Say I'm measuring 10 personality traits and I know the population baseline. I would like to create a chart for individual test-takers to show them their individual percentile ranking on each trait. Thus, the numbers go from 1 (percentile) to 99 (percentile). Given that a 50 is perfectly average, I'd like the graph to show bars going to the left or right from 50 as the origin line. In bar graphs in ggplot, it seems that the origin line defaults to 0. Is there a way to change the origin line to be at 50?
Here's some fake data and default graphing:
df <- data.frame(
names = LETTERS[1:10],
factor = round(rnorm(10, mean = 50, sd = 20), 1)
)
library(ggplot2)
ggplot(data = df, aes(x=names, y=factor)) +
geom_bar(stat="identity") +
coord_flip()
Picking up on #nongkrong's comment, here's some code that will do what I think you want while relabeling the ticks to match the original range and relabeling the axis to avoid showing the math:
library(ggplot2)
ggplot(data = df, aes(x=names, y=factor - 50)) +
geom_bar(stat="identity") +
scale_y_continuous(breaks=seq(-50,50,10), labels=seq(0,100,10)) + ylab("Percentile") +
coord_flip()
This post was really helpful for me - thanks #ulfelder and #nongkrong. However, I wanted to re-use the code on different data without having to manually adjust the tick labels to fit the new data. To do this in a way that retained ggplot's tick placement, I defined a tiny function and called this function in the label argument:
fix.labels <- function(x){
x + 50
}
ggplot(data = df, aes(x=names, y=factor - 50)) +
geom_bar(stat="identity") +
scale_y_continuous(labels = fix.labels) + ylab("Percentile") +
coord_flip()
Related
I'd like to have some labels stacked on top of a geom_bar graph. Here's an example:
df <- data.frame(x=factor(c(TRUE,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE)))
ggplot(df) + geom_bar(aes(x,fill=x)) + opts(axis.text.x=theme_blank(),axis.ticks=theme_blank(),axis.title.x=theme_blank(),legend.title=theme_blank(),axis.title.y=theme_blank())
Now
table(df$x)
FALSE TRUE
3 5
I'd like to have the 3 and 5 on top of the two bars. Even better if I could have the percent values as well. E.g. 3 (37.5%) and 5 (62.5%). Like so:
(source: skitch.com)
Is this possible? If so, how?
To plot text on a ggplot you use the geom_text. But I find it helpful to summarise the data first using ddply
dfl <- ddply(df, .(x), summarize, y=length(x))
str(dfl)
Since the data is pre-summarized, you need to remember to change add the stat="identity" parameter to geom_bar:
ggplot(dfl, aes(x, y=y, fill=x)) + geom_bar(stat="identity") +
geom_text(aes(label=y), vjust=0) +
opts(axis.text.x=theme_blank(),
axis.ticks=theme_blank(),
axis.title.x=theme_blank(),
legend.title=theme_blank(),
axis.title.y=theme_blank()
)
As with many tasks in ggplot, the general strategy is to put what you'd like to add to the plot into a data frame in a way such that the variables match up with the variables and aesthetics in your plot. So for example, you'd create a new data frame like this:
dfTab <- as.data.frame(table(df))
colnames(dfTab)[1] <- "x"
dfTab$lab <- as.character(100 * dfTab$Freq / sum(dfTab$Freq))
So that the x variable matches the corresponding variable in df, and so on. Then you simply include it using geom_text:
ggplot(df) + geom_bar(aes(x,fill=x)) +
geom_text(data=dfTab,aes(x=x,y=Freq,label=lab),vjust=0) +
opts(axis.text.x=theme_blank(),axis.ticks=theme_blank(),
axis.title.x=theme_blank(),legend.title=theme_blank(),
axis.title.y=theme_blank())
This example will plot just the percentages, but you can paste together the counts as well via something like this:
dfTab$lab <- paste(dfTab$Freq,paste("(",dfTab$lab,"%)",sep=""),sep=" ")
Note that in the current version of ggplot2, opts is deprecated, so we would use theme and element_blank now.
Another solution is to use stat_count() when dealing with discrete variables (and stat_bin() with continuous ones).
ggplot(data = df, aes(x = x)) +
geom_bar(stat = "count") +
stat_count(geom = "text", colour = "white", size = 3.5,
aes(label = ..count..),position=position_stack(vjust=0.5))
So, this is our initial plot↓
library(ggplot2)
df <- data.frame(x=factor(c(TRUE,TRUE,TRUE,TRUE,TRUE,FALSE,FALSE,FALSE)))
p <- ggplot(df, aes(x = x, fill = x)) +
geom_bar()
p
As suggested by yuan-ning, we can use stat_count().
geom_bar() uses stat_count() by default. As mentioned in the ggplot2 reference, stat_count() returns two values: count for number of points in bin and prop for groupwise proportion. Since our groups match the x values, both props are 1 and aren’t useful. But we can use count (referred to as “..count..”) that actually denotes bar heights, in our geom_text(). Note that we must include “stat = 'count'” into our geom_text() call as well.
