12/30/2023 0 Comments Rmarkdown plot![]() Rmd files, allowing the resulting combinations of text, code and output to be nicely formatted. I’ll have more to say in a future post about the latest research in this area.RStudio allows the use of Markdown formatting styles within RMarkdown. We present evidence that R Markdown can be used effectively in introductory statistics courses, and discuss its role in the rapidly-changing world of statistical computation. ![]() It provides a solution suitable not only for cutting edge research, but also for use in an introductory statistics course. R Markdown is a new technology that makes creating fully-reproducible statistical analysis simple and painless. Horton argue that teaching students R Markdown helps them to grasp the concept of reproducible research. These assignments include having students deconstruct and reconstruct plots, copy masterful graphs, create one-minute visual revelations, convert tables into `pictures’, and develop interactive visualizations with, e.g., the virtual earth as a plotting canvas.Īnother paper R Markdown: Integrating A Reproducible Analysis Tool into Introductory Statistics by Ben Baumer, Mine Cetinkaya-Rundel, Andrew Bray,Linda Loi and Nicholas J. The focus is on several different types of assignments that exemplify how to incorporate graphics into a course in a pedagogically meaningful way. This article discusses how to make statistical graphics a more prominent element of the undergraduate statistics curricula. The authors Deborah Nolan and Jamis Perrett in their paper Teaching and Learning Data Visualization: Ideas and Assignments paper here argue that statistical graphics should have a more prominent role in an introductory statistics course. Some Recent Research On Reproducible Research And Intro Statistics Two research papers I read recently support this view. This is the way of the near future in Introductory Statistics. My motivation for working in R Markdown is that I want to teach my students that R Markdown is an excellent way to intergrate their R code, writing, plots and output. It would be just as easy to knit to a Word file. ![]() This entire article was written in R markdown in RStudio and knitted to an HTML file. It is a little involved but I think it is much better than the base graphics.This will teach the basics of working with R and RStudio, ggplot2, and R Markdown files. This is how you make a scatter plot in ggplot2. Here is the scatter plot with the regression line. Here is the final code for creating the scatter plot with the regression line. The gray shaded area is the confidence interval. The function for adding a liner model is “lm”. I will keep adding to the plot by plotting the regression line. I added some blue color to the plot based on the body weight scatter145=ggplot(data=TPS145, aes(body.wt,backpack.wt,colour=body.wt)) + This is a starting point and we can add to this plot to really spruce it up. Here is the scatterplot below produced from the above code: Scatter145=ggplot(data=TPS145, aes(body.wt,backpack.wt)) + Now we put this data frame into the ggplot object and name it scatter145 and call the ggplot2 package. We then turn these two vectors into a data frame. We use the “c” command to combine or concatenate into a vector. Here is the data from page 145 in the TPS 4e textbook and how we enter it in. MyGraph <- ggplot(myData, aes(variable for x axis, variable for y axis)) + geom() The general form of a command will look like this: Now let’s make a scatter plot with the example in the TPS4e book Chapter 3, page 145.
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