Adjusted some figures and typos

_R/2017-04-27-tidytext-gender-plots.Rmd

@@ -97,7 +97,7 @@ he_she_counts %>%
   labs(x = "",
        y = 'Relative appearance after "she" compared to "he"',
        fill = "",
-       title = "Gendered verbs across 100,000 plot descriptions",
+       title = "Verbs correlated with gender in 100,000 plot descriptions",
        subtitle = "Words with at least 200 uses after he/she, 12 most skewed in each direction.") +
   scale_y_continuous(labels = c("4X", "2X", "Same", "2X"),
                      breaks = seq(-2, 1)) +
@@ -173,5 +173,5 @@ I'd also note that we could expand the analysis to include not only pronouns but
 
 Again, the full code for this post is available [here](https://github.com/dgrtwo/dgrtwo.github.com/blob/master/_R/2017-04-27-tidytext-gender-plots.Rmd), and I hope others explore this data more deeply.
 
-[^binary]: Gender is not a binary, so please note that this analysis is examining how the Wikipedia description's author refers to the character. For exampele, we miss cases of [singular they](https://en.wikipedia.org/wiki/Gender-neutral_language), but it would be challenging to separate it from the more common plural.
+[^binary]: Gender is not a binary, so please note that this analysis is examining how the Wikipedia description's author refers to the character. For example, we miss cases of [singular they](https://en.wikipedia.org/wiki/Gender-neutral_language), but it would be challenging to separate it from the more common plural.
 [^himself]: I'm also skipping the words "himself" and "herself", which are the most gender-shifted words but aren't interesting for our purposes.

_posts/2017-04-27-tidytext-gender-plots.md

@@ -141,5 +141,5 @@ I'd also note that we could expand the analysis to include not only pronouns but
 
 Again, the full code for this post is available [here](https://github.com/dgrtwo/dgrtwo.github.com/blob/master/_R/2017-04-27-tidytext-gender-plots.Rmd), and I hope others explore this data more deeply.
 
-[^binary]: Gender is not a binary, so please note that this analysis is examining how the Wikipedia description's author refers to the character. For exampele, we miss cases of [singular they](https://en.wikipedia.org/wiki/Gender-neutral_language), but it would be challenging to separate it from the more common plural.
+[^binary]: Gender is not a binary, so please note that this analysis is examining how the Wikipedia description's author refers to the character. For example, we miss cases of [singular they](https://en.wikipedia.org/wiki/Gender-neutral_language), but it would be challenging to separate it from the more common plural.
 [^himself]: I'm also skipping the words "himself" and "herself", which are the most gender-shifted words but aren't interesting for our purposes.