Let’s invoke the libraries we need in this exercise.
library(tidyverse)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.3.6 ✔ purrr 0.3.5
✔ tibble 3.1.8 ✔ dplyr 1.0.10
✔ tidyr 1.2.1 ✔ stringr 1.4.1
✔ readr 2.1.3 ✔ forcats 0.5.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()library(nycflights13)Before analysis, let’s take a look what we have in the dataset.
glimpse(planes)Rows: 3,322
Columns: 9
$ tailnum <chr> "N10156", "N102UW", "N103US", "N104UW", "N10575", "N105UW…
$ year <int> 2004, 1998, 1999, 1999, 2002, 1999, 1999, 1999, 1999, 199…
$ type <chr> "Fixed wing multi engine", "Fixed wing multi engine", "Fi…
$ manufacturer <chr> "EMBRAER", "AIRBUS INDUSTRIE", "AIRBUS INDUSTRIE", "AIRBU…
$ model <chr> "EMB-145XR", "A320-214", "A320-214", "A320-214", "EMB-145…
$ engines <int> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
$ seats <int> 55, 182, 182, 182, 55, 182, 182, 182, 182, 182, 55, 55, 5…
$ speed <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ engine <chr> "Turbo-fan", "Turbo-fan", "Turbo-fan", "Turbo-fan", "Turb…planes%>%
filter(engine == "Turbo-jet")%>%
group_by(manufacturer,model,engine)%>%
summarize(count=n())%>%
arrange(desc(count))`summarise()` has grouped output by 'manufacturer', 'model'. You can override
using the `.groups` argument.# A tibble: 41 × 4
# Groups: manufacturer, model [41]
manufacturer model engine count
<chr> <chr> <chr> <int>
1 BOEING 757-222 Turbo-jet 80
2 BOEING 737-832 Turbo-jet 71
3 AIRBUS INDUSTRIE A319-131 Turbo-jet 50
4 BOEING 757-224 Turbo-jet 41
5 BOEING 737-724 Turbo-jet 32
6 BOEING 757-251 Turbo-jet 29
7 AIRBUS INDUSTRIE A320-211 Turbo-jet 25
8 AIRBUS INDUSTRIE A321-211 Turbo-jet 25
9 MCDONNELL DOUGLAS MD-90-30 Turbo-jet 21
10 AIRBUS INDUSTRIE A319-132 Turbo-jet 20
# … with 31 more rowsplanes%>%
group_by(manufacturer,model) %>%
summarize(count=n(),avg_age = mean(2022-year, na.rm = T))%>%
arrange(desc(count), avg_age)`summarise()` has grouped output by 'manufacturer'. You can override using the
`.groups` argument.# A tibble: 147 × 4
# Groups: manufacturer [35]
manufacturer model count avg_age
<chr> <chr> <int> <dbl>
1 BOEING 737-7H4 361 18.1
2 BOMBARDIER INC CL-600-2B19 162 19.3
3 AIRBUS A320-232 129 16.3
4 AIRBUS INDUSTRIE A320-232 127 23.9
5 BOMBARDIER INC CL-600-2D24 123 14.9
6 BOEING 737-824 122 19.4
7 EMBRAER EMB-145LR 114 21.6
8 BOEING 737-3H4 105 27.6
9 EMBRAER EMB-145XR 104 18.4
10 MCDONNELL DOUGLAS AIRCRAFT CO MD-88 103 32.3
# … with 137 more rows