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--- title : "Data Wrangling 1 " ---

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## Switching windows * `ctrl-1`: script window * `ctrl-2`: console window > Try to run today's script without using your mouse/trackpad # Data wrangling ## Useful packages: [`tidyverse`](https://www.tidyverse.org/packages/) [Cheat sheets on website](https://www.rstudio.com/resources/cheatsheets/) for [Data Wrangling](https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf) ```r library(tidyverse) ``` Remember use `install.packages("tidyverse")` to install a new package. ### Example operations from [here](https://cran.rstudio.com/web/packages/dplyr/vignettes/introduction.html) ## New York City Flights Data from [US Bureau of Transportation Statistics](http://www.transtats.bts.gov/DatabaseInfo.asp?DB_ID=120&Link=0) (see `?nycflights13`) ```r library(nycflights13) ``` Check out the `flights` object ```r head(flights) ``` ``` ## # A tibble: 6 x 19 ## year month day dep_time sched_dep_time dep_delay arr_time ## ## 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 ## # ... with 12 more variables: sched_arr_time , arr_delay , ## # carrier , flight , tailnum , origin , dest , ## # air_time , distance , hour , minute , ## # time_hour ``` ### Object _Structure_ Check out data _structure_ with `glimpse()` ```r glimpse(flights) ``` ``` ## Observations: 336,776 ## Variables: 19 ## $ year 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013,... ## $ month 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,... ## $ day 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,... ## $ dep_time 517, 533, 542, 544, 554, 554, 555, 557, 557, 55... ## $ sched_dep_time 515, 529, 540, 545, 600, 558, 600, 600, 600, 60... ## $ dep_delay 2, 4, 2, -1, -6, -4, -5, -3, -3, -2, -2, -2, -2... ## $ arr_time 830, 850, 923, 1004, 812, 740, 913, 709, 838, 7... ## $ sched_arr_time 819, 830, 850, 1022, 837, 728, 854, 723, 846, 7... ## $ arr_delay 11, 20, 33, -18, -25, 12, 19, -14, -8, 8, -2, -... ## $ carrier "UA", "UA", "AA", "B6", "DL", "UA", "B6", "EV",... ## $ flight 1545, 1714, 1141, 725, 461, 1696, 507, 5708, 79... ## $ tailnum "N14228", "N24211", "N619AA", "N804JB", "N668DN... ## $ origin "EWR", "LGA", "JFK", "JFK", "LGA", "EWR", "EWR"... ## $ dest "IAH", "IAH", "MIA", "BQN", "ATL", "ORD", "FLL"... ## $ air_time 227, 227, 160, 183, 116, 150, 158, 53, 140, 138... ## $ distance 1400, 1416, 1089, 1576, 762, 719, 1065, 229, 94... ## $ hour 5, 5, 5, 5, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5,... ## $ minute 15, 29, 40, 45, 0, 58, 0, 0, 0, 0, 0, 0, 0, 0, ... ## $ time_hour 2013-01-01 05:00:00, 2013-01-01 05:00:00, 2013... ``` # `dplyr` "verbs" * `select()` and `rename()`: Extract existing variables * `filter()` and `slice()`: Extract existing observations * `arrange()` * `distinct()` * `mutate()` and `transmute()`: Derive new variables * `summarise()`: Change the unit of analysis * `sample_n()` and `sample_frac()` ## Useful select functions * "`-`" Select everything but * "`:`" Select range * `contains()` Select columns whose name contains a character string * `ends_with()` Select columns whose name ends with a string * `everything()` Select every column * `matches()` Select columns whose name matches a regular expression * `num_range()` Select columns named x1, x2, x3, x4, x5 * `one_of()` Select columns whose names are in a group of names * `starts_with()` Select