class: center, middle, inverse, title-slide # Tidy data and data wrangling ### Prof. Maria Tackett --- layout: true <div class="my-footer"> <span> <a href="http://datasciencebox.org" target="_blank">datasciencebox.org</a> </span> </div> --- class: middle, center ## [Click for PDF of slides](05-tidy-data-wrangle.pdf) --- class: center, middle # Tidy data --- ## Tidy data >Happy families are all alike; every unhappy family is unhappy in its own way. > >Leo Tolstoy -- .pull-left[ **Characteristics of tidy data:** - Each variable forms a column. - Each observation forms a row. - Each type of observational unit forms a table. ] -- .pull-right[ **Characteristics of untidy data:** !@#$%^&*() ] --- ## .question[ What makes this data not tidy? ] <div class="figure" style="text-align: center"> <img src="img/05/untidy-data/hyperwar-airplanes-on-hand.png" alt="WW2 Army Air Force combat aircraft from [Army Air Forces Statistical Digest, WW II](https://www.ibiblio.org/hyperwar/AAF/StatDigest/aafsd-3.html)" width="80%" /> <p class="caption">WW2 Army Air Force combat aircraft from [Army Air Forces Statistical Digest, WW II](https://www.ibiblio.org/hyperwar/AAF/StatDigest/aafsd-3.html)</p> </div> --- .question[ What makes this data not tidy? ] <br> <div class="figure" style="text-align: center"> <img src="img/05/untidy-data/us-general-economic-characteristic-acs-2017.png" alt="[US Census Fact Finder, General Economic Characteristics, ACS 2017](https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_17_5YR_DP03&amp;src=pt)" width="90%" /> <p class="caption">[US Census Fact Finder, General Economic Characteristics, ACS 2017](https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ACS_17_5YR_DP03&src=pt)</p> </div> --- ## Summary tables .question[ Is each of the following a dataset or a summary table? ] .midi[ .pull-left[ ``` ## # A tibble: 87 x 3 ## name height mass ## <chr> <int> <dbl> ## 1 Luke Skywalker 172 77 ## 2 C-3PO 167 75 ## 3 R2-D2 96 32 ## 4 Darth Vader 202 136 ## 5 Leia Organa 150 49 ## 6 Owen Lars 178 120 ## 7 Beru Whitesun lars 165 75 ## 8 R5-D4 97 32 ## 9 Biggs Darklighter 183 84 ## 10 Obi-Wan Kenobi 182 77 ## # … with 77 more rows ``` ] .pull-right[ ``` ## # A tibble: 3 x 2 ## gender avg_height ## <chr> <dbl> ## 1 feminine 165. ## 2 masculine 177. ## 3 <NA> 181. ``` ] ] --- ## Displaying data ```r starwars %>% select(name, height, mass) ``` -- <br> ## Summarizing data ```r starwars %>% group_by(gender) %>% summarize( avg_height = mean(height, na.rm = TRUE) %>% round(2) ) ``` --- class: center, 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/05/dplyr-part-of-tidyverse.png" width="70%" style="display: block; margin: auto;" /> ] .pull-right[ .small[ - `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 --- ## 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)) - Featured in [TidyTuesday](https://github.com/rfordatascience/tidytuesday)! ```r hotels <- read_csv("data/hotels.csv") ``` --- ## First look: Variables ```r