i've got a data frame all
that look like this:
http://pastebin.com/Xc1HEYyH
Now I want to create a scatter plot with the column headings in the x-axis and the respective values as the data points. For example:
7| x
6| x x
5| x x x x
4| x x x
3| x x
2| x x
1|
---------------------------------------
STM STM STM PIC PIC PIC
cold normal hot cold normal hot
This should be easy, but I can not figure out how.
Regards
The basic idea, if you want to plot using Hadley's ggplot2
is to get your data of the form:
x y
col_names values
And this can be done by using melt
function from Hadley's reshape2
. Do ?melt
to see the possible arguments. However, here since we want to melt the whole data.frame, we just need,
melt(all)
# this gives the data in format:
# variable value
# 1 STM_cold 6.0
# 2 STM_cold 6.0
# 3 STM_cold 5.9
# 4 STM_cold 6.1
# 5 STM_cold 5.5
# 6 STM_cold 5.6
Here, x
will be then column variable
and y
will be corresponding value
column.
require(ggplot2)
require(reshape2)
ggplot(data = melt(all), aes(x=variable, y=value)) +
geom_point(aes(colour=variable))
If you don't want the colours, then just remove aes(colour=variable)
inside geom_point so that it becomes geom_point()
.
Edit: I should probably mention here, that you could also replace geom_point
with geom_jitter
that'll give you, well, jittered points:
Here are two options to consider. The first uses dotplot
from the "lattice" package:
library(lattice)
dotplot(values ~ ind, data = stack(all))
The second uses dotchart
from base R's "graphics" options. To use the dotchart
function, you need to wrap your data.frame
in as.matrix
:
dotchart(as.matrix(all), labels = "")
Note that the points in this graphic are not "jittered", but rather, presented in the order they were recorded. That is to say, the lowest point is the first record, and the highest point is the last record. If you zoomed into the plot for this example, you would see that you have 16 very faint horizontal lines. Each line represents one row from each column. Thus, if you look at the dots for "STM_cold" or any of the other variables that have NA
values, you'll see a few blank lines at the top where there was no data available.
This has its advantages since it might show a trend over time if the values are recorded chronologically, but might also be a disadvantage if there are too many rows in your source data frame.
A bit of a manual version using base R graphics just for fun.
Get the data:
test <- read.table(text="STM_cold STM_normal STM_hot PIC_cold PIC_normal PIC_hot
6.0 6.6 6.3 0.9 1.9 3.2
6.0 6.6 6.5 1.0 2.0 3.2
5.9 6.7 6.5 0.3 1.8 3.2
6.1 6.8 6.6 0.2 1.8 3.8
5.5 6.7 6.2 0.5 1.9 3.3
5.6 6.5 6.5 0.2 1.9 3.5
5.4 6.8 6.5 0.2 1.8 3.7
5.3 6.5 6.2 0.2 2.0 3.5
5.3 6.7 6.5 0.1 1.7 3.6
5.7 6.7 6.5 0.3 1.7 3.6
NA NA NA 0.1 1.8 3.8
NA NA NA 0.2 2.1 4.1
NA NA NA 0.2 1.8 3.3
NA NA NA 0.8 1.7 3.5
NA NA NA 1.7 1.6 4.0
NA NA NA 0.1 1.7 3.7",header=TRUE)
Set up the basic plot:
plot(
NA,
ylim=c(0,max(test,na.rm=TRUE)+0.3),
xlim=c(1-0.1,ncol(test)+0.1),
xaxt="n",
ann=FALSE,
panel.first=grid()
)
axis(1,at=seq_along(test),labels=names(test),lwd=0,lwd.ticks=1)
Plot some points, with some x-axis jitter
ing so they are not printed on top of one another.
invisible(
mapply(
points,
jitter(rep(seq_along(test),each=nrow(test))),
unlist(test),
col=rep(seq_along(test),each=nrow(test)),
pch=19
)
)
Result:
Here's an example using alpha transparency on the points and getting rid of the jitter
as discussed in the below comments with Ananda.
invisible(
mapply(
points,
rep(seq_along(test),each=nrow(test)),
unlist(test),
col=rgb(0,0,0,0.1),
pch=15,
cex=3
)
)
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