Get accessibility data for particular year and table number

Source: R/accessibility.R

The DfT's Journey Time Statistics are outlined here on the gov.uk website.

get_jts_data(
  table,
  year = 2017,
  u_csv = jts_url(),
  clean = TRUE,
  ods = FALSE,
  output_format = "data_frame",
  type = NULL
)

Arguments

table

The title of the table, e.g. "jts0501"
year

The year, e.g. 2017. If "meta" is supplied, get metadata.
u_csv

The base url of the files
clean

Should the dataset be cleaned with jts_clean?
ods

Download and read-in raw ODS files? FALSE by default, which reads-in csv files instead. The .csv files were created to overcome performance limitations of readODS, which cannot read-in large .ods files.
output_format

Which file format should be returned? data_frame by default; sf optional.
type

Options are lsoa, la (local authority district/unitary authority) and lpa (local planning authority); lsoa by default.

Details

The function uses a data frame of existing tables, created by the script 'accessibility_tables.R' in the data-raw folder.

The tables starting JTS01 to JTS03 provide national overview data.

The tables JTS0401 to JTS0409 provide data at the Local Authority level.

The tables JTS0501 to JTS0509 provide the same data at the LSOA level.

The tables beginning JTS09 provide data on accessibility to transport hubs.

And the tables beginning JTS10 contain add other variables.

Data is provided every year from 2014 to 2019 in many cases

Examples

head(jts_tables)

#> # A tibble: 6 x 7
#>   table_code csv_file_name  table_title    table_url    csv_url   service  year 
#>   <chr>      <chr>          <chr>          <chr>        <chr>     <chr>    <chr>
#> 1 jts0101    jts0101-JTS01… Average minim… https://ass… https://… Time: E… meta 
#> 2 jts0102    jts0102-2014.… Average minim… https://ass… https://… Time, r… 2014 
#> 3 jts0102    jts0102-2015.… Average minim… https://ass… https://… Time, r… 2015 
#> 4 jts0102    jts0102-2016.… Average minim… https://ass… https://… Time, r… 2016 
#> 5 jts0102    jts0102-2017.… Average minim… https://ass… https://… Time, r… 2017 
#> 6 jts0103    jts0103-2014.… Average minim… https://ass… https://… Time by… 2014 
head(jts_tables$table_title)

#> [1] "Average minimum travel time to reach the nearest key services by mode of travel: England"                       
#> [2] "Average minimum travel time to reach the nearest key services by mode of travel, rural and urban areas: England"
#> [3] "Average minimum travel time to reach the nearest key services by mode of travel, rural and urban areas: England"
#> [4] "Average minimum travel time to reach the nearest key services by mode of travel, rural and urban areas: England"
#> [5] "Average minimum travel time to reach the nearest key services by mode of travel, rural and urban areas: England"
#> [6] "Average minimum travel time to reach the nearest key services by public transport or walking, region: England"  
metadata = get_jts_data(table = "jts0101", year = "meta")

#> Warning: Multiple column headings found: : 
head(metadata)

#> # A tibble: 6 x 13
#>   Year  Mode  `Places with 10… `Places with 50… `Places with 50…
#>   <fct> <fct>            <dbl>            <dbl>            <dbl>
#> 1 2014  Publ…              9.7             12.7             32.6
#> 2 NA    Cycle              9.2             10.9             32.7
#> 3 NA    Car                7.6              8.5             17.2
#> 4 2015  Publ…              9.8             12.7             32.7
#> 5 NA    Cycle              9.2             10.8             32.2
#> 6 NA    Car                7.5              8.3             16.8
#> # … with 8 more variables: `Primary school` <dbl>, `Secondary school` <dbl>,
#> #   `Further Education` <dbl>, GP <dbl>, Hospital <dbl>, `Food store` <dbl>,
#> #   `Town Centres` <dbl>, NA <fct>
# uncomment on released version
jts0401_2017 = get_jts_data(table = "jts0401", year = 2017)
head(jts0401_2017[1:7])

