# Tidy Animated Verbs Garrick Aden-Buie – [@grrrck](https://twitter.com/grrrck) – [garrickadenbuie.com](https://www.garrickadenbuie.com). David Zimmermann – [@dav\_zim](https://twitter.com/dav_zim) – [datashenanigan.wordpress.com](https://datashenanigan.wordpress.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) - Tidyr Operations: [`gather()`](#gather), [`spread()`](#spread) - 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) ## Installing The in-development version of `tidyexplain` can be installed with `devtools`: ``` r # install.package("devtools") devtools::install_github("gadenbuie/tidy-animated-verbs") library(tidyexplain) ``` ## Mutating Joins ``` r x <- dplyr::data_frame( id = 1:3, x = paste0("x", 1:3) ) y <- dplyr::data_frame( id = (1:4)[-3], y = paste0("y", (1:4)[-3]) ) animate_full_join(x, y, by = c("id"), export = "first") ```  ``` 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 animate_inner_join(x, y, by = "id") ```  ``` r dplyr::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 animate_left_join(x, y, by = "id") ```  ``` r dplyr::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 <- dplyr::bind_rows(y, dplyr::data_frame(id = 2, y = "y5")) 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 animate_left_join(x, y_extra, by = "id", anim_opts = anim_options(title_size = 22)) ```  ``` r dplyr::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 animate_right_join(x, y, by = "id") ```  ``` r dplyr::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 animate_full_join(x, y, by = "id") ```  ``` r dplyr::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 > Filtering joins match observations in the same way as mutating joins, > but affect the observations, not the variables. … Semi-joins are > useful for matching filtered summary tables back to the original rows. > … Anti-joins are useful for diagnosing join mismatches. > [R for Data Science: Filtering > Joins](http://r4ds.had.co.nz/relational-data.html#filtering-joins) ### Semi Join > All rows from `x` where there are matching values in `y`, keeping just > columns from `x`. ``` r animate_semi_join(x, y, by = "id") ```  ``` r dplyr::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 animate_anti_join(x, y, by = "id") ```  ``` r dplyr::anti_join(x, y, by = "id") #> # A tibble: 1 x 2 #> id x #> #> 1 3 x3 ``` ## Set Operations ``` r x <- dplyr::data_frame( x = c(1, 1, 2), y = c("a", "b", "a") ) y <- dplyr::data_frame( x = c(1, 2), y = c("a", "b") ) animate_union(x, y, export = "first") ```  ``` 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 animate_union(x, y) ```  ``` r dplyr::union(x, y) #> # A tibble: 4 x 2 #> x y #> #> 1 2 b #> 2 2 a #> 3 1 b #> 4 1 a ``` ``` r animate_union(y, x) ```  ``` r dplyr::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 animate_union_all(x, y) ```  ``` r dplyr::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 animate_intersect(x, y) ```  ``` r dplyr::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 animate_setdiff(x, y) ```  ``` r dplyr::setdiff(x, y) #> # A tibble: 2 x 2 #> x y #> #> 1 1 b #> 2 2 a ``` ``` r animate_setdiff(y, x) ```  ``` r dplyr::setdiff(y, x) #> # A tibble: 1 x 2 #> x y #> #> 1 2 b ``` ## Tidy Data and `gather()`, `spread()` functionality [Tidy data](http://r4ds.had.co.nz/tidy-data.html#tidy-data-1) follows the following three rules: 1. Each variable has its own column. 2. Each observation has its own row. 3. Each value has its own cell. Many of the tools in the [tidyverse](https://tidyverse.org) expect data to be formatted as a tidy dataset and the [tidyr](https://tidyr.tidyverse.org) package provides functions to help you organize your data into tidy data. ``` r long <- dplyr::data_frame( year = c(2010, 2011, 2010, 2011, 2010, 2011), person = c("Alice", "Alice", "Bob", "Bob", "Charlie", "Charlie"), sales = c(105, 110, 100, 97, 90, 95) ) wide <- dplyr::data_frame( year = 2010:2011, Alice = c(105, 110), Bob = c(100, 97), Charlie = c(90, 95) ) ``` ### Gather > Gather takes multiple columns and collapses into key-value pairs, > duplicating all other columns as needed. You use gather() when you > notice that your column names are not names of variables, but values > of a variable. ``` r set_font_size(4.5, 15) animate_gather(wide, key = "person", value = "sales", -year) ```  ``` r tidyr::gather(wide, key = "person", value = "sales", -year) #> # A tibble: 6 x 3 #> year person sales #> #> 1 2010 Alice 105 #> 2 2011 Alice 110 #> 3 2010 Bob 100 #> 4 2011 Bob 97 #> 5 2010 Charlie 90 #> 6 2011 Charlie 95 ``` ### Spread > Spread a key-value pair across multiple columns. Use it when an a > column contains observations from multiple variables. ``` r animate_spread(long, key = "person", value = "sales") ```  ``` r tidyr::spread(long, key = "person", value = "sales") #> # A tibble: 2 x 4 #> year Alice Bob Charlie #> #> 1 2010 105 100 90 #> 2 2011 110 97 95 ``` ## Learn More [Tidy data](http://r4ds.had.co.nz/tidy-data.html#tidy-data-1) follows the following three rules: 1. Each variable has its own column. 2. Each observation has its own row. 3. Each value has its own cell. Many of the tools in the [tidyverse](https://tidyverse.org) expect data to be formatted as a tidy dataset and the [tidyr](https://tidyr.tidyverse.org) package provides functions to help you organize your data into 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. Use it when an a > column contains observations from multiple variables. `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 your column names are not names of variables, but *values* > of a variable.  ``` 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 ```