9 Data science workflow

(AST230) R for Data Science

Data science tools

A typical data science project

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Import

• Reading data from different sources, e.g., SAS, SPSS, Stata, Excel, SQL, etc.

Tidy

• When your data is tidy, each column is a variable and each row is an observation

Transform

• Transformation includes
  • narrowing in on observations of interest (like all people in one city or all data from the last year),
  • creating new variables that are functions of existing variables (like computing speed from distance and time), and
  • calculating a set of summary statistics (like counts or means).

Together, tidying and transforming are called wrangling because getting your data in a form that’s natural to work with often feels like a fight!

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Visualize

• Visualization is a fundamentally human activity.
• A good visualization will show you things you did not expect or raise new questions about the data.
• A good visualization might also hint that you’re asking the wrong question or that you need to collect different data.

Model

• summarizing data
• Models are complementary tools to visualization.
• Once you have made your questions sufficiently precise, you can use a model to answer them.

Communicate

• Presenting results and writing report

tidyverse

• tidyverse is a collection of R packages

  • ggplot2, tibble, tidyr, readr, purrr, dplyr, and many more

  • tidyverse_packages() \rightarrow shows the complete list of tidyverse packages

• tidyverse packages share a common philosophy of data and R programming and are designed to work together naturally

  • Coding philosophy is different for functions of tidyverse packages compared to the base R packages

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• Hadley Wickham and his colleagues have been working on tidyverse packages at RStudio Inc.

  • Wickham H and Grolemund G (2017). R for data science O’Reilly.

• To load all packages of tidyverse

library(tidyverse)

• No need to load ggplot2 package if you load tidyverse family of packages

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