R data type examples

merge(), subset(), with(), duplicated(), %in%

Basic plot: Bar plot, pie chart, histgrams and density plot, Scatterplots, Labeling points and Add a legend with the legend() function

Boxplot, Notched Boxplot, Boxplots With Point Identification, Violin Plots, Bagplot

Plot x-y Points with Locally Smoothed Mean and Standard Deviation, Enhanced Heat Map, lowess Scatter Plot Smoothing, Plot Error Bars and Confidence Intervals, Plot Group Means and Confidence Intervals

Creating Vectors, Explicit Coercion, Matrices, Lists, Factors, Missing Values, Data Frames, Names, Subsetting, Partial Matching, Removing NA Values, Vectorized Matrix Operations, Control Structures, Functions, Looping on the Command Line, apply, split, Simulation, Plot, Lattice, Regular Expressions，Dates and Times， Classes and Methods

Representing data in R, Simulation basics, Structure of a Data Analysis, hclust, glm, Getting Data, Summarizing data, Data munging basics, Exploratory graphs, Expository graphs, Save the plot file, Hierarchical clustering, K-means clustering, Principal components analysis and singular value decomposition

Multiple testing, Simulation for model checking, {UsingR}, {bootstrap}

Smoothing, loess (Local Polynomial Regression Fitting), {splines}, lm

The bootstrap {boot}, Bootstrapping for prediction {ipred} {randomForest}

Prediction study design, Predicting with regression models (lm, glm), Predicting with trees

ANOVA with multiple factors/variables, Linear regression, logistic regression, Poisson regression, Model checking and model selection,

K-means Clustering

Correlation heatmap;

Two informative PCA plots of principal component analysis

Regression with factor variables, anova, Multiple regression, Regression in the real world,

P-values, arrows, polygon

download data, open browser

Basic least squares, Inference basics, lm, confint

Clustering example: svd, hclust, kmeans

Plotting Classification Trees with the plot.rpart and rattle pckages