• Plotly is a free and open-source graphing library for R.

• Plotly is a free and open-source graphing library for R.

• Plotly’s R graphing library makes interactive, publication-quality graphs.

• Plotly is a free and open-source graphing library for R.

• Plotly’s R graphing library makes interactive, publication-quality graphs.

• You can view the source, report issues or contribute on GitHub.

• Plotly is a free and open-source graphing library for R.

• Plotly’s R graphing library makes interactive, publication-quality graphs.

• You can view the source, report issues or contribute on GitHub.

• plotly is an R package for creating interactive web-based graphs via the open source JavaScript graphing library plotly.js.

• Plotly is a free and open-source graphing library for R.

• Plotly’s R graphing library makes interactive, publication-quality graphs.

• You can view the source, report issues or contribute on GitHub.

• plotly is an R package for creating interactive web-based graphs via the open source JavaScript graphing library plotly.js.

• As of version 2.0 (November 17, 2015), graphs created with the plotly R package are rendered locally through the htmlwidgets framework.

Download from CRAN

• Use the install.package() function to install the plotly R package from CRAN!

install.packages("plotly")

Download from CRAN

• Use the install.package() function to install the plotly R package from CRAN!

install.packages("plotly")

Download from GitHub

• Alternatively, you can install the latest development version of plotly from GitHub via the devtools R package:

devtools::install_github("ropensci/plotly")

Download from CRAN

• Use the install.package() function to install the plotly R package from CRAN!

install.packages("plotly")

Download from GitHub

• Alternatively, you can install the latest development version of plotly from GitHub via the devtools R package:

devtools::install_github("ropensci/plotly")

Note For RStudio Users

• RStudio users should ensure that they are using the latest RStudio release in order to ensure compatibility with the htmlwidgets R package.

By default, the plotly R package runs locally in your web browser or in the RStudio viewer.

library(plotly)
plot_ly(midwest, x = ~percollege, color = ~state, type = "box", height=350, width=800)

Simply printing the plot object will render the chart locally in your web browser or in the RStudio viewer.

Graphs created with the plotly R package are interactive!

Click on legend entries to hide/show traces, click-and-drag on the chart to zoom, double-click to autoscale, shift-and-drag to pan.

See https://plotly.com/r/basic-charts/ for more!

plot_ly(data = iris, x = ~Sepal.Length, y = ~Petal.Length)

plot_ly(data = iris, x = ~Sepal.Length, y = ~Petal.Length, color = ~Species)

plot_ly(diamonds[sample(nrow(diamonds), 1000), ], x = ~carat, y = ~price, color = ~carat, size = ~carat)

plot_ly(
  diamonds[sample(nrow(diamonds), 1000), ], x = ~carat, y = ~price,
  # Hover text:
  text = ~paste("Price: ", price, '$<br>Cut:', cut),
  color = ~carat, size = ~carat
)

mtcars$am[which(mtcars$am == 0)] <- 'Automatic'
mtcars$am[which(mtcars$am == 1)] <- 'Manual'
mtcars$am <- as.factor(mtcars$am)

fig <- plot_ly(mtcars, x = ~wt, y = ~hp, z = ~qsec, color = ~am, colors = c('#BF382A', '#0C4B8E'))
fig <- fig %>% add_markers()
fig <- fig %>% layout(scene = list(xaxis = list(title = 'Weight'),
                     yaxis = list(title = 'Gross horsepower'),
                     zaxis = list(title = '1/4 mile time')))

fig

See https://plot.ly/r/reference/#scatter3d for more information and chart attribute options!

Add mode and type arguements to make line graphs. They’re of course useful for showing change over time:

data("airmiles")
plot_ly(x = time(airmiles), y = airmiles, mode='lines',type='scatter', height=350)

You can show multiple lines by specifying the column in the data frame that separates the lines:

library(tidyr)
library(dplyr)
data("EuStockMarkets")

stocks <- as_tibble(EuStockMarkets) %>%
  gather(index, price) %>%
  mutate(time = rep(time(EuStockMarkets), 4))

plot_ly(x = stocks$time, y = stocks$price, color = stocks$index, type = 'scatter', mode = 'lines')

dens <- with(diamonds, tapply(price, INDEX = cut, density))
df <- data.frame(
  x = unlist(lapply(dens, "[[", "x")),
  y = unlist(lapply(dens, "[[", "y")),
  cut = rep(names(dens), each = length(dens[[1]]$x))
)

fig <- plot_ly(df, x = ~x, y = ~y, color = ~cut) 
fig <- fig %>% add_lines()

fig

See https://plot.ly/r/reference/#scatter for more information and chart attribute options!

Histograms are great for showing how counts of data are distributed. Use the type = "histogram" argument.

plot_ly(x = ~rnorm(50), type = "histogram",height=350, width = 600)

Use alpha to set the opacity to make overlayed histograms easier to see:

fig <- plot_ly(alpha = 0.6)
fig <- fig %>% add_histogram(x = ~rnorm(500))
fig <- fig %>% add_histogram(x = ~rnorm(500) + 1)
fig <- fig %>% layout(barmode = "overlay")

fig

See https://plot.ly/r/reference/#histogram for more information and chart attribute options!

plot_ly(ggplot2::diamonds, y = ~price, color = ~cut, type = "box")

See https://plot.ly/r/reference/#box for more information and chart attribute options!

plot_ly(z = ~volcano) %>% add_surface()

df <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv")
df$hover <- with(df, paste(state, '<br>', "Beef", beef, "Dairy", dairy, "<br>",
                           "Fruits", total.fruits, "Veggies", total.veggies,
                           "<br>", "Wheat", wheat, "Corn", corn))
# give state boundaries a white border
l <- list(color = toRGB("white"), width = 2)
# specify some map projection/options
g <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)

fig <- plot_geo(df, locationmode = 'USA-states')
fig <- fig %>% add_trace(
    z = ~total.exports, text = ~hover, locations = ~code,
    color = ~total.exports, colors = 'Purples'
  )
fig <- fig %>% colorbar(title = "Millions USD")
fig <- fig %>% layout(
    title = '2011 US Agriculture Exports by State<br>(Hover for breakdown)',
    geo = g
  )

fig

With ggplotly() by Plotly, you can convert your ggplot2 figures into interactive ones powered by plotly.js, ready for embedding into Dash applications.

library(plotly)

dat <- data.frame(
    time = factor(c("Lunch","Dinner"), levels=c("Lunch","Dinner")),
    total_bill = c(14.89, 17.23)
)

p <- ggplot(data=dat, aes(x=time, y=total_bill)) +
    geom_bar(stat="identity")

fig <- ggplotly(p)

fig

You can publish your charts to the web with Plotly’s web service.

1. Create a free Plotly account:

• A Plotly account is required to publish charts online. It’s free to get started, and you control the privacy of your charts.

2. Save your authentication credentials

• Find your authentication API keys in your online settings. Set them in your R session with:

Sys.setenv("plotly_username"="your_plotly_username")
Sys.setenv("plotly_api_key"="your_api_key")

• Save these commands in your .Rprofile file to be run every time you start R.

3. Publish your graphs to Plotly with api_create

library(plotly)
p <- plot_ly(midwest, x = ~percollege, color = ~state, type = "box")
api_create(p, filename = "r-docs-midwest-boxplots")

• filename sets the name of the file inside your online plotly account.