class: center, middle, inverse, title-slide # Tidy work in Tidyverse ## R Foundation for Life Scientists ### Marcin Kierczak --- exclude: true count: false <link href="https://fonts.googleapis.com/css?family=Roboto|Source+Sans+Pro:300,400,600|Ubuntu+Mono&amp;subset=latin-ext" rel="stylesheet"> <link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.3.1/css/all.css" integrity="sha384-mzrmE5qonljUremFsqc01SB46JvROS7bZs3IO2EmfFsd15uHvIt+Y8vEf7N7fWAU" crossorigin="anonymous"> <!-- ----------------- Only edit title & author above this ----------------- --> # Tidyverse -- What is it all About? * [Tidyverse](http://www.tidyverse.org) is a collection of packages. * Created by [Hadley Wickham](http://hadley.nz). * Gains popularity, on the way to become a *de facto* standard in data analyses. * Knowing how to use it can increase your salary :-) * A philosophy of programming or a programing paradigm. * Everything is about the flow of *tidy data*. .center[ <img src="data/slide_tidyverse/hex-tidyverse.png", style="height:200px;"> <img src="data/slide_tidyverse/Hadley-wickham2016-02-04.jpeg", style="height:200px;"> <img src="data/slide_tidyverse/RforDataScience.jpeg", style="height:200px;"> ] .vsmall[sources of images: www.tidyverse.org, Wikipedia, www.tidyverse.org] --- name: tidyverse_workflow # Typical Tidyverse Workflow The tidyverse curse? -- > Navigating the balance between base R and the tidyverse is a challenge to learn. [-Robert A. Muenchen](http://r4stats.com/articles/why-r-is-hard-to-learn/) -- .center[<img src="data/slide_tidyverse/tidyverse-flow.png", style="height:400px;">] .vsmall[source: http://www.storybench.org/getting-started-with-tidyverse-in-r/] --- name: intro_to_pipes # Introduction to Pipes .pull-left-50[ .center[<img src="data/slide_tidyverse/pipe_magritte.jpg", style="width:300px;">] .vsmall[Rene Magritt, *La trahison des images*, [Wikimedia Commons](https://en.wikipedia.org/wiki/The_Treachery_of_Images#/media/File:MagrittePipe.jpg)] .center[<img src="data/slide_tidyverse/magrittr.png", style="width:150px;">] ] -- .pull-right-50[ * Let the data flow. * *Ceci n'est pas une pipe* -- `magrittr` * The `%>%` pipe: + `x %>% f` `\(\equiv\)` `f(x)` + `x %>% f(y)` `\(\equiv\)` `f(x, y)` + `x %>% f %>% g %>% h` `\(\equiv\)` `h(g(f(x)))` ] -- .pull-right-50[ instead of writing this: ```r data <- iris data <- head(data, n=3) ``` ] -- .pull-right-50[ write this: ```r iris %>% head(n=3) ``` ``` ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3.0 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa ``` ] --- name: tibble_intro # Tibbles .pull-left-50[ .center[<img src="data/slide_tidyverse/hex-tibble.png">] ```r head(as_tibble(iris)) ``` ``` ## # A tibble: 6 × 5 ## Sepal.Length Sepal.Width Petal.Length Petal.Width Species ## <dbl> <dbl> <dbl> <dbl> <fct> ## 1 5.1 3.5 1.4 0.2 setosa ## 2 4.9 3 1.4 0.2 setosa ## 3 4.7 3.2 1.3 0.2 setosa ## 4 4.6 3.1 1.5 0.2 setosa ## 5 5 3.6 1.4 0.2 setosa ## 6 5.4 3.9 1.7 0.4 setosa ``` ] .pull-right-50[ * `tibble` is one of the unifying features of tidyverse, * it is a *better* `data.frame` realization, * objects `data.frame` can be coerced to `tibble` using `as_tibble()` ```r tibble( x = 1, # recycling y = runif(8), z = x + y^2, outcome = rnorm(8) ) ``` ``` ## # A tibble: 8 × 4 ## x y z outcome ## <dbl> <dbl> <dbl> <dbl> ## 1 1 0.270 1.07 0.169 ## 2 1 0.774 1.60 -1.73 ## 3 1 0.468 1.22 -0.259 ## 4 1 0.918 1.84 1.86 ## 5 1 0.399 1.16 1.18 ## 6 1 0.811 1.66 0.689 ## 7 1 0.266 1.07 -1.61 ## 8 1 