R Markdown

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When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document.

Getting Organized

• Organize your work into projects
• Create one folder for all R Stuff
• Mine is in droppbox
• Create new projects as subfolders
• Use File -> New Project in RStudio

The Working Directory

• This is where R will store things and look for things by default.
• Creating a new project sets the working directory.
• getwd() to see what yours is
• setwd() to set your working directory

What Kind of File?

• RDA, or RDATA: Click on “File” then on “Open File”
• Note the load command in the console for future reference.
• Excel: Save as CSV or TSV from Excel
• Prep file following DataCamp tutorial
• Use fn = file.choose() to get an accurate path.
• read_csv(fn) or tsv to get the file into a dataframe. The _ functions (in readr) are better than the . functions (in base).

Looking for Bad Data

There are some things you should always do, but they won’t find all problems.

The more questions you ask, the more problems you’ll find.

For the dataframe as a whole do head(), tail(), str() and summary().

For numeric variables: - hist(x) - summary(x) - boxplot(x) - plot(density(x))

For qualitative variables.

• table(x)
• barplot(table(x))

Relationships

• plot(x,y) for pairs of numeric variables
• boxplot(x~y) for numeric variable x and categorical variable y mosaicplot(table(x,y)) for pairs of caegorical variables.

Consequences of Cleaning

You need to examine the data you have left after taking care of the bad data.

Do you still have a random sample of your population?