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coedata

coedata is a data package that serves up various datasets made available by the Church of England’s Data Services team. Data sources include:

2021 Census topics made available by coedata include:

  • Number of usual residents in households and communal establishments
  • Country of birth
  • Age by 5 year age bands
  • Ethnic group
  • Religion
  • General health
  • Social Classification (NS-SeC)
  • Economic activity status
  • Highest level of qualification
  • Households by deprivation dimensions
  • Household language
  • Accommodation type
  • Car or van availability
  • Tenure
  • Household composition

Installation

You can install the development version of coedata from GitHub with:

# install.packages("pak")
pak::pak("Church-Army/coedata")

Documentation

Full documentation website on: https://Church-Army.github.io/coedata

Usage

Using coedata to get parish-level census statistics

This is an example of how you could use coedata to get 2021 census data on general health for a selection of parishes. We’ll use the neighbouring parishes of Stifford: St Mary and Grays: St Peter and St Paul.

To get this data, we’ll need:

  • The parish codes of those parishes
  • The ONS ID of the general health dataset

We can find the ONS ID of the general health dataset by using coe_datasets(), which lists the ID of every census dataset that’s a available through this package:

ons_id description
TS001 TS001 - Number of usual residents in households and communal establishments
TS004 TS004 - Country of birth
TS007A TS007A - Age by 5 year age bands
TS021 TS021 - Ethnic group
TS030 TS030 - Religion
TS037 TS037 - General health
TS062 TS062 - Social Classification (NS-SeC)
TS066 TS066 - Economic activity status
TS067 TS067 - Highest level of qualification
TS011 TS011 - Households by deprivation dimensions
TS025 TS025 - Household language
TS044 TS044 - Accommodation type
TS045 TS045 - Car or van availability
TS054 TS054 - Tenure
TS003 TS003 - Household composition

We can see that general health data can be found in the dataset with ID TS037.

Luckily, we already know the parish codes for the parishes we’re interested in. If you’re not sure where to find the parish codes you need, please refer to Finding parish codes.

Now that we have the ONS ID and the parish codes, we can get our census data:

coe_census_parish(ons_id = "TS037", parish_codes = c(580342, 580334))
#> ✔ Reading from "coedata_parish-data".
#> ✔ Range ''TS037''.
#> Church of England Census Data
#> TS037 - General health 
#> Units:  persons 
#> # A tibble: 2 × 7
#>   parish_code population general_health_very_good general_health_good
#>   <chr>            <dbl>                    <dbl>               <dbl>
#> 1 580334           49315                   26735.              16254.
#> 2 580342            6332                    2956.               2160.
#> # ℹ 3 more variables: general_health_fair <dbl>, general_health_bad <dbl>,
#> #   general_health_very_bad <dbl>
#> Parish-level data compiled by the Church of England

Those parishes are both in central London - they are very densely populated!

Note that we can also get relative statistics for the same data by setting relative = TRUE:

coe_census_parish(ons_id = "TS037", parish_codes = c(580342, 580334), relative = TRUE) 
#> Church of England Census Data
#> TS037 - General health 
#> Units:  Proportion of all persons 
#> # A tibble: 2 × 7
#>   parish_code population general_health_very_good general_health_good
#>   <chr>            <dbl>                    <dbl>               <dbl>
#> 1 580334           49315                    0.542               0.330
#> 2 580342            6332                    0.467               0.341
#> # ℹ 3 more variables: general_health_fair <dbl>, general_health_bad <dbl>,
#> #   general_health_very_bad <dbl>
#> Parish-level data compiled by the Church of England

Viewing a parish in its diocesan and national contexts

Sometimes people want to see how their parish compares to its diocese and to the nation as a whole. coedata contains a function that returns this data for any parish and any number of ONS datasets:

coe_parish_snapshot(580342, ons_ids = "TS037")
#> $TS037
#> Church of England Census Data
#> TS037 - General health 
#> Units:  Proportion of all persons 
#> # A tibble: 3 × 9
#>   level   level_code level_name        population general_health_very_good
#>   <chr>   <chr>      <chr>                  <dbl>                    <dbl>
#> 1 parish  580342     Stifford: St Mary       6332                    0.467
#> 2 diocese 8          Chelmsford           3281380                    0.497
#> 3 nation  <NA>       england             56490046                    0.485
#> # ℹ 4 more variables: general_health_good <dbl>, general_health_fair <dbl>,
#> #   general_health_bad <dbl>, general_health_very_bad <dbl>

Finding parish codes

When you’re looking at individual parishes with coedata, you’ll need to identify them with their unique parish codes. If you’re not sure what the parish code is for a parish or church, you can either:

A note on socio-economic classification labels

Most of the statistics used in this package are labelled very intuitively, using labels like general_health_very_good or country_of_birth_middle_east_asia. This is not true of the socio0economic classification data (TS062), which uses labels like ns_sec_L1_3. To understand these labels, please consult the table returned by ns_sec_descriptions().

Thanks and attribution

  • All census data was originally provided by the Office for National Statistics. ONS also maintain the nomis service, which was used to source some of this package’s internal data.
  • Parish-level census data profiles were compiled by Ken Eames, who is a senior statistical researcher at the Church of England’s Data Services. Original parish-level datasets are available on the Church of England website
    • Ken also maintains the parish map from which Church data are read by this package. Thanks again, Ken!
  • Thanks to ropensci for their development and maintenance of the nomisr package, which was used to interface with nomis.
  • Thanks to Jenny Bryan at Posit for the development and maintenance of googlesheets4, which provided a simple, secure way of minimising package size by storing data remotely.

TODO: