hrbrmstr

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 %>%

halhen

library(tidyverse)

# data from https://www.kaggle.com/bls/american-time-use-survey

df.resp <- read_csv('../data/atus/atusresp.csv')
df.act <- read_csv('../data/atus/atusact.csv', col_types=cols(tustarttim = col_character(), tustoptime = col_character())) 
df.sum <- read_csv('../data/atus/atussum.csv')

df.tmp <- df.act %>%
  mutate(activity = case_when(trtier2p == 1301 ~ 'Exercise',

jeroen

library(magick)
library(magrittr)

# Convert to 'ico' format
image_read("https://www.r-project.org/logo/Rlogo.png") %>%
	image_scale("32x32!") %>% # remove the "!" after 32x32 to scale proportionally
	image_write("favicon.ico", format = "ico")


# Favicon can also be png/gif

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'), 

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,

hrbrmstr

library(rgdal)
library(rgeos)
library(ggplot2)
library(readr)
library(tidyr)
library(dplyr)
library(grid)

us <- readOGR("us_states_hexgrid.geojson", "OGRGeoJSON")

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 %>%

bryangoodrich

# Twitter Topic Modeling Using R
# Author: Bryan Goodrich
# Date Created: February 13, 2015
# Last Modified: April 3, 2015
# 
# Use twitteR API to query Twitter, parse the search result, and
# perform a series of topic models for identifying potentially
# useful topics from your query content. This has applications for
# social media, research, or general curiosity
#

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)