9.2 R for statistical computing and graphics
5 min read•july 30, 2024
R is a powerful tool for data journalists, offering robust statistical analysis and stunning visualizations. It enables you to import, clean, and analyze diverse datasets, from CSV files to web APIs, using functions and packages like and .
With R, you can create compelling graphics using ggplot2 and interactive visualizations with packages like plotly. It integrates seamlessly with other tools, supporting reproducible workflows through R Markdown and version control systems like Git.
R Programming Fundamentals
Basic Concepts and Environment
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- R is an open-source programming language and software environment for statistical computing and graphics, providing a wide variety of statistical and graphical techniques
- R uses a command-line interface, where users type commands or submit scripts to perform tasks
- Integrated development environments (IDEs) such as RStudio can enhance the user experience by providing a more user-friendly interface and additional features
Data Structures and Operations
- R has several built-in data structures:
- Vectors: one-dimensional arrays that can contain numeric, character, or logical data
- Matrices: two-dimensional arrays with rows and columns, containing data of the same type
- Data frames: two-dimensional structures with rows and columns, similar to tables in a relational database, that can store data of different types in columns
- Lists: ordered collections of objects that can contain elements of different types, including other lists
- R uses a variety of operators for arithmetic, comparison, and logical operations
- R provides functions for data manipulation, statistical analysis, and graphical visualization
- R supports user-defined functions, which allow users to create custom operations and automate repetitive tasks
- R includes such as loops and conditionals for more advanced programming
Statistical Analysis in R
Built-in Functions and Packages
- R provides a wide range of built-in functions for statistical analysis:
- Summary statistics functions: mean(), median(), sd() (standard deviation), and summary() for generating descriptive statistics of data
- Hypothesis testing functions: t.test() for t-tests, aov() for analysis of variance (), and chisq.test() for chi-squared tests
- Regression analysis functions: lm() for and glm() for generalized linear models
- R has a vast collection of packages (libraries) that extend its functionality and provide additional tools for specific statistical methods and domains
- Packages can be installed from CRAN (Comprehensive R Archive Network) or other repositories using the install.packages() function
- Popular packages for data manipulation and analysis include dplyr, tidyr, and data.table, while ggplot2 is widely used for data visualization
Data Preprocessing and Modeling Techniques
- R provides functions for data preprocessing:
- Handling missing values: is.na() for identifying missing values, na.omit() for removing rows with missing values
- Scaling and normalization: scale() for standardizing variables
- Data splitting: sample() for random sampling, split() for dividing data into subsets
- R supports various statistical modeling techniques:
- Linear models, generalized linear models, and mixed-effects models through built-in functions and packages
- Survival analysis for modeling time-to-event data
- R offers a range of machine learning algorithms:
- Classification methods: decision trees, random forests, support machines
- Clustering methods: k-means, hierarchical clustering
- Machine learning packages such as caret and mlr provide a unified interface for training and evaluating models
Data Visualization with R
Base R Graphics and ggplot2
- R is renowned for its powerful and flexible graphics capabilities, enabling users to create a wide variety of static and interactive visualizations
- The base R graphics system provides a set of high-level functions for creating plots:
- plot() for scatterplots, line() for line graphs, hist() for histograms, and for box plots
- These functions can be customized using additional arguments to control aspects such as colors, labels, axes, and legends
- Multiple plots can be arranged in a single figure using the par() function or layout() for more complex arrangements
- The ggplot2 package, part of the tidyverse collection, is a popular and powerful tool for creating advanced and customizable graphics in R using a layered grammar of graphics
- ggplot2 uses a declarative approach, where users specify the data, aesthetic mappings (x and y axes, color, size), geometries (points, lines, bars), and other plot elements in a step-by-step manner
- ggplot2 supports a wide range of plot types (scatterplots, line plots, bar plots, histograms, box plots) and extensions for specific domains (maps, networks, time series)
Interactive Visualizations and Reporting
- R supports the creation of interactive and dynamic visualizations using packages such as plotly, leaflet (for maps), and shiny (for web applications)
- R can generate publication-quality graphics in various file formats, including PNG, JPEG, PDF, and SVG, using functions like png(), jpeg(), pdf(), and svg()
- R integrates with other tools for data visualization and reporting:
- Markdown and LaTeX through packages like rmarkdown and knitr, enabling the creation of dynamic and reproducible reports and presentations
- Web development frameworks for creating interactive data-driven stories
R for Data Journalism
Data Acquisition and Cleaning
- R is a powerful tool for data journalism, enabling journalists to collect, clean, analyze, and visualize data to support their stories and investigations
- R can import data from various sources:
- CSV files using ()
- Excel spreadsheets using the readxl package
- Databases using packages like RMySQL and RPostgreSQL
- Web APIs using packages such as httr and jsonlite
- R provides functions and packages for data cleaning and preprocessing:
- dplyr and tidyr packages allow users to filter, select, mutate, and reshape data using a consistent and readable syntax
- dplyr functions like filter(), select(), mutate(), and summarise() enable users to subset, transform, and aggregate data efficiently
- tidyr functions such as pivot_longer() and pivot_wider() help in reshaping data between long and wide formats, making it easier to analyze and visualize
Analysis, Visualization, and Reproducibility
- R's statistical analysis capabilities enable journalists to explore and test hypotheses, identify patterns and trends, and build models to support their stories
- Journalists can use R to calculate summary statistics, perform hypothesis tests, and conduct regression analysis to identify relationships between variables
- R's machine learning packages allow journalists to build predictive models and classify data (identifying patterns in campaign contributions, predicting election outcomes)
- R's data visualization tools, such as ggplot2 and interactive visualization packages, enable journalists to create compelling and informative graphics to communicate their findings to a broad audience
- R integrates with other tools in the data journalism workflow:
- SQL databases for data storage
- Version control systems like Git for collaboration and reproducibility
- Web development frameworks for creating interactive data-driven stories
- R supports the creation of reproducible and transparent data journalism projects through tools like R Markdown and Jupyter Notebooks, which combine code, results, and narrative text in a single document, enabling others to verify and build upon the work
Key Terms to Review (16)
ANOVA: ANOVA, or Analysis of Variance, is a statistical method used to test the differences between two or more group means to determine if at least one of the group means is significantly different from the others. This technique helps in identifying whether any of the variations among groups are greater than would be expected by chance, making it a powerful tool in statistical analysis and research.
