Merge pull request #378 from cmu-delphi/ds/style

style: run package through styler

Author: brookslogan

.git-blame-ignore-revs

@@ -0,0 +1 @@
+c65876078a6f9525952b305eaea2fca003adf907

R/archive.R

@@ -4,11 +4,11 @@
 #' grouping by geo value, time value, or any other variables. See the
 #' [correlation
 #' vignette](https://cmu-delphi.github.io/epiprocess/articles/correlation.html)
-#' for examples. 
+#' for examples.
 #'
 #' @param x The `epi_df` object under consideration.
 #' @param var1,var2 The variables in `x` to correlate.
-#' @param dt1,dt2 Time shifts to consider for the two variables, respectively, 
+#' @param dt1,dt2 Time shifts to consider for the two variables, respectively,
 #'   before computing correlations. Negative shifts translate into in a lag
 #'   value and positive shifts into a lead value; for example, if `dt = -1`,
 #'   then the new value on June 2 is the original value on June 1; if `dt = 1`,
@@ -34,51 +34,59 @@
 #'   `method` (same as `cor()`).
 #'
 #' @return An tibble with the grouping columns first (`geo_value`, `time_value`,
-#'   or possibly others), and then a column `cor`, which gives the correlation. 
-#' 
+#'   or possibly others), and then a column `cor`, which gives the correlation.
+#'
 #' @importFrom stats cor
 #' @importFrom rlang .data !! !!! enquo syms
 #' @importFrom tidyselect eval_select
 #' @export
 #' @examples
-#'  
+#'
 #' # linear association of case and death rates on any given day
-#' epi_cor(x = jhu_csse_daily_subset, 
-#'         var1 = case_rate_7d_av, 
-#'         var2 = death_rate_7d_av, 
-#'         cor_by = "time_value")
-#' 
+#' epi_cor(
+#'   x = jhu_csse_daily_subset,
+#'   var1 = case_rate_7d_av,
+#'   var2 = death_rate_7d_av,
+#'   cor_by = "time_value"
+#' )
+#'
 #' # correlation of death rates and lagged case rates
-#' epi_cor(x = jhu_csse_daily_subset, 
-#'         var1 = case_rate_7d_av, 
-#'         var2 = death_rate_7d_av, 
-#'         cor_by = time_value, 
-#'         dt1 = -2)
-#' 
-#' # correlation grouped by location 
-#' epi_cor(x = jhu_csse_daily_subset, 
-#'         var1 = case_rate_7d_av, 
-#'         var2 = death_rate_7d_av, 
-#'         cor_by = geo_value)
-#' 
+#' epi_cor(
+#'   x = jhu_csse_daily_subset,
+#'   var1 = case_rate_7d_av,
+#'   var2 = death_rate_7d_av,
+#'   cor_by = time_value,
+#'   dt1 = -2
+#' )
+#'
+#' # correlation grouped by location
+#' epi_cor(
+#'   x = jhu_csse_daily_subset,
+#'   var1 = case_rate_7d_av,
+#'   var2 = death_rate_7d_av,
+#'   cor_by = geo_value
+#' )
+#'
 #' # correlation grouped by location and incorporates lagged cases rates
-#' epi_cor(x = jhu_csse_daily_subset, 
-#'         var1 = case_rate_7d_av, 
-#'         var2 = death_rate_7d_av, 
-#'         cor_by = geo_value, 
-#'         dt1 = -2)
-epi_cor = function(x, var1, var2, dt1 = 0, dt2 = 0, shift_by = geo_value,
-                   cor_by = geo_value, use = "na.or.complete",
-                   method = c("pearson", "kendall", "spearman")) {
+#' epi_cor(
+#'   x = jhu_csse_daily_subset,
+#'   var1 = case_rate_7d_av,
+#'   var2 = death_rate_7d_av,
+#'   cor_by = geo_value,
+#'   dt1 = -2
+#' )
+epi_cor <- function(x, var1, var2, dt1 = 0, dt2 = 0, shift_by = geo_value,
+                    cor_by = geo_value, use = "na.or.complete",
+                    method = c("pearson", "kendall", "spearman")) {
   # Check we have an `epi_df` object
   if (!inherits(x, "epi_df")) Abort("`x` must be of class `epi_df`.")
 
