Fun with Data Wrangling

A practical, hands‑on data‑wrangling exercise in which participants work with datasets to read, inspect, subset, reorder and match data in R. Participants practice skills such as importing CSV files, filtering data frames, modifying values, building lists, using the %in% operator and applying functional programming tools like map_lgl() to manage and refine data structures.

Keywords

R, Subsetting, in operator

Exercises

Reading in and inspecting data

1. Using the animals.csv, read the .csv file into your environment and assign it to a variable called animals. Be sure to check that your row names are the different animals.

2. Check to make sure that animals is a dataframe.

3. How many rows are in the animals dataframe? How many columns?

Data wrangling

1. Extract the speed value of 40 km/h from the animals dataframe.

2. Return the rows with animals that are the color Tan.

3. Return the rows with animals that have speed greater than 50 km/h and output only the color column. Keep the output as a data frame.

4. Change the color of “Grey” to “Gray”.

5. Create a list called animals_list in which the first element contains the speed column of the animals dataframe and the second element contains the color column of the animals dataframe.

6. Give each element of your list the appropriate name (i.e speed and color).

The %in% operator, reordering and matching

1. In your environment you should have a dataframe called proj_summary which contains quality metric information for an RNA-seq dataset. We have obtained batch information for the control samples in this dataset. Copy and paste the code below to create a dataframe of control samples with the associated batch information:

# Read in proj_summary if needed
proj_summary <- read.table(file = "data/project-summary.txt", header = TRUE, row.names = 1)

# Create ctrl_samples dataframe
ctrl_samples <- data.frame(row.names = c("sample3", "sample10", "sample8", "sample4", "sample15"), 
                           date = c("01/13/2018", "03/15/2018", "01/13/2018", "09/20/2018","03/15/2018"))

1. How many of the ctrl_samples are also in the proj_summary dataframe? Use the %in% operator to compare sample names.

2. Keep only the rows in proj_summary which correspond to those in ctrl_samples. Do this with the %in% operator. Save it to a variable called proj_summary_ctrl.

3. We would like to add in the batch information for the samples in proj_summary_ctrl. Find the rows that match in ctrl_samples.

4. Use cbind() to add a column called batch to the proj_summary_ctrl dataframe. Assign this new dataframe back to proj_summary_ctrl.

BONUS: Using map_lgl()

1. Subset proj_summary to keep only the “high” and “low” samples based on the treatment column. Save the new dataframe to a variable called proj_summary_noctl.

2. Further, subset the dataframe to remove the non-numeric columns “Quality_format”, and “treatment”. Try to do this using the map_lgl() function in addition to is.numeric(). Save the new dataframe back to proj_summary_noctl.