I have observations in the form of ranges
For eg: A 13-20, B 15-30, C 23-40, D 2-11
I want to plot them in R in form of the starting value and the end value for eg. 13 and 20 for A(upper and lower limits if you may say) in order to visualize and find out what ranges are common to certain combinations of observations. Is there a quick way to do this in R ? I think this is a very trivial problem I am having but I cant think of anyway to do it right now.
Here is a solution using ggplot. It's not clear at all what format your data is in, so this assumes a data frame with columns id (A-D), min, and max.
df <- data.frame(id=LETTERS[1:4], min=c(13,15,23,2), max=c(20,30,40,11))
library(ggplot2)
ggplot(df, aes(x=id))+
geom_linerange(aes(ymin=min,ymax=max),linetype=2,color="blue")+
geom_point(aes(y=min),size=3,color="red")+
geom_point(aes(y=max),size=3,color="red")+
theme_bw()
I've added a lot of customization just to give you an idea of how it's done. You use the aes(...) function to tell ggplot which columns in df map to various aesthetics of the graph. So for instance aes(x=id) tells ggplot that the values for the x-axis are to be found in the id column of df, etc.
EDIT: Response to OP's comment.
To change the size of axis text, use the theme(...) function, as in:
ggplot(df, aes(x=id))+
geom_linerange(aes(ymin=min,ymax=max),linetype=2,color="blue")+
geom_point(aes(y=min),size=3,color="red")+
geom_point(aes(y=max),size=3,color="red")+
theme_bw()+
theme(axis.text.x=element_text(size=15))
Here I made the x-axis text bigger. Play around with size=... to get it the way you want. Also read the documentation (?theme) for a list of other formatting options.
It is not clear whether the dataset has range column as string or not i.e. '13-20', '15-30' etc. or if it is two numeric columns as showed in the created example.
matplot(m1, xaxt='n', pch=1, ylab='range')
axis(1, at=seq_len(nrow(m1)), labels=row.names(m1))
s1 <- seq_len(nrow(m1))
arrows(s1, m1[,1], s1, m1[,2], angle=90, length=0.1)
If the data has string column (d1)
library(splitstackshape)
d2 <- setDF(cSplit(d1, 'range', '-'))
matplot(d2[,-1], xaxt='n', pch=1, ylab='range')
axis(1, at=seq_len(nrow(d2)), labels=d2$Col1)
arrows(s1, d2[,2], s1, d2[,3], angle=90, length=0.1)
data
m1 <- matrix(c(13,20, 15,30, 23,40, 2,11),
byrow=TRUE,dimnames=list(LETTERS[1:4],NULL), ncol=2)
d1 <- data.frame(Col1=LETTERS[1:4],
range=c('13-20', '15-30', '23-40', '2-11'), stringsAsFactors=FALSE)
Related
I have two factors and two continuous variables, and I use this to create a two-way facet plot using ggplot2. However, not all of my factor combinations have data, so I end up with dummy facets. Here's some dummy code to produce an equivalent output:
library(ggplot2)
dummy<-data.frame(x=rnorm(60),y=rnorm(60),
col=rep(c("A","B","C","B","C","C"),each=10),
row=rep(c("a","a","a","b","b","c"),each=10))
ggplot(data=dummy,aes(x=x,y=y))+
geom_point()+
facet_grid(row~col)
This produces this figure
Is there any way to remove the facets that don't plot any data? And, ideally, move the x and y axis labels up or right to the remaining plots? As shown in this GIMPed version
I've searched here and elsewhere and unless my search terms just aren't good enough, I can't find the same problem anywhere. Similar issues are often with unused factor levels, but here no factor level is unused, just factor level combinations. So facet_grid(drop=TRUE) or ggplot(data=droplevel(dummy)) doesn't help here. Combining the factors into a single factor and dropping unused levels of the new factor can only produce a 1-dimensional facet grid, which isn't what I want.
Note: my actual data has a third factor level which I represent by different point colours. Thus a single-plot solution allowing me to retain a legend would be ideal.
It's not too difficult to rearrange the graphical objects (grobs) manually to achieve what you're after.
