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library(rCharts)
library(plyr)
library(reshape2)
library(scales)

findata=read.csv("https://raw.github.com/patilv/rChartsTutorials/master/findata.csv")
# These are data regarding NCAA athletic department expenses at public universities. Please see the blog post where these charts were originally used 
# regarding more details on the origins of these data.: http://analyticsandvisualization.blogspot.com/2013/10/subsidies-revenues-and-expenses-of-ncaa.html

findata=findata[,-c(1:2)] # removing first dummy column - the csv quirk - and second column on Rank.

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require(tidyr)
require(dplyr)
#require(ggplot2)

# from: https://twitter.com/JennyBryan/status/646047312830050304
separate(data.frame(x = "howdy"), x, into = 1:6, sep = "(?!^)")

#works with more than one word
separate(data.frame(x = c("howdy", "snake")), x, into = 1:6, sep = "(?!^)")

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## short version
require(ggplot2)

graphdata <- structure(list(X12136 = c(79L, 15L, 60L, 33L, 53L, 89L, 21L, 
25L, 83L, 3L, 64L, 47L, 39L, 1L, 99L, 69L, 9L, 59L, 8L, 10L, 
23L, 74L, 65L, 81L, 42L, 79L, 15L, 60L, 33L, 53L, 89L, 21L, 25L, 
83L, 3L, 64L, 47L, 39L, 1L, 99L, 69L, 9L, 59L, 8L, 10L, 23L, 
74L, 65L, 81L, 42L, 79L, 15L, 60L, 33L, 53L, 89L, 21L, 25L, 83L, 
3L), pheno = structure(list(Condition = structure(c(2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 

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Livecoding D3 in RStudio

This is a proof-of-concept on how one can use RStudio to livecode D3 visualizations.

Usage

You will need to install a couple of packages before getting started

devtools::install_github("yihui/servr")

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require(dplyr)

# input data frame
df1 <- data.frame(trait=rep(1:10, 4), Trait.Code = rep(LETTERS[1:4], each = 10), Alpha_Value= rep(c(NA, "1", NA, "Alphastuff"), each = 10), Number_Value = rep(c(3, NA, 6, NA), each = 10), stringsAsFactors= FALSE)

df1 %>%
mutate(n=row_number()) %>% #add index for later join
filter(!is.na(Alpha_Value), Alpha_Value != "NULL") %>% # Alpha_Value != NULL or NA
group_by(Trait.Code) %>% #group for mutate
mutate(Converted_Value = ifelse(any(is.na(as.numeric(.$Alpha_Value))), NA, as.numeric(Alpha_Value))) %>%

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require(ggplot2)
require(dplyr)
require(plotly)

test <- data.frame(year = rep(2001:2016, each=10), group= rep(LETTERS[1:16], times = 10), count = runif(160))

gg <- ggplot(test, aes(x=year, y = count, group = group, colour = group)) +
           	  geom_point() +
           	  geom_line()
gg

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L.shell.Day5	a.shell.Day5	b.shell.Day5	Hex.shell.Day5	L.shell.Day1	a.shell.Day1	b.shell.Day1	Hex.shell.Day1
61.1824499603039	-22.076387665463	32.2221171514149	#8A9F68	59.1700367857259	-22.0471225002006	34.9234624183896	#859758
56.8621418223068	-24.97288907957	42.0601786964881	#778D3B	51.5169668478454	-23.9252046480325	36.6068073530096	#627A37
62.7257035685657	-22.9733681151521	35.8083291448731	#8AA164	57.7975941354515	-22.202011261358	35.4614903698262	#809651
59.9427072331491	-23.4729626916318	34.455617994546	#809957	55.2358856160348	-21.9162837108123	35.5743258652009	#778C49
63.6548194655096	-24.6867708185501	44.698102412673	#8DA34C	54.0763291124911	-21.8893308495491	35.6211872004396	#748946
67.5740105523698	-23.8259047306477	45.808160158852	#9AAD54	58.3355995894783	-24.1634822371787	37.4696438729551	#7C954D
73.2291440290129	-21.7287032147082	38.4157268985132	#AABB72	61.9082465991772	-20.7769111456906	31.9351191059027	#899D61
58.871658635331	-23.2615793573258	37.1729800997476	#7F964F	57.398673319426	-22.7656

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library(devtools)
library(iotools)
library(R.utils)
library(feather) # install_github("wesm/feather/R")
library(microbenchmark)
library(data.table)
library(readr)
library(ggplot2)
library(plotly)

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library(dplyr)
library(tidyr)

no_col <- max(count.fields("https://gist.githubusercontent.com/bhive01/ad5f2f51b02aed0ccfbd50ee3bb8dd68/raw/e0a66e51c88da13861bfa9f624aef5bb62cbfb32/fruitshape.txt", sep = ","))
cukeshape <- read.table("https://gist.githubusercontent.com/bhive01/ad5f2f51b02aed0ccfbd50ee3bb8dd68/raw/e0a66e51c88da13861bfa9f624aef5bb62cbfb32/fruitshape.txt",sep=",",fill=TRUE,col.names=1:no_col)

tcukeshape <- as.data.frame(t(cukeshape))

tcukeshape %>%
	gather(., fruit, width) %>%