I have inputted some data consisting of three columns, X,Y and Group.
I am looking to get the underling data for a voronoi diagram for each group.
By using
a=deldir(Test.data$X,Test.data$Y,rw=c(0,1,0,1))
I succesfully create the voronoi data for the entire dataset. However I do not know how to iterate this process through the different groups that I have in the dataset.
Does anyone have any ideas? I have expereince with the ggplot function and know in here I can simply add a third dimension, something like
ggplot(Test.data,aes(x=X,y=Y,colour=Group))
Is there a way I can get a similar affect with the deldir() function
Thanks in advance for your help.
Ben
Consider creating a list of groups and then filter dataset. Below lapply() creates a list of deldir objects, one for each distinct group:
groups <- unique(Test.data$groupcol)
deldirList <- lapply(groups, function(g) {
temp <- Test.data[Test.data$groupcol==g,]
deldir(temp$X, temp$Y, rw=c(0,1,0,1))
})
Related
I am a newbie to R. I have a question. For checking the outlier of a variable we generally use:
boxplot(train$rate)
Suppose, the rate is the variable of my datasets and train is my data sets name. But when I have multiple variables like 100 or 150 variables, then it will be very time consuming to check one by one variable's outlier. Is there any function to bring the 100 variables' outlier in one boxplot?
If yes, then which function is used to remove those variable's outlier at one time instead of one by one? Please help to solve this problem.
Thanks in advance
I agree with Rui Barradas that it is bad practice to remove outliers without further thought. As long as the value is valid you should keep it in your data or at least run two separate analyses with and without the influential value. You could use a for loop to apply a function to every variable in your dataset.
train2<-train # Copy old dataset
outvalue<-list() # Create two empty lists
outindex<-list()
for(i in 1:ncol(train2){ # For every column in your dataset
outvalue[[i]]<-boxplot(train2[,i])$out # Plot and get the outlier value
outindex[[i]]<-which(train2[,i] == outvalue[[i]]) # Get the outlier index
train2[outindex[[i]],i] <- NA # Remove the outliers
}
This works and plots the data, but it is quite slow. If you don't want to plot the data but just want the outliers you could look into other outlier functions, the extremevalues package has a function that takes a different approach to identifying outliers and doesn't require a plot.
This uses the getOutliers function from the extremevalues package
outRight<-list()
outLeft<-outRight
for(i in 1:ncol(train2){
outRight[[i]]<-getOutliers(train2[,i])$iRight
outLeft[[i]]<-getOutliers(train2[,i])$iLeft
train2[outRight[[i]],i] <- NA
train2[outLeft[[i]],i] <- NA
}
The function boxplot returns a value. If you see the Value section of its help page you'll see that it's a list with named components, one of which is out. That's the one you seem to be looking for.
bp <- boxplot(train$rate)
bp$out
clean <- train$rate[-which(train$rate %in% bp$out)] # to remove the outliers
I also would not do that. Outliers are data, and normal/likely to occur. By eliminating them you are not taking into account the entirety of your data, a bad practice.
Dear Friends I would appreciate if someone can help me in some question in R.
I have a data frame with 8 variables, lets say (v1,v2,...,v8).I would like to produce groups of datasets based on all possible combinations of these variables. that is, with a set of 8 variables I am able to produce 2^8-1=63 subsets of variables like {v1},{v2},...,{v8}, {v1,v2},....,{v1,v2,v3},....,{v1,v2,...,v8}
my goal is to produce specific statistic based on these groupings and then compare which subset produces a better statistic. my problem is how can I produce these combinations.
thanks in advance
You need the function combn. It creates all the combinations of a vector that you provide it. For instance, in your example:
names(yourdataframe) <- c("V1","V2","V3","V4","V5","V6","V7","V8")
varnames <- names(yourdataframe)
combn(x = varnames,m = 3)
This gives you all permutations of V1-V8 taken 3 at a time.
I'll use data.table instead of data.frame;
I'll include an extraneous variable for robustness.
