đź¤ąâ€Ťâ™€ Animations of tidyverse verbs using R, the tidyverse, and gganimate
No Data
Garrick Aden-Buie â€“ @grrrck â€“ garrickadenbuie.com. Set operations contributed by Tyler Grant Smith.
Mutating Joins â€”
inner_join(),
left_join(),
right_join(),
full_join()
Filtering Joins â€”
semi_join(),
anti_join()
Set Operations â€”
union(),
union_all(),
intersect(),
setdiff()
Tidy Data â€”
spread()and
gather()
Learn more about
Please feel free to use these images for teaching or learning about action verbs from the tidyverse. You can directly download the original animations or static images in svg or png formats, or you can use the scripts to recreate the images locally.
Currently, the animations cover the dplyr two-table verbs and Iâ€™d like to expand the animations to include more verbs from the tidyverse. Suggestions are welcome!
The Relational Data chapter of the R for Data Science book by Garrett Grolemund and Hadley Wickham is an excellent resource for learning more about relational data.
The dplyr two-table verbs vignette and Jenny Bryanâ€™s Cheatsheet for dplyr join functions are also great resources.
The animations were made possible by the newly re-written gganimate package by Thomas Lin Pedersen (original by Dave Robinson). The package readme provides an excellent (and quick) introduction to gganimate.
Thanks to an initial push by David Zimmermann, we have begun work toward a packaged set of functions to generate dynamic explanatory animations from users' actual data. Please visit the pkg branch of the tidyexplain repository for more information (or to contribute!).
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
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
All rows from
xwhere there are matching values iny, and all columns fromxandy.
inner_join(x, y, by = "id") #> # A tibble: 2 x 3 #> id x y #> #> 1 1 x1 y1 #> 2 2 x2 y2
All rows from
x, and all columns fromxandy. Rows inxwith no match inywill haveNAvalues in the new columns.
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
â€¦ If there are multiple matches between
xandy, all combinations of the matches are returned.
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
All rows from y, and all columns from
xandy. Rows inywith no match inxwill haveNAvalues in the new columns.
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
All rows and all columns from both
xandy. Where there are not matching values, returnsNAfor the one missing.
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 match observations in the same way as mutating joins, but affect the observations, not the variables. â€¦ Semi-joins are useful for matching filtered summary tables back to the original rows. â€¦ Anti-joins are useful for diagnosing join mismatches.
R for Data Science: Filtering Joins
All rows from
xwhere there are matching values iny, keeping just columns fromx.
semi_join(x, y, by = "id") #> # A tibble: 2 x 2 #> id x #> #> 1 1 x1 #> 2 2 x2
All rows from
xwhere there are not matching values iny, keeping just columns fromx.
anti_join(x, y, by = "id") #> # A tibble: 1 x 2 #> id x #> #> 1 3 x3
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
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
All unique rows from
xandy.
union(x, y) #> # A tibble: 4 x 2 #> x y #> #> 1 2 b #> 2 2 a #> 3 1 b #> 4 1 a
union(y, x) #> # A tibble: 4 x 2 #> x y #> #> 1 2 a #> 2 1 b #> 3 2 b #> 4 1 a
All rows from
xandy, keeping duplicates.
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
Common rows in both
xandy, keeping just unique rows.
intersect(x, y) #> # A tibble: 1 x 2 #> x y #> #> 1 1 a
All rows from
xwhich are not also rows iny, keeping just unique rows.
setdiff(x, y) #> # A tibble: 2 x 2 #> x y #> #> 1 1 b #> 2 2 a
setdiff(y, x) #> # A tibble: 1 x 2 #> x y #> #> 1 2 b
Tidy data follows the following three rules:
Many of the tools in the tidyverse expect data to be formatted as a tidy dataset and the tidyr package provides functions to help you organize your data into tidy data.
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(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.
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