columns whose name starts with a character string ### `select()` examples Select only the `year`, `month`, and `day` columns: ```r select(flights,year, month, day) ``` ``` ## # A tibble: 336,776 x 3 ## year month day ## ## 1 2013 1 1 ## 2 2013 1 1 ## 3 2013 1 1 ## 4 2013 1 1 ## 5 2013 1 1 ## 6 2013 1 1 ## 7 2013 1 1 ## 8 2013 1 1 ## 9 2013 1 1 ## 10 2013 1 1 ## # ... with 336,766 more rows ``` ### `select()` examples Select everything _except_ the `tailnum`: ```r select(flights,-tailnum) ``` ``` ## # A tibble: 336,776 x 18 ## year month day dep_time sched_dep_time dep_delay arr_time ## ## 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 ## 7 2013 1 1 555 600 -5 913 ## 8 2013 1 1 557 600 -3 709 ## 9 2013 1 1 557 600 -3 838 ## 10 2013 1 1 558 600 -2 753 ## # ... with 336,766 more rows, and 11 more variables: sched_arr_time , ## # arr_delay , carrier , flight , origin , ## # dest , air_time , distance , hour , minute , ## # time_hour ``` Select all columns containing the string `"time"`: ```r select(flights,contains("time")) ``` ``` ## # A tibble: 336,776 x 6 ## dep_time sched_dep_time arr_time sched_arr_time air_time ## ## 1 517 515 830 819 227 ## 2 533 529 850 830 227 ## 3 542 540 923 850 160 ## 4 544 545 1004 1022 183 ## 5 554 600 812 837 116 ## 6 554 558 740 728 150 ## 7 555 600 913 854 158 ## 8 557 600 709 723 53 ## 9 557 600 838 846 140 ## 10 558 600 753 745 138 ## # ... with 336,766 more rows, and 1 more variable: time_hour ``` You can also rename columns with `select()` ```r select(flights,year,carrier,destination=dest) ``` ``` ## # A tibble: 336,776 x 3 ## year carrier destination ## ## 1 2013 UA IAH ## 2 2013 UA IAH ## 3 2013 AA MIA ## 4 2013 B6 BQN ## 5 2013 DL ATL ## 6 2013 UA ORD ## 7 2013 B6 FLL ## 8 2013 EV IAD ## 9 2013 B6 MCO ## 10 2013 AA ORD ## # ... with 336,766 more rows ``` ## `filter()` observations Filter all flights that departed on on January 1st: ```r filter(flights, month == 1, day == 1) ``` ``` ## # A tibble: 842 x 19 ## year month day dep_time sched_dep_time dep_delay arr_time ## ## 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 ## 7 2013 1 1 555 600 -5 913 ## 8 2013 1 1 557 600 -3 709 ## 9 2013 1 1 557 600 -3 838 ## 10 2013 1 1 558 600 -2 753 ## # ... with 832 more rows, and 12 more variables: sched_arr_time , ## # arr_delay , carrier , flight , tailnum , ## # origin , dest , air_time , distance , hour , ## # minute , time_hour ``` ## _Base_ R method This is equivalent to the more verbose code in base R: ```r flights[flights$month == 1 & flights$day == 1, ] ``` ``` ## # A tibble: 842 x 19 ## year month day dep_time sched_dep_time dep_delay arr_time ## ## 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 ## 7 2013 1 1 555 600 -5 913 ## 8 2013 1 1 557 600 -3 709 ## 9 2013 1 1 557 600 -3 838 ## 10 2013 1 1 558 600 -2 753 ## # ... with 832 more rows, and 12 more variables: sched_arr_time , ## # arr_delay , carrier , flight , tailnum , ## # origin , dest , air_time , distance , hour , ## # minute , time_hour ``` Compare with `dplyr` method: ```r filter(flights, month == 1, day == 1) ```

Filter the `flights` data set to keep only evening flights (`dep_time` after 1600) in June.

