Updated NA tests

Fixed NA tests code

FrEDI/testing/configTests_utils.R

@@ -18,23 +18,24 @@
 # }
 
 ## Function to check if column has at least one non NA value
-has_nonNA_values_df <- function(x, groups0="sector", col0="annual_impacts") {
+has_nonNA_values_df <- function(df0, groups0="sector", col0="annual_impacts") {
   # ### Check whether values in x are NA
   # x <- x |> is.na()
   # ### Calculate number of rows
   # y <- tibble(numRows = x |> nrow())
   # ### Number of NA values
   # y <- y |> mutate(numNA = x |> colSums() |> nrow() |> is.null() |> if_else(., 0, 1))
   ### Add counters
-  x    <- x |> mutate(numRows = 1)
-  x    <- x |> mutate(numNA   = 1 * (x[[col0]] |> is.na()))
+  df0   <- df0 |> mutate(numRows = 1)
+  vals0 <- df0[[col0]] |> is.na()
+  x     <- df0 |> mutate(numNA   = 1 * vals0)
   ### Summarize
-  sum0 <- c("numRows", "numNA")
-  y    <- x |> group_by_at(c(groups0)) |> summarize_at(c(sum0), sum)
+  sum0  <- c("numRows", "numNA")
+  y     <- x |> group_by_at(c(groups0)) |> summarize_at(c(sum0), sum)
   ### Whether all results are missing
-  y    <- y |> mutate(allNA = (numRows == numNA))
+  y     <- y |> mutate(allNA = (numRows == numNA))
   ### Filter to values with allNA
-  z    <- y |> filter(allNA)
+  z     <- y |> filter(allNA)
   # ### Get number of rows |>
   # z    <- y |> nrow()
   # # z <- 1 * (z > 0)

FrEDI/testing/testing_general_fredi_test.R

@@ -26,14 +26,15 @@ general_fredi_test <- function(
   #### Check if each sector has at least one non-NA-value
 
   # Get names of sector "group-names" consistent with dplyr
-  sect_names <- newOutputs |> pull(sector) |> unique()
+  # sect_names <- newOutputs |> pull(sector) |> unique()
 
   # For each sector group, check if each sector has nonNA values
-  sect_na_check <- newOutputs |>
-    group_by_at(c("sector")) |>
-    has_nonNA_values_df(groups0="sector", col0="annual_impacts")
-    # group_map(~ has_nonNA_values_df(., col0="annual_impacts")) |>
-    # set_names(sect_names$sector)
+  # sect_na_check <- newOutputs |>
+  #   group_by_at(c("sector")) |>
+  #   group_map(~ has_nonNA_values_df(., col0="annual_impacts")) |>
+  #   set_names(sect_names$sector)
+  sect_na_check <- newOutputs |> has_nonNA_values_df(groups0="sector", col0="annual_impacts")
+
 
   # If there are any sector groups with all NA values, filter to that sector and save to output list
   # sect_na_check$`ATS Extreme Temperature` <- 1