Remember to:

• Consider removing the dplyr package so you can demonstrate installing it.
  • Linux users: you may not want to do this because the source install is slow

Introduction to tabular data

• We will be working with data from the Portal Project.
  • Long-term experimental study of small mammals in Arizona.
  • Download surveys, species, and plots from Datasets into folder.
  • Need to know where the data is: Right click -> Save link as.

• Start/open a project (modeling good practice)

• Dataset is composed of three tables.
• Load these into R using read.csv().

surveys <- read.csv("surveys.csv")
species <- read.csv("species.csv")
plots <- read.csv("plots.csv")

• Display data by clicking on it in Environment
• Three tables
  • surveys - main table, one row for each rodent captured, date on date, location, species ID, sex, and size
  • species - latin species names for each species ID + general taxon
  • plots - information on the experimental manipulations at the site
• Good tabular data structure
  • One table per type of data
    • Tables can be linked together to combine information.
  • Each row contains a single record.
    • A single observation or data point
  • Each column or field contains a single attribute.
    • A single type of information

Packages

• Main way that reusable code is shared in R
• Combination of code, data, and documentation
• R has a rich ecosystem of packages for data manipulation & analysis
• Download and install packages with the R console:
  • install.packages("dplyr")
• Using a package:
  • Load all of the functions in the package: library("dplyr")

Basic dplyr

• Modern data manipulation library for R

surveys <- read.csv("surveys.csv")

• Select a subset of columns.

select(surveys, year, month, day)

• They can occur in any order.

select(surveys, month, day, year)

• Use filter() to get only the rows that meet certain criteria.
  • Combine the data frame to be filtered with a series of conditional statements.
  • Column, condition, value

filter(surveys, species_id == "DS")

• Add new columns with calculated values using mutate()

mutate(surveys, hindfoot_length_cm = hindfoot_length / 10)

• If we look at surveys now will it contain the new column?
• Open surveys
• All of these commands produce new values, data frames in this case
• To store them for later use we need to assign them to a variable

surveys_plus <- mutate(surveys,
                       hindfoot_length_cm = hindfoot_length / 10)

• Or we could overwrite the existing variable if we don’t need it

surveys <- mutate(surveys,
                  hindfoot_length_cm = hindfoot_length / 10)

Do Shrub Volume Data Basics.