![]() ![]() There are many educational activities for which you can use graph paper. The dot paper with the smaller dots can be useful if you want the dots to visually 'disappear' at a distance, so they can be really useful for creating an graph or illustration where you don't want the grid to be obviously a part of the background.īesides the regular Cartesian dot paper, the isometric dot paper on this page is a great tool for creating 3D sketches or creatively working on geometric patterns. The dot paper comes in the same metric and customary unit dimensions as the graph paper, and there are variants with very fine dots or slightly larger (and easier to see) dots, depending on what you need. The last low-level plotting function that we’ll go over in detail is legend() which adds a legend to a plot.While not technically graph paper, you'll also find on this page printable blank dot paper. For example, legend = c("Males, "Females") will create two groups with names Males and Females.Īdditional arguments specifying symbol types ( pch), line types ( lty), line widths ( lwd), background color of symbol types 21 through 25 ( pt.bg) and several other optional arguments. For example, "bottomright" will always put the legend at the bottom right corner of the plot.Ī string vector specifying the text in the legend. Alternatively, you can enter a string indicating where to put the legend (i.e. 18.5 Chapter 8: Matrices and Dataframesġ1.7.7 legend() Table 11.12: Arguments to legend() ArgumentĬoordinates of the legend - for example, x = 0, y = 0 will put the text at the coordinates (0, 0).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. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |