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geom_line in ggplot2

How to make line plots in ggplot2 with geom_line. Examples with code and interactive charts


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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.

Basic Line Plot

library(plotly)

dat1 <- data.frame(
    sex = factor(c("Female","Female","Male","Male")),
    time = factor(c("Lunch","Dinner","Lunch","Dinner"), levels=c("Lunch","Dinner")),
    total_bill = c(13.53, 16.81, 16.24, 17.42)
)

p <- ggplot(data=dat1, aes(x=time, y=total_bill, group=sex)) +
    geom_line() +
    geom_point()

fig <- ggplotly(p)

fig

Add Points

library(plotly)

dat1 <- data.frame(
    sex = factor(c("Female","Female","Male","Male")),
    time = factor(c("Lunch","Dinner","Lunch","Dinner"), levels=c("Lunch","Dinner")),
    total_bill = c(13.53, 16.81, 16.24, 17.42)
)

# Map sex to different point shape, and use larger points
p <- ggplot(data=dat1, aes(x=time, y=total_bill, group=sex, shape=sex)) +
    geom_line() +
    geom_point()

fig <- ggplotly(p)

fig

Styles & Themes

library(plotly)

dat1 <- data.frame(
    sex = factor(c("Female","Female","Male","Male")),
    time = factor(c("Lunch","Dinner","Lunch","Dinner"), levels=c("Lunch","Dinner")),
    total_bill = c(13.53, 16.81, 16.24, 17.42)
)

p <- ggplot(data=dat1, aes(x=time, y=total_bill, group=sex, shape=sex, colour=sex)) +
    geom_line(aes(linetype=sex), size=1) +     # Set linetype by sex
    geom_point(size=5) +         # Use larger points, fill with white
    scale_colour_hue(name="Sex",      # Set legend title
                     l=30)  +                  # Use darker colors (lightness=30)
    scale_shape_manual(name="Sex",
                       values=c(22,21)) +      # Use points with a fill color
    scale_linetype_discrete(name="Sex") +
    xlab("Time of day") + ylab("Total bill") + # Set axis labels
    ggtitle("Average bill for 2 people") +     # Set title
    theme_bw()

fig <- ggplotly(p)

fig

Continuous

library(plotly)

datn <- read.table(header=TRUE, text='
supp dose length
  OJ  0.5  13.23
  OJ  1.0  22.70
  OJ  2.0  26.06
  VC  0.5   7.98
  VC  1.0  16.77
  VC  2.0  26.14
')

p <- ggplot(data=datn, aes(x=dose, y=length, group=supp, colour=supp)) +
    geom_line() +
    geom_point()

fig <- ggplotly(p)

fig

Categorical

library(plotly)

datn <- read.table(header=TRUE, text='
supp dose length
  OJ  0.5  13.23
  OJ  1.0  22.70
  OJ  2.0  26.06
  VC  0.5   7.98
  VC  1.0  16.77
  VC  2.0  26.14
')

datn2 <- datn
datn2$dose <- factor(datn2$dose)
p <- ggplot(data=datn2, aes(x=dose, y=length, group=supp, colour=supp)) +
    geom_line() +
    geom_point()

fig <- ggplotly(p)

fig

Multiple Variables

library(reshape2)
library(plotly)

test_data <-
  data.frame(
    var0 = 100 + c(0, cumsum(runif(49, -20, 20))),
    var1 = 150 + c(0, cumsum(runif(49, -10, 10))),
    date = seq(as.Date("2002-01-01"), by="1 month", length.out=100)
  )

test_data_long <- melt(test_data, id="date")  # convert to long format

p <- ggplot(data=test_data_long,
       aes(x=date, y=value, colour=variable)) +
    geom_line()

fig <- ggplotly(p)

fig

Mulitple Points

library(plotly)
library(data.table)

d=data.table(x=seq(0, 100, by=0.1), y=seq(0,1000))
p <- ggplot(d, aes(x=x, y=y))+geom_line()
#Change the length parameter for fewer or more points
thinned <- floor(seq(from=1,to=dim(d)[1],length=70))
p <- ggplot(d, aes(x=x, y=y))+geom_line()+geom_point(data=d[thinned,],aes(x=x,y=y))

fig <- ggplotly(p)

fig

Styled Lines

library(plotly)

x <- c(10, 20, 50, 10, 20, 50)
mean = c(52.4, 98.2, 97.9, 74.1, 98.1, 97.6)
group = c(1, 1, 1, 2,2,2)
upper = c(13.64, 89, 86.4, 13.64, 89, 86.4)
lower = c(95.4, 99.8, 99.7, 95.4, 99.8, 99.7)
data <- data.frame(x=x,y=mean, group, upper, lower)

p <- ggplot(data, aes(x = x, y= mean, group = as.factor(data$group),
                          colour=as.factor(data$group))) +
  geom_line() + geom_point() +
  geom_line(aes(y=lower),linetype="dotted") +
  geom_line(aes(y=upper),linetype="dotted")+
  scale_color_manual(name="Groups",values=c("red", "blue"))+
  guides(colour = guide_legend(override.aes = list(linetype = 1)))

fig <- ggplotly(p)

fig

Mapping to Groups

library(plotly)

# Data frame with two continuous variables and two factors
set.seed(0)
x <- rep(1:10, 4)
y <- c(rep(1:10, 2)+rnorm(20)/5, rep(6:15, 2) + rnorm(20)/5)
treatment <- gl(2, 20, 40, labels=letters[1:2])
replicate <- gl(2, 10, 40)
d <- data.frame(x=x, y=y, treatment=treatment, replicate=replicate)

p <- ggplot(d, aes(x=x, y=y, colour=treatment, group=interaction(treatment, replicate))) +
    geom_point() + geom_line()

fig <- ggplotly(p)

fig

Add Segment

library(plotly)

x <- rep(1:10, 2)
y <- c(1:10, 1:10+5)
fac <- gl(2, 10)
df <- data.frame(x=x, y=y, fac=fac)

p <- ggplot(df, aes(x=x, y=y, linetype=fac)) +
    geom_line() +
    geom_segment(aes(x=2, y=7, xend=7, yend=7), colour="red") +
    scale_linetype_discrete(guide=guide_legend(override.aes=aes(colour="blue")))

fig <- ggplotly(p)

fig

Add Error Bar

library(plotly)

# sample data
df <- data.frame(condition = rep(LETTERS[1:4], each = 5),
                 E = rep(1:5, times = 4),
                 avg = rnorm(20),
                 se = .3)
# plotting command
p <- ggplot(data = df, aes(x = E,
                      y = avg,
                      color = condition,
                      linetype = condition,
                      shape = condition,
                      fill = condition)) +
  geom_line(size=1) +
  geom_point(size=3) +
  scale_color_manual(values = c(A = "red", B = "red", C = "blue", D = "blue"),
                     guide = "none") +
  scale_linetype_manual(values = c(A = "solid", B = "dashed", C = "solid", D = "dashed"),
                        guide = "none") +
  scale_shape_manual(values = c(A = 24, B = 24, C = 21, D = 21),
                     guide = "none") +
  scale_fill_manual(values = c(A = "white", B = "red", C = "white", D = "blue"),
                    guide = "none") +
  geom_errorbar(aes(x = E, ymin = avg-se, ymax = avg+se, color = NULL, linetype = NULL),
                width=.1, position=position_dodge(width = .1))

fig <- ggplotly(p)

fig

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)