A very newbish question, but say I have data like this:
test_data <-
data.frame(
var0 = 100 + c(0, cumsum(runif(49, -20, 20))),
var1 = 150 + c(0
You need the data to be in "tall" format instead of "wide" for ggplot2. "wide" means having an observation per row with each variable as a different column (like you have now). You need to convert it to a "tall" format where you have a column that tells you the name of the variable and another column that tells you the value of the variable. The process of passing from wide to tall is usually called "melting". You can use tidyr::gather
to melt your data frame:
library(ggplot2)
library(tidyr)
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 %>%
gather(key,value, var0, var1) %>%
ggplot(aes(x=date, y=value, colour=key)) +
geom_line()
Just to be clear the data
that ggplot
is consuming after piping it via gather
looks like this:
date key value
2002-01-01 var0 100.00000
2002-02-01 var0 115.16388
...
2007-11-01 var1 114.86302
2007-12-01 var1 119.30996
Using your data:
test_data <- data.frame(
var0 = 100 + c(0, cumsum(runif(49, -20, 20))),
var1 = 150 + c(0, cumsum(runif(49, -10, 10))),
Dates = seq.Date(as.Date("2002-01-01"), by="1 month", length.out=100))
I create a stacked version which is what ggplot()
would like to work with:
stacked <- with(test_data,
data.frame(value = c(var0, var1),
variable = factor(rep(c("Var0","Var1"),
each = NROW(test_data))),
Dates = rep(Dates, 2)))
In this case producing stacked
was quite easy as we only had to do a couple of manipulations, but reshape()
and the reshape
and reshape2
might be useful if you have a more complex real data set to manipulate.
Once the data are in this stacked form, it only requires a simple ggplot()
call to produce the plot you wanted with all the extras (one reason why higher-level plotting packages like lattice
and ggplot2
are so useful):
require(ggplot2)
p <- ggplot(stacked, aes(Dates, value, colour = variable))
p + geom_line()
I'll leave it to you to tidy up the axis labels, legend title etc.
HTH
The general approach is to convert the data to long format (using melt()
from package reshape
or reshape2
) or gather()
/pivot_longer()
from the tidyr
package:
library("reshape2")
library("ggplot2")
test_data_long <- melt(test_data, id="date") # convert to long format
ggplot(data=test_data_long,
aes(x=date, y=value, colour=variable)) +
geom_line()
Also see this question on reshaping data from wide to long.
I am also new to R but trying to understand how ggplot works I think I get another way to do it. I just share probably not as a complete perfect solution but to add some different points of view.
I know ggplot is made to work with dataframes better but maybe it can be also sometimes useful to know that you can directly plot two vectors without using a dataframe.
Loading data. Original date vector length is 100 while var0 and var1 have length 50 so I only plot the available data (first 50 dates).
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=50)
Plotting
ggplot() + geom_line(aes(x=date,y=var0),color='red') +
geom_line(aes(x=date,y=var1),color='blue') +
ylab('Values')+xlab('date')
However I was not able to add a correct legend using this format. Does anyone know how?
For a small number of variables, you can build the plot manually yourself:
ggplot(test_data, aes(date)) +
geom_line(aes(y = var0, colour = "var0")) +
geom_line(aes(y = var1, colour = "var1"))