Merge pull request #22 from NIEHS/eva_dev

debug again

NAMESPACE

@@ -16,18 +16,36 @@ export(plot_ts_ref)
 export(request_sensor_history)
 export(summarize_hourly_temp)
 export(tile_ts)
-import(RCurl)
-import(data.table)
-import(dplyr)
 import(ggplot2)
 import(ggspatial)
 import(httr)
 import(sftime)
-import(stats)
-import(tidyr)
-import(tidyterra)
 import(utils)
+importFrom(RCurl,url.exists)
+importFrom(data.table,as.data.table)
+importFrom(data.table,data.table)
+importFrom(data.table,fread)
+importFrom(dplyr,between)
+importFrom(dplyr,filter)
+importFrom(dplyr,group_by)
+importFrom(dplyr,n)
+importFrom(dplyr,rename)
+importFrom(dplyr,summarise)
+importFrom(dplyr,summarize)
+importFrom(dplyr,ungroup)
 importFrom(jsonlite,fromJSON)
 importFrom(lubridate,floor_date)
+importFrom(lubridate,with_tz)
 importFrom(methods,new)
+importFrom(sf,st_bbox)
 importFrom(stats,median)
+importFrom(stats,quantile)
+importFrom(terra,as.polygons)
+importFrom(terra,mask)
+importFrom(terra,project)
+importFrom(terra,vect)
+importFrom(tidyr,drop_na)
+importFrom(tidyterra,geom_spatraster)
+importFrom(tidyterra,geom_spatraster_contour)
+importFrom(tidyterra,geom_spatvector)
+importFrom(tidyterra,scale_color_whitebox_c)

R/calib_cws.R

@@ -8,8 +8,11 @@
 #' @return a list with the calibrated observations, the average bias,
 #' the number of reference stations used to compute the bias, and the
 #' start and end time of the observations.
+#' @importFrom dplyr filter group_by summarise ungroup
+#' @importFrom stats median
 calib_cws <- function(x, ref, max_dist = 10000) {
   temp_err <- site_id <- geometry <- network <- temp <- dist_to_ref <- NULL
+  hour <- NULL
   stopifnot(
     "max_dist must be between 0 and 20000" =
       max_dist > 0 & max_dist <= 20000

R/class_hourly_temp.R

@@ -3,6 +3,7 @@ setClass("hourly_temp",
   contains = c("data.frame")
 )
 
+
 setValidity("hourly_temp", function(object) {
   stopifnot(
     "object is not a data.frame" =
@@ -41,6 +42,7 @@ setValidity("hourly_temp", function(object) {
 #' @param network the name of the network
 #' @return a hourly_temp object
 #' @importFrom methods new
+#' @importFrom dplyr rename
 #' @author Eva Marques
 hourly_temp <- function(x,
                         temp = "temp",

R/clean_cws.R

@@ -2,7 +2,7 @@
 #' @param data formatted sftime (with columns: site_id, temp, time)
 #' @param na_thresh threshold of NA to remove a station (0 <= na_tresh <= 1)
 #' @return cleaned data.frame
-#' @import dplyr
+#' @importFrom dplyr group_by n summarize
 manage_na <- function(data, na_thresh = 0.1) {
   stopifnot(
     "threshold must be between 0 and 1" =
@@ -93,6 +93,7 @@ clean_cws <- function(x) {
 #' @param epsg_m crs in meters (default: epsg:32119)
 #' @param res resolution of the squares in meters (default: 100km)
 #' @return sf of polygons
+#' @importFrom terra vect project mask as.polygons
 cut_area <- function(area, epsg_m = "epsg:32119", res = 100000) {
   # project area with a crs in meters
   area_m <- area |>

R/download_ghcnh.R

@@ -71,9 +71,8 @@ find_nearest_ghcnh <- function(lat, lon) {
 #' @return a data.frame with the GHCN-H station raw data
 #' @author Eva Marques
 #' @export
-#' @import RCurl
-#' @import tidyr
-#' @import dplyr
+#' @importFrom RCurl url.exists
+#' @importFrom tidyr drop_na
 #' @import utils
 download_ghcnh_station <- function(site_id, year) {
   temperature <- NULL
@@ -113,6 +112,7 @@ download_ghcnh_station <- function(site_id, year) {
 #' @param te end date
 #' @param area a sf, sfc, SpatRaster or SpatVector object
 #' @return a data.frame with the GHCN-H stations observations in the area
+#' @importFrom dplyr between
 download_ghcnh <- function(ts, te, area) {
   stopifnot(
     "ts and te should be POSIXct objects" =
@@ -143,7 +143,7 @@ download_ghcnh <- function(ts, te, area) {
     return(NULL)
   } else {
     ghcnh <- format_ghcnh(ghcnh)
-    ghcnh <- ghcnh[which(between(ghcnh$time, ts, te)), ]
+    ghcnh <- ghcnh[which(dplyr::between(ghcnh$time, ts, te)), ]
     return(ghcnh)
   }
 }

R/download_purpleair.R

@@ -95,7 +95,18 @@ request_sensor_history <- function(start_ts,
   }
 }
 
-
+#' Load PurpleAir sensors history data
+#' @param nwlat North-west latitude
+#' @param selat South-east latitude
+#' @param nwlng North-west longitude
+#' @param selng South-east longitude
+#' @param location_type Location type
+#' @param start_ts Start timestamp
+#' @param end_ts End timestamp
+#' @param api_key API key for PurpleAir
+#' @param average Average time in minutes
+#' @param fields Fields to be included in the data
+#' @return A data frame with sensors history data
 request_sensors_history <- function(nwlat,
                                     selat,
                                     nwlng,
@@ -147,12 +158,12 @@ request_sensors_history <- function(nwlat,
   }
 }
 
-# to be tested
 #' Download all PurpleAir stations data in area between two dates
 #' @param ts start date
 #' @param te end date
 #' @param area a sf, sfc, SpatRaster or SpatVector object
 #' @param api_key API key for PurpleAir
+#' @importFrom sf st_bbox
 download_pa <- function(ts, te, area, api_key) {
   bounds <- area |>
     format_area() |>
@@ -177,6 +188,8 @@ download_pa <- function(ts, te, area, api_key) {
 #' @param storage_file file path where PurpleAir data is stored
 #' @param api_key API key for PurpleAir
 #' @import utils
+#' @importFrom data.table data.table
+#' @importFrom dplyr between
 load_pa <- function(ts, te, area, storage_file = NULL, api_key = NULL) {
   if (is.null(storage_file)) {
     pa <- download_pa(ts, te, area, api_key)

R/estimate_measurement_error.R

@@ -43,6 +43,7 @@ find_closest_ref <- function(cws, ref) {
 #' with columns site_id and geometry
 #' @return sf (or inherited) object with additional columns temp_ref, temp_err,
 #' ref_id, dist_to_ref
+#' @importFrom dplyr rename
 est_temp_error <- function(cws, ref) {
   # check column names
   cols <- c("site_id", "temp", "geometry", "time")

R/format_observations.R

@@ -10,6 +10,8 @@
 #' @author Eva Marques
 #' @importFrom lubridate floor_date
 #' @importFrom stats median
+#' @importFrom dplyr group_by summarise ungroup
+#' @importFrom data.table as.data.table
 #' @export
 summarize_hourly_temp <- function(x, time, temp, lat, lon) {
   stopifnot(
@@ -47,6 +49,7 @@ summarize_hourly_temp <- function(x, time, temp, lat, lon) {
 #' @param raw_temp_unit the initial temperature unit
 #' @param raw_crs the initial coordinate reference system
 #' @return sftime from hourly_temp class
+#' @importFrom lubridate with_tz floor_date
 #' @author Eva Marques
 #' @export
 format_pa <- function(raw,
@@ -92,6 +95,7 @@ format_pa <- function(raw,
 #' @param raw_temp_unit the initial temperature unit
 #' @param raw_crs the initial coordinate reference system
 #' @return sftime from hourly_temp class
+#' @importFrom lubridate floor_date
 #' @author Eva Marques
 #' @export
 format_wu <- function(raw,
@@ -141,6 +145,7 @@ format_wu <- function(raw,
 #' and columns "Year", "Month", "Day", "Hour", "temperature", "Latitude",
 #' "Longitude", "temperature_Source_Code"
 #' @return sftime from hourly_temp class
+#' @importFrom dplyr rename
 #' @author Eva Marques
 #' @export
 format_ghcnh <- function(raw) {

