remove utf and fallback for connection failure

data-raw/noaa-data.R

@@ -1,15 +1,11 @@
 ## code to prepare `NOAA_data` dataset
+
 library(tibble)
 
 NOAA_data <- tibble(
   variable = c("temperature", "phosphate", "nitrate", "silicate", "oxygen",
   "salinity", "density"),
-  unit = c(
-    "\u00b0 C",
-    rep("\u03bc mol kg-1", 4),
-    "",
-    "kg m-3"
-  ),
+  unit = c("C", rep("micromol kg-1", 4), "", "kg m-3"),
   citation = c(
     vc_cite["temperature"],
     rep(vc_cite["nutrients"], 3),
@@ -19,4 +15,5 @@ NOAA_data <- tibble(
   )
 )
 
+#save(NOAA_data, file = "data/NOAA_data.rda", ascii = TRUE, version = 3)
 usethis::use_data(NOAA_data, overwrite = TRUE)

inst/WORDLIST

@@ -99,9 +99,7 @@ https
 io
 isbn
 knitr
-ncei
 nd
-noaa
 pkgs
 programmatically
 quosure
@@ -119,6 +117,7 @@ testthat
 tibble
 tunnelled
 tunnelling
+unicode
 utrecht
 vdiffr
 worldmap

vignettes/oceanexplorer.Rmd

@@ -116,7 +116,7 @@ Imagine we are interested in phosphate concentrations in the month December from
 
 First, we extract the data by proving the variable, grid resolution, and the averaging period. Make sure to have an internet connection in order to connect to the NOAA server.
 
-```{r setup, eval = curl::has_internet() && interactive()}
+```{r setup, eval = curl::has_internet()}
 # load package
 library(oceanexplorer)
 # get data
@@ -129,37 +129,37 @@ This operation can take a while, but it can be sped-up during future calls by ca
 
 Then we can plot the phosphate data from a depth of 1000 meter below sea level.
 
-```{r plot1, fig.width=7, eval = curl::has_internet() && interactive()}
+```{r plot1, fig.width=7, eval = exists("WOA")}
 plot_NOAA(WOA, depth = 1000)
 ```
 
-```{r save1, echo=FALSE, eval = curl::has_internet() && interactive()}
+```{r save1, echo=FALSE, eval = FALSE}
 ggplot2::ggsave("vignettes/NOAA_plot1.png", plot_NOAA(WOA, depth = 1000), width = 9)
 ```
 
-```{r graph1, echo=FALSE, eval = !curl::has_internet() || !interactive(), out.width="100%"}
+```{r graph1, echo=FALSE, eval = !exists("WOA"), out.width="100%"}
 knitr::include_graphics("NOAA_plot1.png")
 ```
 
 ## Filter Agulhas Basin data point
 
 Finally, we can filter a data point from, for example, the Agulhas Basin.
 
-```{r filter, eval = curl::has_internet() && interactive()}
+```{r filter, eval = exists("WOA")}
 (pts <- filter_NOAA(WOA, depth = 1000, coord = list(lon = 20, lat = -46)))
 ```
 
 We can then also project these extraction points on the world map for future reference.
 
-```{r plot2, fig.width=7, eval = curl::has_internet() && interactive()}
+```{r plot2, fig.width=7, eval = exists("WOA")}
 plot_NOAA(WOA, depth = 1000, points = pts)
 ```
 
-```{r save2, echo=FALSE, eval = curl::has_internet() && interactive()}
+```{r save2, echo=FALSE, eval = FALSE}
 ggplot2::ggsave("vignettes/NOAA_plot2.png", plot_NOAA(WOA, depth = 1000, points = pts), width = 9)
 ```
 
-```{r graph2, echo=FALSE, eval = !curl::has_internet() || !interactive(), out.width="100%"}
+```{r graph2, echo=FALSE, eval = !exists("WOA"), out.width="100%"}
 knitr::include_graphics("NOAA_plot2.png")
 ```