Line Charts in ggplot2

How to make Line Charts in ggplot2 with geom_line in 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.

library(plotly)

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

plotly::ggplotly(p)

Vertical plot orientation

library(plotly)

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

plotly::ggplotly(p)

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)

Step plot:

library(plotly)

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

plotly::ggplotly(p)

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)

Adding colour

library(plotly)

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

plotly::ggplotly(p)

library(plotly)

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

plotly::ggplotly(p)

Adding symbols

library(plotly)

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

plotly::ggplotly(p)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)