Last updated: 2019-04-25

Checks: 5 1

Knit directory: MSTPsummerstatistics/

This reproducible R Markdown analysis was created with workflowr (version 1.3.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: uncommitted changes

The R Markdown is untracked by Git. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20180927)

The command set.seed(20180927) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Repository version: 68d1b40

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .Rproj.user/

Untracked files:
    Untracked:  analysis/introR.Rmd
    Untracked:  analysis/multipleTesting.Rmd

Unstaged changes:
    Modified:   analysis/ProbabilityReview.Rmd
    Modified:   analysis/index.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

There are no past versions. Publish this analysis with wflow_publish() to start tracking its development.

Introduction

Here, we introduce R, a statistical programming language. Doing statistics within a programming language brings many advantages, including allowing one to organize all analyses into program files that can be rerun to replicate analyses. In addition to using R, we will be using RStudio, an integrated development environment (IDE), which assists us in working with R and outputs of our code.

Both R and RStudio are freely available online.

Downloading/Installing R and RStudio


sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.4

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] workflowr_1.3.0 Rcpp_0.12.18    digest_0.6.16   rprojroot_1.3-2
 [5] backports_1.1.2 git2r_0.23.0    magrittr_1.5    evaluate_0.11  
 [9] stringi_1.2.4   fs_1.2.7        rmarkdown_1.10  tools_3.5.1    
[13] stringr_1.3.1   glue_1.3.0      yaml_2.2.0      compiler_3.5.1 
[17] htmltools_0.3.6 knitr_1.20