advanced r programming unit 5 study guides

Key Concepts and Terminology

• Data types in R include numeric, character, logical, and complex which define the kind of data that can be stored and manipulated
• Data structures encompass vectors, matrices, arrays, lists, and data frames each with unique properties and uses
• Tidy data principles ensure data is structured consistently with each variable in a column, each observation in a row, and each type of observational unit in a table
• Exploratory data analysis (EDA) involves summarizing main characteristics, detecting outliers, and identifying patterns through visual and quantitative methods
• Statistical analysis techniques range from descriptive statistics (mean, median, standard deviation) to inferential methods (hypothesis testing, regression analysis) for drawing conclusions from data
  • Descriptive statistics provide a snapshot of key metrics and distributions within a dataset
  • Inferential statistics allow generalizing findings from a sample to a larger population
• Data visualization leverages human visual perception to uncover insights and communicate findings through charts, plots, and interactive dashboards
• Literate programming combines analysis code, documentation, and outputs into a cohesive narrative enhancing reproducibility and collaboration

Data Types and Structures in R

• Vectors are one-dimensional data structures that hold elements of the same data type (numeric, character, logical)
  • Create vectors with the c() function such as num_vec <- c(1, 2, 3) for numeric or char_vec <- c("a", "b", "c") for character vectors
  • Access vector elements using square bracket notation [] with index positions starting at 1
• Matrices are two-dimensional structures with elements of the same data type arranged in rows and columns
  • Construct matrices with the matrix() function specifying data, number of rows, and number of columns
• Lists are flexible structures that can contain elements of different data types including other lists
  • Create lists using the list() function and access elements with double square bracket notation [[]] or the $ operator for named elements
• Data frames are two-dimensional structures similar to matrices but can have columns of different data types
  • Build data frames with the data.frame() function or by reading in external data files
  • Manipulate data frames using functions from packages like dplyr for filtering, selecting, and transforming data
• Factors are special vectors used for categorical data with predefined levels that can be ordered or unordered
  • Convert vectors to factors with the factor() function and specify levels or let R infer them from unique values

Importing and Cleaning Data

• Read in data from various file formats such as CSV, TSV, Excel, or JSON using functions like read.csv(), read.table(), read_excel(), or fromJSON()
  • Specify arguments like file path, header presence, column separators, and data types as needed
• Handle missing data by removing incomplete cases with na.omit() or imputing values using techniques like mean, median, or predictive modeling
• Reshape data between wide and long formats based on analysis requirements using functions like pivot_longer() and pivot_wider() from the tidyr package
• Merge datasets horizontally (adding columns) or vertically (adding rows) using functions like merge(), cbind(), and rbind()
  • Ensure common identifier variables exist for accurate merging and handle mismatches or duplicates
• Perform data type conversions as needed using functions like as.numeric(), as.character(), or as.Date() to ensure variables are in suitable formats for analysis
• Split and combine strings using functions from the stringr package such as str_split(), str_sub(), and str_c() for text data processing tasks

Exploratory Data Analysis Techniques

• Compute summary statistics for numerical variables including measures of central tendency (mean, median) and dispersion (range, variance, standard deviation)
  • Use functions like mean(), median(), min(), max(), quantile(), and sd() to quickly summarize distributions
• Examine frequency distributions for categorical variables using tables or bar charts to identify dominant categories and potential imbalances
  • Generate contingency tables with table() or xtabs() and visualize with barplot() or ggplot2::geom_bar()
• Assess relationships between variables through correlation analysis for numerical data and contingency tables or mosaic plots for categorical data
  • Calculate correlation coefficients with cor() and create scatterplots with plot() or ggplot2::geom_point()
  • Use chisq.test() to assess independence between categorical variables and visualize with mosaicplot()
• Identify potential outliers or unusual observations that may influence analysis results using visual methods like boxplots or by calculating z-scores
• Utilize functional programming techniques with apply() family of functions (apply(), lapply(), sapply()) to efficiently perform operations across data structures

