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I am trying to get a boxplot with 3 different tools in each dataset size like the one below:
ggplot(data1, aes(x = dataset, y = time, color = tool)) + geom_boxplot() +
labs(x = 'Datasets', y = 'Seconds', title = 'Time') +
scale_y_log10() + theme_bw()
But I need to transform x-axis to log scale. For that, I need to numericize each dataset to be able to transform them to log scale. Even without transforming them, they look like the one below:
ggplot(data2, aes(x = dataset, y = time, color = tool)) + geom_boxplot() +
labs(x = 'Datasets', y = 'Seconds', title = 'Time') +
scale_y_log10() + theme_bw()
I checked boxplot parameters and grouping parameters of aes, but could not resolve my problem. At first, I thought this problem is caused by scaling to log, but removing those elements did not resolve the problem.
What am I missing exactly? Thanks...
Files are in this link. "data2" is the numericized version of "data1".
Your question was a tough cookie, but I learned something new from it!
Just using group = dataset is not sufficient because you also have the tool variable to look out for. After digging around a bit, I found this post which made use of the interaction() function.
This is the trick that was missing. You want to use group because you are not using a factor for the x values, but you need to include tool in the separation of your data (hence using interaction() which will compute the possible crosses between the 2 variables).
# This is for pretty-printing the axis labels
my_labs <- function(x){
paste0(x/1000, "k")
}
levs <- unique(data2$dataset)
ggplot(data2, aes(x = dataset, y = time, color = tool,
group = interaction(dataset, tool))) +
geom_boxplot() + labs(x = 'Datasets', y = 'Seconds', title = 'Time') +
scale_x_log10(breaks = levs, labels = my_labs) + # define a log scale with your axis ticks
scale_y_log10() + theme_bw()
This plots
hopefully a trivial one. The code below is hopefully quite simplistic and is just to illustrate the issue (and hence is quite crude). I am just wondering what the best way to set a maximum number of x axis tickmarks might be here.
library(ggplot2)
library(data.table)
data <- data.table(year=c(2009,2009,2009,2009,2010,2010,2010,2010,
2011,2011,2011,2011,2012,2012,2012,2012,2013,2013,2013,2013,
2014,2014,2014,2014,2015,2015,2015,2015),year_quart = c("2009-Q1","2009-Q2",
"2009-Q3","2009-Q4","2010-Q1","2010-Q2","2010-Q3","2010-Q4","2011-Q1","2011-
Q2","2011-Q3","2011-Q4","2012-Q1","2012-Q2","2012-Q3","2012-Q4",
"2013-Q1","2013-Q2","2013-Q3","2013-Q4","2014-Q1","2014-Q2","2014-Q3",
"2014-Q4","2015-Q1","2015-Q2","2015-Q3","2015-Q4"),region = c("EU","EU",
"EU","EU","EU","EU","EU","EU","EU","EU","EU","EU","EU","EU","EU","EU","EU",
"EU","EU","EU","EU","EU","EU","EU","EU","EU","EU","EU"),value = c(390,621,
442,113,586,571,391,432,758,897,696,160,189,567,621,922,402,185,609,812,549,
783,211,974,723,584,745,609))
plot1 <- ggplot(data, aes(factor(year_quart),value, xmin="2009-Q1", xmax="2009-Q4")) +
geom_line(aes(group=region),size=0.4) +
labs(x = "year", y = "value", title = "Title") +
scale_x_discrete(
breaks = unique(data$year_quart),
labels = unique(data$year_quart),
limits = c("2009-Q1","2009-Q2","2009-Q3","2009-Q4","2010-Q1","2010-Q2")
)
So with this code I get a plot which looks OKish. However if I swap
limits=c("2009-Q1","2009-Q2","2009-Q3","2009-Q4","2010-Q1","2010-Q2")
with
limits=c("2009-Q1","2009-Q2","2009-Q3","2009-Q4","2010-Q1","2010-Q2","2010-Q3", "2010-Q4","2011-Q1","2011-Q2","2011-Q3","2011-Q4","2012-Q1","2012-Q2","2012-Q3",
"2012-Q4","2013-Q1","2013-Q2","2013-Q3","2013-Q4","2014-Q1","2014-Q2","2014-Q3",
"2014-Q4","2015-Q1","2015-Q2","2015-Q3","2015-Q4"))
I generate far too many tickmarks to be viewed clearly. So what I would ideally like is, for a certain year/quarter range, specific code that generates a maximum number of (clearly viewable) tickmarks depending on this range.
many thanks in advance!
