style: fix excess line lengths

elipousson · Jan 4, 2024 · e461958 · e461958
1 parent f91e7ab
commit e461958
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Showing 10 changed files with 117 additions and 60 deletions.
diff --git a/R/acs_survey.R b/R/acs_survey.R
@@ -65,7 +65,11 @@ NULL
 acs_survey_match <- function(survey = "acs5",
                              error_call = caller_env()) {
   # See acs_surveys
-  arg_match0(survey, c("acs1", "acs3", "acs5", "acsse"), error_call = error_call)
+  arg_match0(
+    survey,
+    c("acs1", "acs3", "acs5", "acsse"),
+    error_call = error_call
+  )
 }
 
 #' @rdname acs_survey
@@ -119,10 +123,11 @@ acs_survey_ts <- function(survey = "acs5",
 #'   `year_start` variable which is the earliest year for a survey sample
 #'   specified by the survey parameter.
 #' @export
-acs_survey_label <- function(survey = "acs5",
-                             year = 2022,
-                             pattern = "{year_start}-{year} ACS {sample}-year Estimates",
-                             prefix = "") {
+acs_survey_label <- function(
+    survey = "acs5",
+    year = 2022,
+    pattern = "{year_start}-{year} ACS {sample}-year Estimates",
+    prefix = "") {
   sample <- acs_survey_sample(survey)
 
   year_start <- year - (as.integer(sample) - 1)

diff --git a/R/collapse_acs_variables.R b/R/collapse_acs_variables.R
@@ -91,18 +91,34 @@ collapse_acs_variables <- function(data,
     data <- dplyr::summarise(
       data,
       "{variable_col}" := list(unique(.data[[variable_col]])),
-      "{value_col}" := round(sum(.data[[value_col]], na.rm = na.rm), digits = digits),
+      "{value_col}" := round(
+        sum(.data[[value_col]], na.rm = na.rm),
+        digits = digits
+      ),
       "{moe_col}" := round(
-        tidycensus::moe_sum(.data[[moe_col]], estimate = .data[[value_col]], na.rm = na.rm),
+        tidycensus::moe_sum(
+          .data[[moe_col]],
+          estimate = .data[[value_col]],
+          na.rm = na.rm
+        ),
+        digits = digits
+      ),
+      "{perc_cols[[1]]}" := round(
+        sum(.data[[perc_cols[[1]]]], na.rm = na.rm),
         digits = digits
       ),
-      "{perc_cols[[1]]}" := round(sum(.data[[perc_cols[[1]]]], na.rm = na.rm), digits = digits),
       "{perc_cols[[2]]}" := round(
-        tidycensus::moe_sum(.data[[perc_cols[[2]]]], estimate = .data[[perc_cols[[1]]]], na.rm = na.rm),
+        tidycensus::moe_sum(
+          .data[[perc_cols[[2]]]],
+          estimate = .data[[perc_cols[[1]]]],
+          na.rm = na.rm
+        ),
         digits = digits
       ),
       dplyr::across(
-        -any_of(c(name_col, label_col, variable_col, value_col, moe_col, perc_cols)),
+        -any_of(
+          c(name_col, label_col, variable_col, value_col, moe_col, perc_cols)
+        ),
         function(x) {
           list(unique(x))
         }
@@ -113,13 +129,22 @@ collapse_acs_variables <- function(data,
     data <- dplyr::summarise(
       data,
       "{variable_col}" := list(unique(.data[[variable_col]])),
-      "{value_col}" := round(sum(.data[[value_col]], na.rm = na.rm), digits = digits),
+      "{value_col}" := round(
+        sum(.data[[value_col]], na.rm = na.rm),
+        digits = digits
+      ),
       "{moe_col}" := round(
-        tidycensus::moe_sum(.data[[moe_col]], estimate = .data[[value_col]], na.rm = na.rm),
+        tidycensus::moe_sum(
+          .data[[moe_col]],
+          estimate = .data[[value_col]],
+          na.rm = na.rm
+        ),
         digits = digits
       ),
       dplyr::across(
-        -any_of(c(name_col, label_col, variable_col, value_col, moe_col)),
+        -any_of(
+          c(name_col, label_col, variable_col, value_col, moe_col)
+        ),
         function(x) {
           list(unique(x))
         }
@@ -133,9 +158,14 @@ collapse_acs_variables <- function(data,
       # FIXME: This could be a weighted mean if some valid weight is included in
       # the dataset. As is, these values may be invalid if the relative size of
       # the collapsed groups is very different.
-      "{value_col}" := round(mean(.data[[value_col]], na.rm = na.rm), digits = digits),
+      "{value_col}" := round(
+        mean(.data[[value_col]], na.rm = na.rm),
+        digits = digits
+      ),
       dplyr::across(
-        -any_of(c(name_col, label_col, variable_col, value_col, moe_col)),
+        -any_of(
+          c(name_col, label_col, variable_col, value_col, moe_col)
+        ),
         function(x) {
           list(unique(x))
         }