#> # A tibble: 6 x 7
#>   Region   LA_Code  LA_Name     Empl_pop `100EmpPTt` `100EmpPT15n` `100EmpPT30n`
#>   <fct>    <fct>    <fct>          <int>       <dbl>         <dbl>         <dbl>
#> 1 North E… E060000… County Dur…   387726        9.10          2.33          8.97
#> 2 North E… E060000… Darlington     76400        8.42          3.35          9.29
#> 3 North E… E060000… Hartlepool     67192        7.90          3.50          9.63
#> 4 North E… E060000… Middlesbro…   101165        6.26          3.97          9.94
#> 5 North E… E060000… Northumber…   234010       15.2           1.49          6.65
#> 6 North E… E060000… Redcar and…    97971        7.94          2.40          9.62
jts0401_2014 = get_jts_data(table = "jts0401", year = 2014)
head(jts0401_2014[1:7])

#> # A tibble: 6 x 7
#>   Region   LA_Code  LA_Name     Empl_pop `100EmpPTt` `100EmpPT15n` `100EmpPT30n`
#>   <fct>    <fct>    <fct>          <int>       <dbl>         <dbl>         <dbl>
#> 1 North E… E060000… County Dur…   384110       10.6           1.86          8.52
#> 2 North E… E060000… Darlington     76142        8.84          2.88          9.25
#> 3 North E… E060000… Hartlepool     67013        8.04          3.26          9.60
#> 4 North E… E060000… Middlesbro…   100614        7.50          3.43          9.97
#> 5 North E… E060000… Northumber…   232459       16.2           1.32          6.54
#> 6 North E… E060000… Redcar and…    98459        9.06          2.01          9.32
jts0401_2017_sf = get_jts_data(table = "jts0401", year = 2017, output_format = "sf")

#> Joining, by = "LA_Code"
head(jts0401_2014[1:7])

#> # A tibble: 6 x 7
#>   Region   LA_Code  LA_Name     Empl_pop `100EmpPTt` `100EmpPT15n` `100EmpPT30n`
#>   <fct>    <fct>    <fct>          <int>       <dbl>         <dbl>         <dbl>
#> 1 North E… E060000… County Dur…   384110       10.6           1.86          8.52
#> 2 North E… E060000… Darlington     76142        8.84          2.88          9.25
#> 3 North E… E060000… Hartlepool     67013        8.04          3.26          9.60
#> 4 North E… E060000… Middlesbro…   100614        7.50          3.43          9.97
#> 5 North E… E060000… Northumber…   232459       16.2           1.32          6.54
#> 6 North E… E060000… Redcar and…    98459        9.06          2.01          9.32
# jts0401_2017_raw = get_jts_data(table = "jts0401", year = 2017, clean = FALSE)
# head(jts0401_2017_raw[1:7])
# jts0501_2017 = get_jts_data(table = "jts0501", year = 2017)
# head(jts0501_2017[1:7])
# jts0501_2017 = get_jts_data(table = "jts0501", year = 2017, output_format = "sf")
# head(jts0501_2017)
jts0501_meta = get_jts_data(table = "jts0501", year = "meta")

#> Warning: Multiple column headings found: 1618: 
head(jts0501_meta)

#> # A tibble: 6 x 4
#>   `Service users source: Census and u… NA       NA                      NA      
#>   <fct>                                <fct>    <fct>                   <fct>   
#> 1 Field                                Alterna… Description             Paramet…
#> 2 LSOA_Code                            Emp005   LSOA Code               Referen…
#> 3 Region                               Emp001   Region of England       Referen…
#> 4 LA_Code                              Emp002   LA Code                 Referen…
#> 5 LA_Name                              Emp003   LA Name                 Referen…
#> 6 Empl_pop                             Emp004   Service users in LSOA … Number