| data_sets <- c("mtcars", "morley", "rock") | |
| shinyServer(function(input, output) { | |
| # Drop-down selection box for which data set | |
| output$choose_dataset <- renderUI({ | |
| selectInput("dataset", "Data set", as.list(data_sets)) | |
| }) | |
| # Check boxes |
Notes:
I've tried to break up in to separate pieces, but it's not always possible: e.g. knowledge of data structures and subsetting are tidy intertwined.
Level of Bloom's taxonomy listed in square brackets, e.g. http://bit.ly/15gqPEx. Few categories currently assess components higher in the taxonomy.
Install alm package from CRAN
install.packages("alm")
library(alm)Get metrics on a DOI, specifying info=history gives you the detailed data instead of totals. Note that counter is PLOS's views (includes page views of html, downloads of XML and PDF).
| #Get DOIs for subject area from PeerJ | |
| library(httr) | |
| library(plyr) | |
| #JSON for Ecology articles (paginated): | |
| ecol_url = "https://peerj.com/articles/index.json?subject=1100" | |
| dois = list() | |
| repeat { |
| --- | |
| title: "Get verbatim R chunks in R Markdown" | |
| author: "Jenny Bryan" | |
| date: "18 September, 2014" | |
| output: | |
| html_document: | |
| keep_md: TRUE | |
| --- | |
| My periodic revisitation of "how can I include a verbatim R chunk in `.rmd`"? This time I am writing it down! Various proposed solutions: |
2017-08-03: Since I wrote this in 2014, the universe, specifically Kirill Müller (https://github.com/krlmlr), has provided better solutions to this problem. I now recommend that you use one of these two packages:
I love these packages so much I wrote an ode to here.
I use these packages now instead of what I describe below. I'll leave this gist up for historical interest. 😆
| one <- seq(1:10) | |
| two <- rnorm(10) | |
| three <- runif(10, 1, 2) | |
| four <- -10:-1 | |
| df <- data.frame(one, two, three) | |
| df2 <- data.frame(one, two, three, four) | |
| str(df) |
I think the two most important messages that people can get from a short course are:
a) the material is important and worthwhile to learn (even if it's challenging), and b) it's possible to learn it!
For those reasons, I usually start by diving as quickly as possible into visualisation. I think it's a bad idea to start by explicitly teaching programming concepts (like data structures), because the pay off isn't obvious. If you start with visualisation, the pay off is really obvious and people are more motivated to push past any initial teething problems. In stat405, I used to start with some very basic templates that got people up and running with scatterplots and histograms - they wouldn't necessary understand the code, but they'd know which bits could be varied for different effects.
Apart from visualisation, I think the two most important topics to cover are tidy data (i.e. http://www.jstatsoft.org/v59/i10/ + tidyr) and data manipulation (dplyr). These are both important for when people go off and apply
| #' convert positional information to two columns | |
| #' | |
| #' Sometimes text is organized by position. This function | |
| #' turns positional group labels (e.g headers ) into the levels of a grouping variable | |
| #' @param x character vector containing group labels followed by group members | |
| #' @param pattern regular expression that identifies the group labels | |
| fill_down <- function(x, pattern){ | |
| ## find matches of the pattern | |
| x <- as.character(x) | |
| value_matches <- grepl(pattern = pattern, x = x) |
If you were to give recommendations to your "little brother/sister" on things that they need to do to become a data scientist, what would those things be?
I think the "Data Science Venn Diagram" (http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram) is a great place to start. You need three things to be a good data scientist: