Chapter 9 (Part 2)
Dimple K. Patel
2024-02-17
Section 9.9
1. Now we are going to use the geom_histogram function
to make a histogram of the heights in the height data
frame. When reading the documentation for this function we see that it
requires just one mapping, the values to be used for the histogram. Make
a histogram of all the plots. What is the variable containing the
heights? C. Height
library(ggplot2)
library(dslabs)
heights## sex height
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## 3 Male 68.00000
## 4 Male 74.00000
## 5 Male 61.00000
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## 10 Male 67.00000
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## 17 Male 75.00000
## 18 Female 64.00000
## 19 Female 60.00000
## 20 Male 67.00000
## 21 Male 66.00000
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## 23 Male 70.00000
## 24 Male 73.00000
## 25 Male 72.00000
## 26 Male 69.00000
## 27 Male 69.00000
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## 1050 Male 66.000002. Now create a ggplot object using the pipe to assign the heights
data to a ggplot object. Assign height to the x values
through the aes function.
dk<-heights|>ggplot(aes(x=height))3. Now we are ready to add a layer to actually make the histogram.
Use the object created in the previous exercise and the
geom_histogram() function to make the histogram.
dk+geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
4. Note that when we run the code in the previous exercise we get the
warning: stat_bin() using bins = 30. Pick
better value with binwidth. Use the binwidth
argument to change the histogram made in the previous exercise to use
bins of size 1 inch.
dk+geom_histogram(binwidth=1)
5. Instead of a histogram, we are going to make a smooth density plot. In this case we will not make an object, but instead render the plot with one line of code. Change the geometry in the code previously used to make a smooth density instead of a histogram.
dk+geom_density()
6. Now we are going to make a density plot for males and females
separately. We can do this using the group argument. We
assign groups via the aesthetic mapping as each point needs to a group
before making the calculations needed to estimate a density.
heights|>ggplot(aes(x=height, group=sex))+geom_density()
- We can also assign groups through the
colorargument. This has the added benefit that it uses color to distinguish the groups. Change the code above to use color.
heights|>ggplot(aes(x=height, color=sex))+geom_density()
8. We can also assign groups through the fill argument.
This has the added benefit that it uses colors to distinguish the
groups, like this. However, here the second density is drawn over the
other. We can make the curves more visible by using alpha blending to
add transparency. Set the alpha parameter to 0.2 in the geom_density
function to make this change.
heights |> ggplot(aes(height, fill=sex)) + geom_density(alpha=.20) 