Binomial Distribution: A binomial distribution is a discrete probability distribution that models the number of successes in a fixed number of independent Bernoulli trials, where each trial has two possible outcomes (success or failure) and a constant probability of success. This distribution is crucial for understanding hypothesis testing and determining statistical significance, as it allows for the calculation of probabilities related to binary outcomes. Additionally, it can be effectively utilized in R for statistical computing and graphics, making it easier to visualize data and perform analyses.
Boxplot(): The `boxplot()` function in R is a powerful graphical tool used to visualize the distribution of a dataset by displaying its median, quartiles, and potential outliers. It provides a compact summary of the data's central tendency and variability, making it easier to compare distributions across multiple groups. By incorporating elements like whiskers and boxes, this function helps users quickly understand the range and skewness of the data.
Control Structures: Control structures are constructs that dictate the flow of execution in programming, allowing developers to control how and when specific blocks of code are executed. They play a crucial role in decision-making and repetition within programs, enabling the implementation of complex logic and enhancing the program's functionality. In R, control structures like conditionals and loops are essential for processing data effectively and generating meaningful statistical outputs.
Data frame: A data frame is a two-dimensional, tabular data structure used in R that can hold different types of data, such as numeric, character, or factor variables. It is similar to a spreadsheet or a SQL table and allows for easy manipulation and analysis of datasets, making it a fundamental component for statistical computing and graphics in R.
Dplyr: dplyr is an R package designed for data manipulation that provides a set of functions to help users transform and summarize data efficiently. It allows data journalists to perform operations like filtering, selecting, mutating, and summarizing data in a straightforward and intuitive way, making it easier to prepare data for analysis and reporting.
Linear regression: Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data. This technique helps in understanding how the typical value of the dependent variable changes when any one of the independent variables is varied while the others are held fixed. Linear regression is crucial for making predictions, identifying trends, and analyzing relationships within datasets.
List: In programming and data analysis, a list is a collection of ordered elements that can store multiple values in a single variable. Lists in R are particularly versatile, allowing for different types of data structures, such as vectors, matrices, and even other lists, making them essential for statistical computing and graphics tasks.
Missing value imputation: Missing value imputation is the process of replacing missing or null values in a dataset with substituted values to maintain the integrity of the data and ensure accurate analysis. This technique is crucial in statistical computing and graphics because missing data can lead to biased results and hinder the validity of data interpretations. By using imputation methods, analysts can fill in gaps, allowing for better data modeling and visualization while preserving the overall dataset structure.
Normal Distribution: Normal distribution is a statistical concept that describes how data points are spread around a mean, forming a symmetrical bell-shaped curve. This distribution is essential for many statistical methods because it helps to understand patterns in data, identify outliers, and conduct hypothesis testing. The properties of normal distribution make it a fundamental concept in statistical analysis and are particularly relevant for determining the significance of results when analyzing data sets.
Object-oriented programming: Object-oriented programming (OOP) is a programming paradigm based on the concept of 'objects,' which can contain data in the form of fields and code in the form of procedures. This approach allows developers to create modular, reusable code by encapsulating data and behavior together, making it easier to manage complex software systems and enabling better data modeling through classes and inheritance.
Outlier Detection: Outlier detection refers to the process of identifying data points that significantly differ from the rest of a dataset. These data points, known as outliers, can indicate variability in measurement, experimental errors, or novel phenomena. Detecting outliers is crucial because they can skew statistical analyses and lead to misleading conclusions, particularly when calculating summary statistics or performing data visualizations.
Read.csv: The `read.csv` function in R is used to import data from a comma-separated values (CSV) file into R as a data frame. This function is essential for statistical computing and graphics, allowing users to easily access and manipulate datasets for analysis. By specifying parameters such as the file path and whether the first row contains headers, users can customize the import process to suit their needs.
Tidyr: tidyr is an R package designed for data tidying, which means converting data into a format that makes it easier to analyze. It helps users reshape and clean their data by providing functions to handle messy datasets, making it essential for effective data analysis and visualization in statistical computing and graphics.
Vector: In the context of statistical computing and graphics, a vector is a basic data structure that stores a sequence of elements, which can be numbers, characters, or logical values. Vectors are crucial in R programming because they allow for efficient data manipulation and analysis, serving as the foundation for more complex data types like matrices and data frames. Understanding vectors is essential for performing operations like statistical calculations and visualizations in R.
Write.table: The `write.table` function in R is used to export data frames to a file in a tabular format, allowing users to save their datasets for later analysis or sharing. This function is crucial in statistical computing as it provides flexibility in specifying file types, delimiters, and formatting options, making it essential for effective data management and reporting in various contexts.