R/generate_unique_site_id.R

@@ -1,9 +1,9 @@
-# ` @title Generate unique sensor ID
-# ` @description This function generates a unique sensor "id" column based on
-# ` the latitude and longitude of the sensor.
-# ` @param x A data.frame, data.table, sf or sftime with columns "lat" and "lon"
-# ` @return A data frame with an additional column "id"
-# ` @author Eva Marques
+#' @title Generate unique sensor ID
+#' @description This function generates a unique sensor "id" column based on
+#' the latitude and longitude of the sensor.
+#' @param x A data.frame, data.table, sf or sftime with columns "lat" and "lon"
+#' @return A data frame with an additional column "id"
+#' @author Eva Marques
 generate_site_id <- function(x) {
   stopifnot(
     "lat and lon columns are missing" =

R/load_weatherunderground.R

@@ -4,7 +4,7 @@
 #' @param area Area of interest
 #' @param inventory an sf object with inventory of
 #' @return a data.table with the raw WU data
-#' @import data.table
+#' @importFrom data.table fread
 #' @author Eva Marques
 #' Weather Underground stations (see create_wu_inventory function)
 load_wu <- function(ts, te, area, inventory) {

R/plot_cws.R

@@ -177,10 +177,10 @@ tile_ts <- function(data) {
 #' @param data data frame or sftime with all the network, contains "network"
 #' column
 #' @param var continuous variable to boxplot (in general: temp)
-#' @return A ggplo2 object with the boxplots
+#' @return A ggplot2 object with the boxplots
 #' @export
 #' @import ggplot2
-#' @import stats
+#' @importFrom stats quantile
 #' @author Eva Marques
 hourly_boxplot_networks <- function(data, var) {
   network <- NULL
@@ -223,7 +223,7 @@ hourly_boxplot_networks <- function(data, var) {
 
 #' @import ggplot2
 #' @import ggspatial
-#' @import tidyterra
+#' @importFrom tidyterra geom_spatvector
 map_observations <- function(data,
                              var,
                              background,
@@ -276,7 +276,7 @@ map_observations <- function(data,
 
 #' @import ggplot2
 #' @import ggspatial
-#' @import tidyterra
+#' @importFrom tidyterra geom_spatraster scale_color_whitebox_c
 map_observations_imp <- function(data,
                                  var,
                                  imp,
@@ -301,11 +301,11 @@ map_observations_imp <- function(data,
     scale_fill_gradientn(colours = c("white", "grey"), na.value = NA) +
     tidyterra::scale_color_whitebox_c(
       palette = "bl_yl_rd",
-      labels = scales::label_number(suffix = paste0("ºC")),
+      labels = scales::label_number(suffix = paste0("C")),
       n.breaks = 12,
       guide = guide_legend(reverse = TRUE)
     ) +
-    guides(fill = guide_legend(title = "Imperviousness (%)")) +
+    guides(fill = guide_legend(title = "Imperviousness")) +
     labs(
       title = title,
       subtitle = date
@@ -335,8 +335,8 @@ map_observations_imp <- function(data,
 
 #' @import ggplot2
 #' @import ggspatial
-#' @import tidyterra
-#' @import stats
+#' @importFrom tidyterra geom_spatraster geom_spatraster_contour
+#' @importFrom stats quantile
 map_observations_hw <- function(data,
                                 var,
                                 imp,