class: center, middle, inverse, title-slide # Data Science for Economists ## Lecture 5: Data cleaning & wrangling: (1) Tidyverse ### Grant McDermott ### University of Oregon | <a href="https://github.com/uo-ec607">EC 607</a> --- name: toc <style type="text/css"> @media print { .has-continuation { display: block !important; } } </style> # Table of contents 1. [Prologue](#prologue) 2. [Tidyverse basics](#basics) 3. [Data wrangling with dplyr](#dplyr) - [filter](#filter) - [arrange](#arrange) - [select](#select) - [mutate](#mutate) - [summarise](#summarise) - [joins](#joins) 4. [Data tidying with tidyr](#tidyr) - [pivot_longer](#pivot_longer) / [pivot_wider](#pivot_wider) - [separate](#separate) - [unite](#unite) 5. [Summary](#summary) --- class: inverse, center, middle name: prologue # Prologue <html><div style='float:left'></div><hr color='#EB811B' size=1px width=796px></html> --- # What is "tidy" data? ### Resources: - [Vignette](https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html) (from the **tidyr** package) - [Original paper](https://vita.had.co.nz/papers/tidy-data.pdf) (Hadley Wickham, 2014 JSS) -- </br> ### Key points: 1. Each variable forms a column. 2. Each observation forms a row. 3. Each type of observational unit forms a table. -- </br> Basically, tidy data is more likely to be [long (i.e. narrow) format](https://en.wikipedia.org/wiki/Wide_and_narrow_data) than wide format. --- # Checklist ### R packages you'll need today ☑ [**tidyverse**](https://www.tidyverse.org/) ☑ [**nycflights13**](hhttps://github.com/hadley/nycflights13) -- </br> I'll hold off loading these libraries for now. But you can install/update them both with the following command. ```r install.packages(c('tidyverse', 'nycflights13'), repos = 'https://cran.rstudio.com', dependencies = TRUE) ``` **Tip:** If you're on Linux, then I _strongly_ recommend installing the pre-compiled binary versions of these packages from [RSPM](https://packagemanager.rstudio.com/client/#/repos/1/overview) instead of CRAN. The exact repo mirror varies by distro (see the link). But on Ubuntu 20.04, for example, you'd use: ```r install.packages(c('tidyverse', 'nycflights13'), repos = 'https://packagemanager.rstudio.com/all/__linux__/focal/latest', dependencies = TRUE) ``` --- class: inverse, center, middle name: basics # Tidyverse basics <html><div style='float:left'></div><hr color='#EB811B' size=1px width=796px></html> --- # Tidyverse vs. base R Much digital ink has been spilled over the "tidyverse vs. base R" debate. -- I won't delve into this debate here, because I think the answer is [clear](http://varianceexplained.org/r/teach-tidyverse/): We should teach the tidyverse first (or, at least, early). - The documentation and community support are outstanding. - Having a consistent philosophy and syntax makes it easier to learn. - Provides a convenient "front-end" to big data tools that we'll use later in the course. - For data cleaning, wrangling, and plotting, the tidyverse really is a no-brainer.<sup>1</sup> .footnote[ <sup>1</sup> I'm also a huge fan of [**data.table**](http://r-datatable.com/). This package will be the subject of our next lecture. ] -- **But**... this certainly shouldn't put you off learning base R alternatives. - Base R is extremely flexible and powerful (and stable). - There are some things that you'll have to venture outside of the tidyverse for. - A combination of tidyverse and base R is often the best solution to a problem. - Excellent base R data manipulation tutorials: [here](https://www.rspatial.org/intr/index.html) and [here](https://github.com/matloff/fasteR). --- # Tidyverse vs. base R (cont.) One point of convenience is that there is often a direct correspondence between a tidyverse command and its base R equivalent. These generally follow a `tidyverse::snake_case` vs `base::period.case` rule. E.g. Compare: | tidyverse | base | |---|---| | `?readr::read_csv` | `?utils::read.csv` | | `?dplyr::if_else` | `?base::ifelse` | | `?tibble::tibble` | `?base::data.frame` | Etcetera. If you call up the above examples, you'll see that the tidyverse alternative typically offers some enhancements or other useful options (and sometimes restrictions) over its base counterpart. -- **Remember:** There are (almost) always multiple ways to achieve a single goal in R. --- # Tidyverse packages Let's load the tidyverse meta-package and check the output. ```r