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Data wrangling

Useful packages: tidyverse

Cheat sheets on website for Data Wrangling

library(tidyverse)

Remember use install.packages("tidyverse") to install a new package.

Example operations from here

New York City Flights

Data from US Bureau of Transportation Statistics (see ?nycflights13)

library(nycflights13)

Check out the flights object

head(flights)
## # A tibble: 6 x 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
## # ... with 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>

Object Structure

Check out data structure with glimpse()

glimpse(flights)
## Observations: 336,776
## Variables: 19
## $ year           <int> 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,...
## $ day            <int> 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, 55...
## $ sched_dep_time <int> 515, 529, 540, 545, 600, 558, 600, 600, 600, 60...
## $ dep_delay      <dbl> 2, 4, 2, -1, -6, -4, -5, -3, -3, -2, -2, -2, -2...
## $ arr_time       <int> 830, 850, 923, 1004, 812, 740, 913, 709, 838, 7...
## $ sched_arr_time <int> 819, 830, 850, 1022, 837, 728, 854, 723, 846, 7...
## $ arr_delay      <dbl> 11, 20, 33, -18, -25, 12, 19, -14, -8, 8, -2, -...
## $ carrier        <chr> "UA", "UA", "AA", "B6", "DL", "UA", "B6", "EV",...
## $ flight         <int> 1545, 1714, 1141, 725, 461, 1696, 507, 5708, 79...
## $ tailnum        <chr> "N14228", "N24211", "N619AA", "N804JB", "N668DN...
## $ origin         <chr> "EWR", "LGA", "JFK", "JFK", "LGA", "EWR", "EWR"...
## $ dest           <chr> "IAH", "IAH", "MIA", "BQN", "ATL", "ORD", "FLL"...
## $ air_time       <dbl> 227, 227, 160, 183, 116, 150, 158, 53, 140, 138...
## $ distance       <dbl> 1400, 1416, 1089, 1576, 762, 719, 1065, 229, 94...
## $ hour           <dbl> 5, 5, 5, 5, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5,...
## $ minute         <dbl> 15, 29, 40, 45, 0, 58, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ time_hour      <dttm> 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:

select(flights,year, month, 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 rows

select() examples

Select everything except the tailnum:

select(flights,-tailnum)
## # A tibble: 336,776 x 18
##     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
##  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 <int>,
## #   arr_delay <dbl>, carrier <chr>, flight <int>, origin <chr>,
## #   dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
## #   time_hour <dttm>

Select all columns containing the string "time":

select(flights,contains("time"))
## # A tibble: 336,776 x 6
##    dep_time sched_dep_time arr_time sched_arr_time air_time
##       <int>          <int>    <int>          <int>    <dbl>
##  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 <dttm>

You can also rename columns with select()

select(flights,year,carrier,destination=dest)
## # A tibble: 336,776 x 3
##     year carrier destination
##    <int> <chr>   <chr>      
##  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:

filter(flights, month == 1, day == 1)
## # A tibble: 842 x 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
##  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 <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>

Base R method

This is equivalent to the more verbose code in base 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
##    <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
##  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 <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>

Compare with dplyr method:

filter(flights, month == 1, day == 1)

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

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
##    <int> <int> <int>    <int>          <int>     <dbl>    <int>
##  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 <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>

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 (“|”):

filter(flights, month == 1 | month == 2)
## # A tibble: 51,955 x 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
##  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 <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>

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


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
##    <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
##  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 <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>

Select rows with slice():

slice(flights, 1:10)
## # A tibble: 10 x 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
##  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 <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>

arrange() rows

arrange() is similar to filter() except it reorders instead of filtering.

arrange(flights, year, month, day)
## # A tibble: 336,776 x 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
##  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 <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>

Base R method:

flights[order(flights$year, flights$month, flights$day), ]

Descending order: desc()

arrange(flights, desc(arr_delay))
## # A tibble: 336,776 x 19
##     year month   day dep_time sched_dep_time dep_delay arr_time
##    <int> <int> <int>    <int>          <int>     <dbl>    <int>
##  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 <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>

Base R method:

flights[order(desc(flights$arr_delay)), ]

Distinct: Find distinct rows

distinct(
  select(flights,carrier)
)
## # A tibble: 16 x 1
##    carrier
##    <chr>  
##  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):

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
##       <dbl>    <dbl>     <dbl>
##  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:

a1 <- group_by(flights, year, month, day)
a2 <- select(a1, arr_delay, dep_delay)
## Adding missing grouping variables: `year`, `month`, `day`
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
##   <int> <int> <int> <dbl> <dbl>
## 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:

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
##    <int> <int> <int> <dbl> <dbl>
##  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:

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
##    <int> <int> <int> <dbl> <dbl>
##  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)

flights %>%
  group_by(origin) %>%
  summarise(meanDelay = mean(dep_delay,na.rm=T))
## # A tibble: 3 x 2
##   origin meanDelay
##   <chr>      <dbl>
## 1 EWR         15.1
## 2 JFK         12.1
## 3 LGA         10.3

Perform operations by group: mean and sd departure delay by airline (carrier)

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
##    <chr>       <dbl>   <dbl>
##  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).

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

Today’s task

Now complete the task here by yourself or in small groups.

Colophon

This exercise based on code from here.