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Plot.ly Charts using PluginR and Tiki

Derived from http://www.r-statistics.com/2013/12/plotly-beta-collaborative-plotting-with-r/
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The Plotly R graphing library allows you to create and share interactive, publication-quality plots in your browser. Plotly is also built for working together, and makes it easy to post graphs and data publicly with a URL or privately to collaborators.

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Installing Plotly

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{RR(echo="0", cacheby="pagename", wikisyntax="0")} # Installing Plotly # ----------------------- # Install de required packages if you don't have them yet if(!require(devtools)){ install.packages("devtools", repos="http://ftp.heanet.ie/mirrors/cran.r-project.org/") } require("devtools") if(!require(RCurl)){ install.packages("RCurl", repos="http://ftp.heanet.ie/mirrors/cran.r-project.org/") } if(!require(bitops)){ install.packages("bitops", repos="http://ftp.heanet.ie/mirrors/cran.r-project.org/") } if(!require(RJSONIO)){ install.packages("RJSONIO", repos="http://ftp.heanet.ie/mirrors/cran.r-project.org/") } # Next, install plotly (a big thanks to Hadley, who suggested the GitHub route): if(!require(plotly)){ devtools::install_github("plotly/R-api") } # ... # * DONE (plotly) # Then sign-up like this (adapt username and email to your choice) or at https://plot.ly/: require(plotly) response = signup (username = 'yournewusername', email= 'youremail@example.com') # You'll get as output in the R console: # ## Thanks for signing up to plotly! ## ## Your username is: yournewusername ## ## Your temporary password is: yourtemppassword. You use this to log into your plotly account at https://plot.ly/plot. ## ## Your API key is: API_Key. You use this to access your plotly account through the API. ## ## To get started, initialize a plotly object with your username and api_key, e.g. ## >>> p <- plotly(username="yournewusername", key="API_Key") ## Then, make a graph! ## >>> res <- p$plotly(c(1,2,3), c(4,2,1)) # And we’re up and running! You can change and access your password and key in your homepage. {RR}

Overlaid Histograms

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{RR(echo="0", cacheby="pagename", wikisyntax="0")} # 1. Overlaid Histograms: # ------------------------ require(plotly) p <- plotly(username="yournewusername", key="API_Key") x0 = rnorm(500) x1 = rnorm(500)+1 data0 = list(x=x0, type='histogramx', opacity=0.8) data1 = list(x=x1, type='histogramx', opacity=0.8) layout = list(barmode='overlay') response = p$plotly(data0, data1, kwargs=list(layout=layout)) # The script makes a graph. Use the RStudio viewer or add “browseURL(response$url)” to your script # to avoid copy and paste routines of your URL and open the graph directly. #browseURL(response$url) # In Tiki, you can plot the graph in a wiki page by means of an iframe to the reponse$url # #cat(response$url) # # But since Tiki doesn't allow the word "url" in scripts, we will use the index of the value in the response list: cat(unlist(response[1])) cat(unlist(response[1]) ) # This will produce something like: # #"https://plot.ly/~yournewusername/0/" # # Then you just need to include that url in an iframe as usual in Tiki {RR} {iframe name=myPlotlyChart width=800 height=600 align=middle frameborder=0 marginheight=0 marginwidth=0 scrolling=auto src="https://plot.ly/~yournewusername/0/"}

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Log-normal Boxplot

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{RR(echo="1", cacheby="pagename", wikisyntax="0")} # 2. Log-normal Boxplot # ------------------------ require(plotly) p <- plotly(username='USERNAME', key='API_KEY') x <- c(seq(0,0,length=1000),seq(1,1,length=1000),seq(2,2,length=1000)) y <- c(rlnorm(1000,0,1),rlnorm(1000,0,2),rlnorm(1000,0,3)) s <- list( type = 'box', jitter = 0.5 ) layout <- list( title = 'Fun with the Lognormal distribution', yaxis = list( type = 'log' ) ) response <- p$plotly(x,y, kwargs = list(layout = layout, style=s)) #browseURL(response$url) # Again, in Tiki, you can plot the graph in a wiki page by means of an iframe to the reponse$url = unlist(response[1]) {RR} {iframe name=myPlotlyChart width=800 height=600 align=middle frameborder=0 marginheight=0 marginwidth=0 scrolling=auto src="https://plot.ly/~ueb/14/"}

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HeatMaps

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{RR(echo="0", cacheby="pagename", wikisyntax="0")} #################################### # Days of the Week Heatmap Demo # Questions? Email feedback@plot.ly # For more docs, see plot.ly/api #################################### require(RColorBrewer) require(plotly) py <- plotly(username='yournewusername', key='API_Key') x <- c('Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday') y <- c('Morning', 'Afternoon', 'Evening') z <- list( c(1., 20., 30, 50, 1), c(20., 1., 60, 80, 30), c(30., 60., 1., -10, 20) ) # Color brewer YIOrBr colorscale http://colorbrewer2.org # scl=[[0,"rgb(128, 0, 38)"],[0.125,"rgb(189, 0, 38)"],[0.25,"rgb(227, 26, 28)"], # [0.375,"rgb(252, 78, 42)"],[0.5,"rgb(253, 141, 60)"],[0.625,"rgb(254, 178, 76)"], # [0.75,"rgb(254, 217, 118)"],[0.875,"rgb(255, 237, 160)"],[1,"rgb(255, 255, 204)"]] scl <- brewer.pal(9,'YlOrBr') data <- list( x = x, y = y, z = z, scl= list( c(0,"rgb(128, 0, 38)"), c(0.125,"rgb(189, 0, 38)"), c(0.25,"rgb(227, 26, 28)"), c(0.375,"rgb(252, 78, 42)"), c(0.5,"rgb(253, 141, 60)"), c(0.625,"rgb(254, 178, 76)"), c(0.75,"rgb(254, 217, 118)"), c(0.875,"rgb(255, 237, 160)"), c(1,"rgb(255, 255, 204)") ), type = 'heatmap' ) response <- py$plotly(data) # url and filename #unlist(response[1]) #filename <- response$filename {RR} {iframe name=myPlotlyChart width=800 height=600 align=middle frameborder=0 marginheight=0 marginwidth=0 scrolling=auto src="https://plot.ly/~ueb/15/"}

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More types of charts...

For more examples of chart types possible with Plot.ly and R, see:

Collaborating and Sharing

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# Collaborating and Sharing: You’re in Control # --------------------------------------------- # # Keep in mind that: # # (1) You control if graphs are public or private, and who you share with (like Google Docs) # (2) Public sharing in Plotly is free (like GitHub). # # To share privately, press “Share” in our GUI or share with your script. # Users you share with get an email and can edit and comment on graphs. # That means no more emailing data, graphs, screenshots, and spreadsheets around: you can do it all in Plotly. # You can also save and apply custom themes to new data to avoid re-making the same graphs with new data. # Just upload and apply your theme.