Use new regional weather

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: portalr
 Title: Create Useful Summaries of the Portal Data
-Version: 0.4.0
+Version: 0.4.1
 Authors@R: 
     c(person(given = c("Glenda", "M."),
              family = "Yenni",

NEWS.md

@@ -2,6 +2,11 @@
 
 Version numbers follow [Semantic Versioning](https://semver.org/).
 
+# [portalr 0.4.1](https://github.com/weecology/portalr/releases/tag/v0.4.1)
+*2023-08-223*
+
+### Regional weather filling includes more stations
+
 # [portalr 0.4.0](https://github.com/weecology/portalr/releases/tag/v0.4.0)
 *2023-04-21*
 

R/Weather.R

@@ -177,17 +177,7 @@ fill_missing_weather <- function(weather, path = get_default_data_path())
     dplyr::filter(.data$date >= "1980-01-01") %>%
     dplyr::rename(precipitation = "prcp")
 
-  rustys <- read.csv(file.path(path, 'PortalData/Weather/Rustys_regional_weather.csv'),
-                       na.strings = c(""), header = TRUE, stringsAsFactors = FALSE) %>%
-    dplyr::group_by(.data$year, .data$month, .data$day) %>%
-    dplyr::summarize(mintemp = min(.data$templow),
-                     maxtemp = max(.data$temphigh),
-                     meantemp = mean(.data$tempavg),
-                     precipitation = sum(.data$preciptotal)) %>%
-    dplyr::select(dplyr::all_of(c("year", "month", "day", "mintemp", "maxtemp", "meantemp","precipitation"))) %>%
-    dplyr::arrange(.data$year,.data$month, .data$day)
-
-  rodeo <- read.csv(file.path(path, 'PortalData/Weather/Rodeo_regional_weather.csv'),
+  region1 <- read.csv(file.path(path, 'PortalData/Weather/Rodeo_regional_weather.csv'),
                      na.strings = c(""), header = TRUE, stringsAsFactors = FALSE) %>%
     dplyr::group_by(.data$year, .data$month, .data$day) %>%
     dplyr::summarize(mintemp = min(.data$templow),
@@ -197,18 +187,6 @@ fill_missing_weather <- function(weather, path = get_default_data_path())
     dplyr::select(dplyr::all_of(c("year", "month", "day", "mintemp", "maxtemp", "meantemp","precipitation"))) %>%
     dplyr::arrange(.data$year,.data$month, .data$day)
 
-  region1 <- dplyr::full_join(rodeo, rustys, by = c("year", "month", "day")) %>%
-    dplyr::group_by(.data$year, .data$month, .data$day) %>%
-    dplyr::rowwise() %>%
-    dplyr::mutate(mintemp = mean(c(.data$mintemp.y, .data$mintemp.x), na.rm=TRUE),
-                  maxtemp = mean(c(.data$maxtemp.y, .data$maxtemp.x), na.rm=TRUE),
-                  meantemp = mean(c(.data$meantemp.y, .data$meantemp.x), na.rm=TRUE),
-                  precipitation = mean(c(.data$precipitation.y, .data$precipitation.x), na.rm=TRUE)) %>%
-    dplyr::ungroup() %>%
-    dplyr::filter(!(is.na(.data$mintemp) & is.na(.data$maxtemp) &
-                      is.na(.data$meantemp) & is.na(.data$precipitation))) %>%
-    dplyr::select(dplyr::all_of(c("year", "month", "day", "precipitation", "mintemp", "maxtemp", "meantemp")))
-
   region2 <- dplyr::full_join(portal4sw, sansimon, by = c("date", "year", "month", "day")) %>%
     dplyr::group_by(.data$year, .data$month, .data$day, .data$date) %>%
     dplyr::rowwise() %>%

R/portalr-package.R

@@ -1,31 +1,30 @@
+#' @keywords internal
 #' @title Creates summaries of the Portal data
 #'
 #' @description This package is designed to be an interface to the Portal data, which resides online at \url{https://github.com/weecology/portalData}. Its contains a set of functions to download, clean, and summarize the data.
 #'
 #' @name portalr
-#' @docType package
-#' @keywords package
 #'
 #' @importFrom lubridate "%m+%"
 #' @importFrom rlang "!!" "!!!" ":=" .data
 #' @importFrom utils head download.file read.csv unzip read.table tail
 #' @importFrom stats median na.omit
 #' @importFrom httr content GET stop_for_status
 
-NULL
+"_PACKAGE"
 
 ## quiets concerns of R CMD check re: variables used in NSE functions
 if (getRversion() >= "2.15.1") utils::globalVariables(
-  c(".", "n")
-  )
+    c(".", "n")
+)
 
 #' @title Return Citation for Portal Data
 #'
 #' @return An object of class "citation". For more details, see `citation()`
 #' @export
 get_dataset_citation <- function()
 {
-  path <- system.file("CITATION-PORTAL-DATA",
-                      package = "portalr", mustWork = TRUE)
-  utils::readCitationFile(path)
+    path <- system.file("CITATION-PORTAL-DATA",
+                        package = "portalr", mustWork = TRUE)
+    utils::readCitationFile(path)
 }

tests/testthat/test-99-regression.R

@@ -256,9 +256,9 @@ test_that("data generated by default setting is same (weather)", {
   attributes(data) <- attributes(data)[sort(names(attributes(data)))]
 
   # correct for NAs in output
-  expect_known_hash(is.na(data), "8648ecf05f")
+  expect_known_hash(is.na(data), "ab7f91f465")
   data[is.na(data)] <- -999999
-  expect_known_hash(data, "fcc6dc4ca0")
+  expect_known_hash(data, "b302f95927")
 })
 
 test_that("data generated by fill = TRUE is same (weather)", {