Session 4 Individual Activity Key
Emily Rosenthal, Elena Leib & Willa Voorhies
July 27, 2022
For a list of the questions for the activity, go here: https://docs.google.com/document/d/1R31H1g3Ll_UdizIz3MW_9IBnxZXYFicxXytioUtKCzQ/edit?usp=sharing
When you are done, please add your plot(s) to the Jamboard and/or send us an email with your plots!! https://jamboard.google.com/d/1xp1yFPHMp501QYKVeQ-j1jlmBMytRcf3qO0PnKaz704/edit?usp=sharing
Question 1: Set up script
Open an R script and load the covid_attitudes_clean.csv data.
Make sure to load the tidyverse library at the start of your script:
library(tidyverse), and set options(stringsAsFactors = FALSE).
## Load packages and set options ##
library(tidyverse)
options(stringsAsFactors = FALSE)
## Read in data ##
covid_attitudes <- read.csv('covid_attitudes.csv')Question 2: Recreate the plot
Key
First, we need to get our data ready to plot:
1. “definitely” is spelled wrong for the response option “defintely
not”. So, we need to correct that spelling!
We need to change “probably_not” to say “probably”
We also need to make Q35 into a factor and order the levels from definitely to probably not (if we don’t set the levels, then they won’t be in the correct order, R will default them to alphabetical order)
# Set up data frame for plot
df_q35.plot <- covid_attitudes %>%
#remove NA's
drop_na() %>% #Note: this removes rows that have NA is ANY column. If I wanted to remove rows that have NA only in the Q35 column, for instance, this would be drop_na(Q35.take_vaccine.)
# Change probably_not and spell definitely not correctly
mutate(
Q35.take_vaccine. = case_when(
Q35.take_vaccine. == "probably_not" ~ "probably not",
Q35.take_vaccine. == "defintely not" ~ "definitely not" ,
TRUE ~ Q35.take_vaccine.),
# Make Q35 into a factor
Q35.take_vaccine. = factor(
Q35.take_vaccine.,
levels = c("definitely not",
"probably not",
"unsure",
"probably",
"definitely")
)
)
# Setting up our plot
ggplot(df_q35.plot, aes(x = Q35.take_vaccine.,
y = Q101.confidence_in_authority,
fill = Q35.take_vaccine.)) +
# Add the boxplots
geom_boxplot() +
# Change the theme to classic for a simplistic and clean look
theme_classic() +
# Change the x-axis label
xlab("willingness to take a vaccine") +
# Change the y-axis label
ylab("confidence in authority") +
# Change the fill of the boxplots
scale_fill_brewer(palette = "Dark2") +
# Get rid of the legend on the plot (set it equal to "none" or "NULL")
theme(legend.position = "none")
ggsave("q35plot.jpg", width = 5, height = 3) #This saves my plot to my files! And voila! We have recreated the plot!
We want to note that the final product of beautiful, clean code that you see here was not written perfectly the first time or even in this order! We would bet that NO ONE would write this code perfectly on the first go.
The key is to iterate! Write some code, run it, see if it works, and then add something else, run it, and then keep going!
Another tip is to break down the big problem into little ones to solve one step at a time.
Solution Breakdown and Logic
Here is an example of our thought process as we worked on recreating the plot:
1. Get the ggplot() set up with the correct x and y-axes, and add a geom_boxplot. Run the code and see what is there:
ggplot(covid_attitudes, aes(x = Q35.take_vaccine.,
y = Q101.confidence_in_authority)) +
geom_boxplot()## Warning: Removed 157 rows containing non-finite values (stat_boxplot).
2. I noticed that my Q35 levels are in a different order than the ones in the goal plot. This suggests that I need to make Q35 a factor, and set the order of the levels using the levels = c(…) argument. I also notice that “probably not” has an underscore that I want to remove. Also, “definitely wrong” was spelled incorrectly and I want to fix that. To do all this, I made a new data frame that I will use just for this plot.