names(hotels) ``` ``` ## [1] "hotel" "is_canceled" ## [3] "lead_time" "arrival_date_year" ## [5] "arrival_date_month" "arrival_date_week_number" ## [7] "arrival_date_day_of_month" "stays_in_weekend_nights" ## [9] "stays_in_week_nights" "adults" ## [11] "children" "babies" ## [13] "meal" "country" ## [15] "market_segment" "distribution_channel" ## [17] "is_repeated_guest" "previous_cancellations" ## [19] "previous_bookings_not_canceled" "reserved_room_type" ## [21] "assigned_room_type" "booking_changes" ## [23] "deposit_type" "agent" ## [25] "company" "days_in_waiting_list" ## [27] "customer_type" "adr" ## [29] "required_car_parking_spaces" "total_of_special_requests" ## [31] "reservation_status" "reservation_status_date" ``` --- ## Second look: Overview ```r glimpse(hotels) ``` ``` ## Rows: 119,390 ## Columns: 32 ## $ hotel <chr> "Resort Hotel", "Resort Hotel", "Resor… ## $ is_canceled <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,… ## $ lead_time <dbl> 342, 737, 7, 13, 14, 14, 0, 9, 85, 75,… ## $ arrival_date_year <dbl> 2015, 2015, 2015, 2015, 2015, 2015, 20… ## $ arrival_date_month <chr> "July", "July", "July", "July", "July"… ## $ arrival_date_week_number <dbl> 27, 27, 27, 27, 27, 27, 27, 27, 27, 27… ## $ arrival_date_day_of_month <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,… ## $ stays_in_weekend_nights <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,… ## $ stays_in_week_nights <dbl> 0, 0, 1, 1, 2, 2, 2, 2, 3, 3, 4, 4, 4,… ## $ adults <dbl> 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2,… ## $ children <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,… ## $ babies <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,… ## $ meal <chr> "BB", "BB", "BB", "BB", "BB", "BB", "B… ## $ country <chr> "PRT", "PRT", "GBR", "GBR", "GBR", "GB… ## $ market_segment <chr> "Direct", "Direct", "Direct", "Corpora… ## $ distribution_channel <chr> "Direct", "Direct", "Direct", "Corpora… ## $ is_repeated_guest <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,… ## $ previous_cancellations <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,… ## $ previous_bookings_not_canceled <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,… ## $ reserved_room_type <chr> "C", "C", "A", "A", "A", "A", "C", "C"… ## $ assigned_room_type <chr> "C", "C", "C", "A", "A", "A", "C", "C"… ## $ booking_changes <dbl> 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,… ## $ deposit_type <chr> "No Deposit", "No Deposit", "No Deposi… ## $ agent <chr> "NULL", "NULL", "NULL", "304", "240", … ## $ company <chr> "NULL", "NULL", "NULL", "NULL", "NULL"… ## $ days_in_waiting_list <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,… ## $ customer_type <chr> "Transient", "Transient", "Transient",… ## $ adr <dbl> 0.00, 0.00, 75.00, 75.00, 98.00, 98.00… ## $ required_car_parking_spaces <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,… ## $ total_of_special_requests <dbl> 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 3,… ## $ reservation_status <chr> "Check-Out", "Check-Out", "Check-Out",… ## $ reservation_status_date <date> 2015-07-01, 2015-07-01, 2015-07-02, 2… ``` --- ## Select a single column View only the `lead_time` (number of days between booking and arrival date): -- .pull-left[ ```r hotels %>% select(lead_time) ``` ``` ## # A tibble: 119,390 x 1 ## lead_time ## <dbl> ## 1 342 ## 2 737 ## 3 7 ## 4 13 ## 5 14 ## 6 14 ## 7 0 ## 8 9 ## 9 85 ## 10 75 ## # … with 119,380 more rows ``` ] -- .pull-right[ - Start with a data frame - Pass it to the `select()` function. - Second argument