This document contains code for Group 6. 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 ) )Are carnivorous animals less likely to be portrayed as vulnerable or victimized than herbivorous animals?
The first thing we need to do is to recode the data so we know which articles are about:
TaxonPortrayal### 6.1: The first task is to recode the data for portrayal (Vulnerable/Victimized) and Herbivory (yes/No)
# 6.1.1: First, see what species are present
table(CSPdf$Taxon) # print out the unique values of Taxon
# 6.1.2: Recoding the data
group6DF <- CSPdf %>%
mutate(Herbivore = case_when(Taxon %in% c("deer","elephant") ~ "Herbivore",
TRUE ~ "Carnivore"),
Victimized = case_when(Portrayal %in% c("Vulnerable","Victim") ~ "Victim",
TRUE ~ "Not"))
# This is a little complicated but we've created two new columns:
# 1) Herbivore - which tracks if each species is an herbivore or not,
# 2) Victimized - which tracks if the animal in each article is portrayed
# as a victim or not.
# For both columns, you'll need to add the other relevant fields for Herbivore or Victim.
# TRUE ~ the other category is an easy way to say anything that isn't
# an herbivore or a victim portrayal is the other category (Carnivore or Not Victim)
View(group6DF) # inspect outputWe will now cross-tabulate the data to get counts of each combination of type of animal (Herbivore or not) and victimization (victim or not).
### 6.2: Preparing data for visualization
group6cross <- group6DF %>%
group_by(..., ...) %>% # Fill in the correct columns (Hint: Herbivore and Victimized may be relevant)
summarize(Count=...) # get the counts for each combination of the grouping variables
` # 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 group6cross (short for crosstab data frame)
# 6.2.1: We need to clean up the crosstab to present the counts as percentages
# for a better waffle plot output
group6cross <- group6cross %>%
group_by(Herbivore) %>%
mutate(Percentages=round(Count/sum(Count)*100,0))
View(group6cross) # view output
We’re ready now to visualize the data.
### 6.3: Generating a waffle chart
waff6 <- ggplot(group6cross, aes(fill=Victimized, values=Percentages)) # initiate plot
waff6 <- waff6 + geom_waffle() # add on waffle
waff6 # display output
waff6 <- waff6 + facet_wrap(~Herbivore) # create sub-plots by Country
waff6 # display output
waff6 <- waff6 + theme_void() # change the plot visuals
waff6 # display output
waff6 <- waff6 + labs(x="",y="",fill="Portrayal: ") + theme(legend.position="top")
waff6 # display final plotIs ecotourism a more common portrayal in developing countries than in developed ones?
As above, we need to recode the data based on countries that are or are not developing. We also need to recode the topics as either being about ecotourism or not.
### 6.4: Recoding the data based on developing country status and ecoutourism topic
# 6.4.1: See what countries are in the data
table(CSPdf$Country)
# 6.4.2: See what topics are in the data
table(CSPdf$Topic)
# 6.4.3: Recode the data
group6DF2 <- CSPdf %>%
mutate(Developing = case_when(Country %in% c("Indonesia","India","Cambodia") ~ "Developing",
TRUE ~ "Developed"),
Ecotourism = case_when(Topic=="Ecotourism"~"Ecotourism",
TRUE ~ "Not ecotourism"))
## We're basically doing the same steps here as above.
## You will need to modify the Developing command.
## The Ecotourism column command may be OK.Next, we want to get the counts of country (developing or not) and topic (ecotourism or not). We’ll do that by cross-tabulating these data.
### 6.5: Cross tabulating country and topic
group6cross2 <- group6DF2 %>%
group_by(..., ...) %>% # Hint: Developing and Ecotourism
summarize(Count=...) # Hint: n() for counts
# 6.5.1: Get percentages for the cross-tab so it's cleaner to display
group6cross2 <- group6cross2 %>%
group_by(Developing) %>%
mutate(Percentages=round(Count/sum(Count)*100,0))
View(group6cross2) # see outputNow we’re ready to visualize the data.
### 6.6: Generating a waffle chart
waff6 <- ggplot(group6cross2, aes(fill=Ecotourism, values=Percentages)) # initiate plot
waff6 <- waff6 + geom_waffle() # add on waffle
waff6 # display output
waff6 <- waff6 + facet_wrap(~Developing) # create sub-plots by Country
waff6 # display output
waff6 <- waff6 + theme_void() # change the plot visuals
waff6 # display output
waff6 <- waff6 + labs(x="",y="",fill="Portrayal: ") + theme(legend.position="top")
waff6 # display final plot