library(tidyverse) ``` ``` ## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ── ``` ``` ## ✓ ggplot2 3.3.3 ✓ purrr 0.3.4 ## ✓ tibble 3.1.1 ✓ dplyr 1.0.5 ## ✓ tidyr 1.1.3 ✓ stringr 1.4.0 ## ✓ readr 1.4.0 ✓ forcats 0.5.1 ``` ``` ## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ── ## x dplyr::filter() masks stats::filter() ## x dplyr::lag() masks stats::lag() ``` -- We see that we have actually loaded a number of packages (which could also be loaded individually): **ggplot2**, **tibble**, **dplyr**, etc. - We can also see information about the package versions and some [namespace conflicts](https://raw.githack.com/uo-ec607/lectures/master/04-rlang/04-rlang.html#59). --- # Tidyverse packages (cont.) The tidyverse actually comes with a lot more packages than those that are just loaded automatically.<sup>1</sup> ```r tidyverse_packages() ``` ``` ## [1] "broom" "cli" "crayon" "dbplyr" "dplyr" ## [6] "forcats" "ggplot2" "haven" "hms" "httr" ## [11] "jsonlite" "lubridate" "magrittr" "modelr" "pillar" ## [16] "purrr" "readr" "readxl" "reprex" "rlang" ## [21] "rstudioapi" "rvest" "stringr" "tibble" "tidyr" ## [26] "xml2" "tidyverse" ``` We'll use several of these additional packages during the remainder of this course. - E.g. The **lubridate** package for working with dates and the **rvest** package for webscraping. - However, bear in mind that these packages will have to be loaded separately. .footnote[ <sup>1</sup> It also includes a *lot* of dependencies upon installation. This is a matter of some [controversy](http://www.tinyverse.org/). ] --- # Tidyverse packages (cont.) I hope to cover most of the tidyverse packages over the length of this course. Today, however, I'm only really going to focus on two packages: 1. [**dplyr**](https://dplyr.tidyverse.org/) 2. [**tidyr**](https://tidyr.tidyverse.org/) These are the workhorse packages for cleaning and wrangling data. They are thus the ones that you will likely make the most use of (alongside **ggplot2**, which we already met back in Lecture 1). - Data cleaning and wrangling occupies an inordinate amount of time, no matter where you are in your research career. --- # An aside on pipes: %>% We already learned about pipes in our [lecture](https://raw.githack.com/uo-ec607/lectures/master/03-shell/03-shell.html#91) on the bash shell. The tidyverse loads its own pipe operator, denoted `%>%`. I want to reiterate how cool pipes are, and how using them can dramatically improve the experience of reading and writing code. Compare: ```r ## These next two lines of code do exactly the same thing. mpg %>% filter(manufacturer=="audi") %>% group_by(model) %>% summarise(hwy_mean = mean(hwy)) summarise(group_by(filter(mpg, manufacturer=="audi"), model), hwy_mean = mean(hwy)) ``` -- The first line reads from left to right, exactly how I thought of the operations in my head. - Take this object (`mpg`), do this (`filter`), then do this (`group_by`), etc. The second line totally inverts this logical order (the final operation comes first!) - Who wants to read things inside out? --- # An aside on pipes: %>% (cont.) The piped version of the code is even more readable if we write it over several lines. Here it is again and, this time, I'll run it for good measure so you can see the output: ```r mpg %>% filter(manufacturer=="audi") %>% group_by(model) %>% summarise(hwy_mean = mean(hwy)) ``` ``` ## # A tibble: 3 x 2 ## model hwy_mean ## <chr> <dbl> ## 1 a4 28.3 ## 2 a4 quattro 25.8 ## 3 a6 quattro 24 ``` Remember: Using vertical space costs nothing and makes for much more readable/writeable code than cramming things horizontally. -- PS — The pipe is originally from the [**magrittr**](https://magrittr.tidyverse.org/) package ([geddit?](https://en.wikipedia.org/wiki/The_Treachery_of_Images)), which can do some other cool things if you're inclined to explore. --- name: nativepipe # A further aside on the base R pipe: |> The magrittr pipe has proven so successful and popular, that the R core team [recently announced](https://t.co/HVECPENQ5C?amp=1) a "native" pipe would be coming to base R, denoted `|>`.