library(spanishoddata)
library(dplyr)This vignette demonstrates how to get minimal daily aggregated data on the number of trips between municipalities using the spod_quick_get_od() function. With this function, you only get total trips for a single day, and no additional variables that are available in the full v2 (2022 onwards) data set. The advantage of this function is that it is much faster than downloading the full data from source CSV files using spod_get(), as each CSV file for a single day is about 200 MB in size. Also, this way of getting the data is much less demanding on your computer as you are only getting a small table from the internet (less than 1 MB), and no data processing (such as aggregation from more detailed hourly data with extra columns that is happening when you use spod_get() function) is required on your computer.
Make sure you have loaded the package:
library(spanishoddata)
library(dplyr)Choose where {spanishoddata} should download (and convert) the data by setting the data directory following command:
spod_set_data_dir(data_dir = "~/spanish_od_data")The function above will also ensure that the directory is created and that you have sufficient permissions to write to it.
You can also set the data directory with an environment variable:
Sys.setenv(SPANISH_OD_DATA_DIR = "~/spanish_od_data")The package will create this directory if it does not exist on the first run of any function that downloads the data.
To permanently set the directory for all projects, you can specify the data directory globally by setting the SPANISH_OD_DATA_DIR environment variable, e.g. with the following command:
usethis::edit_r_environ()
# Then set the data directory globally, by typing this line in the file:SPANISH_OD_DATA_DIR = "~/spanish_od_data"You can also set the data directory locally, just for the current project. Set the ‘envar’ in the working directory by editing .Renviron file in the root of the project:
file.edit(".Renviron")Note
Setting a local data directory in this case is optional, as the data is downloaded directly from the web API and there is no caching on disk. However, the metadata will be downloaded to check the range of valid dates available at the time of the request. So metadata will be downloaded to a temporary location by default, or to the data directory, if you do set it.
To get the data, use the spod_quick_get_od() function. There is no need to specify whether you need municipalities or districts, as the only municipal level data can be accessed with this function. The min_trips argument specifies the minimum number of trips to include in the data. If you set min_trips to 0, you will get all data for all origin-destination pairs for the specified date.
od_1000 <- spod_quick_get_od(
date = "2022-01-01",
min_trips = 1000
)The data is returned as a tibble and only contanes the requested date, the identifiers of the origin and destination municipalities, the number of trips, and the total length of trips in kilometers.
glimpse(od_1000)glimpse(od_1000)
Rows: 8,524
Columns: 5
$ date <date> 2022-01-01, 2022-01-01, 2022-01-01, 2022…
$ id_origin <chr> "01001", "01002", "01002", "01002", "0100…
$ id_destination <chr> "01059", "01036", "01002", "01054_AM", "0…
$ n_trips <int> 2142, 1215, 8899, 1105, 2250, 4621, 1992,…
$ trips_total_length_km <int> 27130, 13743, 26700, 10603, 12228, 69999,…od_1000# A tibble: 8,524 × 5
