Problem Set 5

Please submit this homework as an R Markdown (Rmd) file. See the introduction to Problem Set 1 if you need more information about the Rmd format.

File name

Your file should use the following naming scheme

[last name]_ENS623_SP18_PS5.Rmd

For example,

Lammens_ENS623_SP18_PS5.Rmd

Grading note

Problem 1 and 3 are worth 10 points (partial credit given) and Problem 2 and the Bonus are worth 5 points.

Problem 1 - Plotting relationships between variables

In class on Wednesday, I demonstrated how to use ggplot2 to make an x-y scatter plot comparing Sepal.Length and Petal.Length in the iris data set.

Recall that you must first install ggplot2 if you have not done so already:

install.packages("ggplot2")

Then load it into your environment:

library(ggplot2)

Then the code to create the actual plot looks like this:

data(iris)

ggplot(data = iris, aes( x = Sepal.Length, y = Petal.Length, colour = Species )) + 
  geom_point()

I very briefly showed you that you can add the term geom_smoth(method = "lm") to add linear regression lines onto your plot, as such:

ggplot(data = iris, aes( x = Sepal.Length, y = Petal.Length, colour = Species )) + 
  geom_point() +
  geom_smooth(method = "lm")

Use the code above, but replace Petal.Length with Sepal.Width. Make a new x-y scatter plot with linear regression lines. Describe how accounting for the three different species might influence how you interpret your data, in comparison to an x-y scatter plot where you do not seperate the species.

Hint: You might want to make an x-y scatter plot that does not color the different species.

Problem 2 - Working with syntax in ggplot2

Below are two code chunks, both which create histograms and density plots using the iris data set.

Chunk 1

ggplot() +
  geom_histogram(data = iris, aes(x = Petal.Length, y = ..density.., fill = Species), 
                 position = "dodge") +
  geom_density(data = iris, aes(x = Petal.Length, colour = Species)) +
  theme_bw() 

Chunk 2

ggplot(data = iris, aes(x = Petal.Length)) +
  geom_histogram( aes(y = ..density.., fill = Species), 
                 position = "dodge") +
  geom_density( aes(colour = Species)) +
  theme_bw() 

Describe the differences between the code chunks.

Do Chunks 1 and 2 produce the same plots?

Based on the answers to the above two questions, what do you think the role of defining arguments in the ggplot() function is?

Bonus

How do you interpret the figure produced by the code below?

ggplot(data = iris, aes(x = Petal.Length)) +
  geom_histogram( aes(y = ..density.., fill = Species), 
                 position = "dodge") +
  #facet_grid( Species ~ . ) +
  geom_density() +
  theme_bw() 

Problem 3 - Systematic Review

Read the Pullin and Stewart 2006 article posted on BlackBoard. During our next class, we will discuss systematic reviews. In preparation for that discussion, write a reading response of about 200 words explaining the ways a systematic review of the literature differs from how you have approached writing reviews in the past (e.g., term papers, final papers, etc.).