I'm struggeling with the histogram function in my exploratory analysis. I would like to run a couple of variables in my dataset through a histogram function and for each add the title and a line at the arithmetic mean. This is how far I've got (but the main title is still missing):
histo.abline <-function(x){
hist(x)
abline(v = mean(x, na.rm = TRUE), col = "blue", lwd = 4)}
sapply(dataset[c(7:10)], histo.abline)
I tried to add a main argument in the histogram function but it just doesn't pick the right variable name of my dataset vector. When I put main=x there, it says returns NULL for each variable. Colnames, names and other functions didn't work either. Could you help me?
you can try to do it with ggplot:
library(ggplot)
histo.abline <-function(dataset,colnum){
p<-ggplot(dataset,aes(dataset[,colnum]))+geom_histogram(bins=5,fill=I("blue"),col=I("red"), alpha=I(.2))+
geom_vline(xintercept = mean(dataset[,colnum], na.rm = TRUE))+xlab(as.character(names(dataset)[colnum]))
return(p)
}
since you have not provided data lets work with mtcars and create a list of histograms
dataset=mtcars
listOfHistograms<-lapply(3:7,function(x) histo.abline(dataset,x))
your list has 5 histograms that you can plot for instance the first by:
print(listOfHistograms[[1]])
More histogram options for ggplot here: https://www.r-bloggers.com/how-to-make-a-histogram-with-ggplot2/
hope this helps
EDIT: Multiple Plot in one graph
One way to do it is through cowplot library:
library(cowplot)
plot_grid(plotlist=listOfHistograms[1:4])
This is my code to create a boxplot in R that has 4 boxplots in one.
psnr_x265_256 <- c(39.998,39.998, 40.766, 38.507,38.224,40.666,38.329,40.218,44.746,38.222)
psnr_x264_256 <- c(39.653, 38.106,37.794,36.13,36.808,41.991,36.718,39.26,46.071,36.677)
psnr_xvid_256 <- c(33.04564,33.207269,32.715427,32.104696,30.445141,33.135261,32.669766, 31.657039,31.53103,31.585865)
psnr_mpeg2_256 <- c(32.4198,32.055051,31.424819,30.560274,30.740421,32.484694, 32.512268,32.04659,32.345848, 31)
all_errors = cbind(psnr_x265_256, psnr_x264_256, psnr_xvid_256,psnr_mpeg2_256)
modes = cbind(rep("PSNR",10))
journal_linear_data <-data.frame(psnr_x265_256, psnr_x264_256, psnr_xvid_256,psnr_mpeg2_256)
yvars <- c("psnr_x265_256","psnr_x264_256","psnr_xvid_256","psnr_mpeg2_256")
xvars <- c("x265","x264","xvid","mpeg2")
bmp(filename="boxplot_PSNR_256.bmp")
boxplot(journal_linear_data[,yvars], xlab=xvars, ylab="PSNR")
dev.off()
This is the image I get.
I want to have the corresponding values for each boxplot in x axis "x265","x264","xvid","mpeg2".
Do you have any idea how to fix this?
There are multiple ways of changing the labels for your boxplot variables. Probably the simplest way is changing the column names of your data frame:
colnames(journal_linear_data) <- c("x265","x264","xvid","mpeg2")
Even simpler: you could do this right at the creation of your data frame too:
journal_linear_data <- data.frame(x265=psnr_x265_256, x264=psnr_x264_256, xvid=psnr_xvid_256, mpeg2=psnr_mpeg2_256)
If you run into the problem of your labels not being shown or overlapping due to too few space, try rotating the x labels using the las parameter, e.g. las=2 or las=3.
I'm plotting some Q-Q plots using the qqplot function. It's very convenient to use, except that I want to color the data points based on their IDs. For example:
library(qualityTools)
n=(rnorm(n=500, m=1, sd=1) )
id=c(rep(1,250),rep(2,250))
myData=data.frame(x=n,y=id)
qqPlot(myData$x, "normal",confbounds = FALSE)
So the plot looks like:
I need to color the dots based on their "id" values, for example blue for the ones with id=1, and red for the ones with id=2. I would greatly appreciate your help.
You can try setting col = myData$y. I'm not sure how the qqPlot function works from that package, but if you're not stuck with using that function, you can do this in base R.
Using base R functions, it would look something like this:
# The example data, as generated in the question
n <- rnorm(n=500, m=1, sd=1)
id <- c(rep(1,250), rep(2,250))
myData <- data.frame(x=n,y=id)
# The plot
qqnorm(myData$x, col = myData$y)
qqline(myData$x, lty = 2)
Not sure how helpful the colors will be due to the overplotting in this particular example.
Not used qqPlot before, but it you want to use it, there is a way to achieve what you want. It looks like the function invisibly passes back the data used in the plot. That means we can do something like this:
# Use qqPlot - it generates a graph, but ignore that for now
plotData <- qqPlot(myData$x, "normal",confbounds = FALSE, col = sample(colors(), nrow(myData)))
# Given that you have the data generated, you can create your own plot instead ...
with(plotData, {
plot(x, y, col = ifelse(id == 1, "red", "blue"))
abline(int, slope)
})
Hope that helps.
I would like to ask a follow-up question related to the answer given in this post [Gantt style time line plot (in base R) ] on Gantt plots in base r. I feel like this is worth a new question as I think these plots have a broad appeal. I'm also hoping that a new question would attract more attention. I also feel like I need more space than the comments of that question to be specific.
The following code was given by #digEmAll . It takes a dataframe with columns referring to a start time, end time, and grouping variable and turns that into a Gantt plot. I have modified #digEmAll 's function very slightly to get the bars/segments in the Gantt plot to be contiguous to one another rather than having a gap. Here it is:
plotGantt <- function(data, res.col='resources',
start.col='start', end.col='end', res.colors=rainbow(30))
{
#slightly enlarge Y axis margin to make space for labels
op <- par('mar')
par(mar = op + c(0,1.2,0,0))
minval <- min(data[,start.col])
maxval <- max(data[,end.col])
res.colors <- rev(res.colors)
resources <- sort(unique(data[,res.col]),decreasing=T)
plot(c(minval,maxval),
c(0.5,length(resources)+0.5),
type='n', xlab='Duration',ylab=NA,yaxt='n' )
axis(side=2,at=1:length(resources),labels=resources,las=1)
for(i in 1:length(resources))
{
yTop <- i+0.5
yBottom <- i-0.5
subset <- data[data[,res.col] == resources[i],]
for(r in 1:nrow(subset))
{
color <- res.colors[((i-1)%%length(res.colors))+1]
start <- subset[r,start.col]
end <- subset[r,end.col]
rect(start,yBottom,end,yTop,col=color)
}
}
par(op) # reset the plotting margins
}
Here are some sample data. You will notice that I have four groups 1-4. However, not all dataframes have all four groups. Some only have two, some only have 3.
mydf1 <- data.frame(startyear=2000:2009, endyear=2001:2010, group=c(1,1,1,1,2,2,2,1,1,1))
mydf2 <- data.frame(startyear=2000:2009, endyear=2001:2010, group=c(1,1,2,2,3,4,3,2,1,1))
mydf3 <- data.frame(startyear=2000:2009, endyear=2001:2010, group=c(4,4,4,4,4,4,3,2,3,3))
mydf4 <- data.frame(startyear=2000:2009, endyear=2001:2010, group=c(1,1,1,2,3,3,3,2,1,1))
Here I run the above function, but specify four colors for plotting:
plotGantt(mydf1, res.col='group', start.col='startyear', end.col='endyear',
res.colors=c('red','orange','yellow','gray99'))
plotGantt(mydf2, res.col='group', start.col='startyear', end.col='endyear',
res.colors=c('red','orange','yellow','gray99'))
plotGantt(mydf3, res.col='group', start.col='startyear', end.col='endyear',
res.colors=c('red','orange','yellow','gray99'))
plotGantt(mydf4, res.col='group', start.col='startyear', end.col='endyear',
res.colors=c('red','orange','yellow','gray99'))
These are the plots:
What I would like to do is modify the function so that:
1) it will plot on the y-axis all four groups regardless of whether they actually appear in the data or not.
