Step 1: Generate a dataset. To create a normal distribution plot, we first need to generate a dataset that follows a normal distribution. We can use the rnorm function in R to generate a random sample of numbers that follow a normal distribution with a specified mean and standard deviation. For example, let’s generate a sample of 1000 numbers

You can use geom_smooth() to add confidence interval lines to a plot in ggplot2: library (ggplot2) some_ggplot + geom_point() + geom_smooth(method=lm) The following examples show how to use this syntax in practice with the built-in mtcars dataset in R. Example 1: Add Confidence Interval Lines in ggplot2
Mastering Software Development in R. 4.1 Basic Plotting With ggplot2. The ggplot2 package allows you to quickly plot attractive graphics and to visualize and explore data. Objects created with ggplot2 can also be extensively customized with ggplot2 functions (more on that in the next subsection), and because ggplot2 is built using grid graphics
I have a ggplot command. ggplot( rates.by.groups, aes(x=name, y=rate, colour=majr, group=majr) ) inside a function. But I would like to be able to use a parameter of the function to pick out the column to use as colour and group.
Part of R Language Collective. 2. I want to alter a ggplot2 plot in R using the ggplot_build and ggplot_gtable functions and use it afterwards in a plot_grid. Example code to make the plot: library (ggplot2) library (cowplot) p1
By default, ggplot2 uses (I believe) a color palette based on evenly-spaced hue values. There are other functions built into the library that use either Brewer palettes or Viridis colorspaces. There are other functions built into the library that use either Brewer palettes or Viridis colorspaces.
Vector helpers. ggplot2 also provides a handful of helpers that are useful for creating visualisations. cut_interval () cut_number () cut_width () Discretise numeric data into categorical. mean_cl_boot () mean_cl_normal () mean_sdl () median_hilow () A selection of summary functions from Hmisc.
gganimate is an extension of the ggplot2 package for creating animated ggplots. It provides a range of new functionality that can be added to the plot object in order to customize how it should change with time. Key features of gganimate: transitions: you want your data to change. views: you want your viewpoint to change.

This R tutorial describes how to create a histogram plot using R software and ggplot2 package. The function geom_histogram() is used. You can also add a line for the mean using the function geom_vline .

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    • how to use ggplot in r