# Session 7: Quick plotting with base R
####################
# What to Remember #
####################
# Notes: Graphs are created iteratively - run the following code one line at a time
attach(mtcars)
plot(wt, mpg)
abline(lm(mpg~wt))
title("Regression of MPG on Weight")
#############
# Data Used #
#############
# Notes:
library(readxl)
facebook <- read.delim("data/facebook.tsv")
reddit <- read.csv("data/reddit.csv")
race <- read.csv("data/race-comparison.csv")
supermarket <- read_excel("data/Supermarket Transactions.xlsx", sheet = "Data")
###############
# Strip Chart #
###############
# default
stripchart(mtcars$mpg)
# change the type the point style w/pch
stripchart(facebook$tenure, pch = 16)
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# Histogram #
#############
# default
hist(facebook$tenure)
# add different parameters to change breaks, color, title, etc.
hist(facebook$tenure, breaks = 100, col = "grey",
main = "Facebook User Tenure", xlab = "Tenure (Days)")
# get probabilities versus counts by entering probability = TRUE
hist(facebook$tenure, breaks = 100, col = "grey", probability = TRUE)
# you can also compare the histogram to a normal curve to see how it deviates
# there's a lot going on here so this is just for your reference
## remove missing values
x <- na.omit(facebook$tenure)
## create the histogram
h <- hist(x, breaks = 100, col = "grey", main = "Facebook User Tenure",
xlab = "Tenure (Days)")
## create normal curve data
xfit <- seq(min(x), max(x), length = 100)
yfit <- dnorm(xfit, mean = mean(x), sd = sd(x))
yfit <- yfit * diff(h$mids[1:2]) * length(x)
## add normal curve to the histogram
lines(xfit, yfit, col = "red", lwd = 2)
################
# Density Plot #
################
# first you need to calculate the density (be sure to remove missing data with
# na.rm = TRUE)
d <- density(facebook$tenure, na.rm = TRUE)
# you can now use plot() to plot the density data
plot(d)
# you can fill area under the curve with polygon()
polygon(d, col = "red", border = "blue")
############
# Box Plot #
############
# default
boxplot(facebook$tenure)
# change to horizontal
boxplot(facebook$tenure, horizontal = TRUE)
# add notches
boxplot(facebook$tenure, horizontal = TRUE, notch = TRUE, col = "grey40")
boxplot(mtcars$mpg, horizontal = TRUE, notch = TRUE, col = "grey40") # better example
# you can also add mean value points but you can only do it to vertical boxplots
boxplot(mtcars$mpg, notch = TRUE)
points(mean(mtcars$mpg), pch=18, col = "red")
#############
# Your Turn #
#############
# Using the facebook data visually assess the continous variables.
# What insights do you find?
# Example 1 - age of users
hist(facebook$age, breaks = 100, col = "grey", main = "Age of Facebook Users",
xlab = "Age (Years)")
# Example 2 - friend count of users
hist(facebook$friend_count, breaks = 100, col = "grey")
hist(log10(facebook$friend_count), breaks = 100, col = "grey")
# Example 3 - anyone want to share?
#############
# Bar Chart #
#############
# Notes:
# bar charts plot the count of each category so you first need to calculate
# the counts
table(reddit$dog.cat)
# embed this in the barplot() function
barplot(table(reddit$dog.cat))
# include and/or change features such as title, horizontal alignment, category
# names, color
pets <- table(reddit$dog.cat)
par(las = 1)
barplot(pets, main = "Reddit User Animal Preferences", horiz = TRUE,
names.arg = c("Cats", "Dogs", "Turtles"), col = 'cyan')
# plot this data
library(dplyr)
state <- reddit %>%
group_by(state) %>%
tally() %>%
arrange(n) %>%
filter(state != "")
par(mar = c(3,8,1,1), las = 1)
barplot(state$n, names.arg = state$state, horiz = TRUE)
############
# Dot Plot #
############
# Notes:
# now plot the same state data with a dot plot
dotchart(state$n, labels = state$state, cex = .7)
