I want to perform basket analysis and draw a paracoord plot however I receive an error.
Content of this error is: :
Error in m[j, i] : subscript out of bounds.In addition: Warning message:
In cbind(pl, pr) :
number of rows of result is not a multiple of vector length (arg 2)
I am using data from: Link.
First I am transforming this to fit basket analysis, name of the original excel files is Online_Retail:
library(arules)
library(arulesViz)
library(plyr)
items <- ddply(Online_Retail, c("CustomerID", "InvoiceDate"), function(df1)paste(df1$Description, collapse = ","))
items1 <- items["V1"]
write.csv(items1, "groceries1.csv", quote=FALSE, row.names = FALSE, col.names = FALSE)
trans1 <- read.transactions("groceries1.csv", format = "basket", sep=",",skip=1)
And to draw paracoord I have created such a code:
rules.trans2<-apriori(data=trans1, parameter=list(supp=0.001,conf = 0.05),
appearance=list(default="rhs", lhs="ROSES REGENCY TEACUP AND SAUCER"), control=list(verbose=F))
sorted.plot <- sort(rules.trans2, by="support", decreasing = TRUE)
plot(sorted.plot, method="paracoord", control=list(reorder=TRUE, verbose = TRUE))
Why my code for paracoord is not working? how can I fix it? What should I change?
This is, unfortunately, a bug in arulesViz. This will be fixed in the next release (arulesViz 1.3-3). The fix is already available in the development version on GitHub: https://github.com/mhahsler/arulesViz
Related
I've recently been attempting to evaluate output from k-modes (a cluster label), relative to a so-called True cluster label (labelled 'class' below).
In other words: I've been attempting to external validate the clustering output. However, when I tried external validation measures from the 'fpc' package, I was unsuccessful (error term posted below script).
I've attached my code for the mushroom dataset. I would appreciate if anyone could show me how to successful execute these external validation measures in the context of categorical data.
Any help appreciated.
# LIBRARIES
install.packages('klaR')
install.packages('fpc')
library(klaR)
library(fpc)
#MUSHROOM DATA
mushrooms <- read.csv(file = "https://raw.githubusercontent.com/miachen410/Mushrooms/master/mushrooms.csv", header = FALSE)
names(mushrooms) <- c("edibility", "cap-shape", "cap-surface", "cap-color",
"bruises", "odor", "gill-attachment", "gill-spacing",
"gill-size", "gill-color", "stalk-shape", "stalk-root",
"stalk-surface-above-ring", "stalk-surface-below-ring",
"stalk-color-above-ring", "stalk-color-below-ring", "veil-type",
"veil-color", "ring-number", "ring-type", "spore-print-color",
"population", "habitat")
names(mushrooms)[names(mushrooms)=="edibility"] <- "class"
indexes <- apply(mushrooms, 2, function(x) any(is.na(x) | is.infinite(x)))
colnames(mushrooms)[indexes]
table(mushrooms$class)
str(mushrooms)
#REMOVING CLASS VARIABLE
mushroom.df <- subset(mushrooms, select = -c(class))
#KMODES ANALYSIS
result.kmode <- kmodes(mushroom.df, 2, iter.max = 50, weighted = FALSE)
#EXTERNAL VALIDATION ATTEMPT
mushrooms$class <- as.factor(mushrooms$class)
class <- as.numeric(mushrooms$class))
clust_stats <- cluster.stats(d = dist(mushroom.df),
class, result.kmode$cluster)
#ERROR TERM
Error in silhouette.default(clustering, dmatrix = dmat) :
NA/NaN/Inf in foreign function call (arg 1)
In addition: Warning message:
In dist(mushroom.df) : NAs introduced by coercion
I am using WarbleR in R to do some acoustic analyses. As freq_range couldn't detect all the bottom frequencies very well, I have created a data frame manually with all the right bottom frequencies, loaded this into R and turned it into a selection table. Traq_freq_contour and compare.methods and freq_DTW all work fine (although freq_DTW does give a warning message:
Warning message: In (0:(n - 1)) * f : NAs produced by integer overflow
However. If I try to do the function cross_correlation, I get the following error:
Error in if (ncol(spc1$amp) > ncol(spc2$amp)) { :
argument is of length zero
I do not get this error with a selection table with the bottom and top frequency added with the freq_range function in R instead of manually. What could be the issue here? The selection tables both look similar:
This is the selection table partly made by R through freq_range:
And this is the one with the bottom frequencies added manually (which has more sound files than the one before):
This is part of the code I use:
#Comparing methods for quantitative analysis of signal structure
compare.methods(X = stnew, flim = c(0.6,2.5), bp = c(0.6,2.5), methods = c("XCORR", "dfDTW"))
#Measure acoustic parameters with spectro_analysis
paramsnew <- spectro_analysis(stnew, bp = c(0.6,2), threshold = 20)
write.csv(paramsnew, "new_acoustic_parameters.csv", row.names = FALSE)
#Remove parameters derived from fundamental frequency
paramsnew <- paramsnew[, grep("fun|peakf", colnames(paramsnew), invert = TRUE)]
#Dynamic time warping
dm <- freq_DTW(stnew, length.out = 30, flim = c(0.6,2), bp = c(0.6,2), wl = 300, img = TRUE)
str(dm)
#Spectrographic cross-correlation
xcnew <- cross_correlation(stnew, wl = 300, na.rm = FALSE)
str(xc)
Any idea what I'm doing wrong?
