diff --git a/_R/2017-04-26-tidytext-plots.Rmd b/_R/2017-04-26-tidytext-plots.Rmd
index ed31970..047f32b 100644
--- a/_R/2017-04-26-tidytext-plots.Rmd
+++ b/_R/2017-04-26-tidytext-plots.Rmd
@@ -193,7 +193,7 @@ peak_decile %>%
 
 We see that the words in the start and the end are the most specific to their particular deciles: for example, almost half of the occurrences of the word "fictional" occurred in the first 10% of the story. The middle sections have words that are more spread out (having, say, 14% of their occurrences in that section rather than the expected 10%), but they still are words that make sense in the story structure.
 
-Let's visualize the full trend for the words overrepreseted at each point.
+Let's visualize the full trend for the words overrepresented at each point.
 
 ```{r sparklines, fig.width = 8, fig.height = 8, echo = FALSE}
 peak_decile %>%
@@ -203,7 +203,7 @@ peak_decile %>%
   ungroup() %>%
   inner_join(decile_counts, by = "word") %>%
   mutate(word = reorder(word, peak_decile + .001 * fraction_peak)) %>%
-  ggplot(aes(decile, n / number, color = word)) +
+  ggplot(aes(decile.y, n / number, color = word)) +
   geom_line(show.legend = FALSE, size = 1) +
   geom_hline(lty = 2, yintercept = .1, alpha = .5) +
   facet_wrap(~ word, ncol = 6) +
@@ -246,4 +246,4 @@ In short, if we had to summarize the *average* story that humans tell, it would
 This was a pretty simple analysis of story arcs (for a more in-depth example, see the [research described here](https://www.theatlantic.com/technology/archive/2016/07/the-six-main-arcs-in-storytelling-identified-by-a-computer/490733/)), and it doesn't tell us too much we wouldn't have been able to guess. 
 (Except perhaps that characters are most likely to be drunk right in the middle of a story. How can we monetize that insight?)
 
-What I like about this approach is how quickly you can gain insights with simple quantitative methods (counting, taking the median) applied to a large text dataset. In future posts, I'll be diving deeper into these plots and showing what else we can learn.
\ No newline at end of file
+What I like about this approach is how quickly you can gain insights with simple quantitative methods (counting, taking the median) applied to a large text dataset. In future posts, I'll be diving deeper into these plots and showing what else we can learn.