From dbe965486b0706ad6f77a8f1c42e74774fd4dad6 Mon Sep 17 00:00:00 2001
From: albhasan <albhasan@gmail.com>
Date: Fri, 3 Nov 2023 10:05:19 -0300
Subject: [PATCH] Closes #142

---
 episodes/07-plot-ggplot2.Rmd | 34 +++++++++++++++++-----------------
 1 file changed, 17 insertions(+), 17 deletions(-)

diff --git a/episodes/07-plot-ggplot2.Rmd b/episodes/07-plot-ggplot2.Rmd
index 03d25e28..75c7834b 100644
--- a/episodes/07-plot-ggplot2.Rmd
+++ b/episodes/07-plot-ggplot2.Rmd
@@ -25,21 +25,21 @@ library(dplyr)
 ```
 
 Plotting our data is one of the best ways to quickly explore it and the various
-relationships between variables. There are three main plotting systems in R, the
-[base plotting system](https://www.statmethods.net/graphs/), the
+relationships between variables. There are three main plotting systems in R,
+the [base plotting system](https://www.statmethods.net/graphs/), the
 [lattice](https://www.statmethods.net/advgraphs/trellis.html) package, and the
-[ggplot2](https://www.statmethods.net/advgraphs/ggplot2.html) package. Today and
-tomorrow we'll be learning about the ggplot2 package, because it is the most
-effective for creating publication quality graphics. In this episode, we will
-introduce the key features of a ggplot and make a few example plots. We will
-expand on these concepts and see how they apply to geospatial data types when we
-start working with geospatial data in the [R for Raster and Vector
+[ggplot2](https://www.statmethods.net/advgraphs/ggplot2.html) package. Today
+and tomorrow we'll be learning about the ggplot2 package, because it is one of
+the most popular for creating publication quality graphics. In this episode, we
+will introduce the key features of a ggplot and make a few example plots. We
+will expand on these concepts and see how they apply to geospatial data types
+when we start working with geospatial data in the [R for Raster and Vector
 Data](https://datacarpentry.org/r-raster-vector-geospatial/) lesson.
 
 :::::::::::::::::::::::::::::::::::::::  instructor
 
 - This episode introduces `geom_col` and `geom_histogram`. These geoms are used
-  in the rest of the workshop, along with geoms specifically for geospatial 
+  in the rest of the workshop, along with geoms specifically for geospatial
   data.
 - Emphasize that we will go much deeper into visualization and creating
   publication-quality graphics later in the workshop.
@@ -73,7 +73,7 @@ for histograms.
 
 ```{r lifeExp-vs-gdpPercap-scatter, fig.cap="Histogram of life expectancy by country showing bimodal distribution with modes at 45 and 75", message=FALSE}
 library("ggplot2")
-ggplot(data = gapminder, aes(x = lifeExp)) +   
+ggplot(data = gapminder, aes(x = lifeExp)) +
   geom_histogram()
 ```
 
@@ -89,7 +89,7 @@ tells `ggplot` we want to visually represent the
 distribution of one variable (in our case "lifeExp"):
 
 ```{r lifeExp-vs-gdpPercap-scatter2, fig.cap="Histogram of life expectancy by country showing bimodal distribution with modes at 45 and 75"}
-ggplot(data = gapminder, aes(x = lifeExp)) +   
+ggplot(data = gapminder, aes(x = lifeExp)) +
   geom_histogram()
 ```
 
@@ -106,7 +106,7 @@ expectancy:
 ## Solution to challenge 1
 
 ```{r ch1-sol}
-ggplot(data = gapminder, aes(x = gdpPercap)) +   
+ggplot(data = gapminder, aes(x = gdpPercap)) +
  geom_histogram()
 ```
 
@@ -131,7 +131,7 @@ We will plot countries on the x-axis (listed in alphabetic order
 by default) and gdp per capita on the y-axis.
 
 ```{r hist-subset-gapminder, fig.cap="Barplot of GDP per capita. Country names on x-axis overlap and are not readable"}
-ggplot(data = gapminder_small, aes(x = country, y = gdpPercap)) + 
+ggplot(data = gapminder_small, aes(x = country, y = gdpPercap)) +
   geom_col()
 ```
 
@@ -140,7 +140,7 @@ x-axis labels. A quick fix to this is the add the `coord_flip()`
 function to the end of our plot code.
 
 ```{r hist-subset-gapminder-flipped, fig.cap="Barplot showing GDP per capita. Country names on the y-axis are readable"}
-ggplot(data = gapminder_small, aes(x = country, y = gdpPercap)) + 
+ggplot(data = gapminder_small, aes(x = country, y = gdpPercap)) +
   geom_col() +
   coord_flip()
 ```
@@ -185,10 +185,10 @@ The default behavior for `postion` in `geom_col()`
 is "stack".
 
 ```{r gpd-per-cap}
-ggplot(gapminder_small_2, 
-       aes(x = country, y = gdpPercap, 
+ggplot(gapminder_small_2,
+       aes(x = country, y = gdpPercap,
        fill = as.factor(year))) +
-   geom_col(position = "dodge") + 
+   geom_col(position = "dodge") +
    coord_flip()
 ```