R is effective.“Happy families are all alike; every unhappy family is unhappy in its own way” - Leo Tolstoy
library(tidyverse)Tidy data has three related characteristics
Often we need to wrangle our data to extract meaning. This includes calculating new variables, summary statistics, grouping by variables, renaming, reordering, selecting subsets of data, filtering by various conditions, etc.
We can accomplish a great deal of wrangling by learning just seven key verbs. Each of these functions takes a data frame as input and returns a data frame as output.
To demonstrate data wrangling we will use a dataset of characteristics of all flights departing from New York City (JFK, LGA, EWR) in 2013. You may need to install the package first using the commented line of code (note you only need to do this once).
#install.packages(nycflights13)
library(nycflights13) We first explore the data a bit. Examine the documentation as well.
glimpse(flights)## Rows: 336,776
## Columns: 19
## $ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, …
## $ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ day <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ dep_time <int> 517, 533, 542, 544, 554, 554, 555, 557, 557, 558, 558,…
## $ sched_dep_time <int> 515, 529, 540, 545, 600, 558, 600, 600, 600, 600, 600,…
## $ dep_delay <dbl> 2, 4, 2, -1, -6, -4, -5, -3, -3, -2, -2, -2, -2, -2, -…
## $ arr_time <int> 830, 850, 923, 1004, 812, 740, 913, 709, 838, 753, 849…
## $ sched_arr_time <int> 819, 830, 850, 1022, 837, 728, 854, 723, 846, 745, 851…
## $ arr_delay <dbl> 11, 20, 33, -18, -25, 12, 19, -14, -8, 8, -2, -3, 7, -…
## $ carrier <chr> "UA", "UA", "AA", "B6", "DL", "UA", "B6", "EV", "B6", …
## $ flight <int> 1545, 1714, 1141, 725, 461, 1696, 507, 5708, 79, 301, …
## $ tailnum <chr> "N14228", "N24211", "N619AA", "N804JB", "N668DN", "N39…
## $ origin <chr> "EWR", "LGA", "JFK", "JFK", "LGA", "EWR", "EWR", "LGA"…
## $ dest <chr> "IAH", "IAH", "MIA", "BQN", "ATL", "ORD", "FLL", "IAD"…
## $ air_time <dbl> 227, 227, 160, 183, 116, 150, 158, 53, 140, 138, 149, …
## $ distance <dbl> 1400, 1416, 1089, 1576, 762, 719, 1065, 229, 944, 733,…
## $ hour <dbl> 5, 5, 5, 5, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 6, …
## $ minute <dbl> 15, 29, 40, 45, 0, 58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 59, …
## $ time_hour <dttm> 2013-01-01 05:00:00, 2013-01-01 05:00:00, 2013-01-01 …names(flights)## [1] "year" "month" "day" "dep_time"
## [5] "sched_dep_time" "dep_delay" "arr_time" "sched_arr_time"
## [9] "arr_delay" "carrier" "flight" "tailnum"
## [13] "origin" "dest" "air_time" "distance"
## [17] "hour" "minute" "time_hour"head(flights)## # A tibble: 6 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## # … with 11 more variables: 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>The head() function returns “A tibble: 6 x 19” and then the first six rows of the flights data. A tibble is a tweaked, opinionated version of the R data frame.
There are a few differences a tidyverse tibble and an R data frame. Two of the main ones are described below.
First, it provides more information than a data frame. When you print a tibble, it will show the first ten rows and all of the columns that fit on the screen, along with the type of each column. Try this with the flights data. You can modify the number of rows and columns shown using the print() function.
Question: Can you print the first three rows and all columns of the flights data? Check the documentation!
print(flights, n = 3, width = Inf)## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## arr_delay carrier flight tailnum origin dest air_time distance hour minute
## <dbl> <chr> <int> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 11 UA 1545 N14228 EWR IAH 227 1400 5 15
## 2 20 UA 1714 N24211 LGA IAH 227 1416 5 29
## 3 33 AA 1141 N619AA JFK MIA 160 1089 5 40
## time_hour
## <dttm>
## 1 2013-01-01 05:00:00
## 2 2013-01-01 05:00:00
## 3 2013-01-01 05:00:00
## # … with 336,773 more rowsSecond, tibbles are somewhat more strict than data frames when it comes to subsetting data.
select()The select() function picks off one or more columns by name.
