The goal of jtstats is to enable easy import of the Department for Transport’s Journey Times Statistics (JTS).
You can install the development version of jtstats from GitHub with:
# install.packages("remotes")
remotes::install_github("datasciencecampus/jtstats-r")
# # Or for local development, uncomment the following::
# remotes::install_local(".")
# devtools::load_all() # or Ctlr+Shift+BLoad the package as follows:
library(jtstats)For the purposes of this README we will also load the tidyverse metapackage:
library(tidyverse)To see what tables are available you can browse the JTS website on gov.uk. Alternatively, you can check the datasets from within R:
dim(jts_tables)
#> [1] 192 6
head(jts_tables)
#> # A tibble: 6 × 6
#> table_type table_title table_code sheet csv_file table_url
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Journey times to key services… Average mi… jts0101 JTS0… jts0101… https://…
#> 2 Journey times to key services… Average mi… jts0102 2014 jts0102… https://…
#> 3 Journey times to key services… Average mi… jts0102 2015 jts0102… https://…
#> 4 Journey times to key services… Average mi… jts0102 2016 jts0102… https://…
#> 5 Journey times to key services… Average mi… jts0102 2017 jts0102… https://…
#> 6 Journey times to key services… Average mi… jts0102 2019 jts0102… https://…As the above output shows, there are 192 separate tables that constitute the JTS dataset. JTS tables are divided into different table types:
unique(jts_tables$table_type)
#> [1] "Journey times to key services (JTS01)"
#> [2] "User access to key services by journey time (JTS02)"
#> [3] "Number of key services by journey time (JTS03)"
#> [4] "Journey times to key services by local authority (JTS04)"
#> [5] "Journey times to key services by lower super output area (JTS05)"
#> [6] "Journey times connectivity (JTS09)"
#> [7] "Ad hoc journey times analysis (JTS10)"You can search for specific JTS tables with lookup_jts_table():
jts04_gps = lookup_jts_table(type = "jts04", purpose = "GPs")
#> Matching tables by type (jts04):
#> Travel time, destination and origin indicators for employment centres by mode of travel and local authority, England
#> Travel time, destination and origin indicators for primary schools by mode of travel and local authority, England
#> Travel time, destination and origin indicators for secondary schools by mode of travel and local authority, England
#> Travel time, destination and origin indicators for further education by mode of travel and local authority, England
#> Travel time, destination and origin indicators for GPs by mode of travel and local authority, England
#> Travel time, destination and origin indicators for hospitals by mode of travel and local authority, England
#> Travel time, destination and origin indicators for food stores by mode of travel and local authority, England
#> Travel time, destination and origin indicators for town centres by mode of travel and local authority, England
#> Travel time, destination and origin indicators to Pharmacy by cycle and car, local authority, England
#>
#> Matching tables by purpose (jts04):
#> Travel time, destination and origin indicators for GPs by mode of travel and local authority, England
jts04_gps$table_title
#> [1] "Travel time, destination and origin indicators for GPs by mode of travel and local authority, England"
#> [2] "Travel time, destination and origin indicators for GPs by mode of travel and local authority, England"
#> [3] "Travel time, destination and origin indicators for GPs by mode of travel and local authority, England"
#> [4] "Travel time, destination and origin indicators for GPs by mode of travel and local authority, England"
#> [5] "Travel time, destination and origin indicators for GPs by mode of travel and local authority, England"
#> [6] "Travel time, destination and origin indicators for GPs by mode of travel and local authority, England"
jts04_gps$sheet
#> [1] "2014" "2015_REVISED" "2016" "2017" "2019"
#> [6] "LA_Metadata"As an example, our packages allow easy retrieval of data on the average journey time to employment centres (with 100 to 499 jobs) by public transport simply by running the following lines of code:
jts_df = get_jts(type = "jts05", purpose = "employment", sheet = 2019)
#> Matching tables by type (jts05):
#> Travel time, destination and origin indicators for Employment centres by mode of travel, Lower Super Output Area (LSOA), England
#> Travel time, destination and origin indicators for Primary schools by mode of travel, Lower Super Output Area (LSOA), England
#> Travel time, destination and origin indicators for Secondary schools by mode of travel, Lower Super Output Area (LSOA), England
#> Travel time, destination and origin indicators for Further education by mode of travel, Lower Super Output Area (LSOA), England
#> Travel time, destination and origin indicators for GPs by mode of travel, Lower Super Output Area (LSOA), England
#> Travel time, destination and origin indicators for Hospitals by mode of travel, Lower Super Output Area (LSOA), England
#> Travel time, destination and origin indicators for Food stores by mode of travel, Lower Super Output Area (LSOA), England
#> Travel time, destination and origin indicators for town centres by mode of travel, Lower Super Output Area (LSOA), England
#> Travel time, destination and origin indicators to Pharmacies by cycle and car, Lower Super Output Area (LSOA), England
#>
#> Matching tables by purpose (jts05):
#> Travel time, destination and origin indicators for Employment centres by mode of travel, Lower Super Output Area (LSOA), England
#>
#> Matching tables by sheet (jts05, employment, 2019):
#> jts0501-2019_REVISED.csv
#> Warning: One or more parsing issues, call `problems()` on your data frame for details,
#> e.g.:
#> dat <- vroom(...)
