diff --git a/NAMESPACE b/NAMESPACE
index 780edcf..dfcd2b6 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -20,7 +20,7 @@ export(get_taxon_rank)
export(get_utm_zone)
export(long2UTM)
export(order_cols)
-export(removeEmptyTables)
+export(remove_empty_tables)
export(replace_blanks)
export(te_check)
export(utm_to_ll)
diff --git a/NEWS.md b/NEWS.md
index eda3964..f2e0e6c 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,10 @@
+# QCkit v0.1.8 (not yet released)
+
+2024-06-27
+* bug fixes for `generate_ll_from_utm()`
+* add function `remove_empty_tables()` (and associated unit tests)
+* update documentation for `replace blanks()` to indicate it can replace blanks with more than just NA
+
# QCkit v0.1.7
2024-05-08
* Updated the `replace_blanks()` function to accept any missing value code a user inputs (but it still defaults to NA).
diff --git a/R/geography.R b/R/geography.R
index accdfd5..2b89c94 100644
--- a/R/geography.R
+++ b/R/geography.R
@@ -425,7 +425,11 @@ generate_ll_from_utm <- function(df,
# Separate df with just coordinates. We'll filter out any NA rows.
coord_df <- df %>%
- dplyr::select(`_UTMJOINCOL`, {{EastingCol}}, {{NorthingCol}}, {{ZoneCol}}, {{DatumCol}})
+ dplyr::select(`_UTMJOINCOL`,
+ {{EastingCol}},
+ {{NorthingCol}},
+ {{ZoneCol}},
+ {{DatumCol}})
withr::with_envvar(c("PROJ_LIB" = ""), { # This is a fix for the proj library bug in R (see pinned post "sf::st_read() of geojson not getting CRS" in IMData General Discussion).
coord_df <- coord_df %>%
@@ -437,14 +441,19 @@ generate_ll_from_utm <- function(df,
na_row_count <- nrow(df) - nrow(coord_df)
if (na_row_count > 0) {
- warning(paste(na_row_count, "rows are missing UTM coordinates, zone, and/or datum information."), call. = FALSE)
+ warning(paste(
+ na_row_count,
+ "rows are missing UTM coordinates, zone, and/or datum information."),
+ call. = FALSE)
}
## Set up CRS for lat/long data
- latlong_CRS <- sp::CRS(glue::glue("+proj=longlat +datum={latlong_datum}")) # CRS for our new lat/long values
+ latlong_CRS <- sp::CRS(glue::glue("+proj=longlat +datum={latlong_datum}"))
+ # CRS for our new lat/long values
# Loop through each datum and zone in the data
- zones_datums <- dplyr::select(coord_df, {{ZoneCol}}, {{DatumCol}}) %>% # Get vector of zones present in data
+ zones_datums <- dplyr::select(coord_df, {{ZoneCol}}, {{DatumCol}}) %>%
+ # Get vector of zones present in data
unique()
new_coords <- sapply(1:nrow(zones_datums), function(zone_datum_index) {
@@ -454,7 +463,8 @@ generate_ll_from_utm <- function(df,
zone_num <- current_zone
north_south <- ""
} else {
- zone_num <- stringr::str_extract(current_zone, "\\d+") # sp::CRS wants zone number only, e.g. 11, not 11N
+ zone_num <- stringr::str_extract(current_zone, "\\d+")
+ # sp::CRS wants zone number only, e.g. 11, not 11N
zone_letter <- tolower(stringr::str_extract(current_zone, "[A-Za-z]"))
if (!is.na(zone_letter) && zone_letter == "s") {
north_south <- " +south"
@@ -469,17 +479,21 @@ generate_ll_from_utm <- function(df,
filtered_df <- coord_df %>%
dplyr::filter((!!rlang::ensym(ZoneCol) == current_zone & !!rlang::ensym(DatumCol) == current_datum))
sp_utm <- sp::SpatialPoints(filtered_df %>%
- dplyr::select({{EastingCol}}, {{NorthingCol}}) %>%
+ dplyr::select({{EastingCol}},
+ {{NorthingCol}}) %>%
as.matrix(),
- proj4string = utm_CRS) # Convert UTM columns into a SpatialPoints object
- sp_geo <- sp::spTransform(sp_utm, latlong_CRS) %>% # Transform UTM to Lat/Long
+ proj4string = utm_CRS)
+ # Convert UTM columns into a SpatialPoints object
+ sp_geo <- sp::spTransform(sp_utm, latlong_CRS) %>%
