class: center, middle, inverse, title-slide .title[ # Fundamentals of R: Data Manipulation ] .subtitle[ ## <br><br> STA35A: Statistical Data Science 1 ] .author[ ### Xiao Hui Tai ] .date[ ### October 9, 2024 ] --- layout: true <!-- <div class="my-footer"> --> <!-- <span> --> <!-- <a href="https://datasciencebox.org" target="_blank">datasciencebox.org</a> --> <!-- </span> --> <!-- </div> --> --- <style type="text/css"> .tiny .remark-code { font-size: 50%; } .small .remark-code { font-size: 80%; } </style> ## Reminders/Announcements - HW 1 is due tomorrow at 9pm - HW 2 will be posted on Friday afternoon on the course website - Schedule for next week (week 4): - Monday: regular lecture; lab due 9 PM - Wednesday: Oscar Rivera will do review during regular lecture time (same room) - Thursday: **no lab**, instead 12-1PM OH (XHT; virtual, will post link on Piazza); 3-4 PM OH (OR); HW 2 due - Friday: midterm during regular lecture time (same room); **no homework**; 1-2 PM OH (OR) --- ## Midterm - Midterm will cover material until Monday, Oct 14 - Closed-book - You don't need your computers - No make-up exams - Drop policy for exams: 1 midterm may be dropped --- ## Today - Data manipulation tools --- ## 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 <- readr::read_csv("data/hotels.csv") ``` .footnote[ Source: [TidyTuesday](https://github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-02-11/readme.md) ] --- ## First question: What is in the data set? .tiny[ ``` r dplyr::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", "GB… ## $ market_segment <chr> "Direct", "Direct", "Dir… ## $ distribution_channel <chr> "Direct", "Direct", "Dir… ## $ is_repeated_guest <dbl> 0, 0, 0, 0, 0, 0, 0, 0, … ## $ previous_cancellations <dbl> 0, 0, 0, 0, 0, 0, 0, 0, … ## $ previous_bookings_not_canceled <dbl> 0, 0, 0, 0, 0, 0, 0, 0, … ## $ reserved_room_type <chr> "C", "C", "A", "A", "A",… ## $ assigned_room_type <chr> "C", "C", "C", "A", "A",… ## $ booking_changes <dbl> 3, 4, 0, 0, 0, 0, 0, 0, … ## $ deposit_type <chr> "No Deposit", "No Deposi… ## $ agent <chr> "NULL", "NULL", "NULL", … ## $ company <chr> "NULL", "NULL", "NULL", … ## $ days_in_waiting_list <dbl> 0, 0, 0, 0, 0, 0, 0, 0, … ## $ customer_type <chr> "Transient", "Transient"… ## $ adr <dbl> 0.00, 0.00, 75.00, 75.00… ## $ required_car_parking_spaces <dbl> 0, 0, 0, 0, 0, 0, 0, 0, … ## $ total_of_special_requests <dbl> 0, 0, 0, 0, 1, 1, 0, 1, … ## $ reservation_status <chr> "Check-Out", "Check-Out"… ## $ reservation_status_date <date> 2015-07-01, 2015-07-01,… ``` ] --- ## `select()`: 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 × 1 ## lead_time ## <dbl> ## 1 342 ## 2 737 ## 3 7 ## 4 13 ## 5 14 ## 6 14 ## # ℹ 119,384 more rows ``` - **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 - This is an alternative to `hotels$lead_time` --- ## Select multiple columns View only the `hotel` type and `lead_time` columns: ``` r select(hotels, hotel, lead_time) ``` ``` ## # A tibble: 119,390 × 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 ## # ℹ 119,384 more rows ``` --- ## `select()` to exclude variables - We saw earlier that `select()` keeps variables - `select()` can also exclude variables, using the `-` sign .small[ ``` r hotels %>% select(-agent) ``` ``` ## # A tibble: 119,390 × 31 ## hotel is_canceled lead_time arrival_date_year ## <chr> <dbl> <dbl> <dbl> ## 1 Resort Hotel 0 342 2015 ## 2 Resort Hotel 0 737 2015 ## 3 Resort Hotel 0 7 2015 ## 4 Resort Hotel 0 13 2015 ## 5 Resort Hotel 0 14 2015 ## 6 Resort Hotel 0 14 2015 ## # ℹ 119,384 more rows ## # ℹ 27 more variables: arrival_date_month <chr>, ## # 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>, … ``` ] --- ## `select()` a range of variables - Instead of writing out all the variable names, `select()` also accepts a **range of variables** - This follows the order they are listed in the data frame ``` r hotels %>% select(hotel:arrival_date_month) ``` ``` ## # A