0.431 1.19 1.22 ``` ] --- name: tibble2 # More on Tibbles * When you print a `tibble`: + all columns that fit the screen are shown, + first 10 rows are shown, + data type for each column is shown. ```r as_tibble(cars) %>% print(n = 5) ``` ``` ## # A tibble: 5 × 2 ## speed dist ## <dbl> <dbl> ## 1 4 2 ## 2 4 10 ## 3 7 4 ## 4 7 22 ## 5 8 16 ``` * `my_tibble %>% print(n = 50, width = Inf)`, * `options(tibble.print_min = 15, tibble.print_max = 25)`, * `options(dplyr.print_min = Inf)`, * `options(tibble.width = Inf)` --- name: tibble2 # Subsetting Tibbles ```r vehicles <- as_tibble(cars[1:5,]) vehicles[['speed']] vehicles[[1]] vehicles$speed # Using placeholders vehicles %>% .$dist vehicles %>% .[['dist']] vehicles %>% .[[2]] ``` ``` ## [1] 4 4 7 7 8 ## [1] 4 4 7 7 8 ## [1] 4 4 7 7 8 ## [1] 2 10 4 22 16 ## [1] 2 10 4 22 16 ## [1] 2 10 4 22 16 ``` -- **Note!** Not all old R functions work with tibbles, than you have to use `as.data.frame(my_tibble)`. --- name: tibbles_partial_matching # Tibbles are Stricter than `data.frames` ```r cars <- cars[1:5,] ``` ```r cars$spe # partial matching ``` ``` ## [1] 4 4 7 7 8 ``` ```r vehicles$spe # no partial matching ``` ``` ## Warning: Unknown or uninitialised column: `spe`. ``` ``` ## NULL ``` ```r cars$gear ``` ``` ## NULL ``` ```r vehicles$gear ``` ``` ## Warning: Unknown or uninitialised column: `gear`. ``` ``` ## NULL ``` --- name: loading_data # Loading Data In `tidyverse` you import data using `readr` package that provides a number of useful data import functions: * `read_delim()` a generic function for reading *-delimited files. There are a number of convenience wrappers: + `read_csv()` used to read comma-delimited files, + `read_csv2()` reads semicolon-delimited files, `read_tsv()` that reads tab-delimited files. * `read_fwf` for reading fixed-width files with its wrappers: + fwf_widths() for width-based reading, + fwf_positions() for positions-based reading and + read_table() for reading white space-delimited fixed-width files. * `read_log()` for reading Apache-style logs. -- The most commonly used `read_csv()` has some familiar arguments like: * `skip` -- to specify the number of rows to skip (headers), * `col_names` -- to supply a vector of column names, * `comment` -- to specify what character designates a comment, * `na` -- to specify how missing values are represented. --- name: readr # Importing Data Using `readr` When reading and parsing a file, `readr` attempts to guess proper parser for each column by looking at the 1000 first rows. ```r tricky_dataset <- read_csv(readr_example('challenge.csv')) ``` ``` ## Rows: 2000 Columns: 2 ``` ``` ## ── Column specification ──────────────────────────────────────────────────────── ## Delimiter: "," ## dbl (1): x ## date (1): y ``` ``` ## ## ℹ Use `spec()` to retrieve the full column specification for this data. ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. ``` OK, so there are some parsing failures. We can examine them more closely using `problems()` as suggested in the above output. --- name: readr_problems # Looking at Problematic Columns ```r (p <- problems(tricky_dataset)) ``` ``` ## # A tibble: 0 × 5 ## # … with 5 variables: row <int>, col <int>, expected <chr>, actual <chr>, ## # file <chr> ``` OK, let's see which columns cause trouble: ```r p %$% table(col) ``` ``` ## < table of extent 0 > ``` Looks like the problem occurs only in the `x` column. --- name: readr_problems_fixing # Fixing Problematic Columns So, how can we fix the problematic columns? 