Let’s say we want a dataset that only contains the variables dep_delay and arr_delay.
select(flights, dep_delay, arr_delay)## # A tibble: 336,776 x 2
## dep_delay arr_delay
## <dbl> <dbl>
## 1 2 11
## 2 4 20
## 3 2 33
## 4 -1 -18
## 5 -6 -25
## 6 -4 12
## 7 -5 19
## 8 -3 -14
## 9 -3 -8
## 10 -2 8
## # … with 336,766 more rowsWe can also use select() to exclude variables. Let’s exclude dep_delay but keep all other variables.
select(flights, -dep_delay)## # A tibble: 336,776 x 18
## year month day dep_time sched_dep_time arr_time sched_arr_time arr_delay
## <int> <int> <int> <int> <int> <int> <int> <dbl>
## 1 2013 1 1 517 515 830 819 11
## 2 2013 1 1 533 529 850 830 20
## 3 2013 1 1 542 540 923 850 33
## 4 2013 1 1 544 545 1004 1022 -18
## 5 2013 1 1 554 600 812 837 -25
## 6 2013 1 1 554 558 740 728 12
## 7 2013 1 1 555 600 913 854 19
## 8 2013 1 1 557 600 709 723 -14
## 9 2013 1 1 557 600 838 846 -8
## 10 2013 1 1 558 600 753 745 8
## # … with 336,766 more rows, and 10 more variables: carrier <chr>, flight <int>,
## # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## # hour <dbl>, minute <dbl>, time_hour <dttm>We can also use select() to select a range of variables. Here, we select the first three variables representing the departure day.
select(flights, year:day)## # A tibble: 336,776 x 3
## year month day
## <int> <int> <int>
## 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 rowsarrange()The arrange() function orders rows (observations) in specific ways.
Let’s arrange the data by descending departure delays, with large departure delays on top.
arrange(flights, desc(dep_delay))## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 9 641 900 1301 1242 1530
## 2 2013 6 15 1432 1935 1137 1607 2120
## 3 2013 1 10 1121 1635 1126 1239 1810
## 4 2013 9 20 1139 1845 1014 1457 2210
## 5 2013 7 22 845 1600 1005 1044 1815
## 6 2013 4 10 1100 1900 960 1342 2211
## 7 2013 3 17 2321 810 911 135 1020
## 8 2013 6 27 959 1900 899 1236 2226
## 9 2013 7 22 2257 759 898 121 1026
## 10 2013 12 5 756 1700 896 1058 2020
## # … with 336,766 more rows, and 11 more variables: 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>Or with low departure delays on top.
arrange(flights, dep_delay)## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 12 7 2040 2123 -43 40 2352
## 2 2013 2 3 2022 2055 -33 2240 2338
## 3 2013 11 10 1408 1440 -32 1549 1559
## 4 2013 1 11 1900 1930 -30 2233 2243
## 5 2013 1 29 1703 1730 -27 1947 1957
## 6 2013 8 9 729 755 -26 1002 955
## 7 2013 10 23 1907 1932 -25 2143 2143
## 8 2013 3 30 2030 2055 -25 2213 2250
## 9 2013 3 2 1431 1455 -24 1601 1631
## 10 2013 5 5 934 958 -24 1225 1309
## # … with 336,766 more rows, and 11 more variables: 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>What if we only want to examine the dep_delay and arr_delay columns? We can combine arrange() and select()!
select(arrange(flights, desc(dep_delay)), dep_delay, arr_delay)## # A tibble: 336,776 x 2
## dep_delay arr_delay
## <dbl> <dbl>
## 1 1301 1272
## 2 1137 1127
## 3 1126 1109
## 4 1014 1007
## 5 1005 989
## 6 960 931
## 7 911 915
## 8 899 850
## 9 898 895
## 10 896 878
## # … with 336,766 more rowsIt is not easy to understand what is going on in the code chunk above.
The pipe is a technique for passing information from one process to another.
