README.md

Exploratory Data Analysis(EDA) using dplyr and ggplot2: Workshop Materials for Lumohacks 2018.

In statistics, exploratory data analysis (EDA) is an approach to analyzing data sets often with visual methods to summarize their main characteristics, check data quality, and help form hypothesis.

In this workshop, we will work through a EDA problem using data wrangling and visualization:

1. Data wrangling: we will use dplyr package which contains 5 main functions:

• select(): get a subset of columns
• filter(): get a subset of rows
• mutate(): create a new column
• group_by(): define groups according to the values in one or more columns
• summarise(): reduce many rows down to a single value of interest.

2. Visualization: we will use ggplot2 to do some visualizations:

• set up a plot with ggplot()
• Choose which variables to plot using argument mapping = aes(x, y) in ggplot()
• Choose which type of plot using geom_
• Add title and subtitle using labels

By the end of this workshop, you will have a better understanding of the basic techniques to do EDA on your own analysis.

Materials

• Markdown
• Rmarkdown

Environment Setup Instructions:

• Run the Rmarkdown in Rstudio locally
• Run the Rmarkdown in Rstudio cloud

Requirements

• R
• Rstudio
• dplyr
• ggplot2