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.
smr04_data_filepath <- eider_example("random_smr04_data.csv")
smr04_data_filepath
#> [1] "/tmp/Rtmplms5lk/Rinstb684ea25c01/eider/extdata/random_smr04_data.csv"In this specific vignette, we are using simulated SMR04
data. 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.
smr04_data <- utils::read.csv(smr04_data_filepath) %>%
dplyr::mutate(
admission_date = lubridate::ymd(admission_date),
discharge_date = lubridate::ymd(discharge_date)
)
dplyr::glimpse(smr04_data)
#> Rows: 217
#> Columns: 7
#> $ id <int> 1, 3, 3, 3, 11, 11, 2, 19, 19, 19, 16, 16, 4, 4, 16…
#> $ admission_date <date> 2015-07-15, 2016-05-03, 2016-05-04, 2016-05-05, 20…
#> $ discharge_date <date> 2015-07-15, 2016-05-04, 2016-05-05, 2016-05-06, 20…
#> $ cis_marker <int> 26, 20, 20, 20, 33, 33, 56, 70, 70, 70, 59, 59, 67,…
#> $ episode_within_cis <int> 1, 1, 2, 3, 1, 2, 1, 1, 2, 3, 1, 2, 1, 2, 1, 2, 3, …
#> $ admission_type <int> 34, 19, NA, NA, 12, NA, 34, 40, NA, NA, 11, NA, 20,…
#> $ specialty <chr> "G22", "G61", "G21", "G4", "G63", "G6", "G22", "G1"…(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.)
Each row in this table corresponds to one episode; multiple episodes may be associated with the same stay. This simplified table has 7 columns:
id, which is a numeric patient ID;admission_date and discharge_date, which
are the dates of admission and discharge for each episode;cis_marker, which is a unique number associated with
each stay (note that this is not necessarily unique for different
patient IDs);episode_within_cis, which is the episode number within
each stay; andspecialty, which is the specialty code for the
episode.We begin with a simple example: counting the number of episodes
(i.e. number of rows) associated with a psychotherapy specialty, which
corresponds to any of the codes "G6", "G61",
"G62", or "G63". This is a fairly
straightforward count transformation_type, with a filter to
select for those values. We can, in principle, express this as a
compound filter with type "or", as the following shows:
{
"filter": {
"type": "or",
"subfilter": {
"g6": {
"column": "specialty",
"type": "in",
"value": "G6"
},
"g61": {
"column": "specialty",
"type": "in",
"value": "G61"
},
"g62": {
"column": "specialty",
"type": "in",
"value": "G62"
},
"g63": {
"column": "specialty",
"type": "in",
"value": "G63"
}
}
}
}However, for filters with type "in", eider
lets the user specify multiple values to compare against, which is much
more compact. The resulting feature definition is as follows:
pt_episodes_filepath <- eider_example("psychotherapy_episodes.json")
writeLines(readLines(pt_episodes_filepath))
#> {
#> "source_table": "smr04",
#> "transformation_type": "count",
#> "grouping_column": "id",
#> "absent_default_value": 0,
#> "output_feature_name": "num_psychotherapy_episodes",
#> "filter": {
#> "column": "specialty",
#> "type": "in",
#> "value": [
#> "G6",
#> "G61",
#> "G62",
#> "G63"
#> ]
#> }
#> }res <- run_pipeline(
data_sources = list(smr04 = smr04_data_filepath),
feature_filenames = pt_episodes_filepath
)
dplyr::glimpse(res$features)
#> Rows: 20
#> Columns: 2
#> $ id <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1…
#> $ num_psychotherapy_episodes <int> 3, 5, 2, 3, 5, 1, 1, 3, 0, 2, 1, 6, 4, 3, 9…Next, we will count the number of stays associated with a
psychotherapy specialty. This is slightly more complicated, as each row
corresponds to an episode, not a stay: thus, we cannot simply count the
number of rows in the table. Instead, we will need to count the number
of unique cis_marker values associated with a psychotherapy
specialty, as each cis_marker corresponds to a different
stay. This means a transformation type of "nunique", and an
aggregation column of "cis_marker".
pt_stays_filepath <- eider_example("psychotherapy_stays.json")
writeLines(readLines(pt_stays_filepath))
#> {
#> "source_table": "smr04",
#> "transformation_type": "nunique",
#> "grouping_column": "id",
#> "absent_default_value": 0,
#> "aggregation_column": "cis_marker",
#> "output_feature_name": "num_psychotherapy_stays",
#> "filter": {
#> "column": "specialty",
#> "type": "in",
#> "value": [
#> "G6",
#> "G61",
#> "G62",
#> "G63"
#> ]
#> }
#> }res <- run_pipeline(
data_sources = list(smr04 = smr04_data_filepath),
feature_filenames = pt_stays_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, …
#> $ num_psychotherapy_stays <int> 2, 4, 2, 3, 4, 1, 1, 2, 0, 2, 1, 4, 3, 2, 6, 1…The last three features will be concerned with the number of days spent in hospital. In principle, this can be easily calculated from the data we already have: by taking the discharge date and subtracting the admission date, we can obtain the length of each episode.
eider does not yet possess the functionality to
preprocess tables in this way (by adding new columns). However, the
package does allow you to perform this calculation yourself
(e.g. using dplyr), and then add the new table to the
pipeline as a new data source. Specifically, data sources do not
necessarily need to be CSV filenames; they can simply be data frames
themselves.