   # Check that we have variables to do computations on
   if (missing(var1)) Abort("`var1` must be specified.")
   if (missing(var2)) Abort("`var2` must be specified.")
-  var1 = enquo(var1)
-  var2 = enquo(var2)
-  
+  var1 <- enquo(var1)
+  var2 <- enquo(var2)
+
   # Defuse grouping variables. This looks a bit more involved since we want to
   # accomodate the option of specifying multiple variables for each grouping.
   # Hence use the power of tidyselect::eval_select(), which can accomodate any
@@ -88,26 +96,33 @@ epi_cor = function(x, var1, var2, dt1 = 0, dt2 = 0, shift_by = geo_value,
   # * cor_by = c(a, b)
   # * cor_by = c("a", "b")
   # and so on, and similarly for shift_by. Note: make sure to follow with !!!
-  cor_by = syms(names(eval_select(enquo(cor_by), x)))
-  shift_by = syms(names(eval_select(enquo(shift_by), x)))
+  cor_by <- syms(names(eval_select(enquo(cor_by), x)))
+  shift_by <- syms(names(eval_select(enquo(shift_by), x)))
 
   # Which method?
-  method = match.arg(method)
+  method <- match.arg(method)
 
   # Perform time shifts, then compute appropriate correlations and return
   return(x %>%
-         dplyr::group_by(!!!shift_by) %>%
-         dplyr::arrange(.data$time_value) %>%
-         dplyr::mutate(var1 = shift(!!var1, n = dt1),
-                       var2 = shift(!!var2, n = dt2)) %>%
-         dplyr::ungroup() %>%
-         dplyr::group_by(!!!cor_by) %>%
-         dplyr::summarize(cor = cor(x = .data$var1, y = .data$var2,
-                                    use = use, method = method)))
+    dplyr::group_by(!!!shift_by) %>%
+    dplyr::arrange(.data$time_value) %>%
+    dplyr::mutate(
+      var1 = shift(!!var1, n = dt1),
+      var2 = shift(!!var2, n = dt2)
+    ) %>%
+    dplyr::ungroup() %>%
+    dplyr::group_by(!!!cor_by) %>%
+    dplyr::summarize(cor = cor(
+      x = .data$var1, y = .data$var2,
+      use = use, method = method
+    )))
 }
 
 # Function to perform time shifts, lag or lead
-shift = function(var, n) {
-  if (n < 0) return(dplyr::lag(var, -n))
-  else return(dplyr::lead(var, n))
+shift <- function(var, n) {
+  if (n < 0) {
+    return(dplyr::lag(var, -n))
+  } else {
+    return(dplyr::lead(var, n))
+  }
 }

R/correlation.R

@@ -1,41 +1,41 @@
-#' Subset of JHU daily state cases and deaths 
+#' Subset of JHU daily state cases and deaths
 #'
 #' This data source of confirmed COVID-19 cases and deaths
 #' is based on reports made available by the Center for
 #' Systems Science and Engineering at Johns Hopkins University.
-#' This example data ranges from Mar 1, 2020 to Dec 31, 2021, and is limited to 
+#' This example data ranges from Mar 1, 2020 to Dec 31, 2021, and is limited to
 #' California, Florida, Texas, New York, Georgia, and Pennsylvania.
 #'
 #' @format A tibble with 4026 rows and 6 variables:
 #' \describe{
-#'   \item{geo_value}{the geographic value associated with each row 
+#'   \item{geo_value}{the geographic value associated with each row
 #'       of measurements.}
 #'   \item{time_value}{the time value associated with each row of measurements.}
-#'   \item{case_rate_7d_av}{7-day average signal of number of new 
+#'   \item{case_rate_7d_av}{7-day average signal of number of new
 #'       confirmed COVID-19 cases per 100,000 population, daily}
-#'   \item{death_rate_7d_av}{7-day average signal of number of new confirmed 
+#'   \item{death_rate_7d_av}{7-day average signal of number of new confirmed
 #'       deaths due to COVID-19 per 100,000 population, daily}
 #'   \item{cases}{Number of new confirmed COVID-19 cases, daily}
-#'   \item{cases_7d_av}{7-day average signal of number of new confirmed 
+#'   \item{cases_7d_av}{7-day average signal of number of new confirmed
 #'       COVID-19 cases, daily}
 #' }
-#' @source This object contains a modified part of the 
+#' @source This object contains a modified part of the
 #'   \href{https://github.com/CSSEGISandData/COVID-19}{COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University}
-#'   as \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{republished in the COVIDcast Epidata API}. 
+#'   as \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{republished in the COVIDcast Epidata API}.
 #'   This data set is licensed under the terms of the
 #'   \href{https://creativecommons.org/licenses/by/4.0/}{Creative Commons Attribution 4.0 International license}
 #'   by the Johns Hopkins University on behalf of its Center for Systems Science
 #'   in Engineering. Copyright Johns Hopkins University 2020.
 #'
 #' Modifications:
-#' * \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{From the COVIDcast Epidata API}: 
-#'   These signals are taken directly from the JHU CSSE 
-#'   \href{https://github.com/CSSEGISandData/COVID-19}{COVID-19 GitHub repository} 
-#'   without changes. The 7-day average signals are computed by Delphi by 
-#'   calculating moving averages of the preceding 7 days, so the signal for 
-#'   June 7 is the average of the underlying data for June 1 through 7, 
+#' * \href{https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html}{From the COVIDcast Epidata API}:
+#'   These signals are taken directly from the JHU CSSE
+#'   \href{https://github.com/CSSEGISandData/COVID-19}{COVID-19 GitHub repository}
+#'   without changes. The 7-day average signals are computed by Delphi by
+#'   calculating moving averages of the preceding 7 days, so the signal for
+#'   June 7 is the average of the underlying data for June 1 through 7,
 #'   inclusive.
-#' * Furthermore, the data has been limited to a very small number of rows, 
+#' * Furthermore, the data has been limited to a very small number of rows,
 #'   the signal names slightly altered, and formatted into a tibble.
 "jhu_csse_daily_subset"
 
