# tidyexplain
**Repository Path**: tidyfriday/tidyexplain
## Basic Information
- **Project Name**: tidyexplain
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: CC0-1.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-09-20
- **Last Updated**: 2020-12-20
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# 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()`](#intersection),
[`setdiff()`](#set-difference)
- [**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/relational-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 gganimate.
### Dynamic Animations
Thanks to an initial push by [David Zimmermann](https://github.com/DavZim), we
have begun work toward a packaged set of functions to generate dynamic
explanatory animations from users' actual data.
Please visit the [pkg branch](https://github.com/gadenbuie/tidyexplain/tree/pkg)
of the tidyexplain repository for more information (or to contribute!).
## Mutating Joins
> A mutating join allows you to combine variables from two tables. It
> first matches observations by their keys, then copies across variables
> from one table to the other.
> [R for Data Science: Mutating
> joins](http://r4ds.had.co.nz/relational-data.html#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
> 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
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
> Set operations are occasionally useful when you want to break a single
> complex filter into simpler pieces. All these operations work with a
> complete row, comparing the values of every variable. These expect the
> x and y inputs to have the same variables, and treat the observations
> like sets.
> [R for Data Science: Set
> operations](http://r4ds.had.co.nz/relational-data.html#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
[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
```