Load the necessary libraries.
library(grid);
library(gtable);
Turn your ggplot2 plot into a grob.
gg <- ggplot(data = dummy, aes(x = x,y = y)) +
geom_point() +
facet_grid(row ~ col);
grob <- ggplotGrob(gg);
Working out which facets to remove, and which axes to move where depends on the grid-structure of your grob. gtable_show_layout(grob) gives a visual representation of your grid structure, where numbers like (7, 4) denote a panel in row 7 and column 4.
Remove the empty facets.
# Remove facets
idx <- which(grob$layout$name %in% c("panel-2-1", "panel-3-1", "panel-3-2"));
for (i in idx) grob$grobs[[i]] <- nullGrob();
Move the x axes up.
# Move x axes up
# axis-b-1 needs to move up 4 rows
# axis-b-2 needs to move up 2 rows
idx <- which(grob$layout$name %in% c("axis-b-1", "axis-b-2"));
grob$layout[idx, c("t", "b")] <- grob$layout[idx, c("t", "b")] - c(4, 2);
Move the y axes to the right.
# Move y axes right
# axis-l-2 needs to move 2 columns to the right
# axis-l-3 needs ot move 4 columns to the right
idx <- which(grob$layout$name %in% c("axis-l-2", "axis-l-3"));
grob$layout[idx, c("l", "r")] <- grob$layout[idx, c("l", "r")] + c(2, 4);
Plot.
# Plot
grid.newpage();
grid.draw(grob);
Extending this to more facets is straightforward.
Maurits Evers solution worked great, but is quite cumbersome to modify.
An alternative solution is to use facet_manual from {ggh4x}.
This is not equivalent though as it uses facet_wrap, but allows appropriate placement of the facets.
# devtools::install_github("teunbrand/ggh4x")
library(ggplot2)
dummy<-data.frame(x=rnorm(60),y=rnorm(60),
col=rep(c("A","B","C","B","C","C"),each=10),
row=rep(c("a","a","a","b","b","c"),each=10))
design <- "
ABC
#DE
##F
"
ggplot(data=dummy,aes(x=x,y=y))+
geom_point()+
ggh4x::facet_manual(vars(row,col), design = design, labeller = label_both)
Created on 2022-02-25 by the reprex package (v2.0.0)
One possible solution, of course, would be to create a plot for each factor combination separately and then combine them using grid.arrange() from gridExtra. This would probably lose my legend and would be an all around pain, would love to hear if anyone has any better suggestions.
This particular case looks like a job for ggpairs (link to a SO example). I haven't used it myself, but for paired plots this seems like the best tool for the job.
In a more general case, where you're not looking for pairs, you could try creating a column with a composite (pasted) factor and facet_grid or facet_wrap by that variable (example)
I started learning R for data analysis and, most importantly, for data visualisation.
Since I am still in the switching process, I am trying to reproduce the activities I was doing with Graphpad Prism or Origin Pro in R. In most of the cases everything was smooth, but I could not find a smart solution for plotting multiple y columns in a single graph.
What I usually get from the softwares I use for data visualisations look like this:
Each single black trace is a measurement, and I would like to obtain the same plot in R. In Prism or Origin, this will take a single copy-paste in a XY graph.
I exported the matrix of data (one X, which indicates the time, and multiple Y values, which are the traces you see in the image).
I imported my data in R with the following commands:
library(ggplot2) #loaded ggplot2
Data <- read.csv("Directory/File.txt", header=F, sep="") #imported data
DF <- data.frame(Data) #transformed data into data frame
If I plot my data now, I obtain a series of columns, where the first one (called V1) is the X axis and all the others (V2 to V140) are the traces I want to put on the same graph.
To plot the data, I tried different solutions:
ggplot(data=DF, aes(x=DF$V1, y=DF[V2:V140]))+geom_line()+theme_bw() #did not work
plot(DF, xy.coords(x=DF$V1, y=DF$V2:V140)) #gives me an error
plot(DF, xy.coords(x=V1, y=c(V2:V10))) #gives me an error
I tried the matplot, without success, following the EZH guide:
The code I used is the following: matplot(x=DF$V1, type="l", lty = 2:100)
The only solution I found would be to individually plot a command for each single column, but it is a crazy solution. The number of columns varies among my data, and manually enter commands for 140 columns is insane.