This will get you your subsetted data frames:
nn<-8L
dt<-setnames(as.data.table(cbind(1:100,matrix(rnorm(100*nn),ncol=nn))),
c("id",paste0("V",1:nn)))
#should be a smarter (read: more easily generalized) way to produce this,
# but it's eluding me for now...
#basically, this generates the indices to include when subsetting
x<-cbind(rep(c(0,1),each=128),
rep(rep(c(0,1),each=64),2),
rep(rep(c(0,1),each=32),4),
rep(rep(c(0,1),each=16),8),
rep(rep(c(0,1),each=8),16),
rep(rep(c(0,1),each=4),32),
rep(rep(c(0,1),each=2),64),
rep(c(0,1),128)) *
t(matrix(rep(1:nn),2^nn,nrow=nn))
#now get the correct column names for each subset
# by subscripting the nonzero elements
incl<-lapply(1:(2^nn),function(y){paste0("V",1:nn)[x[y,][x[y,]!=0]]})
#now subset the data.table for each subset
ans<-lapply(1:(2^nn),function(y){dt[,incl[[y]],with=F]})
You said you wanted some statistics from each subset, in which case it may be more useful to instead specify the last line as:
ans2<-lapply(1:(2^nn),function(y){unlist(dt[,incl[[y]],with=F])})
#exclude the first row, which is null
means<-lapply(2:(2^nn),function(y){mean(ans2[[y]])})
I have an environment I'm using as a hashmap (inspired by this question) that's storing a count of references to that key in another document.
Now I'm hoping to draw up a bar plot of the keys vs values ordered by those with the highest value.
To me it seems that the easiest way to do the sort (while keeping shared indexes between keys and values) would be to put my data into a data frame. So step 1, i extracted the keys and values from the map to two lists as below:
i<-0
keys <- list()
values <- list()
for (key in ls(contactsMap)){
keys[i]<-key
values[i]<-contactsMap[[key]]
i<- i + 1
}
values.num <- as.numeric(values) # to avoid character lists.
But now i cant seem to be able to get them to form a simple 2 column data frame.
I'm extremely new to R and I'm sure its something simple I'm missing, but no amount of searching will shed any light on the matter.
any suggestions on how to form a data frame from two lists or, more directly, how to sort two lists while keeping the relationship between them would be amazing.
Thanks.
df <- data.frame(keys, values) #and then you can sort it like this:
df[order(df$keys),]
I have a data set, and I am trying to create a new variable with random values that are associated with a particular subset.
For example, given the data frame:
data(iris)
iris=iris
I want another variable that associates each value of iris$Species with a random number (between 0 and 1). This can be accomplished in a circuitous fashion by creating a data frame:
df=data.frame(unique(iris$Species),runif(length(unique(iris$Species))))
And merging it with the original data frame:
iris=merge(iris,df,by.x="Species",by.y="unique.iris.Species.")
This accomplishes what I want, but it is inelegant. Furthermore, if I wanted to replicate this process many times over different variables this process would be burdensome. What I would hope for is some quick indexing method that would hopefully look something like:
iris$Species.unif=runif(length(unique(iris$Species)))[iris$Species]
Given that indexing in R is typically very slick, I expect there is some way of doing this that I am not aware of.
Thank you in advance.
You may want to try by using levels:
iris <- iris
iris$species_unif <- iris$Species
levels(iris$species_unif ) <- runif(length(levels(iris$Species)))
I have a set of data that looks like this,
species<-"ABC"
ind<-rep(1:4,each=24)
hour<-rep(seq(0,23,by=1),4)
depth<-runif(length(ind),1,50)
df<-data.frame(cbind(species,ind,hour,depth))
df$depth<-as.numeric(df$depth)
In this example, the column "ind" has more levels and they don't have always the same length (here each individual has 4 levels, but in reality some individuals have thousands of rows of data, while other only a few lines).
What I would like to do is to have an outer loop or function that will select all the rows from each individual ("ind") and generate a boxplot using the depth/hour columns.
This is the idea that I have in mind,
for (i in 1:length(unique(df$ind))){
data<-df[df$ind==df$ind[i],]
individual[i]<-data
plot.boxplot<-function(data){
boxplot(depth~hour,dat=data,xlab="Hour of day",ylab="Depth (m)")
}
}
par(mfrow=c(2,2),mar=c(5,4,3,1))
plot.boxplot(individual)
I realized that this loop might be inappropriate, but I am still learning. I can do the boxplot for each individual at a time, but I would like a faster, more efficient way of selecting the data for each individual and creating or storing boxplot results. This will be very useful for when I have many more individuals (instead of doing one at a time...). Thanks a lot in advance.
What about something like this?
par(mfrow=c(2,2))
invisible(
by(df,df$ind,
function(x)
boxplot(depth~hour,data=x,xlab="Hour of day",ylab="Depth (m)")
)
)
To provide some explanation, this runs a boxplot for each group of cases in df defined by df$ind. The invisible wrapper just makes it so that the bunch of output used for the boxplot is not written to the console.