Prefer is.null() (and is.na()) over missing()

NEWS.md

@@ -24,6 +24,8 @@
 
   - `source = "all"` will return all available metadata (including KCL and Europe).
 
+- new function `runRegression()` for extracting 'dilution lines' from air quality and other data. Online manual will be updated with principles and examples. 
+
 - `calendarPlot()` now automatically creates its own `labels` if `breaks` are specified. For example, `c(0, 10, 20)` will create the labels `c("0 - 10", "10 - 20")`. `labels` can still be used to override the default values. (#341)
 
 - return tibble from `timeAverage()`.
@@ -32,9 +34,7 @@
 
 - Move regression formula off main plot for `polarPlot()` for clarity and label slope as 'm'.
 
-- new function `runRegression()` for extracting 'dilution lines' from air quality and other data. Online manual will be updated with principles and examples. 
-
-- Tweak seasonal trend decomposition using STL to allow the seasonal amplitude to vary more. Affects `smoothTrend` and `TheilSen`.
+- Tweak seasonal trend decomposition using STL to allow the seasonal amplitude to vary more. Affects `smoothTrend()` and `TheilSen()`.
 
 ## Bug Fixes
 
@@ -44,6 +44,8 @@
 
 - The `year` argument of `importMeta()` is now respected when `source = "kcl"` and `"europe"`.
 
+- Several of the directional analysis plot family (e.g., `polarFreq()`) have been refactored to use `is.null()` or `is.na()` over `missing()`. While predominantly an internal change, this should be make these functions easier to use inside of other functions (e.g., `function(data, breaks = NA) polarFreq(data, breaks = breaks))` will now run successfully).
+
 - For `calendarPlot` when annotated with ws or wd arrows, use max ws/wd that corresponds to hour of maximum pollutant concentration and not simple the max ws/wd for a day.
 
 # openair 2.17-0

R/percentileRose.R

@@ -453,9 +453,7 @@ percentileRose <- function(
 
   ## nice intervals for pollutant concentrations
   tmp <- (results.grid$x ^ 2 + results.grid$y ^ 2) ^ 0.5
-  if (missing(intervals)) intervals <- pretty(c(min(tmp, na.rm = TRUE), max(tmp, na.rm = TRUE)))
-
-
+  if (is.null(intervals)) intervals <- pretty(c(min(tmp, na.rm = TRUE), max(tmp, na.rm = TRUE)))
 
   labs <- intervals ## the labels
 

R/polarAnnulus.R

@@ -150,7 +150,7 @@ polarAnnulus <-
            type = "default",
            statistic = "mean",
            percentile = NA,
-           limits = c(0, 100),
+           limits = NULL,
            cols = "default",
            width = "normal",
            min.bin = 1,
@@ -159,7 +159,7 @@ polarAnnulus <-
            force.positive = TRUE,
            k = c(20, 10),
            normalise = FALSE,
-           key.header = "",
+           key.header = statistic,
            key.footer = pollutant,
            key.position = "right",
            key = TRUE,
@@ -183,7 +183,6 @@ polarAnnulus <-
     percentile <- 50
   }
 
-  if (missing(key.header)) key.header <- statistic
   if (key.header[1] == "weighted.mean") key.header <- c("weighted", "mean")
   if (key.header[1] == "percentile") key.header <- c(paste(percentile, "th", sep = ""), "percentile")
   if (key.header[1] == "cpf") key.header <- c("CPF", "probability")
@@ -510,7 +509,7 @@ polarAnnulus <-
   ## auto-scaling
   nlev <- 200 # preferred number of intervals
   ## handle missing breaks arguments
-  if (missing(limits)) {
+  if (any(is.null(limits)) | any(is.na(limits))) {
     # breaks <- pretty(results.grid$z, n = nlev)
     breaks <- seq(min(results.grid$z, na.rm = TRUE), max(results.grid$z, na.rm = TRUE),
                   length.out = nlev

R/polarDiff.R

@@ -42,10 +42,13 @@ polarDiff <- function(
     after,
     pollutant = "nox",
     x = "ws",
-    limits = NA,
+    limits = NULL,
     plot = TRUE,
     ...
 ) {
+  if (is.null(limits)){
+    limits <- NA
+  }
 
   # extra args setup
   Args <- list(...)

R/polarFreq.R

@@ -116,12 +116,12 @@
 #' \dontrun{polarFreq(mydata, pollutant = "pm25", statistic
 #' ="weighted.mean", offset = 50, ws.int = 25, trans = FALSE) }
 polarFreq <- function(mydata,
-                      pollutant = "",
+                      pollutant = NULL,
                       statistic = "frequency",
                       ws.int = 1,
                       wd.nint = 36,
                       grid.line = 5,
-                      breaks = seq(0, 5000, 500),
+                      breaks = NULL,
                       cols = "default",
                       trans = TRUE,
                       type = "default",
@@ -183,7 +183,7 @@ polarFreq <- function(mydata,
     trellis.par.set(fontsize = list(text = extra.args$fontsize))
   }
 
-  if (!missing(pollutant)) vars <- c(vars, pollutant)
+  if (!is.null(pollutant)) vars <- c(vars, pollutant)
 
   ## data checks
   mydata <- checkPrep(mydata, vars, type, remove.calm = FALSE)
@@ -198,19 +198,20 @@ polarFreq <- function(mydata,
   mydata <- cutData(mydata, type, ...)
 
   ## if pollutant chosen but no statistic - use mean, issue warning
-  if (!missing(pollutant) & missing(statistic)) {
+  if (statistic == "frequency" & !is.null(pollutant)) {
+    cli::cli_warn(c("x" = "{.code statistic == 'frequency'} incompatible with a defined {.field pollutant}.",
+                    "i" = "Setting {.field statistic} to {.code 'mean'}."))
     statistic <- "mean"
-    warning("No statistic chosen, using mean")
   }
 
   ## if statistic chosen but no pollutant stop
-  if (!missing(statistic) & missing(pollutant)) {
-    stop("No pollutant chosen, please choose one e.g. pollutant = 'nox'")
+  if (statistic != "frequency" & is.null(pollutant)) {
+    cli::cli_abort(c("x" = "No {.field pollutant} chosen",
+                     "i" = "Please choose a {.field pollutant}, e.g., {.code pollutant = 'nox'}"))
   }
 
-  if (!missing(breaks)) trans <- FALSE ## over-ride transform if breaks supplied
+  if (!(any(is.null(breaks)) | any(is.na(breaks)))) trans <- FALSE ## over-ride transform if breaks supplied
 
-  if (missing(key.header)) key.header <- statistic
   if (key.header == "weighted.mean") key.header <- c("contribution", "(%)")
 
   ## apply square root transform?
@@ -319,7 +320,7 @@ polarFreq <- function(mydata,
 
   nlev <- 200
   ## handle missing breaks arguments
-  if (missing(breaks)) {
+  if (any(is.null(breaks)) | any(is.na(breaks))) {
     breaks <- unique(c(0, pretty(results.grid$weights, nlev)))
     br <- pretty((c(0, results.grid$weights) ^ coef), n = 10) ## breaks for scale
   } else {