Data Science Skills in R

Teaching the tools to get computers to do cool science

Creative Commons License

Working with Data

Assignment

Learning Objectives

Following this assignment students should be able to:

  • install and load an R package
  • understand the data manipulation functions of dplyr
  • execute a simple import and analyze data scenario

Reading

Lecture Notes

Exercises

  1. Shrub Volume Data Basics (10 pts)

    Dr. Granger is interested in studying the factors controlling the size and carbon storage of shrubs. She has conducted an experiment looking at the effect of three different treatments on shrub volume at four different locations. She has placed the data file on the web for you to download:

    Download this into your data folder and get familiar with the data by importing it using read.csv() and then:

    1. Check the column names in the data using the function names().
    2. Use str() to show the structure of the data frame and its individual columns.

    3. Print out the first few rows of the data using the function head().

      Use dplyr to complete the remaining tasks.

    4. Select the data from the length column and print it out.
    5. Select the data from the site and experiment columns and print it out.
    6. Filter the data for all of the plants with heights greater than 5 and print out the result.
    7. Create a new data frame called shrub_data_w_vols that includes all of the original data and a new column containing the volumes, and display it.
    [click here for output]

  2. Shrub Volume Aggregation (10 pts)

    This is a follow-up to Shrub Volume Data Basics.

    Dr. Granger wants some summary data of the plants at her sites and for her experiments. Make sure you have her shrub dimensions data.

    This code calculates the average height of a plant at each site:

    shrub_dims <- read.csv('data/shrub-volume-data.csv')
by_site <- group_by(shrub_dims, site)
avg_height <- summarize(by_site, avg_height = mean(height))
    1. Modify the code to calculate and print the average height of a plant in each experiment.
    2. Use max() to determine the maximum height of a plant at each site.
    [click here for output]

  3. Shrub Volume Join (15 pts)

    This is a follow-up to Shrub Volume Aggregation.

    Dr. Granger has kept a separate table that describes the manipulation for each experiment. Add the experiments data to your data folder.

    Import the experiments data and then use inner_join to combine it with the shrub dimensions data to add a manipulation column to the shrub data.

    [click here for output]

  4. Portal Data Manipulation (15 pts)

    Download a copy of the Portal Teaching Database surveys table and load it into R using read.csv().

    Do not use pipes for this exercise.

    1. Use select() to create a new data frame with just the year, month, day, and species_id columns in that order.
    2. Use mutate(), select(), and na.omit() to create a new data frame with the year, species_id, and weight in kilograms of each individual, with no null weights. The weight in the table is given in grams so you will need to divide it by 1000.
    3. Use the filter() function to get all of the rows in the data frame for the species ID SH.
    4. Use the group_by() and summarize() functions to get a count of the number of individuals in each species ID.
    5. Use the group_by() and summarize() functions to get a count of the number of individuals in each species ID in each year.
    6. Use the filter(), group_by(), and summarize() functions to get the mean mass of species DO in each year.
    [click here for output]

  5. Portal Data Manipulation Pipes (15 pts)

    Download a copy of the Portal Teaching Database surveys table and load it into R using read.csv().

    Use pipes (%>%) to combine the following operations to manipulate the data.

    1. Use mutate(), select(), and na.omit() to create a new data frame with the year, species_id, and weight in kilograms of each individual, with no null weights.
    2. Use the filter() and select() to get the year, month, day, and species_id columns for all of the rows in the data frame where species_id is SH.
    3. Use the group_by() and summarize() functions to get a count of the number of individuals in each species ID.
    4. Use the group_by() and summarize() functions to get a count of the number of individuals in each species ID in each year.
    5. Use the filter(), group_by(), and summarize() functions to get the mean mass of species DO in each year.
    [click here for output]

  6. Fix the Code (15 pts)

    This is a follow-up to Shrub Volume Aggregation. If you haven’t already downloaded the shrub volume data do so now and store it in your data directory.

    The following code is supposed to import the shrub volume data and calculate the average shrub volume for each site and, separately, for each experiment

    read.csv("data/shrub-volume-data.csv")
shrub_data %>%
  mutate(volume = length * width * height) %>%
  group_by(site) %>%
  summarize(mean_volume = max(volume))
shrub_data %>%
  mutate(volume = length * width * height)
  group_by(experiment) %>%
  summarize(mean_volume = mean(volume))
    1. Fix the errors in the code so that it does what it’s supposed to
    2. Add a comment to the top of the code explaining what it does
    [click here for output]

  7. Portal Data Joins (20 pts)

    Download copies of the following Portal Teaching Database tables:

    Load them into R using read.csv().

    1. Use inner_join() to create a table that contains the information from both the surveys table and the species table.
    2. Use inner_join() twice to create a table that contains the information from all three tables.
    3. Use inner_join() and filter() to get a data frame with the information from the surveys and plots tables where the plot_type is Control.
    4. We want to do an analysis comparing the size of individuals on the Control plots to the Long-term Krat Exclosures. Create a data frame with the year, genus, species, weight and plot_type for all cases where the plot type is either Control or Long-term Krat Exclosure. Only include cases where Taxa is Rodent. Remove any records where the weight is missing.
    [click here for output]

Assignment submission & checklist