Since we want both counts and percentages in our labels, we’ll need some calculations and string pasting in our “label” aesthetic instead of just “..count..”. I prefer to add a line of code to create a wrapper percent formatting function from the “scales” package (ships along with “ggplot2”).
pct_format = scales::percent_format(accuracy = .1)
p <- p + geom_text(
aes(
label = sprintf(
'%d (%s)',
..count..,
pct_format(..count.. / sum(..count..))
)
),
stat = 'count',
nudge_y = .2,
colour = 'royalblue',
size = 5
)
p
Of course, you can further edit the labels with colour, size, nudges, adjustments etc.
Likely a dumb question, but I cannot seem to find a solution: I am trying to graph a categorical variable on the x-axis (3 groups) and a continuous variable (% of 0 - 100) on the y-axis. When I do so, I have to clarify that the geom_bar is stat = "identity" or use the geom_col.
However, the values still show up at 4000 on the y-axis, even after following the comments from Y-scale issue in ggplot and from Why is the value of y bar larger than the actual range of y in stacked bar plot?.
Here is how the graph keeps coming out:
I also double checked that the x variable is a factor and the y variable is numeric. Why would this still be coming out at 4000 instead of 100, like a percentage?
EDIT:
The y-values are simply responses from participants. I have a large dataset (N = 600) and the y-value are a percentage from 0-100 given by each participant. So, in each group (N = 200 per group), I have a value for the percentage. I wanted to visually compare the three groups based on the percentages they gave.
This is the code I used to plot the graph.
df$group <- as.factor(df$group)
df$confid<- as.numeric(df$confid)
library(ggplot2)
plot <-ggplot(df, aes(group, confid))+
geom_col()+
ylab("confid %") +
xlab("group")
Are you perhaps trying to plot the mean percentage in each group? Otherwise, it is not clear how a bar plot could easily represent what you are looking for. You could perhaps add error bars to give an idea of the spread of responses.
Suppose your data looks like this:
set.seed(4)
df <- data.frame(group = factor(rep(1:3, each = 200)),
confid = sample(40, 600, TRUE))
Using your plotting code, we get very similar results to yours:
library(ggplot2)
plot <-ggplot(df, aes(group, confid))+
geom_col()+
ylab("confid %") +
xlab("group")
plot
However, if we use stat_summary, we can instead plot the mean and standard error for each group:
ggplot(df, aes(group, confid)) +
stat_summary(geom = "bar", fun = mean, width = 0.6,
fill = "deepskyblue", color = "gray50") +
geom_errorbar(stat = "summary", width = 0.5) +
geom_point(stat = "summary") +
ylab("confid %") +
xlab("group")
I am trying to simply add a legend to my Nyquist plot where I am plotting 2 sets of data: 1 is an experimental set (~600 points), and 2 is a data frame calculated using a transfer function (~1000 points)
I need to plot both and label them. Currently I have them both plotted okay but when i try to add the label using scale_colour_manual no label appears. Also a way to move this label around would be appreciated!! Code Below.
pdf("nyq_2elc.pdf")
nq2 <- ggplot() + geom_point(data = treat, aes(treat$V1,treat$V2), color = "red") +
geom_point(data = circuit, aes(circuit$realTF,circuit$V2), color = "blue") +
xlab("Real Z") + ylab("-Imaginary Z") +
scale_colour_manual(name = 'hell0',
values =c('red'='red','blue'='blue'), labels = c('Treatment','EQ')) +
ggtitle("Nyquist Plot and Equivilent Circuit for 2 Electrode Treatment Setup at 0 Minutes") +
xlim(0,700) + ylim(0,700)
print(nq2)
dev.off()
Ggplot works best with long dataframes, so I would combine the datasets like this:
treat$Cat <- "treat"
circuit$Cat <- "circuit"
CombData <- data.frame(rbind(treat, circuit))
ggplot(CombData, aes(x=V1, y=V2, col=Cat))+geom_point()
This should give you the legend you want.
You probably have to change the names/order of the columns of dataframes treat and circuit so they can be combined, but it's hard to tell because you're not giving us a reproducible example.
I am plotting some payment distribution information and I aggregated the data after scaling it to log-normal (base-e). The histograms turn out great but I want to modify the x-axis to display the non-log equivalents.
