This document contains code for Group 4. 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 ) )Is there a difference between shark attacks and portrayals of sharks as threats globally?
If there isn’t data on shark attacks around the world, it could be possible to evaluate a related version of this question by focusing on articles that do and do not have HWI as a topic, subset to the articles focused on sharks. The assumption here is that HWI is a reflection of shark attacks.
What is the relationship between portrayals and topics for sharks?
The first task is to subset the data to articles focused on sharks. I also suspect that it would be beneficial to simplify the set of Portrayals further. I provide some code to recode the data based on the values of Portrayals.
### 4.1: Subset data to sharks
group4sub <- CSPdf %>%
filter(...) # How would you filter to the articles where the Taxon is "shark"?
### 4.2: Recategorize the data based on the Portrayal field
# 4.2.1: First get a sense of the different portrayals for sharks
table(group4sub$Portrayal)
# 4.2.2: Recategorize data based on the shark's portrayal
group4sub1 <- group4sub %>%
mutate(SharkPortrayal = case_when(Portrayal %in% c("Dangerous", "Controversial") ~"Aggressor",
Portrayal %in% c("Vulnerable","Victim") ~ "Vulnerable"))
# create a new column called SharkPortrayal that recategorizes the data
# based on Portrayal, then store the outputs in group4sub1
# How would you add on other portrayals?
View(group4sub1) # view your outputWe first need to cross-tabulate the data for portrayals and topics for the data subset to sharks.
### 4.3: Cross tabulate portrayals and topics
group4cross <- group4sub %>%
group_by(...,...) %>%
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 group4cross (short for crosstab data frame)
View(group4cross) # view outputWe’re ready to plot the data.
### 4.4 Visualize data
g4 <- ggplot(group4cross, aes(..., ..., ...)) # initiate a plot using group4cross - what columns would you want to use?
g4 # inspect intermediate output
g4 <- g4 + 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
g4 # inspect intermediate output
g4 <- g4 + theme_bw() + labs(x="",y="",size="Count:") # modify the plot appearance
g4 # inspect intermediate output
g4 <- g4 + scale_size_continuous(range=c(1,10)) + geom_text(aes(label = Count)) # add text information and change the size of the points
g4 <- g4 + theme(axis.text.x = element_text(angle = 45, hjust=1),
legend.position = "top") # change the plot visualization
g4 # display the plot