tibble: 119,390 × 5 ## hotel is_canceled lead_time arrival_date_year ## <chr> <dbl> <dbl> <dbl> ## 1 Resort Hotel 0 342 2015 ## 2 Resort Hotel 0 737 2015 ## 3 Resort Hotel 0 7 2015 ## 4 Resort Hotel 0 13 2015 ## 5 Resort Hotel 0 14 2015 ## 6 Resort Hotel 0 14 2015 ## # ℹ 119,384 more rows ## # ℹ 1 more variable: arrival_date_month <chr> ``` --- ## `select()` variables with certain characteristics ``` r hotels %>% select(starts_with("arrival")) ``` ``` ## # A tibble: 119,390 × 4 ## arrival_date_year arrival_date_month arrival_date_week_number ## <dbl> <chr> <dbl> ## 1 2015 July 27 ## 2 2015 July 27 ## 3 2015 July 27 ## 4 2015 July 27 ## 5 2015 July 27 ## 6 2015 July 27 ## # ℹ 119,384 more rows ## # ℹ 1 more variable: arrival_date_day_of_month <dbl> ``` --- ## `select()` variables with certain characteristics ``` r hotels %>% select(ends_with("type")) ``` ``` ## # A tibble: 119,390 × 4 ## reserved_room_type assigned_room_type deposit_type ## <chr> <chr> <chr> ## 1 C C No Deposit ## 2 C C No Deposit ## 3 A C No Deposit ## 4 A A No Deposit ## 5 A A No Deposit ## 6 A A No Deposit ## # ℹ 119,384 more rows ## # ℹ 1 more variable: customer_type <chr> ``` --- ## 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`. ] --- ## `select()`, then `arrange()` What if we wanted to select these columns, and then arrange the data in order of lead time? ``` r hotels %>% select(hotel, lead_time) %>% arrange(lead_time) ``` ``` ## # A tibble: 119,390 × 2 ## hotel lead_time ## <chr> <dbl> ## 1 Resort Hotel 0 ## 2 Resort Hotel 0 ## 3 Resort Hotel 0 ## 4 Resort Hotel 0 ## 5 Resort Hotel 0 ## 6 Resort Hotel 0 ## # ℹ 119,384 more rows ``` --- ## Pipes In programming, a pipe is a technique for **passing information from one process to another**. In R, the symbol is `%>%`. Also: `|>`. .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(lead_time) ``` ``` ## # A tibble: 119,390 × 2 ## hotel lead_time ## <chr> <dbl> ## 1 Resort Hotel 0 ## 2 Resort Hotel 0 ## 3 Resort Hotel 0 ## 4 Resort Hotel 0 ## 5 Resort Hotel 0 ## 6 Resort Hotel 0 ## # ℹ 119,384 more rows ``` ] ] --- ## Pipes In programming, a pipe is a technique for **passing information from one process to another**. In R, the symbol is `%>%`. Also: `|>`. .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(lead_time) ``` ``` ## # A tibble: 119,390 × 2 ## hotel lead_time ## <chr> <dbl> ## 1 Resort Hotel 0 ## 2 Resort Hotel 0 ## 3 Resort Hotel 0 ## 4 Resort Hotel 0 ## 5 Resort Hotel 0 ## 6 Resort Hotel 0 ## # ℹ 119,384 more rows ``` ] ] --- ## Pipes In programming, a pipe is a technique for **passing information from one process to another**. In R, the symbol is `%>%`. Also: `|>`. .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`. ] .pull-right[ .small[ ``` r hotels %>% select(hotel, lead_time) %>% arrange(lead_time) #<< ``` ``` ## # A tibble: 119,390 × 2 ## hotel lead_time ## <chr> <dbl> ## 1 Resort Hotel 0 ## 2 Resort Hotel 0 ## 3 Resort Hotel 0 ## 4 Resort Hotel 0 ## 5 Resort Hotel 0 ## 6 Resort Hotel 0 ## # ℹ 119,384 more rows ``` ] ] Note that the pipe operator is implemented in the package `magrittr`, but is automatically loaded when we use `library(dplyr)` or `library(tidyverse)`. --- ## 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: ``` r find("keys") %>% start_car() %>% drive(to = "work") %>% park() ``` --- ## Simple example - We can write `exp(1)` with pipes as `1 %>% exp`, and `log(exp(1))` as `1 %>% exp %>% log` ``` r exp(1) ``` ``` ## [1] 2.718282 ``` ``` r 1 %>% exp ``` ``` ## [1] 2.718282 ``` ``` r 1 %>% exp %>% log ``` ``` ## [1] 1 ``` - Tidyverse functions are at their best when composed together using the pipe operator --- ## `arrange()` in ascending or descending order - We saw earlier that `arrange()` defaults to ascending order - For descending order, use `desc()` .pull-left[ ``` r hotels %>% select(hotel, lead_time) %>% arrange(lead_time) ``` ``` ## # A tibble: 119,390 × 2 ## hotel lead_time ## <chr> <dbl> ## 1 Resort Hotel 0 ## 2 Resort Hotel 0 ## 3 Resort Hotel 0 ## 4 Resort Hotel 0 ## 5 Resort Hotel 0 ## 6 Resort Hotel 0 ## # ℹ 119,384 more rows ``` ] .pull-right[ ``` r hotels %>% select(hotel, lead_time) %>% arrange(desc(lead_time)) ``` ``` ## # A tibble: 119,390 × 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 ## # ℹ 119,384 more rows ``` ] --- ## `slice()` for certain row numbers This is an alternative indexing option for `hotels[1:5, ]` ``` r hotels %>% slice(1:5) ``` ``` ## # A tibble: 5 × 32 ## hotel is_canceled lead_time arrival_date_year ## <chr> <dbl> <dbl> <dbl> ## 1 Resort Hotel 0 342 2015 ## 2 Resort Hotel 0 737 2015 ## 3 Resort Hotel 0 7 2015 ## 4 Resort Hotel 0 13 2015 ## 5 Resort Hotel 0 14 2015 ## # ℹ 28 more variables: arrival_date_month <chr>, ## # 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>, … ``` --- ## Reminder: comments in R - Any text following `#` will be printed as is, and won't be run as code - This is useful for leaving comments and for temporarily disabling certain lines of code (for debugging, trying out different things) .tiny[ ``` 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 × 32 ## hotel is_canceled lead_time arrival_date_year ## <chr> <dbl> <dbl> <dbl> ## 1 Resort Hotel 0 342 2015 ## 2 Resort Hotel 0 737 2015 ## 3 Resort Hotel 0 7 2015 ## 4 Resort Hotel 0 13 2015 ## 5 Resort Hotel 0 14 2015 ## # ℹ 28 more variables: arrival_date_month <chr>, ## # 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>, … ``` ] --- ## `filter()` to select a subset of rows .small[ ``` r # bookings in City Hotels hotels %>% filter(hotel == "City Hotel") ``` ``` ## # A tibble: 79,330 × 32 ## hotel is_canceled lead_time arrival_date_year ## <chr> <dbl> <dbl> <dbl> ## 1 City Hotel 0 6 2015 ## 2 City Hotel 1 88 2015 ## 3 City Hotel 1 65 2015 ## 4 City Hotel 1 92 2015 ## 5 City Hotel 1 100 2015 ## 6 City Hotel 1 79 2015 ## # ℹ 79,324 more rows ## # ℹ 28 more variables: arrival_date_month <chr>, ## # 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>, … ``` ] <small>What was the base R alternative that we saw?</small> --- ## `filter()` for many conditions at once ``` r hotels %>% filter( adults == 0, children >= 1 ) %>% select(adults, babies, children) ``` ``` ## # A tibble: 223 × 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 ## # ℹ 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 ) %>% select(adults, babies, children) ``` ``` ## # A tibble: 223 × 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 ## # ℹ 217 more rows ``` --- ## Reminder: 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` || | --- ## `distinct()` to filter for unique rows .small[ .pull-left[ ``` r hotels %>% distinct(market_segment) ``` ``` ## # A tibble: 8 × 1 ## market_segment ## <chr> ## 1 Direct ## 2 Corporate ## 3 Online TA ## 4 Offline TA/TO ## 5 Complementary ## 6 Groups ## # ℹ 2 more rows ``` ] .pull-left[ Recall: `arrange()` to order alphabetically ``` r hotels %>% distinct(market_segment) %>% arrange(market_segment) ``` ``` ## # A tibble: 8 × 1 ## market_segment ## <chr> ## 1 Aviation ## 2 Complementary ## 3 Corporate ## 4 Direct ## 5 Groups ## 6 Offline TA/TO ## # ℹ 2 more rows ``` ] ] --- ## `distinct()` using more than one variable ``` r hotels %>% distinct(hotel, market_segment) %>% #<< arrange(hotel, market_segment) ``` ``` ## # A tibble: 14 × 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 ## # ℹ 8 more rows ``` --- ## `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 × 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 ## # ℹ 119,384 more rows ``` <small>What are these functions doing? How do to the same in base R?