What would you suggest?
Thank you in advance.
Here there are also some data attached.Data: single X, multiple Y
I tried using the matplot(). I used a very sample data which has no trend at all. so th eoutput from my code shall look terrible, but my main focus is on the code. Since you have already tried matplot() ,just recheck with below solution if you had done it right!
set.seed(100)
df = matrix(sample(1:685765,50000,replace = T),ncol = 100)
colnames(df)=c("x",paste0("y", 1:99))
dt=as.data.frame(df)
matplot(dt[["x"]], y = dt[,c(paste0("y",1:99))], type = "l")
If you want to plot in base R, you have to make a plot and add lines one at a time, however that isn't hard to do.
we start by making some sample data. Since the data in the link seemed to all be on the same scale, I will assume your data frame only has y values and the x value is stored separately.
plotData <- as.data.frame(matrix(sort(rnorm(500)),ncol = 5))
xval <- sort(sample(200, 100))
Now we can initialize a plot with the first column.
plot(xval, plotData[[1]], type = "l",
ylim = c(min(plotData), max(plotData)))
type = "l" makes a line plot instead of a scatter plot
ylim = c(min(plotData), max(plotData)) makes sure the y-axis will fit all the data.
Now we can add the rest of the values.
apply(plotData[-1], 2, lines, x = xval)
plotData[-1] removes the column we already plotted,
apply function with 2 as the second parameter means we want to execute a function on every column,
lines defines the function we are applying to the columns. lines adds a new line to the current plot.
x = xval passes an extra parameter (x) to the lines function.
if you wat to plot the data using ggplot2, the data should be transformed to long format;
library(ggplot2)
library(reshape2)
dat <- read.delim('AP.txt', header = F)
# plotting only first 9 traces
# my rstudio will crach if I plot the full data;
df <- melt(dat[1:10], id.vars = 'V1')
ggplot(df, aes(x = V1, y = value, color = variable)) + geom_line()
# if you want all traces to be in same colour, you can use
ggplot(df, aes(x = V1, y = value, group = variable)) + geom_line()
I am trying to plot (on the same graph) two sets of data versus date from two different data frames. Both data frames have the same exact dates for each of the two measurements. I would like to plot these two sets of data on the same graph, with different colors. However, I can't get them on the same graph at all. R is already reading the date as date. I tried this:
qplot( date , NO3, data=qual.arn)
+ qplot( qual.arn$date , qual.arn$DIS.O2, "O2(aq)" , add=T)
and received this error.
Error in add_ggplot(e1, e2, e2name) :
argument "e2" is missing, with no default
I tried using the ggplot function instead of qplot, but I couldn't even plot one graph this way.
ggplot(date=qual.no3.s, aes(date,NO3))
Error: ggplot2 doesn't know how to deal with data of class uneval
PLEASE HELP. Thank you!
Since you didn't provide any data (please do so in future), here's a made up dataset for demonstrate a solution. There are (at least) two ways to do this: the right way and the wrong way. Both yield equivalent results in this very simple case.
# set up minimum reproducible example
set.seed(1) # for reproducible example
dates <- seq(as.Date("2015-01-01"),as.Date("2015-06-01"), by=1)
df1 <- data.frame(date=dates, NO3=rpois(length(dates),25))
df2 <- data.frame(date=dates, DIS.O2=rnorm(length(dates),50,10))
ggplot is designed to use data in "long" format. This means that all the y-values (the concentrations) are in a single column, and there is separate column which identifies the corresponding category ("NO3" or "DIS.O2" in your case). So first we merge the two data-sets based on date, then use melt(...) to convert from "wide" (categories in separate columns) to "long" format. Then we let ggplot worry about legends, colors, etc.
library(ggplot2)
library(reshape2) # for melt(...)
