wranglEHR is a data wrangling and cleaning tool for CC-HIC. It is
designed to run against the CC-HIC EAV table structure (which at present
exists in PostgreSQL and SQLite flavours). We are about to undergo a
major rewrite to OHDSI CDM version 6, so this package will be in flux.
Please see the R vignettes for further details on how to use the
package to perform the most common tasks:
extract_demographics()produces a table for time invariant dataitems.extract_timevarying()produces a table for longitudinal dataitems.clean()cleans the above tables according to pre-defined standards.
This package is designed to work in concert with inspectEHR which
provides data quality evaluation for the CC-HIC.
# install directly from github with
remotes::install_github("DocEd/wranglEHR")
library(wranglEHR)# Connect to the database (will use the internal test db)
ctn <- setup_dummy_db()
# Extract static variables. Rename on the fly.
dtb <- extract_demographics(
connection = ctn,
episode_ids = 1:10, # specify for episodes
code_names = c("NIHR_HIC_ICU_0017", "NIHR_HIC_ICU_0019"),
rename = c("height", "weight")
)
head(dtb)
#> # A tibble: 6 × 2
#> episode_id height
#> <int> <dbl>
#> 1 1 2.34
#> 2 2 2.01
#> 3 3 4.00
#> 4 4 -0.318
#> 5 5 2.44
#> # … with 1 more row
# Extract time varying variables. Rename on the fly.
ltb <- extract_timevarying(
ctn,
episode_ids = 1:10,
code_names = "NIHR_HIC_ICU_0108",
rename = "hr")
#> 3e-04 hours to process
#> WEE! How sublime was that?!
head(ltb)
#> # A tibble: 6 × 3
#> episode_id time hr
#> <int> <dbl> <int>
#> 1 1 0 91
#> 2 1 1 78
#> 3 1 2 102
#> 4 1 3 94
#> 5 1 4 69
#> # … with 1 more row
# Pull out to any arbitrary temporal resolution and custom define the
# behaviour for information recorded at resolution higher than you are sampling.
# only extract the first 24 hours of data
ltb_2 <- extract_timevarying(
ctn,
episode_ids = 1:10,
code_names = "NIHR_HIC_ICU_0108",
rename = "hr",
cadence = 2, # 1 row every 2 hours
coalesce_rows = mean, # use mean to downsample to our 2 hour cadence
time_boundaries = c(0, 24)
)
#> 0.00026 hours to process
#> HUZZAH! How cat's meow was that?!
head(ltb_2)
#> # A tibble: 6 × 3
#> episode_id time hr
#> <int> <dbl> <dbl>
#> 1 1 0 84.5
#> 2 1 2 102
#> 3 1 4 81.3
#> 4 1 6 80
#> 5 1 8 80.3
#> # … with 1 more row
## Don't forget to turn the lights out as you leave.
DBI::dbDisconnect(ctn)If you find a bug, please file a minimal reproducible example on github.
- https://www.ohdsi.org/analytic-tools/achilles-for-data-characterization/
- Kahn, Michael G.; Callahan, Tiffany J.; Barnard, Juliana; Bauck, Alan E.; Brown, Jeff; Davidson, Bruce N.; Estiri, Hossein; Goerg, Carsten; Holve, Erin; Johnson, Steven G.; Liaw, Siaw-Teng; Hamilton-Lopez, Marianne; Meeker, Daniella; Ong, Toan C.; Ryan, Patrick; Shang, Ning; Weiskopf, Nicole G.; Weng, Chunhua; Zozus, Meredith N.; and Schilling, Lisa (2016) “A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data,” eGEMs (Generating Evidence & Methods to improve patient outcomes): Vol. 4: Iss. 1, Article 18.