To view the full wiki, click here: Full healthyR Wiki
healthyR is a comprehensive R package designed to streamline hospital data analysis workflows. It provides a consistent, intuitive framework for analyzing common administrative and clinical data problems, helping healthcare analysts and data scientists quickly generate insights from hospital data.
healthyR takes the guesswork out of common hospital data analysis tasks:
You can install the released version of healthyR from CRAN with:
install.packages("healthyR")
And the development version from GitHub with:
# install.packages("devtools")
# devtools::install_github("spsanderson/healthyR")
install.packages("healthyR", repos = c('https://spsanderson.r-universe.dev', 'https://cloud.r-project.org'))
library(healthyR)
library(dplyr)
library(timetk)
# Load your hospital data
# Analyze time series patterns with time signatures
data_with_time_features <- ts_signature_tbl(
.data = your_data,
.date_col = admission_date
)
# Visualize average length of stay trends
ts_alos_plt(
.data = your_data,
.date_col = discharge_date,
.value_col = length_of_stay,
.by_grouping = "month"
)
# Create service line classifications
data_with_service_line <- your_data %>%
mutate(
service_line = service_line_vec(
.data = .,
.dx_col = principal_dx,
.px_col = principal_px,
.drg_col = drg_number
)
)
healthyR provides powerful tools for temporal analysis of hospital data.
Add time-based features to your data for advanced analysis:
library(healthyR)
library(timetk)
ts_signature_tbl(.data = m4_daily, .date_col = date)
#> # A tibble: 17,578 × 31
#> id date value index.num diff year year.iso half quarter month
#> <fct> <date> <dbl> <dbl> <dbl> <int> <int> <int> <int> <int>
#> 1 D410 1978-06-23 9109. 267408000 NA 1978 1978 1 2 6
#> 2 D410 1978-06-24 9103. 267494400 86400 1978 1978 1 2 6
#> 3 D410 1978-06-25 9116. 267580800 86400 1978 1978 1 2 6
#> 4 D410 1978-06-26 9116. 267667200 86400 1978 1978 1 2 6
#> 5 D410 1978-06-27 9106. 267753600 86400 1978 1978 1 2 6
#> 6 D410 1978-06-28 9094. 267840000 86400 1978 1978 1 2 6
#> 7 D410 1978-06-29 9094. 267926400 86400 1978 1978 1 2 6
#> 8 D410 1978-06-30 9084. 268012800 86400 1978 1978 1 2 6
#> 9 D410 1978-07-01 9081. 268099200 86400 1978 1978 2 3 7
#> 10 D410 1978-07-02 9047. 268185600 86400 1978 1978 2 3 7
#> # ℹ 17,568 more rows
#> # ℹ 21 more variables: month.xts <int>, month.lbl <ord>, day <int>, hour <int>,
#> # minute <int>, second <int>, hour12 <int>, am.pm <int>, wday <int>,
#> # wday.xts <int>, wday.lbl <ord>, mday <int>, qday <int>, yday <int>,
#> # mweek <int>, week <int>, week.iso <int>, week2 <int>, week3 <int>,
#> # week4 <int>, mday7 <int>
Identify patterns and outliers in time series data:
library(healthyR)
library(timetk)
library(dplyr)
ts_signature_tbl(.data = m4_daily, .date_col = date, .pad_time = TRUE, id) %>%
ts_median_excess_plt(
.date_col = date
, .value_col = value
, .x_axis = week
, .ggplot_group_var = year
, .years_back = 5
)
Create quadrant charts for performance analysis (e.g., comparing length of stay vs readmission risk):
suppressPackageStartupMessages(library(healthyR))
suppressPackageStartupMessages(library(tibble))
suppressPackageStartupMessages(library(dplyr))
gartner_magic_chart_plt(
.data = tibble(x = rnorm(100, 0, 1), y = rnorm(100, 0, 1))
, .x_col = x
, .y_col = y
, .y_lab = "los"
, .x_lab = "RA"
, .plot_title = "Test Title"
, .top_left_label = "Top Left lbl"
, .top_right_label = "Top Right lbl"
, .bottom_left_label = "Bottom Left lbl"
, .bottom_right_label = "Bottom Right lbl"
)
Automatically classify patients into service lines based on clinical codes:
# Example: Classify patients by service line
df <- data.frame(
dx_col = "F10.10", # ICD-10 diagnosis code
px_col = NA, # Procedure code (if applicable)
drg_col = "896" # DRG number
)
service_line_vec(
.data = df,
.dx_col = dx_col,
.px_col = px_col,
.drg_col = drg_col
)
ts_signature_tbl() - Add time-based features to your datats_alos_plt() - Plot average length of stay over timets_readmit_rate_plt() - Visualize readmission ratests_median_excess_plt() - Identify excess utilization patternsts_census_los_daily_tbl() - Calculate daily census and LOS metricsts_plt() - General time series plottinglos_ra_index_summary_tbl() - Calculate LOS and readmission indiceslos_ra_index_plt() - Visualize performance indicesgartner_magic_chart_plt() - Create quadrant performance chartsdiverging_bar_plt() - Show positive/negative deviationsdiverging_lollipop_plt() - Lollipop charts for variance analysisservice_line_vec() - Classify patients into service linesservice_line_augment() - Add service line column to your datacategory_counts_tbl() - Get frequency counts by categorytop_n_tbl() - Extract top N records by criterianamed_item_list() - Create named lists for Excel exportcolor_blind() - Color-blind friendly palettehr_scale_fill_colorblind() - ggplot2 fill scale for accessibilityhr_scale_color_colorblind() - ggplot2 color scale for accessibilitysave_to_excel() - Export data to Excel with timestampopt_bin() - Calculate optimal bin size for histogramssql_left(), sql_right(), sql_mid() - SQL-style string operationshealthyR includes reference datasets for service line classification:
dx_cc_mapping - ICD-10 diagnosis code mappings for condition
categoriespx_cc_mapping - ICD-10 procedure code mappings for procedure
categoriesWe welcome contributions! Please see our Contributing Guidelines for details on:
If you encounter issues or have questions:
If you use healthyR in your research or publications, please cite it:
citation("healthyR")
MIT © Steven P. Sanderson II, MPH
Steven P. Sanderson II, MPH - Website: https://www.spsanderson.com - GitHub: @spsanderson - ORCID: 0009-0006-7661-8247