flights %>%
arrange(desc(dep_delay)) %>%
select(dep_delay, arr_delay)## # A tibble: 336,776 x 2
## dep_delay arr_delay
## <dbl> <dbl>
## 1 1301 1272
## 2 1137 1127
## 3 1126 1109
## 4 1014 1007
## 5 1005 989
## 6 960 931
## 7 911 915
## 8 899 850
## 9 898 895
## 10 896 878
## # … with 336,766 more rowsWhen reading code “in English”, say “and then” whenever you see a pipe.
Question: How would you read the code chunk above in English? What is it accomplishing?
slice()Slice selects rows based on their position.
Here we slice off the first 5 rows of the flights data.
flights %>%
slice(1:5)## # A tibble: 5 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## # … with 11 more variables: 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>Question: What is the code chunk below accomplishing? Guess before running the code.
Return the flights with the top 5 departure delays.
flights %>%
arrange(desc(dep_delay)) %>%
slice(1:5)## # A tibble: 5 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 9 641 900 1301 1242 1530
## 2 2013 6 15 1432 1935 1137 1607 2120
## 3 2013 1 10 1121 1635 1126 1239 1810
## 4 2013 9 20 1139 1845 1014 1457 2210
## 5 2013 7 22 845 1600 1005 1044 1815
## # … with 11 more variables: 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>To add comments to code, use the pound sign. This is helpful for debugging as well - you can temporarily disable a line.
Question: What will happen if you comment out the line containing arrange() in the code chunk above? Try it.
Commenting out the line of code containing arrange() returns the first five flights in the flights data (not arranging in descending order of departure delay).
filter()filter() selects rows satisfying certain conditions.
We can use a single condition. Here we select all rows where the destination airport is RDU.
flights %>%
filter(dest == "RDU")## # A tibble: 8,163 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 800 810 -10 949 955
## 2 2013 1 1 832 840 -8 1006 1030
## 3 2013 1 1 851 851 0 1032 1036
## 4 2013 1 1 917 920 -3 1052 1108
## 5 2013 1 1 1024 1030 -6 1204 1215
## 6 2013 1 1 1127 1129 -2 1303 1309
## 7 2013 1 1 1157 1205 -8 1342 1345
## 8 2013 1 1 1240 1235 5 1415 1415
## 9 2013 1 1 1317 1325 -8 1454 1505
## 10 2013 1 1 1449 1450 -1 1651 1640
## # … with 8,153 more rows, and 11 more variables: 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>We can use more than one condition. Here we select all rows where the destination airport is RDU and the arrival delay is less than 0.
flights %>%
filter(dest == "RDU", arr_delay < 0)## # A tibble: 4,232 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 800 810 -10 949 955
## 2 2013 1 1 832 840 -8 1006 1030
## 3 2013 1 1 851 851 0 1032 1036
## 4 2013 1 1 917 920 -3 1052 1108
## 5 2013 1 1 1024 1030 -6 1204 1215
## 6 2013 1 1 1127 1129 -2 1303 1309
## 7 2013 1 1 1157 1205 -8 1342 1345
## 8 2013 1 1 1317 1325 -8 1454 1505
## 9 2013 1 1 1505 1510 -5 1654 1655
## 10 2013 1 1 1800 1800 0 1945 1951
## # … with 4,222 more rows, and 11 more variables: 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>More complex conditions are possible!
Question: In plain English, what is the code below accomplishing?
