Examples: PIS data

library(eider)
library(magrittr)

This series of vignettes in the Gallery section aim to demonstrate the functionality of eider through examples that are similar to real-life usage. To do this, we have created a series of randomly generated datasets that are stored with the package. You can access these datasets using the eider_example() function, which will return the path to where the dataset is stored in your installation of R.

pis_data_filepath <- eider_example("random_pis_data.csv")

pis_data_filepath
#> [1] "/tmp/RtmpNLxY4o/Rinstb2e339c6b83/eider/extdata/random_pis_data.csv"

The data

In this specific vignette, we are using simulated Prescribing Information System (PIS). Our dataset does not contain every column specified in here, but serves as a useful example of how real-life data may be treated using eider.

pis_data <- utils::read.csv(pis_data_filepath) %>%
  dplyr::mutate(paid_date = lubridate::ymd(paid_date))

dplyr::glimpse(pis_data)
#> Rows: 100
#> Columns: 4
#> $ id          <int> 19, 19, 19, 7, 3, 18, 2, 5, 2, 6, 10, 2, 15, 4, 15, 6, 15,…
#> $ paid_date   <date> 2017-12-15, 2016-08-11, 2015-07-07, 2017-03-14, 2015-08-0…
#> $ bnf_section <int> 113, 106, 105, 112, 111, 106, 108, 104, 109, 110, 109, 115…
#> $ num_items   <int> 3, 5, 1, 1, 1, 3, 4, 3, 1, 4, 4, 5, 3, 5, 1, 2, 4, 2, 3, 1…

(Note that when the data is loaded by eider, the date columns are automatically converted to the date type for you: you do not need to do the manual processing above.)

This simplified table has 4 columns:

  • id, which is a numeric patient ID;
  • paid_date, which is the date the prescription was paid for;
  • bnf_section, which is a code for the type of drug prescribed;
  • num_items, which is the number of items prescribed.

Feature 1: Number of unique prescription types

A simple example of a feature here is the number of unique prescription type each patient has received, which corresponds to the number of distinct values of bnf_section per id.

The JSON required uses the nunique transformation type, and we must specify the column over which we want to take the distinct values using "aggregation_column": "bnf_section".

unique_bnf_filepath <- eider_example("distinct_bnf_prescriptions.json")
writeLines(readLines(unique_bnf_filepath))
#> {
#>   "source_table": "pis",
#>   "transformation_type": "nunique",
#>   "grouping_column": "id",
#>   "absent_default_value": 0,
#>   "aggregation_column": "bnf_section",
#>   "output_feature_name": "unique_bnf_sections"
#> }
res <- run_pipeline(
  data_sources = list(pis = pis_data_filepath),
  feature_filenames = unique_bnf_filepath
)

dplyr::glimpse(res$features)
#> Rows: 20
#> Columns: 2
#> $ id                  <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, …
#> $ unique_bnf_sections <int> 3, 3, 4, 6, 2, 5, 3, 2, 5, 2, 4, 1, 4, 4, 3, 6, 3,…

Feature 2: Number of drugs prescribed since 2016

A slightly more complicated example involves summing up the total number of items prescribed, but only counting those transactions since 2016—in other words, those where the paid_date is on or after 1 January 2016.

To do this, we perform a sum over the num_items column, and use a filter to remove any rows that are prior to this date. The filter has the type "date_gt_eq", which means greater than or equal to.

drugs_since_2016_filepath <- eider_example("num_prescriptions_since_2016.json")
writeLines(readLines(drugs_since_2016_filepath))
#> {
#>   "source_table": "pis",
#>   "transformation_type": "sum",
#>   "grouping_column": "id",
#>   "absent_default_value": 0,
#>   "aggregation_column": "num_items",
#>   "output_feature_name": "num_prescriptions_since_2016",
#>   "filter": {
#>     "column": "paid_date",
#>     "type": "date_gt_eq",
#>     "value": "2016-01-01"
#>   }
#> }
res <- run_pipeline(
  data_sources = list(pis = pis_data_filepath),
  feature_filenames = drugs_since_2016_filepath
)

dplyr::glimpse(res$features)
#> Rows: 20
#> Columns: 2
#> $ id                           <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,…
#> $ num_prescriptions_since_2016 <int> 10, 5, 19, 21, 5, 8, 12, 6, 8, 3, 4, 6, 7…

Feature 3: Maximum number of items prescribed in a single transaction

As a warm-up to feature 4, we will write a small feature that looks up the maximum value of num_items for each patient in the table. This is a max transformation type, which is very similar to the nunique and sum that we have seen above, except that we run a different aggregation function on the num_items column: instead of counting the unique values or summing them, we pick out the maximum value.

max_items_filepath <- eider_example("max_drugs_in_transaction.json")
writeLines(readLines(max_items_filepath))
#> {
#>   "source_table": "pis",
#>   "transformation_type": "max",
#>   "grouping_column": "id",
#>   "absent_default_value": 0,
#>   "aggregation_column": "num_items",
#>   "output_feature_name": "max_drugs_in_transaction"
#> }
res <- run_pipeline(
  data_sources = list(pis = pis_data_filepath),
  feature_filenames = max_items_filepath
)

dplyr::glimpse(res$features)
#> Rows: 20
#> Columns: 2
#> $ id                       <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,…
#> $ max_drugs_in_transaction <int> 4, 3, 5, 7, 5, 4, 4, 4, 6, 4, 5, 6, 3, 4, 3, …

Feature 4: Maximum number of items prescribed in a single day

Now, consider a slightly more complicated request: what is the largest number of items that were prescribed to a patient in a single day? Clearly, this is also a max transformation type, but we need to now somehow group together any rows that belong to the same patient and the same date, and add up those values.

To do this, we can use eider’s preprocessing functionality, which is described more thoroughly in the preprocessing vignette. Specifically, we can:

  • group by the id and paid_date columns;
  • then replace the values in the num_items column with the sum of those values.

In JSON, these instructions can be specified using the "preprocessing" key:

{
    ...,
    "preprocessing": {
        "on": ["id", "paid_date"],
        "replace_with_sum": "num_items"
    }
}

The full JSON file is the same as in Feature 3, but just with this preprocessing block added in:

max_items_day_filepath <- eider_example("max_drugs_in_day.json")
writeLines(readLines(max_items_day_filepath))
#> {
#>   "source_table": "pis",
#>   "transformation_type": "max",
#>   "grouping_column": "id",
#>   "absent_default_value": 0,
#>   "aggregation_column": "num_items",
#>   "preprocess": {
#>     "on": [
#>       "id",
#>       "paid_date"
#>     ],
#>     "replace_with_sum": "num_items"
#>   },
#>   "output_feature_name": "max_drugs_in_day"
#> }
res <- run_pipeline(
  data_sources = list(pis = pis_data_filepath),
  feature_filenames = c(max_items_filepath, max_items_day_filepath)
)

dplyr::glimpse(res$features)
#> Rows: 20
#> Columns: 3
#> $ id                       <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,…
#> $ max_drugs_in_transaction <int> 4, 3, 5, 7, 5, 4, 4, 4, 6, 4, 5, 6, 3, 4, 3, …
#> $ max_drugs_in_day         <int> 4, 3, 8, 7, 5, 6, 8, 4, 6, 4, 5, 6, 3, 4, 3, …

Notice the differences between the two feature columns above: in the second (max_drugs_in_day) we have successfully aggregated transactions which happened on the same day, and thus the values (where they differ) are larger.