Hello: I am running windows 10 -- R3.5.1 -- RStudio Version 1.1.456
I would like to know why when I replace a column value it still appears in subsequent routines: My example: r1$B1 is a Factor: It is created from the first character of a list of CPT codes, r1$CPT. head(r1$CPT, N= 25) [1] A4649 A4649 C9359 C1713 A0394 A0398 903 Levels: 00000 00001 00140 00160 00670 00810 00940 01400 01470 01961 01968 10160 11000 11012 11042 11043 11044 11045 11100 11101 11200 11201 11401 11402 ... l8699 str(r1$CPT) Factor w/ 903 levels "00000","00001",..: 773 773 816 783 739 741 743 739 739 741 ... And I want only those CPT's with leading alpha char in this column so I set the numeric leading char to Z r1$B1 <- str_sub(r1$CPT,1,1) r1$B1 <- as.factor(r1$B1) #Redundant levels(r1$B1)[levels(r1$B1) %in% c('1','2','3','4','5','6','7','8','9','0')] <- 'Z' When I check what I have done I find l & L unique(r1$B1) #[1] A C Z L G Q U J V E S l D P #Levels: Z A C D E G J l L P Q S U V So I change l to L r1$B1[r1$B1 == 'l'] <- 'L' When I check again I have l & L but l = 0 table(r1$B1) # Z A C D E G J l L P Q S U V #19639 1673 546 2 8 147 281 0 664 1 64 36 114 14 When I go to find those rows as if they existed, they are not accounted for? tmp <- subset(r1, B1 == "l") print(tmp) Empty data.table (0 rows) of 9 cols: SavingsReversed,productID,ProviderID,PatientGender,ModCnt,Editnumber2... And I have actually visually inspected the whole darn column, sheesh! So I ignore it temporarily. Now later on it resurfaces in a tutorial I am following for caret pkg. preProcess(r1b, method = c("center", "scale"), thresh = 0.95, pcaComp = NULL, na.remove = TRUE, k = 5, knnSummary = mean, outcome = NULL, fudge = 0.2, numUnique = 3, verbose = FALSE, freqCut = 95/5, uniqueCut = 10, cutoff = 0.9, rangeBounds = c(0, 1)) # Warning in preProcess.default(r1b, method = c("center", "scale"), thresh = 0.95, : # These variables have zero variances: B1l <-------------yes this is a remnant of the r1$B1 clean-up # Created from 23141 samples and 22 variables # # Pre-processing: # - centered (22) # - ignored (0) # - scaled (22) So my questions are, in consideration of regression modelling accuracy: Why is this happening? How do I remove it? Or is it irrelevant and leave it be? As always, thank you for you support. WHP Confidentiality Notice This message is sent from Zelis. ...{{dropped:13}} ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.