R Data Visualization

R provides a variety of data visualization libraries, including:
- ggplot2: A popular and flexible library for creating elegant and informative plots.
- plotly: A library for creating interactive, web-based plots.
- base graphics: A built-in library for creating basic plots, such as histograms and scatter plots.
Key functions for data visualization:
- ggplot(): Creates a ggplot object.
- geom_point(): Adds points to a ggplot object.
- theme(): Customizes the appearance of a ggplot object.
- plot(): Creates a basic plot using base graphics.

R provides a variety of data manipulation libraries, including:
- dplyr: A popular library for data manipulation and analysis.
- tidyr: A library for data transformation and cleaning.
- data.table: A library for fast and efficient data manipulation.
Key functions for data manipulation:
- filter(): Subsets a data frame based on conditions.
- arrange(): Sorts a data frame by one or more columns.
- mutate(): Creates new columns in a data frame.
- merge(): Combines two or more data frames based on a common column.
- pivot_longer(): Converts a data frame from wide to long format.
- pivot_wider(): Converts a data frame from long to wide format.

R provides a variety of libraries for statistical modeling, including:
- stats: A built-in library for statistical modeling and analysis.
- MASS: A library for statistical modeling and analysis.
- car: A library for statistical modeling and analysis.
Key functions for statistical modeling:
- lm(): Fits a linear regression model.
- glm(): Fits a generalized linear model.
- t.test(): Performs a t-test.
- anova(): Performs an analysis of variance.
- cor(): Calculates the correlation between two vectors.

R provides a variety of libraries for machine learning, including:
- caret: A library for building and testing machine learning models.
- dplyr: A library for data manipulation and machine learning.
- xgboost: A library for extreme gradient boosting.
Key functions for machine learning:
- train(): Trains a machine learning model using caret.
- predict(): Makes predictions using a trained machine learning model.
- confusionMatrix(): Calculates the confusion matrix for a machine learning model.
- xgb.train(): Trains an extreme gradient boosting model.
- xgb.predict(): Makes predictions using an extreme gradient boosting model.

R provides various data visualization libraries, including ggplot2, plotly, and base graphics.
ggplot2 is a popular and flexible library for creating elegant and informative plots.
plotly is a library for creating interactive, web-based plots.
base graphics is a built-in library for creating basic plots, such as histograms and scatter plots.
Key functions for data visualization include:
- ggplot() creates a ggplot object.
- geom_point() adds points to a ggplot object.
- theme() customizes the appearance of a ggplot object.
- plot() creates a basic plot using base graphics.

R provides various data manipulation libraries, including dplyr, tidyr, and data.table.
dplyr is a popular library for data manipulation and analysis.
tidyr is a library for data transformation and cleaning.
data.table is a library for fast and efficient data manipulation.
Key functions for data manipulation include:
- filter() subsets a data frame based on conditions.
- arrange() sorts a data frame by one or more columns.
- mutate() creates new columns in a data frame.
- merge() combines two or more data frames based on a common column.
- pivot_longer() converts a data frame from wide to long format.
- pivot_wider() converts a data frame from long to wide format.

R provides various libraries for statistical modeling, including stats, MASS, and car.
stats is a built-in library for statistical modeling and analysis.
MASS is a library for statistical modeling and analysis.
car is a library for statistical modeling and analysis.
Key functions for statistical modeling include:
- lm() fits a linear regression model.
- glm() fits a generalized linear model.
- t.test() performs a t-test.
- anova() performs an analysis of variance.
- cor() calculates the correlation between two vectors.

R provides various libraries for machine learning, including caret, dplyr, and xgboost.
caret is a library for building and testing machine learning models.
dplyr is a library for data manipulation and machine learning.
xgboost is a library for extreme gradient boosting.
Key functions for machine learning include:
- train() trains a machine learning model using caret.
- predict() makes predictions using a trained machine learning model.
- confusionMatrix() calculates the confusion matrix for a machine learning model.
- xgb.train() trains an extreme gradient boosting model.
- xgb.predict() makes predictions using an extreme gradient boosting model.