Your answers to 1. (a).
file_url <- "https://raw.githubusercontent.com/ntaback/UofT_STA130/master/week2/Week2PracticeProblems-student.Rmd"
download.file(url = file_url , destfile = "Week2PracticeProblems-student.Rmd")Look for the file “Week2PracticeProblems-student.Rmd” under the Files tab then click on it to open.
Change the subtitle to “Week 2 Practice Problems Solutions” and change the author to your name and student number.
Type your answers below each question. Remember that R code chunks can be inserted directly into the notebook by choosing Insert R from the Insert menu (see Using R Markdown for Class Assignments). In addition this R Markdown cheatsheet, and reference are great resources as you get started with R Markdown.
Exercise 4.4 - 4.6 in the textbook use data that come with R. The dataset is in the nycflights13 package, which you must first load with the command library(nycflights13).
Bring your output for this question to tutorial on Friday January 19 (either a hardcopy or on your laptop).
library(tidyverse)
library(nycflights13)
glimpse(flights) # if you run this command you can see the variables in the data
glimpse(planes)
glimpse(weather)The data frames in the nycflights13 are described in the help documentation for the package. This can be accessed from the help tab or, for example, by running help(topic = "flights", package = "nycflights13") to access the documentation for the flights data frame.
Below is some code to help you get started. Wherever you see FILL IN CODE HERE is a prompt for you to figure out what code should inserted at this point. In other words, FILL IN CODE HERE is not valid R code.
library(tidyverse)
library(nycflights13)
# Explore flights data frame
# The number of distinct planes in the flights data frame
flights %>% summarize(num_planes = n_distinct(tailnum))
# The number of planes with non-missing plane tail number in the flights data frame
flights %>% summarize(sum(is.na(tailnum) == FALSE))
# Create a data frame called flight_tailnum that contains
# only planes with tailnum info. in the flights data frame
flight_tailnum <- flights %>% filter(is.na(tailnum) == FALSE) %>% select(tailnum)
# Explore planes data frame
# The number of distinct planes in the planes data frame
planes %>% summarize(num_planes = n_distinct(tailnum))
# The number of planes with non-missing plane year
# manufacture date in planes data frame
planes %>% summarize(sum(is.na(year) == FALSE))
# The number of planes with missing plane year
# manufacture date in planes data frame
planes %>% summarize(sum(is.na(year) == TRUE))
# Create a data frame where year in planes data frame is renamed to
# plane_year then choose rows where plane year is not missing
plane_year <- planes %>% rename(plane_year = year) %>%
FILL IN CODE HERE %>% FILL IN CODE HERE
# Join flight_tailnum and plane_year and calculate oldest plane
FILL IN CODE HERE %>% inner_join(FILL IN CODE HERE) %>% summarise(FILL IN CODE HERE)
# Calculate the number of planes that flew from NYC that are in planes table
FILL IN CODE HERE %>% inner_join(FILL IN CODE HERE, by = "tailnum") %>% summarize(num_plane=n_distinct(tailnum))library(tidyverse)
library(nycflights13)
# how many planes have a missing date of manufacture?
planes %>% mutate(missing_year = is.na(year) == FILL IN CODE HERE) %>% count(FILL IN CODE HERE)
# five most common manufacturers
planes %>%
count(FILL IN CODE HERE) %>%
arrange(desc(n)) %>%
top_n(5)
# distribution of manufacturer over time
# combine smaller manufacturers into 'other' category
planes1 <- planes %>%
count(manufacturer,year) %>%
mutate(manufacturer_cat = ifelse(n >= 5, manufacturer, "Other")) %>%
filter(is.na(year) == FALSE)
# use recode function in dplyr to recode some manufacturer names
planes1$manufacturer_cat <- recode(planes1$manufacturer_cat, "AIRBUS INDUSTRIE" = "AIRBUS", FILL IN CODE HERE)
# plot distribution of manufacturer over time
planes1 %>% ggplot(aes(x = FILL IN CODE HERE, y = FILL IN CODE HERE)) + geom_col(aes(fill = FILL IN CODE HERE))library(tidyverse)
library(nycflights13)
# Distribution of temperature in July 2013
weather %>% filter(FILL IN CODE HERE) %>% ggplot(aes(FILL IN CODE HERE)) + FILL IN CODE HERE
# Outliers in wind speed
weather %>% ggplot(aes(FILL IN CODE HERE)) + geom_boxplot() + labs(x = "")
# Relationship between dewpoint and humidity
weather %>% ggplot(aes(FILL IN CODE HERE)) + FILL IN CODE HERE
# Relationship between visibility and precipitation
weather %>% ggplot(aes(FILL IN CODE HERE)) + FILL IN CODE HEREThe Respiratory Virus Detection Surveillance System collects data from select laboratories across Canada on the number of tests performed and the number of tests positive for influenza and other respiratory viruses. Data are reported on a weekly basis year-round to the Centre for Immunization and Respiratory Infectious Diseases (CIRID), Public Health Agency of Canada. These data are also summarized in the weekly FluWatch report. Visit the website.
Bring your output for this question to tutorial on Friday January 19 (either a hardcopy or on your laptop).
rvest. This code will “scrape” the table from the website and load it into an R data frame. Run the following code to download Table 1 directly into the a data frame called fludat.NB: You will not be responsible for understanding how this code and how the rvest library works (i.e., there will not be a test question on this topic). But, if you are intrested in scraping data from the web see.
# Uncomment next line if the rvest package is not installed
# install.packages("rvest")
library(rvest)
library(tidyverse)
url <- "https://www.canada.ca/en/public-health/services/surveillance/respiratory-virus-detections-canada/2017-2018/respiratory-virus-detections-isolations-week-1-ending-january-6-2018.html"
# download and read table into flu_dat
flu_dat <- url %>%
read_html() %>%
html_nodes(xpath = '/html/body/main/div[1]/div[2]/details[1]/table') %>%
html_table()
# clean up the file
fludat <- flu_dat[[1]]
dat <- as.data.frame(sapply(select(fludat,2:23), as.numeric))
fludat <- cbind(`Reporting Laboratory`=fludat[,1],dat)Answer the following questions:
Create a tidy version of fludat. Explain how you made the data tidy.
Which provinces and territories have the highest positive rates for Flu A and Flu B.
The odds of testing positive for, say, Flu A in a province or territory is:
\[\frac{\hat p_{\text province}}{(1-\hat p_{\text province})},\] where \(\hat p_{\text province}\) is the proportion that tested positive for Flu A. The odds ratio of testing positive for, say Flu A, in Newfoundland versus Ontario is:
\[\frac{\hat p_{\text Newfoundland}/(1-\hat p_{\text Newfoundland})}{\hat p_{\text Ontario}/(1-\hat p_{\text Ontario})} \]
ggplot library to plot the odds ratios. Explain why you selected this type of plot.SAT_2010 data in the mdsr library (see page 39, 41 of MDSR) where you specify different lengths of histogram bins using ggplot().binwidth argument. What do you notice?binwidth has the values 10, 15, and 20.Which histogram is the most accurate representation of the distribution of math scores?
geom_histogram(): aes(y=..density..); alpha; fill; and colour (a list of colours is here and see here for alpha, fill, and colour)) . The density argument changes the \(y\)-axis to relative frequency, and aes(y=..count..) specifies that frequency should be used on the \(y\)-axis. Here is starter code:library(mdsr)
library(tidyverse)
SAT_2010 %>% ggplot(aes(x=math)) + geom_histogram(aes(y=..density..),binwidth = 10,fill="darkgrey",colour="black",alpha=.1) Try different values of alpha and colours to create a histogram that’s easy to interpret. Also, try the histogram with frequency and relative frequency on the \(y\)-axis. Which is easier to interpret?