1. Data Analysis Module: Basic Visualizations
Programming in R
Data Analysis Module: Basic Visualizations

2. Data Analysis Module
Basic Descriptive Statistics and Confidence Intervals
Basic Visualizations
Histograms
Pie Charts
Bar Charts
Scatterplots
Ttests
One Sample
Paired
Independent Two Sample
ANOVA
Chi Square and Odds
Regression Basics

3. Data Analysis: Visualizations
Visualization is just as important to exploratory data analysis as descriptive statistics. In these notes, we will go through four primary visualization tools that every analyst uses.

4. Data Analysis: Visualizations
Visualization 1: Histogram
Features:
Typically univariate display
Demonstrates distribution/spread
Used with continuous data

5. Data Analysis: Visualizations
Visualization 1: Histogram
Basic R Code:
hist(x, freq=FALSE, col="Red", main="Figure i: xx", xlab=“label", ylab=“label",xlim= c(0,n))
Note:
Freq=TRUE will generate the frequencies on the y axis. FALSE will generate the percentages.

6. Data Analysis: Visualizations
Visualization 2: Pie Charts
Features:
Typically univariate display
Demonstrates percent of a whole
Used with categorical data

7. Data Analysis: Visualizations
Visualization 2: Pie Chart
Basic R Code:
Create a table
tablex<-table(x)
(2) Generate the Pie Chart
pie(tablex)
(3) Add preferred colors and labels
colors<-c("Yellow", "Red", "Blue", "Cyan")
pie(tablex, col=colors, main=“figure i")

8. Data Analysis: Visualizations
Visualization 3: Bar Charts
Features:
Typically univariate display
Demonstrates frequency by category
Typically categorical data

9. Data Analysis: Visualizations
Visualization 3: Bar Chart
Basic R Code:
barplot(table(x), col=colors, horiz=TRUE, main = “figure i: Bar Chart", xlab = “Count of Stuff")
Note:
horiz = TRUE will generate a horizontal chart. horiz = FALSE will generate a vertical chart.

10. Data Analysis: Visualizations
Visualization 4: Scatterplot
Features:
Typically bivariate display
Demonstrates relationship between two continuous variables
Standard visual for correlation

11. Data Analysis: Visualizations
Visualization 4: Scatterplot
Basic R Code:
plot(x, y, main=“figure i: scatterplot", xlab = “x", ylab=“y", pch= 19, ylim = c(0,n), xlim = c(0, n))
Note:
X is the independent variable, Y is the dependent variable. Ylim and xlim are establishing the range of the axes.

12. Data Analysis: Visualizations
Cool pch values