```r filter(flights,dep_time>1600,month==6) ``` ``` ## # A tibble: 10,117 x 19 ## year month day dep_time sched_dep_time dep_delay arr_time ## ## 1 2013 6 1 1602 1505 57 1721 ## 2 2013 6 1 1602 1605 -3 1824 ## 3 2013 6 1 1602 1610 -8 1748 ## 4 2013 6 1 1603 1610 -7 1839 ## 5 2013 6 1 1603 1545 18 1726 ## 6 2013 6 1 1605 1608 -3 1742 ## 7 2013 6 1 1605 1600 5 1801 ## 8 2013 6 1 1605 1614 -9 1801 ## 9 2013 6 1 1608 1600 8 1807 ## 10 2013 6 1 1609 1615 -6 1817 ## # ... with 10,107 more rows, and 12 more variables: sched_arr_time , ## # arr_delay , carrier , flight , tailnum , ## # origin , dest , air_time , distance , hour , ## # minute , time_hour ```

## Other _boolean_ expressions `filter()` is similar to `subset()` except it handles any number of filtering conditions joined together with `&`. You can also use other boolean operators, such as _OR_ ("|"): ```r filter(flights, month == 1 | month == 2) ``` ``` ## # A tibble: 51,955 x 19 ## year month day dep_time sched_dep_time dep_delay arr_time ## ## 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 ## 7 2013 1 1 555 600 -5 913 ## 8 2013 1 1 557 600 -3 709 ## 9 2013 1 1 557 600 -3 838 ## 10 2013 1 1 558 600 -2 753 ## # ... with 51,945 more rows, and 12 more variables: sched_arr_time , ## # arr_delay , carrier , flight , tailnum , ## # origin , dest , air_time , distance , hour , ## # minute , time_hour ```

Filter the `flights` data set to keep only flights where the `distance` is greater than 1000 OR the `air_time` is more than 100

```r filter(flights,distance>1000|air_time>100) ``` ``` ## # A tibble: 222,479 x 19 ## year month day dep_time sched_dep_time dep_delay arr_time ## ## 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 ## 7 2013 1 1 555 600 -5 913 ## 8 2013 1 1 557 600 -3 838 ## 9 2013 1 1 558 600 -2 753 ## 10 2013 1 1 558 600 -2 849 ## # ... with 222,469 more rows, and 12 more variables: sched_arr_time , ## # arr_delay , carrier , flight , tailnum , ## # origin , dest , air_time , distance , hour , ## # minute , time_hour ```

## Select rows with `slice()`: ```r slice(flights, 1:10) ``` ``` ## # A tibble: 10 x 19 ## year month day dep_time sched_dep_time dep_delay arr_time ## ## 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 ## 7 2013 1 1 555 600 -5 913 ## 8 2013 1 1 557 600 -3 709 ## 9 2013 1 1 557 600 -3 838 ## 10 2013 1 1 558 600 -2 753 ## # ... with 12 more variables: sched_arr_time , arr_delay , ## # carrier , flight , tailnum , origin , dest , ## # air_time , distance , hour , minute , ## # time_hour ``` ## `arrange()` rows `arrange()` is similar to `filter()` except it reorders instead of filtering. ```r arrange(flights, year, month, day) ``` ``` ## # A tibble: 336,776 x 19 ## year month day dep_time sched_dep_time dep_delay arr_time ## ## 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 ## 7 2013 1 1 555 600 -5 913 ## 8 2013 1 1 557 600 -3 709 ## 9 2013 1 1 557 600 -3 838 ## 10 2013 1 1 558 600 -2 753 ## # ... with 336,766 more rows, and 12 more variables: sched_arr_time , ## # arr_delay , carrier , flight , tailnum , ## # origin , dest , air_time , distance , hour , ## # minute , time_hour ``` _Base_ R method: ```r flights[order(flights$year, flights$month, flights$day), ] ``` ## Descending order: `desc()` ```r arrange(flights, desc(arr_delay)) ``` ``` ## # A tibble: 336,776 x 19 ## year month day dep_time sched_dep_time dep_delay arr_time ## ## 1 2013 1 9 641 900 1301 1242 ## 2 2013 6 15 1432 1935 1137 1607 ## 3 2013 1 10 1121 1635 1126 1239 ## 4 2013 9 20 1139 1845 1014 1457 ## 5 2013 7 22 845 1600 1005 1044 ## 6 2013 4 10 1100 1900 960 1342 ## 7 2013 3 17 2321 810 911 135 ## 8 2013 7 22 2257 759 898 121 ## 9 2013 12 5 756 1700 896 1058 ## 10 2013 5 3 