Fix spelling mistakes

jmgirard · Oct 26, 2018 · 4a3eeec · 4a3eeec
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diff --git a/R/instrument_oop.R b/R/instrument_oop.R
@@ -244,7 +244,7 @@ instruments <- function() {
 
 #' Load a specific instrument object
 #'
-#' The circumplex pacakge includes information about numerous circumplex
+#' The circumplex package includes information about numerous circumplex
 #' instruments including instructions for scoring and standardizing items to be
 #' used in conjunction with the \code{score} and \code{standardize} functions.
 #' This function loads the information for a specific instrument into memory.

diff --git a/R/tidying_functions.R b/R/tidying_functions.R
@@ -120,7 +120,7 @@ score <- function(.data, items, instrument, na.rm = TRUE, prefix = "", suffix =
 #'   to use in standardizing the scale scores (default = 1). See \code{?norms}
 #'   to see the normative samples available for an instrument.
 #' @param prefix Optional. A string to include at the beginning of the newly
-#'   calcualted scale variables' names, before the scale name and \code{suffix}
+#'   calculated scale variables' names, before the scale name and \code{suffix}
 #'   (default = "").
 #' @param suffix Optional. A string to include at the end of the newly
 #'   calculated scale variables' names, after the scale name and \code{prefix}

diff --git a/docs/articles/intermediate-ssm-analysis.html b/docs/articles/intermediate-ssm-analysis.html
diff --git a/docs/articles/introduction-to-ssm-analysis.html b/docs/articles/introduction-to-ssm-analysis.html
diff --git a/docs/reference/instrument.html b/docs/reference/instrument.html
diff --git a/docs/reference/standardize.html b/docs/reference/standardize.html
diff --git a/man/instrument.Rd b/man/instrument.Rd
diff --git a/man/standardize.Rd b/man/standardize.Rd
diff --git a/vignettes/intermediate-ssm-analysis.Rmd b/vignettes/intermediate-ssm-analysis.Rmd
@@ -261,7 +261,7 @@ ggsave(filename = "bordpd_gender.png", plot = p, width = 7.5, height = 4,
 ```
 
 ## Wrap-up
-In this vignette, we learned how to generalize the SSM analyses to multiple groups and measures, how to conduct contrast analyses, how to make basic customizations to tables and figures, and how to export tables and figures to external files. In the next vignette, "Advanced Circumplex Vizualiation," we will learn more advanced customization options for the SSM figures and other circumplex visualizations. (Note that the next vignette is still in progress.)
+In this vignette, we learned how to generalize the SSM analyses to multiple groups and measures, how to conduct contrast analyses, how to make basic customizations to tables and figures, and how to export tables and figures to external files. In the next vignette, "Advanced Circumplex Visualization," we will learn more advanced customization options for the SSM figures and other circumplex visualizations. (Note that the next vignette is still in progress.)
 
 ## References
 * Gurtman, M. B. (1992). Construct validity of interpersonal personality measures: The interpersonal circumplex as a nomological net. _Journal of Personality and Social Psychology, 63_(1), 105–118.

diff --git a/vignettes/introduction-to-ssm-analysis.Rmd b/vignettes/introduction-to-ssm-analysis.Rmd
@@ -381,7 +381,7 @@ The output of the function has been saved in the `results` object, which we can
 summary(results)
 ```
 
-That was pretty easy! We can now write up these results. However, the `circumplex` package has some features that can make what we just did even easier. First, because the first three arguments of the `ssm_analyze()` function are always the same, we can omit their names. Second, because we organized the `jz2017s` data frame to have the circumplex scale variables adjacent and in order from PA to NO, we can simplify their specification by using the `PA:NO` shortcut. Finally, because the use of octant scales is so common, the `circumplex` package comes with a convenience function for outputing their angular displacements: `octants()`. Note how, even when using these shortcuts, the results are the same except for minor stochastic differences in the confidence intervals due to the randomness inherent to bootstrapping. (To get the exact same results, we could use the `set.seed()` function to control the random number generator in R.)
+That was pretty easy! We can now write up these results. However, the `circumplex` package has some features that can make what we just did even easier. First, because the first three arguments of the `ssm_analyze()` function are always the same, we can omit their names. Second, because we organized the `jz2017s` data frame to have the circumplex scale variables adjacent and in order from PA to NO, we can simplify their specification by using the `PA:NO` shortcut. Finally, because the use of octant scales is so common, the `circumplex` package comes with a convenience function for outputting their angular displacements: `octants()`. Note how, even when using these shortcuts, the results are the same except for minor stochastic differences in the confidence intervals due to the randomness inherent to bootstrapping. (To get the exact same results, we could use the `set.seed()` function to control the random number generator in R.)
 
 ```{r summary1b}
 results2 <- ssm_analyze(jz2017s, PA_z:NO_z, octants())