I'am trying to print a plot, depending on a variable with 12 terms. This plot is the result of cluster classification on sequences, using OM distance.
I print this plot on one pdf page :
pdf("YYY.pdf", height=11,width=20)
seqIplot(XXX.seq, group=XXX$variable, cex.legend = 2, cex.plot = 1.5, border = NA, sortv =XXX.om)
dev.off()
But the printing is to small ... so i try to print this on 2 pages, like this :
pdf("YYY.pdf", height=11,width=20)
seqIplot(XXX.seq, group=XXX$variable, variable="1":"6", cex.legend = 2, cex.plot = 1.5, border = NA, sortv =XXX.om)
seqIplot(XXX.seq, group=XXX$variable, variable="7":"12", cex.legend = 2, cex.plot = 1.5, border = NA, sortv = XXX.om)
dev.off()
But it doesn't work ... Do you know how I can ask R to separate terms' variables into two groups, so as to print 6 graphics per pdf page ?
The solution is to plot separately the subset of groups you want on each page. Here is an example using the biofam data provided by TraMineR. The group variable p02r04 is religious participation which takes 10 different values.
library(TraMineR)
data(biofam)
bs <- seqdef(biofam[,10:25])
group <- factor(biofam$p02r04)
lv <- levels(group)
sel <- (group %in% lv[1:6])
seqIplot(bs[sel,], group=group[sel], sortv="from.end", withlegend=FALSE)
seqIplot(bs[!sel,], group=group[!sel], sortv="from.end")
If you are sorting the index plot with a variable you should indeed take the same subset of the sort variable, e.g. sortv=XXX.om[sel] in your case.
I don't know if I understood your question, you could post some data in order to help us reproduce what you want, maybe this helps. To plot six graphs in one page you should adjust the mfrow parameter, is that what you wanted?
pdf("test.pdf")
par(mfrow=c(3,2))
plot(1:10, 21:30)
plot(1:10, 21:30, pch=20)
hist(rnorm(1000))
barplot(VADeaths)
...
dev.off()
Related
Plotting several clusters using seqdplot in TraMineR can make the legend messy, especially in combination with numerous states. This calls for additional options for modifying the legend which is available with the function seqlegend. However, I have a hard time combining a state distribution plot (seqdplot) with a separate modified legend (seqlegend). Ideally one wants to plot the clusters (e.g. 9) without a legend and then add the separate legend in the available bottom right row, but instead the separate legend is generating a new plot window. Can anyone help?
Here's an example using the biofam data. With the data I use in my own research the legend becomes much more messy since I have 11 states.
#Data
library(TraMineR)
library(WeightedCluster)
data(biofam)
biofam.seq <- seqdef(biofam[501:600, 10:25])
#OM distances
biofam.om <- seqdist(biofam.seq, method = "OM", indel = 3, sm = "TRATE")
#9 clusters
wardCluster <- hclust(as.dist(biofam.om), method = "ward.D2")
cluster9 <- cutree(wardCluster, k = 9)
#State distribution plot
seqdplot(biofam.seq, group = cluster9, with.legend = F)
#Separate legend
seqlegend(biofam.seq, title = "States", ncol = 2)
#Combine state distribution plot and separate legend
#??
Thank you.
The seqplot function does not allow to control the number of columns of the legend, nor does it allow to add a legend title. So you have to compose the plot yourself by generating a separated plot for each group with the legend disabled and adding the legend afterwards. Here is how you can do that:
cluster9 <- factor(cluster9)
levc <- levels(cluster9)
lev <- length(levc)
par(mfrow=c(5,2))
for (i in 1:lev)
seqdplot(biofam.seq[cluster9 == levc[i],], border=NA, main=levc[i], with.legend=FALSE)
seqlegend(biofam.seq, ncol=4, cex = 1.2, title='States')
========================
Update, Oct 1, 2018 =================
Since TraMineR V 2.0-9, the seqplot family of functions now support (when applicable) the argument ncol to control the number of columns in the legend. To add a title to the legend, you still have to proceed as shown above.
AFAIK seqlegend() doesn't work when the other plots you are plotting utilizes the groups arguments. In your case the only thing seqlegend() is adding is a title "States". If you are looking to add a legend so you can customize what is in the legend and so forth, you can accomplish that by providing the corresponding alphabet and states that are used in your analysis.
The package's website has several walkthroughs and guides enumerating the various options and so forth: Link to their webiste
#Data
library(TraMineR)
library(WeightedCluster)
data(biofam)
## Generate alphabet and states
alphabet <- 0:7
states <- letters[seq_along(alphabet)]
biofam.seq <- seqdef(biofam[501:600, 10:25], states = states, alphabet = alphabet)
#OM distances
biofam.om <- seqdist(biofam.seq, method = "OM", indel = 3, sm = "TRATE")
#9 clusters
wardCluster <- hclust(as.dist(biofam.om), method = "ward.D2")
cluster9 <- cutree(wardCluster, k = 9)
#State distribution plot
seqdplot(biofam.seq, group = cluster9, with.legend = TRUE)
We can plot candle stick chart using chart series function chartSeries(Cl(PSEC)) I have created some custom values (I1,I2 and I3) which I want to plot together(overlay) outside the candle stick pattern. I have used addTA() for this purpose
chartSeries(Cl(PSEC)), TA="addTA(I1,col=2);addTA(I2,col=3);addTA(I3,col=4)")
The problem is that it plots four plots for Cl(PSEC),I1,I2 and I3 separately instead of two plots which I want Cl(PSEC) and (I1,I2,I3)
EDITED
For clarity I am giving a sample code with I1, I2 and I3 variable created for this purpose
library(quantmod)
PSEC=getSymbols("PSEC",auto.assign=F)
price=Cl(PSEC)
I1=SMA(price,3)
I2=SMA(price,10)
I3=SMA(price,15)
chartSeries(price, TA="addTA(I1,col=2);addTA(I2,col=3);addTA(I3,col=4)")
Here is an option which preserves largely your original code.
You can obtain the desired result using the option on=2 for each TA after the first:
library(quantmod)
getSymbols("PSEC")
price <- Cl(PSEC)
I1 <- SMA(price,3)
I2 <- SMA(price,10)
I3 <- SMA(price,15)
chartSeries(price, TA=list("addTA(I1, col=2)", "addTA(I2, col=4, on=2)",
"addTA(I3, col=5, on=2)"), subset = "last 6 months")
If you want to overlay the price and the SMAs in one chart, you can use the option on=1 for each TA.
Thanks to #hvollmeier who made me realize with his answer that I had misunderstood your question in the previous version of my answer.
PS: Note that several options are described in ?addSMA(), including with.col which can be used to select a specific column of the time series (Cl is the default column).
If I understand you correctly you want the 3 SMAs in a SUBPLOT and NOT in your main chart window.You can do the following using newTA.
Using your data:
PSEC=getSymbols("PSEC",auto.assign=F)
price=Cl(PSEC)
Now plotting a 10,30,50 day SMA in a window below the main window:
chartSeries(price['2016'])
newSMA <- newTA(SMA, Cl, on=NA)
newSMA(10)
newSMA(30,on=2)
newSMA(50,on=2)
The key is the argument on. Use on = NA in defining your new TA function, because the default value foron is 1, which is the main window. on = NA plots in a new window. Then plot the remaining SMAs to the same window as the first SMA. Style the colours etc.to your liking :-).
You may want to consider solving this task using plotting with the newer quantmod charts in the quantmod package (chart_Series as opposed to chartSeries).
Pros:
-The plots look cleaner and better (?)
-have more flexibility via editing the pars and themes options to chart_Series (see other examples here on SO for the basics of things you can do with pars and themes)
Cons:
-Not well documented.
PSEC=getSymbols("PSEC",auto.assign=F)
price=Cl(PSEC)
chart_Series(price, subset = '2016')
add_TA(SMA(price, 10))
add_TA(SMA(price, 30), on = 2, col = "green")
add_TA(SMA(price, 50), on = 2, col = "red")
# Make plot all at once (this approach is useful in shiny applications):
print(chart_Series(price, subset = '2016', TA = 'add_TA(SMA(price, 10), yaxis = list(0, 10));
add_TA(SMA(price, 30), on = 2, col = "purple"); add_TA(SMA(price, 50), on = 2, col = "red")'))
Could you explain me if there is a way to extract outliers from box plot. I have plotted a box plot and I want to extract only the outliers.
Here is the code for the box plot.
# melting down
require(reshape)
melt_nx <- melt(nx, id.vars = c("x", "y"))
boxplot(data = melt_nx, main = "NX", value ~ variable, las = 2,
par(mar = c(15, 5, 4, 2) + 0.1),
names = c("We1", "We2", "we3"))
Is it possible from the box plot to extract the outliers only?
The boxplot function returns a list with one of it node-names as "out". These are the values that are beyond the "whiskers". I don't know about executing par within the argument list but if you want these particular values, then use this:
vals <- boxplot(data = melt_nx, main = "NX", value ~ variable, las = 2,
names = c("We1", "We2", "we3"))
vals$out
And do read all these help pages:
?boxplot
?boxplot.stats
?bxp
?fivenum
I know this has been answered, but for me there is an alternative method using the Boxplot method from the car package. Note the capital B in the Boxplot function call.
