How can I put my axis labels in a convenient format in lattice?
require(stats)
xyplot(lat*1000000 ~ long, data = quakes)
gives me y-labels like -3.5e+0.7. I would want lattice to write the whole number.
(maybe it is easy, but I can't find a solution.)
Thank you in advance!
Create your own labels and pass them to the scales argument.
y_at <- pretty(quakes$lat*1e6)
y_labels <- formatC(y_at, digits = 0, format = "f")
xyplot(
lat*1000000 ~ long,
data = quakes,
scales = list(
y = list(
at = y_at,
labels = y_labels
)
)
)
For the formatting step, there are lots of alternatives to formatC. Take a look at format, prettyNum and sprintf to get you started.
If you want to do this with dates, then note that scales accepts a format argument for that purpose.
There are a couple of "global options" that might affect how values are printed. In this case scipen is the one you want to move:
old_op <- options(scipen=10)
xyplot(lat*1000000 ~ long, data = quakes)
options(old_op)
# probably better to restore it so the rest of you session is more "normal"
Related
My problem is multifaceted.
I would like to plot multiple columns saved in a data frame. Those columns do not have an x variable but would essentially be 1 to 101 consistent for all. I have seen that I can transfer them into long format but most ggplot options require an X. I tried zoo which does what I want it to, but the x-label is all jumbled and I am not aware of how to fix it. (Example of data below, and plot)
df <- zoo(HIP_131_Y0_LC_walk1[1:9])
plot(df)
I have multiple data frames saved in a list so ultimately would like to run a function and apply to all. The zoo function solves step one but I am not able to apply to all the data frames in the list.
graph<-lapply(myfiles,function(x) zoo(x) )
print(graph)
Ideally I would like to also mark minimum and maximum, which I am aware can be done with ggplot but not zoo.
Thank you so much for your help in advance
Assuming that the problem is overlapped panel names there are numerous solutions to this:
abbreviate the names using abbreviate. We show this for plot.zoo and autoplot.zoo .
put the panel name in the upper left. We show this for plot.zoo using a custom panel.
Use a header on each panel. We show this using xyplot.zoo and using ggplot.
The examples below use the test input in the Note at the end. (Next time please provide a complete example including all input in reproducible form.)
The first two examples below abbreviates the panel names and using plot.zoo and autoplot.zoo (which uses ggplot2). The third example uses xyplot.zoo (which uses lattice). This automatically uses headers and is probably the easiest solution.
library(zoo)
plot(z, ylab = abbreviate(names(z), 8))
library(ggplot2)
zz <- setNames(z, abbreviate(names(z), 8))
autoplot(zz)
library (lattice)
xyplot(z)
(click on plots to see expanded; continued after plots)
This fourth example puts the panel names in the upper left of the panel themselves using plot.zoo with a custom panel.
pnl <- function(x, y, ..., pf = parent.frame()) {
legend("topleft", names(z)[pf$panel.number], bty = "n", inset = -0.1)
lines(x, y)
}
plot(z, panel = pnl, ylab = "")
(click on plot to see it expanded)
We can also get headers with autoplot.zoo similar to in lattice above.
library(ggplot2)
autoplot(z, facets = ~ Series, col = I("black")) +
theme(legend.position = "none")
(click to expand; continued after graphics)
List
If you have a list of vectors L (see Note at end for a reproducible example of such a list) then this will produce a zoo object:
do.call("merge", lapply(L, zoo))
Note
Test input used above.
library(zoo)
set.seed(123)
nms <- paste0(head(state.name, 9), "XYZ") # long names
m <- matrix(rnorm(101*9), 101, dimnames = list(NULL, nms))
z <- zoo(m)
L <- split(m, col(m)) # test list using m in Note
I want to add a greek character to the x-axis of my histogram plot in R.
I can write the greek character alone or with the hat, but the problem is that I need this character to be come with a hat and asterisk () together. More specifically, I want the something like hat(phi^). Here is what I have done:
x = rnorm(1000)
hist( x, nclass = 100, cex.lab=1.5, xlab = expression(hat(phi^*)),
ylab="Frequency", main="", cex.axis=1.5 )
Thanks.
What about using ggplot2 instead of base R. You can then use latex2exp::TeX to use (some) LaTeX expressions in the axes labels.
set.seed(2018)
x = rnorm(1000)
library(ggplot2)
library(latex2exp)
ggplot(data.frame(x = x), aes(x)) +
geom_histogram(bins = 100) +
theme_minimal() +
xlab(TeX("$\\widehat{\\phi^*}$"))
You need to escape the backslashes with an extra backslash and wrap math expressions inside $ delimiters (just as in regular LaTeX inline math). I used \widehat{}, but you can also use hat{} instead.
Here is the best looking solution that I found:
hist( x, nclass = 100, cex.lab=1.5, xlab = expression(hat(phi)~"*"),
ylab="Frequency", main="", cex.axis=1.5 )
To get the star under the hat, I don't know of any nice looking solution with base plot functions. I think #Maurits Evers' method is the best compromise between complexity and prettiness of the result.
But here is anyway a more "advanced" (and maybe a little bit over the top, but well...) solution. It is based on this blog post: http://iltabiai.github.io/tips/latex/2015/09/15/latex-tikzdevice-r.html
To make it work, you will need to install the tikzDevice package.
First, load the packages and create the data-set.
library(tikzDevice)
library(ggplot2)
dat <- data.frame(x = rnorm(1000))
Then create a TeX file that will contain the "translation" of your R plot in "tikz" language.
tikz(file = "plot_test.tex", width = 5, height = 5, standAlone = TRUE)
ggplot(dat, aes(x = x)) +
geom_histogram(color="white") + theme_bw() +
labs( x = "$\\widehat{\\phi^*}$")
dev.off()
Then, you can either directly copy or call the LaTeX code into your own LaTeX document (then the standAlone=TRUE argument is not necessary), or you can use these two very useful function to generate a pdf version and see the result.
tools::texi2dvi("plot_test.tex", pdf=TRUE)
system(paste(getOption('pdfviewer'), "plot_test.pdf"))
I have a dummy variable call it "drink" and a corresponding age variable that represents a precise age estimate (several decimal points) for each person in a dataset. I want to first "bin" the age variable, extracting the mean value for each bin based on the "drink" dummy, and then graph the result. My code to do so looks like this:
df$bins <- cut(df$age, seq(from = 17, to = 31, by = .2), include.lowest = TRUE)
df.plot <- ddply(df, .(bins), summarise, avg.drink = mean(drinks_alcohol))
qplot(bins, avg.drink, data = df.plot)
This works well enough, but the x-axis in the graph is unreadable because it corresponds to the length size of the bins. Is there a way to make the modify the X-axis to show, for example, ages 19-23 only, with the "ticks" still aligning with the correct bins? For example, in my current code there is a bin for (19, 19.2] and another bin for (20, 20.2]. I would want only the bins that start in whole numbers to be identified on the X-axis with the first number (19, 20), not the second (19.2, 20.2) shown.
Is there any straightforward way to do this?
The most direct way to specify axis labels is with the appropriate scale function... in the case of factors on the x axis, scale_x_discrete. It will use whatever labels you give it with the labels argument, or you can give it a function that formats things as you like.
To "manually" specify the labels, you just need to create a vector of appropriate length. In this case, if you factor values go are intervals beginning with seq(17, 31.8, by = 0.2) and you want to label bins beginning with integers, then your labels vector will be
bin_starts = seq(17, 31.8, by = 0.2)
bin_labels = ifelse(bin_starts - trunc(bin_starts) < 0.0001, as.character(bin_starts), "")
(I use the a - b < 0.0001 in case of precision problems, though it shouldn't be a problem in this particular case).
A more robust solution would to label the factor levels with the number at the start of the interval from the beginning. cut also has a labels argument.
my_breaks = seq(17, 32, by = 0.2)
df$bins <- cut(df$age, breaks = my_breaks, labels = head(my_breaks, -1),
include.lowest = TRUE)
You could then fairly easily write a formatter (following templates from the scales package) to print only the ones you want:
int_only = function(x) {
# test if we can coerce to numeric, if not do nothing
if (any(is.na(as.numeric(x)))) return(x)
# otherwise convert to numeric and return integers and blanks as labels
x = as.numeric(x)
return(ifelse(x - trunc(x) < 1e-10, as.character(x), ""))
}
Then, using the nicely formatted data created above, you should be able to pass int_only as a formatter function to labels to get the labels you want. (Note: untested! necessary tweaks left as an exercise for the reader, though I'll gladly accept edits :) )
I've been trying to write out an R script that will plot the date-temp series for a set of locations that are identified by a Deployment_ID.