</small> --- ## `count()` to create frequency tables .pull-left[ ``` r # alphabetical order by default hotels %>% count(market_segment) #<< ``` ``` ## # A tibble: 8 × 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 ## # ℹ 2 more rows ``` ] .pull-right[ ``` r # descending frequency order hotels %>% count(market_segment, sort = TRUE) #<< ``` ``` ## # A tibble: 8 × 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 ## # ℹ 2 more rows ``` ] - Base R version: `table()` --- ## `count()` and `arrange()` .pull-left[ ``` r # ascending frequency order hotels %>% count(market_segment) %>% arrange(n) #<< ``` ``` ## # A tibble: 8 × 2 ## market_segment n ## <chr> <int> ## 1 Undefined 2 ## 2 Aviation 237 ## 3 Complementary 743 ## 4 Corporate 5295 ## 5 Direct 12606 ## 6 Groups 19811 ## # ℹ 2 more rows ``` ] .pull-right[ ``` r # descending frequency order # just like adding sort = TRUE hotels %>% count(market_segment) %>% arrange(desc(n)) #<< ``` ``` ## # A tibble: 8 × 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 ## # ℹ 2 more rows ``` ] --- ## `count()` for multiple variables ``` r hotels %>% count(hotel, market_segment) ``` ``` ## # A tibble: 14 × 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 ## # ℹ 8 more rows ``` --- ## `summarize()` for summary stats ``` r # mean average daily rate for all bookings hotels %>% summarize(mean_adr = mean(adr)) ``` ``` ## # A tibble: 1 × 1 ## mean_adr ## <dbl> ## 1 102. ``` - `summarize()` **changes the data frame** entirely - **Rows are collapsed** into a single summary statistic - **Columns that are irrelevant** to the calculation are **removed** --- ## `summarize()` is often used with `group_by()` - For **grouped operations** - There are **two types** of `hotel`, city and resort hotels - We want the mean daily rate for bookings at **city vs. resort** hotels ``` r hotels %>% group_by(hotel) %>% summarize(mean_adr = mean(adr)) ``` ``` ## # A tibble: 2 × 2 ## hotel mean_adr ## <chr> <dbl> ## 1 City Hotel 105. ## 2 Resort Hotel 95.0 ``` - `group_by()` can be used with **more than one group** --- ## Multiple summary statistics `summarize` can be used for multiple summary statistics as well ``` r hotels %>% summarize( n = n(), # frequencies min_adr = min(adr), mean_adr = mean(adr), median_adr = median(adr), max_adr = max(adr) ) ``` ``` ## # A tibble: 1 × 5 ## n min_adr mean_adr median_adr max_adr ## <int> <dbl> <dbl> <dbl> <dbl> ## 1 119390 -6.38 102. 94.6 5400 ``` --- ## Data manipulation using `dplyr` .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 - `summarize`: reduce variables to values - `group_by`: for grouped operations - ... (many more) ] ] --- ## Exercise: NYC Flights data This data frame contains data on all 336,776 flights that departed from New York City in 2013. It is available as part of the `nycflights13` package. ``` r nycflights13::flights ``` ``` ## # A tibble: 336,776 × 19 ## year month day dep_time sched_dep_time dep_delay arr_time ## <int> <int> <int> <int> <int> <dbl> <int> ## 1 2013 1 1 517 515 2 830 ## 2 2013 1 1 533 529 4 850 ## 3 2013 1 1 542 540 2 923 ## 4 2013 1 1 544 545 -1 1004 ## 5 2013 1 1 554 600 -6 812 ## 6 2013 1 1 554 558 -4 740 ## # ℹ 336,770 more rows ## # ℹ 12 more variables: sched_arr_time <int>, arr_delay <dbl>, ## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, ## # dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, ## # minute <dbl>, time_hour <dttm> ``` --- ## Exercise: NYC Flights data Select the `carrier` column. Select the `carrier` and `tailnum` columns. Sort the data by origin. Filter only flights with carrier `OO`. Filter only flights with carrier `OO`, originating in `LGA`. Create a new variable that indicates whether or not the flight departed late. Create a new variable for the mean departure delay by day. Repeat all the operations using base R. --- ## Summary -- - Data manipulation tools - `select()`: selects columns by name - `arrange()`: reorders rows - `slice()`: selects rows using index(es) - `filter()`: selects rows matching criteria - `distinct()`: filter for unique rows - `mutate()`: adds new variables - `summarize()`: reduces variables to values - `group_by()`: for grouped operations