Worksheet 3 - Data Manipulation

So far, we’ve covered

• Dataframes
• Subsetting
• Logicals

1. What is wrong with this code example? Answer before running.

df <- data.frame(id = c("Jason","Paul","Mary", "Robert","Toby","Nina","Robin","James"), x = 1:10, y = rnorm(10))

2. Fix each of the following common data frame subsetting errors:

# Find rows where cyl equals 4
mtcars[mtcars$cyl = 4, ]

# Exclude only rows 1 through 4
mtcars[-1:4, ]

# Return only rows for cylinders less than 5
mtcars[mtcars$cyl <= 5]

# Return only rows for cylinders that are 4 or 6.
mtcars[mtcars$cyl == 4 | 6, ]

3. Why does mtcars[1:20] return a error? How does it differ from the similar mtcars[1:20, ]?

4. Load the ggplot2 library. There should be a dataset called diamonds. You can verify that by typing in data(diamonds)

• How big is this dataset (number of rows and columns)?
• Create a new data.frame called small_diamonds that only contains rows 1 through 9 and 19 through 23. You can do this in one or two steps. *How many records of “Ideal” cut diamonds?

Bonus: Using the str() command, investigate the following brand new data type.

• Given a linear model - Extract the residuals for the fitted values

mod <- lm(mpg ~ wt, data = mtcars)

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