MrFlick

# withX() will create an environment for the first parameter and all of your named parameters
# if the first parameter is not named, it will be called "X" in the enviroment
# there may be exactly one unnamed expression
# this unnamed parameter will be eval'ed in the envir defined by your named parameters
# If no object is returned from the expression, the newest value of the first parameter is returned invisibly
# this can avoid repeating awkward expressions

#this is a lot like `with()` but instead of working within the variable, 
#   the value itself is added to the enviroment as a variable

bryangoodrich

loadInput <- function() {
    structure(c(0.051267, -0.092742, -0.21371, -0.375, -0.51325, 
    -0.52477, -0.39804, -0.30588, 0.016705, 0.13191, 0.38537, 0.52938, 
    0.63882, 0.73675, 0.54666, 0.322, 0.16647, -0.046659, -0.17339, 
    -0.47869, -0.60541, -0.62846, -0.59389, -0.42108, -0.11578, 0.20104, 
    0.46601, 0.67339, -0.13882, -0.29435, -0.26555, -0.16187, -0.17339, 
    -0.28283, -0.36348, -0.30012, -0.23675, -0.06394, 0.062788, 0.22984, 
    0.2932, 0.48329, 0.64459, 0.46025, 0.6273, 0.57546, 0.72523, 
    0.22408, 0.44297, 0.322, 0.13767, -0.0063364, -0.092742, -0.20795, 
    -0.20795, -0.43836, -0.21947, -0.13882, 0.18376, 0.22408, 0.29896, 

jennybc

Inspired by recent @johnmyleswhite tweets on indexing (though they focus more on NA stuff)

My standard advice to R novices is "don't worry about numeric vs integer too much, for now", but I just created an indexing example that scares me.

I did not realize it was sooo easy to create this gotcha when indexing with numeric (which I know is not a great idea).

pdparker

# Transform .Rmd files to slidy files

.SUFFIXES: .Rmd .html .md

all: Day1Part1-Introduction.md Day1Part1-Introduction.html Day1Part1-session2.md Day1Part1-session2.html \
	Day1Part1-session3.md Day1Part1-session3.html Day1Part2-session1.md Day1Part2-session1.html \
	Day1Part2-session2.md Day1Part2-session2.html

#markdown
%.md: %.Rmd

bryangoodrich

import array
class WeightedQuickUnion:
    def __init__(self, N):
        self.count = N
        self.id = array.array('i', range(N))
        self.size = array.array('i', [1] * N)
    
    def connected(self, p, q):
        return self.find(p) == self.find(q)

trinker

```r
p_load_gh("jcheng5/bubbles", "trinker/qdapTools")

x <- runif(26)
colfunc <- colorRampPalette(c("white", "red"))

cols <- colfunc(length(sort(unique(x))))


bubbles(value = x, label = LETTERS,

abresler

### Parsing Netsdaily for a Polarity Plot -- working with JSON

packages <-
	c('magrittr', 'dplyr', 'qdap', 'jsonlite', 'ggplot2')
lapply(packages, library, character.only = T)
url <-
	'http://www.netsdaily.com/comments/load_comments/8261850'

data <-
	url %>%

leeper

# R Function to Draw Edward Tufte-style Slopeplots

slopegraph <-
function(
df,
xlim = c(.5,ncol(df)+.5),
ylim = c(min(df)-diff(range(df))/100,max(df)+diff(range(df))/100),
main = NULL,
bty = 'n',
yaxt = 'n',

walkerke

library(idbr) 
library(ggplot2)
library(gganimate)
library(tweenr)
library(countrycode)
library(dplyr)

idb_api_key("Your API key goes here")

africa_fips <- countrycode(c('Nigeria', 'Uganda', 'Tanzania', 'Ghana'), 

jimhester

library(gh)
library(tidyverse)
library("magick")
library(glue)

dir.create("data")

repos <- gh::gh("/orgs/:org/repos", org = "tidyverse", .limit = Inf) %>% map_chr("name")

urls <- repos %>%