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)
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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$date > as.Date("2013-01-01"), ]
p <- ggplot(recent, aes(date, unemploy)) + geom_line()
plotly::ggplotly(p)
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Step plot:
library(plotly)
recent <- economics[economics$date > as.Date("2013-01-01"), ]
p <- ggplot(recent, aes(date, unemploy)) + geom_step()
plotly::ggplotly(p)
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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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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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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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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)
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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 = 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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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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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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