Using nycflights13 package I have prepared the following analyses.
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Analysis-1 Number of tailnumbers per grouped number of seats.
planes %>%
mutate (seat_bracket= case_when (seats<= 100 ~ "(0-100]" , seats<= 200 ~ "(100-200]" ,seats<= 300 ~ "(200-300]" , seats<= 400 ~ "(300-400]" ,TRUE ~ "(400-HI]" )) %>%
group_by (seat_bracket) %>%
summarise (count= n ()) %>%
print (n= Inf )
# A tibble: 5 × 2
seat_bracket count
<chr> <int>
1 (0-100] 820
2 (100-200] 2207
3 (200-300] 98
4 (300-400] 196
5 (400-HI] 1
Analysis-2 Average, min and max speed with respect to type, engine and number of engines.
First, we check the distinct values of speed column.
planes %>%
group_by (speed) %>%
summarise (count= n ()) %>%
arrange (desc (speed)) %>%
print (n= Inf )
# A tibble: 14 × 2
speed count
<int> <int>
1 432 8
2 232 1
3 202 1
4 167 1
5 162 2
6 127 1
7 126 1
8 112 1
9 108 1
10 107 1
11 105 2
12 95 1
13 90 2
14 NA 3299
We see that most of the speed data is non-existent. With the remaining data here is the table for average, min and max of speed by type, engine and number of engines.
options (dplyr.summarise.inform= FALSE )
planes %>%
filter (! is.na (speed)) %>%
group_by (type,engine,engines) %>%
summarise (avg_speed= round (mean (speed),2 ),min_speed= min (speed),max_speed= max (speed)) %>%
arrange (type,engine,engines) %>%
print (n= Inf )
# A tibble: 7 × 6
# Groups: type, engine [6]
type engine engines avg_speed min_speed max_speed
<chr> <chr> <int> <dbl> <int> <int>
1 Fixed wing multi engine Reciprocating 2 145. 90 167
2 Fixed wing multi engine Reciprocating 4 232 232 232
3 Fixed wing multi engine Turbo-jet 2 432 432 432
4 Fixed wing multi engine Turbo-prop 2 202 202 202
5 Fixed wing single engine 4 Cycle 1 108 108 108
6 Fixed wing single engine Reciprocating 1 108. 90 127
7 Rotorcraft Turbo-shaft 1 112 112 112