diff --git a/R/fmt_acs.R b/R/fmt_acs.R
@@ -30,9 +30,9 @@ fmt_acs_county <- function(data,
                            columns = all_of(name_col),
                            ...) {
   if (is.null(state) && any(data[["geography"]] == "state")) {
-   state <- unique(
-     data[data[["geography"]] == "state", name_col]
-   )
+    state <- unique(
+      data[data[["geography"]] == "state", name_col]
+    )
   }
 
   pattern <- glue(pattern)

diff --git a/R/get_acs_metadata.R b/R/get_acs_metadata.R
@@ -125,9 +125,15 @@ label_acs_metadata <- function(data,
                                perc = TRUE,
                                geoid_col = "GEOID",
                                variable_col = "variable") {
-  data <- label_acs_table_metadata(data, survey, year, variable_col = variable_col)
+  data <- label_acs_table_metadata(
+    data, survey, year,
+    variable_col = variable_col
+  )
 
-  data <- label_acs_column_metadata(data, survey, year, variable_col = variable_col)
+  data <- label_acs_column_metadata(
+    data, survey, year,
+    variable_col = variable_col
+  )
 
   if (perc && all(has_name(data, geoid_col))) {
     data <- join_acs_percent(data, geoid_col = geoid_col)

diff --git a/R/get_decennial_ts.R b/R/get_decennial_ts.R
@@ -54,9 +54,10 @@ get_decennial_ts <- function(geography,
   )
 
   if (has_length(year, 1)) {
-    if (!all(year %in% c(2000, 2010, 2020))) {
+    allow_years <- c(2000, 2010, 2020)
+    if (!all(year %in% allow_years)) {
       cli::cli_abort(
-        c("{.arg year} can't include any values other than {c(2000, 2010, 2020)}.",
+        c("{.arg year} can't include any values other than {allow_years}.",
           "i" = "Try using NHGIS for earlier decennial Census data:
         {.url https://www.nhgis.org/}"
         )

diff --git a/R/join_tigris_geometry.R b/R/join_tigris_geometry.R
@@ -77,7 +77,11 @@ join_tigris_geometry <- function(data = NULL,
     \(geography, state, county) {
       switch(geography,
         "block" = tigris::blocks(state = state, county = county, ...),
-        "block group" = tigris::block_groups(state = state, county = county, ...),
+        "block group" = tigris::block_groups(
+          state = state,
+          county = county,
+          ...
+        ),
         "tract" = tigris::tracts(state = state, county = county, ...),
         "county" = tigris::counties(state = state, ...),
         "state" = tigris::states(...)

diff --git a/R/select_acs.R b/R/select_acs.R
@@ -30,7 +30,7 @@
 #'     county = "Baltimore city"
 #'   )
 #'
-#' select_acs(edu_data)
+#'   select_acs(edu_data)
 #' }
 #' }
 #' @importFrom dplyr select

diff --git a/R/xwalk.R b/R/xwalk.R
@@ -195,8 +195,10 @@ make_area_xwalk <- function(area,
   if (coverage && st_is_all_predicate(block_xwalk, area)) {
     cli::cli_bullets(
       c(
-        "!" = "All features in {.arg block_xwalk} already intersect with {.arg area}",
-        "*" = "Setting {.arg coverage} to {.code FALSE} to avoid inaccurate results."
+        "!" = "All features in {.arg block_xwalk} already
+        intersect with {.arg area}",
+        "*" = "Setting {.arg coverage} to {.code FALSE} to
+        avoid inaccurate results."
       )
     )
 