date id_origin id_destination n_trips trips_total_length_km
<date> <chr> <chr> <int> <int>
1 2022-01-01 01001 01059 2142 27130
2 2022-01-01 01002 01036 1215 13743
3 2022-01-01 01002 01002 8899 26700
4 2022-01-01 01002 01054_AM 1105 10603
5 2022-01-01 01002 01010 2250 12228
6 2022-01-01 01009_AM 01059 4621 69999
7 2022-01-01 01009_AM 01009_AM 1992 16395
8 2022-01-01 01009_AM 01051 2680 18554
9 2022-01-01 01010 01002 2147 11578
10 2022-01-01 01017_AM 01017_AM 1847 12695
# ℹ 8,514 more rows
# ℹ Use `print(n = ...)` to see more rowsTo get only trips of a certain length, use the distances argument.
od_long <- spod_quick_get_od(
date = "2022-01-01",
min_trips = 0,
distances = c("10-50km", "50+km")
)glimpse(od_long)Rows: 247,208
Columns: 5
$ date <date> 2022-01-01, 2022-01-01, 2022-01-01, 2022…
$ id_origin <chr> "08015", "08015", "08015", "08015", "0801…
$ id_destination <chr> "08285", "17902_AM", "43014", "08007", "0…
$ n_trips <int> 5, 1, 5, 165, 210, 111, 1486, 39, 52, 166…
$ trips_total_length_km <int> 339, 161, 924, 5052, 2955, 2453, 29630, 1…od_long# A tibble: 247,208 × 5
date id_origin id_destination n_trips trips_total_length_km
<date> <chr> <chr> <int> <int>
1 2022-01-01 08015 08285 5 339
2 2022-01-01 08015 17902_AM 1 161
3 2022-01-01 08015 43014 5 924
4 2022-01-01 08015 08007 165 5052
5 2022-01-01 08015 08030 210 2955
6 2022-01-01 08015 08051 111 2453
7 2022-01-01 08015 08121 1486 29630
8 2022-01-01 08015 08122_AM 39 1886
9 2022-01-01 08015 08300_AM 52 1301
10 2022-01-01 08015 08902 166 2042
# ℹ 247,198 more rows
# ℹ Use `print(n = ...)` to see more rowsTo get only trips between certain municipalities, use the id_origin and id_destination arguments.
You can get all valid munincipality identifiers with the spod_get_zones("muni", ver = 2) function. This function will need to download some spatial data, so it might take some time and you might want to setup the data download folder with spod_setup_cache() if you have not done so before.
municipalities <- spod_get_zones("muni", ver = 2)
head(municipalities)Let us select all locations with Madrid in the name:
madrid_muni_ids <- municipalities |>
filter(str_detect(name, "Madrid")) |>
pull(id)
madrid_muni_ids[1] "28073" "28079" "28127" "45087"Now let use use these IDs as origins to gett all trips from Madrid to the rest of Spain:
flows_from_Madrid <- spod_quick_get_od(
date = "2022-01-01",
min_trips = 0,
id_origin = madrid_muni_ids
)glimpse(flows_from_Madrid)Rows: 2,232
Columns: 5
$ date <date> 2022-01-01, 2022-01-01, 2022-01-01, 2022…
$ id_origin <chr> "28073", "28073", "28073", "28073", "2807…
$ id_destination <chr> "28007", "28066", "28079", "45081", "FR10…
$ n_trips <int> 1239, 1505, 3730, 237, 2, 10, 11, 5, 9, 2…
$ trips_total_length_km <int> 11120, 7268, 75798, 3385, 82, 296, 1036, …# A tibble: 2,232 × 5
date id_origin id_destination n_trips trips_total_length_km
<date> <chr> <chr> <int> <int>
1 2022-01-01 28073 28007 1239 11120
2 2022-01-01 28073 28066 1505 7268
3 2022-01-01 28073 28079 3730 75798
4 2022-01-01 28073 45081 237 3385
5 2022-01-01 28073 FR102 2 82
6 2022-01-01 28073 28177 10 296
7 2022-01-01 28073 45165 11 1036
8 2022-01-01 28073 46011_AM 5 1621
9 2022-01-01 28073 21076_AM 9 4102
10 2022-01-01 28073 46120_AM 2 631
# ℹ 2,222 more rows
# ℹ Use `print(n = ...)` to see more rowsSimilarly, you can set limits on the destination municipalities:
barcelona_muni_ids <- municipalities |>
filter(str_detect(name, "Barcelona")) |>
pull(id)
barcelona_muni_idsmadrid_barcelona_od <- spod_quick_get_od(
date = "2022-01-01",
min_trips = 0,
id_origin = madrid_muni_ids,
id_destination = barcelona_muni_ids
)madrid_barcelona_od# A tibble: 4 × 5
date id_origin id_destination n_trips trips_total_length_km
<date> <chr> <chr> <int> <int>
1 2022-01-01 28073 08019 2 1334
2 2022-01-01 28079 08019 1661 838281
3 2022-01-01 28127 08019 26 13699
4 2022-01-01 45087 08019 2 1184You can now proceed to analyse these flows or visualise them as in the static and interactive flow maps tutorials.