1. We can explicitely tell what parser to use: ```r tricky_dataset <- read_csv(readr_example('challenge.csv'), col_types = cols(x = col_double(), y = col_character())) tricky_dataset %>% tail(n = 5) ``` ``` ## # A tibble: 5 × 2 ## x y ## <dbl> <chr> ## 1 0.164 2018-03-29 ## 2 0.472 2014-08-04 ## 3 0.718 2015-08-16 ## 4 0.270 2020-02-04 ## 5 0.608 2019-01-06 ``` As you can see, we can still do better by parsing the `y` column as *date*, not as *character*. --- name: readr_problems_fixing2 # Fixing Problematic Columns cted. But knowing that the parser is guessed based on the first 1000 lines, we can see what sits past the 1000-th line in the data: ```r tricky_dataset %>% head(n = 1002) %>% tail(n = 4) ``` ``` ## # A tibble: 4 × 2 ## x y ## <dbl> <chr> ## 1 4569 <NA> ## 2 4548 <NA> ## 3 0.238 2015-01-16 ## 4 0.412 2018-05-18 ``` It seems, we were very unlucky, because up till 1000-th line there are only integers in the x column and `NA`s in the y column so the parser cannot be guessed correctly. To fix this: ```r tricky_dataset <- read_csv(readr_example('challenge.csv'), guess_max = 1001) ``` ``` ## Rows: 2000 Columns: 2 ``` ``` ## ── Column specification ──────────────────────────────────────────────────────── ## Delimiter: "," ## dbl (1): x ## date (1): y ``` ``` ## ## ℹ Use `spec()` to retrieve the full column specification for this data. ## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. ``` --- name: readr_writing # Writing to a File The `readr` package also provides functions useful for writing tibbled data into a file: * `write_csv()` * `write_tsv()` * `write_excel_csv()` They **always** save: * text in UTF-8, * dates in ISO8601 But saving in csv (or tsv) does mean you loose information about the type of data in particular columns. You can avoid this by using: * `write_rds()` and `read_rds()` to read/write objects in R binary rds format, * use `write_feather()` and `read_feather()` from package `feather` to read/write objects in a fast binary format that other programming languages can access. --- name: basic_data_transformations # Basic Data Transformations with `dplyr` Let us create a tibble: ```r (bijou <- as_tibble(diamonds) %>% head(n = 10)) ``` ``` ## # A tibble: 10 × 10 ## carat cut color clarity depth table price x y z ## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl> ## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 ## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 ## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 ## 4 0.29 Premium I VS2 62.4 58 334 4.2 4.23 2.63 ## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 ## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48 ## 7 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47 ## 8 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53 ## 9 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49 ## 10 0.23 Very Good H VS1 59.4 61 338 4 4.05 2.39 ``` .center[<img src="data/slide_tidyverse/diamonds.png", style="height:200px">] --- name: filter # Picking Observations using `filter()` ```r bijou %>% filter(cut == 'Ideal' | cut == 'Premium', carat >= 0.23) %>% head(n = 5) ``` ``` ## # A tibble: 2 × 10 ## carat cut color clarity depth table price x y z ## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl> ## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 ## 2 0.29 Premium I VS2 62.4 58 334 4.2 4.23 2.63 ``` Be careful with floating point comparisons! Also, rows with comparison resulting in `NA` are skipped