dplyr Aggregation

Remember to

• Load surveys.csv data into surveys

Basic aggregation

• Aggregation combines rows into groups based on one of more columns.
• Calculates combined values for each group.
• First step, group the data frame.
• Let’s group it by species_id
• group_by
• Arguments: 1) table to work on; 2) columns to group by

group_by(surveys, species_id)

• Different looking kind of data.frame
  • Source, grouping, and data type information
• Store the data frame in a variable to use in the next step

surveys_by_species <- group_by(surveys, species_id)

• After grouping a data frame use summarize() to calculate values for each group.
• Count the number of rows for each group (individuals in each species).
• summarize
• Arguments
  • Table to work on, which needs to be a grouped table
  • One additional argument for each calculation we want to do for each group
    • New column name to store calculated value
    • =
    • Calculation that we want to perform for each group
    • We’ll use the function n which is a special function that counts the rows in the table

summarize(surveys_by_species, abundance = n())

• Can group by multiple columns
• Count the number of individuals in each species and plot

surveys_by_species_plot <- group_by(surveys, species_id, plot_id)
species_plot_counts <- summarize(surveys_by_species_plot, abundance = n())

• Use any function that returns a single value from a vector.
• E.g., mean, max, min
• We’ll calculate the average weight of each species on each plot

species_weight <- summarize(surveys_by_species_plot, avg_weight = mean(weight))

• Open table
• Why did we get NA?
  • mean(weight) returns NA when weight has missing values (NA)
• Can fix using mean(weight, na.rm = TRUE)

species_weight <- summarize(surveys_by_species,
                            avg_weight = mean(weight, na.rm = TRUE))

• Still has NaN for species that have never been weighed
• Can filter using !is.na

filter(species_weight, !is.na(avg_weight))

Do Shrub Volume Aggregation.