Scatter Plots in ggplot2
How to make Scatter Plots in ggplot2 with Plotly.
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Plotly is a free and open-source graphing library for R. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.
Default point plot
library(plotly)
library(ggplot2)
p <- ggplot(mtcars, aes(wt, mpg))
p <- p + geom_point()
ggplotly(p)
Click to copy Add colour
library(plotly)
library(ggplot2)
p <- ggplot(mtcars, aes(wt, mpg))
p <- p + geom_point(aes(colour = factor(cyl)))
ggplotly(p)
Click to copy Changing shapes of data points
library(plotly)
library(ggplot2)
p <- ggplot(mtcars, aes(wt, mpg))
p <- p + geom_point(aes(shape = factor(cyl)))
ggplotly(p)
Click to copy Changing size of data points
library(plotly)
library(ggplot2)
p <- ggplot(mtcars, aes(wt, mpg))
p <- p + geom_point(aes(size = qsec))
ggplotly(p)
Click to copy Manually setting aesthetics
library(plotly)
library(ggplot2)
p <- ggplot(mtcars, aes(wt, mpg)) + geom_point(colour = "red", size = 3)
ggplotly(p)
Click to copy Optional shape arguments
For shapes that have a border (like shape 21), you can colour the inside and outside separately. Use the stroke aesthetic to modify the width of the border.
library(plotly)
library(ggplot2)
p <-
ggplot(mtcars, aes(wt, mpg)) +
geom_point(shape = 21, colour = "black", fill = "white", size = 5, stroke = 5)
ggplotly(p)
Click to copy Mix multiples shapes
You can create interesting shapes by layering multiple points of different sizes.
Default plot:
library(plotly)
library(ggplot2)
p <- ggplot(mtcars, aes(mpg, wt, shape = factor(cyl)))
p <-
p +
geom_point(aes(colour = factor(cyl)), size = 4) +
geom_point(colour = "grey90", size = 1.5)
ggplotly(p)
Click to copy Mixed shapes:
library(plotly)
library(ggplot2)
p <- ggplot(mtcars, aes(mpg, wt, shape = factor(cyl)))
p <-
p +
geom_point(colour = "black", size = 4.5) +
geom_point(colour = "pink", size = 4) +
geom_point(aes(shape = factor(cyl)))
ggplotly(p)
Click to copy Liner Regression
library(plotly)
library(ggplot2)
dat <- data.frame(cond = rep(c("A", "B"), each=10),
xvar = 1:20 + rnorm(20,sd=3),
yvar = 1:20 + rnorm(20,sd=3))
p <- ggplot(dat, aes(x=xvar, y=yvar)) +
geom_point(shape=1) + # Use hollow circles
geom_smooth(method=lm) # Add linear regression line
ggplotly(p)
Click to copy library(plotly)
library(ggplot2)
dat <- data.frame(cond = rep(c("A", "B"), each=10),
xvar = 1:20 + rnorm(20,sd=3),
yvar = 1:20 + rnorm(20,sd=3))
p <- ggplot(dat, aes(x=xvar, y=yvar)) +
geom_point(shape=1) +
geom_smooth()
Click to copy Without confidence boundary area:
library(plotly)
library(ggplot2)
dat <- data.frame(cond = rep(c("A", "B"), each=10),
xvar = 1:20 + rnorm(20,sd=3),
yvar = 1:20 + rnorm(20,sd=3))
p <- ggplot(dat, aes(x=xvar, y=yvar)) +
geom_point(shape=1) + # Use hollow circles
geom_smooth(method=lm, # Add linear regression line
se=FALSE) # Don't add shaded confidence region
ggplotly(p)
Click to copy Multiple regressions:
library(plotly)
library(ggplot2)
x <- 1:10
dd <- rbind(data.frame(x=x,fac="a", y=x+rnorm(10)),
data.frame(x=2*x,fac="b", y=x+rnorm(10)))
coef <- lm(y~x:fac, data=dd)$coefficients
p <- qplot(data=dd, x=x, y=y, color=fac)+
geom_abline(slope=coef["x:faca"], intercept=coef["(Intercept)"])+
geom_abline(slope=coef["x:facb"], intercept=coef["(Intercept)"])
ggplotly(p)
Click to copy Constrained slope
library(plotly)
library(ggplot2)
n <- 20
x1 <- rnorm(n); x2 <- rnorm(n)
y1 <- 2 * x1 + rnorm(n)
y2 <- 3 * x2 + (2 + rnorm(n))
A <- as.factor(rep(c(1, 2), each = n))
df <- data.frame(x = c(x1, x2), y = c(y1, y2), A = A)
fm <- lm(y ~ x + A, data = df)
p <- ggplot(data = cbind(df, pred = predict(fm)), aes(x = x, y = y, color = A))
p <- p + geom_point() + geom_line(aes(y = pred))
ggplotly(p)
Click to copy Stat Summary
library(plotly)
library(ggplot2)
hist <- data.frame(date=Sys.Date() + 0:13, counts=1:14)
hist <- transform(hist, weekday=factor(weekdays(date), levels=c('Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday')))
p <- ggplot(hist, aes(x=weekday, y=counts, group=1)) +
geom_point(stat='summary', fun.y=sum) +
stat_summary(fun.y=sum, geom="line")
ggplotly(p)
Click to copy Line order
library(plotly)
library(ggplot2)
dat <- data.frame(x = sample(1:10), y = sample(1:10), order = sample(1:10))
p <- ggplot(dat[order(dat$order),], aes(x, y)) + geom_point() + geom_text(aes(y = y + 0.25,label = order)) +
geom_path()
ggplotly(p)
Click to copy Adding horizontal line
library(plotly)
library(ggplot2)
p <- ggplot(mtcars,aes(mpg,qsec))+geom_point() +
geom_segment(aes(x=15,xend=20,y=18,yend=18))
ggplotly(p)
Click to copy Adding points to line
library(plotly)
library(ggplot2)
df <- data.frame(time=as.factor(c(1,1,2,2,3,3,4,4,5,5)),
value=as.numeric(c(7, 8, 9, 10, 10, 11, 10.5, 11.4, 10.9, 11.6)),
side=as.factor(c("E","F","E","F","E","F","E","F","E","F")))
p <- ggplot(df, aes(time, value, group=side, colour=side)) +
geom_line(size=1)
p <- p + geom_point()
ggplotly(p)
Click to copy What About Dash?
Dash for R is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.
Learn about how to install Dash for R at https://dashr.plot.ly/installation.
Everywhere in this page that you see fig, you can display the same figure in a Dash for R application by passing it to the figure argument of the Graph component from the built-in dashCoreComponents package like this:
library(plotly)
fig <- plot_ly()
# fig <- fig %>% add_trace( ... )
# fig <- fig %>% layout( ... )
library(dash)
library(dashCoreComponents)
library(dashHtmlComponents)
app <- Dash$new()
app$layout(
htmlDiv(
list(
dccGraph(figure=fig)
)
)
)
app$run_server(debug=TRUE, dev_tools_hot_reload=FALSE)
Click to copy