If our goal is to make the tick labels more readable, we can always rotate them using the axis.text.x argument in theme:
ggplot(data, aes(factor(year_quart),value, xmin="2009-Q1", xmax="2009-Q4")) +
geom_line(aes(group=region),size=0.4) +
labs(x = "year", y = "value", title = "Title") +
scale_x_discrete(
breaks = unique(data$year_quart),
labels = unique(data$year_quart),
limits=c("2009-Q1","2009-Q2","2009-Q3","2009-Q4","2010-Q1","2010-Q2","2010-Q3", "2010-Q4","2011-Q1","2011-Q2","2011-Q3","2011-Q4","2012-Q1","2012-Q2","2012-Q3",
"2012-Q4","2013-Q1","2013-Q2","2013-Q3","2013-Q4","2014-Q1","2014-Q2","2014-Q3",
"2014-Q4","2015-Q1","2015-Q2","2015-Q3","2015-Q4")
) +
theme(axis.text.x = element_text(angle = 45))
I have a set of code that produces multiple plots using facet_wrap:
ggplot(summ,aes(x=depth,y=expr,colour=bank,group=bank)) +
geom_errorbar(aes(ymin=expr-se,ymax=expr+se),lwd=0.4,width=0.3,position=pd) +
geom_line(aes(group=bank,linetype=bank),position=pd) +
geom_point(aes(group=bank,pch=bank),position=pd,size=2.5) +
scale_colour_manual(values=c("coral","cyan3", "blue")) +
facet_wrap(~gene,scales="free_y") +
theme_bw()
With the reference datasets, this code produces figures like this:
I am trying to accomplish two goals here:
Keep the auto scaling of the y axis, but make sure only 1 decimal place is displayed across all the plots. I have tried creating a new column of the rounded expr values, but it causes the error bars to not line up properly.
I would like to wrap the titles. I have tried changing the font size as in Change plot title sizes in a facet_wrap multiplot, but some of the gene names are too long and will end up being too small to read if I cram them on a single line. Is there a way to wrap the text, using code within the facet_wrap statement?
Probably cannot serve as definite answer, but here are some pointers regarding your questions:
Formatting the y-axis scale labels.
First, let's try the direct solution using format function. Here we format all y-axis scale labels to have 1 decimal value, after rounding it with round.
formatter <- function(...){
function(x) format(round(x, 1), ...)
}
mtcars2 <- mtcars
sp <- ggplot(mtcars2, aes(x = mpg, y = qsec)) + geom_point() + facet_wrap(~cyl, scales = "free_y")
sp <- sp + scale_y_continuous(labels = formatter(nsmall = 1))
The issue is, sometimes this approach is not practical. Take the leftmost plot from your figure, for example. Using the same formatting, all y-axis scale labels would be rounded up to -0.3, which is not preferable.
The other solution is to modify the breaks for each plot into a set of rounded values. But again, taking the leftmost plot of your figure as an example, it'll end up with just one label point, -0.3
Yet another solution is to format the labels into scientific form. For simplicity, you can modify the formatter function as follow:
formatter <- function(...){
function(x) format(x, ..., scientific = T, digit = 2)
}
Now you can have a uniform format for all of plots' y-axis. My suggestion, though, is to set the label with 2 decimal places after rounding.
Wrap facet titles
This can be done using labeller argument in facet_wrap.
