Fix README issue

R/apply_weekly_lag.R

@@ -2,9 +2,11 @@
 #'
 #' @description Applying a week lag to the data will create raster images
 #' showing the mobility a week before the date of interest.
-#' This function works only for QuadKeys without NAs.
+#' This function works only for QuadKeys reported without NAs for
+#' `n_crisis` and `percent_change` variables .
 #'
-#' @param data A data.frame
+#' @param data A data.frame with the columns `quadkey`,
+#' `day`, `hour` and `n_crisis`.
 #'
 #' @importFrom rlang .data
 #'
@@ -26,9 +28,12 @@
 #'     package = "quadkeyr"
 #'   ), "/"),
 #'   colnames = c(
-#'     "lat", "lon",
-#'     "quadkey", "date_time",
-#'     "n_crisis", "percent_change"
+#'     "lat",
+#'     "lon",
+#'     "quadkey",
+#'     "date_time",
+#'     "n_crisis",
+#'     "percent_change"
 #'   ),
 #'   coltypes = list(
 #'     lat = "d",
@@ -40,29 +45,62 @@
 #'   )
 #' )
 #'
-#' apply_weekly_lag(files)
+#' apply_weekly_lag(data = files)
 apply_weekly_lag <- function(data) {
-  out_data <- c()
+  # First I must check that we have all the days and months
+  if (nrow(missing_combinations(data)) != 0) {
+    mc <- missing_combinations(data)
+    # create the combination of QuadKeys,
+    # days and hours missing in a grid
+    missing_data <- expand.grid(
+      quadkey = unique(data$quadkey),
+      day = mc$day,
+      hour = mc$hour
+    )
+
+    # Add the missing data to the original files
+    # Now I have quadkey, day and hour columns complete
+    data <- dplyr::bind_rows(data, missing_data) |>
+      dplyr::arrange(.data$day, .data$hour)
+  }
+
+  # I am only considering cases where there aren't NAs
+  # Let's remove the QuadKeys with 100% NAs for n_crisis
+  qk_data_without_NA  <-  data |>
+    dplyr::group_by(.data$quadkey) |>
+    dplyr::summarise(empty = !is.na(sum(.data$n_crisis))) |>
+    dplyr::filter(.data$empty == FALSE) |>
+    dplyr::ungroup()
+
+  data <- data |>
+    dplyr::filter(.data$quadkey %in% qk_data_without_NA$quadkey)
+
+  # QuadKey that appears in all the combination of possible days and hours
+  # should occur `qk_rep` times.
+  # If a QuadKey is reported fewer times than that, we will remove it
+  # to avoid discontinuous sequences of days and subsequent gaps.
+  min_date <-  min(data$day)
+  max_date <- max(data$day)
+  days <- as.numeric(max_date - min_date)
+  qk_rep <- (days + 1) * 3
+
+  qk_reg <- data |>
+    dplyr::count(.data$quadkey) |>
+    dplyr::filter(.data$n == qk_rep)
+
+  data <- data |>
+    dplyr::filter(.data$quadkey %in% qk_reg$quadkey)
+
+  # Now that this is all sorted,
+  # let's create the lag column
+  quadkey_lag <- data |>
+    dplyr::group_by(.data$quadkey) |>
+    dplyr::arrange(.data$day, .data$hour, .by_group = TRUE) |>
+    dplyr::mutate(n_crisis_lag_7 = dplyr::lag(.data$n_crisis,
+                               n = (7 * 3))) |>
+    dplyr::mutate(percent_change_7 = ((.data$n_crisis_lag_7 - .data$n_crisis) /
+                                        .data$n_crisis) * 100)
+}
 
-  for (i in unique(data$quadkey)) {
-    inter <- data |>
-      dplyr::filter(.data$quadkey == i)
 
-    # I am only considering cases where there aren't NAs
-    if (!is.na(sum(inter$n_crisis))) {
-      quadkey_lag <- inter |>
-        dplyr::group_by(.data$quadkey, .data$hour) |>
-        dplyr::mutate(n_crisis_lag_7 = dplyr::lag(as.numeric(.data$n_crisis),
-          n = 7
-        )) |>
-        dplyr::mutate(percent_change_7 = ((
-          .data$n_crisis_lag_7 - .data$n_crisis
-        ) /
-          .data$n_crisis) * 100)
 
-      out_data <- rbind(out_data, quadkey_lag)
-    }
-  }
-
-  return(out_data)
-}

R/get_coords.R

@@ -72,11 +72,7 @@ get_qk_coord <- function(data) {
 #' (`tileX`, `tileY`), a `quadkey` and a `geometry` column.
 #'
 #' @keywords internal
-#' @export
-#'
 #' @examples
-#'
-#'
 #' grid <- create_qk_grid(
 #'   xmin = -59,
 #'   xmax = -40,

R/grid_to_polygon.R

@@ -135,7 +135,6 @@ grid_to_polygon <- function(data) {
 #' Their value is not necessary.
 #'
 #' @keywords internal
-#' @export
 #'
 #' @examples
 #'

R/polygon_to_raster.R

@@ -77,7 +77,7 @@ polygon_to_raster <- function(data,
   # detect and reposrt combination of dates and times missing
   if (nrow(missing_combinations(data)) > 0) {
     warning("These combinations of days and times are missing in the dataset")
-    missing_combinations(data)
+    print(missing_combinations(data))
   }
 
 
@@ -104,7 +104,6 @@ polygon_to_raster <- function(data,
         next
       }
 
-
       data_raster <- data |>
         dplyr::filter(.data$day == as.Date(i,
           origin = "1970-01-01"

