feat: addition of accel data

R/bibentries.R

@@ -30,6 +30,13 @@ bibentries <- c(
     pages = "e3298",
     year = "2017",
     publisher = "PeerJ Inc."
+  ),
+  huang2019multilevel = bibentry("article",
+    title = "Multilevel matrix-variate analysis and its application to accelerometry-measured physical activity in clinical populations",
+    author = "Huang, Lei and Bai, Jiawei and Ivanescu, Andrada and Harris, Tamara and Maurer, Mathew and Green, Philip and Zipunnikov, Vadim",
+    journal = "Journal of the American Statistical Association",
+    year = "2019",
+    publisher = "Taylor & Francis"
   )
 )
 # nolint end

R/chf.R

@@ -0,0 +1,22 @@
+#' Congestive heart failure accelerometry data
+#'
+#' @description
+#' Activity data from a study of congestive heart failure.
+#'
+#' @source <https://www.dropbox.com/scl/fi/vnuldgqnkfa532hzoazjj/chfData.zip>
+#' @references `r format_bib("huang2019multilevel")`
+#' @format A tibble with 13,300 rows and 9 variables:
+#' \describe{
+#'   \item{id}{Unique identifier for each participant in the study.}
+#'   \item{gender}{Participant's gender, recorded as male or female.}
+#'   \item{age}{Age of the participant, in years.}
+#'   \item{bmi}{Body Mass Index (BMI) of the participant.}
+#'   \item{event_week}{The week number relative to a critical event.}
+#'   \item{event_type}{Type of event recorded, such as hospitalization.}
+#'   \item{week}{Week number during the study, indicating the time frame of data collection.}
+#'   \item{day}{Day of the week (e.g. Monday, Tuesday) when the activity data was recorded.}
+#'   \item{activity}{Activity levels recorded by accelerometer (resolution is 1 minute, so 24 h/day * 60 min/h = 1440 min/day).}
+#' }
+#' @examples
+#' chf
+"chf"

README.Rmd

@@ -25,7 +25,7 @@ A collection of datasets containing functional data
 
 ## Installation
 
-You can install the development version of gmeans from [GitHub](https://github.com/) with:
+You can install the development version of tidyfundata from [GitHub](https://github.com/) with:
 
 ```{r, eval = FALSE}
 # install.packages("pak")

README.md

@@ -16,7 +16,7 @@ A collection of datasets containing functional data
 
 ## Installation
 
-You can install the development version of gmeans from
+You can install the development version of tidyfundata from
 [GitHub](https://github.com/) with:
 
 ``` r

data-raw/chf.R

@@ -0,0 +1,17 @@
+library(dplyr)
+
+# data source: https://www.dropbox.com/scl/fi/vnuldgqnkfa532hzoazjj/chfData.zip
+covar <- readr::read_csv(here::here("data-raw", "covariate.csv"), )
+activity <- readr::read_csv(here::here("data-raw", "activity.csv"))
+
+chf <- covar |>
+  inner_join(activity, by = join_by(id)) |>
+  tidyfun::tf_gather(activity.1:activity.1440, key = activity) |>
+  mutate(day = ordered(day,
+    levels = c(
+      "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"
+    ),
+    labels = c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun")
+  ))
+
+usethis::use_data(chf, overwrite = TRUE, compress = "xz")