is variable we want to select: `lead_time` - The result is a data frame with 119,300 and 1 column: --dplyr functions always expect a data frame and always yield a data frame. ] --- ## Select multiple columns View only the `hotel` type and `lead_time`: -- .pull-left[ ```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 ## 7 Resort Hotel 0 ## 8 Resort Hotel 9 ## 9 Resort Hotel 85 ## 10 Resort Hotel 75 ## # … with 119,380 more rows ``` ] -- .pull-right[ .discussion[ 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[ .vocab[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 ## 7 Resort Hotel 0 ## 8 Resort Hotel 9 ## 9 Resort Hotel 85 ## 10 Resort Hotel 75 ## # … with 119,380 more rows ``` ] -- .pull-right[ .vocab[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 ## 7 City Hotel 629 ## 8 City Hotel 629 ## 9 City Hotel 629 ## 10 City Hotel 629 ## # … with 119,380 more rows ``` ] --- class: middle, center # 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 ## 7 City Hotel 629 ## 8 City Hotel 629 ## 9 City Hotel 629 ## 10 City Hotel 629 ## # … with 119,380 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 ## 7 City Hotel 629 ## 8 City Hotel 629 ## 9 City Hotel 629 ## 10 City Hotel 629 ## # … with 119,380 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 ## 7 City Hotel 629 ## 8 City Hotel 629 ## 9 City Hotel 629 ## 10 City Hotel 629 ## # … with 119,380 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. -- .discussion[ Any guesses as to why the package is called magrittr? ] -- .pull-left[ <img src="img/05/magritte.jpg" width="100%" style="display: block; margin: auto;" /> ] .pull-right[ <img src="img/05/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, 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: - Read the pipe as "and then" ```r find("keys") %>% start_car() %>% drive(to = "work") %>% park() ``` --- ## What about other arguments? Use the dot to - send results to a function argument other than first one or - use the previous result for multiple arguments ```r hotels %>% filter(hotel == "Resort Hotel") %>% * lm(adr ~ lead_time, data = .) ``` ``` ## ## Call: ## lm(formula = adr ~ lead_time, data = .) ## ## Coefficients: ## (Intercept) lead_time ## 93.16876 0.01925 ``` --- class: middle, center # Working with a single data frame --- class: middle ### You have a single data frame, and you want to process it and prepare it for anlaysis! --- ## `select` to keep variables ```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 ## 7 Resort Hotel 0 ## 8 Resort Hotel 9 ## 9 Resort Hotel 85 ## 10 Resort Hotel 75 ## # … with 119,380 more rows ``` --- ## `select` to exclude variables .small[ ```r hotels %>% * select(-agent) ``` ``` ## # A tibble: 119,390 x 31 ## hotel is_canceled lead_time arrival_date_ye… arrival_date_mo… ## <chr> <dbl> <dbl> <dbl> <chr> ## 1 Reso… 0 342 2015 July ## 2 Reso… 0 737 2015 July ## 3 Reso… 0 7 2015 July ## 4 Reso… 0 13 2015 July ## 5 Reso… 0 14 2015 July ## 6 Reso… 0 14 2015 July ## 7 Reso… 0 0 2015 July ## 8 Reso… 0 9 2015 July ## 9 Reso… 1 85 2015 July ## 10 Reso… 1 75 2015 July ## # … with 119,380 more rows, and 26 more variables: ## # arrival_date_week_number <dbl>, arrival_date_day_of_month <dbl>, ## # stays_in_weekend_nights <dbl>, stays_in_week_nights <dbl>, adults <dbl>, ## # children <dbl>, babies <dbl>, meal <chr>, country <chr>, ## # market_segment <chr>, distribution_channel <chr>, is_repeated_guest <dbl>, ## # previous_cancellations <dbl>, previous_bookings_not_canceled <dbl>, ## # reserved_room_type <chr>, assigned_room_type <chr>, booking_changes <dbl>, ## # deposit_type <chr>, company <chr>, days_in_waiting_list <dbl>, ## # customer_type <chr>, adr <dbl>, required_car_parking_spaces <dbl>, ## # total_of_special_requests <dbl>, reservation_status <chr>, ## # reservation_status_date <date> ``` ] --- ## `select` a range of variables ```r hotels %>% * select(hotel:arrival_date_month) ``` ``` ## # A tibble: 119,390 x 5 ## hotel is_canceled lead_time arrival_date_year arrival_date_month ## <chr> <dbl> <dbl> <dbl> <chr> ## 1 Resort Hotel 0 342 2015 July ## 2 Resort Hotel 0 737 2015 July ## 3 Resort Hotel 0 7 2015 July ## 4 Resort Hotel 0 13 2015 July ## 5 Resort Hotel 0 14 2015 July ## 6 Resort Hotel 0 14 2015 July ## 7 Resort Hotel 0 0 2015 July ## 8 Resort Hotel 0 9 2015 July ## 9 Resort Hotel 1 85 2015 July ## 10 Resort Hotel 1 75 2015 July ## # … with 119,380 more rows ``` --- ## `arrange` in ascending / descending order .pull-left[ ```r hotels %>% select(adults, children, babies) %>% * arrange(babies) ``` ``` ## # A tibble: 119,390 x 3 ## adults children babies ## <dbl> <dbl> <dbl> ## 1 2 0 0 ## 2 2 0 0 ## 3 1 0 0 ## 4 1 0 0 ## 5 2 0 0 ## 6 2 0 0 ## 7 2 0 0 ## 8 2 0 0 ## 9 2 0 0 ## 10 2 0 0 ## # … with 119,380 more rows ``` ] .pull-right[ ```r hotels %>% select(adults, children, babies) %>% * arrange(desc(babies)) ``` ``` ## # A tibble: 119,390 x 3 ## adults children babies ## <dbl> <dbl> <dbl> ## 1 2 0 10 ## 2 1 0 9 ## 3 2 0 2 ## 4 2 0 2 ## 5 2 0 2 ## 6 2 0 2 ## 7 2 0 2 ## 8 2 0 2 ## 9 2 0 2 ## 10 2 0 2 ## # … with 119,380 more rows ``` ] --- ## `slice` for certain row numbers .midi[ ```r # first five hotels %>% * slice(1:5) ``` ``` ## # A tibble: 5 x 32 ## hotel is_canceled lead_time arrival_date_ye… arrival_date_mo… arrival_date_we… ## <chr> <dbl> <dbl> <dbl> <chr> <dbl> ## 1 Reso… 0 342 2015 July 27 ## 2 Reso… 0 737 2015 July 27 ## 3 Reso… 0 7 2015 July 27 ## 4 Reso… 0 13 2015 July 27 ## 5 Reso… 0 14 2015 July 27 ## # … with 26 more variables: arrival_date_day_of_month <dbl>, ## # stays_in_weekend_nights <dbl>, stays_in_week_nights <dbl>, adults <dbl>, ## # children <dbl>, babies <dbl>, meal <chr>, country <chr>, ## # market_segment <chr>, distribution_channel <chr>, is_repeated_guest <dbl>, ## # previous_cancellations <dbl>, previous_bookings_not_canceled <dbl>, ## # reserved_room_type <chr>, assigned_room_type <chr>, booking_changes <dbl>, ## # deposit_type <chr>, agent <chr>, company <chr>, days_in_waiting_list <dbl>, ## # customer_type <chr>, adr <dbl>, required_car_parking_spaces <dbl>, ## # total_of_special_requests <dbl>, reservation_status <chr>, ## # reservation_status_date <date> ``` ] --- .tip[ In R, you can use the `#` (hashtag or pound sign, depending on your age 😜) for adding comments to your code. Any text following `#` will be printed as is, and won't be run as R code. This is useful for leaving comments in your code and for temporarily disabling certain lines of code while debugging. ] .small[ ```r hotels %>% # slice the first five