Ideally, each page of the output pdf would have the name of the Deployment_ID (check), a graph with proper axes (check) and correct scaling of the x-axis to best show the date-temp series for that specific Deployment_ID (not check).
At the moment, the script makes a pdf that shows each ID over the full range of the dates in the date column (i.e. 1988-2010), instead of just the relevant dates (i.e. just 2005), which squishes the scatterplot down into uselessness.
I'm pretty sure it's something to do with how you define xlim, but I can't figure out how to have R access the date min and the date max for each factor as it draws the plots.
Script I have so far:
#Get CSV to read data from, change the file path and name
data <- read.csv(file.path("C:\Users\Person\Desktop\", "SampleData.csv"))
#Make Date real date - must be in yyyy/mm/dd format from the csv to do so
data$Date <- as.Date(data$Date)
#Call lattice to library, note to install.packages(lattice) if you don't have it
library(lattice)
#Make the plots with lattice, this takes a while.
dataplot <- xyplot(data$Temp~data$Date|factor(data$Deployment_ID),
data=data,
stack = TRUE,
auto.key = list(space = "right"),
layout = c(1,1),
ylim = c(-10,40)
)
#make the pdf
pdf("Dataplots_SampleData.pdf", onefile = TRUE)
#print to the pdf? Not really sure how this works. Takes a while.
print(dataplot)
dev.off()
Use the scales argument. give this a try
dataplot <- xyplot(data$Temp~data$Date|factor(data$Deployment_ID),
data=data,
stack = TRUE,
auto.key = list(space = "right"),
layout = c(1,1),
scales= list( relation ="free")
)
I am very, very new to R so please forgive the basic nature of my question. In short, I have done a lot of Google searching to try to answer this, but I find that even the basic guides available, and simple discussions on forums are assuming more prior knowledge than I have, especially when it comes to outlining what all of the coding terms are and what changing them means for a plot.
In short I have a tab formatted table with three columns of data that I wish to plot densities for on a single graph. I would like the lines to be different patterns (dotted, dashed etc. whatever makes it easy to tell them apart, I cannot use colours as my supervisor is colour blind).
I have code that reads in the data and makes accessible the columns I am interested in:
mydata <- read.table("c:/Users/Demon/Desktop/Thesis/Fst_all_genome.txt", header=TRUE,
sep="\t")
fstdata <- data.frame(Fst_ceu_mkk =rnorm(10),
Fst_ceu_yri =rnorm(10),
Fst_mkk_yri =rnorm(10))
Where do I go from here?
Appendix A of 'An Introduction to R' has a nice walkthrough tutorial you can do in ten minutes; it teaches among other things about line types etc
After that, plotting densities was explained dozens of times here too; search in the search box above for eg '[r] density'. There is also the R Graph Gallery (possibly down right now) and more.
A nice, free guide I often recommend is John Verzani's simpleR which stresses graphs a lot and will teach you what you need here.
Two options for you to explore using high-level graphics.
# dummy data
d = data.frame(x = rnorm(10), y = rnorm(10), z = rnorm(10))
You first need to reshape the data from wide to long format,
require(reshape2)
m = melt(d)
ggplot2 graphics
require(ggplot2)
ggplot(data = m, mapping = aes(x = value, linetype = variable)) +
geom_line(stat = "density")
Lattice graphics
Using the same melt()ed data,
require(lattice)
densityplot( ~ value, data = m, group = variable,
auto.key = TRUE, par.settings = col.whitebg())
If you need something very simple, you could do simply:
plot(density(mydata$col_1))
lines(density(mydata$col_2), lty = 2)
lines(density(mydata$col_2), lty = 3)
If the second and third density curves are far away from the first, you'll need define xy limits of the plotting region explicitly:
dens1 <- density(mydata$col_1)
dens2 <- density(mydata$col_2)
dens3 <- density(mydata$col_3)
plot(dens1, xlim = range(dens1$x, dens2$x, dens3$x),
ylim = range(dens1$y, dens2$y, dens3$y))
lines(density(mydata$col_2), lty = 2)
lines(density(mydata$col_2), lty = 3)
Hope this helps.