13: Package Building
13.5 Submit a package to CRAN
13: Package Building
13.5 Submit a package to CRAN - Video Tutorials & Practice Problems
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<v Voiceover>Now that the</v> package is built and passed all the checks, if you want to get it to CRAN, now's the time. CRAN is probably the best way to get it out to the masses. You used to have to upload it through FTP, then send an email to the core team telling them that your package was ready. They have thankfully put together a nice little website where you can easily submit your package. You have to put your name and your email address and the name and email address should match the maintainer field of the description file. You upload your file using regular choose file dialogue and you click upload package. Within maybe even less than a day, your package will be up on CRAN and built, ready for everyone to use. Doesn't get much simpler than that.
Start typing, then use the up and down arrows to select an option from the list.
Table of contents
- Part 1: R as a Tool — Introduction3m
- 1: Getting Started with R30m
- 2: The Basic Building Blocks in R40m
- 3: Advanced Data Structures in R36m
- 4: Reading Data into R54m
- 5: Making Statistical Graphs1h 8m
- Learning objectives0m
- 5.1 Find the diamonds in the data1m
- 5.2 Make histograms with base graphics1m
- 5.3 Make scatterplots with base graphics1m
- 5.4 Make boxplots with base graphics1m
- 5.5 Get familiar with ggplot22m
- 5.6 Plot histograms and densities with ggplot23m
- 5.7 Make scatterplots with ggplot25m
- 5.8 Make boxplots and violin plots with ggplot24m
- 5.9 Make line plots8m
- 5.10 Create small multiples4m
- 5.11 Control colors and shapes1m
- 5.12 Add themes to graphs2m
- 5.13 Use Web graphics29m
- 6: Basics of Programming51m
- Learning objectives0m
- 6.1 Write the classic "Hello, World!" example2m
- 6.2 Understand the basics of function arguments10m
- 6.3 Return a value from a function2m
- 6.4 Gain flexibility with do.call3m
- 6.5 Use "if" statements to control program flow2m
- 6.6 Stagger "if" statements with "else"5m
- 6.7 Check multiple statements with switch3m
- 6.8 Run checks on entire vectors5m
- 6.9 Check compound statements5m
- 6.10 Iterate with a for loop6m
- 6.11 Iterate with a while loop1m
- 6.12 Control loops with break and next2m
- 7: Data Munging1h 15m
- Learning objectives0m
- 7.1 Repeat an operation on a matrix using apply4m
- 7.2 Repeat an operation on a list3m
- 7.3 Apply a function over multiple lists with mapply4m
- 7.4 Perform group summaries with the aggregate function5m
- 7.5 Do group operations with the plyr Package17m
- 7.6 Combine datasets3m
- 7.7 Join datasets5m
- 7.8 Switch storage paradigms5m
- 7.9 Use tidyr2m
- 7.10 Get faster group operations21m
- 8: In-Depth with dplyr23m
- 9: Manipulating Strings39m
- 10: Reports and Slideshows with knitr36m
- Learning objectives0m
- 10.1 Understand the basics of LaTeX7m
- 10.2 Weave R code into LaTeX using knitr5m
- 10.3 Understand the basics of Markdown2m
- 10.4 Understand the basics of RMarkdown4m
- 10.5 Weave R code into Markdown using knitr2m
- 10.6 Convert Markdown files to Word1m
- 10.7 Convert Markdown to PDF1m
- 10.8 Create slideshows with RMarkdown3m
- 10.9 Write equations with RMarkdown7m
- 11: Include HTML Widgets in HTML Documents22m
- 12: Shiny22m
- 13: Package Building23m
- 14: Rcpp for Faster Code33m
- Part 1 - Summary1m
- Part 2: R for Statistics, Modeling and Machine Learning — Introduction2m
- 15: Basic Statistics56m
- 16: Linear Models1h 38m
- Learning objectives0m
- 16.1 Fit simple linear models10m
- 16.2 Explore the data8m
- 16.3 Fit multiple regression models19m
- 16.4 Fit logistic regression10m
- 16.5 Fit Poisson regression7m
- 16.6 Analyze survival data12m
- 16.7 Assess model quality with residuals5m
- 16.8 Compare models7m
- 16.9 Judge accuracy using cross-validation9m
- 16.10 Estimate uncertainty with the bootstrap6m
- 16.11 Choose variables using stepwise selection2m
- 17: Other Models47m
- 18: Time Series30m
- 19: Clustering20m
- 20: More Machine Learning26m
- 21: Network Analysis36m
- 22: Automatic Parameter Tuning with Caret10m
- 23: Fit a Bayesian Model with RStan15m
- Part 2 - SummaryComing soon