# The right way: combine the data-sets, then plot
df.mrg <- merge(df1,df2, by="date", all=TRUE)
gg.df <- melt(df.mrg, id="date", variable.name="Component", value.name="Concentration")
ggplot(gg.df, aes(x=date, y=Concentration, color=Component)) +
geom_point() + labs(x=NULL)
The "wrong" way to do this is by making separate calls to geom_point(...) for each layer. In your particular case this might be simpler, but in the long run it's better to use the other method.
# The wrong way: plot two sets of points
ggplot() +
geom_point(data=df1, aes(x=date, y=NO3, color="NO2")) +
geom_point(data=df2, aes(x=date, y=DIS.O2, color="DIS.O2")) +
scale_color_manual(name="Component",values=c("red", "blue")) +
labs(x=NULL, y="Concentration")
I have a data frame with a quantitative variable, x, and several different factors, f1, f2, ...,fn. The number of levels is not constant across factors.
I want to create a (single) plot of densities of x by factor level fi.
I know how to hand code this for a specific factor. For example, here is the plot for a factor with two levels.
# set up the background plot
plot(density(frame$x[frame$f1=="level1"]))
# add curves
lines(density(frame$x[frame$f1=="level2"]))
I could also do this like so:
# set up the background plot
plot(NA)
# add curves
lines(density(frame$x[frame$f1=="level1"]))
lines(density(frame$x[frame$f1=="level2"]))
What I'd like to know is how can I do this if I only specify the factor as input. I don't even know how to write a for loop that would do what I need, and I have the feeling that the 'R way' would avoid for loops.
Bonus: For the plots, I would like to specify limiting values for the axes. Right now I do this in this way:
xmin=min(frame$x[frame$f1=="level1"],frame$x[frame$f1=="level2"])
How can I include this type of calculation in my script?
I'm assuming your data is in the format (data frame called df)
f1 f2 f3 fn value
A........................... value 1
A............................value 2
.............................
B............................value n-1
B............................value n
In that cause, lattice (or ggplot2) will be very useful.
library(lattice)
densityplot(~value, groups = f1, data = df, plot.points = FALSE)
This should get you close to what you are looking for, I think.
Greg
You could also do:
# create an empty plot. You may want to add xlab, ylab etc
# EDIT: also add some appropriate axis limits with xlim and ylim
plot(0, 0, "n", xlim=c(0, 10), ylim=c(0, 2))
levels <- unique(frame$f1)
for (l in levels)
{
lines(density(frame$x[frame$f1==l]))
}
ggplot2 code
library(ggplot2)
ggplot(data, aes(value, colour = f1)) +
stat_density(position = "identity")
I often have to make stacked barplots to compare variables, and because I do all my stats in R, I prefer to do all my graphics in R with ggplot2. I would like to learn how to do two things:
First, I would like to be able to add proper percentage tick marks for each variable rather than tick marks by count. Counts would be confusing, which is why I take out the axis labels completely.
Second, there must be a simpler way to reorganize my data to make this happen. It seems like the sort of thing I should be able to do natively in ggplot2 with plyR, but the documentation for plyR is not very clear (and I have read both the ggplot2 book and the online plyR documentation.
My best graph looks like this, the code to create it follows:
The R code I use to get it is the following:
library(epicalc)
### recode the variables to factors ###
recode(c(int_newcoun, int_newneigh, int_neweur, int_newusa, int_neweco, int_newit, int_newen, int_newsp, int_newhr, int_newlit, int_newent, int_newrel, int_newhth, int_bapo, int_wopo, int_eupo, int_educ), c(1,2,3,4,5,6,7,8,9, NA),
c('Very Interested','Somewhat Interested','Not Very Interested','Not At All interested',NA,NA,NA,NA,NA,NA))
### Combine recoded variables to a common vector
Interest1<-c(int_newcoun, int_newneigh, int_neweur, int_newusa, int_neweco, int_newit, int_newen, int_newsp, int_newhr, int_newlit, int_newent, int_newrel, int_newhth, int_bapo, int_wopo, int_eupo, int_educ)
### Create a second vector to label the first vector by original variable ###
a1<-rep("News about Bangladesh", length(int_newcoun))
a2<-rep("Neighboring Countries", length(int_newneigh))
[...]
