15 - Introduction to R

Answers to exercises

:::

Access http://richardtwatson.com/data/manheim.txt which contains details of the sales of three car models: X, Y, and Z.

1a.

Compute the number of sales for each type of model.

library(readr)
url <-  'http://richardtwatson.com/data/manheim.txt'
t <-  read_delim(url,delim=',')
t %>% 
 group_by(model)  %>%
 summarize(frequency = n())

1c.

Report the mean price for each model.

library(readr)
library(sqldf)
url <-  'http://richardtwatson.com/data/manheim.txt'
t <-  read_delim(url,delim=',')
t %>% 
 group_by(model)  %>%
 summarize(mean = mean(price))

Use the 'Import Dataset' feature of RStudio to read http://richardtwatson.com/data/electricityprices.csv which contains details of electricity prices for a major city.

2a.

What is the maximum cost?

max(electricityprices$cost)

2c.

What is the mean cost?

mean(electricityprices$cost)

::::::::::::::::::::::: question 3.

Read http://richardtwatson.com/data/electricityprices.csv containing details of the wealth of various countries and complete the following exercises.

3a.

Sort the table by GDP per capita.

library(readr)
library(sqldf)
url <-  'http://people.terry.uga.edu/rwatson/data/wealth.csv'
t <-  read_delim(url,delim = ',')
ts <-  sqldf('select * from t order by `GDP per capita`;')

3c.

Compute the ratio of US GDP per capita to the average GDP per capita.

library(readr)
library(sqldf)
url <-  'http://people.terry.uga.edu/rwatson/data/wealth.csv'
sqldf('Select `GDP per capita`/(select avg(`GDP per capita`) from t) as "US ratio" from t where Country = "United States";')

Get the list of failed US banks.

5a.

Determine how many banks failed in each state.

# download the table as csv file and read the file using RStudio's Import Dataset
library(sqldf)
sqldf('Select ST, count(*) from banklist group by ST')

5c.

How many banks were closed each year (hint: use the lubridate package and strptime() as sqldf does not have a YEAR function)

library(sqldf)
options(sqldf.driver = "SQLite") # to avoid a conflict with RMySQL
library(lubridate)
# banklist$closeYear <-  year(strptime(banklist$Closing.Date, "%d-%b-%y"))
temp <-  strptime(banklist$Closing.Date, "%d-%b-%y")
banklist$closeYear <-  year(temp)
sqldf('Select closeYear, count(*) from banklist group by closeYear;')

::::::::: question 6.

Get Table01.xls of U.S. broccoli data on farms and area harvested. Get rid of unwanted rows to create a spreadsheet for the area harvested with one header row and the 50 states. Change cells without integer values to 0 and save the file in CSV format for reading with R.

6a.

Reshape the data so that each observation contains state name, year, and area harvested.

# Read the file using RStudio's Import Datase
# set the column names as R puts an X in front of the year.
colnames(broccoli) <-  c('state',1974,1978,1982,1987,1992,1997,2002,2007)
library(reshape)
m <-  melt(broccoli,id='state')
colnames(m) <-  c('state','year','acres')

6c.

Compute total hectares harvested each year for which data are available.

library(sqldf)
sqldf('select year, sum(hectares) as "Total area" from m group by year;')