@@ -46,7 +46,7 @@
 #' provided to us by health system partners, and also contains confirmed
 #' COVID-19 cases based on reports made available by the Center for
 #' Systems Science and Engineering at Johns Hopkins University.
-#' This example data ranges from June 1, 2020 to Dec 1, 2021, and 
+#' This example data ranges from June 1, 2020 to Dec 1, 2021, and
 #' is also limited to California, Florida, Texas, and New York.
 #'
 #' @format An `epi_archive` data format. The data table DT has 129,638 rows and 5 columns:
@@ -88,23 +88,23 @@
 #' @return Boolean
 #'
 #' @noRd
-some_package_is_being_unregistered = function(parent_n = 0L) {
-  calls = sys.calls()
+some_package_is_being_unregistered <- function(parent_n = 0L) {
+  calls <- sys.calls()
   # `calls` will include the call to this function; strip out this call plus
   # `parent_n` additional requested calls to make it like we're reasoning about
   # the desired call. This could prevent potential false positives from
   # triggering if, in a later version, we decide to loosen the `call_name`
   # checks below to something that would be `TRUE` for the name of this function
   # or one of the undesired call ancestors.
-  calls_to_inspect = utils::head(calls, n = -(parent_n + 1L))
+  calls_to_inspect <- utils::head(calls, n = -(parent_n + 1L))
   # Note that `utils::head(sys.calls(), n=-1L)` isn't equivalent, due to lazy
   # argument evaluation. Note that copy-pasting the body of this function
   # without this `utils::head` operation isn't always equivalent to calling it;
   # e.g., within the `value` argument of a package-level `delayedAssign`,
   # `sys.calls()` will return `NULL` is some or all cases, including when its
   # evaluation has been triggered via `unregister`.
-  simple_call_names = purrr::map_chr(calls_to_inspect, function(call) {
-    maybe_simple_call_name = rlang::call_name(call)
+  simple_call_names <- purrr::map_chr(calls_to_inspect, function(call) {
+    maybe_simple_call_name <- rlang::call_name(call)
     if (is.null(maybe_simple_call_name)) NA_character_ else maybe_simple_call_name
   })
   # `pkgload::unregister` is an (the?) exported function that forces
@@ -127,11 +127,11 @@ some_package_is_being_unregistered = function(parent_n = 0L) {
 #' different than when using `delayedAssign` directly.
 #'
 #' @noRd
-delayed_assign_with_unregister_awareness = function(x, value,
-                                                    eval.env = rlang::caller_env(),
-                                                    assign.env = rlang::caller_env()) {
-  value_quosure = rlang::as_quosure(rlang::enexpr(value), eval.env)
-  this_env = environment()
+delayed_assign_with_unregister_awareness <- function(x, value,
+                                                     eval.env = rlang::caller_env(),
+                                                     assign.env = rlang::caller_env()) {
+  value_quosure <- rlang::as_quosure(rlang::enexpr(value), eval.env)
+  this_env <- environment()
   delayedAssign(x, eval.env = this_env, assign.env = assign.env, value = {
     if (some_package_is_being_unregistered()) {
       withCallingHandlers(
@@ -144,26 +144,30 @@ delayed_assign_with_unregister_awareness = function(x, value,
         # all.)
         rlang::eval_bare(rlang::quo_get_expr(value_quosure), rlang::quo_get_env(value_quosure)),
         error = function(err) {
-          Abort(paste("An error was raised while attempting to evaluate a promise",
-                      "(prepared with `delayed_assign_with_unregister_awareness`)",
-                      "while an `unregister` or `unregister_namespace` call",
-                      "was being evaluated.",
-                      "This can happen, for example, when `devtools::load_all`",
-                      "reloads a package that contains a buggy promise,",
