This document contains code for Group 2. It is your task to modify the code in places where I have left ... as a placeholder for a variable name. Please feel free to consult with myself and Quyen for guidance.
## Libraries for interacting with data
library(readr) # reads in spreadsheet data
library(dplyr) # lets you wrangle data more effectively
library(tidyr) # lets us interact with data with additional functions
## Libraries for plotting
library(ggplot2) # plotting library
library(waffle) # square pie chart (waffle chart) library
## Libraries for spatial data
library(sf) # a nicer way to deal with spatial data
library(spData) # spatial datasetsNext, we’re going to import the class dataset.
CSPdf <- read_tsv("https://raw.githubusercontent.com/EA30POM/Fall2021/main/data/OTEC_CSP.tsv") # use the function read_tsv from readr to pull in a spreadsheet from this URL.
# Then store the output in an object called "CSPdf"
# CSPdf stands for Conservation Science Partners data frameWe can always pull up a viewer of our data by using these commands:
## Version 1:
CSPdf %>% # take CSPdf and feed it into the next command
head( ) %>% # look at the first 6 rows
View( ) # open the spreadsheet viewer
# akin to x --> f(x) --> g(x)
## Alternatively, version 2:
View( head( CSPdf ) ) # akin to g( f( x ) )What is the relationship between the topic of climate change and portrayals of (endangered) species?
The first thing we need to do is to recode the data so we know which articles are about:
Climate Change (or some other topic)If you’d like, you can modify the first commands below to also recategorize the articles based on whether or not the species is an Endangered species. Alternatively, because there are so few articles where the topic is climate change (\(n=8\) out of a total \(n=971\)), you could reformulate the question to be on “How do portrayals vary by endangered vs. non-endangered species”?
### 2.1: Recategorize data
group2DF <- CSPdf %>%
mutate(TopicCat = case_when(Topic=="Climate Change"~"Climate Change",
Topic!="Climate Change"~"Other"))
# create a new column called TopicCat that records if the article is about Climate Change or not (Other)
### 2.2: Create a cross-tabulation of TopicCat and Portrayal
group2cross <- group2DF %>% # start from the group 2 data
group_by(TopicCat,Portrayal) %>% # bucket the data based on TopicCat and Portrayal
summarise(Count=...) # get the counts for each combination of TopicCat and Portrayal
` # How would you correct the ... in the code above?
# Hint: n() may be helpful - it counts the instances for each combination of variables
# save the output in the object group2cross (short for crosstab data frame)
### 2.3: Visualizing your output
# 2.3.1: Balloon plot
g2 <- ggplot(group2cross, aes(..., ..., ...)) # initiate a plot using group2cross - what columns would you want to use?
g2 # inspect intermediate output
g2 <- g2 + geom_point(aes(size = Count), colour = "skyblue") # plot points for each combination of topic and portrayal - change the size of the point based on the counts
g2 # inspect intermediate output
g2 <- g2 + theme_bw() + labs(x="",y="",size="Count:") # modify the plot appearance
g2 # inspect intermediate output
g2 <- g2 + scale_size_continuous(range=c(1,10)) + geom_text(aes(label = Count)) # add text information and change the size of the points
g2 <- g2 + theme(axis.text.x = element_text(angle = 45, hjust=1),
legend.position = "top") # change the plot visualization
g2 # display the plotHow are tigers portrayed in Asia and outside of Asia?
As above, we need to recode the data to be based on whether or not the Country of each article is in Asia or not. We also need to focus on articles about tigers.
### 2.4: Subset the data to filter to tigers
group2sub <- CSPdf %>%
filter(...) # subset to the rows where the Taxon is "tiger"
# assign the output to group2sub
### 2.5: Categorize the countries as being in Asia or not
# 2.5.1: See what the countries are
table(group2sub$Country)
group2sub <- group2sub %>%
mutate(InAsia=case_when(Country %in% c("Japan","India")~"Asia",
TRUE~"Not Asia")
# complete the re-labelling for the other countries
# TRUE~"Placeholder" just means that any other values of Country are "Not Asia"
View(group2sub) # see your dataThe next step is to cross-tabulate the data to prepare the data for visualization.
### 2.6: Cross tabulate portrayals for the countries that are in or outside of Asia
group2cross2 <- group2sub %>%
group_by(...,...) %>% # Categorize the data based on whether or not they are InAsia and the Portrayal of species
summarise(Count=...) # get the counts for each combination of InAsia and Portrayal
` # How would you correct the ... in the code above?
# Hint: n() may be helpful - it counts the instances for each combination of variables
# save the output in the object group2cross2 (short for crosstab data frame number 2)
View(group2cross2)We’re ready to plot the data now.
### 2.7: Visualize the data
waff2 <- ggplot(group2cross2, aes(fill=Portrayal, values=Count)) # initiate plot
waff2 <- waff2 + geom_waffle() # add on waffle charts
waff2 # display output
waff2 <- waff2 + facet_wrap(~InAsia) # create sub-plots by InAsia
waff2 # display output
waff2 <- waff2 + theme_void() # change the plot visuals
waff2 # display output
waff2 <- waff2 + labs(x="",y="",fill="Portrayal: ") + theme(legend.position="top")
waff2 # display final plot