My current axis displays [0:2.5:10] values
Alternatively, I would like to see values for exp(2.5), exp(5), etc.
Any suggestions on how to accomplish this? Anything I can add to my plotting statement to scale the x-axis values? Maybe there's a better approach - thoughts?
Current code:
ggplot(plotData, aes_string(pay, fill = pt)) + geom_histogram(bins = 50) + facet_wrap(~M_P)
Answered...Final plot:
Not sure if this is exactly what you are after but you can change the text of the x axis labels to whatever you want using scale_x_continuous.
Here's without:
ggplot(data = cars) + geom_histogram(aes(x = speed), binwidth = 1)
Here's with:
ggplot(data = cars) + geom_histogram(aes(x = speed), binwidth = 1) +
scale_x_continuous(breaks=c(5,10,15,20,25), labels=c(exp(5), exp(10), exp(15), exp(20), exp(25)))
I'm an R/ggplot newbie. I would like to create a geom_line plot of a continuous variable time series and then add a layer composed of events. The continuous variable and its timestamps is stored in one data.frame, the events and their timestamps are stored in another data.frame.
What I would really like to do is something like the charts on finance.google.com. In those, the time series is stock-price and there are "flags" to indicate news-events. I'm not actually plotting finance stuff, but the type of graph is similar. I am trying to plot visualizations of log file data. Here's an example of what I mean...
If advisable (?), I would like to use separate data.frames for each layer (one for continuous variable observations, another for events).
After some trial and error this is about as close as I can get. Here, I am using example data from data sets that come with ggplot. "economics" contains some time-series data that I'd like to plot and "presidential" contains a few events (presidential elections).
library(ggplot2)
data(presidential)
data(economics)
presidential <- presidential[-(1:3),]
yrng <- range(economics$unemploy)
ymin <- yrng[1]
ymax <- yrng[1] + 0.1*(yrng[2]-yrng[1])
p2 <- ggplot()
p2 <- p2 + geom_line(mapping=aes(x=date, y=unemploy), data=economics , size=3, alpha=0.5)
p2 <- p2 + scale_x_date("time") + scale_y_continuous(name="unemployed [1000's]")
p2 <- p2 + geom_segment(mapping=aes(x=start,y=ymin, xend=start, yend=ymax, colour=name), data=presidential, size=2, alpha=0.5)
p2 <- p2 + geom_point(mapping=aes(x=start,y=ymax, colour=name ), data=presidential, size=3)
p2 <- p2 + geom_text(mapping=aes(x=start, y=ymax, label=name, angle=20, hjust=-0.1, vjust=0.1),size=6, data=presidential)
p2
Questions:
This is OK for very sparse events, but if there's a cluster of them (as often happens in a log file), it gets messy. Is there some technique I can use to neatly display a bunch of events occurring in a short time interval? I was thinking of position_jitter, but it was really hard for me to get this far. google charts stacks these event "flags" on top of each other if there's a lot of them.
I actually don't like sticking the event data in the same scale as the continuous measurement display. I would prefer to put it in a facet_grid. The problem is that the facets all must be sourced from the same data.frame (not sure if that's true). If so, that also seems not ideal (or maybe I'm just trying to avoid using reshape?)
Now I like ggplot as much as the next guy, but if you want to make the Google Finance type charts, why not just do it with the Google graphics API?!? You're going to love this:
install.packages("googleVis")
library(googleVis)
dates <- seq(as.Date("2011/1/1"), as.Date("2011/12/31"), "days")
happiness <- rnorm(365)^ 2
happiness[333:365] <- happiness[333:365] * 3 + 20
Title <- NA
Annotation <- NA
df <- data.frame(dates, happiness, Title, Annotation)
df$Title[333] <- "Discovers Google Viz"
df$Annotation[333] <- "Google Viz API interface by Markus Gesmann causes acute increases in happiness."
### Everything above here is just for making up data ###
## from here down is the actual graphics bits ###
AnnoTimeLine <- gvisAnnotatedTimeLine(df, datevar="dates",
numvar="happiness",
titlevar="Title", annotationvar="Annotation",
options=list(displayAnnotations=TRUE,
legendPosition='newRow',
width=600, height=300)
)
# Display chart
plot(AnnoTimeLine)
# Create Google Gadget
cat(createGoogleGadget(AnnoTimeLine), file="annotimeline.xml")
and it produces this fantastic chart:
As much as I like #JD Long's answer, I'll put one that is just in R/ggplot2.
The approach is to create a second data set of events and to use that to determine positions. Starting with what #Angelo had:
library(ggplot2)
data(presidential)
data(economics)
Pull out the event (presidential) data, and transform it. Compute baseline and offset as fractions of the economic data it will be plotted with. Set the bottom (ymin) to the baseline. This is where the tricky part comes. We need to be able to stagger labels if they are too close together. So determine the spacing between adjacent labels (assumes that the events are sorted). If it is less than some amount (I picked about 4 years for this scale of data), then note that that label needs to be higher. But it has to be higher than the one after it, so use rle to get the length of TRUE's (that is, must be higher) and compute an offset vector using that (each string of TRUE must count down from its length to 2, the FALSEs are just at an offset of 1). Use this to determine the top of the bars (ymax).
events <- presidential[-(1:3),]
baseline = min(economics$unemploy)
delta = 0.05 * diff(range(economics$unemploy))
events$ymin = baseline
events$timelapse = c(diff(events$start),Inf)
events$bump = events$timelapse < 4*370 # ~4 years
offsets <- rle(events$bump)
events$offset <- unlist(mapply(function(l,v) {if(v){(l:1)+1}else{rep(1,l)}}, l=offsets$lengths, v=offsets$values, USE.NAMES=FALSE))
events$ymax <- events$ymin + events$offset * delta
Putting this together into a plot:
ggplot() +
geom_line(mapping=aes(x=date, y=unemploy), data=economics , size=3, alpha=0.5) +
geom_segment(data = events, mapping=aes(x=start, y=ymin, xend=start, yend=ymax)) +
geom_point(data = events, mapping=aes(x=start,y=ymax), size=3) +
geom_text(data = events, mapping=aes(x=start, y=ymax, label=name), hjust=-0.1, vjust=0.1, size=6) +
scale_x_date("time") +
scale_y_continuous(name="unemployed \[1000's\]")
You could facet, but it is tricky with different scales. Another approach is composing two graphs. There is some extra fiddling that has to be done to make sure the plots have the same x-range, to make the labels all fit in the lower plot, and to eliminate the x axis in the upper plot.
xrange = range(c(economics$date, events$start))
p1 <- ggplot(data=economics, mapping=aes(x=date, y=unemploy)) +
geom_line(size=3, alpha=0.5) +
scale_x_date("", limits=xrange) +
scale_y_continuous(name="unemployed [1000's]") +
opts(axis.text.x = theme_blank(), axis.title.x = theme_blank())
ylims <- c(0, (max(events$offset)+1)*delta) + baseline
p2 <- ggplot(data = events, mapping=aes(x=start)) +
geom_segment(mapping=aes(y=ymin, xend=start, yend=ymax)) +
geom_point(mapping=aes(y=ymax), size=3) +
geom_text(mapping=aes(y=ymax, label=name), hjust=-0.1, vjust=0.1, size=6) +
scale_x_date("time", limits=xrange) +
scale_y_continuous("", breaks=NA, limits=ylims)
#install.packages("ggExtra", repos="http://R-Forge.R-project.org")
library(ggExtra)
align.plots(p1, p2, heights=c(3,1))
Plotly is an easy way to make ggplots interactive. To display events, coerce them into factors which can be displayed as an aesthetic, like color.
The end result is a plot that you can drag the cursor over. The plots display data of interest:
Here is the code for making the ggplot:
# load data
data(presidential)
data(economics)
# events of interest
events <- presidential[-(1:3),]
# strip year from economics and events data frames
economics$year = as.numeric(format(economics$date, format = "%Y"))
# use dplyr to summarise data by year
#install.packages("dplyr")
library(dplyr)
econonomics_mean <- economics %>%
group_by(year) %>%
summarise(mean_unemployment = mean(unemploy))
# add president terms to summarized data frame as a factor
president <- c(rep(NA,14), rep("Reagan", 8), rep("Bush", 4), rep("Clinton", 8), rep("Bush", 8), rep("Obama", 7))
econonomics_mean$president <- president
# create ggplot
p <- ggplot(data = econonomics_mean, aes(x = year, y = mean_unemployment)) +
geom_point(aes(color = president)) +
geom_line(alpha = 1/3)
It only takes one line of code to make the ggplot into a plotly object.
# make it interactive!
#install.packages("plotly")
library(plotly)
ggplotly(p)
Considering you are plotting time series and qualitative information, most economic book use the area of plotting to indicate a structural change or event on data so i recommend to use something like this:
library(ggplot2)
data(presidential)
data(economics)
ggplot() +
geom_rect(aes(xmin = start,
xmax = end,
ymin = 0, ymax = Inf,
fill = name),
data = presidential,
show.legend = F) +
geom_text(aes(x = start+500,
y = 2000,
label = name,
angle = 90),
data = presidential) +
geom_line(aes(x = date, y = unemploy),
data= economics) +
scale_fill_brewer(palette = "Blues") +
labs(x = "time", y = "unemploy")