smarterliner.blogg.se - Rmarkdown bullet points

#Rmarkdown bullet points how to#
#Rmarkdown bullet points code#

We’ve seen a few code chunk options earlier this semester, but this document revisits them.įirst, knit, your new file (and give it a name, when prompted). State 2 aspects of Chaz’s analysis that are not reproducible. He writes down the degrees of freedom, the T-stat, and the p-value.Ĭhaz copies his graph to Word and writes a conclusion in context of the problem. He deletes these 3 rows in Excel.Ĭhaz uses the t.test function in Excel to run the test. Chaz decides that these 3 students should be removed from the analysis because, if they had stayed enrolled, their GPAs would have been different than 0. These three students have GPAs of 0 because they were suspended for repeatedly refusing to wear masks indoors. He changes the labels and the limits on the y-axis using Point-and-Click Excel operations.įrom his boxplots, he see that there are 3 outliers in the non-athlete group.

He uses Excel to make a set of side-by-side boxplots. He writes the null and alternative hypotheses in words and in statistical notation. He decides that a two-sample t-test is an appropriate procedure for this data (recall from Intro Stat that this procedure is appropriate for comparing a quantitative response (GPA) across two groups). He has two variables: whether or not a student is a student athlete and GPA.

Your friend Chaz is doing a data analysis project in Excel to compare the average GPA of student athletes with the average GPA of non-student athletes.

R Markdown combines the “coding” steps with the “write-up” steps into one coherent document that contains the code, all figures and tables, and any explanations. (Note: You can override this feature, but it is not recommended unless you are intentionally providing someone with borken code.) R Markdown makes it easy for you to make your analysis reproducible for a couple of reasons:Īn R Markdown file will not knit unless all of your code runs, meaning that you won’t accidentally give someone code that doesn’t work. An analysis is not reproducible if this isn’t the case. That is, an analysis is reproducible if you provide enough information that the person sitting next to you can obtain identical results as long as they follow your procedures. Reproducibility is a concept that has recently gained popularity in the sciences for describing analyses that another researcher is able to repeat. By the end of this section, we want to be able to use some of the R Markdown options to make a nice-looking document (so that you can implement some of these options in your first mini-project).

#Rmarkdown bullet points how to#

We’ve been using R Markdown for a while now, but have not yet talked about most of its features or how to do anything except insert a new code chunk. 16.3.4 Implementing a Bootstrap in R - part 2.16.3.3 Side Quest: Interation - the loop.16 Confidence Intervals via Resampling (aka the Bootstrap).15.1.2 Making a variable from Date Components.14 Introduction to Working with Dates in R.12 Intro to Text Manipulation in R via the stringr package.11.1 Stacking Rows and Appending Columns.11 Introduction to Merging Data Tables in R.9.5 Tidying crash data ( pivot_longer + separate + pivot_wider).

9.4 Parsing Functions from the readr package.9.3 Tidying longitudinal data ( pivot_longer).8.3 An example of an issue with Factors.8 Working with Factors in R (An Introduction to the forcats package).4.9.2 Combining group_by with other commands.4 Introduction to Data Wrangling via the dplyr package.1.2 What are R, R Studio, and R Markdown?.0.3.2 Skill Mastery by Grade (Approximate Guidelines).