매끄러운 자바 스크립트 라이브러리를 R에 사용해 봅시다.

 

 

This tool helps review multiple outputs in an efficient manner and saves much needed space in documents and Shiny applications, while creating a user friendly experience.

These carousels can be used directly from the R console, from RStudio, in Shiny apps and R Markdown documents.

Installation

CRAN

install.packages('slickR')

Github (dev)

devtools::install_github('metrumresearchgroup/slickR')

Examples

There are many options within the slick carousel, to get started with the basics we will show a few examples in the rest of the post. If you want to try out any of the examples you can go to this site where they are rendered and can be tested out.


library(svglite)
library(lattice)
library(ggplot2)
library(rvest) 
library(reshape2)
library(dplyr)
library(htmlwidgets)
library(slickR)

Images

Some web scraping for the images example….

#NBA Team Logos
nbaTeams=c("ATL","BOS","BKN","CHA","CHI","CLE","DAL","DEN","DET","GSW",
    "HOU","IND","LAC","LAL","MEM","MIA","MIL","MIN","NOP","NYK",
    "OKC","ORL","PHI","PHX","POR","SAC","SAS","TOR","UTA","WAS")
teamImg=sprintf("https://i.cdn.turner.com/nba/nba/.element/img/4.0/global/logos/512x512/bg.white/svg/%s.svg",nbaTeams)

#Player Images
a1=read_html('http://www.espn.com/nba/depth')%>%html_nodes(css = '#my-teams-table a')
a2=a1%>%html_attr('href')
a3=a1%>%html_text()
team_table=read_html('http://www.espn.com/nba/depth')%>%html_table()
team_table=team_table[[1]][-c(1,2),]
playerTable=team_table%>%melt(,id='X1')%>%arrange(X1,variable)
playerName=a2[grepl('[0-9]',a2)]
playerId=do.call('rbind',lapply(strsplit(playerName,'[/]'),function(x) x[c(8,9)]))
playerId=playerId[playerId[,1]!='phi',]
playerTable$img=sprintf('http://a.espncdn.com/combiner/i?img=/i/headshots/nba/players/full/%s.png&w=350&h=254',playerId[,1])

Grouped Images

There are players on each team, so lets group the starting five together and have each dot correspond with a team:

slickR(
  obj = playerTable$img,
  slideId = c('ex2'),
  slickOpts = list(
    initialSlide = 0,
    slidesToShow = 5,
    slidesToScroll = 5,
    focusOnSelect = T,
    dots = T
  ),
  height = 100,width='100%'
)

Replacing the dots

Sometimes the dots won’t be informative enough so we can switch them with an HTML object, such as text or other images.
We can pass to the customPaging argument javascript code using the htmlwidgets::JS function.

Replace dots with text

cP1=JS("function(slick,index) {return '<a>'+(index+1)+'</a>';}")

slickR(
  obj = playerTable$img,
  slideId = 'ex3',
  dotObj = teamImg,
  slickOpts = list(
    initialSlide = 0,
    slidesToShow = 5,
    slidesToScroll = 5,
    focusOnSelect = T,
    dots = T,
    customPaging = cP1
  ),
  height=100,width='100%'
)

Replace dots with Images

cP2=JS("function(slick,index) {return '<a><img src= ' + dotObj[index] + '  width=100% height=100%></a>';}")


#Replace dots with Images
s1 <- slickR(
  obj = playerTable$img,
  dotObj = teamImg,
  slickOpts = list(
    initialSlide = 0,
    slidesToShow = 5,
    slidesToScroll = 5,
    focusOnSelect = T,
    dots = T,
    customPaging = cP2
  ),height = 100,width='100%'
)

#Putting it all together in one slickR call
s2 <- htmltools::tags$script(
  sprintf("var dotObj = %s", 
          jsonlite::toJSON(teamImg))
)

htmltools::browsable(htmltools::tagList(s2, s1))

