class: center, middle, inverse, title-slide # Working with a single data frame ## <br><br> Introduction to Data Science with R and Tidyverse ### based on datasciencebox.org --- layout: true <div class="my-footer"> <span> Introduction to Data Science with R and Tidyverse | Lukas Jürgensmeier, Matteo Fina, Jan Bischoff | based on <a href="https://datasciencebox.org" target="_blank">datasciencebox.org</a> </span> </div> --- class: middle # We... .huge[.green[have]] a single data frame .huge[.pink[want]] to slice it, and dice it, and juice it, and process it --- ## Data: Hotel bookings - Data from two hotels: one resort and one city hotel - Observations: Each row represents a hotel booking ```r hotels <- read_csv("data/hotels.csv") ``` --- class: middle # `select`, `arrange`, and `slice` --- ## `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 ## # ... with 119,384 more rows ``` --- ## `select` to exclude variables .small[ ```r hotels %>% * select(-agent) ``` ``` ## # A tibble: 119,390 x 31 ## hotel is_ca~1 lead_~2 arriv~3 arriv~4 arriv~5 arriv~6 stays~7 ## <chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> ## 1 Resort~ 0 342 2015 July 27 1 0 ## 2 Resort~ 0 737 2015 July 27 1 0 ## 3 Resort~ 0 7 2015 July 27 1 0 ## 4 Resort~ 0 13 2015 July 27 1 0 ## 5 Resort~ 0 14 2015 July 27 1 0 ## 6 Resort~ 0 14 2015 July 27 1 0 ## # ... with 119,384 more rows, 23 more variables: ## # 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>, ... ``` ] --- ## `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_~1 ## <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 ## # ... with 119,384 more rows, and abbreviated variable name ## # 1: arrival_date_month ``` --- ## `select` variables with certain characteristics ```r hotels %>% * select(starts_with("arrival")) ``` ``` ## # A tibble: 119,390 x 4 ## arrival_date_year arrival_date_month arrival_date_wee~1 arriv~2 ## <dbl> <chr> <dbl> <dbl> ## 1 2015 July 27 1 ## 2 2015 July 27 1 ## 3 2015 July 27 1 ## 4 2015 July 27 1 ## 5 2015 July 27 1 ## 6 2015 July 27 1 ## # ... with 119,384 more rows, and abbreviated variable names ## # 1: arrival_date_week_number, 2: arrival_date_day_of_month ``` --- ## `select` variables with certain characteristics ```r hotels %>% * select(ends_with("type")) ``` ``` ## # A tibble: 119,390 x 4 ## reserved_room_type assigned_room_type deposit_type customer_t~1 ## <chr> <chr> <chr> <chr> ## 1 C C No Deposit Transient ## 2 C C No Deposit Transient ## 3 A C No Deposit Transient ## 4 A A No Deposit Transient ## 5 A A No Deposit Transient ## 6 A A No Deposit Transient ## # ... with 119,384 more rows, and abbreviated variable name ## # 1: customer_type ``` --- ## Select helpers - `starts_with()`: Starts with a prefix - `ends_with()`: Ends with a suffix - `contains()`: Contains a literal string - `num_range()`: Matches a numerical range like x01, x02, x03 - `one_of()`: Matches variable names in a character vector - `everything()`: Matches all variables - `last_col()`: Select last variable, possibly with an offset - `matches()`: Matches a regular expression (a sequence of symbols/characters expressing a string/pattern to be searched for within text) .footnote[ See help for any of these functions for more info, e.g. `?everything`. ] --- ## `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 ## # ... with 119,384 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 ## # ... with 119,384 more rows ``` ] --- ## `slice` for certain row numbers .midi[ ```r # first five hotels %>% * slice(1:5) ``` ``` ## # A tibble: 5 x 32 ## hotel is_ca~1 lead_~2 arriv~3 arriv~4 arriv~5 arriv~6 stays~7 ## <chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> ## 1 Resort~ 0 342 2015 July 27 1 0 ## 2 Resort~ 0 737 2015 July 27 1 0 ## 3 Resort~ 0 7 2015 July 27 1 0 ## 4 Resort~ 0 13 2015 July 27 1 0 ## 5 Resort~ 0 14 2015 July 27 1 0 ## # ... with 24 more variables: 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>, ... ``` ] --- .tip[ In R, you can use the `#` 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_ca~1 lead_~2 arriv~3 arriv~4 arriv~5 arriv~6 stays~7 ## <chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> ## 1 Resort~ 0 342 2015 July 27 1 0 ## 2 Resort~ 0 737 2015 July 27 1 0 ## 3 Resort~ 0 7 2015 July 27 1 0 ## 4 Resort~ 0 13 2015 July 27 1 0 ## 5 Resort~ 0 14 2015 July 27 1 0 ## # ... with 24 more variables: stays_in_week_nights <dbl>, ## # adults <dbl>, children <dbl>, babies <dbl>, meal <chr>, ... ``` ] --- class: middle # `filter` --- ## `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_ca~1 lead_~2 arriv~3 arriv~4 arriv~5 arriv~6 stays~7 ## <chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> ## 1 City H~ 0 6 2015 July 27 1 0 ## 2 City H~ 1 88 2015 July 27 1 0 ## 3 City H~ 1 65 2015 July 27 1 0 ## 4 City H~ 1 92 2015 July 27 1 2 ## 5 City H~ 1 100 2015 July 27 2 0 ## 6 City H~ 1 79 2015 July 27 2 0 ## # ... with 79,324 more rows, 24 more variables: ## # 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>, ... ``` ] --- ## `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 ## # ... with 217 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 ## # ... with 217 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` || | --- .your-turn[ ### Your turn! Time to actually play around with the Hotels dataset! - Go to Posit Cloud and start `application-exercise-04-hotels-data-wrangling`. - Open the R Markdown document `hotels-datawrangling.Rmd` and complete Exercises 1 - 4. ] --- class: middle # `distinct` and `count` --- ## `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 ... ``` ] ] --- ## `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 ``` ] ] --- .your-turn[ ### Your turn! - Go back to Posit Cloud and continue `application-exercise-04-hotels-data-wrangling`. - Open the R Markdown document `hotels-datawrangling.Rmd` and complete Exercises 5 and 6. ] --- class: middle # `mutate` --- ## `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 ## # ... with 119,384 more rows ``` --- ## Little ones in resort and city hotels .midi[ .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 ## # ... with 3,923 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: 5,403 x 2 ## hotel little_ones ## <chr> <dbl> ## 1 City Hotel 1 ## 2 City Hotel 1 ## 3 City Hotel 2 ## 4 City Hotel 1 ## 5 City Hotel 1 ## 6 City Hotel 1 ## # ... with 5,397 more rows ``` ] ] --- .question[ 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 ``` ] --- class: middle # `summarise` and `group_by` --- ## `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. ``` -- .pull-left-wide[ .tip[ `summarise()` changes the data frame entirely, it collapses rows down to a single summary statistic, 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! ] .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 ``` --- .your-turn[ ### Your turn! Time to actually play around with the Hotels dataset! - Go back to Posit Cloud and continue `application-exercise-04-hotels-data-wrangling`. - Open the R Markdown document `hotels-datawrangling.Rmd` and complete Exercises 7 and 8. ]