A dplyr use case. Data about flights to and from Budapest between 2007 and 2012, from the Budapest satRdays dataviz challenge.

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