rows # this line is a comment #select(hotel) %>% # this one doesn't run slice(1:5) # this line runs ``` ``` ## # A tibble: 5 x 32 ## hotel is_canceled lead_time arrival_date_ye… arrival_date_mo… arrival_date_we… ## <chr> <dbl> <dbl> <dbl> <chr> <dbl> ## 1 Reso… 0 342 2015 July 27 ## 2 Reso… 0 737 2015 July 27 ## 3 Reso… 0 7 2015 July 27 ## 4 Reso… 0 13 2015 July 27 ## 5 Reso… 0 14 2015 July 27 ## # … with 26 more variables: arrival_date_day_of_month <dbl>, ## # stays_in_weekend_nights <dbl>, stays_in_week_nights <dbl>, adults <dbl>, ## # children <dbl>, babies <dbl>, meal <chr>, country <chr>, ## # market_segment <chr>, distribution_channel <chr>, is_repeated_guest <dbl>, ## # previous_cancellations <dbl>, previous_bookings_not_canceled <dbl>, ## # reserved_room_type <chr>, assigned_room_type <chr>, booking_changes <dbl>, ## # deposit_type <chr>, agent <chr>, company <chr>, days_in_waiting_list <dbl>, ## # customer_type <chr>, adr <dbl>, required_car_parking_spaces <dbl>, ## # total_of_special_requests <dbl>, reservation_status <chr>, ## # reservation_status_date <date> ``` ] --- ## `slice` for certain row numbers .midi[ ```r # last five last_row <- nrow(hotels) # nrow() gives the number of rows in a data frame hotels %>% * slice((last_row - 4):last_row) ``` ``` ## # A tibble: 5 x 32 ## hotel is_canceled lead_time arrival_date_ye… arrival_date_mo… arrival_date_we… ## <chr> <dbl> <dbl> <dbl> <chr> <dbl> ## 1 City… 0 23 2017 August 35 ## 2 City… 0 102 2017 August 35 ## 3 City… 0 34 2017 August 35 ## 4 City… 0 109 2017 August 35 ## 5 City… 0 205 2017 August 35 ## # … with 26 more variables: arrival_date_day_of_month <dbl>, ## # stays_in_weekend_nights <dbl>, stays_in_week_nights <dbl>, adults <dbl>, ## # children <dbl>, babies <dbl>, meal <chr>, country <chr>, ## # market_segment <chr>, distribution_channel <chr>, is_repeated_guest <dbl>, ## # previous_cancellations <dbl>, previous_bookings_not_canceled <dbl>, ## # reserved_room_type <chr>, assigned_room_type <chr>, booking_changes <dbl>, ## # deposit_type <chr>, agent <chr>, company <chr>, days_in_waiting_list <dbl>, ## # customer_type <chr>, adr <dbl>, required_car_parking_spaces <dbl>, ## # total_of_special_requests <dbl>, reservation_status <chr>, ## # reservation_status_date <date> ``` ] --- ## `filter` to select a subset of rows .midi[ ```r # bookings in City Hotels hotels %>% * filter(hotel == "City Hotel") ``` ``` ## # A tibble: 79,330 x 32 ## hotel is_canceled lead_time arrival_date_ye… arrival_date_mo… ## <chr> <dbl> <dbl> <dbl> <chr> ## 1 City… 0 6 2015 July ## 2 City… 1 88 2015 July ## 3 City… 1 65 2015 July ## 4 City… 1 92 2015 July ## 5 City… 1 100 2015 July ## 6 City… 1 79 2015 July ## 7 City… 0 3 2015 July ## 8 City… 1 63 2015 July ## 9 City… 1 62 2015 July ## 10 City… 1 62 2015 July ## # … with 79,320 more rows, and 27 more variables: ## # arrival_date_week_number <dbl>, arrival_date_day_of_month <dbl>, ## # stays_in_weekend_nights <dbl>, stays_in_week_nights <dbl>, adults <dbl>, ## # children <dbl>, babies <dbl>, meal <chr>, country <chr>, ## # market_segment <chr>, distribution_channel <chr>, is_repeated_guest <dbl>, ## # previous_cancellations <dbl>, previous_bookings_not_canceled <dbl>, ## # reserved_room_type <chr>, assigned_room_type <chr>, booking_changes <dbl>, ## # deposit_type <chr>, agent <chr>, company <chr>, days_in_waiting_list <dbl>, ## # customer_type <chr>, adr <dbl>, required_car_parking_spaces <dbl>, ## # total_of_special_requests <dbl>, reservation_status <chr>, ## # reservation_status_date <date> ``` ] --- ## `filter` for many conditions at once ```r hotels %>% filter( * adults == 0, * children >= 1 ) %>% select(adults, babies, children) ``` ``` ## # A tibble: 223 x 3 ## adults babies children ## <dbl> <dbl> <dbl> ## 1 0 0 3 ## 2 0 0 2 ## 3 0 0 2 ## 4 0 0 2 ## 5 0 0 2 ## 6 0 0 3 ## 7 0 1 2 ## 8 0 0 2 ## 9 0 0 2 ## 10 0 0 2 ## # … with 213 more rows ``` --- ## `filter` for more complex conditions ```r # bookings with no adults and some children or babies in the room hotels %>% filter( adults == 0, * children >= 1 | babies >= 1 # | means or ) %>% select(adults, babies, children) ``` ``` ## # A tibble: 223 x 3 ## adults babies children ## <dbl> <dbl> <dbl> ## 1 0 0 3 ## 2 0 0 2 ## 3 0 0 2 ## 4 0 0 2 ## 5 0 0 2 ## 6 0 0 3 ## 7 0 1 2 ## 8 0 0 2 ## 9 0 0 2 ## 10 0 0 2 ## # … with 213 more rows ``` --- ## Logical operators in R <br> operator | definition || operator | definition ------------|------------------------------||--------------|---------------- `<` | less than ||`x`&nbsp;&#124;&nbsp;`y` | `x` OR `y` `<=` | less than or equal to ||`is.na(x)` | test if `x` is `NA` `>` | greater than ||`!is.na(x)` | test if `x` is not `NA` `>=` | greater than or equal to ||`x %in% y` | test if `x` is in `y` `==` | exactly equal to ||`!(x %in% y)` | test if `x` is not in `y` `!=` | not equal to ||`!x` | not `x` `x & y` | `x` AND `y` || | --- class: middle, center ## Demo --- ## `distinct` to filter for unique rows ... and `arrange` to order alphabetically .small[ .pull-left[ ```r hotels %>% * distinct(market_segment) %>% arrange(market_segment) ``` ``` ## # A tibble: 8 x 1 ## market_segment ## <chr> ## 1 Aviation ## 2 Complementary ## 3 Corporate ## 4 Direct ## 5 Groups ## 6 Offline TA/TO ## 7 Online TA ## 8 Undefined ``` ] .pull-right[ ```r hotels %>% * distinct(hotel, market_segment) %>% arrange(hotel, market_segment) ``` ``` ## # A tibble: 14 x 2 ## hotel market_segment ## <chr> <chr> ## 1 City Hotel Aviation ## 2 City Hotel Complementary ## 3 City Hotel Corporate ## 4 City Hotel Direct ## 5 City Hotel Groups ## 6 City Hotel Offline TA/TO ## 7 City Hotel Online TA ## 8 City Hotel Undefined ## 9 Resort Hotel Complementary ## 10 Resort Hotel Corporate ## 11 Resort Hotel Direct ## 12 Resort Hotel Groups ## 13 Resort Hotel Offline TA/TO ## 14 Resort Hotel Online TA ``` ] ] --- ## `count` to create frequency tables .pull-left[ ```r # alphabetical order by default hotels %>% * count(market_segment) ``` ``` ## # A tibble: 8 x 2 ## market_segment n ## <chr> <int> ## 1 Aviation 237 ## 2 Complementary 743 ## 3 Corporate 5295 ## 4 Direct 12606 ## 5 Groups 19811 ## 6 Offline TA/TO 24219 ## 7 Online TA 56477 ## 8 Undefined 2 ``` ] -- .pull-right[ ```r # descending frequency order hotels %>% count(market_segment, * sort = TRUE) ``` ``` ## # A tibble: 8 x 2 ## market_segment n ## <chr> <int> ## 1 Online TA 56477 ## 2 Offline TA/TO 24219 ## 3 Groups 19811 ## 4 Direct 12606 ## 5 Corporate 5295 ## 6 Complementary 743 ## 7 Aviation 237 ## 8 Undefined 2 ``` ] --- ## `count` and `arrange` .pull-left[ ```r # ascending frequency order hotels %>% count(market_segment) %>% * arrange(n) ``` ``` ## # A tibble: 8 x 2 ## market_segment n ## <chr> <int> ## 1 Undefined 2 ## 2 Aviation 237 ## 3 Complementary 743 ## 4 Corporate 5295 ## 5 Direct 12606 ## 6 Groups 19811 ## 7 Offline TA/TO 24219 ## 8 Online TA 56477 ``` ] .pull-right[ ```r # descending frequency order # just like adding sort = TRUE hotels %>% count(market_segment) %>% * arrange(desc(n)) ``` ``` ## # A tibble: 8 x 2 ## market_segment n ## <chr> <int> ## 1 Online TA 56477 ## 2 Offline TA/TO 24219 ## 3 Groups 19811 ## 4 Direct 12606 ## 5 Corporate 5295 ## 6 Complementary 743 ## 7 Aviation 237 ## 8 Undefined 2 ``` ] --- ## `count` for multiple variables ```r hotels %>% * count(hotel, market_segment) ``` ``` ## # A tibble: 14 x 3 ## hotel market_segment n ## <chr> <chr> <int> ## 1 City Hotel Aviation 237 ## 2 City Hotel Complementary 542 ## 3 City Hotel Corporate 2986 ## 4 City Hotel Direct 6093 ## 5 City Hotel Groups 13975 ## 6 City Hotel Offline TA/TO 16747 ## 7 City Hotel Online TA 38748 ## 8 City Hotel Undefined 2 ## 9 Resort Hotel Complementary 201 ## 10 Resort Hotel Corporate 2309 ## 11 Resort Hotel Direct 6513 ## 12 Resort Hotel Groups 5836 ## 13 Resort Hotel Offline TA/TO 7472 ## 14 Resort Hotel Online TA 17729 ``` --- ## order matters when you `count` .midi[ .pull-left[ ```r # hotel type first hotels %>% * count(hotel, market_segment) ``` ``` ## # A tibble: 14 x 3 ## hotel market_segment n ## <chr> <chr> <int> ## 1 City Hotel Aviation 237 ## 2 City Hotel Complementary 542 ## 3 City Hotel Corporate 2986 ## 4 City Hotel Direct 6093 ## 5 City Hotel Groups 13975 ## 6 City Hotel Offline TA/TO 16747 ## 7 City Hotel Online TA 38748 ## 8 City Hotel Undefined 2 ## 9 Resort Hotel Complementary 201 ## 10 Resort Hotel Corporate 2309 ## 11 Resort Hotel Direct 6513 ## 12 Resort Hotel Groups 5836 ## 13 Resort Hotel Offline TA/TO 7472 ## 14 Resort Hotel Online TA 17729 ``` ] .pull-right[ ```r # market segment first hotels %>% * count(market_segment, hotel) ``` ``` ## # A tibble: 14 x 3 ## market_segment hotel n ## <chr> <chr> <int> ## 1 Aviation City Hotel 237 ## 2 Complementary City Hotel 542 ## 3 Complementary Resort Hotel 201 ## 4 Corporate City Hotel 2986 ## 5 Corporate Resort Hotel 2309 ## 6 Direct City Hotel 6093 ## 7 Direct Resort Hotel 6513 ## 8 Groups City Hotel 13975 ## 9 Groups Resort Hotel 5836 ## 10 Offline TA/TO City Hotel 16747 ## 11 Offline TA/TO Resort Hotel 7472 ## 12 Online TA City Hotel 38748 ## 13 Online TA Resort Hotel 17729 ## 14 Undefined City Hotel 2 ``` ] ] --- class: middle, center ## Demo --- ## `mutate` to add a new variable ```r hotels %>% * mutate(little_ones = children + babies) %>% select(children, babies, little_ones) %>% arrange(desc(little_ones)) ``` ``` ## # A tibble: 119,390 x 3 ## children babies little_ones ## <dbl> <dbl> <dbl> ## 1 10 0 10 ## 2 0 10 10 ## 3 0 9 9 ## 4 2 1 3 ## 5 2 1 3 ## 6 2 1 3 ## 7 3 0 3 ## 8 2 1 3 ## 9 2 1 3 ## 10 3 0 3 ## # … with 119,380 more rows ``` --- ## Little ones in resort and city hotels .small[ .pull-left[ ```r # Resort Hotel hotels %>% mutate(little_ones = children + babies) %>% filter( little_ones >= 1, hotel == "Resort Hotel" ) %>% select(hotel, little_ones) ``` ``` ## # A