<sup>1</sup> For example: ```r mtcars |> subset(cyl==4) |> head() mtcars |> subset(cyl==4) |> d => lm(mpg ~ disp, data = d) ``` .footnote[<sup>1</sup> That's actually a `|` followed by a `>`. The default font on these slides just makes it look extra fancy.] -- At the time of writing this native pipe is only available in the [development](https://stat.ethz.ch/R-manual/R-devel/library/base/html/pipeOp.html) version of R. (I'll show an in-class demo.) This native pipe complements some other new cool features, like support for ["lambda" functions](https://stackoverflow.com/questions/16501/what-is-a-lambda-function) in R. - So, worth watching this space. --- class: inverse, center, middle name: dplyr # dplyr <html><div style='float:left'></div><hr color='#EB811B' size=1px width=796px></html> --- # Aside: dplyr 1.0.0 release Some of the **dplyr** features that we'll cover today were introduced in [version 1.0.0](https://www.tidyverse.org/blog/2020/06/dplyr-1-0-0/) of the package. - Version 1.0.0 is a big deal since it marks a stable code base for the package going forward. However, at the time of writing these slides, it had only come out very recently. - Please make sure that you are running at least **dplyr** 1.0.0 before continuing. ```r packageVersion('dplyr') ``` ``` ## [1] '1.0.5' ``` ```r # install.packages('dplyr') ## install updated version if < 1.0.0 ``` -- *Note:* **dplyr** 1.0.0 also notifies you about grouping variables every time you do operations on or with them. YMMV, but, personally, I find these messages annoying and so prefer to [switch them off](https://twitter.com/MattCowgill/status/1278463099272491008). ```r options(dplyr.summarise.inform = FALSE) ## Add to .Rprofile to make permanent ``` --- # Key dplyr verbs There are five key dplyr verbs that you need to learn. 1. `filter`: Filter (i.e. subset) rows based on their values. 2. `arrange`: Arrange (i.e. reorder) rows based on their values. 3. `select`: Select (i.e. subset) columns by their names: 4. `mutate`: Create new columns. 5. `summarise`: Collapse multiple rows into a single summary value.<sup>1</sup> .footnote[ <sup>1</sup> `summarize` with a "z" works too. R doesn't discriminate against uncivilised nations of the world. ] -- </br> Let's practice these commands together using the `starwars` data frame that comes pre-packaged with dplyr. --- name: filter # 1) dplyr::filter We can chain multiple filter commands with the pipe (`%>%`), or just separate them within a single filter command using commas. ```r starwars %>% filter( species == "Human", height >= 190 ) ``` ``` ## # A tibble: 4 x 14 ## name height mass hair_color skin_color eye_color birth_year sex gender ## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> ## 1 Dart… 202 136 none white yellow 41.9 male mascu… ## 2 Qui-… 193 89 brown fair blue 92 male mascu… ## 3 Dooku 193 80 white fair brown 102 male mascu… ## 4 Bail… 191 NA black tan brown 67 male mascu… ## # … with 5 more variables: homeworld <chr>, species <chr>, films <list>, ## # vehicles <list>, starships <list> ``` --- # 1) dplyr::filter *cont.* Regular expressions work well too. ```r starwars %>% filter(grepl("Skywalker", name)) ``` ``` ## # A tibble: 3 x 14 ## name height mass hair_color skin_color eye_color birth_year sex gender ## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> ## 1 Luke… 172 77 blond fair blue 19 male mascu… ## 2 Anak… 188 84 blond fair blue 41.9 male mascu… ## 3 Shmi… 163 NA black fair brown 72 fema… femin… ## # … with 5 more variables: homeworld <chr>, species <chr>, films <list>, ## # vehicles <list>, starships <list> ``` --- # 1) dplyr::filter *cont.* A very common `filter` use case is identifying (or removing) missing data cases. ```r starwars %>% filter(is.na(height)) ``` ``` ## # A tibble: 6 x 14 ## name height mass hair_color skin_color eye_color birth_year sex gender ## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> ## 1 Arve… NA NA brown fair brown NA male mascu… ## 2 Finn NA NA black dark dark NA male mascu… ## 3 Rey NA NA brown light hazel NA fema… femin… ## 4 Poe … NA NA brown light brown NA male mascu… ## 5 BB8 NA NA none none black NA none mascu… ## 6 Capt… NA NA unknown unknown unknown NA <NA> <NA> ## # … with 5 more variables: homeworld <chr>, species <chr>, films <list>, ## # vehicles <list>, starships <list> ``` -- </br> To remove missing observations, simply use negation: `filter(!is.na(height))`. Try this yourself. --- name: arrange # 2) dplyr::arrange ```r starwars %>% arrange(birth_year) ``` ``` ## # A tibble: 87 x 14 ## name height mass hair_color skin_color eye_color birth_year sex gender ## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> ## 1 Wick… 88 20 brown brown