Merge pull request #138 from behrman/visualize-typos

Fix typos - 3 Data visualisation

visualize.Rmd

@@ -19,7 +19,7 @@ library(ggplot2)
 
 ## A code template
 
-Let's use our first graph to answer a question: Do cars with big engines use more fuel than cars with small engines? You probably already have an answer, but try to make your answer precise. What does the relationship between engine size and fuel efficieny look like? Is it positive? Negative? Linear? Nonlinear?
+Let's use our first graph to answer a question: Do cars with big engines use more fuel than cars with small engines? You probably already have an answer, but try to make your answer precise. What does the relationship between engine size and fuel efficiency look like? Is it positive? Negative? Linear? Nonlinear?
 
 You can test your answer with the `mpg` data set in the `ggplot2` package. The data set contains observations collected by the EPA on 38 models of car. Among the variables in `mpg` are 
 
@@ -63,7 +63,7 @@ The rest of this chapter will show you how to complete and extend this template
 
 > "The greatest value of a picture is when it forces us to notice what we never expected to see."---John Tukey
 
-In the plot above, one group of points seems to fall outside of the linear trend. These cars have a higher mileage than you might expect. How can you explain these cars? 
+In the plot below, one group of points seems to fall outside of the linear trend. These cars have a higher mileage than you might expect. How can you explain these cars? 
 
 ```{r, echo = FALSE}
 knitr::include_graphics("images/visualization-1.png")
@@ -73,7 +73,7 @@ Let's hypothesize that the cars are hybrids. One way to test this hypothesis is
 
 You can add a third variable, like `class`, to a two dimensional scatterplot by mapping it to an _aesthetic_.
 
-An aesthetic is a visual property of the objects in your plot. Aesthetics include things like the size, the shape, or the color of your points. You can display a point (like the one below) in different ways by changing the values of its aesthetic properties. Since we already use the word "value" to describe data, let's use the word "level" to describe aesthetic properties. Here we change the levels of a point's size, shape, and color to make the point small, trianglular, or blue.
+An aesthetic is a visual property of the objects in your plot. Aesthetics include things like the size, the shape, or the color of your points. You can display a point (like the one below) in different ways by changing the values of its aesthetic properties. Since we already use the word "value" to describe data, let's use the word "level" to describe aesthetic properties. Here we change the levels of a point's size, shape, and color to make the point small, triangular, or blue.
 
 ```{r, echo = FALSE}
 knitr::include_graphics("images/visualization-2.png")
@@ -111,7 +111,7 @@ ggplot(data = mpg) +
   geom_point(mapping = aes(x = displ, y = hwy, shape = class))
 ```
 
-What happened to the suv's? `ggplot2` will only use six shapes at a time. Additional groups will go unplotted when you use this aesthetic.
+What happened to the suvs? `ggplot2` will only use six shapes at a time. Additional groups will go unplotted when you use this aesthetic.
 
 For each aesthetic, you set the name of the aesthetic to the variable to display, and you do this within the `aes()` function. The `aes()` function gathers together each of the aesthetic mappings used by a layer and passes them to the layer's mapping argument. The syntax highlights a useful insight because you also set `x` and `y` to variables within `aes()`. The insight is that the x and y locations of a point are themselves aesthetics, visual properties that you can map to variables to display information about the data. 
 
@@ -546,7 +546,7 @@ knitr::include_graphics("images/visualization-stats.png")
 
 ## Coordinate systems
 
-Let's leave the cartesian coordinate system and examine the polar coordinate system. We will begin with a riddle: how is a bar chart similar to a coxcomb plot, like the one below?
+Let's leave the Cartesian coordinate system and examine the polar coordinate system. We will begin with a riddle: how is a bar chart similar to a coxcomb plot, like the one below?
 
 ```{r echo = FALSE, message = FALSE, fig.show='hold', fig.width=3, fig.height=4}
 ggplot(data = diamonds) +