I am trying to visualise a biparite network using the biparite package in R. My data consists of 4 columns in a spreadsheet. The columns contain 1) plant species names2) bee species names 3) site 4) interaction frequency. I first read the data into R from a CSV file, then convert it to a web using the helper function frame2webs. When I then try to visualise the network with plotweb() I get the error message:
Error in web[rind, cind, drop = FALSE] : incorrect number of dimensions
My code looks like this:
library(bipartite)
bee <- read.csv('TestFile.csv')
bees <- as.data.frame(bee)
BeeWeb <- frame2webs(bees, type.out = "array")
plotweb(BeeWeb)
I've also tried:
BeeWeb <- frame2webs(bees,
varnames = c("higher","lower","webID","freq"),
type.out = "array")
Please help! I am new to R and am struggling to make this work. Cheers!
Not sure what your data look like, but this happens to me when I have a single factor level in either the "higher" or "lower" column, type.out is "list", and emptylist is TRUE.
This is due to a problem in empty, a function that frame2webs only calls when type.out is "list" and emptylist is TRUE. empty finds the dimensions of your data using NROW and NCOL, which interpret a single row of input as a vertical vector. When there's only one factor level in "lower" or "higher", the input to empty is a one-row array. empty interprets this row as a column, hence the 'incorrect number of dimensions' error.
Two simple workarounds:
Set type.out to "array"
Set emptylist to FALSE
Related
I am trying to construct a table of power spectra and run into this problem:
Define the table:
V <- tibble(month=double(),day=double(),hour=double(),minutes=double(),
frequency=double(),power=double(),period=double())
compute the spectrum:
S <- spec.pgram(Spec2d$Inst,spans=windowSize,log="yes")
which creates an object of class "spec"
I need to extract the data from S and put it into V. When I try:
V$frequency <- S$freq
I get this error message:
Error: Assigned data `S$freq` must be compatible with existing data.
x Existing data has 0 rows.
x Assigned data has 48 rows.
ℹ Only vectors of size 1 are recycled.
which doesn't make sense to me. I have tried to coerce S$freq into different different types of objects but nothing works.
S$freq is a vector of length 48 as in the error message
What is going on? Is there a workaround?
Don't initialise the dataframe/tibble first. Try :
S <- spec.pgram(Spec2d$Inst,spans=windowSize,log="yes")
V <- data.frame(frequency = S$freq)
I'm running a large number of meta-analyses with metafor. To get an overview of the results, I wanted to put together vectors containing the main estimates (to combine them in a dataframe later on). Yet, for some of these calculations, I do not have enough primary studies yet, so R will not be able to create a model for this particular domain. Hence, I will get an error message when I try to create a vector at the end.
library(metafor)
r1<-c(NA,NA)
n1<-c(NA,NA)
data1<-data.frame(r1,n1)
escalc1<-escalc(measure="COR", ri=r1,ni=n1, data = data1, method=REML)
rma1<-rma(yi,vi, data=escalc1)
#note the program will not be able to calculate rma1, because k = 0.
r2<-c(.3,.2)
n2<-c(100,200)
data2<-data.frame(r2,n2)
escalc2<-escalc(measure="COR", ri=r2,ni=n2, data = data2, method=REML)
rma2<-rma(yi,vi, data=escalc2)
#it will create an object for rma2 though
estimates<-c(rma1$beta, rma2$beta)
#as rma2 exists but rma1 doesn't, R will no let me create a vector here
Is there a way to tell R to check if the object exists first and to put in NAs for all cases where no object has been created yet? Specifically, I want R to replace rma1$beta (which does not exist) with NA in the last line of code. Is that possible?
You can use tryCatch to tell R what to do as an alternative if an error occurs, e.g.,
library(metafor)
r1<-c(NA,NA)
n1<-c(NA,NA)
data1<-data.frame(r1,n1)
escalc1<-escalc(measure="COR", ri=r1,ni=n1, data = data1)
e1 <- tryCatch({
rma1<-rma(yi,vi, data=escalc1);
rma1$beta}, error = function(e) NA)
r2<-c(.3,.2)
n2<-c(100,200)
data2<-data.frame(r2,n2)
escalc2<-escalc(measure="COR", ri=r2,ni=n2, data = data2)
e2 <- tryCatch({
rma2<-rma(yi,vi, data=escalc2);
rma2$beta}, error = function(e) NA)
estimates<-c(e1, e2)
#[1] NA 0.2356358
Let say that I have these vectors:
time <- c(306,455,1010,210,883,1022,310,361,218,166)
status <- c(1,1,0,1,1,0,1,1,1,1)
gender <- c(1,1,1,1,1,1,2,2,1,1)
And I turn it into these data frame:
dataset <- data.frame(time, status, gender)
I want to list the factors in the third column using this function (p/s: pardon the immaturity. I'm still learning):
getFactor<-function(dataset){
result <- list()
result["Factors"] <- unique(dataset[[3]])
return(result)
}
And all I get is this:
getFactor(dataset)
$Factors
[1] 1
Warning message:
In result["Factors"] <- unique(dataset[[3]]) :
number of items to replace is not a multiple of replacement length
I tried using levels, but all I get is an empty list. My question is (1) why does this happen? and (2) is there any other way that I can get the list of the factor in a function?
Solution is simple, you just need double brackets around "Factors" :)
In the function
result[["Factors"]] <- unique(dataset[[3]])
That should be the line.
The double brackets return an element, single brackets return that selection as a list.
Sounds silly, by try this
test <- list()
class(test["Factors"])
class(test[["Factors"]])
The first class will be of type 'list'. The second will be of type 'NULL'. This is because the single brackets returns a subset as a list, and the double brackets return the element itself. It's useful depending on the scenario. The element in this case is "NULL" because nothing has been assigned to it.