by default! ```r bijou %>% filter(near(0.23, carat) | is.na(carat)) %>% head(n = 5) ``` ``` ## # A tibble: 3 × 10 ## carat cut color clarity depth table price x y z ## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl> ## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 ## 2 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 ## 3 0.23 Very Good H VS1 59.4 61 338 4 4.05 2.39 ``` --- name: arrange # Rearranging Observations using `arrange()` ```r bijou %>% arrange(cut, carat, desc(price)) ``` ``` ## # A tibble: 10 × 10 ## carat cut color clarity depth table price x y z ## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl> ## 1 0.22 Fair E VS2 65.1 61 337 3.87 3.78 2.49 ## 2 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 ## 3 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 ## 4 0.23 Very Good H VS1 59.4 61 338 4 4.05 2.39 ## 5 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48 ## 6 0.24 Very Good I VVS1 62.3 57 336 3.95 3.98 2.47 ## 7 0.26 Very Good H SI1 61.9 55 337 4.07 4.11 2.53 ## 8 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 ## 9 0.29 Premium I VS2 62.4 58 334 4.2 4.23 2.63 ## 10 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 ``` The `NA`s always end up at the end of the rearranged tibble. --- name: select # Selecting Variables with `select()` Simple `select` with a range: ```r bijou %>% select(color, clarity, x:z) %>% head(n = 4) ``` ``` ## # A tibble: 4 × 5 ## color clarity x y z ## <ord> <ord> <dbl> <dbl> <dbl> ## 1 E SI2 3.95 3.98 2.43 ## 2 E SI1 3.89 3.84 2.31 ## 3 E VS1 4.05 4.07 2.31 ## 4 I VS2 4.2 4.23 2.63 ``` -- Exclusive `select`: ```r bijou %>% select(-(x:z)) %>% head(n = 4) ``` ``` ## # A tibble: 4 × 7 ## carat cut color clarity depth table price ## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> ## 1 0.23 Ideal E SI2 61.5 55 326 ## 2 0.21 Premium E SI1 59.8 61 326 ## 3 0.23 Good E VS1 56.9 65 327 ## 4 0.29 Premium I VS2 62.4 58 334 ``` --- name: select2 # Selecting Variables with `select()` cted. `rename` is a variant of `select`, here used with `everything()` to move `x` to the beginning and rename it to `var_x` ```r bijou %>% rename(var_x = x) %>% head(n = 5) ``` ``` ## # A tibble: 5 × 10 ## carat cut color clarity depth table price var_x y z ## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl> ## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 ## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 ## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 ## 4 0.29 Premium I VS2 62.4 58 334 4.2 4.23 2.63 ## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 ``` -- use `everything()` to bring some columns to the front: ```r bijou %>% select(x:z, everything()) %>% head(n = 4) ``` ``` ## # A tibble: 4 × 10 ## x y z carat cut color clarity depth table price ## <dbl> <dbl> <dbl> <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> ## 1 3.95 3.98 2.43 0.23 Ideal E SI2 61.5 55 326 ## 2 3.89 3.84 2.31 0.21 Premium E SI1 59.8 61 326 ## 3 4.05 4.07 2.31 0.23 Good E VS1 56.9 65 327 ## 4 4.2 4.23 2.63 0.29 Premium I VS2 62.4 58 334 ``` --- name: mutate # Create/alter new Variables with `mutate` ```r bijou %>% mutate(p = x + z, q = p + y) %>% select(-(depth:price)) %>% head(n = 5) ``` ``` ## # A tibble: 5 × 9 ## carat cut color clarity x y z p q ## <dbl> <ord> <ord> <ord> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 0.23 Ideal E SI2 3.95 3.98 2.43 6.38 10.4 ## 2 0.21 Premium E SI1 3.89 3.84 2.31 6.2 10.0 ## 3 0.23 Good E VS1 4.05 4.07 2.31 6.36 10.4 ## 