R Data Visualization

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.

Data Manipulation

• 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.

Statistical Modeling

• 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.

Machine Learning

• 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.

Data Visualization

• 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.

Data Manipulation

• 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.

Statistical Modeling

• 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.

Machine Learning

• 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.