Data transformation

Learning objectives

• Identify computer programming as a form of problem solving
• Practice decomposing an analytical goal into a set of discrete, computational tasks
• Identify the verbs for a language of data manipulation
• Clarify confusing aspects of data transformation from R for Data Science
• Practice transforming data

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Diamonds Example(s)

• If you know the order of arguments, you don't have to specify the arguments explicitly
• It's best practice to name arguments explicitly though

1. Identify inputs

data("diamonds")

2. Filter

diamonds_ideal <- filter(.data = diamonds, cut == "Ideal")
//if I knew the order of arguments, this would also work: 
diamonds_ideal <- filter(diamonds, "Ideal")

• The first argument in dplyr functions is usually called .data so as not to be confused with the function data()

3. Summarize

summarize(.data = diamonds_ideal, avg_price = mean(price))

• For calculating statistics for sub-groups in your dataset, use group_by (which defines a grouping structure)

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Tidyverse

We <3 Hadley Wickham

The functions to know are in the slides

• You can use British English or American English
• The = and <- are for assigning objects name
• The == operation checks for equivalence
• Imagine the %>% operator as the coding equivalent of the words "and then"
  EX. flights %>% group_by(dest)