a17<-rep("Education", length(int_educ))
Interest2<-c(a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17)
### Create a Weighting vector of the proper length ###
Interest.weight<-rep(weight, 17)
### Make and save a new data frame from the three vectors ###
Interest.df<-cbind(Interest1, Interest2, Interest.weight)
Interest.df<-as.data.frame(Interest.df)
write.csv(Interest.df, 'C:\\Documents and Settings\\[name]\\Desktop\\Sweave\\InterestBangladesh.csv')
### Sort the factor levels to display properly ###
Interest.df$Interest1<-relevel(Interest$Interest1, ref='Not Very Interested')
Interest.df$Interest1<-relevel(Interest$Interest1, ref='Somewhat Interested')
Interest.df$Interest1<-relevel(Interest$Interest1, ref='Very Interested')
Interest.df$Interest2<-relevel(Interest$Interest2, ref='News about Bangladesh')
Interest.df$Interest2<-relevel(Interest$Interest2, ref='Education')
[...]
Interest.df$Interest2<-relevel(Interest$Interest2, ref='European Politics')
detach(Interest)
attach(Interest)
### Finally create the graph in ggplot2 ###
library(ggplot2)
p<-ggplot(Interest, aes(Interest2, ..count..))
p<-p+geom_bar((aes(weight=Interest.weight, fill=Interest1)))
p<-p+coord_flip()
p<-p+scale_y_continuous("", breaks=NA)
p<-p+scale_fill_manual(value = rev(brewer.pal(5, "Purples")))
p
update_labels(p, list(fill='', x='', y=''))
I'd very much appreciate any tips, tricks or hints.
Your second problem can be solved with melt and cast from the reshape package
After you've factored the elements in your data.frame called you can use something like:
install.packages("reshape")
library(reshape)
x <- melt(your.df, c()) ## Assume you have some kind of data.frame of all factors
x <- na.omit(x) ## Be careful, sometimes removing NA can mess with your frequency calculations
x <- cast(x, variable + value ~., length)
colnames(x) <- c("variable","value","freq")
## Presto!
ggplot(x, aes(variable, freq, fill = value)) + geom_bar(position = "fill") + coord_flip() + scale_y_continuous("", formatter="percent")
As an aside, I like to use grep to pull in columns from a messy import. For example:
x <- your.df[,grep("int.",df)] ## pulls all columns starting with "int_"
And factoring is easier when you don't have to type c(' ', ...) a million times.
for(x in 1:ncol(x)) {
df[,x] <- factor(df[,x], labels = strsplit('
Very Interested
Somewhat Interested
Not Very Interested
Not At All interested
NA
NA
NA
NA
NA
NA
', '\n')[[1]][-1]
}
You don't need prop.tables or count etc to do the 100% stacked bars. You just need +geom_bar(position="stack")
About percentages insted of ..count.. , try:
ggplot(mtcars, aes(factor(cyl), prop.table(..count..) * 100)) + geom_bar()
but since it's not a good idea to shove a function into the aes(), you can write custom function to create percentages out of ..count.. , round it to n decimals etc.
You labeled this post with plyr, but I don't see any plyr in action here, and I bet that one ddply() can do the job. Online plyr documentation should suffice.
If I am understanding you correctly, to fix the axis labeling problem make the following change:
# p<-ggplot(Interest, aes(Interest2, ..count..))
p<-ggplot(Interest, aes(Interest2, ..density..))
As for the second one, I think you would be better off working with the reshape package. You can use it to aggregate data into groups very easily.
In reference to aL3xa's comment below...
library(ggplot2)
r<-rnorm(1000)
d<-as.data.frame(cbind(r,1:1000))
ggplot(d,aes(r,..density..))+geom_bar()
Returns...
alt text http://www.drewconway.com/zia/wp-content/uploads/2010/04/density.png
The bins are now densities...
Your first question: Would this help?
geom_bar(aes(y=..count../sum(..count..)))
Your second question; could you use reorder to sort the bars? Something like
aes(reorder(Interest, Value, mean), Value)
(just back from a seven hour drive - am tired - but I guess it should work)