Let’s construct this new data frame:
smr04_with_days_data <- smr04_data %>%
dplyr::mutate(days_in_hospital = as.numeric(discharge_date - admission_date))
dplyr::glimpse(smr04_with_days_data)
#> Rows: 217
#> Columns: 8
#> $ id <int> 1, 3, 3, 3, 11, 11, 2, 19, 19, 19, 16, 16, 4, 4, 16…
#> $ admission_date <date> 2015-07-15, 2016-05-03, 2016-05-04, 2016-05-05, 20…
#> $ discharge_date <date> 2015-07-15, 2016-05-04, 2016-05-05, 2016-05-06, 20…
#> $ cis_marker <int> 26, 20, 20, 20, 33, 33, 56, 70, 70, 70, 59, 59, 67,…
#> $ episode_within_cis <int> 1, 1, 2, 3, 1, 2, 1, 1, 2, 3, 1, 2, 1, 2, 1, 2, 3, …
#> $ admission_type <int> 34, 19, NA, NA, 12, NA, 34, 40, NA, NA, 11, NA, 20,…
#> $ specialty <chr> "G22", "G61", "G21", "G4", "G63", "G6", "G22", "G1"…
#> $ days_in_hospital <dbl> 0, 1, 1, 1, 2, 0, 0, 2, 2, 2, 0, 0, 4, 0, 2, 1, 0, …In the subsequent sections, we’ll provide this new data frame as a
data source to run_pipeline().
With this new column, we can now calculate the total number of days
each patient has spent in hospital. This just requires a
sum transformation, where we act on the column that we just
added, called days_in_hospital.
total_days_filepath <- eider_example("days_in_smr04.json")
writeLines(readLines(total_days_filepath))
#> {
#> "source_table": "smr04_with_days",
#> "transformation_type": "sum",
#> "grouping_column": "id",
#> "absent_default_value": 0,
#> "aggregation_column": "days_in_hospital",
#> "output_feature_name": "total_days_in_hospital"
#> }Notice that the feature above specifies a different
"source_table". This new identifier can then be passed to
run_pipeline(), together with the data frame that we
calculated above.
res <- run_pipeline(
data_sources = list(smr04_with_days = smr04_with_days_data),
feature_filenames = total_days_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, 1…
#> $ total_days_in_hospital <dbl> 6, 15, 6, 8, 13, 5, 12, 9, 3, 7, 7, 7, 11, 6, 1…In this feature, we are going to calculate the longest single stay in hospital for each patient. To do this, we need to add up the number of days spent in each episode within the same stay, before we take the maximum of these values.
Because the overall action is to take the maximum, the
max transformation type is appropriate here. However, the
summation must be accomplished through a preprocessing step. In this
step, we need to group the data on the id and
cis_marker columns, and then replace the values of
days_in_hospital with the sums of the days for all
episodes. This will give us a table where each row still corresponds to
an episode, but the days_in_hospital column has been
modified to contain values for each stay.
For more details on preprocessing, see the corresponding vignette.
longest_stay_filepath <- eider_example("longest_stay.json")
writeLines(readLines(longest_stay_filepath))
#> {
#> "source_table": "smr04_with_days",
#> "transformation_type": "max",
#> "grouping_column": "id",
#> "absent_default_value": 0,
#> "preprocess": {
#> "on": [
#> "id",
#> "cis_marker"
#> ],
#> "replace_with_sum": [
#> "days_in_hospital"
#> ]
#> },
#> "aggregation_column": "days_in_hospital",
#> "output_feature_name": "longest_stay"
#> }res <- run_pipeline(
data_sources = list(smr04_with_days = smr04_with_days_data),
feature_filenames = longest_stay_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, 15, 16,…
#> $ longest_stay <dbl> 4, 5, 4, 3, 4, 3, 5, 6, 1, 3, 5, 3, 6, 4, 4, 3, 7, 7, 7, 6Just for good measure, let’s run the entire pipeline with all four of the features above in one go.
res <- run_pipeline(
data_sources = list(
smr04 = smr04_data_filepath,
smr04_with_days = smr04_with_days_data
),
feature_filenames = c(
pt_episodes_filepath,
pt_stays_filepath,
total_days_filepath,
longest_stay_filepath
)
)
dplyr::glimpse(res$features)
#> Rows: 20
#> Columns: 5
#> $ id <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1…
#> $ num_psychotherapy_episodes <int> 3, 5, 2, 3, 5, 1, 1, 3, 0, 2, 1, 6, 4, 3, 9…
#> $ num_psychotherapy_stays <int> 2, 4, 2, 3, 4, 1, 1, 2, 0, 2, 1, 4, 3, 2, 6…
#> $ total_days_in_hospital <dbl> 6, 15, 6, 8, 13, 5, 12, 9, 3, 7, 7, 7, 11, …
#> $ longest_stay <dbl> 4, 5, 4, 3, 4, 3, 5, 6, 1, 3, 5, 3, 6, 4, 4…