Fixes typos in visualization.Rmd

visualize.Rmd

@@ -17,7 +17,7 @@ This chapter focusses on ggplot2, one of the core members of the tidyverse. To a
 library(tidyverse)
 ```
 
-That one line of code loads the core tidyverse; packages which you will use in almost every data analysis. It also tells you which functions from the tidyverse conflicts with functions in base R (or from other packages you might have loaded). 
+That one line of code loads the core tidyverse; packages which you will use in almost every data analysis. It also tells you which functions from the tidyverse conflict with functions in base R (or from other packages you might have loaded). 
 
 If you run this code and get the error message "there is no package called ‘tidyverse’", you'll need to first install it, then run `library()` once again.
 
@@ -154,9 +154,9 @@ ggplot(data = mpg) +
   geom_point(mapping = aes(x = displ, y = hwy, shape = class))
 ```
 
-What happened to the SUVs? ggplot2 will only use six shapes at a time. By default, additional groups will go unplotted when you use this aesthetic.
+What happened to the SUVs? ggplot2 will only use six shapes at a time. By default, additional groups will go unplotted when you use the shape aesthetic.
 
-For each aesthetic, you use the `aes()` associate the name of the aesthetic with a variable to display. 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 about `x` and `y`: 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. 
+For each aesthetic, you use `aes()` to associate the name of the aesthetic with a variable to display. 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 about `x` and `y`: 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. 
 
 Once you map an aesthetic, ggplot2 takes care of the rest. It selects a reasonable scale to use with the aesthetic, and it constructs a legend that explains the mapping between levels and values. For x and y aesthetics, ggplot2 does not create a legend, but it creates an axis line with tick marks and a label. The axis line acts as a legend; it explains the mapping between locations and values.
 
@@ -359,8 +359,7 @@ ggplot(data = mpg) +
     
 ggplot(data = mpg) +
   geom_smooth(
-    mapping = aes(x = displ, y = hwy, colour = drv),
-    show.legend = FALSE
+    mapping = aes(x = displ, y = hwy, group = drv)
   )
 ```
 
@@ -470,7 +469,7 @@ On the x-axis, the chart displays `cut`, a variable from `diamonds`. On the y-ax
 * smoothers fit a model to your data and then plot predictions from the
   model.
 
-* boxplots compute a robust summary of the distribution and display as 
+* boxplots compute a robust summary of the distribution and then display a 
   specially formatted box.
 
 The algorithm used to calculate new values for a graph is called a __stat__, short for statistical transformation. The figure below describes how this process works with `geom_bar()`.