#############
# Your Turn #
#############
# Using the Reddit data...
# 1) assess the frequency of education levels
reddit_ed <- reddit %>%
group_by(education) %>%
tally() %>%
filter(education != "NA") %>%
arrange(n)
par(mar = c(5,15,1,1), las = 1)
barplot(reddit_ed$n, names.arg = reddit_ed$education, horiz = TRUE)
# 2) assess how the different cheeses rank with Reddit users. What do you find?
cheese <- reddit %>%
group_by(cheese) %>%
tally() %>%
filter(cheese != "NA") %>%
arrange(n)
dotchart(cheese$n, labels = cheese$cheese, bg = "yellow")
# reset margins
par(mar = c(5, 4, 4, 2))
################
# Scatter Plot #
################
# Notes:
# default (add some color with col = ? or change the point style with pch = ?)
plot(x = race$White_unemployment, y = race$Black_unemployment)
plot(x = race$Black_unemployment, y = race$black_college)
# you can fit lines to the scatter plot to assess its linearity; note that you
# need to use "~" rather than "x =" and "y ="
plot(White_unemployment ~ Black_unemployment, data = race)
abline(lm(White_unemployment ~ Black_unemployment, data = race), col = "red")
lines(lowess(race$White_unemployment ~ race$Black_unemployment), col = "blue")
# quickly assess multiple scatter plots with pairs()
pairs(race)
pairs(race[, c("White_unemployment", "Black_unemployment",
"white_college", "black_college")])
###############
# Line Charts #
###############
# Notes:
# apply different "type = ?" for line, step, and line-point charts
## turn this default to a line chart using "type = ?"
plot(x = race$Year, y = race$black_college)
## how about a step chart
plot(x = race$Year, y = race$black_college)
## and a line chart with dots
plot(x = race$Year, y = race$black_college)
# you can plot multiple lines on a chart
plot(x = race$Year, y = race$Black_hs, type = "l", ylim = c(0, max(race$Black_hs)))
lines(x = race$Year, y = race$black_college, col = "red")
lines(x = race$Year, y = race$Black_unemployment, col = "blue", lty = 2)
# and you probably want to add a legend
legend("topleft", legend = c("HS Rate", "College Rate", "Unemployment"),
col = c("black", "red", "blue"), lty = c(1, 1, 2))
#####################
# Box Plots...again #
#####################
# Notes:
# turn this single variable boxplot into two box plots comparing gender
boxplot(supermarket$Revenue)
boxplot(Revenue ~ Gender, data = supermarket)
# add a third variable for marital status
boxplot(Revenue ~ Gender + `Marital Status`, data = supermarket)
#############
# Your Turn #
#############
# Using the supermarket data analyze revenue by date, homeownership, city, product
# family, etc. Don't forget you can summarize the data using dplyr like you
# learned about earlier...example:
## total revenues by date
revenue_by_date <- supermarket %>%
group_by(`Purchase Date`) %>%
summarise(Revenue = sum(Revenue, na.rm = TRUE))
plot(Revenue ~ `Purchase Date`, data = revenue_by_date, type = "l", col = "grey")
lines(lowess(revenue_by_date$Revenue ~ revenue_by_date$`Purchase Date`, f = 1/4), col = "blue")
## revenue by product category
par(mar = c(5, 10, 4, 2))
boxplot(Revenue ~ `Product Category`, data = supermarket, horizontal = TRUE)
# reset margins
par(mar = c(5, 4, 4, 2))
######################
# Bar Charts...again #
######################
# Notes:
# we can get frequency tables across two groups
counts <- table(supermarket$`Marital Status`, supermarket$Children)
# you can plot these results either stacked or side-by-side with barplot()
barplot(counts, col = c("darkblue", "red"), legend = c("Married", "Single"))
barplot(counts, col = c("darkblue", "red"), legend = c("Married", "Single"), beside = TRUE)
# you can turn these counts into proportions
proportions <- prop.table(counts)
# now plot these results in a similar manner as above
barplot(proportions, col = c("darkblue", "red"), legend = c("Married", "Single"))
barplot(proportions, col = c("darkblue", "red"), legend = c("Married", "Single"), beside = TRUE)
#############
# Your Turn #
#############
# Using the reddit data compare counts of...
# 1) product family by homeownership
Q1 <- table(supermarket$`Product Family`, supermarket$Homeowner)
barplot(Q1, beside = TRUE, legend.text = TRUE)
# 2) annual income by homeownership
supermarket$`Annual Income` <- factor(supermarket$`Annual Income`,
levels = c("$10K - $30K", "$30K - $50K",
"$50K - $70K", "$70K - $90K",
"$90K - $110K", "$110K - $130K",
"$130K - $150K", "$150K +"))
Q2 <- prop.table(table(supermarket$`Annual Income`, supermarket$Homeowner))
barplot(Q2, legend.text = TRUE,
args.legend = list(x = "topleft", cex = 1, bty = "n", y.intersp=1.5))
# 3) country by gender
Q3 <- prop.table(table(supermarket$Gender, supermarket$Country))
barplot(Q3, beside = TRUE, legend.text = c("Female", "Male"), col = c("pink", "blue"),
args.legend = list(x = "topleft", cex = 1.5, bty = "n", y.intersp=1.5))
# 4) etc.