View dark_base16-tomorrow-night.rstheme
/* rs-theme-name: Dark UI Base16 Tomorrow Night */ | |
/* rs-theme-is-dark: TRUE */ | |
/* Dark UI from Randy3k's Wombat (https://github.com/randy3k/dotfiles/blob/master/.R/rstudio/themes/Wombat.rstheme) */ | |
/* "Tomorrow night" color scheme adapted from chriskempson's Base16 (https://github.com/chriskempson/base16). */ | |
.body { | |
background: #ffffff; | |
} |
View triangle.py
# triangle.py | |
import math | |
def triangle_perimeter(a, b, c): | |
return a + b + c | |
def triangle_area(a, b, c): | |
p = triangle_perimeter(a, b, c) / 2 | |
x = math.sqrt(p * (p - a) * (p - b) * (p - c)) | |
return round(x, 2) |
View dta_to_sav.r
# package dependency | |
library(haven) | |
path_in = "new_EB with more var_2004-2016.dta" | |
out_tsv = "new_EB.tsv" | |
out_sav = "new_EB.sav" | |
# name repair shows the issue with _merge | |
x = haven::read_dta(p, .name_repair = "universal") | |
names(x)[ names(x) == "._merge" ] = "MERGEVAR" |
View sample.r
# change of PRNG in R >= 3.6.0 | |
# use former PRNG | |
RNGkind(sample.kind = "Rounding") | |
# use new/fixed PRNG | |
RNGkind(sample.kind = "default") | |
# see ?Random for details |
View disc2019.R
library(cartography) | |
library(sp) | |
# Load data | |
data(nuts2006) | |
# Get a SpatialLinesDataFrame of countries borders | |
nuts0.contig.spdf <- getBorders(nuts0.spdf) | |
# Get the GDP per capita | |
nuts0.df$gdpcap <- nuts0.df$gdppps2008/nuts0.df$pop2008*1000000 |
View 0-download-all-ergm-sources.R
## | |
## download all `ergm` sources from v3.0-1 to today | |
## | |
library(rvest) | |
d <- "ergm-sources" | |
dir.create(d, showWarnings = FALSE) | |
# current source |
View gist:ad24bb323d2603dbdd3af35086147c83
R to python useful data wrangling snippets | |
The dplyr package in R makes data wrangling significantly easier. | |
The beauty of dplyr is that, by design, the options available are limited. | |
Specifically, a set of key verbs form the core of the package. | |
Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe. | |
Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R. | |
The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package). | |
dplyr is organised around six key verbs |
View pairs_plot.R
library(tidyverse) | |
library(patchwork) | |
plot_pair <- function(data, x, y) { | |
ggplot(data, aes_string(x = x, y = y, color = "Species", shape = "Species")) + | |
geom_point() + | |
scale_color_brewer(palette = "Dark2") + | |
theme(legend.position = "none", plot.title = element_text(size = 7)) + | |
labs(x = NULL, y = NULL, title = paste0(y, " ~ ", x)) |
View user6631314.do
clear | |
input str500 x | |
"-73.974651 40.73868,-73.973391 40.73897,-73.97323 40.73955,-73.97298 40.7415304,-73.97247 40.7423406,-73.97229 40.7428504,-73.97213 40.74338,-73.971871 40.7440804,-73.97141 40.7446706,-73.97103 40.7455605,-73.970301 40.7465204,-73.96929 40.7472" | |
"-73.970301 40.7465204,-73.96929 40.7472" | |
end | |
// keep only the first pair of coordinates | |
replace x = regexr(x, ",.*", "") | |
// split by space, converting to numeric |
View session_info.r
# ============================================================================== | |
# SESSION_INFO | |
# | |
# A script to deal with package dependencies that will | |
# | |
# - detach all packages except base ones | |
# - install its own package dependencies | |
# - look for session_info.txt and parse it for packages | |
# - ensure the packages are installed and up to date | |
# |
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