| library(tidyverse) | |
| # using expression() for the text formatting: | |
| ggplot(mtcars, | |
| aes(disp, | |
| mpg)) + | |
| geom_point() + | |
| # ~ for spaces, and * for no-space between (unquoted) expressions | |
| ylab(expression(Anthropogenic~SO[4]^{"2-"}~(ngm^-3))) + | |
| xlab(expression(italic(delta)^13*C[ap]*"‰")) + |
| #!/usr/bin/env Rscript | |
| json_in <- file('stdin', 'r') | |
| lat_newp <- '{"t":"RawBlock","c":["latex","\\\\newpage"]}' | |
| doc_newp <- '{"t":"RawBlock","c":["openxml","<w:p><w:r><w:br w:type=\\"page\\"/></w:r></w:p>"]}' | |
| ast <- paste(readLines(json_in, warn=FALSE), collapse="\n") |
| echo sha, contributor, date, message > log.csv | |
| git log --date=local --pretty=format:'%h, %an, %ad, "%s"' >> log.csv |
| # Depends on the `beepr` package by @rasmusab: https://cran.r-project.org/web/packages/beepr/README.html | |
| # Code greatly improved by flodel on Stack Exchange Code Review (thanks flodel) | |
| # Planned improvements/expansions: new end-of-timer sounds, add functionality for starting a separate R session so that it doesn't bogart the current one. | |
| timer <- function(interval, units = c("secs", "mins", "hours", "days", "weeks")) { | |
| units <- match.arg(units) | |
| num_sec <- interval * switch(units, secs = 1, mins = 60, hours = 3600, | |
| days = 86400, weeks = 604800) | |
| Sys.sleep(num_sec) | |
| if (require(beepr)) beep(2) else message("TIMER DONE YO!") |
| #Script to scrape NARS website | |
| library(rvest) | |
| library(dplyr) | |
| nars <- read_html("https://www.epa.gov/national-aquatic-resource-surveys/data-national-aquatic-resource-surveys") | |
| links <- nars %>% | |
| html_nodes(".file-link, a") %>% | |
| html_attr("href") %>% | |
| tbl_df() %>% | |
| filter(grepl("files",value)) %>% |
| # you need my horse library from here: https://github.com/dill/horse | |
| library(horse) | |
| library(magrittr) | |
| library(knitr) | |
| setup_twitter_oauth("you auth stuff", | |
| "goes here", | |
| "you can't use mine", | |
| "it's mine") |
Playing with adding + operator for easily joining geojson objects together into a single
valid geojson object. This is relatively easy with lists, but not so easy with json unless you speak json.
Get lawn package too for viewing data
devtools::install_github("ropensci/geojsonio")
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:
| #' 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) |
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