# Modify cyl into factors
mtcars2$cyl <- c("Four Cylinder", "Six Cylinder", "Eight Cylinder")[match(mtcars2$cyl, c(4,6,8))]
# Redraw the graph
sp <- ggplot(mtcars2, aes(x = mpg, y = qsec)) + geom_point() +
facet_wrap(~cyl, scales = "free_y", labeller = labeller(cyl = label_wrap_gen(width = 10)))
sp <- sp + scale_y_continuous(labels = formatter(nsmall = 2))
It must be noted that the wrap function detects space to separate labels into lines. So, in your case, you might need to modify your variables.
This only solved the first part of the question. You can create a function to format your axis and use scale_y_continous to adjust it.
df <- data.frame(x=rnorm(11), y1=seq(2, 3, 0.1) + 10, y2=rnorm(11))
library(ggplot2)
library(reshape2)
df <- melt(df, 'x')
# Before
ggplot(df, aes(x=x, y=value)) + geom_point() +
facet_wrap(~ variable, scale="free")
# label function
f <- function(x){
format(round(x, 1), nsmall=1)
}
# After
ggplot(df, aes(x=x, y=value)) + geom_point() +
facet_wrap(~ variable, scale="free") +
scale_y_continuous(labels=f)
scale_*_continuous(..., labels = function(x) sprintf("%0.0f", x)) worked in my case.
I am trying to make a labeled bubble plot with ggplot2 in R. Here is the simplified scenario:
I have a data frame with 4 variables: 3 quantitative variables, x, y, and z, and another variable that labels the points, lab.
I want to make a scatter plot, where the position is determined by x and y, and the size of the points is determined by z. I then want to place text labels beside the points (say, to the right of the point) without overlapping the text on top of the point.
If the points did not vary in size, I could try to simply modify the aesthetic of the geom_text layer by adding a scaling constant (e.g. aes(x=x+1, y=y+1)). However, even in this simple case, I am having a problem with positioning the text correctly because the points do not scale with the output dimensions of the plot. In other words, the size of the points remains constant in a 500x500 plot and a 1000x1000 plot - they do not scale up with the dimensions of the outputted plot.
Therefore, I think I have to scale the position of the label by the size (e.g. dimensions) of the output plot, or I have to get the radius of the points from ggplot somehow and shift my text labels. Is there a way to do this in ggplot2?
Here is some code:
# Stupid data
df <- data.frame(x=c(1,2,3),
y=c(1,2,3),
z=c(1,2,1),
lab=c("a","b","c"), stringsAsFactors=FALSE)
# Plot with bad label placement
ggplot(aes(x=x, y=y), data=df) +
geom_point(aes(size=z)) +
geom_text(aes(label=lab),
colour="red") +
scale_size_continuous(range=c(5, 50), guide="none")
EDIT: I should mention, I tried hjust and vjust inside of geom_text, but it does not produce the desired effect.
# Trying hjust and vjust, but it doesn't look nice
ggplot(aes(x=x, y=y), data=df) +
geom_point(aes(size=z)) +
geom_text(aes(label=lab), hjust=0, vjust=0.5,
colour="red") +
scale_size_continuous(range=c(5, 50), guide="none")
EDIT: I managed to get something that works for now, thanks to Henrik and shujaa. I will leave the question open just in case someone shares a more general solution.
Just a blurb of what I am using this for: I am plotting a map, and indicating the amount of precipitation at certain stations with a point that is sized proportionally to the amount of precipitation observed. I wanted to add a station label beside each point in an aesthetically pleasing manner. I will be making more of these plots for different regions, and my output plot may have a different resolution or scale (e.g. due to different projections) for each plot, so a general solution is desired. I might try my hand at creating a custom position_jitter, like baptiste suggested, if I have time during the weekend.
It appears that position_*** don't have access to the scales used by other layers, so it's a no go. You could make a clone of GeomText that shifts the labels according to the size mapped,
but it's a lot of effort for a very kludgy and fragile solution,
geom_shiftedtext <- function (mapping = NULL, data = NULL, stat = "identity",
position = "identity",
parse = FALSE, ...) {
GeomShiftedtext$new(mapping = mapping, data = data, stat = stat, position = position,
parse = parse, ...)
}
require(proto)
GeomShiftedtext <- proto(ggplot2:::GeomText, {
objname <- "shiftedtext"
draw <- function(., data, scales, coordinates, ..., parse = FALSE, na.rm = FALSE) {
data <- remove_missing(data, na.rm,
c("x", "y", "label"), name = "geom_shiftedtext")
lab <- data$label
if (parse) {
lab <- parse(text = lab)
}
with(coord_transform(coordinates, data, scales),
textGrob(lab, unit(x, "native") + unit(0.375* size, "mm"),
unit(y, "native"),
hjust=hjust, vjust=vjust, rot=angle,
gp = gpar(col = alpha(colour, alpha),
fontfamily = family, fontface = fontface, lineheight = lineheight))
)
}
})
df <- data.frame(x=c(1,2,3),
y=c(1,2,3),
z=c(1.2,2,1),
lab=c("a","b","c"), stringsAsFactors=FALSE)
ggplot(aes(x=x, y=y), data=df) +
geom_point(aes(size=z), shape=1) +
geom_shiftedtext(aes(label=lab, size=z),
hjust=0, colour="red") +
scale_size_continuous(range=c(5, 100), guide="none")
This isn't a very general solution, because you'll need to tweak it every time, but you should be able to add to the x value for the text some value that's linear depending on z.
I had luck with
ggplot(aes(x=x, y=y), data=df) +
geom_point(aes(size=z)) +
geom_text(aes(label=lab, x = x + .06 + .14 * (z - min(z))),
colour="red") +
scale_size_continuous(range=c(5, 50), guide="none")
but, as the font size depends on your window size, you would need to decide on your output size and tweak accordingly. I started with x = x + .05 + 0 * (z-min(z)) and calibrated the intercept based on the smallest point, then when I was happy with that I adjusted the linear term for the biggest point.
Another alternative. Looks OK with your test data, but you need to check how general it is.
dodge <- abs(scale(df$z))/4
ggplot(data = df, aes(x = x, y = y)) +
geom_point(aes(size = z)) +
geom_text(aes(x = x + dodge), label = df$lab, colour = "red") +
scale_size_continuous(range = c(5, 50), guide = "none")
Update
Just tried position_jitter, but the width argument only takes one value, so right now I am not sure how useful that function would be. But I would be happy to find that I am wrong. Example with another small data set:
df3 <- mtcars[1:10, ]
ggplot(data = df3, aes(x = wt, y = mpg)) +
geom_point(aes(size = qsec), alpha = 0.1) +
geom_text(label = df3$carb, position = position_jitter(width = 0.1, height = 0)) +
scale_size_continuous(range = c(5, 50), guide = "none")
I am using ggplot to generate a chart that summarises a race made up from several laps. There are 24 participants in the race,numbered 1-12, 14-25; I am plotting out a summary measure for each participant using ggplot, but ggplot assumes I want the number range 1-25, rather than categories 1-12, 14-25.
What's the fix for this? Here's the code I am using (the data is sourced from a Google spreadsheet).
