The bigvis approach

[jbao]

Hi @nickdelgrosso ,

thanks for the awesome talk yesterday and the interesting topic. I'm wondering if you've heard about the bigvis package in R, which implements a similar heatmap approach and the result looks good enough with a more or less default configuration.

library('bigvis')
library('ggplot2')

dat <- read.csv('xxx.csv')

binwidth <- 0.1
summarized <- with(dat, condense(bin(x,binwidth), bin(y,binwidth)))

ggplot(summarized, aes(x,y,fill=.count)) + 
  geom_tile() + 
  scale_fill_gradient(low = "white", high = "black", trans='log') +
  theme(panel.background=element_rect(fill="blue", colour="blue"))

I'd like to know your opinion on this, and I can try to write it up in more details if you're interested;-)

[nickdelgrosso]

Hi!

Thank you for the complement--I'm glad you liked it! I didn't know about the bigvis package, but from your description, it sounds like a good R-lang equivalent to Datashader. Thanks for the recommendation!

Best wishes,

Nick

On 2018-04-14 00:28, jbao wrote:

Hi @nickdelgrosso [1] ,

thanks for the awesome talk yesterday and the interesting topic. I'm wondering if you've heard about the bigvis package [2] in R, which implements a similar heatmap approach and the result looks good enough with a more or less default configuration.

library('bigvis') library('ggplot2')

dat <- read.csv('xxx.csv')

binwidth <- 0.1 summarized <- with(dat, condense(bin(x,binwidth), bin(y,binwidth)))

ggplot(summarized, aes(x,y,fill=.count)) + geom_tile() + scale_fill_gradient(low = "white", high = "black", trans='log') + theme(panel.background=element_rect(fill="blue", colour="blue"))

I'd like to know your opinion on this, and I can try to write it up in more details if you're interested;-)

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Links:

[1] https://github.com/nickdelgrosso [2] https://github.com/hadley/bigvis [3] https://github.com/nickdelgrosso/datashader_talk/issues/2 [4] https://github.com/notifications/unsubscribe-auth/ADoHd4yQI0wCkAb6oEKZZ8nkBzhJPsqZks5toSaYgaJpZM4TUsko