On Github grssnbchr / zrug-rddj
Timo Grossenbacher
Presentation available under grssnbchr.github.io/zrug-rddj
Formerly: MSc in Geography & Computer Science UZH
Mar - Oct 2014: Tages-Anzeiger
Since Nov 2014 I work in the team of SRF Data as coder and journalist ("journocoder")
Some examples
More: research & ideas, less: service center
pitch ideas, receive / collect / scrape / enforce (BGÖ) data
preprocess > visualize > analyze > find the story
publication on srf.ch -> overview, interactivity
publication in radio and/or TV -> anecdotes, details, aspects
Dual-use goods & conventional arms exports
R is a Swiss Army Knife > it reads all kinds of weird s**t
R allows for automation > example
R empowers reproducibility and, ultimately, transparency > we publish most of our analyses on GitHub
base, dplyr, tidyr, maggritr, ggplot2, extrafont, animation, readxl, xml2, jsonlite, RSQLite, googlesheets, stringr, rpremraj/mailR, R2HTML, knitr, slidify, readr, caret, sp, maptools, etc.
Error reporting in RStudio - in general, the console in RStudio
The plethora of packages doesn't make it better (jsonlite, rjson, RJSONIO). You still need to use list() and complicated lapply() calls to produce nested data structures - and JSON is all about nested data structures.
What about something like that?
my_dataset %>% group_by(facet) %>% to_json("output.json")
The language... especially Standard Evaluation vs. Non-Standard Evaluation ... and stuff like paste() or paste0().
direct_matches %<>% mutate_(.dots = setNames( list( interp(~ as.numeric(sub("\\D*(\\d+).*", "\\1", a)), a = as.name(combined))), combined ) )
rddj.info - a resource collection for doing DDJ with R
Algorithmic Accountibility
@grssnbchr or timo.grossenbacher(at)srf.ch
This presentation is available (and reproducible) under github.com/grssnbchr/zrug-rddj