Plotting Multiple Curves Same Graph and Same Scale

Plotting multiple curves same graph and same scale

(The typical method would be to use plot just once to set up the limits, possibly to include the range of all series combined, and then to use points and lines to add the separate series.) To use plot multiple times with par(new=TRUE) you need to make sure that your first plot has a proper ylim to accept the all series (and in another situation, you may need to also use the same strategy for xlim):

# first plot
plot(x, y1, ylim=range(c(y1,y2)))

# second plot  EDIT: needs to have same ylim
par(new = TRUE)
plot(x, y2, ylim=range(c(y1,y2)), axes = FALSE, xlab = "", ylab = "")

This next code will do the task more compactly, by default you get numbers as points but the second one gives you typical R-type-"points":

  matplot(x, cbind(y1,y2))
  matplot(x, cbind(y1,y2), pch=1)

Plot two graphs in same plot in R

lines() or points() will add to the existing graph, but will not create a new window. So you'd need to do

plot(x,y1,type="l",col="red")
lines(x,y2,col="green")

how to plot two graphs together while share the same scale of x-axis

You should first make sure to calculate common xmin-xmax to both series.

Then with patwhwork a suggested in comments or cowplot:

xmin <- min(df$ADY ,df2$AESTDY)
xmax <- max(df$ADY ,df2$AESTDY)

p1 <- ggplot(df, aes(colour=SDTM_LabN)) + 
        geom_line(aes(x=ADY,y=result)) +
        coord_cartesian(xlim = c(xmin,xmax))

p2 <- ggplot(df2, aes(colour=AEDECOD)) + 
        geom_segment(aes(x=AESTDY, xend=AEENDY, y=AEDECOD, yend=AEDECOD),) +
        xlab("Duration") + 
        coord_cartesian(xlim = c(xmin,xmax))

library(cowplot)
plot_grid(plotlist = list(p1,p2),align='v',ncol=1)

Creating the same scale for graphs with different scales

There are at least two options to achieve your desired result.

1. If you want to stick with plot.grid you could achieve your desired result by setting the same limits for the x and the y scale in each of your plots. To this end compute the overall minimum and maximum in your data:

library(ggplot2)

rsq <- lapply(1:length(unique(iris$Species)), function(i) {
  cor(iris[iris$Species == unique(iris$Species)[i], "Sepal.Length"], iris[iris$Species == unique(iris$Species)[i], "Petal.Length"])^2
})

d_list <- split(iris, iris$Species)

xmin <- min(unlist(lapply(d_list, function(x) min(x["Sepal.Length"]))))
xmax <- max(unlist(lapply(d_list, function(x) max(x["Sepal.Length"]))))
ymin <- min(unlist(lapply(d_list, function(x) min(x["Petal.Length"]))))
ymax <- max(unlist(lapply(d_list, function(x) max(x["Petal.Length"]))))

p.list <- lapply(1:length(unique(iris$Species)), function(i) {
  ggplot(iris[iris$Species == unique(iris$Species)[i], ], aes(x = Sepal.Length, y = Petal.Length)) +
    geom_point() +
    scale_x_continuous(limits = c(xmin, xmax)) +
    scale_y_continuous(limits = c(ymin, ymax)) +
    theme_bw() +
    geom_text(aes(x = min(Sepal.Length), y = max(Petal.Length), label = paste0("R= ", round(rsq[[i]], 2))))
})

cowplot::plot_grid(
  plotlist = p.list[1:3], nrow = 1, ncol = 3,
  labels = "AUTO"
) 

2. As a second approach you could make use of faceting as I mentioned in my comment. To this end bind your datasets into one and add an id variable. Additionally put the r-square values in a data frame too. Doing so allows you to make your plot using facetting:

library(ggplot2)
library(dplyr)

d_list <- split(iris, iris$Species)

rsq_list <- lapply(d_list, function(x) {
  data.frame(
    rsq = cor(x["Sepal.Length"], x["Petal.Length"])[1, 1]^2,
    Sepal.Length = min(x["Sepal.Length"]),
    Petal.Length = max(x["Petal.Length"])
  )
})

d_bind <- dplyr::bind_rows(d_list)
rsq_bind <- dplyr::bind_rows(rsq_list, .id = "Species")

ggplot(d_bind, aes(x = Sepal.Length, y = Petal.Length)) +
  geom_point() +
  theme_bw() +
  geom_text(data = rsq_bind, aes(label = paste0("R= ", round(rsq, 2)))) +
  facet_wrap(~Species)

Plot multiple similar data in same graph

I'll clarify the ideas I wrote in a comment.
First, let's get some data:

x = 470:0.1:484;
z1 = cos(x)/2;
z2 = sin(x)/3;
z3 = cos(x+0.2)/2.3;

I'll plot just three data sets, all of this is trivial to extend to any number of data sets.

Idea 1: multiple axes

The idea here is simply to use subplot to create a small-multiple type plot:

ytick = [-0.5,0.0,0.5];
ylim = [-0.9,0.9]);
figure

h1 = subplot(3,1,1);
plot(x,z1);
set(h1,'ylim',ylim,'ytick',ytick);
title('z1')

h2 = subplot(3,1,2);
plot(x,z2);
set(h2,'ylim',ylim,'ytick',ytick);
title('z2')

h3 = subplot(3,1,3);
plot(x,z3);
set(h3,'ylim',ylim,'ytick',ytick);
title('z3')

Note that it is possible to, e.g., remove the tick labels from the top two plot, leaving only labels on the bottom one. You can then also move the axes so that they are closer together (which might be necessary if there are lots of these lines in the same plot):

set(h1,'xticklabel',[],'box','off')
set(h2,'xticklabel',[],'box','off')
set(h3,'box','off')
set(h1,'position',[0.13,0.71,0.8,0.24])
set(h2,'position',[0.13,0.41,0.8,0.24])
set(h3,'position',[0.13,0.11,0.8,0.24])
axes(h1)
title('')
ylabel('z1')
axes(h2)
title('')
ylabel('z2')
axes(h3)
title('')
ylabel('z3')

Idea 2: same axes, plot with offset

This is the simpler approach, as you're dealing only with a single axis. @Zizy Archer already showed how easy it is to shift data if they're all in a single 2D matrix Z. Here I'll just plot z1, z2+2, and z3+4. Adjust the offsets to your liking. Next, I set the 'ytick' property to create the illusion of separate graphs, and set the 'yticklabel' property so that the numbers along the y-axis match the actual data plotted. The end result is similar to the multiple axes plots above, but they're all in a single axes:

figure
plot(x,z1);
hold on
plot(x,z2+2);
plot(x,z3+4);
ytick = [-0.5,0.0,0.5];
set(gca,'ytick',[ytick,ytick+2,ytick+4]);
set(gca,'yticklabel',[ytick,ytick,ytick]);
text(484.5,0,'z1')
text(484.5,2,'z2')
text(484.5,4,'z3')

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