brown 8 male mascu… ## 2 IG-88 200 140 none metal red 15 none mascu… ## 3 Luke… 172 77 blond fair blue 19 male mascu… ## 4 Leia… 150 49 brown light brown 19 fema… femin… ## 5 Wedg… 170 77 brown fair hazel 21 male mascu… ## 6 Plo … 188 80 none orange black 22 male mascu… ## 7 Bigg… 183 84 black light brown 24 male mascu… ## 8 Han … 180 80 brown fair brown 29 male mascu… ## 9 Land… 177 79 black dark brown 31 male mascu… ## 10 Boba… 183 78.2 black fair brown 31.5 male mascu… ## # … with 77 more rows, and 5 more variables: homeworld <chr>, species <chr>, ## # films <list>, vehicles <list>, starships <list> ``` -- *Note:* Arranging on a character-based column (i.e. strings) will sort alphabetically. Try this yourself by arranging according to the "name" column. --- # 2) dplyr::arrange *cont.* We can also arrange items in descending order using `arrange(desc())`. ```r starwars %>% arrange(desc(birth_year)) ``` ``` ## # A tibble: 87 x 14 ## name height mass hair_color skin_color eye_color birth_year sex gender ## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> ## 1 Yoda 66 17 white green brown 896 male mascu… ## 2 Jabb… 175 1358 <NA> green-tan… orange 600 herm… mascu… ## 3 Chew… 228 112 brown unknown blue 200 male mascu… ## 4 C-3PO 167 75 <NA> gold yellow 112 none mascu… ## 5 Dooku 193 80 white fair brown 102 male mascu… ## 6 Qui-… 193 89 brown fair blue 92 male mascu… ## 7 Ki-A… 198 82 white pale yellow 92 male mascu… ## 8 Fini… 170 NA blond fair blue 91 male mascu… ## 9 Palp… 170 75 grey pale yellow 82 male mascu… ## 10 Clie… 183 NA brown fair blue 82 male mascu… ## # … with 77 more rows, and 5 more variables: homeworld <chr>, species <chr>, ## # films <list>, vehicles <list>, starships <list> ``` --- name: select # 3) dplyr::select Use commas to select multiple columns out of a data frame. (You can also use "first:last" for consecutive columns). Deselect a column with "-". ```r starwars %>% select(name:skin_color, species, -height) ``` ``` ## # A tibble: 87 x 5 ## name mass hair_color skin_color species ## <chr> <dbl> <chr> <chr> <chr> ## 1 Luke Skywalker 77 blond fair Human ## 2 C-3PO 75 <NA> gold Droid ## 3 R2-D2 32 <NA> white, blue Droid ## 4 Darth Vader 136 none white Human ## 5 Leia Organa 49 brown light Human ## 6 Owen Lars 120 brown, grey light Human ## 7 Beru Whitesun lars 75 brown light Human ## 8 R5-D4 32 <NA> white, red Droid ## 9 Biggs Darklighter 84 black light Human ## 10 Obi-Wan Kenobi 77 auburn, white fair Human ## # … with 77 more rows ``` --- # 3) dplyr::select *cont.* You can also rename some (or all) of your selected variables in place. ```r starwars %>% select(alias=name, crib=homeworld, sex=gender) ``` ``` ## # A tibble: 87 x 3 ## alias crib sex ## <chr> <chr> <chr> ## 1 Luke Skywalker Tatooine masculine ## 2 C-3PO Tatooine masculine ## 3 R2-D2 Naboo masculine ## 4 Darth Vader Tatooine masculine ## 5 Leia Organa Alderaan feminine ## 6 Owen Lars Tatooine masculine ## 7 Beru Whitesun lars Tatooine feminine ## 8 R5-D4 Tatooine masculine ## 9 Biggs Darklighter Tatooine masculine ## 10 Obi-Wan Kenobi Stewjon masculine ## # … with 77 more rows ``` -- If you just want to rename columns without subsetting them, you can use `rename`. Try this now by replacing `select(...)` in the above code chunk with `rename(...)`. --- # 3) dplyr::select *cont.* The `select(contains(PATTERN))` option provides a nice shortcut in relevant cases. ```r starwars %>% select(name, contains("color")) ``` ``` ## # A tibble: 87 x 4 ## name hair_color skin_color eye_color ## <chr> <chr> <chr> <chr> ## 1 Luke Skywalker blond fair blue ## 2 C-3PO <NA> gold yellow ## 3 R2-D2 <NA> white, blue red ## 4 Darth Vader none white yellow ## 5 Leia Organa brown light brown ## 6 Owen Lars brown, grey light blue ## 7 Beru Whitesun lars brown light blue ## 8 R5-D4 <NA> white, red red ## 9 Biggs Darklighter black light brown ## 10 Obi-Wan Kenobi auburn, white fair blue-gray ## # … with 77 more rows ``` --- # 3) dplyr::select *cont.