Addresses spell check warnings

DESCRIPTION

@@ -6,7 +6,7 @@ Description: A tool for exploring correlations.
     It makes it possible to easily perform routine tasks when
     exploring correlation matrices such as ignoring the diagonal,
     focusing on the correlations of certain variables against others,
-    or rearranging and visualising the matrix in terms of the
+    or rearranging and visualizing the matrix in terms of the
     strength of the correlations.
 Authors@R: 
     c(person(given = "Edgar",

NEWS.md

@@ -8,7 +8,7 @@
 
 - Improves `correlate()` for database backed tables
 
-- Fixes compatability issues with `dplyr` 
+- Fixes compatibility issues with `dplyr` 
 
 # corrr 0.3.2
 
@@ -52,7 +52,7 @@ The `diagonal` argument of `as_matrix` and `as_matrix.cor_df` is now an optional
 
 ## Fixes
 
-- When `legend = TRUE` (now the default setting), `rplot` and `network_plot` generate a single, unlabelled legend referring to the size of the correlations.
+- When `legend = TRUE` (now the default setting), `rplot` and `network_plot` generate a single, unlabeled legend referring to the size of the correlations.
 
 ## Other
 

R/correlate.R

@@ -10,7 +10,7 @@
 #'   \item A tibble (see \code{\link[tibble]{tibble}})
 #'   \item An additional class, "cor_df"
 #'   \item A "rowname" column
-#'   \item Standardised variances (the matrix diagonal) set to missing values by
+#'   \item Standardized variances (the matrix diagonal) set to missing values by
 #'   default (\code{NA}) so they can be ignored in calculations.
 #' }
 #' 

R/output.R

@@ -69,10 +69,10 @@ fashion.default <- function(x, decimals = 2, leading_zeros = FALSE, na_print = "
 #' 
 #' @param rdf Correlation data frame (see \code{\link{correlate}}) or object
 #'   that can be coerced to one (see \code{\link{as_cordf}}).
-#' @param legend Boolean indicating whether a legend mapping the colours to the correlations should be displayed.
+#' @param legend Boolean indicating whether a legend mapping the colors to the correlations should be displayed.
 #' @param shape \code{\link{geom_point}} aesthetic.
 #' @param print_cor Boolean indicating whether the correlations should be printed over the shapes.
-#' @param colours,colors Vector of colours to use for n-colour gradient.
+#' @param colours,colors Vector of colors to use for n-color gradient.
 #' @return Plots a correlation data frame
 #' @export
 #' @examples 
@@ -104,13 +104,13 @@ rplot.default <- function(rdf, ...) {
 #' 
 #' Output a network plot of a correlation data frame in which variables that are
 #' more highly correlated appear closer together and are joined by stronger
-#' paths. Paths are also coloured by their sign (blue for positive and red for
+#' paths. Paths are also colored by their sign (blue for positive and red for
 #' negative). The proximity of the points are determined using multidimensional
 #' clustering.
 #' 
 #' @param min_cor Number from 0 to 1 indicating the minimum value of
 #'   correlations (in absolute terms) to plot.
-#' @param colours,colors Vector of colours to use for n-colour gradient.
+#' @param colours,colors Vector of colors to use for n-color gradient.
 #' @param repel Should variable labels repel each other? If TRUE, text is added
 #'   via \code{\link[ggrepel]{geom_text_repel}} instead of \code{\link[ggplot2]{geom_text}}
 #' @param curved Should the paths be curved? If TRUE, paths are added via

R/reshape.R

@@ -60,7 +60,7 @@ focus_ <- function(x, ..., .dots, mirror) {
 
 #' Conditionally focus correlation data frame
 #' 
-#' Apply a predicate function to each colum of correlations. Columns that
+#' Apply a predicate function to each column of correlations. Columns that
 #' evaluate to TRUE will be included in a call to \code{\link{focus}}.
 #' 
 #' @param x Correlation data frame or object to be coerced to one via
@@ -104,7 +104,7 @@ focus_if.default <- function(x, .predicate, ..., mirror = FALSE) {
 #'   matrix diagonal) should be dropped? Will automatically be set to TRUE if
 #'   mirror is FALSE.
 #' @param remove.dups Removes duplicate entries, without removing all NAs
-#' @return tbl with three colums (x and y variables, and their correlation)
+#' @return tbl with three columns (x and y variables, and their correlation)
 #' @export
 #' @examples
 #' x <- correlate(mtcars)

R/retract.R

@@ -1,4 +1,4 @@
-#' Creates a data frame from a streched correlation table
+#' Creates a data frame from a stretched correlation table
 #' 
 #' \code{retract} does the opposite of what \code{stretch} does
 #' 

README.Rmd

@@ -43,7 +43,7 @@ Using `corrr` typically starts with `correlate()`, which acts like the base corr
 
 - A `tbl` with an additional class, `cor_df`
 - An extra "rowname" column
-- Standardised variances (the matrix diagonal) set to missing values (`NA`) so they can be ignored.
+- Standardized variances (the matrix diagonal) set to missing values (`NA`) so they can be ignored.
 