4 0.29 Premium I VS2 4.2 4.23 2.63 6.83 11.1 ## 5 0.31 Good J SI2 4.34 4.35 2.75 7.09 11.4 ``` -- or with `transmute` (only the transformed variables will be retained) ```r bijou %>% transmute(carat, cut, sum = x + y + z) %>% head(n = 5) ``` ``` ## # A tibble: 5 × 3 ## carat cut sum ## <dbl> <ord> <dbl> ## 1 0.23 Ideal 10.4 ## 2 0.21 Premium 10.0 ## 3 0.23 Good 10.4 ## 4 0.29 Premium 11.1 ## 5 0.31 Good 11.4 ``` --- name: grouped_summaries # Group and Summarize ```r bijou %>% group_by(cut) %>% summarize(max_price = max(price), mean_price = mean(price), min_price = min(price)) ``` ``` ## # A tibble: 5 × 4 ## cut max_price mean_price min_price ## <ord> <int> <dbl> <int> ## 1 Fair 337 337 337 ## 2 Good 335 331 327 ## 3 Very Good 338 337. 336 ## 4 Premium 334 330 326 ## 5 Ideal 326 326 326 ``` -- ```r bijou %>% group_by(cut, color) %>% summarize(max_price = max(price), mean_price = mean(price), min_price = min(price)) %>% head(n = 4) ``` ``` ## # A tibble: 4 × 5 ## # Groups: cut [3] ## cut color max_price mean_price min_price ## <ord> <ord> <int> <dbl> <int> ## 1 Fair E 337 337 337 ## 2 Good E 327 327 327 ## 3 Good J 335 335 335 ## 4 Very Good H 338 338. 337 ``` --- name: other_data_manipulations # Other data manipulation tips ```r bijou %>% group_by(cut) %>% summarize(count = n()) ``` ``` ## # A tibble: 5 × 2 ## cut count ## <ord> <int> ## 1 Fair 1 ## 2 Good 2 ## 3 Very Good 4 ## 4 Premium 2 ## 5 Ideal 1 ``` -- When you need to regroup within the same pipe, use `ungroup()`. --- name: concept_of_tidy_data # The Concept of Tidy Data Data are tidy *sensu Wickham* if: * each and every observation is represented as exactly one row, * each and every variable is represented by exactly one column, * thus each data table cell contains only one value. <img src="data/slide_tidyverse/tidy_data.png" width="2560" style="display: block; margin: auto auto auto 0;" /> Usually data are untidy in only one way. However, if you are unlucky, they are really untidy and thus a pain to work with... --- name: tidy_data # Tidy Data <img src="data/slide_tidyverse/tidy_data.png" style="height:100px"> -- .center[**Are these data tidy?**] .pull-left-70[ <table class="table table-striped table-hover table-responsive table-condensed" style=""> <thead> <tr> <th style="text-align:center;"> Sepal.Length </th> <th style="text-align:center;"> Sepal.Width </th> <th style="text-align:center;"> Petal.Length </th> <th style="text-align:center;"> Petal.Width </th> <th style="text-align:center;"> Species </th> </tr> </thead> <tbody> <tr> <td style="text-align:center;"> 5.1 </td> <td style="text-align:center;"> 3.5 </td> <td style="text-align:center;"> 1.4 </td> <td style="text-align:center;"> 0.2 </td> <td style="text-align:center;"> setosa </td> </tr> <tr> <td style="text-align:center;"> 4.9 </td> <td style="text-align:center;"> 3.0 </td> <td style="text-align:center;"> 1.4 </td> <td style="text-align:center;"> 0.2 </td> <td style="text-align:center;"> setosa </td> </tr> <tr> <td style="text-align:center;"> 4.7 </td> <td style="text-align:center;"> 3.2 </td> <td style="text-align:center;"> 1.3 </td> <td style="text-align:center;"> 0.2 </td> <td style="text-align:center;"> setosa </td> </tr> </tbody> </table> ] -- .pull-right-30[ <table class="table table-striped table-hover table-responsive table-condensed" style=""> <thead> <tr> <th style="text-align:center;"> Species </th> <th style="text-align:center;"> variable </th> <th style="text-align:center;"> value </th> </tr> </thead> <tbody> <tr> <td style="text-align:center;"> setosa </td> <td style="text-align:center;"> Sepal.Length </td> <td style="text-align:center;"> 5.1 </td> </tr> <tr> <td style="text-align:center;"> setosa </td> <td style="text-align:center;"> Sepal.Length </td> <td style="text-align:center;"> 4.9 </td> </tr> <tr> <td style="text-align:center;"> setosa </td> <td style="text-align:center;"> Sepal.Length </td> <td style="text-align:center;"> 4.7 </td> </tr> </tbody> </table> ] <br>&nbsp;<hr><br> -- .pull-left-50[ <table class="table table-striped table-hover table-responsive table-condensed" style=""> <thead> <tr> <th style="text-align:center;"> Sepal.L.W </th> <th style="text-align:center;"> Petal.L.W </th> <th style="text-align:center;"> Species </th> </tr> </thead> <tbody> <tr> <td style="text-align:center;"> 5.1/3.5 </td> <td style="text-align:center;"> 1.4/0.2 </td> <td style="text-align:center;"> setosa </td> </tr> <tr> <td style="text-align:center;"> 4.9/3 </td> <td style="text-align:center;"> 1.4/0.2 </td> <td style="text-align:center;"> setosa </td> </tr> <tr> <td style="text-align:center;"> 4.7/3.2 </td> <td style="text-align:center;"> 1.3/0.2 </td> <td style="text-align:center;"> setosa </td> </tr> </tbody> </table> ] -- .pull-right-50[ <table class="table table-striped table-hover table-responsive table-condensed" style=""> <tbody> <tr> <td style="text-align:left;"> Sepal.Length </td> <td style="text-align:center;"> 5.1 </td> <td style="text-align:center;"> 4.9 </td> <td style="text-align:center;"> 4.7 </td> <td style="text-align:center;"> 4.6 </td> </tr> <tr> <td style="text-align:left;"> Sepal.Width </td> <td style="text-align:center;"> 3.5 </td> <td style="text-align:center;"> 3.0 </td> <td style="text-align:center;"> 3.2 </td> <td style="text-align:center;"> 3.1 </td> </tr> <tr> <td style="text-align:left;"> Petal.Length </td> <td style="text-align:center;"> 1.4 </td> <td style="text-align:center;"> 1.4 </td> <td style="text-align:center;"> 1.3 </td> <td style="text-align:center;"> 1.5 </td> </tr> <tr> <td style="text-align:left;"> Petal.Width </td> <td style="text-align:center;"> 0.2 </td> <td style="text-align:center;"> 0.2 </td> <td style="text-align:center;"> 0.2 </td> <td style="text-align:center;"> 0.2 </td> </tr> <tr> <td style="text-align:left;"> Species </td> <td style="text-align:center;"> setosa </td> <td style="text-align:center;"> setosa </td> <td style="text-align:center;"> setosa </td> <td style="text-align:center;"> setosa </td> </tr> </tbody> </table> ] --- name: tidying_data_pivot_longer # Tidying Data with `tidyr::pivot_longer` If some of your column names are actually values of a variable, use `pivot_longer` (replaces `gather`): ```r bijou2 %>% head(n = 5) ``` ``` ## # A tibble: 5 × 3 ## cut `2008` `2009` ## <ord> <int> <dbl> ## 1 Ideal 326 331. ## 2 Premium 326 331. ## 3 Good 327 332. ## 4 Premium 334 339. ## 5 Good 335 340. ``` ```r bijou2 %>% pivot_longer(c(`2008`, `2009`), names_to = 'year', values_to = 'price') %>% head(n = 5) ``` ``` ## # A tibble: 5 × 3 ## cut year price ## <ord> <chr> <dbl> ## 1 Ideal 2008 326 ## 2 Ideal 2009 331. ## 3 Premium 2008 326 ## 4 Premium 2009 331. ## 5 Good 2008 327 ``` --- name: tidying_data_pivot_wider # Tidying Data with `tidyr::pivot_wider` If some of your observations are scattered across many rows, use `pivot_wider` (replaces `gather`): ```r bijou3 ``` ``` ## # A tibble: 9 × 5 ## cut price clarity dimension measurement ## <ord> <int> <ord> <chr> <dbl> ## 1 Ideal 