* The `select(..., everything())` option is another useful shortcut if you only want to bring some variable(s) to the "front" of a data frame. ```r starwars %>% select(species, homeworld, everything()) %>% head(5) ``` ``` ## # A tibble: 5 x 14 ## species homeworld name height mass hair_color skin_color eye_color ## <chr> <chr> <chr> <int> <dbl> <chr> <chr> <chr> ## 1 Human Tatooine Luke… 172 77 blond fair blue ## 2 Droid Tatooine C-3PO 167 75 <NA> gold yellow ## 3 Droid Naboo R2-D2 96 32 <NA> white, bl… red ## 4 Human Tatooine Dart… 202 136 none white yellow ## 5 Human Alderaan Leia… 150 49 brown light brown ## # … with 6 more variables: birth_year <dbl>, sex <chr>, gender <chr>, ## # films <list>, vehicles <list>, starships <list> ``` -- </br> *Note:* The new `relocate` function available in dplyr 1.0.0 has brought a lot more functionality to ordering of columns. See [here](https://www.tidyverse.org/blog/2020/03/dplyr-1-0-0-select-rename-relocate/). --- name: mutate # 4) dplyr::mutate You can create new columns from scratch, or (more commonly) as transformations of existing columns. ```r starwars %>% select(name, birth_year) %>% mutate(dog_years = birth_year * 7) %>% mutate(comment = paste0(name, " is ", dog_years, " in dog years.")) ``` ``` ## # A tibble: 87 x 4 ## name birth_year dog_years comment ## <chr> <dbl> <dbl> <chr> ## 1 Luke Skywalker 19 133 Luke Skywalker is 133 in dog years. ## 2 C-3PO 112 784 C-3PO is 784 in dog years. ## 3 R2-D2 33 231 R2-D2 is 231 in dog years. ## 4 Darth Vader 41.9 293. Darth Vader is 293.3 in dog years. ## 5 Leia Organa 19 133 Leia Organa is 133 in dog years. ## 6 Owen Lars 52 364 Owen Lars is 364 in dog years. ## 7 Beru Whitesun lars 47 329 Beru Whitesun lars is 329 in dog yea… ## 8 R5-D4 NA NA R5-D4 is NA in dog years. ## 9 Biggs Darklighter 24 168 Biggs Darklighter is 168 in dog year… ## 10 Obi-Wan Kenobi 57 399 Obi-Wan Kenobi is 399 in dog years. ## # … with 77 more rows ``` --- # 4) dplyr::mutate *cont.* *Note:* `mutate` is order aware. So you can chain multiple mutates in a single call. ```r starwars %>% select(name, birth_year) %>% mutate( dog_years = birth_year * 7, ## Separate with a comma comment = paste0(name, " is ", dog_years, " in dog years.") ) ``` ``` ## # A tibble: 87 x 4 ## name birth_year dog_years comment ## <chr> <dbl> <dbl> <chr> ## 1 Luke Skywalker 19 133 Luke Skywalker is 133 in dog years. ## 2 C-3PO 112 784 C-3PO is 784 in dog years. ## 3 R2-D2 33 231 R2-D2 is 231 in dog years. ## 4 Darth Vader 41.9 293. Darth Vader is 293.3 in dog years. ## 5 Leia Organa 19 133 Leia Organa is 133 in dog years. ## 6 Owen Lars 52 364 Owen Lars is 364 in dog years. ## 7 Beru Whitesun lars 47 329 Beru Whitesun lars is 329 in dog yea… ## 8 R5-D4 NA NA R5-D4 is NA in dog years. ## 9 Biggs Darklighter 24 168 Biggs Darklighter is 168 in dog year… ## 10 Obi-Wan Kenobi 57 399 Obi-Wan Kenobi is 399 in dog years. ## # … with 77 more rows ``` --- # 4) dplyr::mutate *cont.* Boolean, logical and conditional operators all work well with `mutate` too. ```r starwars %>% select(name, height) %>% filter(name %in% c("Luke Skywalker", "Anakin Skywalker")) %>% mutate(tall1 = height > 180) %>% mutate(tall2 = ifelse(height > 180, "Tall", "Short")) ## Same effect, but can choose labels ``` ``` ## # A tibble: 2 x 4 ## name height tall1 tall2 ## <chr> <int> <lgl> <chr> ## 1 Luke Skywalker 172 FALSE Short ## 2 Anakin Skywalker 188 TRUE Tall ``` --- # 4) dplyr::mutate *cont.* Lastly, combining `mutate` with the new `across` feature in dplyr 1.0.0+ allows you to easily work on a subset of variables. For example: ```r starwars %>% select(name:eye_color) %>% * mutate(across(where(is.character), toupper)) %>% head(5) ``` ``` ## # A tibble: 5 x 6 ## name height mass hair_color skin_color eye_color ## <chr> <int> <dbl> <chr> <chr> <chr> ## 1 LUKE SKYWALKER 172 77 BLOND FAIR BLUE ## 2 C-3PO 167 75 <NA> GOLD YELLOW ## 3 R2-D2 96 32 <NA> WHITE, BLUE RED ## 4 DARTH VADER 202 136 NONE WHITE YELLOW ## 5 LEIA ORGANA 150 49 BROWN LIGHT BROWN ``` -- </br> *Note:* This workflow (i.e. combining `mutate` and `across`) supersedes the old "scoped" variants of `mutate` that you might have used previously. More details [here](https://www.tidyverse.org/blog/2020/04/dplyr-1-0-0-colwise/) and [here](https://dplyr.tidyverse.org/dev/articles/colwise.html). --- name: summarise # 5) dplyr::summarise Particularly useful in combination with the `group_by` command. ```r starwars %>% group_by(species, gender) %>% summarise(mean_height = mean(height, na.rm = TRUE)) ``` ``` ## # A tibble: 42 x 3 ## # Groups: species [38] ## species gender mean_height ## <chr> <chr> <dbl> ## 1 Aleena masculine 79 ## 2 Besalisk masculine 198 ## 3 Cerean masculine 198 ## 4 Chagrian masculine 196 ## 5 Clawdite feminine 168 ## 6 Droid feminine 96 ## 7 Droid masculine 140 ## 8 Dug masculine 112 ## 9 Ewok masculine 88 ## 10 Geonosian masculine 183 ## # … with 32 more rows ``` --- # 5) dplyr::summarise *cont.