-                      "because reloading can cause old package-level promises to",
-                      "be forced via `pkgload::unregister` and",
-                      "`pkgload:::unregister_namespace`, due to",
-                      "https://github.com/r-lib/pkgload/pull/157.",
-                      "If this is the current situation, you might be able to",
-                      "be successfully reload the package again after",
-                      "`unloadNamespace`-ing it (but this situation will",
-                      "keep re-occurring every other `devtools::load`",
-                      "and every `devtools:document` until the bug or situation",
-                      "generating the promise's error has been resolved)."
-                      ),
-                class = "epiprocess__promise_evaluation_error_during_unregister",
-                parent = err)
-        })
+          Abort(
+            paste(
+              "An error was raised while attempting to evaluate a promise",
+              "(prepared with `delayed_assign_with_unregister_awareness`)",
+              "while an `unregister` or `unregister_namespace` call",
+              "was being evaluated.",
+              "This can happen, for example, when `devtools::load_all`",
+              "reloads a package that contains a buggy promise,",
+              "because reloading can cause old package-level promises to",
+              "be forced via `pkgload::unregister` and",
+              "`pkgload:::unregister_namespace`, due to",
+              "https://github.com/r-lib/pkgload/pull/157.",
+              "If this is the current situation, you might be able to",
+              "be successfully reload the package again after",
+              "`unloadNamespace`-ing it (but this situation will",
+              "keep re-occurring every other `devtools::load`",
+              "and every `devtools:document` until the bug or situation",
+              "generating the promise's error has been resolved)."
+            ),
+            class = "epiprocess__promise_evaluation_error_during_unregister",
+            parent = err
+          )
+        }
+      )
     } else {
       rlang::eval_bare(rlang::quo_get_expr(value_quosure), rlang::quo_get_env(value_quosure))
     }
@@ -189,14 +193,14 @@ delayed_assign_with_unregister_awareness = function(x, value,
 # binding may have been created with the same name as the package promise, and
 # this binding will stick around even when the package is reloaded, and will
 # need to be `rm`-d to easily access the refreshed package promise.
-delayed_assign_with_unregister_awareness("archive_cases_dv_subset", as_epi_archive(archive_cases_dv_subset_dt, compactify=FALSE))
+delayed_assign_with_unregister_awareness("archive_cases_dv_subset", as_epi_archive(archive_cases_dv_subset_dt, compactify = FALSE))
 
 #' Subset of JHU daily cases from California and Florida
 #'
 #' This data source of confirmed COVID-19 cases
 #' is based on reports made available by the Center for
 #' Systems Science and Engineering at Johns Hopkins University.
-#' This example data is a snapshot as of Oct 28, 2021 and captures the cases 
+#' This example data is a snapshot as of Oct 28, 2021 and captures the cases
 #' from June 1, 2020 to May 31, 2021
 #' and is limited to California and Florida.
 #'
@@ -222,7 +226,7 @@ delayed_assign_with_unregister_awareness("archive_cases_dv_subset", as_epi_archi
 #' This data source of confirmed COVID-19 cases and deaths
 #' is based on reports made available by the Center for
 #' Systems Science and Engineering at Johns Hopkins University.
-#' This example data ranges from Mar 1, 2020 to Dec 31, 2021, 
+#' This example data ranges from Mar 1, 2020 to Dec 31, 2021,
 #' and is limited to Massachusetts and Vermont.
 #'
 #' @format A tibble with 16,212 rows and 5 variables: