Plotly is a web application for creating and sharing data visualizations. Plotly can work with several programming languages and applications including R, Python, and Microsoft Excel. We’re going to concentrate on creating different graphs with Plotly and sharing those graphs.
Installing Plotly is easy:
install.packages("plotly")
library(plotly)If you want to share your visualizations on https://plot.ly/ you should make an account on their site.
A basic scatterplot is easy to make, with the added benefit of tooltips that appear when your mouse hovers over each point. Specify a scatterplot by indicating type = "scatter". Notice that the arguments for the x and y variables as specified as formulas, with the tilde operator (~) preceding the variable that you’re plotting.
library(plotly)
plot_ly(mtcars, x = ~wt, y = ~mpg, type = "scatter")## Warning: package 'plotly' was built under R version 3.3.2## Warning: package 'ggplot2' was built under R version 3.3.2You can add color to your scatterplot points according to a categorical variable in the data frame you use with plot_ly().
plot_ly(mtcars, x = ~wt, y = ~mpg, type = "scatter", color = ~factor(cyl))You can also show continuous variables with color in scatterplots.
plot_ly(mtcars, x = ~wt, y = ~mpg, type = "scatter", color = ~disp)By specifying the size argument you can make each point in your scatterplot a different size.
plot_ly(mtcars, x = ~wt, y = ~mpg, type = "scatter",
color = ~factor(cyl), size = ~hp)You can create a three-dimensional scatterplot with the type = "scatter3d" argument. If you click and drag these scatterplots you can view them from different angles.
set.seed(2016-07-21)
temp <- rnorm(100, mean = 30, sd = 5)
pressue <- rnorm(100)
dtime <- 1:100
plot_ly(x = ~temp, y = ~pressue, z = ~dtime,
type = "scatter3d", color = ~temp)Line graphs are the default graph for plot_ly(). They’re of course useful for showing change over time:
data("airmiles")
plot_ly(x = ~time(airmiles), y = ~airmiles, type = "scatter", mode = "lines")You can show multiple lines by specifying the column in the data frame that separates the lines:
library(plotly)
library(tidyr)
library(dplyr)
data("EuStockMarkets")
stocks <- as.data.frame(EuStockMarkets) %>%
gather(index, price) %>%
mutate(time = rep(time(EuStockMarkets), 4))
plot_ly(stocks, x = ~time, y = ~price, color = ~index, type = "scatter", mode = "lines")## Warning: package 'tidyr' was built under R version 3.3.2A histogram is great for showing how counts of data are distributed. Use the type = "histogram" argument.
plot_ly(x = ~precip, type = "histogram")Boxplots are wonderful for comparing how different datasets are distributed. Specify type = "box" to create a boxplot.
plot_ly(iris, y = ~Petal.Length, color = ~Species, type = "box")Heatmaps are useful for displaying three dimensional data in two dimensions, using color for the third dimension. Create a heatmap by using the type = "heatmap" argument.
terrain1 <- matrix(rnorm(100*100), nrow = 100, ncol = 100)
plot_ly(z = ~terrain1, type = "heatmap")Why limit yourself to two dimensions when you can render three dimensions on a computer!? Create moveable 3D surfaces with type = "surface".
terrain2 <- matrix(sort(rnorm(100*100)), nrow = 100, ncol = 100)
plot_ly(z = ~terrain2, type = "surface")Choropleth maps illustrate data across geographic areas by shading regions with different colors. Choropleth maps are easy to make with Plotly though they require more setup compared to other Plotly graphics.
# Create data frame
state_pop <- data.frame(State = state.abb, Pop = as.vector(state.x77[,1]))
# Create hover text
state_pop$hover <- with(state_pop, paste(State, '<br>', "Population:", Pop))
# Make state borders white
borders <- list(color = toRGB("red"))
# Set up some mapping options
map_options <- list(
scope = 'usa',
projection = list(type = 'albers usa'),
showlakes = TRUE,
lakecolor = toRGB('white')
)plot_ly(z = ~state_pop$Pop, text = ~state_pop$hover, locations = ~state_pop$State,
type = 'choropleth', locationmode = 'USA-states',
color = state_pop$Pop, colors = 'Blues', marker = list(line = borders)) %>%
layout(title = 'US Population in 1975', geo = map_options)