R/read_and_format_files.R

@@ -12,6 +12,9 @@
 #' @param coltypes Column specifications (as strings).
 #' See vignette("readr", package = "readr") for more details.
 #' documentation.
+#' @param keep_format Vector of column names, 
+#' besides `date_time`, `day` and `quadkey`, that you 
+#' don't want to convert to a number.
 #'
 #' @seealso \code{\link{format_fb_data}}
 #' @seealso \code{\link[readr]{read_csv}}
@@ -50,7 +53,8 @@
 #' head(files)
 read_fb_mobility_files <- function(path_to_csvs,
                                    colnames,
-                                   coltypes) {
+                                   coltypes,
+                                   keep_format = NULL) {
 
   # This data always have the same format
   fnames <- list.files(
@@ -84,8 +88,8 @@ read_fb_mobility_files <- function(path_to_csvs,
     }
   )
 
-
-  data <- format_fb_data(data)
+  data <- format_fb_data(data,
+                         keep_format = keep_format)
 
   if (nrow(missing_combinations(data)) > 0) {
     message(paste(
@@ -103,10 +107,16 @@ read_fb_mobility_files <- function(path_to_csvs,
 #' @description This function removes unnecessary characters such as `\\N`
 #' and ensures that the format of the date and QuadKeys is correct.
 #'
-#' @param data A data.frame with a `quadkey`, `date_time`, `country` columns
-#' and other numeric variables
+#' @param data A data.frame with a `quadkey` and `date_time` columns
+#' and other variables
+#' @param keep_format Vector of column names, 
+#' besides `date_time`, `day` and `quadkey`, that you 
+#' don't want to convert to a number.
 #'
-#' @return A data.frame.
+#' @return A data.frame without `\N`, 
+#' `quadkey` without scientific notation and
+#' a new column `day` and `hour`
+#' 
 #' @export
 #'
 #' @seealso \code{\link{read_fb_mobility_files}}
@@ -115,7 +125,8 @@ read_fb_mobility_files <- function(path_to_csvs,
 #'
 #' data(result_read_fb_mobility_data)
 #' format_fb_data(data = result_read_fb_mobility_data)
-format_fb_data <- function(data) {
+format_fb_data <- function(data,
+                           keep_format = NULL) {
 
   # remove scientific notation
   data$quadkey <- format(data$quadkey,
@@ -125,6 +136,7 @@ format_fb_data <- function(data) {
   # change date format
   data$day <- lubridate::date(data$date_time)
 
+  # get the hour
   data$hour <- as.numeric(format(as.POSIXct(data$date_time,
     format = "%Y-%m-%d %H%M"
   ),
@@ -138,7 +150,8 @@ format_fb_data <- function(data) {
       ~ ifelse(. == "\\N", NA, .)
     )) |>
     dplyr::mutate(dplyr::across(
-      -c("date_time", "day", "quadkey"), # tidyselect
+      -c("date_time", "day", "quadkey", 
+         dplyr::all_of(keep_format)), # tidyselect
       as.numeric
     ))
 
@@ -163,6 +176,7 @@ format_fb_data <- function(data) {
 #'
 #' # Sample dataset
 #' data <- data.frame(
+#'   country = c("US", "MX", "MX"), 
 #'   day = c("2023-01-01", "2023-01-03", "2023-01-05"),
 #'   hour = c(0, 8, 16)
 #' )
@@ -187,6 +201,5 @@ missing_combinations <- function(data) {
     data,
     by = c("day", "hour")
   )
-
   return(missing_combinations)
 }

R/regular_grid.R

@@ -39,7 +39,6 @@ regular_qk_grid <- function(data) {
     zoom = qk_zoom
   )
 
-
   if (nrow(grid$data) == nrow(data)) {
     warning("The grid is already complete, this function is not necessary")
     return(list(
@@ -124,7 +123,6 @@ add_regular_polygon_grid <- function(data) {
   ))
 }
 
-
 #' Get regular QuadKey polygon grid derived from
 #' the bounding box of the `quadkey` column of a data.frame.
 #'

README.Rmd

@@ -48,9 +48,11 @@ to address and retrieve specific map tiles,
 facilitating rapid display within 
 mapping applications.
 
-```{r qk, echo = FALSE, out.width= "50%", fig.align='center', fig.cap= "The QuadKey of any tile starts with the QuadKey of its parent tile (the containing tile at the previous level). Image extracted from Microsoft's Bing Maps Tile System webpage."}
-knitr::include_graphics("vignettes/quadkeys.jpg")
+
+```{r qks, echo = FALSE, out.width= "70%", fig.align='center'}
+knitr::include_graphics("vignettes/quadkeys.png")
 ```
+<p style="text-align: center">The QuadKey of any tile starts with the QuadKey of its parent tile (the containing tile at the previous level). Image extracted from Microsoft's Bing Maps Tile System webpage.</p>
 
 ---
 

README.md

@@ -38,17 +38,13 @@ spatial position as a sequence of characters. They provide an efficient
 method to address and retrieve specific map tiles, facilitating rapid
 display within mapping applications.
 
-<div class="figure" style="text-align: center">
-
-<img src="vignettes/quadkeys.jpg" alt="The QuadKey of any tile starts with the QuadKey of its parent tile (the containing tile at the previous level). Image extracted from Microsoft's Bing Maps Tile System webpage." width="50%" />
-<p class="caption">
+<img src="vignettes/quadkeys.png" width="70%" style="display: block; margin: auto;" />
+<p style="text-align: center">
 The QuadKey of any tile starts with the QuadKey of its parent tile (the
 containing tile at the previous level). Image extracted from Microsoft’s
 Bing Maps Tile System webpage.
 </p>
 
-</div>
-
 ------------------------------------------------------------------------
 
 The goal of `quadkeyr` is to: