R for Research: Glossary

Use RStudio to write and run R programs.

Use Git to track changes in a project

Build and maintain a project directory that is easy to navigate.

Understand when and how to

Organizing your data tables according to tidy data principles will make them easier for you and others to use for analysis.

Never modify your raw data. Always make a copy before making any changes.

Keep track of all of the steps you take to clean your data.

Organize your data according to tidy data principles.

Record metadata in a separate plain text file.

Avoid using multiple tables within one spreadsheet.

Avoid spreading data across multiple tabs (but do use a new tab to record data cleaning or manipulations).

Record zeros as zeros.

Use an appropriate null value to record missing data.

Don’t use formatting to convey information or to make your spreadsheet look pretty.

Place comments in a separate column.

Record units in column headers.

Include only one piece of information in a cell.

Avoid spaces, numbers and special characters in column headers.

Avoid special characters in your data.

Use extreme caution when working with date data.

Splitting dates into their component values can make them easier to handle.

Always copy your original spreadsheet file and work with a copy so you don’t affect the raw data.

Use data validation to prevent accidentally entering invalid data.

Data stored in common spreadsheet formats will often not be read correctly into data analysis software, introducing errors into your data.

Exporting data from spreadsheets to formats like CSV or TSV puts it in a format that can be used consistently by most programs.

OpenRefine is a powerful, free and open source tool that can be used for data cleaning.

OpenRefine will automatically track any steps allowing you to backtrack as needed and providing a record of all work done

OpenRefine can import a variety of file types.

OpenRefine can be used to explore data using filters.

Clustering in OpenRefine can help to identify different values that might mean the same thing.

OpenRefine can transform the values of a column.

OpenRefine provides a way to sort and filter data without affecting the raw data.

OpenRefine also provides ways to get overviews of numerical data.

All changes are being tracked in OpenRefine, and this information can be used for scripts for future analyses or reproducing an analysis.

Cleaned data or entire projects can be exported from OpenRefine.

Projects can be shared with collaborators, enabling them to see, reproduce and check all data cleaning steps you performed.

Other examples and resources online are good for learning more about OpenRefine

Use RStudio to write and run R programs.

Use install.packages() to install packages (libraries).

Access individual values by location using [].

Access slices of data using [low:high].

Access arbitrary sets of data using [c(...)].

Use logical operations and logical vectors to access subsets of data.

Use read_csv to read tabular data in R.

Use factors to represent categorical data in R.

Use the dplyr package to manipulate dataframes.

Use select() to choose variables from a dataframe.

Use filter() to choose data based on values.

Use group_by() and summarize() to work with subsets of data.

Use mutate() to create new variables.

Use the tidyr package to change the layout of dataframes.

Use gather() to go from wide to long format.

Use spread() to go from long to wide format.

ggplot2 is a flexible and useful tool for creating plots in R.

The data set and coordinate system can be defined using the ggplot function.

Additional layers, including geoms, are added using the + operator.

Boxplots are useful for visualizing the distribution of a continuous variable.

Barplot are useful for visualizing categorical data.

Faceting allows you to generate multiple plots based on a categorical variable.