The error "number of items to replace is not a multiple of replacement length" is because you've asked it to put 3 things into a single element (that element is a list). When you use double brackets you actually put it inside a list, where you can have multiple elements, so it can work!
Hope that makes sense!
Currently, when you create your data frame, dataset$gender is double vector (which R will automatically do if everything in it is numbers). If you want it to be a factor, you can declare it that way at the beginning:
dataset <- data.frame(time, status, gender = as.factor(gender))
Or coerce it to be a factor later:
dataset$gender <- as.factor(gender)
Then getting a vector of the levels is simple, without writing a function:
level_vector <- levels(dataset$gender)
level_vector
You're also subsetting lists & data frames incorrectly in your function. To call the third column of dataset, use dataset[,3]. The first element of a list is called by list[[1]]
I would like to perform a HCPC on the columns of my dataset, after performing a CA. For some reason I also have to specify at the start, that all of my columns are of type 'factor', just to loop over them afterwards again and convert them to numeric. I don't know why exactly, because if I check the type of each column (without specifying them as factor) they appear to be numeric... When I don't load and convert the data like this, however, I get an error like the following:
Error in eigen(crossprod(t(X), t(X)), symmetric = TRUE) : infinite or
missing values in 'x'
Could this be due to the fact that there are columns in my dataset that only contain 0's? If so, how come that it works perfectly fine by reading everything in first as factor and then converting it to numeric before applying the CA, instead of just performing the CA directly?
The original issue with the HCPC, then, is the following:
# read in data; 40 x 267 data frame
data_for_ca <- read.csv("./data/data_clean_CA_complete.csv",row.names=1,colClasses = c(rep('factor',267)))
# loop over first 267 columns, converting them to numeric
for(i in 1:267)
data_for_ca[[i]] <- as.numeric(data_for_ca[[i]])
# perform CA
data.ca <- CA(data_for_ca,graph = F)
# perform HCPC for rows (i.e. individuals); up until here everything works just fine
data.hcpc <- HCPC(data.ca,graph = T)
# now I start having trouble
# perform HCPC for columns (i.e. variables); use their coordinates that are stocked in the CA-object that was created earlier
data.cols.hcpc <- HCPC(data.ca$col$coord,graph = T)
The code above shows me a dendrogram in the last case and even lets me cut it into clusters, but then I get the following error:
Error in catdes(data.clust, ncol(data.clust), proba = proba, row.w =
res.sauv$call$row.w.init) : object 'data.clust' not found
It's worth noting that when I perform MCA on my data and try to perform HCPC on my columns in that case, I get the exact same error. Would anyone have any clue as how to fix this or what I am doing wrong exactly? For completeness I insert a screenshot of the upper-left corner of my dataset to show what it looks like:
Thanks in advance for any possible help!
I know this is old, but because I've been troubleshooting this problem for a while today:
HCPC says that it accepts a data frame, but any time I try to simply pass it $col$coord or $colcoord from a standard ca object, it returns this error. My best guess is that there's some metadata it actually needs/is looking for that isn't in a data frame of coordinates, but I can't figure out what that is or how to pass it in.
The current version of FactoMineR will actually just allow you to give HCPC the whole CA object and tell it whether to cluster the rows or columns. So your last line of code should be:
data.cols.hcpc <- HCPC(data.ca, cluster.CA = "columns", graph = T)
I have the following block of code. I am a complete beginner in R (a few days old) so I am not sure how much of the code will I need to share to counter my problem. So here is all of it I have written.
mdata <- read.csv("outcome-of-care-measures.csv",colClasses = "character")
allstate <- unique(mdata$State)
allstate <- allstate[order(allstate)]
spldata <- split(mdata,mdata$State)
if (num=="best") num <- 1
ranklist <- data.frame("hospital" = character(),"state" = character())
for (i in seq_len(length(allstate))) {
if (outcome=="heart attack"){
pdata <- spldata[[i]]
pdata[,11] <- as.numeric(pdata[,11])
bestof <- pdata[!is.na(as.numeric(pdata[,11])),][]
inorder <- order(bestof[,11],bestof[,2])
if (num=="worst") num <- nrow(bestof)
hospital <- bestof[inorder[num],2]
state <- allstate[i]
ranklist <- rbind(ranklist,c(hospital,state))
}
}
allstate is a character vector of states.
outcome can have values similar to "heart attack"
num will be numeric or "best" or "worst"
I want to create a data frame ranklist which will have hospital names and the state names which follow a certain criterion.
However I keep getting the error
invalid factor level, NA generated
I know it has something to do with rbind but I cannot figure out what is it. I have tried googling about this, and also tried troubleshooting using other similar queries on this site too. I have checked any of my vectors I am trying to bind are not factors. I also tried forcing the coercion by setting the hospital and state as.character() during assignment, but didn't work.
I would be grateful for any help.
Thanks in advance!
Since this is apparently from a Coursera assignment I am not going to give you a solution but I am going to hint at it: Have a look at the help pages for read.csv and data.frame. Both have the argument stringsAsFactors. What is the default, true or false? Do you want to keep the default setting? Is colClasses = "character" in line 1 necessary? Use the str function to check what the classes of the columns in mdata and ranklist are. read.csv additionally has an na.strings argument. If you use it correctly, also the NAs introduced by coercion warning will disappear and line 16 won't be necessary.
Finally, don't grow a matrix or data frame inside a loop if you know the final size beforehand. Initialize it with the correct dimensions (here 52 x 2) and assign e.g. the i-th hospital to the i-th row and first column of the data frame. That way rbind is not necessary.
By the way you did not get an error but a warning. R didn't interrupt the loop it just let you know that some values have been coerced to NA. You can also simplify the seq_len statement by using seq_along instead.