class: center, middle, inverse, title-slide # Grammar of data wrangling ## <br><br> Introduction to Data Science ### <a href="https://introds.org/">introds.org</a> ### <br> Dr. Mine Çetinkaya-Rundel --- layout: true <div class="my-footer"> <span> <a href="https://introds.org" target="_blank">introds.org</a> </span> </div> --- class: middle # Grammar of data wrangling --- ## A grammar of data wrangling... ... based on the concepts of functions as verbs that manipulate data frames .pull-left[ <img src="img/dplyr-part-of-tidyverse.png" width="70%" style="display: block; margin: auto;" /> ] .pull-right[ .midi[ - `select`: pick columns by name - `arrange`: reorder rows - `slice`: pick rows using index(es) - `filter`: pick rows matching criteria - `distinct`: filter for unique rows - `mutate`: add new variables - `summarise`: reduce variables to values - `group_by`: for grouped operations - ... (many more) ] ] --- ## Rules of **dplyr** functions - First argument is *always* a data frame - Subsequent arguments say what to do with that data frame - Always return a data frame - Don't modify in place --- ## Data: Hotel bookings - Data from two hotels: one resort and one city hotel - Observations: Each row represents a hotel booking - Goal for original data collection: Development of prediction models to classify a hotel booking's likelihood to be cancelled ([Antonia et al., 2019](https://www.sciencedirect.com/science/article/pii/S2352340918315191#bib5)) ```r hotels <- read_csv("data/hotels.csv") ``` .footnote[ Source: [TidyTuesday](https://github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-02-11/readme.md) ] --- ## First look: Variables ```r names(hotels) ``` ``` ## [1] "hotel" ## [2] "is_canceled" ## [3] "lead_time" ## [4] "arrival_date_year" ## [5] "arrival_date_month" ## [6] "arrival_date_week_number" ## [7] "arrival_date_day_of_month" ## [8] "stays_in_weekend_nights" ## [9] "stays_in_week_nights" ## [10] "adults" ## [11] "children" ## [12] "babies" ## [13] "meal" ## [14] "country" ## [15] "market_segment" ## [16] "distribution_channel" ## [17] "is_repeated_guest" ## [18] "previous_cancellations" ... ``` --- ## Second look: Overview ```r glimpse(hotels) ``` ``` ## Rows: 119,390 ## Columns: 32 ## $ hotel <chr> "Resort Hotel", "Resort… ## $ is_canceled <dbl> 0, 0, 0, 0, 0, 0, 0, 0,… ## $ lead_time <dbl> 342, 737, 7, 13, 14, 14… ## $ arrival_date_year <dbl> 2015, 2015, 2015, 2015,… ## $ arrival_date_month <chr> "July", "July", "July",… ## $ arrival_date_week_number <dbl> 27, 27, 27, 27, 27, 27,… ## $ arrival_date_day_of_month <dbl> 1, 1, 1, 1, 1, 1, 1, 1,… ## $ stays_in_weekend_nights <dbl> 0, 0, 0, 0, 0, 0, 0, 0,… ## $ stays_in_week_nights <dbl> 0, 0, 1, 1, 2, 2, 2, 2,… ## $ adults <dbl> 2, 2, 1, 1, 2, 2, 2, 2,… ## $ children <dbl> 0, 0, 0, 0, 0, 0, 0, 0,… ## $ babies <dbl> 0, 0, 0, 0, 0, 0, 0, 0,… ## $ meal <chr> "BB", "BB", "BB", "BB",… ## $ country <chr> "PRT", "PRT", "GBR", "G… ## $ market_segment <chr> "Direct", "Direct", "Di… ## $ distribution_channel <chr> "Direct", "Direct", "Di… ... ``` --- ## Select a single column View only `lead_time` (number of days between booking and arrival date): ```r select(hotels, lead_time) ``` ``` ## # A tibble: 119,390 x 1 ## lead_time ## <dbl> ## 1 342 ## 2 737 ## 3 7 ## 4 13 ## 5 14 ## 6 14 ## # … with 119,384 more rows ``` --- ## Select a single column .pull-left[ ```r *select( hotels, lead_time ) ``` ] .pull-right[ - Start with the function (a verb): `select()` ] --- ## Select a single column .pull-left[ ```r select( * hotels, lead_time ) ``` ] .pull-right[ - Start with the function (a verb): `select()` - First argument: data frame we're working with , `hotels` ] --- ## Select a single column .pull-left[ ```r select( hotels, * lead_time ) ``` ] .pull-right[ - Start with the function (a verb): `select()` - First argument: data frame we're working with , `hotels` - Second argument: variable we want to select, `lead_time` ] --- ## Select a single column .pull-left[ ```r select( hotels, lead_time ) ``` ``` ## # A tibble: 119,390 x 1 ## lead_time ## <dbl> ## 1 342 ## 2 737 ## 3 7 ## 4 13 ## 5 14 ## 6 14 ## # … with 119,384 more rows ``` ] .pull-right[ - Start with the function (a verb): `select()` - First argument: data frame we're working with , `hotels` - Second argument: variable we want to select, `lead_time` - Result: data frame with 119390 rows and 1 column ] --- .tip[ dplyr functions always expect a data frame and always yield a data frame. ] ```r select(hotels, lead_time) ``` ``` ## # A tibble: 119,390 x 1 ## lead_time ## <dbl> ## 1 342 ## 2 737 ## 3 7 ## 4 13 ## 5 14 ## 6 14 ## # … with 119,384 more rows ``` --- ## Select multiple columns View only the `hotel` type and `lead_time`: -- .pull-left[ ```r select(hotels, hotel, lead_time) ``` ``` ## # A tibble: 119,390 x 2 ## hotel lead_time ## <chr> <dbl> ## 1 Resort Hotel 342 ## 2 Resort Hotel 737 ## 3 Resort Hotel 7 ## 4 Resort Hotel 13 ## 5 Resort Hotel 14 ## 6 Resort Hotel 14 ## # … with 119,384 more rows ``` ] -- .pull-right[ .question[ What if we wanted to select these columns, and then arrange the data in descending order of lead time? ] ] --- ## Data wrangling, step-by-step .pull-left[ Select: ```r hotels %>% select(hotel, lead_time) ``` ``` ## # A tibble: 119,390 x 2 ## hotel lead_time ## <chr> <dbl> ## 1 Resort Hotel 342 ## 2 Resort Hotel 737 ## 3 Resort Hotel 7 ## 4 Resort Hotel 13 ## 5 Resort Hotel 14 ## 6 Resort Hotel 14 ## # … with 119,384 more rows ``` ] -- .pull-right[ Select, then arrange: ```r hotels %>% select(hotel, lead_time) %>% arrange(desc(lead_time)) ``` ``` ## # A tibble: 119,390 x 2 ## hotel lead_time ## <chr> <dbl> ## 1 Resort Hotel 737 ## 2 Resort Hotel 709 ## 3 City Hotel 629 ## 4 City Hotel 629 ## 5 City Hotel 629 ## 6 City Hotel 629 ## # … with 119,384 more rows ``` ] --- class: middle # Pipes --- ## What is a pipe? In programming, a pipe is a technique for passing information from one process to another. -- .pull-left[ - Start with the data frame `hotels`, and pass it to the `select()` function, ] .pull-right[ .small[ ```r *hotels %>% select(hotel, lead_time) %>% arrange(desc(lead_time)) ``` ``` ## # A tibble: 119,390 x 2 ## hotel lead_time ## <chr> <dbl> ## 1 Resort Hotel 737 ## 2 Resort Hotel 709 ## 3 City Hotel 629 ## 4 City Hotel 629 ## 5 City Hotel 629 ## 6 City Hotel 629 ## # … with 119,384 more rows ``` ] ] --- ## What is a pipe? In programming, a pipe is a technique for passing information from one process to another. .pull-left[ - Start with the data frame `hotels`, and pass it to the `select()` function, - then we select the variables `hotel` and `lead_time`, ] .pull-right[ .small[ ```r hotels %>% * select(hotel, lead_time) %>% arrange(desc(lead_time)) ``` ``` ## # A tibble: 119,390 x 2 ## hotel lead_time ## <chr> <dbl> ## 1 Resort Hotel 737 ## 2 Resort Hotel 709 ## 3 City Hotel 629 ## 4 City Hotel 629 ## 5 City Hotel 629 ## 6 City Hotel 629 ## # … with 119,384 more rows ``` ] ] --- ## What is a pipe? In programming, a pipe is a technique for passing information from one process to another. .pull-left[ - Start with the data frame `hotels`, and pass it to the `select()` function, - then we select the variables `hotel` and `lead_time`, - and then we arrange the data frame by `lead_time` in descending order. ] .pull-right[ .small[ ```r hotels %>% select(hotel, lead_time) %>% * arrange(desc(lead_time)) ``` ``` ## # A tibble: 119,390 x 2 ## hotel lead_time ## <chr> <dbl> ## 1 Resort Hotel 737 ## 2 Resort Hotel 709 ## 3 City Hotel 629 ## 4 City Hotel 629 ## 5 City Hotel 629 ## 6 City Hotel 629 ## # … with 119,384 more rows ``` ] ] --- ## Aside The pipe operator is implemented in the package **magrittr**, though we don't need to load this package explicitly since **tidyverse** does this for us. -- .question[ Any guesses as to why the package is called magrittr? ] -- .pull-left[ <img src="img/magritte.jpg" width="90%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="img/magrittr.jpg" width="100%" style="display: block; margin: auto;" /> ] --- ## How does a pipe work? - You can think about the following sequence of actions - find keys, unlock car, start car, drive to work, park. -- - Expressed as a set of nested functions in R pseudocode this would look like: ```r park(drive(start_car(find("keys")), to = "work")) ``` -- - Writing it out using pipes give it a more natural (and easier to read) structure: ```r find("keys") %>% start_car() %>% drive(to = "work") %>% park() ``` --- ## A note on piping and layering - `%>%` used mainly in **dplyr** pipelines, *we pipe the output of the previous line of code as the first input of the next line of code* -- - `+` used in **ggplot2** plots is used for "layering", *we create the plot in layers, separated by `+`* --- ## dplyr .midi[ ❌ ```r hotels + select(hotel, lead_time) ``` ``` ## Error in select(hotel, lead_time): object 'hotel' not found ``` ✅ ```r hotels %>% select(hotel, lead_time) ``` ``` ## # A tibble: 119,390 x 2 ## hotel lead_time ## <chr> <dbl> ## 1 Resort Hotel 342 ## 2 Resort Hotel 737 ## 3 Resort Hotel 7 ... ``` ] --- ## ggplot2 .midi[ ❌ ```r ggplot(hotels, aes(x = hotel, fill = deposit_type)) %>% geom_bar() ``` ``` ## Error: `mapping` must be created by `aes()` ## Did you use %>% instead of +? ``` ✅ ```r ggplot(hotels, aes(x = hotel, fill = deposit_type)) + geom_bar() ``` <img src="w3-d03-grammar-wrangle_files/figure-html/unnamed-chunk-23-1.png" width="25%" style="display: block; margin: auto;" /> ] --- ## Code styling Many of the styling principles are consistent across `%>%` and `+`: - always a space before - always a line break after (for pipelines with more than 2 lines) ❌ ```r ggplot(hotels,aes(x=hotel,y=deposit_type))+geom_bar() ``` ✅ ```r ggplot(hotels, aes(x = hotel, y = deposit_type)) + geom_bar() ```