Exercises: Session 4

suppressPackageStartupMessages(library(tidyverse))

suppressPackageStartupMessages(library(stringr))

1. Warm up with the iris data frame.

• Show the first 3 rows
• Show the last 3 rows
• Show 3 random rows without repetition

2. Using the iris data set,

• Find the mean value of all 4 measurements
• Find the mean value of all 4 measurements for each Species

3. Using the iris data set,

• Sort the observations by Sepal.Width in decreasing order.

4. Using the iris data`m set,

• Count the number of flowers of each Species

5. Using the iris data set,

• Count the number of observations where Petal.Length is longer than Sepal.Width

6. Using the iris data set,

• Find the Species with the most number of observations where the Sepal.Length is less then the mean Sepal.Length of all observations

7. Using the iris data set,

• Convert the data frame from the current wide format to a tall format, with just 3 columns: Species, Measurement, Value.

8. Using the mtcars data set,

• Find the mean weight of all cars with mpg > 20 and cyl = 4.

9. Using the mtcars data set,

• Add a new column named bmi that is equal to (hp*mpg/wt)

10. Using the mtcars data set

• Find all rows whose car names have numbers in them.

11. Using the iris data set, together with spread and any other operation necessary

• Create a new data frame df that has only 3 columns (Species, Measure, Value) where Measure takes on the values Sepal.Length, Sepal.Width, Petal.Length or Petal.Width. Show the first 5 rows.
• Show the mean value and counts for each Species and Measure of df

12. Using the df data set, apply spread to

• give each different treatement its own column.

df <- data.frame(subject=rep(1:4,3),
                 treatment = rep(c("A", "B", "C"), each=4),
                 value = rnorm(12))

13. Using the expt data set, together with separate and any other operation necessary

• Find the average blood pressure for each treatment group (A, B or C).

Note: You are assumed not to have access to the pid and treat values separate.y.

pid <- rep(1:4, 3)
treat <- rep(c('A','B','C'), each = 4)
bp <- rnorm(12, 120, 25)
expt <- data.frame(name=paste(pid, treat, sep='-'), bp=bp)
rm(pid)
rm(treat)
expt