ECON 223 - Intro to Statistical Programming Week 03

Study Notes

Data Exploration in R

rm(list=ls()): Clears the workspace in R.
The iris dataset contains 150 observations with 5 variables.
Load the dataset using data(iris); view it with View(iris) for a spreadsheet-like interface.
Use head(iris) and tail(iris) to view the first and last few rows, respectively.
dim(iris) reports the dimensions: 150 rows and 5 columns.
Access column names with names(iris) or colnames(iris).
Get the structure of the dataset using str(iris) to identify data types.

Summary Statistics

Use summary(iris) to obtain descriptive statistics for numeric variables and counts for factors.
Extract specific column data using $ notation, e.g., iris$Petal.Length.
Calculate mean, median, variance, and standard deviation using functions like mean(), median(), var(), and sd() respectively.
Use min() and max() for minimum and maximum values, and range() for both.
Utilize quantile() to find specific quantiles, e.g., 25th and 75th percentiles.

One-way Tables

Create frequency tables with table(iris$Species) to see counts for different species.
Use proportions(table(iris$Species)) to get proportions of each species category.

Selecting Data

Select columns by name directly, e.g., iris[, "Sepal.Length"].
Use vectors to select multiple columns, e.g., iris[, c("Sepal.Length", "Sepal.Width")].
Select a range of columns using numerical indices, e.g., iris[, 3:5].
Select rows by indices, e.g., iris[1:8, ].

Data Attachment and Detachment

Use attach(iris) to enable direct access to columns without data frame notation.
Detach the dataset with detach(iris) to return to the default behavior.

Visualization Techniques

Histograms

Create histograms using hist() for visualizing distributions, e.g., hist(iris$Sepal.Length).
Customize the histogram with color, titles, and axis labels.
Adjust bin sizes with the breaks argument to control the granularity of the histogram.

Box Plots

Generate box plots with boxplot(iris$Petal.Length) for visual summary and identification of outliers.
Compare distributions of two variables in a single box plot, e.g., using boxplot(iris$Petal.Length, iris$Sepal.Length).

Scatter Plots

Create scatter plots with plot() for bivariate analysis, visualizing the relationship between two variables like Sepal Length and Petal Length.
Label axes and provide titles to make plots informative.

Group Summary Statistics

Use aggregate() to calculate grouped statistics, such as means or standard deviations, by categorical variables like Species.

Sorting Data

Order data based on a specific variable; e.g., to find the top or bottom rows by Petal Length, use order() within subsetting.

These points encapsulate key operations and functions relevant for data exploration and visual representation in R using the iris dataset, essential for learning statistical programming.