#> problems(dat)Imagine you’re interested in how average travel time to GP services changed between 2017 and 2019. You can do that as follows:
jts_las_gps_2017 = get_jts(type = "jts04", purpose = "GPs", sheet = 2017)
#> Matching tables by type (jts04):
#> Travel time, destination and origin indicators for employment centres by mode of travel and local authority, England
#> Travel time, destination and origin indicators for primary schools by mode of travel and local authority, England
#> Travel time, destination and origin indicators for secondary schools by mode of travel and local authority, England
#> Travel time, destination and origin indicators for further education by mode of travel and local authority, England
#> Travel time, destination and origin indicators for GPs by mode of travel and local authority, England
#> Travel time, destination and origin indicators for hospitals by mode of travel and local authority, England
#> Travel time, destination and origin indicators for food stores by mode of travel and local authority, England
#> Travel time, destination and origin indicators for town centres by mode of travel and local authority, England
#> Travel time, destination and origin indicators to Pharmacy by cycle and car, local authority, England
#>
#> Matching tables by purpose (jts04):
#> Travel time, destination and origin indicators for GPs by mode of travel and local authority, England
#>
#> Matching tables by sheet (jts04, GPs, 2017):
#> jts0405-2017.csv
jts_las_gps_2019 = get_jts(type = "jts04", purpose = "GPs", sheet = 2019)
#> Matching tables by type (jts04):
#> Travel time, destination and origin indicators for employment centres by mode of travel and local authority, England
#> Travel time, destination and origin indicators for primary schools by mode of travel and local authority, England
#> Travel time, destination and origin indicators for secondary schools by mode of travel and local authority, England
#> Travel time, destination and origin indicators for further education by mode of travel and local authority, England
#> Travel time, destination and origin indicators for GPs by mode of travel and local authority, England
#> Travel time, destination and origin indicators for hospitals by mode of travel and local authority, England
#> Travel time, destination and origin indicators for food stores by mode of travel and local authority, England
#> Travel time, destination and origin indicators for town centres by mode of travel and local authority, England
#> Travel time, destination and origin indicators to Pharmacy by cycle and car, local authority, England
#>
#> Matching tables by purpose (jts04):
#> Travel time, destination and origin indicators for GPs by mode of travel and local authority, England
#>
#> Matching tables by sheet (jts04, GPs, 2019):
#> jts0405-2019.csv
names(jts_las_gps_2017)
#> [1] "Region" "LA_Code" "LA_Name" "GP_pop" "GPPTt"
#> [6] "GPPT15n" "GPPT30n" "GPPT45n" "GPPT60n" "GPPT15pct"
#> [11] "GPPT30pct" "GPPT45pct" "GPPT60pct" "GPCyct" "GPCyc15n"
#> [16] "GPCyc30n" "GPCyc45n" "GPCyc60n" "GPCyc15pct" "GPCyc30pct"
#> [21] "GPCyc45pct" "GPCyc60pct" "GPCart" "GPCar15n" "GPCar30n"
#> [26] "GPCar45n" "GPCar60n" "GPCar15pct" "GPCar30pct" "GPCar45pct"
#> [31] "GPCar60pct"jts_geo = get_jts(type = "jts04", purpose = "GPs", sheet = 2017, geo = TRUE)
#> Matching tables by type (jts04):
#> Travel time, destination and origin indicators for employment centres by mode of travel and local authority, England
#> Travel time, destination and origin indicators for primary schools by mode of travel and local authority, England
#> Travel time, destination and origin indicators for secondary schools by mode of travel and local authority, England
#> Travel time, destination and origin indicators for further education by mode of travel and local authority, England
#> Travel time, destination and origin indicators for GPs by mode of travel and local authority, England
#> Travel time, destination and origin indicators for hospitals by mode of travel and local authority, England
#> Travel time, destination and origin indicators for food stores by mode of travel and local authority, England
#> Travel time, destination and origin indicators for town centres by mode of travel and local authority, England
#> Travel time, destination and origin indicators to Pharmacy by cycle and car, local authority, England
#>
#> Matching tables by purpose (jts04):
#> Travel time, destination and origin indicators for GPs by mode of travel and local authority, England
#>
#> Matching tables by sheet (jts04, GPs, 2017):
#> jts0405-2017.csv
names(jts_geo)
#> [1] "OBJECTID" "lad11cd" "lad11cdo" "lad11nm" "lad11nmw"
#> [6] "GlobalID" "SHAPE_Length" "SHAPE_Area" "geometry" "Region"
#> [11] "LA_Name" "GP_pop" "GPPTt" "GPPT15n" "GPPT30n"
#> [16] "GPPT45n" "GPPT60n" "GPPT15pct" "GPPT30pct" "GPPT45pct"
#> [21] "GPPT60pct" "GPCyct" "GPCyc15n" "GPCyc30n" "GPCyc45n"
#> [26] "GPCyc60n" "GPCyc15pct" "GPCyc30pct" "GPCyc45pct" "GPCyc60pct"
#> [31] "GPCart" "GPCar15n" "GPCar30n" "GPCar45n" "GPCar60n"
#> [36] "GPCar15pct" "GPCar30pct" "GPCar45pct" "GPCar60pct"
jts_geo %>%
select(GPPT15pct) %>%
plot()
We can make a slightly more sophisticated plot with tmap as follows:
library(tmap)
#>
#> Attaching package: 'tmap'
#> The following object is masked from 'package:datasets':
#>
#> rivers
uk = rnaturalearth::ne_countries(country = "United Kingdom", returnclass = "sf", scale = "medium")
ie = rnaturalearth::ne_countries(country = "Ireland", returnclass = "sf", scale = "medium")
tm_shape(uk, bbox = sf::st_bbox(jts_geo)) +
tm_polygons() +
tm_shape(ie) +
tm_polygons() +
tm_shape(jts_geo) +
tm_polygons("GPPT15pct", palette = "Blues", title = "% people who live\nwithin 15 minutes\nof GP by public transport") +
tm_layout(legend.position = c("right", "top"))
#> tm_polygons: Deprecated tmap v3 code detected. Code translated to v4