Scatter Plots in ggplot2

How to make Scatter Plots in ggplot2 with Plotly.

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Default point plot

library(plotly)
library(ggplot2)

p <- ggplot(mtcars, aes(wt, mpg))
p <-  p + geom_point()

ggplotly(p)

Add colour

library(plotly)
library(ggplot2)

p <- ggplot(mtcars, aes(wt, mpg))
p <-  p + geom_point(aes(colour = factor(cyl)))

ggplotly(p)

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)

Changing size of data points

library(plotly)
library(ggplot2)

p <- ggplot(mtcars, aes(wt, mpg))
p <-  p + geom_point(aes(size = qsec))

ggplotly(p)

Manually setting aesthetics

library(plotly)
library(ggplot2)

p <-  ggplot(mtcars, aes(wt, mpg)) + geom_point(colour = "red", size = 3)

ggplotly(p)

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)

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)

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)

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)

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()

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)

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)

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)

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)

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)

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)

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)

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)