Plots

To place plots directly into slickR we use svglite to convert a plot into svg code using xmlSVG and then convert it into a character object

plotsToSVG=list(
  #Standard Plot
    xmlSVG({plot(1:10)},standalone=TRUE),
  #lattice xyplot
    xmlSVG({print(xyplot(x~x,data.frame(x=1:10),type="l"))},standalone=TRUE),
  #ggplot
    xmlSVG({show(ggplot(iris,aes(x=Sepal.Length,y=Sepal.Width,colour=Species))+
                   geom_point())},standalone=TRUE), 
  #lattice dotplot
    xmlSVG({print(dotplot(variety ~ yield | site , data = barley, groups = year,
                          key = simpleKey(levels(barley$year), space = "right"),
                          xlab = "Barley Yield (bushels/acre) ",
                          aspect=0.5, layout = c(1,6), ylab=NULL))
            },standalone=TRUE) 
)

#make the plot self contained SVG to pass into slickR 
s.in=sapply(plotsToSVG,function(sv){paste0("data:image/svg+xml;utf8,",as.character(sv))})
slickR(s.in,slideId = 'ex4',slickOpts = list(dots=T), height = 200,width = '100%')

Synching Carousels

There are instances when you have many outputs at once and do not want to go through all, so you can combine two carousels one for viewing and one for searching.

slickR(rep(s.in,each=5),slideId = c('ex5up','ex5down'),
       slideIdx = list(1:20,1:20),
       synchSlides = c('ex5up','ex5down'),
       slideType = rep('img',4),
       slickOpts = list(list(slidesToShow=1,slidesToScroll=1),
                        list(dots=F,slidesToScroll=1,slidesToShow=5,
                             centerMode=T,focusOnSelect=T)
                        ),
       height=100,width = '100%'
       )

Iframes

Since the carousel can accept any html element we can place iframes in the carousel.
There are times when you may want to put in different DOMs rather than an image in slick. Using slideType you can specify which DOM is used in the slides.
For example let’s put the help Rd files from ggplot2 into a slider using the helper function getHelp. (Dont forget to open the output to a browser to view the iframe contents).

geom_filenames=ls("package:ggplot2",pattern = '^geom')

slickR(unlist(lapply(geom_filenames,getHelp,pkg = 'ggplot2')),slideId = 'ex6',slideType = 'iframe',height = '400px',width='100%',slickOpts = list(dots=T,slidesToShow=2,slidesToScroll=2))

htmlwidgets

Finally, we can really leverage R and place self contained htmlwidgets in iframes (like leaflets and plotly) and use them in a carousel. This solves a problem of how to run many htmlwidgets at once outside of Shiny.

library(leaflet)
library(plotly)

l <- leaflet() %>% addTiles()
htmlwidgets::saveWidget(l,'leaflet.html')

p <- iris%>%ggplot(aes(x=Sepal.Length,y=Sepal.Width))+geom_point()
pL= ggplotly(p)
htmlwidgets::saveWidget(pL,'ggplotly.html')

slickR(c(rep(paste0(readLines('leaflet.html'),collapse='\n'),4),
         rep(paste0(readLines('ggplotly.html'),collapse='\n'),4)),
       slideId = c('leaf','plot'),
       slideIdx = list(1:4,5:8),
       slideType = rep('iframe',2),
       slickOpts = list(list(dots=T,slidesToShow=2,slidesToScroll=2),
                        list(dots=T,slidesToShow=2,slidesToScroll=2)),
       height='200px',width='100%')

Jonathan Sidi joined Metrum Research Group in 2016 after working for several years on problems in applied statistics, financial stress testing and economic forecasting in both industrial and academic settings. To learn more about additional open-source software packages developed by Metrum Research Group please visit the Metrum website. Contact: For questions and comments, feel free to email me at: yonis@metrumrg.com or open an issue for bug fixes or enhancements at github.

소스: slickR – the last carousel you’ll ever need | R-bloggers