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NEWS.md

@@ -11,7 +11,7 @@ discretisation procedure (such as the levels of `geom_contour_fill()`/`ggplot2::
 and allows you to treat them as continuous. 
 
 - Related to that, `geom_contour_fill()` now gains a new computed aesthetic called `level_d`,
-which is the same as `level` but forces ggplot2 to automaticallly use the new discretised scale.
+which is the same as `level` but forces ggplot2 to automatically use the new discretised scale.
 
 - `AnchorBreaks()` gains a `bins` argument to mimic the default functionality of 
 `MakeBreaks()`. 
@@ -212,7 +212,7 @@ the rest of the variables. Sorry, thermodynamics is not my forte! (thanks @PaoCo
 There has been some changes in the interface of some functions for the sake of 
 consistency.
 
-- In `Derivate()` (and it's derivated functions --see what I did there?), the
+- In `Derivate()` (and it's derived functions --see what I did there?), the
 `data` argument has been moved back. This is because this function is intended to
 be called inside a `data.table` of `mutate()` call, where you don't need to 
 explicitly specify the data.

R/geom_contour_fill.R

@@ -39,7 +39,7 @@
 #' @section Computed variables:
 #' \describe{
 #'  \item{level}{An ordered factor that represents bin ranges.}
-#'  \item{level_d}{Same as `level`, but autimatically uses [scale_fill_discretised()]}
+#'  \item{level_d}{Same as `level`, but automatically uses [scale_fill_discretised()]}
 #'  \item{level_low,level_high,level_mid}{Lower and upper bin boundaries for each band, as well the mid point between the boundaries.}
 #'  }
 #'

vignettes/Visualization-tools.Rmd

@@ -163,7 +163,7 @@ ggplot(temperature[lev == 300], aes(lon, lat, z = air.z)) +
     geom_contour_fill(aes(fill = stat(level)))
 ```
 
-A big difference between the two variables is that one is compatible with continuous scales while the other only works with discrete scales. The advantage of using `level_mid` is that it preserves the actual continous nature of the data, which allows the use of meaningful continuous scales such as `scale_fill_divergent()` and results in proper colours when breaks are not equally spaced. The disadvantage is that the scale function has no information about the discretisation, and thus is very hard to use *synchronise* the breaks that define the levels of the contours and the breaks shown in the color guide.
+A big difference between the two variables is that one is compatible with continuous scales while the other only works with discrete scales. The advantage of using `level_mid` is that it preserves the actual continuous nature of the data, which allows the use of meaningful continuous scales such as `scale_fill_divergent()` and results in proper colours when breaks are not equally spaced. The disadvantage is that the scale function has no information about the discretisation, and thus is very hard to use *synchronise* the breaks that define the levels of the contours and the breaks shown in the color guide.
 
 A third important computed variable is `level_d`, which is identical to `level` but forces the scale to match the breaks in the data automatically.