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Resource Introduction |
Each student will be expected to introduce an R-related resource that is relevant to their research interests in a 5 minute presentation during a class session. Most students elect to introduce an R package, but you could also introduce a data source, interesting blog, etc., as long as it's relevant to the course. If you are aren't sure, please ask Professor Wilson. The objectives are:
- Learn how to find/download/install a new package and learn how to use it
- Teach your peers about existing R packages that may be useful in their research
The presentation must include:
- Brief introduction: what does the package do and why is it useful? (1-2 slides, 1 minute)
- Author introduction: a short background (affiliation and other packages, etc.) on at least one of the package authors (1 slide, 1 minute)
- Simple demonstration of package code: example input/output from the examples or custom coded examples (2-3 slides, 3 minutes)
There will not be time to actually run any code on-the-fly during your presentation. Instead, use RMarkdown to create a presentation (html) or markdown (md) file so that you can simply display it during class (like the lectures). Then paste your github link into the schedule spreadsheet so we can easily open/view it during class. For the first few weeks, you may simply share your screen and use a powerpoint or other presentation software.
To select a package, I recommend starting with the views on CRAN for a topic of interest. Then read the narrative in the task view for something interesting to you and install the package in R with install.package("packagename") in R and take a look at what it can do. Most package functions include sample code that performs a function. For example, if I was introducing the dplyr package, I might choose the filter() function. If you look in the help you will find a section called "Examples" that you can use for your example code in your presentation. All you have to do is copy-paste it from the help into the R console and them summarize what it's doing in your presentation. For example, I might demonstrate (from the filter() documentation) that the following code will select all characters from Star Wars that are human.
library(dplyr)
filter(starwars, species == "Human")## # A tibble: 35 x 14
## name height mass hair_color skin_color eye_color birth_year sex gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 Luke… 172 77 blond fair blue 19 male mascu…
## 2 Dart… 202 136 none white yellow 41.9 male mascu…
## 3 Leia… 150 49 brown light brown 19 fema… femin…
## 4 Owen… 178 120 brown, gr… light blue 52 male mascu…
## 5 Beru… 165 75 brown light blue 47 fema… femin…
## 6 Bigg… 183 84 black light brown 24 male mascu…
## 7 Obi-… 182 77 auburn, w… fair blue-gray 57 male mascu…
## 8 Anak… 188 84 blond fair blue 41.9 male mascu…
## 9 Wilh… 180 NA auburn, g… fair blue 64 male mascu…
## 10 Han … 180 80 brown fair brown 29 male mascu…
## # … with 25 more rows, and 5 more variables: homeworld <chr>, species <chr>,
## # films <list>, vehicles <list>, starships <list>
Here is an example presentation about the ggrepel package that was built using this R Markdown file, which is also visible on github.