Remember to

• download surveys.csv.
• download species.csv.
• Consider removing the dplyr package so you can demonstrate installing it.

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

• Start a new project (modeling good practice)

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

• Select:
  • select(surveys, year, month, day)
• Filter:
  • filter(surveys, species_id == "DS")
  • filter(surveys, species_id == "DS", year > 1995)
  • filter(surveys, species_id == "DS" | species_id == "DM")
• Mutate:
  • mutate(surveys, hindfoot_length_cm = hindfoot_length / 10)

Do Exercise 2 - Shrub Volume Data Basics.

Aggregation

• Group by:
  • group_by(surveys, species_id)
  • Different looking kind of data.frame
    • Source, grouping, and data type information

surveys_by_species <- group_by(surveys, species_id)

• Grouping with summarize():
  • summarize(surveys_by_species, avg_weight = mean(weight))
  • Real data problem:
    • mean(weight) when weight has missing values (NA)
      • Returns NA
      • mean(weight, na.rm=TRUE)

Do Exercise 3 - Shrub Volume Aggregation.

Joins

• inner_join in dplyr works like JOIN in SQL

species <- read.csv("species.csv")
combined <- inner_join(surveys, species, by = "species_id")
head(combined)

Do Exercise 4 - Shrub Volume Join.

Pipes

• Combine a series of data manipulation actions
• Intermediate variables
  • Step-wise approach
  • Can get cumbersome with lots of variable objects in the environment

surveys_DS <- filter(surveys, species_id == "DS")
surveys_DS_by_yr <- group_by(surveys_DS, year)
avg_weight_DS_by_yr <- summarize(surveys_DS_by_yr,
                                 avg_weight = mean(weight, na.rm=TRUE))

• Pipes:
  • Operator:
    • %>%
  • Operation:
    • %>% takes the output of one command and passes it as input to the next command
    • x %>% f(y) translates to f(x, y)
    • surveys %>% filter(species_id == "DS")

surveys %>%
  filter(species_id == "DS") %>%
  group_by(year) %>%
  summarize(avg_weight = mean(weight, na.rm=TRUE))

Do Exercise 5 - Fix the Code.