Line Charts in ggplot2

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

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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)