* Note that including "na.rm = TRUE" (or, its alias "na.rm = T") is usually a good idea with summarise functions. Otherwise, any missing value will propogate to the summarised value too. ```r ## Probably not what we want starwars %>% summarise(mean_height = mean(height)) ``` ``` ## # A tibble: 1 x 1 ## mean_height ## <dbl> ## 1 NA ``` ```r ## Much better starwars %>% summarise(mean_height = mean(height, na.rm = TRUE)) ``` ``` ## # A tibble: 1 x 1 ## mean_height ## <dbl> ## 1 174. ``` --- # 5) dplyr::summarise *cont.* The same `across`-based workflow that we saw with `mutate` a few slides back also works with `summarise`. For example: ```r starwars %>% group_by(species) %>% * summarise(across(where(is.numeric), mean, na.rm=T)) %>% head(5) ``` ``` ## # A tibble: 5 x 4 ## species height mass birth_year ## <chr> <dbl> <dbl> <dbl> ## 1 Aleena 79 15 NaN ## 2 Besalisk 198 102 NaN ## 3 Cerean 198 82 92 ## 4 Chagrian 196 NaN NaN ## 5 Clawdite 168 55 NaN ``` -- </br> *Note:* Again, this functionality supersedes the old "scoped" variants of `summarise` that you used prior to dplyr 1.0.0. Details [here](https://www.tidyverse.org/blog/2020/04/dplyr-1-0-0-colwise/) and [here](https://dplyr.tidyverse.org/dev/articles/colwise.html). --- # Other dplyr goodies `group_by` and `ungroup`: For (un)grouping. - Particularly useful with the `summarise` and `mutate` commands, as we've already seen. -- `slice`: Subset rows by position rather than filtering by values. - E.g. `starwars %>% slice(c(1, 5))` -- `pull`: Extract a column from as a data frame as a vector or scalar. - E.g. `starwars %>% filter(gender=="female") %>% pull(height)` -- `count` and `distinct`: Number and isolate unique observations. - E.g. `starwars %>% count(species)`, or `starwars %>% distinct(species)` - You could also use a combination of `mutate`, `group_by`, and `n()`, e.g. `starwars %>% group_by(species) %>% mutate(num = n())`. --- # Other dplyr goodies (cont.) There are also a whole class of [window functions](https://cran.r-project.org/web/packages/dplyr/vignettes/window-functions.html) for getting leads and lags, ranking, creating cumulative aggregates, etc. - See `vignette("window-functions")`. -- </br> The final set of dplyr "goodies" are the family of join operations. However, these are important enough that I want to go over some concepts in a bit more depth... - We will encounter and practice these many more times as the course progresses. --- name: joins # Joins One of the mainstays of the dplyr package is merging data with the family [join operations](https://cran.r-project.org/web/packages/dplyr/vignettes/two-table.html). - `inner_join(df1, df2)` - `left_join(df1, df2)` - `right_join(df1, df2)` - `full_join(df1, df2)` - `semi_join(df1, df2)` - `anti_join(df1, df2)` (You find find it helpful to to see visual depictions of the different join operations [here](https://r4ds.had.co.nz/relational-data.html).) -- For the simple examples that I'm going to show here, we'll need some data sets that come bundled with the [**nycflights13**](http://github.com/hadley/nycflights13) package. - Load it now and then inspect these data frames in your own console. ```r library(nycflights13) flights planes ``` --- # Joins (cont.) Let's perform a [left join](https://stat545.com/bit001_dplyr-cheatsheet.html#left_joinsuperheroes-publishers) on the flights and planes datasets. - *Note*: I'm going subset columns after the join, but only to keep text on the slide. -- ```r left_join(flights, planes) %>% select(year, month, day, dep_time, arr_time, carrier, flight, tailnum, type, model) ``` ``` ## Joining, by = c("year", "tailnum") ``` ``` ## # A tibble: 336,776 x 10 ## year month day dep_time arr_time carrier flight tailnum type model ## <int> <int> <int> <int> <int> <chr> <int> <chr> <chr> <chr> ## 1 2013 1 1 517 830 UA 1545 N14228 <NA> <NA> ## 2 2013 1 1 533 850 UA 1714 N24211 <NA> <NA> ## 3 2013 1 1 542 923 AA 1141 N619AA <NA> <NA> ## 4 2013 1 1 544 1004 B6 725 N804JB <NA> <NA> ## 5 2013 1 1 554 812 DL 461 N668DN <NA> <NA> ## 6 2013 1 1 554 740 UA 1696 N39463 <NA> <NA> ## 7 2013 1 1 555 913 B6 507 N516JB <NA> <NA> ## 8 2013 1 1 557 709 EV 5708 N829AS <NA> <NA> ## 9 2013 1 1 557 838 B6 79 N593JB <NA> <NA> ## 10 2013 1 1 558 753 AA 301 N3ALAA <NA> <NA> ## # … with 336,766 more