The document outlines a use case for the R package 'dplyr', detailing various operations on flight data, including filtering, summarizing, and visualizing passenger and flight statistics for Budapest. It involves data cleaning and manipulation tasks, such as renaming columns and aggregating flight metrics by year and month. The document also includes code snippets for generating insights on incoming and outgoing flights, as well as visualizations using ggplot2.

1. dplyr use case
Romain François
@romain_francois
#satRday/Budapest
romainfrancois

2. %>%

4. enjoy(cool(bake(shape(beat(append(bowl(rep("flour",
2), "yeast", "water", "milk", "oil"), "flour", until
= "soft"), duration = "3mins"), as = "balls", style =
"slightly-flat"), degrees = 200, duration =
"15mins"), duration = "5mins"))
bowl(rep("flour", 2), "yeast", "water", "milk", "oil") %>%
append("flour", until = "soft") %>%
beat(duration = "3mins") %>%
shape(as = "balls", style = "slightly-flat") %>%
bake(degrees = 200, duration = "15mins") %>%
cool(buns, duration = "5mins") %>%
enjoy()

6. tmp <- tempfile( fileext = ".xlsx")
download.file("http://bit.ly/2bPaiyU", tmp )
data <- readxl::read_excel(tmp)

7. All caps column names with spaces
> names(data)
[1] "DESTINATION" "COMMERCIAL FLAG" "CITY" "REGION"
[5] "COUNTRY" "FLIGH DIRECTION" "FLIGHT TYPE" "DATE"
[9] "DATE YEAR" "DATE HALF YEAR" "DATE YEAR QUARTER" "DATE YEAR MONTH"
[13] "NBR OF PASSENGERS" "CARGO WEIGHT" "NBR OF FLIGHTS" "SEAT CAPACITY"

8. > unique( data[["COMMERCIAL FLAG"]] )
[1] "Commercial"
> summary( as.factor( data[["REGION"]] ) )
China VH China ZB China ZS Germany ED Germany ET India VA India VI
68 127 3 2160 9 3 1
Japan RJ Russia UI Russia UL Russia UN Russia UR Russia US Russia UU
63 1 167 7 62 59 340
Russia UW Spain GC Spain LE USA KA USA KB USA KC USA KD
6 205 870 4 7 5 19
USA KE USA KI USA KJ USA KM USA KP USA KT USA KW
3 1 114 2 4 2 5
NA's
14520

9. > data %>% select( starts_with( "DATE") )
# A tibble: 18,837 x 5
DATE DATE YEAR DATE HALF YEAR DATE YEAR QUARTER DATE YEAR MONTH
<time> <dbl> <chr> <chr> <chr>
1 2010-11-01 2010 2010H2 2010Q4 201011
2 2010-12-01 2010 2010H2 2010Q4 201012
3 2011-01-01 2011 2011H1 2011Q1 201101
4 2007-05-01 2007 2007H1 2007Q2 200705
5 2011-07-01 2011 2011H2 2011Q3 201107
6 2010-07-01 2010 2010H2 2010Q3 201007
7 2010-04-01 2010 2010H1 2010Q2 201004
8 2012-06-01 2012 2012H1 2012Q2 201206
9 2009-02-01 2009 2009H1 2009Q1 200902
10 2009-10-01 2009 2009H2 2009Q4 200910
# ... with 18,827 more rows

10. > data %>%
+ filter( `NBR OF PASSENGERS` == 0 & `CARGO WEIGHT` == 0 ) %>%
+ group_by( CITY, `DATE YEAR MONTH`, `FLIGH DIRECTION` ) %>%
+ summarise( flights = sum( `NBR OF FLIGHTS` ) ) %>%
+ arrange( desc(flights) )
Source: local data frame [2,417 x 4]
Groups: CITY, DATE YEAR MONTH [2,023]
CITY DATE YEAR MONTH FLIGH DIRECTION flights
<chr> <chr> <chr> <dbl>
1 Timisoara 200911 Incoming 26
2 Arad 200701 Incoming 23
3 Arad 200801 Outgoing 22
4 Cluj Napoca 201110 Incoming 22
5 Arad 200801 Incoming 21
# ...
Empty flights