Full disclosure, I built this up incrementally as well! It took me a little while to realize that “defintely not” was spelled wrong, and I kept getting weird NAs in my data from setting the levels to a name that didn’t match the actual data. Eventually, I figured that out!
df_q35.plot <- covid_attitudes %>%
drop_na() %>%
mutate(
Q35.take_vaccine. = case_when(
Q35.take_vaccine. == "probably_not" ~ "probably not",
Q35.take_vaccine. == "defintely not" ~ "definitely not" ,
TRUE ~ Q35.take_vaccine.
),
Q35.take_vaccine. = factor(
Q35.take_vaccine.,
levels = c(c("definitely not",
"probably not",
"unsure",
"probably",
"definitely")
)
)
) If you are getting NAs after you make something a factor, it is almost always because something went wrong in that process. And often the error is spelling something wrong when setting the levels of the factor.
This is a good lesson in making sure your code is doing what you think it is doing! Also, after you filter something, check to be sure it is actually filtered out!
3. Next, I replotted using this new data frame:
ggplot(df_q35.plot, aes(x = Q35.take_vaccine.,
y = Q101.confidence_in_authority)) +
geom_boxplot()
Excellent, now we are getting somewhere!
4. Next, I changed the theme, and the axis labels in one go, so my code looked like this:
ggplot(df_q35.plot, aes(x = Q35.take_vaccine.,
y = Q101.confidence_in_authority)) +
# Add the boxplots
geom_boxplot() +
# Change the theme to classic for a simplistic and clean look
theme_classic() +
# Change the x-axis label
xlab("willingness to take a vaccine") +
# Change the y-axis label
ylab("confidence in authority")
5. Now, I’m ready to add some color! I added + scale_fill_brewer(palette = “Dark2”) and ran the code below. However, when I ran it, nothing changed color!
ggplot(df_q35.plot, aes(x = Q35.take_vaccine.,
y = Q101.confidence_in_authority)) +
# Add the boxplots
geom_boxplot() +
# Change the theme to classic for a simplistic and clean look
theme_classic() +
# Change the x-axis label
xlab("willingness to take a vaccine") +
# Change the y-axis label
ylab("confidence in authority") +
# Change the fill of the boxplots
scale_fill_brewer(palette = "Dark2")
Why wasn’t it working? I thought maybe I hadn’t typed in the argument correctly, so I looked up the help file for scale_fill_brewer. Also, sometimes it wants scale_color_brewer, not the fill one, so I tried that, too.
6. After neither of those worked, I realized that ggplot didn’t know what I was trying to fill! It can’t read my mind that I want it to fill the boxplots. I need to tell it what to fill, and I want to fill each boxplot by what its label is. So I want to fill by Q35. Therefore, I added a fill argument to aes in the original ggplot call and ran the code:
ggplot(df_q35.plot, aes(x = Q35.take_vaccine.,
y = Q101.confidence_in_authority,
fill = Q35.take_vaccine.)) +
# Add the boxplots
geom_boxplot() +
# Change the theme to classic for a simplistic and clean look
theme_classic() +
# Change the x-axis label
xlab("willingness to take a vaccine") +
# Change the y-axis label
ylab("confidence in authority") +
# Change the fill of the boxplots
scale_fill_brewer(palette = "Dark2")
It worked yay!
7. Now that I got the fill to work by adding a fill argument to aes, it created a legend for me, and I don’t want a legend! (Note that adding fill or color arguments to aes will ALWAYS create a legend for you!) So I needed to add a line of code to remove the legend.
ggplot(df_q35.plot, aes(x = Q35.take_vaccine.,
y = Q101.confidence_in_authority,
fill = Q35.take_vaccine.)) +
# Add the boxplots
geom_boxplot() +
# Change the theme to classic for a simplistic and clean look
theme_classic() +
# Change the x-axis label
xlab("willingness to take a vaccine") +
# Change the y-axis label
ylab("confidence in authority") +
# Change the fill of the boxplots
scale_fill_brewer(palette = "Dark2") +
# Get rid of the legend on the plot (set it equal to "none" or "NULL")
theme(legend.position = "none")
8. After I ran this code, I checked it against the example in the group activity and saw that it looks the same! So we are done! And I did a little dance – whooo! Great work!