rows ``` --- # Joins (cont.) (*continued from previous slide*) Note that dplyr made a reasonable guess about which columns to join on (i.e. columns that share the same name). It also told us its choices: ``` *## Joining, by = c("year", "tailnum") ``` However, there's an obvious problem here: the variable "year" does not have a consistent meaning across our joining datasets! - In one it refers to the *year of flight*, in the other it refers to *year of construction*. -- Luckily, there's an easy way to avoid this problem. - See if you can figure it out before turning to the next slide. - Try `?dplyr::join`. --- # Joins (cont.) (*continued from previous slide*) You just need to be more explicit in your join call by using the `by = ` argument. - You can also rename any ambiguous columns to avoid confusion. ```r left_join( flights, planes %>% rename(year_built = year), ## Not necessary w/ below line, but helpful by = "tailnum" ## Be specific about the joining column ) %>% select(year, month, day, dep_time, arr_time, carrier, flight, tailnum, year_built, type, model) %>% head(3) ## Just to save vertical space on the slide ``` ``` ## # A tibble: 3 x 11 ## year month day dep_time arr_time carrier flight tailnum year_built type ## <int> <int> <int> <int> <int> <chr> <int> <chr> <int> <chr> ## 1 2013 1 1 517 830 UA 1545 N14228 1999 Fixe… ## 2 2013 1 1 533 850 UA 1714 N24211 1998 Fixe… ## 3 2013 1 1 542 923 AA 1141 N619AA 1990 Fixe… ## # … with 1 more variable: model <chr> ``` --- # Joins (cont.) (*continued from previous slide*) Last thing I'll mention for now; note what happens if we again specify the join column... but don't rename the ambiguous "year" column in at least one of the given data frames. ```r left_join( flights, planes, ## Not renaming "year" to "year_built" this time by = "tailnum" ) %>% select(contains("year"), month, day, dep_time, arr_time, carrier, flight, tailnum, type, model) %>% head(3) ``` ``` ## # A tibble: 3 x 11 ## year.x year.y month day dep_time arr_time carrier flight tailnum type model ## <int> <int> <int> <int> <int> <int> <chr> <int> <chr> <chr> <chr> ## 1 2013 1999 1 1 517 830 UA 1545 N14228 Fixe… 737-… ## 2 2013 1998 1 1 533 850 UA 1714 N24211 Fixe… 737-… ## 3 2013 1990 1 1 542 923 AA 1141 N619AA Fixe… 757-… ``` -- Make sure you know what "year.x" and "year.y" are. Again, it pays to be specific. --- class: inverse, center, middle name: tidyr # tidyr <html><div style='float:left'></div><hr color='#EB811B' size=1px width=796px></html> --- # Key tidyr verbs 1. `pivot_longer`: Pivot wide data into long format (i.e. "melt").<sup>1</sup> 2. `pivot_wider`: Pivot long data into wide format (i.e. "cast").<sup>2</sup> 3. `separate`: Separate (i.e. split) one column into multiple columns. 4. `unite`: Unite (i.e. combine) multiple columns into one. .footnote[ <sup>1</sup> Updated version of `tidyr::gather`. <sup>2</sup> Updated version of `tidyr::spread`. ] -- </br> Let's practice these verbs together in class. - Side question: Which of `pivot_longer` vs `pivot_wider` produces "tidy" data? --- name: pivot_longer # 1) tidyr::pivot_longer ```r stocks = data.frame( ## Could use "tibble" instead of "data.frame" if you prefer time = as.Date('2009-01-01') + 0:1, X = rnorm(2, 0, 1), Y = rnorm(2, 0, 2), Z = rnorm(2, 0, 4) ) stocks ``` ``` ## time X Y Z ## 1 2009-01-01 0.7630456 -0.7002949 -3.417682 ## 2 2009-01-02 -0.4579186 -0.7498116 -6.052036 ``` ```r stocks %>% pivot_longer(-time, names_to="stock", values_to="price") ``` ``` ## # A tibble: 6 x 3 ## time stock price ## <date> <chr> <dbl> ## 1 2009-01-01 X 0.763 ## 2 2009-01-01 Y -0.700 ## 3 2009-01-01 Z -3.42 ## 4 2009-01-02 X -0.458 ## 5 2009-01-02 Y -0.750 ## 6 2009-01-02 Z -6.05 ``` --- # 1) tidyr::pivot_longer *cont.