Visualization Tools and Methods

• Create basic plots using the built-in graphics package including scatterplots (plot()), line graphs (lines()), bar charts (barplot()), and histograms (hist())
  • Customize plot appearance with arguments like col, pch, lty, main, xlab, and ylab
• Utilize the ggplot2 package for advanced and layered visualizations following the grammar of graphics principles
  • Begin with ggplot() and add layers with geoms (geometric objects) like geom_point(), geom_line(), geom_bar(), and geom_histogram()
  • Map variables to aesthetic attributes within aes() such as x, y, color, fill, shape, or size
  • Enhance plots with additional layers for labels (labs()), themes (theme()), facets (facet_wrap(), facet_grid()), and statistical transformations (stat_summary(), stat_smooth())
• Employ interactive visualization packages like plotly or rbokeh for creating dynamic and interactive plots that allow zooming, panning, and hovering
• Generate geospatial visualizations using packages like leaflet or ggmap for creating interactive maps with markers, polygons, or heatmaps
• Produce publication-quality graphs by adjusting fonts, colors, legends, and overall layout to effectively communicate key findings

Statistical Analysis in R

• Perform hypothesis tests to assess relationships or differences between variables while accounting for sampling variability
  • Conduct t-tests (t.test()) for comparing means between two groups and ANOVA (aov()) for comparing means across multiple groups
  • Employ chi-squared tests (chisq.test()) for assessing independence between categorical variables
  • Utilize correlation tests (cor.test()) for examining relationships between numerical variables
• Construct confidence intervals to estimate population parameters based on sample statistics and desired confidence levels
• Fit regression models to predict outcomes or assess variable importance using functions like lm() for linear regression or glm() for generalized linear models
  • Interpret model coefficients, p-values, and goodness-of-fit metrics to draw conclusions and assess model performance
• Apply resampling techniques like bootstrapping (boot package) or cross-validation (caret package) to assess model stability and generalization
• Conduct power analysis (pwr package) to determine required sample sizes for detecting effects of interest with desired power levels

Advanced Data Manipulation

• Leverage the dplyr package for efficient data manipulation using a consistent grammar of data transformation functions
  • Filter rows with filter(), select columns with select(), create new variables with mutate(), and summarize data with summarize()
  • Combine dplyr functions using the pipe operator (%>%) for readable and sequential data processing workflows
• Perform data reshaping with the tidyr package to convert between wide and long formats based on analysis needs
  • Use pivot_longer() to convert wide data to long format and pivot_wider() to convert long data to wide format
• Handle missing data using techniques like complete case analysis (na.omit()), imputation using tidyr::replace_na() or mice package, or advanced methods like multiple imputation
• Manipulate text data using string processing functions from the stringr package such as str_sub(), str_split(), str_detect(), and str_replace()
• Iterate over data structures using loops (for, while) or apply functions (apply(), lapply(), sapply()) for repetitive operations or function application
• Employ functional programming principles with purrr package for working with vectors and lists using functions like map(), reduce(), and safely()

Practical Applications and Case Studies

• Analyze customer churn in a telecommunications company by exploring demographic and usage patterns, building predictive models, and identifying key drivers of churn
  • Utilize dplyr for data preprocessing, ggplot2 for visualization, and caret for building and evaluating machine learning models
• Conduct market basket analysis on retail transaction data to uncover product associations and inform cross-selling strategies
  • Employ the arules package for association rule mining and the arulesViz package for visualizing item sets and rules
• Perform sentiment analysis on social media data to assess brand perception and track sentiment over time
  • Leverage the tidytext package for text data processing, the syuzhet package for sentiment scoring, and ggplot2 for visualizing sentiment trends
• Analyze time series data to forecast sales demand and optimize inventory management in a supply chain setting
  • Utilize the forecast package for time series modeling, the lubridate package for handling date/time data, and ggplot2 for creating time series plots
• Conduct geospatial analysis to optimize delivery routes and identify optimal locations for new retail stores
  • Employ packages like sf for spatial data handling, leaflet for interactive map creation, and the TSP package for solving the Traveling Salesman Problem
• Develop interactive dashboards using the flexdashboard package or Shiny framework to enable real-time monitoring and exploration of key performance metrics
  • Integrate visualizations, tables, and interactive controls for a user-friendly and dynamic data presentation