10 Data Science Workflow and Importing Data

Author

Affiliation

Md Rasel Biswas

ISRT, University of Dhaka

1 Data science tools

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

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!

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.

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

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

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)