Return flights with the destination airport RDU or GSO that either arrived or departed early.
flights %>%
filter(dest %in% c("RDU", "GSO"),
arr_delay < 0 | dep_delay < 0)## # A tibble: 6,203 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 800 810 -10 949 955
## 2 2013 1 1 832 840 -8 1006 1030
## 3 2013 1 1 851 851 0 1032 1036
## 4 2013 1 1 917 920 -3 1052 1108
## 5 2013 1 1 1024 1030 -6 1204 1215
## 6 2013 1 1 1127 1129 -2 1303 1309
## 7 2013 1 1 1157 1205 -8 1342 1345
## 8 2013 1 1 1317 1325 -8 1454 1505
## 9 2013 1 1 1449 1450 -1 1651 1640
## 10 2013 1 1 1505 1510 -5 1654 1655
## # … with 6,193 more rows, and 11 more variables: 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>The table of logical operators below will be helpful as you work with filtering.
| operator | definition |
|---|---|
< |
is less than? |
<= |
is less than or equal to? |
> |
is greater than? |
>= |
is greater than or equal to? |
== |
is exactly equal to? |
!= |
is not equal to? |
x & y |
is x AND y? |
x | y |
is x OR y? |
is.na(x) |
is x NA? |
!is.na(x) |
is x not NA? |
x %in% y |
is x in y? |
!(x %in% y) |
is x not in y? |
!x |
is not x? |
The final operator only makes sense if x is logical (TRUE / FALSE).
mutate()mutate() creates a new variable.
In the code chunk below, air_time is converted to hours, and a new variable mph is created, corresponding to the miles per hour of the flight.
flights %>%
mutate(hours = air_time / 60,
mph = distance / hours) %>%
select(air_time, distance, hours, mph)## # A tibble: 336,776 x 4
## air_time distance hours mph
## <dbl> <dbl> <dbl> <dbl>
## 1 227 1400 3.78 370.
## 2 227 1416 3.78 374.
## 3 160 1089 2.67 408.
## 4 183 1576 3.05 517.
## 5 116 762 1.93 394.
## 6 150 719 2.5 288.
## 7 158 1065 2.63 404.
## 8 53 229 0.883 259.
## 9 140 944 2.33 405.
## 10 138 733 2.3 319.
## # … with 336,766 more rowsUsing <= in the mutate creates a new variable on_time that is TRUE if the flight is on time and FALSE if it is not.
flights %>%
mutate(on_time = arr_delay <= 0) %>%
select(arr_delay, on_time)## # A tibble: 336,776 x 2
## arr_delay on_time
## <dbl> <lgl>
## 1 11 FALSE
## 2 20 FALSE
## 3 33 FALSE
## 4 -18 TRUE
## 5 -25 TRUE
## 6 12 FALSE
## 7 19 FALSE
## 8 -14 TRUE
## 9 -8 TRUE
## 10 8 FALSE
## # … with 336,766 more rowsQuestion: What do you think will happen if you take the mean of the on_time variable?
It should give us the proportion of on time flights (as judged by arrival delay).
summarize()summarize calculates summary statistics. It collapses rows into summary statistics and removes columns irrelevant to the calculation.
Be sure to name your columns!
flights %>%
summarize(mean_dep_delay = mean(dep_delay))## # A tibble: 1 x 1
## mean_dep_delay
## <dbl>
## 1 NAQuestion: The code chunk above should return an NA. What is going wrong? Try to fix it to find the mean departure delay.
There are missing departure delays in the data and NAs are contagious.
flights %>%
summarize(prop_on_time = mean(arr_delay <= 0, na.rm = TRUE))## # A tibble: 1 x 1
## prop_on_time
## <dbl>
## 1 0.594group_by()group_by() is used for grouped operations. It’s very powerful when paired with summarize to calculate summary statistics by group.
Here we find the proportion of flights that are on time for each month of the year.
flights %>%
group_by(month) %>%
summarize(prop_on_time = mean(arr_delay <= 0, na.rm = TRUE))## # A tibble: 12 x 2
## month prop_on_time
## <int> <dbl>
## 1 1 0.578
## 2 2 0.572
## 3 3 0.609
## 4 4 0.546
## 5 5 0.638
## 6 6 0.539
## 7 7 0.530
## 8 8 0.596
## 9 9 0.747
## 10 10 0.657
## 11 11 0.643
## 12 12 0.467We can calculate more than one summary statistic in summarize(). In addition to the proportion on time for each month, let’s find the maximum delay, median delay, and the count of flights in each month.