326 SI2 x 3.95 ## 2 Premium 326 SI1 x 3.89 ## 3 Good 327 VS1 x 4.05 ## 4 Ideal 326 SI2 y 3.98 ## 5 Premium 326 SI1 y 3.84 ## 6 Good 327 VS1 y 4.07 ## 7 Ideal 326 SI2 z 2.43 ## 8 Premium 326 SI1 z 2.31 ## 9 Good 327 VS1 z 2.31 ``` ```r bijou3 %>% pivot_wider(names_from=dimension, values_from=measurement) %>% head(n = 4) ``` ``` ## # A tibble: 3 × 6 ## cut price clarity x y z ## <ord> <int> <ord> <dbl> <dbl> <dbl> ## 1 Ideal 326 SI2 3.95 3.98 2.43 ## 2 Premium 326 SI1 3.89 3.84 2.31 ## 3 Good 327 VS1 4.05 4.07 2.31 ``` --- name: tidying_data_separate # Tidying Data with `separate` If some of your columns contain more than one value, use `separate`: ```r bijou4 ``` ``` ## # A tibble: 5 × 4 ## cut price clarity dim ## <ord> <int> <ord> <chr> ## 1 Ideal 326 SI2 3.95/3.98/2.43 ## 2 Premium 326 SI1 3.89/3.84/2.31 ## 3 Good 327 VS1 4.05/4.07/2.31 ## 4 Premium 334 VS2 4.2/4.23/2.63 ## 5 Good 335 SI2 4.34/4.35/2.75 ``` ```r bijou4 %>% separate(dim, into = c("x", "y", "z"), sep = "/", convert = T) ``` ``` ## # A tibble: 5 × 6 ## cut price clarity x y z ## <ord> <int> <ord> <dbl> <dbl> <dbl> ## 1 Ideal 326 SI2 3.95 3.98 2.43 ## 2 Premium 326 SI1 3.89 3.84 2.31 ## 3 Good 327 VS1 4.05 4.07 2.31 ## 4 Premium 334 VS2 4.2 4.23 2.63 ## 5 Good 335 SI2 4.34 4.35 2.75 ``` --- name: tidying_data_separate # Tidying Data with `unite` If some of your columns contain more than one value, use `separate`: ```r bijou5 ``` ``` ## # A tibble: 5 × 7 ## cut price clarity_prefix clarity_suffix x y z ## <ord> <int> <chr> <chr> <dbl> <dbl> <dbl> ## 1 Ideal 326 SI 2 3.95 3.98 2.43 ## 2 Premium 326 SI 1 3.89 3.84 2.31 ## 3 Good 327 VS 1 4.05 4.07 2.31 ## 4 Premium 334 VS 2 4.2 4.23 2.63 ## 5 Good 335 SI 2 4.34 4.35 2.75 ``` ```r bijou5 %>% unite(clarity, clarity_prefix, clarity_suffix, sep='') ``` ``` ## # A tibble: 5 × 6 ## cut price clarity x y z ## <ord> <int> <chr> <dbl> <dbl> <dbl> ## 1 Ideal 326 SI2 3.95 3.98 2.43 ## 2 Premium 326 SI1 3.89 3.84 2.31 ## 3 Good 327 VS1 4.05 4.07 2.31 ## 4 Premium 334 VS2 4.2 4.23 2.63 ## 5 Good 335 SI2 4.34 4.35 2.75 ``` **Note:** that `sep` is here interpreted as the position to split on. It can also be a *regular expression* or a delimiting string/character. Pretty flexible approach! --- name: missing_complete # Completing Missing Values Using `complete` ```r bijou %>% head(n = 10) %>% select(cut, clarity, price) %>% mutate(continent = sample(c('AusOce', 'Eur'), size = 10, replace = T)) -> missing_stones ``` ```r missing_stones %>% complete(cut, continent) ``` ``` ## # A tibble: 13 × 4 ## cut continent clarity price ## <ord> <chr> <ord> <int> ## 1 Fair AusOce VS2 337 ## 2 Fair Eur <NA> NA ## 3 Good AusOce <NA> NA ## 4 Good Eur VS1 327 ## 5 Good Eur SI2 335 ## 6 Very Good AusOce VVS2 336 ## 7 Very Good AusOce VVS1 336 ## 8 Very Good AusOce VS1 338 ## 9 Very Good Eur SI1 337 ## 10 Premium AusOce VS2 334 ## 11 Premium Eur SI1 326 ## 12 Ideal AusOce <NA> NA ## 13 Ideal Eur SI2 326 ``` --- name: more_tidyverse # Some Other Friends * `stringr` for string manipulation and regular expressions, * `forcats` for working with factors, * `lubridate` for working with dates. --- name: end-slide class: end-slide # Thank you. Questions? .end-text[ <p class="smaller"> <span class="small" style="line-height: 1.2;">Graphics from </span><img src="./assets/freepik.jpg" style="max-height:20px; vertical-align:middle;"><br> Created: 27-Sep-2021 • Roy Francis • <a href="https://www.scilifelab.se/">SciLifeLab</a> • <a href="https://nbis.se/">NBIS</a> </p> ]