+ # Transform UTM to Lat/Long
tibble::as_tibble()
# Add lat/long columns back into the original dataframe
latlong <- tibble::tibble(`_UTMJOINCOL` = filtered_df$`_UTMJOINCOL`,
decimalLatitude = sp_geo[[2]],
decimalLongitude = sp_geo[[1]],
- LatLong_CRS = latlong_CRS@projargs) # Store the coordinate reference system PROJ string in the dataframe
+ LatLong_CRS = latlong_CRS@projargs)
+ # Store the coordinate reference system PROJ string in the dataframe
return(latlong)
diff --git a/R/remove_empty_tables.R b/R/remove_empty_tables.R
index 289ea6e..a042031 100644
--- a/R/remove_empty_tables.R
+++ b/R/remove_empty_tables.R
@@ -11,9 +11,9 @@
#' item_b = mtcars,
#' item_c = iris)
#'
-#' tidy_list <- removeEmptyTables(test_list)
+#' tidy_list <- remove_empty_tables(test_list)
#'
-removeEmptyTables <- function(df_list) {
+remove_empty_tables <- function(df_list) {
non_empty_list <- purrr::compact(df_list) # Remove empty dataframes
tables_removed <- setdiff(names(df_list), names(non_empty_list)) # Get names of removed dataframes
diff --git a/R/replace_blanks.R b/R/replace_blanks.R
index 85813f7..3f028d2 100644
--- a/R/replace_blanks.R
+++ b/R/replace_blanks.R
@@ -1,7 +1,8 @@
-#' Replaces all blank cells with NA
+#' Replaces all blank cells a missing value code of your choice
#'
-#' @details `replace_blanks()` is particularly useful for exporting data from a
-#' database (such as access) and converting it to a data package with metadata.
+#' @description `replace_blanks()` is particularly useful for exporting data
+#' from a database (such as access) and converting it to a data package with
+#' metadata.
#'
#' `replace_blanks()` will import all .csv files in the specified working
#' directory. The files are then written back out to the same directory,
@@ -10,9 +11,23 @@
#' If no missing value is specified, the function defaults to replacing all
#' blanks with "NA".
#'
-#' One exception is if a .csv contains NO data (i.e. just column names and no
-#' data in any of the cells). In this case, the blanks will not be replaced with
-#' NA (as the function cannot determine how many NAs to include).
+#' Please keep in mind the "missing" is a general term for all data
+#' not present in the data file or data package. Although you may have a very
+#' good reason for not providing data and that data may not, from the data
+#' package creator's perspective, be "missing" (maybe you never intended to
+#' collect it) from a data package user's perspective any data that is not in
+#' the data package is effectively "missing" from the data package. Therefore,
+#' it is critical to document in metadata any data that are absent with an
+#' appropriate "missingValueCode" and "missingValueDefinition". These terms are
+#' defined by the metadata schema and are broadly used to apply to any data not
+#' present.
+#'
+#' This function will replace all empty cells and all cells with NA with a
+#' "missingValueCode" of your choice (although it defaults to NA).
+#'
+#' @details One exception is if a .csv contains NO data (i.e. just column names
+#' and no data in any of the cells). In this case, the blanks will not be
+#' replaced with NA (as the function cannot determine how many NAs to include).
#'
#' @param directory String. Path to the file(s) to have blanks replaced with
#' NAs. Defaults to the working directory of the project (here::here())
diff --git a/docs/404.html b/docs/404.html
index 8f26c83..c454bf5 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -4,92 +4,117 @@
-
+
Page not found (404) • QCkit
-
-
-
+
+
+
-
+
+
-
- Skip to contents
+
-