Line Charts in ggplot2

How to make Line Charts in ggplot2 with geom_line in Plotly.


New to Plotly?

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.

library(plotly)

p <-    
 ggplot(economics_long, aes(date, value01, colour = variable)) +
  geom_line()

plotly::ggplotly(p)
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197019801990200020100.000.250.500.751.00
variablepcepoppsavertuempmedunemploydatevalue01

Vertical plot orientation

library(plotly)

p <-  ggplot(economics, aes(unemploy, date)) + geom_line(orientation = "y")

plotly::ggplotly(p)
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Step plot

geom_step() is useful when you want to highlight exactly when the y value changes.

Default line plot:

library(plotly)

recent <- economics[economics$date > as.Date("2013-01-01"), ]
p <-  ggplot(recent, aes(date, unemploy)) + geom_line()

plotly::ggplotly(p)
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2013-012013-072014-012014-072015-019000100001100012000
dateunemploy

Step plot:

library(plotly)

recent <- economics[economics$date > as.Date("2013-01-01"), ]
p <-  ggplot(recent, aes(date, unemploy)) + geom_step()

plotly::ggplotly(p)
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2013-012013-072014-012014-072015-019000100001100012000
dateunemploy

Path plot

geom_path() lets you explore how two variables are related over time, e.g. unemployment and personal savings rate.

library(plotly)

m <- ggplot(economics, aes(unemploy/pop, psavert))
p <-  m + geom_path()

plotly::ggplotly(p)
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Adding colour

library(plotly)

m <- ggplot(economics, aes(unemploy/pop, psavert))
p <-  m + geom_path(aes(colour = as.numeric(date)))

plotly::ggplotly(p)
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0.020.030.040.0551015
050001000015000as.numeric(date)unemploy/poppsavert
library(plotly)

p <-    
 ggplot(economics, aes(date, unemploy)) +
  geom_line(colour = "red")

plotly::ggplotly(p)
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Adding symbols

library(plotly)

c <- ggplot(economics, aes(x = date, y = pop))
p <-  c + geom_line(arrow = arrow())

plotly::ggplotly(p)
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19701980199020002010200000225000250000275000300000325000
datepop
library(plotly)

c <- ggplot(economics, aes(x = date, y = pop))
p <-    
 c + geom_line(
  arrow = arrow(angle = 15, ends = "both", type = "closed")
)

plotly::ggplotly(p)
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19701980199020002010200000225000250000275000300000325000
datepop
library(plotly)

df <- data.frame(x = 1:3, y = c(4, 1, 9))
base <- ggplot(df, aes(x, y))
p <-  base + geom_path(size = 10)

plotly::ggplotly(p)
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library(plotly)

df <- data.frame(x = 1:3, y = c(4, 1, 9))
base <- ggplot(df, aes(x, y))
p <-  base + geom_path(size = 10, lineend = "round")

plotly::ggplotly(p)
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library(plotly)

df <- data.frame(x = 1:3, y = c(4, 1, 9))
base <- ggplot(df, aes(x, y))
p <-  base + geom_path(size = 10, linejoin = "mitre", lineend = "butt")

plotly::ggplotly(p)
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Adding breaks to the line

You can use NAs to break the line.

library(plotly)

df <- data.frame(x = 1:5, y = c(1, 2, NA, 4, 5))
p <-  ggplot(df, aes(x, y)) + geom_point() + geom_line()

plotly::ggplotly(p)
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Setting line type, colour, size

library(plotly)

x <- seq(0.01, .99, length.out = 100)
df <- data.frame(
  x = rep(x, 2),
  y = c(qlogis(x), 2 * qlogis(x)),
  group = rep(c("a","b"),
  each = 100)
)
p <- ggplot(df, aes(x=x, y=y, group=group))
p <-  p + geom_line(linetype = 2)

plotly::ggplotly(p)
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library(plotly)

x <- seq(0.01, .99, length.out = 100)
df <- data.frame(
  x = rep(x, 2),
  y = c(qlogis(x), 2 * qlogis(x)),
  group = rep(c("a","b"),
  each = 100)
)
p <- ggplot(df, aes(x=x, y=y, group=group))
p <-  p + geom_line(aes(colour = group), linetype = 2)

plotly::ggplotly(p)
Click to copy
library(plotly)

x <- seq(0.01, .99, length.out = 100)
df <- data.frame(
  x = rep(x, 2),
  y = c(qlogis(x), 2 * qlogis(x)),
  group = rep(c("a","b"),
  each = 100)
)
p <- ggplot(df, aes(x=x, y=y, group=group))
p <-  p + geom_line(aes(colour = x))

plotly::ggplotly(p)
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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()

ggplotly(p)
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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()

ggplotly(p)
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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()

ggplotly(p)
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LunchDinner14151617
SexFemaleMale(Female,1)(Male,1)Average bill for 2 peopleTime of dayTotal bill

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()

ggplotly(p)
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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()

ggplotly(p)
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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()

ggplotly(p)
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2002200420062008201080100120140
variablevar0var1datevalue

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))

ggplotly(p)
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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)))

ggplotly(p)
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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()

ggplotly(p)
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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")))

ggplotly(p)
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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))

ggplotly(p)
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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)
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