1133 2055 878 1250 ## # ... with 336,766 more rows, and 12 more variables: sched_arr_time , ## # arr_delay , carrier , flight , tailnum , ## # origin , dest , air_time , distance , hour , ## # minute , time_hour ``` _Base_ R method: ```r flights[order(desc(flights$arr_delay)), ] ``` ## Distinct: Find distinct rows ```r distinct( select(flights,carrier) ) ``` ``` ## # A tibble: 16 x 1 ## carrier ## ## 1 UA ## 2 AA ## 3 B6 ## 4 DL ## 5 EV ## 6 MQ ## 7 US ## 8 WN ## 9 VX ## 10 FL ## 11 AS ## 12 9E ## 13 F9 ## 14 HA ## 15 YV ## 16 OO ``` ## Mutate: Derive new variables Adds columns with calculations based on other columns. Average air speed (miles/hour): ```r mutate(flights,ave_speed=distance/(air_time/60))%>% select(distance, air_time,ave_speed) ``` ``` ## # A tibble: 336,776 x 3 ## distance air_time ave_speed ## ## 1 1400 227 370. ## 2 1416 227 374. ## 3 1089 160 408. ## 4 1576 183 517. ## 5 762 116 394. ## 6 719 150 288. ## 7 1065 158 404. ## 8 229 53 259. ## 9 944 140 405. ## 10 733 138 319. ## # ... with 336,766 more rows ``` ## Chaining Operations Learn to performing multiple operations sequentially with a _pipe_ character (`%>%`) 1. Group by a variable 2. Select some columns 3. Summarize observations 4. Filter by results With temporary objects: ```r a1 <- group_by(flights, year, month, day) a2 <- select(a1, arr_delay, dep_delay) ``` ``` ## Adding missing grouping variables: `year`, `month`, `day` ``` ```r a3 <- summarise(a2, arr = mean(arr_delay, na.rm = TRUE), dep = mean(dep_delay, na.rm = TRUE)) a4 <- filter(a3, arr > 30 | dep > 30) head(a4) ``` ``` ## # A tibble: 6 x 5 ## # Groups: year, month [3] ## year month day arr dep ## ## 1 2013 1 16 34.2 24.6 ## 2 2013 1 31 32.6 28.7 ## 3 2013 2 11 36.3 39.1 ## 4 2013 2 27 31.3 37.8 ## 5 2013 3 8 85.9 83.5 ## 6 2013 3 18 41.3 30.1 ``` If you don’t want to save the intermediate results: wrap the function calls inside each other: ```r filter( summarise( select( group_by(flights, year, month, day), arr_delay, dep_delay ), arr = mean(arr_delay, na.rm = TRUE), dep = mean(dep_delay, na.rm = TRUE) ), arr > 30 | dep > 30 ) ``` ``` ## Adding missing grouping variables: `year`, `month`, `day` ``` ``` ## # A tibble: 49 x 5 ## # Groups: year, month [11] ## year month day arr dep ## ## 1 2013 1 16 34.2 24.6 ## 2 2013 1 31 32.6 28.7 ## 3 2013 2 11 36.3 39.1 ## 4 2013 2 27 31.3 37.8 ## 5 2013 3 8 85.9 83.5 ## 6 2013 3 18 41.3 30.1 ## 7 2013 4 10 38.4 33.0 ## 8 2013 4 12 36.0 34.8 ## 9 2013 4 18 36.0 34.9 ## 10 2013 4 19 47.9 46.1 ## 11 2013 4 22 37.8 30.6 ## 12 2013 4 25 33.7 23.3 ## 13 2013 5 8 39.6 43.2 ## 14 2013 5 23 62.0 51.1 ## 15 2013 5 24 24.3 30.3 ## 16 2013 6 2 26.1 34.0 ## 17 2013 6 10 28.0 30.6 ## 18 2013 6 13 63.8 45.8 ## 19 2013 6 18 37.6 36.0 ## 20 2013 6 24 51.2 47.2 ## 21 2013 6 25 41.5 43.1 ## 22 2013 6 26 27.3 30.6 ## 23 2013 6 27 44.8 40.9 ## 24 2013 6 28 45.0 48.8 ## 25 2013 6 30 43.5 44.2 ## 26 2013 7 1 58.3 56.2 ## 27 2013 7 7 40.3 36.6 ## 28 2013 7 8 29.6 37.3 ## 29 2013 7 9 31.3 30.7 ## 30 2013 7 10 59.6 52.9 ## 31 2013 7 22 62.8 46.7 ## 32 2013 7 23 45.0 44.7 ## 33 2013 7 28 49.8 37.7 ## 34 2013 8 1 36.0 34.6 ## 35 2013 8 8 55.5 43.3 ## 36 2013 8 9 43.3 34.7 ## 37 2013 8 22 30.0 33.6 ## 38 2013 8 28 35.2 40.5 ## 39 2013 9 2 45.5 53.0 ## 40 2013 9 12 58.9 50.0 ## 41 2013 10 7 39.0 39.1 ## 42 2013 10 11 18.9 31.2 ## 43 2013 12 5 51.7 52.3 ## 44 2013 12 8 36.9 21.5 ## 45 2013 12 9 42.6 34.8 ## 46 2013 12 10 44.5 26.5 ## 47 2013 12 14 46.4 28.4 ## 48 2013 12 17 55.9 40.7 ## 49 2013 12 23 32.2 32.3 ``` Arguments are distant from function -> difficult to read! ## Chaining Operations `%>%` (from the dplyr package) allows you to _pipe_ together various commands. `x %>% f(y)` turns into `f(x, y)` So you can use it to rewrite multiple operations that you can read left-to-right, top-to-bottom: ```r flights %>% group_by(year, month, day) %>% select(arr_delay, dep_delay) %>% summarise( arr = mean(arr_delay, na.rm = TRUE), dep = mean(dep_delay, na.rm = TRUE) ) %>% filter(arr > 30 | dep > 30) ``` ``` ## Adding missing grouping variables: `year`, `month`, `day` ``` ``` ## # A tibble: 49 x 5 ## # Groups: year, month [11] ## year month day arr dep ## ## 1 2013 1 16 34.2 24.6 ## 2 2013 1 31 32.6 28.7 ## 3 2013 2 11 36.3 39.1 ## 4 2013 2 27 31.3 37.8 ## 5 2013 3 8 85.9 83.5 ## 6 2013 3 18 41.3 30.1 ## 7 2013 4 10 38.4 33.0 ## 8 2013 4 12 36.0 34.8 ## 9 2013 4 18 36.0 34.9 ## 10 2013 4 19 47.9 46.1 ## 11 2013 4 22 37.8 30.6 ## 12 2013 4 25 33.7 23.3 ## 13 2013 5 8 39.6 43.2 ## 14 2013 5 23 62.0 51.1 ## 15 2013 5 24 24.3 30.3 ## 16 2013 6 2 26.1 34.0 ## 17 2013 6 10 28.0 30.6 ## 18 2013 6 13 63.8 45.8 ## 19 2013 6 18 37.6 36.0 ## 20 2013 6 24 51.2 47.2 ## 21 2013 6 25 41.5 43.1 ## 22 2013 6 26 27.3 30.6 ## 23 2013 6 27 44.8 40.9 ## 24 2013 6 28 45.0 48.8 ## 25 2013 6 30 43.5 44.2 ## 26 2013 7 1 58.3 56.2 ## 27 2013 7 7 40.3 36.6 ## 28 2013 7 8 29.6 37.3 ## 29 2013 7 9 31.3 30.7 ## 30 2013 7 10 59.6 52.9 ## 31 2013 7 22 62.8 46.7 ## 32 2013 7 23 45.0 44.7 ## 33 2013 7 28 49.8 37.7 ## 34 2013 8 1 36.0 34.6 ## 35 2013 8 8 55.5 43.3 ## 36 2013 8 9 43.3 34.7 ## 37 2013 8 22 30.0 33.6 ## 38 2013 8 28 35.2 40.5 ## 39 2013 9 2 45.5 53.0 ## 40 2013 9 12 58.9 50.0 ## 41 2013 10 7 39.0 39.1 ## 42 2013 10 11 18.9 31.2 ## 43 2013 12 5 51.7 52.3 ## 44 2013 12 8 36.9 21.5 ## 45 2013 12 9 42.6 34.8 ## 46 2013 12 10 44.5 26.5 ## 47 2013 12 14 46.4 28.4 ## 48 2013 12 17 55.9 40.7 ## 49 2013 12 23 32.2 32.3 ``` ## Analyze by group with `group_by()` Perform operations by _group_: mean departure delay by airport (`origin`) ```r flights %>% group_by(origin) %>% summarise(meanDelay = mean(dep_delay,na.rm=T)) ``` ``` ## # A tibble: 3 x 2 ## origin meanDelay ## ## 1 EWR 15.1 ## 2 JFK 12.1 ## 3 LGA 10.3 ``` Perform operations by _group_: mean and sd departure delay by airline (`carrier`) ```r flights %>% group_by(carrier) %>% summarise(meanDelay = mean(dep_delay,na.rm=T), sdDelay = sd(dep_delay,na.rm=T)) ``` ``` ## # A tibble: 16 x 3 ## carrier meanDelay sdDelay ## ## 1 9E 16.7 45.9 ## 2 AA 8.59 37.4 ## 3 AS 5.80 31.4 ## 4 B6 13.0 38.5 ## 5 DL 9.26 39.7 ## 6 EV 20.0 46.6 ## 7 F9 20.2 58.4 ## 8 FL 18.7 52.7 ## 9 HA 4.90 74.1 ## 10 MQ 10.6 39.2 ## 11 OO 12.6 43.1 ## 12 UA 12.1 35.7 ## 13 US 3.78 28.1 ## 14 VX 12.9 44.8 ## 15 WN 17.7 43.3 ## 16 YV 19.0 49.2 ```

Flights from which `origin` airport go the farthest (on average)? Hint: Group by airport (`origin`) then calculate the maximum flight distance (`distance`).

```r flights %>% group_by(origin) %>% summarise(meanDist = mean(distance,na.rm=T))%>% arrange(desc(meanDist))%>% slice(1)%>% select(origin) ``` ``` ## # A tibble: 1 x 1 ## origin ## ## 1 JFK ```

# Today's task Now [complete the task here](CS_03.html) by yourself or in small groups. ## Colophon This exercise based on code from [here](http://spatial.ly/2012/06/mapping-worlds-biggest-airlines/).

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