Packages

• Packages are the main way that reusable code is shared in R
• Combination of code, data, and documentation
• Install to get the package on your computer

• Tell programs that you want to use the package

• Installing packages: install.packages('dplyr')
• Using a package
  • Load all of the functions: library('dplyr')
  • Can result in namespace issues: different packages same name
  • An alternative is to call the package directly using ::
  • stats::filter() vs. dplyr::filter(), no import needed

Basic Dplyr

Does a lot of the same things we’ve learned to do in SQL.

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

• Select: select(surveys, year, month, day)
• Filter: filter(surveys, species_id = 'DS'), filter(surveys, species_id = 'DS', year > 1995)
• Mutate: mutate(surveys, weight_kg = weight / 1000)

Problem 3, 1-5

Group and Summarize

Like in SQL we can also group by and aggregate

• Group by: surveys_by_species <- group_by(surveys, species_id)
• Different looking kind of data frame, and it has groups
• To use those groups we use summarize:

summarize(surveys_by_species, avg_weight = mean(weight))

Problem 3, 6-7

Pipes

• Combine a bunch of these kinds of manipulation
• Standard approach is intermediate variables

surveys_DS <- filter(surveys, species_id == 'DS')`
surveys_DS_by_yr <- group_by(surveys_DS, year)
surveys_DS_by_yr_avg_weight <- summarize(surveys_DS_by_yr, avg_weight = mean(weight))

• This can get a little cumbersome
• Pipes are an alternative
• Pipe %>% takes the output of the one command and passes it as input to next command, so x %>% f(y) == f(x, y)

surveys %>% filter(species_id == 'DS')

surveys %>% filter(species_id == 'DS') %>% group_by(year) %>% summarize(avg_weight = mean(weight))

surveys %>%
  filter(species_id == 'DS') %>%
  group_by(year) %>%
  summarize(avg_weight = mean(weight))

Using Dplyr with databases

We can also use Dplyr to access data directly from a database.

• No need to export files from the database
• Let’s the database do the heavy lifting: faster, no RAM limits

portaldb <- src_sqlite("portal_mammals.sqlite")
surveys <- tbl(portaldb, "surveys")
surveys
query <- "SELECT year, month, day, genus, species
          FROM surveys JOIN species
          ON surveys.species_id = species.species_id"
tbl(portaldb, sql(query))