2) Have the same color associated with each group for every plot regardless of how many groups there are. As you can see, mydf2 has four groups and all four colors are plotted (1-red, 2-orange, 3-yellow, 4-gray). These colors are actually plotted with the same groups for mydf3 as that only contains groups 2,3,4 and the colors are picked in reverse order. However mydf1 and mydf4 have different colors plotted for each group as they do not have any group 4's. Gray is still the first color chosen but now it is used for the lowest occurring group (group2 in mydf1 and group3 in mydf3).
It appears to me that the main thing I need to work on is the vector 'resources' inside the function, and have that not just contain the unique groups but all. When I try manually overriding to make sure it contains all the groups, e.g. doing something as simple as resources <-as.factor(1:4) then I get an error:
'Error in rect(start, yBottom, end, yTop, col = color) : cannot mix zero-length and non-zero- length coordinates'
Presumably the for loop does not know how to plot data that do not exist for groups that don't exist.
I hope that this is a replicable/readable question and it's clear what I'm trying to do.
EDIT: I realize that to solve the color problem, I could just specify the colors for the 3 groups that exist in each of these sample dfs. However, my intention is to use this plot as an output to a function whereby it wouldn't be known ahead of time if all of the groups exist for a particular df.
I slightly modified your function to account for NA in start and end dates :
plotGantt <- function(data, res.col='resources',
start.col='start', end.col='end', res.colors=rainbow(30))
{
#slightly enlarge Y axis margin to make space for labels
op <- par('mar')
par(mar = op + c(0,1.2,0,0))
minval <- min(data[,start.col],na.rm=T)
maxval <- max(data[,end.col],na.rm=T)
res.colors <- rev(res.colors)
resources <- sort(unique(data[,res.col]),decreasing=T)
plot(c(minval,maxval),
c(0.5,length(resources)+0.5),
type='n', xlab='Duration',ylab=NA,yaxt='n' )
axis(side=2,at=1:length(resources),labels=resources,las=1)
for(i in 1:length(resources))
{
yTop <- i+0.5
yBottom <- i-0.5
subset <- data[data[,res.col] == resources[i],]
for(r in 1:nrow(subset))
{
color <- res.colors[((i-1)%%length(res.colors))+1]
start <- subset[r,start.col]
end <- subset[r,end.col]
rect(start,yBottom,end,yTop,col=color)
}
}
par(mar=op) # reset the plotting margins
invisible()
}
In this way, if you simply append all your possible group values to your data you'll get them printed on the y axis. e.g. :
mydf1 <- data.frame(startyear=2000:2009, endyear=2001:2010,
group=c(1,1,1,1,2,2,2,1,1,1))
# add all the group values you want to print with NA dates
mydf1 <- rbind(mydf1,data.frame(startyear=NA,endyear=NA,group=1:4))
plotGantt(mydf1, res.col='group', start.col='startyear', end.col='endyear',
res.colors=c('red','orange','yellow','gray99'))
About the colors, at the moment the ordered res.colors are applied to the sorted groups; so the 1st color in res.colors is applied to 1st (sorted) group and so on...
I need a plot of different density lines, each in another color. This is an example code (but much smaller), using the built-in data.fame USArrests. I hope it is ok to use it?
colors <- heat.colors(3)
plot(density(USArrests[,2], bw=1, kernel="epanechnikov", na.rm=TRUE),col=colors[1])
lines1E <- function(x)lines(density(x,bw=1,kernel="epanechnikov",na.rm=TRUE))
lines1EUSA <- colwise(lines1E)(USArrests[,3:4])`
Currently the code produces with colwise() just one color. How can I get each line with another color? Or is there ab better way to plot several density lines with different colors?
I don't quite follow your example, so I've created my own example data set. First, create a matrix with three columns:
m = matrix(rnorm(60), ncol=3)
Then plot the density of the first column:
plot(density(m[,1]), col=2)
Using your lines1E function as a template:
lines1E = function(x) {lines(density(x))}
We can add multiple curves to the plot:
colwise(lines1E)(as.data.frame(m[ ,2:3]))
Personally, I would just use:
##Added in NA for illustration
m = matrix(rnorm(60), ncol=3)
m[1,] = NA
plot(density(m[,1], na.rm=T))
sapply(2:ncol(m), function(i) lines(density(m[,i], na.rm=T), col=i))
to get: