Description
- (a) Clean up the workspace using the rm() function. Use the data() function to display the built-in datasets you can access. Use the R help to learn more about the ‘longley’ dataset: ?longley.
- Print only the records in the ‘longley’ dataset that are from the years 1947-1950: longley[longley$Year==1947:1950,]. attach(longley).
- You track your commute times for two weeks and record the following (in minutes):17
plot(Unemployed ∼ Year). - Change the type of plot to a line: plot(Unemployed ∼ Year, type =”l”)
16 20 24 22 15 21 15 17 22.
- Enter these numbers into R and find the 5-number summary.
- You find a data entry error, the number 24 should have been 18. Using R, replace the incorrect value without reentering the entire set of data and find the new 5-number summary.
- Use R to count the number of times your commute was at least 20 minutes.
- Use R to calculate the percent of your commutes that were less than 17 minutes.
- Using the maltreat.dta dataset, explore the variable ethnic using tab1(ethnic). There are spelling mistakes that need to be corrected. Correct mis-spelt names, and create a numeric, categorical variable ethncity. The “Jola” cleaning code for part (i) has been provided. Finish the remaining part of the code and produce the final (clean) bar chart.
- Replace ethnic = “Jola” if ethnic value starts with a “J”.
- Replace ethnic = “Mandinka” if ethnic value starts with an “M”
- Replace ethnic = “Serahule” if ethnic value starts with an “S”
- Replace ethnic = “Wollof” if ethnic value starts with a “W”
| library(“readstata13”)
maltreat <- read.dta13(“data/maltreat.dta”) # Original ethnic (string) variable tab1(maltreat$ethnic, col = “grey”) # convert it to a new factor variable ethnicity maltreat$ethnicity <- as.factor(maltreat$ethnic) # explore the levels (unclean) levels(maltreat$ethnicity) # clean up for Jola levels(maltreat$ethnicity)[startsWith(levels(maltreat$ethnicity), “J”)] <- “Jola” |
Distribution of maltreat$ethnic
Frequency
