diff --git a/creating-scatter.Rmd b/creating-scatter.Rmd
index e0abace..ff8258c 100644
--- a/creating-scatter.Rmd
+++ b/creating-scatter.Rmd
@@ -25,7 +25,7 @@ add_lines(p)
 knitr::include_graphics("images/scatter-intro.svg")
 ```
 
-Section \@ref(intro-plotly) introduced 'aesthetic mapping' arguments (unique to the R package) which make it easier to map data to visual properties (e.g., `color`, `linetype`, etc.). In addition to these arguments, **dplyr** groupings can be used to ensure there is at least one geometry per group. The top panel of Figure \@ref(fig:scatter-intro) demonstrates how `group_by()` could be used to effectively wrap the time series from Figure \@ref(fig:scatter-intro) by year, which can be useful for visualizing annual seasonality. Another approach to generating at least one geometry per 'group' is to provide categorical variable to a relevant aesthetic (e.g., `color`), as shown in the bottom panel of Figure \@ref(fig:scatter-intro).
+Section \@ref(intro-plotly) introduced 'aesthetic mapping' arguments (unique to the R package) which make it easier to map data to visual properties (e.g., `color`, `linetype`, etc.). In addition to these arguments, **dplyr** groupings can be used to ensure there is at least one geometry per group. The top panel of Figure \@ref(fig:scatter-lines) demonstrates how `group_by()` could be used to effectively wrap the time series from Figure \@ref(fig:scatter-intro) by year, which can be useful for visualizing annual seasonality. Another approach to generating at least one geometry per 'group' is to provide categorical variable to a relevant aesthetic (e.g., `color`), as shown in the bottom panel of Figure \@ref(fig:scatter-lines).
 
 \index{plot\_ly()@\texttt{plot\_ly()}!group\_by()@\texttt{group\_by()} vs. \texttt{split}}