@@ -373,7 +375,8 @@ rbind_area_coverage <- function(area,
     st_make_valid_coverage(block_xwalk, area),
     error = function(cnd) {
       cli_abort(
-        c("Valid spatial coverage for the area of {.arg block_xwalk} outside the {.arg area_xwalk} can't be created.",
+        c("Valid spatial coverage for the area of {.arg block_xwalk}
+          outside the {.arg area_xwalk} can't be created.",
           "*" = "Set {.code coverage = FALSE} and try again."
         ),
         parent = cnd,
@@ -524,7 +527,11 @@ summarise_weighted_sum <- function(data,
       digits = digits
     ),
     "{moe_col}" := round(
-      tidycensus::moe_sum(.data[[moe_col]], .data[[value_col]] * .data[[weight_col]]),
+      tidycensus::moe_sum(
+        moe = .data[[moe_col]],
+        estimate = .data[[value_col]] * .data[[weight_col]],
+        na.rm = na.rm
+      ),
       digits = digits
     ),
     .by = all_of(c(name_col, variable_col))
@@ -544,7 +551,11 @@ summarise_weighted_mean <- function(data,
   dplyr::summarise(
     data,
     "{value_col}" := round(
-      stats::weighted.mean(.data[[value_col]], w = .data[[weight_col]], na.rm = na.rm),
+      stats::weighted.mean(
+        x = .data[[value_col]],
+        w = .data[[weight_col]],
+        na.rm = na.rm
+      ),
       digits = digits
     ),
     # FIXME: Explore options to calculate an interpolated MOE per this method:

diff --git a/tests/testthat/test-filter_acs.R b/tests/testthat/test-filter_acs.R
@@ -1,38 +1,38 @@
 test_that("filter_acs works", {
-    edu_data <- get_acs_geographies(
-      c("county", "state"),
-      table = "B15003",
-      state = "MD",
-      county = "Baltimore city"
-    )
+  edu_data <- get_acs_geographies(
+    c("county", "state"),
+    table = "B15003",
+    state = "MD",
+    county = "Baltimore city"
+  )
 
-    expect_s3_class(
-      filter_acs(edu_data, vars = "B15003_017"),
-      "data.frame"
-    )
+  expect_s3_class(
+    filter_acs(edu_data, vars = "B15003_017"),
+    "data.frame"
+  )
 
-    expect_s3_class(
-      filter_acs(edu_data, vars = 17),
-      "data.frame"
-    )
+  expect_s3_class(
+    filter_acs(edu_data, vars = 17),
+    "data.frame"
+  )
 
-    expect_s3_class(
-      filter_acs(edu_data, drop_vars = 1),
-      "data.frame"
-    )
+  expect_s3_class(
+    filter_acs(edu_data, drop_vars = 1),
+    "data.frame"
+  )
 
-    expect_s3_class(
-      filter_acs(edu_data, drop_vars = 1),
-      "data.frame"
-    )
+  expect_s3_class(
+    filter_acs(edu_data, drop_vars = 1),
+    "data.frame"
+  )
 
-    expect_s3_class(
-      filter_acs(edu_data, geography = "county"),
-      "data.frame"
-    )
+  expect_s3_class(
+    filter_acs(edu_data, geography = "county"),
+    "data.frame"
+  )
 
-    expect_s3_class(
-      filter_acs(edu_data, column = "Master's degree"),
-      "data.frame"
-    )
+  expect_s3_class(
+    filter_acs(edu_data, column = "Master's degree"),
+    "data.frame"
+  )
 })
diff --git a/tests/testthat/test-xwalk.R b/tests/testthat/test-xwalk.R
@@ -9,7 +9,7 @@ test_that("xwalk functions work", {
   expect_s3_class(block_xwalk, "sf")
 
   area_xwalk <- make_area_xwalk(
-    area = sf::st_buffer(block_xwalk[1,], dist = 5000),
+    area = sf::st_buffer(block_xwalk[1, ], dist = 5000),
     block_xwalk = block_xwalk
   )