| describe_data_df <- function(x) { | |
| res_1 <- paste0("data is a dataframe with ", ncol(x), " colums", ":") | |
| res_2 <- sapply(x, class) | |
| res_2 <- sapply(1:length(res_2), function(x) { | |
| paste0("- Column ", x, " is named '", names(res_2)[x], "' and is of class ", res_2[x], ".") | |
| }) | |
| res_2 <- paste(res_2, collapse = "\n") | |
| res <- paste(res_1, res_2, sep = "\n") |
| # Animated dplyr joins with gganimate | |
| # * Garrick Aden-Buie | |
| # * garrickadenbuie.com | |
| # * MIT License: https://opensource.org/licenses/MIT | |
| # Note: I used Fira Sans and Fira Mono fonts. | |
| # Use search and replace to use a different font if Fira is not available. | |
| library(tidyverse) | |
| library(gganimate) |
| # data from http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/population-distribution-demography/geostat | |
| # Originally seen at http://spatial.ly/2014/08/population-lines/ | |
| # So, this blew up on both Reddit and Twitter. Two bugs fixed (southern Spain was a mess, | |
| # and some countries where missing -- measure twice, submit once, damnit), and two silly superflous lines removed after | |
| # @hadleywickham pointed that out. Also, switched from geom_segment to geom_line. | |
| # The result of the code below can be seen at http://imgur.com/ob8c8ph | |
| library(tidyverse) |
Docker is a tool for bundling together applications and their dependencies into images that can than be run as containers on many different types of computers.
Docker and other containerization tools are useful to scientists because:
| prior_version = 3.3 | |
| library_path = .libPaths()[1] | |
| library_path = substr(library_path, 1, nchar(library_path)-3) | |
| old_library_path=paste0(library_path, prior_version) | |
| old_packages = list.files(old_library_path) | |
| install.packages(old_packages) |
Seminar Topic: Reproducible Computational Analysis - How to start a new project
Time: March 24, 2015 from 9:00am- 12:00 noon
Location: Biomedical & Physical Sciences Bldg, Room 2245
Instructor: Dr. Titus Brown
Description
Computational science projects, from data analysis to modeling, can benefit dramatically from a little up-front investment in automation; starting off with version control and automated building of results will pay off in efficiency, agility, and both transparency and reproducibility of the results. However, most computational researchers have never been exposed to a completely automated analysis pipeline. I will demonstrate the process of initiating a new project, building a few initial scripts, and automating the generation of results, as well as building some graphs. While the topic will be from my own research in bioinformatics, the overall approach should apply to anyone doing data analysis or simulations.
This gist lets you keep IPython notebooks in git repositories. It tells git to ignore prompt numbers and program outputs when checking that a file has changed.
To use the script, follow the instructions given in the script's docstring.
For further details, read this blogpost.
The procedure outlined here is inspired by this answer on Stack Overflow.
