class: center, middle, inverse, title-slide # W3 Notes ### K Arnold --- <style>.remark-slide-number {display: none; }</style> ## Hotel Bookings Dataset See Application Exercise for details ```r hotels <- paste0( "https://raw.githubusercontent.com/", "rfordatascience/tidytuesday/", "master/data/2020/2020-02-11/hotels.csv" ) %>% read_csv() ``` --- ## Inline R code R Markdown input ```r The `hotels` dataset has data about `r nrow(hotels)` bookings. ``` .center[ 🔽 ] > The `hotels` dataset has data about 119390 bookings. --- ## `select` to keep variables ```r hotels %>% * select(hotel, lead_time) ``` --- ## `select` to exclude variables .small[ ```r hotels %>% * select(-agent) ``` ] --- ## `select` variables with certain characteristics ```r hotels %>% * select(starts_with("arrival")) ``` --- ## `arrange` in ascending / descending order .pull-left[ ```r hotels %>% select(adults, children, babies) %>% * arrange(babies) ``` ] .pull-right[ ```r hotels %>% select(adults, children, babies) %>% * arrange(desc(babies)) ``` ] --- ## `slice` for certain row numbers .midi[ ```r # first five hotels %>% * slice(1:5) ``` ] Alternative: ```r hotels %>% * slice_head(5) ``` --- ## Comments In R, as in Python, `#` can be used to comment a line to describe it or to (temporarily) disable it. (Don't leave commented-out code in your reports.) .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 ``` ] --- ## `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) ``` ] (but `slice_tail(5)` would be easier.) --- ## `filter` to select a subset of rows .midi[ ```r # bookings in City Hotels hotels %>% * filter(hotel == "City Hotel") ``` ] --- ## `filter` for many conditions at once ```r hotels %>% filter( * adults == 0, * children >= 1 ) %>% select(adults, babies, children) ``` --- ## `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) ``` --- ## 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` || | --- ## `mutate` to add a new variable ```r hotels %>% * mutate(kids = children + babies) %>% select(children, babies, kids) %>% arrange(desc(kids)) ``` --- ## Kids in resort and city hotels .midi[ .pull-left[ ```r # Resort Hotel hotels %>% mutate(kids = children + babies) %>% filter( kids >= 1, hotel == "Resort Hotel" ) %>% select(hotel, kids) ``` ] .pull-right[ ```r # City Hotel hotels %>% mutate(kids = children + babies) %>% filter( kids >= 1, hotel == "City Hotel" ) %>% select(hotel, kids) ``` ] ] --- .question[ What is happening in the following chunk? ] .midi[ ```r hotels %>% mutate(kids = children + babies) %>% count(hotel, kids) %>% mutate(prop = n / sum(n)) ``` ``` ## # A tibble: 12 x 4 ## hotel kids 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)) ``` -- <br> .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[ ❌ ```r hotels %>% summarise(mean(adr)) ``` ✅ ```r hotels %>% summarise(mean_adr = mean(adr)) ``` ] --- # `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)) ``` --- ## 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()) ``` ] .pull-right[ ```r hotels %>% count(hotel) ``` ] --- # 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) ) ```