bhive01

library(tidyverse)
library(plotly)

df1 <-
structure(list(L = c(70.3645369132914, 65.4398664741782, 68.666743417908, 
77.365430427141, 70.0547601723453, 70.6484131213004, 71.4986501504573, 
66.1120379621642, 73.2774689981553, 69.562646734966, 69.1207716412117, 
70.0365107157227, 70.5193220131868, 64.283933268896, 61.3740110269355, 
68.0741114422373, 68.5654828591618, 70.8832243213418, 64.2919216622958, 
69.292675612121, 68.8174984026827, 66.8429923577925, 74.8780440157137, 

bhive01

library(Rvision)
library(tidyverse)

vid_loc <- paste0(find.package("Rvision"), "/sample_vid/SampleVideo_1080x720_5mb.mp4")
vid1 <- video(vid_loc)

res <- data.frame(frame_pos = seq(2,nframes(vid1), 5), total_movement = NA)

old <- readFrame(vid1, 1)

bhive01

Keybase proof

I hereby claim:

• I am bhive01 on github.
• I am bhive01 (https://keybase.io/bhive01) on keybase.
• I have a public key whose fingerprint is 8786 42F9 9118 5EAB 328A 14DE 3733 D0FF 46A0 E89F

To claim this, I am signing this object:

bhive01

torso <- function(d, n=5L, ...) {
	stopifnot(length(n) == 1L)
	center <- as.integer(nrow(d)/2)
	left <- as.integer(center-n/3)
	right <- as.integer(center + n/3)
	d[seq.int(from= left, to = right, by = 1L),]
}

topntail <- function(d, n=4L) {
	rbind(head(d,n), tail(d,n))

bhive01

library(tidyverse)
library(GGally)

nba = read.csv("http://datasets.flowingdata.com/ppg2008.csv")

nba1 <- 
	sample_n(nba, 40) %>% 
		as_tibble() %>%
		mutate(league = 1)

bhive01

library(tidyverse)

# This is the example I used to show pwalk iterating and printing out things
#perhaps this is a horrible example and I'm using it incorrectly?
# if you substitute pmap for pwalk, you get exactly the same output. 

# make a simple plotting function
plot_points <- function(t) ggplot(t, aes(mpg, wt)) + geom_point()

#function that prints cylinder info, ggplot, and table 

bhive01

library(tidyverse)
library(animation)
library(tweenr)
library(gtools)

income <- read_csv("income.csv")
income_levels <- mixedsort(unique(income$income_level))
income_levels <- c(income_levels[16], income_levels[1:15])

income <- income %>% mutate(income_level=factor(income_level,

bhive01

library(tidyverse)
lagshow <- 
	data.frame(x= rep(1:10, 14),
				y = rep(1:10, 14),
				color = rep(rep(c("A", "B"), each = 10), 7),
				lag = rep(-3:3, each=20)
				) %>%
	mutate(x = if_else(color == "A", x-lag, x))
				
ggplot(lagshow, aes(x = x, y = y, colour = color, shape = color)) +

bhive01

################################################
#Correlation matrix plot
raw.data <- raw.data[complete.cases(raw.data),]


c<-cor(raw.data[colstart:colend])

#Correlation p-value tests
cor.pvalues <- function(X){

bhive01

library(tidyverse)

#-----------------------------------------------------------------------------
# Some complicated function to calculate things.
#-----------------------------------------------------------------------------
complex_func <- function(df) {
  cat("processing: ", first(df[['ID']]), "\n")

  # 100 lines of code go here.