• Combine a series of data manipulation actions
• Do each action in sequential order

Intermediate variables

• Run a command
• Store the output in a variable
• Use that variable later in the code

• Repeat

• Determine the mean weight of DS in each year

ds_data <- filter(surveys, species_id == "DS")
ds_data_by_year <- group_by(ds_data, year)
ds_weight_by_year <- summarize(ds_data_by_year,
                               avg_weight = mean(weight, na.rm = TRUE))

Do Portal Data Manipulation Exercise 1-2

Pipes

• Intermediate variables can get cumbersome if their are lots of steps.
• %>% (“pipe”) takes the output of one command and passes it as input to the next command
• Want to take the mean of a vector
• Normally we would run the mean function with the vector as the input:

x = c(1, 2, 3)
mean(x)

• Instead we could pipe the vector into the function

x %>% mean()

• So x becomes the first argument in mean
• If we want to add other arguments they get added to the function call

x = c(1, 2, 3, NA)
mean(x, na.rm = TRUE)
x %>% mean(na.rm = TRUE)

• Questions?

surveys %>%
  filter(species_id == "DS", !is.na(weight))

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

• Shortcut: Ctrl-shift-m

Do Portal Data Manipulation Pipes 1.