Summarize

Useful summary functions

• sum(), prod()
• mean(), median()
• sd(), IQR(), mad()
• min(), quantile(), max()
• n()
• sum() and mean() for logical variables
• first(), last(), nth()

Don’t forget na.rm = TRUE if needed!

Mean arrival and dep delay

flights %>%
  summarize(mean(arr_delay, na.rm = TRUE))

## # A tibble: 1 x 1
##   `mean(arr_delay, na.rm = TRUE)`
##                             <dbl>
## 1                            6.90

flights %>%
  summarize(mean(dep_delay, na.rm = TRUE))

## # A tibble: 1 x 1
##   `mean(dep_delay, na.rm = TRUE)`
##                             <dbl>
## 1                            12.6

flights %>%
  summarize(
    mean(arr_delay, na.rm = TRUE),
    mean(dep_delay, na.rm = TRUE),
    sd(arr_delay, na.rm = TRUE),
    sd(dep_delay, na.rm = TRUE)
  )

## # A tibble: 1 x 4
##   `mean(arr_delay, na.rm = TRUE)` `mean(dep_del… `sd(arr_del… `sd(dep_del…
##                             <dbl>          <dbl>        <dbl>        <dbl>
## 1                            6.90           12.6         44.6         40.2

flights %>%
  summarize_at(
    vars(arr_delay, dep_delay),
    funs(mean(., na.rm = TRUE), sd(., na.rm = TRUE))
  )

## # A tibble: 1 x 4
##   arr_delay_mean dep_delay_mean arr_delay_sd dep_delay_sd
##            <dbl>          <dbl>        <dbl>        <dbl>
## 1           6.90           12.6         44.6         40.2

flights %>%
  group_by(origin) %>%
  summarize(
    mean(arr_delay, na.rm = TRUE),
    mean(dep_delay, na.rm = TRUE)
  )

## # A tibble: 3 x 3
##   origin `mean(arr_delay, na.rm = TRUE)` `mean(dep_delay, na.rm = TRUE)`
##   <chr>                            <dbl>                           <dbl>
## 1 EWR                               9.11                            15.1
## 2 JFK                               5.55                            12.1
## 3 LGA                               5.78                            10.3

Air time by carrier

total_airtime_by_carrier <-
  flights %>% 
    group_by(carrier) %>% 
    summarize(acc_air_time = sum(air_time, na.rm = TRUE)) %>% 
    ungroup()

total_airtime_by_carrier

## # A tibble: 16 x 2
##    carrier acc_air_time
##    <chr>          <dbl>
##  1 9E           1500801
##  2 AA           6032306
##  3 AS            230863
##  4 B6           8170975
##  5 DL           8277661
##  6 EV           4603614
##  7 F9            156357
##  8 FL            321132
##  9 HA            213096
## 10 MQ           2282880
## 11 OO              2421
## 12 UA          12237728
## 13 US           1756507
## 14 VX           1724104
## 15 WN           1780402
## 16 YV             35763

total_airtime_by_carrier %>% 
  arrange(acc_air_time) %>%
  mutate(carrier = forcats::fct_inorder(carrier)) %>%
  ggplot() +
  geom_col(aes(carrier, acc_air_time / 60 / 24 / 365)) +
  coord_flip()

Carriers for long-distance routes

carrier_arranged_by_mean_distance <-
  flights %>%
  group_by(carrier) %>% 
  summarize(mean_distance = mean(distance)) %>% 
  ungroup() %>%
  arrange(desc(mean_distance))

carrier_arranged_by_mean_distance

## # A tibble: 16 x 2
##    carrier mean_distance
##    <chr>           <dbl>
##  1 HA               4983
##  2 VX               2499
##  3 AS               2402
##  4 F9               1620
##  5 UA               1529
##  6 AA               1340
##  7 DL               1237
##  8 B6               1069
##  9 WN                996
## 10 FL                665
## 11 MQ                570
## 12 EV                563
## 13 US                553
## 14 9E                530
## 15 OO                501
## 16 YV                375

carrier_arranged_by_mean_distance %>% 
  ggplot() +
  geom_col(aes(x = forcats::fct_inorder(carrier), y = mean_distance))

Worst plane

flights %>% 
  filter(is.na(dep_time)) %>%
  group_by(tailnum) %>% 
  summarize(not_departed = n()) %>% 
  ungroup() %>% 
  filter(!is.na(tailnum)) %>%
  arrange(desc(not_departed))

## # A tibble: 1,449 x 2
##    tailnum not_departed
##    <chr>          <int>
##  1 N725MQ            29
##  2 N713MQ            28
##  3 N723MQ            27
##  4 N15574            26
##  5 N722MQ            26
##  6 N13949            24
##  7 N14573            24
##  8 N735MQ            24
##  9 N11535            23
## 10 N11565            23
## # ... with 1,439 more rows

flights %>% 
  group_by(tailnum) %>% 
  summarize(not_departed = sum(is.na(dep_time))) %>% 
  ungroup()

## # A tibble: 4,044 x 2
##    tailnum not_departed
##    <chr>          <int>
##  1 D942DN             0
##  2 N0EGMQ            17
##  3 N10156             7
##  4 N102UW             0
##  5 N103US             0
##  6 N104UW             0
##  7 N10575            17
##  8 N105UW             0
##  9 N107US             0
## 10 N108UW             0
## # ... with 4,034 more rows

flights %>% 
  filter(is.na(dep_time)) %>%
  count(tailnum) %>%
  arrange(desc(n))

## # A tibble: 1,450 x 2
##    tailnum     n
##    <chr>   <int>
##  1 <NA>     2512
##  2 N725MQ     29
##  3 N713MQ     28
##  4 N723MQ     27
##  5 N15574     26
##  6 N722MQ     26
##  7 N13949     24
##  8 N14573     24
##  9 N735MQ     24
## 10 N11535     23
## # ... with 1,440 more rows

Short-distance routes per airline

flights %>%
  group_by(carrier) %>% 
  summarize(short_distance_ratio = mean(distance < 300)) %>% 
  ungroup() %>%
  arrange(desc(short_distance_ratio)) %>%
  ggplot() +
  geom_col(aes(x = forcats::fct_inorder(carrier), y = short_distance_ratio))