Grouping & Joining Data

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

Readings

• R for Data Science - Data transformation 5.6
• R for Data Science - Relational Data 13.1-13.4

Optional Resources:

• Analyzing data with dplyr
• dplyr vignette

Lecture Notes

• dplyr Aggregation
• dplyr Joins
• Converting between data frames and vectors

Place this code at the start of the assignment to load all the required packages.

library(dplyr)

Exercises

1. 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. Check if the file shrub-volume-data.csv is already in your work space (your instructor may have already added them). If not download the shrub dimensions data.

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

   shrub_dims <- read.csv('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.
   Expected outputs for Shrub Volume Aggregation: 1

2. Shrub Volume Join (10 pts)

   This is a follow-up to Shrub Volume Aggregation.

   In addition to the main data table on shrub dimensions, Dr. Granger has two additional data tables. The first describes the manipulation for each experiment. The second provides information about the different sites. Check if the files shrub-volume-experiments.csv and shrub-volume-sites.csv are in your work space (your instructor may have already added them). If not download the experiments data and the sites data.

   1. 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.
   2. Import the sites data and then combine it with both the data on shrub dimensions and the data on experiments to produce a single data frame that contains all of the data.
   Expected outputs for Shrub Volume Join: 1

3. Portal Data Aggregation (10 pts)

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

   1. Use the group_by() and summarize() functions to get a count of the number of individuals in each species ID.
   2. Use the group_by() and summarize() functions to get a count of the number of individuals in each species ID in each year.
   3. Use the filter(), group_by(), and summarize() functions to get the mean mass of species DO in each year.
   Expected outputs for Portal Data Aggregation: 1

4. Fix the Code (15 pts)

   This is a follow-up to Shrub Volume Aggregation. If you don’t already have the shrub volume data in your working directory download it.

   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("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
   Expected outputs for Fix the Code: 1

5. Portal Data Joins (15 pts)

   If surveys.csv, species.csv, and plots.csv are not available in your workspace download them:

   • surveys.csv
   • species.csv
   • plots.csv

   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.
   Expected outputs for Portal Data Joins: 1

6. Portal Data dplyr Review (20 pts)

   If surveys.csv, species.csv, and plots.csv are not available in your workspace download them:

   • surveys.csv
   • species.csv
   • plots.csv

   Load them into R using read.csv().

   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.

   Expected outputs for Portal Data dplyr Review: 1

7. Extracting vectors from data frames (10 pts)

   Using the Portal data surveys table:

   1. Use $ to extract the weight column into a vector
   2. Use [] to extract the month column into a vector
   3. Extract the hindfoot_length column into a vector and calculate the mean hindfoot length ignoring null values.
   Expected outputs for Extracting vectors from data frames: 1

8. Building data frames from vectors (10 pts)

   You have data on the length, width, and height of 10 individuals of the yew Taxus baccata stored in the following vectors:

   length <- c(2.2, 2.1, 2.7, 3.0, 3.1, 2.5, 1.9, 1.1, 3.5, 2.9)
   width <- c(1.3, 2.2, 1.5, 4.5, 3.1, NA, 1.8, 0.5, 2.0, 2.7)
   height <- c(9.6, 7.6, 2.2, 1.5, 4.0, 3.0, 4.5, 2.3, 7.5, 3.2)
   

   Make a data frame that contains these three vectors as columns along with a genus column containing the name Taxus on all rows and a species column containing the word baccata on all rows.

   Expected outputs for Building data frames from vectors: 1

Assignment submission & checklist