11. > data %>%
+ filter( `SEAT CAPACITY` < `NBR OF PASSENGERS` ) %>%
+ group_by( CITY ) %>%
+ summarise(
+ flights = sum( `NBR OF FLIGHTS` ),
+ passengers = sum( `NBR OF PASSENGERS`),
+ capacity = sum( `SEAT CAPACITY`)
+ ) %>%
+ arrange( desc(flights) )
# A tibble: 8 x 4
CITY flights passengers capacity
<chr> <dbl> <dbl> <dbl>
1 Hurghada 32 5229 4665
2 Tenerife Sur 4 760 756
3 Djerba 3 530 510
Empty flights

13. flights <- data %>%
`names<-`( casefold(gsub( " ", "_", names(data))) ) %>%
rename( direction = fligh_direction, type = flight_type ) %>%
mutate(
year = date_year,
month = as.numeric(substr(date_year_month, 5, 6)),
no_passengers = nbr_of_passengers==0,
no_weight = cargo_weight==0
) %>%
group_by( year, month, country, city, direction ) %>%
summarise(
passengers = sum(nbr_of_passengers),
weight = sum(cargo_weight),
capacity = sum(seat_capacity),
flights = sum(nbr_of_flights),
scheduled = sum(nbr_of_flights[type == "Scheduled"] ),
non_scheduled = flights - scheduled,
flights_no_passengers = sum( nbr_of_flights[no_passengers] ),
flights_no_weight = sum( nbr_of_flights[no_weight] ),
flights_empty = sum( nbr_of_flights[no_passengers & no_weight]),
filling = passengers / capacity
) %>%
arrange( year, month, country, city, direction ) %>%
ungroup

14. > glimpse(flights)
Observations: 14,772
Variables: 14
$ year <dbl> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007,...
$ month <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ country <chr> "Albania", "Albania", "Austria", "Austria", "Azerbaijan", "...
$ city <chr> "Tirana", "Tirana", "Wien", "Wien", "Baku", "Brussels", "Br...
$ direction <chr> "Incoming", "Outgoing", "Incoming", "Outgoing", "Incoming",...
$ passengers <dbl> 2448, 1429, 2530, 3126, 0, 4310, 5362, 2760, 3269, 0, 37, 1...
$ weight <dbl> 0, 0, 0, 0, 597393, 598235, 7343, 0, 0, 0, 0, 0, 0, 0, 0, 2...
$ capacity <dbl> 4024, 4024, 5684, 5673, 2365, 8951, 8922, 4320, 4320, 50, 5...
$ flights <dbl> 44, 44, 91, 90, 15, 95, 87, 24, 24, 1, 1, 24, 24, 3, 3, 68,...
$ scheduled <dbl> 44, 44, 91, 89, 15, 95, 87, 24, 24, 0, 0, 24, 24, 0, 0, 68,...
$ non_scheduled <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 3, 3, 0, 3, 0, 0, 0,...
$ flights_no_passengers <dbl> 0, 0, 0, 1, 15, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ flights_no_weight <dbl> 44, 44, 91, 90, 0, 0, 0, 24, 24, 1, 1, 24, 24, 3, 3, 0, 3, ...
$ flights_empty <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...

