Quick start

Discover what is available:

There are a few helper functions to help you find out what data sets are available as well as the corresponding editions and versions The ons_available_datasets() function will return a dataframe with information about all available datasets. The id column is what you need to download a dataset.

datasets <-  ons_available_datasets()

 datasets() %>% 
  select(id)
                                    id
1                               cpih01
2                     mid-year-pop-est
3                   ashe-table-7-hours
4                ashe-table-7-earnings
5                   ashe-table-8-hours
6                ashe-table-8-earnings
7                           opss-rates
8                      opss-membership
9                wellbeing-year-ending
10           wellbeing-local-authority
...

Once you have picked a dataset, you need pick the edition you want. This can be done using ons_available_editions().

# Discover the available editions for a particular dataset
ons_available_editions(id = "mid-year-pop-est")

edition
<chr>
1   mid-2018-april-2019-geography           
2   mid-2019-april-2020-geography           
3   time-series

Finally, you need to find out what what versions are availble for a specific edition of a dataset.

# Discover the available versions for a particular edition

ons_available_versions("mid-year-pop-est", "time-series")

  version
1       1
2       2
3       3
4       4

Download the data

You should now be ready to download the data. Start by specifying where you want the data to downloaded to. The monstr_pipeline_defaults() returns a default folder structure (without creating it). You can specify the a file path base using the download_root argument. If you do not specify download_root, the base file path will be your project root if you are using Rstudio projects and wherever you working directory is set to otherwise. The output from monstr_pipeline_defaults() is then fed to ons_datasets_setup() which queries the ONS API to get the relevant information to prepare for downloading the data. Finally, ons_download() downloads the data. The rest of the piped code reads in, cleans and saves a clean version of the data.

monstr_pipeline_defaults(download_root="/path/to/download/root/") %>% 
  ons_datasets_setup() %>% # Uses the monstr 'standards' for location and format
    ons_dataset_by_id("weekly-deaths-local-authority") %>%
  ons_download(format="csv") %>%
    monstr_read_file() %>%  
    monstr_clean() %>%
    monstr_write_clean(format="all")

Further Examples

Download the latest weekly-deaths-local-authority data as a csv.

ons_datasets_setup(monstr_pipeline_defaults()) %>%
    ons_dataset_by_id("weekly-deaths-local-authority") %>%
    ons_download(format="csv")

# file will be in `{{root}}/data/raw/ons/weekly-deaths-local-authority/time-series/vN.csv`
# metadata about the file will be in `{{root}}/data/raw/ons/weekly-deaths-local-authority/time-series/vN.csv.meta.json`

Similarly it can be downloaded as an xls

ons_datasets_setup(monstr_pipeline_defaults()) %>%
    ons_dataset_by_id("weekly-deaths-local-authority") %>%
    ons_download(format="xls")

Specific versions can be selected.

datasets <- ons_datasets_setup(monstr_pipeline_defaults())
## get the metadata about v4 of the time-series edition of weekly-deaths-local-authority dataset.
wdla4_meta <- datasets %>% ons_dataset_by_id("weekly-deaths-local-authority", edition="time-series", version=4)

# download it
wdla4_meta  %>% 
  monstr_pipeline_defaults() %>% 
  ons_download(format="csv")

# Or get the latest
wdla_latest <- datasets %>% ons_dataset_by_id("weekly-deaths-local-authority", edition="time-series")


# csv for the web meta data about the schema of the data.
wdla_latest %>% ons_download(format="csv")