![]() ![]() 18.5 Chapter 8: Matrices and Dataframesġ6.3 Using if, then statements in functionsĪ good function is like a person who knows what to wear for each occasion – it should put on different things depending on the occasion.18.4 Chapter 7: Indexing vectors with.17.4 Loops over multiple indices with a design matrix.17.3 Updating a container object with a loop.17.2 Creating multiple plots with a loop.17.1.2 Adding the integers from 1 to 100.16.4.4 Storing and loading your functions to and from a function file with source().16.4.2 Using stop() to completely stop a function and print an error.16.3 Using if, then statements in functions.16.2.3 Including default values for arguments.16.2 The structure of a custom function.16.1 Why would you want to write your own function?.15.5.2 Transforming skewed variables prior to standard regression.15.5.1 Adding a regression line to a plot.15.5 Logistic regression with glm(family = "binomial".15.4 Regression on non-Normal data with glm().15.3 Comparing regression models with anova().15.2.6 Getting an ANOVA from a regression model with aov().15.2.5 Center variables before computing interactions!.15.2.4 Including interactions in models: y ~ x1 * x2.15.2.3 Using predict() to predict new data from a model.15.2.2 Getting model fits with fitted.values.15.2.1 Estimating the value of diamonds with lm().14.7 Repeated measures ANOVA using the lme4 package.14.6 Getting additional information from ANOVA objects.14.5 Type I, Type II, and Type III ANOVAs.14.1 Full-factorial between-subjects ANOVA.13.5.1 Getting APA-style conclusions with the apa function.13.1 A short introduction to hypothesis tests.12.3.1 Complex plot layouts with layout().12.3 Arranging plots with par(mfrow) and layout().11.10 Test your R might! Purdy pictures.11.8 Saving plots to a file with pdf(), jpeg() and png().11.7.5 Combining text and numbers with paste().10.6 Test your R might!: Mmmmm…caffeine.9.6.3 Reading files directly from a web URL.9.1.1 Why object and file management is so important.8.7 Test your R might! Pirates and superheroes.7.3.1 Ex: Fixing invalid responses to a Happiness survey.7.2.2 Counts and percentages from logical vectors.6.2.3 Sample statistics from random samples.6.2.2 Additional numeric vector functions.4.4.4 Example: Pirates of The Caribbean.4.3.1 Commenting code with the # (pound) sign.4.3 A brief style guide: Commenting and spacing.4.2.1 Send code from an source to the console.1.5.2 Getting R help and inspiration online.To practice the basics of plotting in R interactively, try this course from DataCamp. The Advanced Graphs section describes how to customize and annotate graphs, and covers more statistically complex types of graphs. These include density plots (histograms and kernel density plots), dot plots, bar charts (simple, stacked, grouped), line charts, pie charts (simple, annotated, 3D), boxplots (simple, notched, violin plots, bagplots) and Scatterplots (simple, with fit lines, scatterplot matrices, high density plots, and 3D plots). The remainder of the section describes how to create basic graph types. Despite the learning curve associated with it, mastering graphing in R can help data scientists, statisticians, and researchers effectively communicate their findings and insights, making it a powerful tool in the field of data science and analytics.Ĭreating a Graph provides an overview of creating and saving graphs in R. This is especially true with 'ggplot2', which offers a coherent system for describing and building graphs. R's graphing capabilities are not only versatile but also highly customizable, providing control over nearly every graphical parameter. Using graphs in R often begins with data cleaning and preparation, followed by defining the type of graph, customizing the plot's aesthetics such as colors, scales, and theme, and finally rendering the plot. The 'ggplot2' package, a part of the tidyverse, has revolutionized the way R users create high-quality and complex plots due to its layering concept, which allows for a step-by-step, intuitive build-up of a plot. ![]() It supports high-level graphics including generic plotting system, grid graphics, and lattice graphics. With R, users can create simple charts such as pie, bar, and line graphs to more sophisticated plots like scatter plots, box plots, heat maps, and histograms. Graphs are a powerful tool for data visualization, enabling complex data patterns, trends, and relationships to be more comprehensible. R offers a rich set of built-in functions and packages for creating various types of graphs. One of the main reasons data analysts turn to R is for its strong graphic capabilities. ![]()
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |