Load necessary packages:

library(ggplot2)
library(dplyr)
library(nycflights13)
library(knitr)

# Install this new package to read in CSV files:
library(readr)

1 Example

1.1 R Markdown

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:

mtcars %>% 
  head() %>% 
  kable()
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1

1.2 Including Plots

You can also embed plots, for example:

ggplot(mtcars, aes(x=disp, y=mpg)) +
  geom_point()

2 Question 1

Here is a scatterplot of number of Starbucks/Dunkin Donuts locations per 1000 individuals over median income for 1024 census tracks in Western Massachusetts:

DD_vs_SB <- read_csv("https://rudeboybert.github.io/STAT135/content/PS07/DD_vs_SB.csv")

# Add your code to create plot below:
ggplot(data = DD_vs_SB, aes(x = median_income, y = shops_per_1000, color = Type)) + 
  geom_point() + facet_wrap("Type") +geom_smooth(method="lm")

3 Question 2

Here is a plot comparing beer vs spirit vs wine consumption worldwide:

drinks <- read_csv("https://rudeboybert.github.io/STAT135/content/PS07/drinks.csv")

# Add your code to create plot below:
ggplot(data = drinks, aes(x = type, y = servings)) + 
  geom_boxplot()

#Or faceted histogram:
ggplot(data = drinks, mapping = aes(x = servings)) +
  geom_histogram()+facet_wrap("type")

4 Question 3

Here is a table that shows the median departure delay for each airline leaving Newark:

# Add your code to create table below:
delay <- flights %>%
  filter(origin == "EWR") %>%
  group_by(carrier) %>%
  summarise(median = quantile(dep_delay, 0.5, na.rm = TRUE))
kable(delay)
carrier median
9E -5
AA -3
AS -3
B6 -3
DL -2
EV -1
MQ -2
OO -1
UA 0
US -4
VX -1
WN 1