 ### API
 
@@ -59,7 +59,7 @@ Reshape structure (`tbl` or `cor_df` out):
 - `focus()` on select columns and rows.
 - `stretch()` into a long format.
 
-Output/visualisations (console/plot out):
+Output/visualizations (console/plot out):
 
 - `fashion()` the correlations for pretty printing.
 - `rplot()` the correlations with shapes in place of the values.

README.md

@@ -45,7 +45,7 @@ following structure:
 
   - A `tbl` with an additional class, `cor_df`
   - An extra “rowname” column
-  - Standardised variances (the matrix diagonal) set to missing values
+  - Standardized variances (the matrix diagonal) set to missing values
     (`NA`) so they can be ignored.
 
 ### API
@@ -66,7 +66,7 @@ Reshape structure (`tbl` or `cor_df` out):
   - `focus()` on select columns and rows.
   - `stretch()` into a long format.
 
-Output/visualisations (console/plot out):
+Output/visualizations (console/plot out):
 
   - `fashion()` the correlations for pretty printing.
   - `rplot()` the correlations with shapes in place of the values.

cran-comments.md

@@ -4,7 +4,7 @@
 * Adds `dice()` function, wraps `focus(x,..., mirror = TRUE)`
 * Adds `retract()` function, opposite of `stretch()` 
 * Improves `correlate()` for database backed tables
-* Fixes compatability issues with `dplyr` 
+* Fixes compatibility issues with `dplyr` 
 
 ## Test environments
 * Local windows 10 install, R 3.6.0

vignettes/using-corrr.Rmd

@@ -15,15 +15,15 @@ library(corrr)
 knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
 ```
 
-corrr is a package for exploring **corr**elations in **R**. It makes it possible to easily perform routine tasks when exploring correlation matrices such as ignoring the diagonal, focusing on the correlations of certain variables against others, or rearranging and visualising the matrix in terms of the strength of the correlations.
+corrr is a package for exploring **corr**elations in **R**. It makes it possible to easily perform routine tasks when exploring correlation matrices such as ignoring the diagonal, focusing on the correlations of certain variables against others, or rearranging and visualizing the matrix in terms of the strength of the correlations.
 
 ## Using corrr
 
 Using `corrr` starts with `correlate()`, which acts like the base correlation function `cor()`. It differs by defaulting to pairwise deletion, and returning a correlation data frame (`cor_df`) of the following structure:
 
 - A `tbl` with an additional class, `cor_df`
 - An extra "rowname" column
-- Standardised variances (the matrix diagonal) set to missing values (`NA`) so they can be ignored.
+- Standardized variances (the matrix diagonal) set to missing values (`NA`) so they can be ignored.
 
 To work with further, let's create a correlation data frame using `correlate()` from the `mtcars` data that comes with R:
 
@@ -35,7 +35,7 @@ d
 
 ## Why a correlation data frame?
 
-At first, a correlation data frame might seem like an unneccessary complexity compared to the traditional matrix. However, the purpose of corrr is to help use explore these correlations, not to do mathematical or statistical operations. Thus, by having the correlations in a data frame, we can make use of packages that help us work with data frames like `dplyr`, `tidyr`, `ggplot2`, and focus on using data pipelines. Lets look at some examples:
+At first, a correlation data frame might seem like an unnecessary complexity compared to the traditional matrix. However, the purpose of corrr is to help use explore these correlations, not to do mathematical or statistical operations. Thus, by having the correlations in a data frame, we can make use of packages that help us work with data frames like `dplyr`, `tidyr`, `ggplot2`, and focus on using data pipelines. Lets look at some examples:
 
 ```{r, message=F, warning=F}
 library(dplyr)
@@ -53,7 +53,7 @@ d %>%
   select(rowname, mpg, cyl, disp)
 ```
 
-Furthermore, by having the diagonal set to missing, we don't need to put in extra effort to ignore them when summarising the correlations. For example:
+Furthermore, by having the diagonal set to missing, we don't need to put in extra effort to ignore them when summarizing the correlations. For example:
 
 ```{r, warning = FALSE, message = FALSE}
 # Compute mean of each column
@@ -77,7 +77,7 @@ Reshape structure (`tbl` or `cor_df` out):
 - `focus()` on select columns and rows.
 - `stretch()` into a long format.
 
-Output/visualisations (console/plot out):
+Output/visualizations (console/plot out):
 
 - `fashion()` the correlations for pretty printing.
 - `rplot()` a shape for each correlation.
@@ -100,7 +100,7 @@ d %>%
   fashion()   # Print in nice format 
 ```
 
-Alternatively, we can visualise these correlations (let's clear the lower triangle for a change):
+Alternatively, we can visualize these correlations (let's clear the lower triangle for a change):
 
 ```{r, warning = FALSE}
 d %>%