Here n() calculates the current group size.
flights %>%
group_by(month) %>%
summarize(prop_on_time = mean(arr_delay <= 0, na.rm = TRUE),
max_delay = max(arr_delay, na.rm = TRUE),
median_delay = median(arr_delay, na.rm = TRUE),
count = n())## # A tibble: 12 x 5
## month prop_on_time max_delay median_delay count
## <int> <dbl> <dbl> <dbl> <int>
## 1 1 0.578 1272 -3 27004
## 2 2 0.572 834 -3 24951
## 3 3 0.609 915 -6 28834
## 4 4 0.546 931 -2 28330
## 5 5 0.638 875 -8 28796
## 6 6 0.539 1127 -2 28243
## 7 7 0.530 989 -2 29425
## 8 8 0.596 490 -5 29327
## 9 9 0.747 1007 -12 27574
## 10 10 0.657 688 -7 28889
## 11 11 0.643 796 -6 27268
## 12 12 0.467 878 2 28135Finally, let’s see what the proportion on time is for EWR, JFK, and LGA.
flights %>%
group_by(origin) %>%
summarize(on_time = mean(dep_delay <= 0, na.rm = TRUE))## # A tibble: 3 x 2
## origin on_time
## <chr> <dbl>
## 1 EWR 0.552
## 2 JFK 0.616
## 3 LGA 0.668count()count counts the unique values of one or more variables. It creates frequency tables.
flights %>%
count(origin)## # A tibble: 3 x 2
## origin n
## <chr> <int>
## 1 EWR 120835
## 2 JFK 111279
## 3 LGA 104662Question: What is the code chunk below doing?
The code chunk below is finding the count of flights departing from EWR, JFK, and LGA and mutating a new variable, the proportion of flights from NYC departing from each of the three airports.
flights %>%
count(origin) %>%
mutate(prop = n / sum(n))## # A tibble: 3 x 3
## origin n prop
## <chr> <int> <dbl>
## 1 EWR 120835 0.359
## 2 JFK 111279 0.330
## 3 LGA 104662 0.311year, month, day, dep_time, dep_delay, and dep_delay_hours (the departure delay in hours). Note you may need to use mutate() to make one or more of these variables.flights %>%
filter(!is.na(dep_time)) %>%
mutate(dep_delay_hours = dep_delay / 60) %>%
select(year:dep_time, dep_delay, dep_delay_hours)## # A tibble: 328,521 x 6
## year month day dep_time dep_delay dep_delay_hours
## <int> <int> <int> <int> <dbl> <dbl>
## 1 2013 1 1 517 2 0.0333
## 2 2013 1 1 533 4 0.0667
## 3 2013 1 1 542 2 0.0333
## 4 2013 1 1 544 -1 -0.0167
## 5 2013 1 1 554 -6 -0.1
## 6 2013 1 1 554 -4 -0.0667
## 7 2013 1 1 555 -5 -0.0833
## 8 2013 1 1 557 -3 -0.05
## 9 2013 1 1 557 -3 -0.05
## 10 2013 1 1 558 -2 -0.0333
## # … with 328,511 more rowstailnum), use a group_by() paired with summarize() to find the sample size, mean, and standard deviation of flight distances. Then, pick off the top 5 and bottom 5 airplanes in terms of mean distance traveled per flight.flights %>%
group_by(tailnum) %>%
summarize(n_dist = n(),
mean_dist = mean(distance, na.rm = TRUE),
sd_dist = sd(distance, na.rm = TRUE)) %>%
arrange(desc(mean_dist)) %>%
slice(c(1:5,((n()-4):n()))) ## # A tibble: 10 x 4
## tailnum n_dist mean_dist sd_dist
## <chr> <int> <dbl> <dbl>
## 1 N380HA 40 4983 0
## 2 N381HA 25 4983 0
## 3 N382HA 26 4983 0
## 4 N383HA 26 4983 0
## 5 N384HA 33 4983 0
## 6 N945UW 285 176. 31.2
## 7 N956UW 222 174. 31.4
## 8 N959UW 213 174. 34.3
## 9 N948UW 232 174. 32.7
## 10 N955UW 225 173. 32.9