tibble: 3,929 x 2 ## hotel little_ones ## <chr> <dbl> ## 1 Resort Hotel 1 ## 2 Resort Hotel 2 ## 3 Resort Hotel 2 ## 4 Resort Hotel 2 ## 5 Resort Hotel 1 ## 6 Resort Hotel 1 ## 7 Resort Hotel 2 ## 8 Resort Hotel 2 ## 9 Resort Hotel 1 ## 10 Resort Hotel 1 ## # … with 3,919 more rows ``` ] .pull-right[ ```r # City Hotel hotels %>% mutate(little_ones = children + babies) %>% filter( little_ones > 1, hotel == "City Hotel" ) %>% select(hotel, little_ones) ``` ``` ## # A tibble: 2,140 x 2 ## hotel little_ones ## <chr> <dbl> ## 1 City Hotel 2 ## 2 City Hotel 2 ## 3 City Hotel 2 ## 4 City Hotel 2 ## 5 City Hotel 2 ## 6 City Hotel 2 ## 7 City Hotel 2 ## 8 City Hotel 2 ## 9 City Hotel 2 ## 10 City Hotel 3 ## # … with 2,130 more rows ``` ] ] --- .discussion[ What is happening in the following chunk? ] .midi[ ```r hotels %>% mutate(little_ones = children + babies) %>% count(hotel, little_ones) %>% mutate(prop = n / sum(n)) ``` ``` ## # A tibble: 12 x 4 ## hotel little_ones n prop ## <chr> <dbl> <int> <dbl> ## 1 City Hotel 0 73923 0.619 ## 2 City Hotel 1 3263 0.0273 ## 3 City Hotel 2 2056 0.0172 ## 4 City Hotel 3 82 0.000687 ## 5 City Hotel 9 1 0.00000838 ## 6 City Hotel 10 1 0.00000838 ## 7 City Hotel NA 4 0.0000335 ## 8 Resort Hotel 0 36131 0.303 ## 9 Resort Hotel 1 2183 0.0183 ## 10 Resort Hotel 2 1716 0.0144 ## 11 Resort Hotel 3 29 0.000243 ## 12 Resort Hotel 10 1 0.00000838 ``` ] --- ## `summarise` for summary stats ```r # mean average daily rate for all bookings hotels %>% * summarise(mean_adr = mean(adr)) ``` ``` ## # A tibble: 1 x 1 ## mean_adr ## <dbl> ## 1 102. ``` -- .tip[ `summarise()` changes the data frame entirely, it collapses rows down to a single summary statistics, and removes all columns that are irrelevant to the calculation. ] --- .tip[ `summarise()` also lets you get away with being sloppy and not naming your new column, but that's not recommended! ] .midi[ .pull-left[ ❌ ```r hotels %>% summarise(mean(adr)) ``` ``` ## # A tibble: 1 x 1 ## `mean(adr)` ## <dbl> ## 1 102. ``` ] .pull-right[ ✅ ```r hotels %>% summarise(mean_adr = mean(adr)) ``` ``` ## # A tibble: 1 x 1 ## mean_adr ## <dbl> ## 1 102. ``` ] ] --- ## `group_by` for grouped operations ```r # mean average daily rate for all booking at city and resort hotels hotels %>% * group_by(hotel) %>% summarise(mean_adr = mean(adr)) ``` ``` ## # A tibble: 2 x 2 ## hotel mean_adr ## <chr> <dbl> ## 1 City Hotel 105. ## 2 Resort Hotel 95.0 ``` --- ## Calculating frequencies The following two give the same result, so `count` is simply short for `group_by` then determine frequencies .pull-left[ ```r hotels %>% group_by(hotel) %>% summarise(n = n()) ``` ``` ## # A tibble: 2 x 2 ## hotel n ## <chr> <int> ## 1 City Hotel 79330 ## 2 Resort Hotel 40060 ``` ] .pull-right[ ```r hotels %>% count(hotel) ``` ``` ## # A tibble: 2 x 2 ## hotel n ## <chr> <int> ## 1 City Hotel 79330 ## 2 Resort Hotel 40060 ``` ] --- ## Multiple summary statistics `summarise` can be used for multiple summary statistics as well ```r hotels %>% summarise( min_adr = min(adr), mean_adr = mean(adr), median_adr = median(adr), max_adr = max(adr) ) ``` ``` ## # A tibble: 1 x 4 ## min_adr mean_adr median_adr max_adr ## <dbl> <dbl> <dbl> <dbl> ## 1 -6.38 102. 94.6 5400 ``` --- class: middle, center ## Demo