-join) - [**Set Operations**](#set-operations) — [`union()`](#union), [`union_all()`](#union-all), [`intersect()`](#intersect), [`setdiff()`](#setdiff) - [**Tidy Data**](#tidy-data) — [`spread()` and `gather()`](#spread-and-gather) - Learn more about - [Using the animations and images](#usage) - [Relational Data](#relational-data) - [gganimate](#gganimate) ## Background ### Usage Please feel free to use these images for teaching or learning about action verbs from the [tidyverse](https://tidyverse.org). You can directly download the [original animations](images/) or static images in [svg](images/static/svg/) or [png](images/static/png/) formats, or you can use the [scripts](R/) to recreate the images locally. Currently, the animations cover the [dplyr two-table verbs](https://dplyr.tidyverse.org/articles/two-table.html) and I’d like to expand the animations to include more verbs from the tidyverse. [Suggestions are welcome\!](https://github.com/gadenbuie/tidy-animated-verbs/issues) ### Relational Data The [Relational Data](http://r4ds.had.co.nz/relation-data.html) chapter of the [R for Data Science](http://r4ds.had.co.nz/) book by Garrett Grolemund and Hadley Wickham is an excellent resource for learning more about relational data. The [dplyr two-table verbs vignette](https://dplyr.tidyverse.org/articles/two-table.html) and Jenny Bryan’s [Cheatsheet for dplyr join functions](http://stat545.com/bit001_dplyr-cheatsheet.html) are also great resources. ### gganimate The animations were made possible by the newly re-written [gganimate](https://github.com/thomasp85/gganimate#README) package by [Thomas Lin Pedersen](https://github.com/thomasp85) (original by [Dave Robinson](https://github.com/dgrtwo)). The [package readme](https://github.com/thomasp85/gganimate#README) provides an excellent (and quick) introduction to gganimte. ## Mutating Joins ``` r x #> # A tibble: 3 x 2 #> id x #> #> 1 1 x1 #> 2 2 x2 #> 3 3 x3 y #> # A tibble: 3 x 2 #> id y #> #> 1 1 y1 #> 2 2 y2 #> 3 4 y4 ``` ### Inner Join > All rows from `x` where there are matching values in `y`, and all > columns from `x` and `y`.  ``` r inner_join(x, y, by = "id") #> # A tibble: 2 x 3 #> id x y #> #> 1 1 x1 y1 #> 2 2 x2 y2 ``` ### Left Join > All rows from `x`, and all columns from `x` and `y`. Rows in `x` with > no match in `y` will have `NA` values in the new columns.  ``` r left_join(x, y, by = "id") #> # A tibble: 3 x 3 #> id x y #> #> 1 1 x1 y1 #> 2 2 x2 y2 #> 3 3 x3 ``` ### Left Join (Extra Rows in y) > … If there are multiple matches between `x` and `y`, all combinations > of the matches are returned.  ``` r y_extra # has multiple rows with the key from `x` #> # A tibble: 4 x 2 #> id y #> #> 1 1 y1 #> 2 2 y2 #> 3 4 y4 #> 4 2 y5 left_join(x, y_extra, by = "id") #> # A tibble: 4 x 3 #> id x y #> #> 1 1 x1 y1 #> 2 2 x2 y2 #> 3 2 x2 y5 #> 4 3 x3 ``` ### Right Join > All rows from y, and all columns from `x` and `y`. Rows in `y` with no > match in `x` will have `NA` values in the new columns.  ``` r right_join(x, y, by = "id") #> # A tibble: 3 x 3 #> id x y #> #> 1 1 x1 y1 #> 2 2 x2 y2 #> 3 4 y4 ``` ### Full Join > All rows and all columns from both `x` and `y`. Where there are not > matching values, returns `NA` for the one missing.  ``` r full_join(x, y, by = "id") #> # A tibble: 4 x 3 #> id x y #> #> 1 1 x1 y1 #> 2 2 x2 y2 #> 3 3 x3 #> 4 4 y4 ``` ## Filtering Joins ### Semi Join > All rows from `x` where there are matching values in `y`, keeping just > columns from `x`.  ``` r semi_join(x, y, by = "id") #> # A tibble: 2 x 2 #> id x #> #> 1 1 x1 #> 2 2 x2 ``` ### Anti Join > All rows from `x` where there are not matching values in `y`, keeping > just columns from `x`.  ``` r anti_join(x, y, by = "id") #> # A tibble: 1 x 2 #> id x #> #> 1 3 x3 ``` ## Set Operations ``` r x #> # A tibble: 3 x 2 #> x y #> #> 1 1 a #> 2 1 b #> 3 2 a y #> # A tibble: 2 x 2 #> x y #> #> 1 1 a #> 2 2 b ``` ### Union > All unique rows from `x` and `y`.  ``` r union(x, y) #> # A tibble: 4 x 2 #> x y #> #> 1 2 b #> 2 2 a #> 3 1 b #> 4 1 a ```  ``` r union(y, x) #> # A tibble: 4 x 2 #> x y #> #> 1 2 a #> 2 1 b #> 3 2 b #> 4 1 a ``` ### Union All > All rows from `x` and `y`, keeping duplicates.  ``` r union_all(x, y) #> # A tibble: 5 x 2 #> x y #> #> 1 1 a #> 2 1 b #> 3 2 a #> 4 1 a #> 5 2 b ``` ### Intersection > Common rows in both `x` and `y`, keeping just unique rows.  ``` r intersect(x, y) #> # A tibble: 1 x 2 #> x y #> #> 1 1 a ``` ### Set Difference > All rows from `x` which are not also rows in `y`, keeping just unique > rows.  ``` r setdiff(x, y) #> # A tibble: 2 x 2 #> x y #> #> 1 1 b #> 2 2 a ```  ``` r setdiff(y, x) #> # A tibble: 1 x 2 #> x y #> #> 1 2 b ``` ## Tidy Data  ``` r wide #> # A tibble: 2 x 4 #> id x y z #> #> 1 1 a c e #> 2 2 b d f long #> # A tibble: 6 x 3 #> id key val #> #> 1 1 x a #> 2 2 x b #> 3 1 y c #> 4 2 y d #> 5 1 z e #> 6 2 z f ``` ### Spread and Gather `spread(data, key, value)` > Spread a key-value pair across multiple columns. `gather(data, key = "key", value = "value", ...)` > Gather takes multiple columns and collapses into key-value pairs, > duplicating all other columns as needed. You use `gather()` when you > notice that you have columns that are not variables.  ``` r gather(wide, key, val, x:z) #> # A tibble: 6 x 3 #> id key val #> #> 1 1 x a #> 2 2 x b #> 3 1 y c #> 4 2 y d #> 5 1 z e #> 6 2 z f spread(long, key, val) #> # A tibble: 2 x 4 #> id x y z #> #> 1 1 a c e #> 2 2 b d f ```