16. > places <- with( distinct( flights, city, country ), paste(city, country, sep = ", ") )
>
> p <- progress_estimated(length(places))
> coords <- tbl_df( bind_rows( lapply(places, function(place){
+ res <- geocode( place )[1, ]
+ Sys.sleep( .2 ) # otherwise geocode wont let us get all the data
+ p$tick()$print()
+ res
+ }) ) )
|==== | 8% ~2 m remaining
> glimpse(cities)
Observations: 484
Variables: 10
$ city <chr> "Mazari Sharif", "Bagram Air Base", "Kabul", "Kandahar", "Tirana...
$ country <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Alb...
$ interpretedPlace <chr> "Mazari Sharif, Afghanistan", "Bagram Airfield, Bagram, Afghanis...
$ longitude <dbl> 67.117951, 69.273567, 69.207486, 65.737175, 19.818698, -2.214323...
$ latitude <dbl> 36.69262, 34.94448, 34.55535, 31.62887, 41.32755, 31.61825, 22.7...
$ xmin <dbl> 67.060633, 69.272218, 68.949509, 65.669574, 19.753568, -2.404461...
$ xmax <dbl> 67.235212, 69.274916, 69.445953, 65.830765, 19.882078, -1.352005...
$ ymin <dbl> 36.65382, 34.94313, 34.34549, 31.59740, 41.29512, 31.18248, 20.8...
$ ymax <dbl> 36.75264, 34.94582, 34.76192, 31.67252, 41.36684, 32.04475, 23.6...
$ uncertainty <dbl> 6692, 194, 31670, 7302, 6535, 51589, 150488, 239610, 12129, 1738...

18. Who went to Budapest
in summer 2011 ?

19. flights %>%
filter(
direction == "Incoming",
month %in% c(7,8),
year == 2011
) %>%
group_by( country ) %>%
summarise(
passengers = sum(passengers),
flights = sum(flights)
) %>%
arrange( desc(passengers) )
Summer
Goes to Budapest

20. flights %>%
filter(
direction == "Incoming",
month %in% c(7,8),
year == 2011
) %>%
group_by( country ) %>%
summarise(
passengers = sum(passengers),
flights = sum(flights)
) %>%
arrange( desc(passengers) )
Who

21. flights %>%
filter(
direction == "Incoming",
month %in% c(7,8),
year == 2011
) %>%
group_by( country ) %>%
summarise(
passengers = sum(passengers),
flights = sum(flights)
) %>%
arrange( desc(passengers) )
Summarise

22. flights %>%
filter(
direction == "Incoming",
month %in% c(7,8),
year == 2011
) %>%
group_by( country ) %>%
summarise(
passengers = sum(passengers),
flights = sum(flights)
) %>%
arrange( desc(passengers) ) Organise

23. flights %>%
filter(
direction == "Incoming",
month %in% c(7,8),
year == 2011
) %>%
group_by( country ) %>%
summarise(
passengers = sum(passengers),
flights = sum(flights)
) %>%
arrange( desc(passengers) ) Organise

24. When are people
leaving Budapest ?

25. data <- flights %>%
filter( direction == "Outgoing" ) %>%
mutate(
year = as.factor(year),
month = factor( month.abb[month], levels = month.abb )
) %>%
group_by( year, month ) %>%
summarise( passengers = sum( passengers ))
ggplot( data ) +
aes( x = month, y = passengers,
group = year, color = year
) +
geom_line() +
geom_point()
Leaving Budapest

26. data <- flights %>%
filter( direction == "Outgoing" ) %>%
mutate(
year = as.factor(year),
month = factor( month.abb[month], levels = month.abb )
) %>%
group_by( year, month ) %>%
summarise( passengers = sum( passengers ))
ggplot( data ) +
aes( x = month, y = passengers,
group = year, color = year
) +
geom_line() +
geom_point()
Collapse #passengers
by year/month

27. data <- flights %>%
filter( direction == "Outgoing" ) %>%
mutate(
year = as.factor(year),
month = factor( month.abb[month], levels = month.abb )
) %>%
group_by( year, month ) %>%
summarise( passengers = sum( passengers ))
ggplot( data ) +
aes( x = month, y = passengers,
group = year, color = year
) +
geom_line() +
geom_point()
Make year and month factors

28. data <- flights %>%
filter( direction == "Outgoing" ) %>%
mutate(
year = as.factor(year),
month = factor( month.abb[month], levels = month.abb )
) %>%
group_by( year, month ) %>%
summarise( passengers = sum( passengers ))
ggplot( data ) +
aes( x = month, y = passengers,
group = year, color = year
) +
geom_line() +
geom_point()
viz with ggplot2

29. When are people leaving Budapest ?

30. satRday flights fever
Flights from/to Budapest
Budapest #satRday dataviz competition
Romain François Cecile Sauder

32. Questions

33. Question