Question 3
Here are some ways you could explore the example questions with graphs.
# Creating a covid_clean data frame that drops the rows that have NA values
covid_clean <- covid_attitudes %>% drop_na()1. Does the belief that scientists understand covid vary by community?
q84.plot <- covid_attitudes %>%
drop_na() %>%
mutate(Q84.community = case_when(Q84.community == "ruralArea" ~ "rural area",
TRUE ~ Q84.community),
Q84.community.factor = factor(Q84.community,
levels = c("rural area",
"suburb",
"small city/town",
"large city")))
ggplot(q84.plot, aes(x = Q84.community.factor,
y = Q16.Belief_scientists_understand_covid,
fill = Q84.community.factor)) +
geom_violin() +
geom_boxplot(color = "black", width = .1) +
theme_classic() +
xlab("community") +
ylab("Belief that scientists understand covid") +
theme(legend.position = "none")
Again, this was made in an iterative fashion! For example, at the end we added boxplots, and also decided to order the levels of community in order to go from smallest to largest.
2. Does attention to news relate to confidence in the US government? Is this true for all age groups?
ggplot(covid_clean, aes(x = Q10.rank_attention_to_news,
y = Q14.confidence_us_government)) +
geom_point() +
geom_smooth(method = "lm") +
facet_wrap(~Q40.age) +
theme_bw() +
xlab("Attention to news (1-10)") +
ylab("Confidence in U.S. Gov't (1-10)")## `geom_smooth()` using formula 'y ~ x'
3. How do age and community relate to willingness to take a vaccine?
df_plot_takeVax <- covid_clean %>%
# Fix the known issues with the Q35 column
mutate(Q35.take_vaccine. =
case_when(Q35.take_vaccine. == "defintely not" ~ "definitely not",
Q35.take_vaccine. == "probably_not" ~ "probably not",
TRUE ~ Q35.take_vaccine.),
# Make Q35 into a factor and order the levels
Q35.take_vaccine. = factor(Q35.take_vaccine.,
levels = c("definitely",
"probably",
"unsure",
"probably not",
"definitely not")),
# Make Q84 intoa factor and order the levels
Q84.community = factor(Q84.community,
levels = c("rural area",
"suburb",
"small city/town",
"large city"))) %>%
# Remove age groups that have less than 10 participants!
group_by(Q40.age) %>%
filter(n() > 10) %>%
# Remove communities that have less than 10 participants
group_by(Q84.community) %>%
filter(n() > 50) %>%
ungroup()First, I tried it this way, with just community to see what it looked like.
ggplot(df_plot_takeVax, aes(x = Q35.take_vaccine., fill = Q84.community)) +
geom_bar(position = "dodge")
Then, I decided I wanted to facet by community, and have side by side bars for age.
ggplot(df_plot_takeVax, aes(x = Q35.take_vaccine., fill = Q40.age)) +
geom_bar(position = "dodge") +
facet_wrap(~Q84.community) +
theme_classic() +
xlab("Willingness to take vaccine")
This is not the most beautiful plot (i.e., I wouldn’t put it in publication), but for a cursory exploration of your data, it will do! I thought this would be the easiest for comparison. However, depending on what comparisons you are interested in, you could have plotted community side-by-side and faceted by age range. Or you could have picked only a few key age ranges of interest to look at.
4. How does education level relate to belief that scientists understand covid?
df_plot_ed <- covid_clean %>%
# Remove education levels that have less than 10 people
group_by(Q74.education) %>%
filter(n() > 10) %>%
ungroup() %>%
# Make education into a factor, and order the levels by ed level
mutate(Q74.education = factor(Q74.education, levels = c("highschool graduate", "some college", "2 year degree", " 4 year degree", "professional degree", "doctorate")))
ggplot(df_plot_ed, aes(x = Q74.education,
y = Q16.Belief_scientists_understand_covid,
fill = Q74.education)) +
geom_violin() +
theme_classic() +
xlab("Education") +
ylab("Beleif that scientists understand covid") +
theme(legend.position = "none")