* Let's quickly save the "tidy" (i.e. long) stocks data frame for use on the next slide. ```r ## Write out the argument names this time: i.e. "names_to=" and "values_to=" tidy_stocks = stocks %>% pivot_longer(-time, names_to="stock", values_to="price") ``` --- name: pivot_wider # 2) tidyr::pivot_wider ```r tidy_stocks %>% pivot_wider(names_from=stock, values_from=price) ``` ``` ## # A tibble: 2 x 4 ## time X Y Z ## <date> <dbl> <dbl> <dbl> ## 1 2009-01-01 0.763 -0.700 -3.42 ## 2 2009-01-02 -0.458 -0.750 -6.05 ``` ```r tidy_stocks %>% pivot_wider(names_from=time, values_from=price) ``` ``` ## # A tibble: 3 x 3 ## stock `2009-01-01` `2009-01-02` ## <chr> <dbl> <dbl> ## 1 X 0.763 -0.458 ## 2 Y -0.700 -0.750 ## 3 Z -3.42 -6.05 ``` -- </br> Note that the second example — which has combined different pivoting arguments — has effectively transposed the data. --- # Aside: Remembering the pivot_* syntax There's a long-running joke about no-one being able to remember Stata's "reshape" command. ([Exhibit A](https://twitter.com/helleringer143/status/1117234887902285836).) It's easy to see this happening with the `pivot_*` functions too. However, I find that I never forget the commands as long as I remember the argument order is *"names"* then *"values"*. --- name: separate # 3) tidyr::separate ```r economists = data.frame(name = c("Adam.Smith", "Paul.Samuelson", "Milton.Friedman")) economists ``` ``` ## name ## 1 Adam.Smith ## 2 Paul.Samuelson ## 3 Milton.Friedman ``` ```r economists %>% separate(name, c("first_name", "last_name")) ``` ``` ## first_name last_name ## 1 Adam Smith ## 2 Paul Samuelson ## 3 Milton Friedman ``` -- </br> This command is pretty smart. But to avoid ambiguity, you can also specify the separation character with `separate(..., sep=".")`. --- # 3) tidyr::separate *cont.* A related function is `separate_rows`, for splitting up cells that contain multiple fields or observations (a frustratingly common occurence with survey data). ```r jobs = data.frame( name = c("Jack", "Jill"), occupation = c("Homemaker", "Philosopher, Philanthropist, Troublemaker") ) jobs ``` ``` ## name occupation ## 1 Jack Homemaker ## 2 Jill Philosopher, Philanthropist, Troublemaker ``` ```r ## Now split out Jill's various occupations into different rows jobs %>% separate_rows(occupation) ``` ``` ## # A tibble: 4 x 2 ## name occupation ## <chr> <chr> ## 1 Jack Homemaker ## 2 Jill Philosopher ## 3 Jill Philanthropist ## 4 Jill Troublemaker ``` --- name: unite # 4) tidyr::unite ```r gdp = data.frame( yr = rep(2016, times = 4), mnth = rep(1, times = 4), dy = 1:4, gdp = rnorm(4, mean = 100, sd = 2) ) gdp ``` ``` ## yr mnth dy gdp ## 1 2016 1 1 101.6175 ## 2 2016 1 2 100.4228 ## 3 2016 1 3 102.3959 ## 4 2016 1 4 101.8645 ``` ```r ## Combine "yr", "mnth", and "dy" into one "date" column gdp %>% unite(date, c("yr", "mnth", "dy"), sep = "-") ``` ``` ## date gdp ## 1 2016-1-1 101.6175 ## 2 2016-1-2 100.4228 ## 3 2016-1-3 102.3959 ## 4 2016-1-4 101.8645 ``` --- # 4) tidyr::unite *cont.* Note that `unite` will automatically create a character variable. You can see this better if we convert it to a tibble. ```r gdp_u = gdp %>% unite(date, c("yr", "mnth", "dy"), sep = "-") %>% as_tibble() gdp_u ``` ``` ## # A tibble: 4 x 2 ## date gdp ## <chr> <dbl> ## 1 2016-1-1 102. ## 2 2016-1-2 100. ## 3 2016-1-3 102. ## 4 2016-1-4 102. ``` -- If you want to convert it to something else (e.g. date or numeric) then you will need to modify it using `mutate`. See the next slide for an example, using the [lubridate](https://lubridate.tidyverse.org/) package's super helpful date conversion functions. --- # 4) tidyr::unite *cont.* *(continued from previous slide)* ```r library(lubridate) gdp_u %>% mutate(date = ymd(date)) ``` ``` ## # A tibble: 4 x 2 ## date gdp ## <date> <dbl> ## 1 2016-01-01 98.5 ## 2 2016-01-02 102. ## 3 2016-01-03 101. ## 4 2016-01-04 101. ``` --- # Other tidyr goodies Use `crossing` to get the full combination of a group of variables.<sup>1</sup> ```r crossing(side=c("left", "right"), height=c("top", "bottom")) ``` ``` ## # A tibble: 4 x 2 ## side height ## <chr> <chr> ## 1 left bottom ## 2 left top ## 3 right bottom ## 4 right top ``` .footnote[ <sup>1</sup> Base R alternative: `expand.grid`. ] -- See `?expand` and `?complete` for more specialised functions that allow you to fill in (implicit) missing data or variable combinations in existing data frames. - You'll encounter this during your next assignment. --- class: inverse, center, middle name: summary # Summary <html><div style='float:left'></div><hr color='#EB811B' size=1px width=796px></html> --- # Key verbs ### dplyr 1. `filter` 2. `arrange` 3. `select` 4. `mutate` 5. `summarise` ### tidyr 1. `pivot_longer` 2. `pivot_wider` 3. `separate` 4. `unite` -- Other useful items include: pipes (`%>%`), grouping (`group_by`), joining functions (`left_join`, `inner_join`, etc.). --- class: inverse, center, middle # Next lecture: Data cleaning and wrangling: (2) data.table <html><div style='float:left'></div><hr color='#EB811B' size=1px width=796px></html>