sskey='0AmbQbL4Lrd61dHlibmxYa2JyT05Na2pGVUxLWVJYRWc'
library("ggplot2")
require(RCurl)
gsqAPI = function(key,query,gid){ return( read.csv( paste( sep="", 'http://spreadsheets.google.com/tq?', 'tqx=out:csv', '&tq=', curlEscape(query), '&key=', key, '&gid=', curlEscape(gid) ) ) ) }
sin2011racestatsX=gsqAPI(sskey,'select A,B,G',gid='13')
sin2011proximity=gsqAPI(sskey,'select A,B,C',gid='12')
h=sin2011proximity
k=sin2011racestatsX
l=subset(h,lap==1)
ggplot() +
geom_step(aes(x=h$car, y=h$pos, group=h$car)) +
scale_x_discrete(limits =c('VET','WEB','HAM','BUT','ALO','MAS','SCH','ROS','SEN','PET','BAR','MAL','','SUT','RES','KOB','PER','BUE','ALG','KOV','TRU','RIC','LIU','GLO','AMB'))+
xlab(NULL) + opts(title="F1 2011 Korea \nRace Summary Chart", axis.text.x=theme_text(angle=-90, hjust=0)) +
geom_point(aes(x=l$car, y=l$pos, pch=3, size=2)) +
geom_point(aes(x=k$driverNum, y=k$classification,size=2), label='Final') +
geom_point(aes(x=k$driverNum, y=k$grid, col='red')) +
ylab("Position")+
scale_y_discrete(breaks=1:24,limits=1:24)+ opts(legend.position = "none")
Expanding on my cryptic comment, try this:
#Convert these to factors with the appropriate labels
# Note that I removed the ''
h$car <- factor(h$car,labels = c('VET','WEB','HAM','BUT','ALO','MAS','SCH','ROS','SEN','PET','BAR','MAL',
'SUT','RES','KOB','PER','BUE','ALG','KOV','TRU','RIC','LIU','GLO','AMB'))
k$driverNum <- factor(k$driverNum,labels = c('VET','WEB','HAM','BUT','ALO','MAS','SCH','ROS','SEN','PET','BAR','MAL',
'SUT','RES','KOB','PER','BUE','ALG','KOV','TRU','RIC','LIU','GLO','AMB'))
l=subset(h,lap==1)
ggplot() +
geom_step(aes(x=h$car, y=h$pos, group=h$car)) +
geom_point(aes(x=l$car, y=l$pos, pch=3, size=2)) +
geom_point(aes(x=k$driverNum, y=k$classification,size=2), label='Final') +
geom_point(aes(x=k$driverNum, y=k$grid, col='red')) +
ylab("Position") +
scale_y_discrete(breaks=1:24,limits=1:24) + opts(legend.position = "none") +
opts(title="F1 2011 Korea \nRace Summary Chart", axis.text.x=theme_text(angle=-90, hjust=0)) + xlab(NULL)
Calling scale_x_discrete is no longer necessary. And stylistically, I prefer putting opts and xlab stuff at the end.
Edit
A few notes in response to your comment. Many of your difficulties can be eased by a more streamlined use of ggplot. Your data is in an awkward format:
#Summarise so we can use geom_linerange rather than geom_step
d1 <- ddply(h,.(car),summarise,ymin = min(pos),ymax = max(pos))
#R has a special value for missing data; use it!
k$classification[k$classification == 'null'] <- NA
k$classification <- as.integer(k$classification)
#The other two data sets should be merged and converted to long format
d2 <- merge(l,k,by.x = "car",by.y = "driverNum")
colnames(d2)[3:5] <- c('End of Lap 1','Final Position','Grid Position')
d2 <- melt(d2,id.vars = 1:2)
#Now the plotting call is much shorter
ggplot() +
geom_linerange(data = d1,aes(x= car, ymin = ymin,ymax = ymax)) +
geom_point(data = d2,aes(x= car, y= value,shape = variable),size = 2) +
opts(title="F1 2011 Korea \nRace Summary Chart", axis.text.x=theme_text(angle=-90, hjust=0)) +
labs(x = NULL, y = "Position", shape = "")
A few notes. You were setting aesthetics to fixed values (size = 2) which should be done outside of aes(). aes() is for mapping variables (i.e. columns) to aesthetics (color, shape, size, etc.). This allows ggplot to intelligently create the legend for you.
Merging the second two data sets and then melting it creates a grouping variable for ggplot to use in the legend. I used the shape aesthetic since a few values overlap; using color may make that hard to spot. In general, ggplot will resist mixing aesthetics into a single legend. If you want to use shape, color and size you'll get three legends.
I prefer setting labels using labs, since you can do them all in one spot. Note that setting the aesthetic label to "" removes the legend title.