This is the code that does it for me, it returns the row numbers of the outliers which you can then use in your dataframe to filter out or extract, etc...
outliers<-Boxplot(x~y, data=df, id.method="y")
Note that the extracted values are of type Character. Then to exclude them you could do something like:
df2 <- df[-as.numeric(outliers),]
Hope this helps a little
The following is some code that produces various spider graphs:
# Data must be given as the data frame, where the first cases show maximum.
maxmin <- data.frame(
total=c(5, 1),
phys=c(15, 3),
psycho=c(3, 0),
social=c(5, 1),
env=c(5, 1))
# data for radarchart function version 1 series, minimum value must be omitted from above.
RNGkind("Mersenne-Twister")
set.seed(123)
dat <- data.frame(
total=runif(3, 1, 5),
phys=rnorm(3, 10, 2),
psycho=c(0.5, NA, 3),
social=runif(3, 1, 5),
env=c(5, 2.5, 4))
dat <- rbind(maxmin,dat)
op <- par(mar=c(1, 2, 2, 1),mfrow=c(2, 2))
radarchart(dat, axistype=1, seg=5, plty=1, vlabels=c("Total\nQOL", "Physical\naspects",
"Phychological\naspects", "Social\naspects", "Environmental\naspects"),
title="(axis=1, 5 segments, with specified vlabels)")
radarchart(dat, axistype=2, pcol=topo.colors(3), plty=1, pdensity=30, pfcol=topo.colors(3),
title="(topo.colors, fill, axis=2)")
radarchart(dat, axistype=3, pty=32, plty=1, axislabcol="grey", na.itp=FALSE,
title="(no points, axis=3, na.itp=FALSE)")
radarchart(dat, axistype=1, plwd=1:5, pcol=1, centerzero=TRUE,
seg=4, caxislabels=c("worst", "", "", "", "best"),
title="(use lty and lwd but b/w, axis=1,\n centerzero=TRUE, with centerlabels)")
par(op)
The output of the graphs consists of two sets of line segments with different colors. Where did the second set of line segments come from? Also what is a good way to graph multiple items on the same spider graph?
You should mention that you are using the fmsb library to create the graph. The code you show is the example in the documentation. The puzzling thing at first glance is why three sets of lines are shown (not two as you imply with "second set") while there are 5 records in dat.
It is all in that same documentation you took the code from:
row 1 = the maximum values (defined in `maxmin` in the example code)
row 2 = minimum values (defined in `maxmin` in the example code)
row 3 to 5 are example data points, each row leading to one of the
three line segments that you see in the example graphs.
Just read the documentation for radarchart {fmsb} again and play with the numbers in the example as you do so. It should be pretty clear what is happening and what options you have for your own data. You can add as many data-rows and create corresponding lines as you wish. But these do tend to become unreadable if you overdo it.
I'm afraid I have a spplot() question again.
I want the colors in my spplot() to represent absolute values, not automatic values as spplot does it by default.
I achieve this by making a factor out of the variable I want to draw (using the command cut()). This works very fine, but the color-key doesn't look good at all.
See it yourself:
library(sp)
data(meuse.grid)
gridded(meuse.grid) = ~x+y
meuse.grid$random <- rnorm(nrow(meuse.grid), 7, 2)
meuse.grid$random[meuse.grid$random < 0] <- 0
meuse.grid$random[meuse.grid$random > 10] <- 10
# making a factor out of meuse.grid$ random to have absolute values plotted
meuse.grid$random <- cut(meuse.grid$random, seq(0, 10, 0.1))
spplot(meuse.grid, c("random"), col.regions = rainbow(100, start = 4/6, end = 1))
How can I have the color.key on the right look good - I'd like to have fewer ticks and fewer labels (maybe just one label on each extreme of the color.key)
Thank you in advance!
[edit]
To make clear what I mean with absolute values: Imagine a map where I want to display the sea height. Seaheight = 0 (which is the min-value) should always be displayed blue. Seaheight = 10 (which, just for the sake of the example, is the max-value) should always be displayed red. Even if there is no sea on the regions displayed on the map, this shouldn't change.
I achieve this with the cut() command in my example. So this part works fine.
THIS IS WHAT MY QUESTION IS ABOUT
What I don't like is the color description on the right side. There are 100 ticks and each tick has a label. I want fewer ticks and fewer labels.
The way to go is using the attribute colorkey. For example:
## labels
labelat = c(1, 2, 3, 4, 5)
labeltext = c("one", "two", "three", "four", "five")
## plot
spplot(meuse.grid,
c("random"),
col.regions = rainbow(100, start = 4/6, end = 1),
colorkey = list(
labels=list(
at = labelat,
labels = labeltext
)
)
)
First, it's not at all clear what you are wanting here. There are many ways to make the color.key look "nice" and that is to understand first the data being passed to spplot and what is being asked of it. cut() is providing fully formatted intervals like (2.3, 5.34] which will need to be handled a different way, increasing the margins in the plot, specific formatting and spacing for the labels, etc. etc. This just may not be what you ultimately want.
Perhaps you just want integer values, rounded from the input values?
library(sp)
data(meuse.grid)
gridded(meuse.grid) = ~x+y
meuse.grid$random <- rnorm(nrow(meuse.grid), 7, 2)
Round the values (or trunc(), ceil(), floor() them . . .)
meuse.grid$rclass <- round(meuse.grid$random)
spplot(meuse.grid, c("rclass"), col.regions = rainbow(100, start = 4/6, end = 1))