I want to use ordinal data (1,2,3,4) of 59 observables with 322 variables and daisy, eventually leading to a cluster analysis. I use the script below on an excel input file (csv). After daisy, the following error message occurs:
> Error in daisy(grb, stand = TRUE) : ungültiger Typ character für
> Spaltennummern 1 In addition: Warning messages: 1: In data.matrix(x) :
> NAs introduced by coercion 2: In daisy(grb, stand = TRUE) :
> Binärvariable 2, 3, 4,
The grb data contain the row names in the first column, and the column titles in the first line. daisy seems to think it has to use the first column: How can I tell it no to do that? The input data look ok.
Secondly, daisy thinks the data a binaries, but they are oridnals, 1-4. How to correct this? Any help is greatly appreciated.
Script:
library(readr)
grb <- read_delim("~/R/Projects/Datasets/Ges.csv",
";", escape_double = FALSE, trim_ws = TRUE)
attach(grb)
library(cluster)
data(grb)
head(grb, 2)
d.d <- daisy(grb, stand = TRUE)
grb.hc <- hclust(d.d, method = "ward.D2")
library("factoextra")
fviz_dend(grb.hc, cex=.5, horiz = TRUE)
I am using Titanic dataset, to extract some data out of it.
and then I tried to extract the rules based on Aprior :
library(arules)
rules<- apriori(df)
Then I asked for two metrics lift, and oddsRatio
metrics <- interestMeasure(rules, c("oddsRatio","lift"),transactions = df)
rules<-sort(rules, decreasing = TRUE, by = "lift")
inspect(head(rules ))
head(metrics)
But, I need to sort the results based on oddsRatio, so I did
dataFramedRules <-quality(rules)
rules<-cbind(dataFramedRules,metrics)
Everything was well before the last line
rules<-sort(rules, decreasing = TRUE, by = "oddsRatio")
but in the last line it complains with:
Error in [.data.frame(x, order(x, na.last = na.last, decreasing = decreasing)) :
undefined columns selected
it seems that it cannot recognized the binded column oddsRatio.
How can I fix it?
The problem get solved by the following code:
quality(rules) <- cbind(quality(rules), oddsRatio = interestMeasure(rules, measure ="oddsRatio", df))
inspect(head(sort(rules, by = "oddsRatio")))
I am currently facing an error mentioned below which is related to NULL values being coerced to a data frame. The data set does contain nulls, however I have tried both is.na() and is.null() functions to replace the null values with something else. The data is stored on hdfs and is stored in a pig.hive format. I have also attached the code below. The code works fine if I remove v[,25] from the key.
Code:
AM = c("AN");
UK = c("PP");
sample.map <- function(k,v){
key <- data.frame(acc = v[!which(is.na(v[,1],1],
year = substr(v[!which(is.na(v[,1]),2],1,4),
month = substr(v[!which(is.na(v[,1]),2],5,6))
value <- data.frame(v[,3],count=1)
keyval(key,value)
}
sample.reduce <- function(key,v){
AT <- sum(v[which(v[,1] %in% AM=="TRUE"),2])
UnknownT <- sum(v[which(v[,1] %in% UK=="TRUE"),2])
Total <- AT + UnknownT
d <- data.frame(AT,UnknownT,Total)
keyval(key,d)
}
out <- mapreduce(input ="/user/hduser/input",
output = "/user/hduser/output",
input.format = make.input.format("pig.hive", sep = "\u0001")
output.format = make.output.format("csv", sep = ","),
map= sample.map)
reduce = sample.reduce)
Error:
Warning in asMethod(object) : NAs introduced by coercion
Warning in split.default(1:rmr.length(y), unique(ind), drop = TRUE) : data length is not a multiple of split variable
Warning in rmr.split(x, x, FALSE, keep.rownames = FALSE) : number of items to replace is not a multiple of replacement length Warning in split.default(1:rmr.length(y), unique(ind), drop = TRUE) :
data length is not a multiple of split variable
Warning in rmr.split(v, ind, lossy = lossy, keep.rownames = TRUE) : number of items to replace is not a multiple of replacement length
Error in as(x, class(k)) :
no method or default for coercing “NULL” to “data.frame”
Calls: <Anonymous> ... apply.reduce -> c.keyval -> reduce.keyval -> lapply -> FUN -> as No traceback available
UPDATE
I have added the sample data and edited the code above. Hope this helps!
Sample Data:
NULL,"2014-03-14","PP"
345689202,"2014-03-14","AN"
234539390,"2014-03-14","PP"
123125444,"2014-03-14","AN"
NULL,"2014-03-14","AN"
901828393,"2014-03-14","AN"
There are some issues with as which have been identified recently. I don't see why as can't handle this by default, but you can modify coerce which handles the conversion with an S4 method to call as.data.frame.
setMethod("coerce",c("NULL","data.frame"), function(from, to, strict=TRUE) as.data.frame(from))
[1] "coerce"
as(NULL,"data.frame")
data frame with 0 columns and 0 rows