STAT823- Homework 3: Data Cleaning and Management Solved

Directions
Launch RStudio and open a new RMarkdown file or use the class RMarkdown template provided and save it on your working directory as a .Rmd file. At the end of the activity, save your pdf generated from RMarkdown+Knitr and submit it in the Blackboard.

Show all your work. Late submission will attract a penalty of 10 points per day after the due date.

If you have questions, please post them on the lesson discussion board.

 

1.        (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.

(b)      Print only the records in the ‘longley’ dataset that are from the years 1947-1950: longley[longley$Year==1947:1950,]. attach(longley).

(c)      
  

plot(Unemployed ∼ Year).

(d)      Change the type of plot to a line: plot(Unemployed ∼ Year, type ="l")

2.        You track your commute times for two weeks and record the following (in minutes):17

16 20 24 22 15 21 15 17 22.

(a)      Enter these numbers into R and find the 5-number summary.

(b)      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.

(c)      Use R to count the number of times your commute was at least 20 minutes.

(d)      Use R to calculate the percent of your commutes that were less than 17 minutes.

3.        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.

(i)          Replace ethnic = “Jola” if ethnic value starts with a “J”.

(ii)        Replace ethnic = “Mandinka” if ethnic value starts with an “M”

(iii)      Replace ethnic = “Serahule” if ethnic value starts with an “S”

 

(iv)      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