factors

I have a data.frame with columns of factors, on which I want to compute a max (or min, or quantiles). I can't use these functions on factors, but I want to. Here's some example : set.seed(3) df1 <- data.frame(id = rep(1:5,each=2),height=sample(c("low","medium","high"),size = 10,replace=TRUE)) df1$height <- factor(df1$height,c("low","medium","high")) df1$height_num <- as.numeric(df1$height) # > df1 # id height height_num # 1 1 low 1 # 2 1 high 3 # 3 2 medium 2 # 4 2 low 1 # 5 3 medium 2 # 6 3 medium 2 # 7 4 low 1 # 8 4 low 1 # 9 5 medium 2 # 10 5 medium 2 I can easily do this: aggregate(height

Here is the link to the problem. The problem asks the number of solutions to the Diophantine equation of the form 1/x + 1/y = 1/z (where z = n! ). Rearranging the given equation clearly tells that the answer is the number of factors of z 2 . So the problem boils down to finding the number of factors of n! 2 ( n factorial squared). My algorithm is as follows Make a Boolean look up table for all primes <= n using Sieve of Eratosthenes algorithm. Iterate over all primes P <= n and find its exponent in n! . I did this using step function formula. Let the exponent be K , then the exponent of P in n

Let's create some factors first: F1 <- factor(c(1,2,20,10,25,3)) F2 <- factor(paste0(F1, " years")) F3 <- F2 levels(F3) <- paste0(sort(F1), " years") F4 <- factor(paste0(F1, " years"), levels=paste0(sort(F1), " years")) then take a look at them: > F1 [1] 1 2 20 10 25 3 Levels: 1 2 3 10 20 25 > F2 [1] 1 years 2 years 20 years 10 years 25 years 3 years Levels: 1 years 10 years 2 years 20 years 25 years 3 years > F3 [1] 1 years 3 years 10 years 2 years 20 years 25 years Levels: 1 years 2 years 3 years 10 years 20 years 25 years > F4 [1] 1 years 2 years 20 years 10 years 25 years 3 years Levels: 1

I have a for loop that checks if a number is a factor of a number, then checks if that factor is prime, and then it adds it to an array. Depending on the original number, I will get an error saying fatal error: Can't form range with end < start This happens almost every time, but for some numbers it works fine. The only numbers I have found to work with it are 9, 15, and 25. Here is the code: let num = 16 // or any Int var primes = [Int]() for i in 2...(num/2) { if ((num % i) == 0) { var isPrimeFactor = true for l in 2...i-1 { if ((i%l) == 0) { isPrimeFactor = false; }//end if }//end for if

I got a survey data.frame they are 100 columns and each columns have 2 factors - Yes or No. However some survey have answers like, Yes! or Nope or Yay or Nah... which really they are yes or no. My question is how can I achieve my converting all values in other columns based on their factor level? e.g if factor level is 1 replace text to Yes else No. My second question is, sometimes I am left with the 3rd level that isn't used, how can I remove all unused factors in ALL columns in data frame. I got more than 100 columns. We can loop over the columns and replace the levels using %in% df1[] <-

I am having trouble converting the columns of 2 data frames in a list to numeric. Right now both data frames have 2 columns consisting of factors. I want to convert them to numeric so that I can do mathematical operations on them. Below is sample code: library(XML) bal <- "http://www.baseball-reference.com/teams/BAL/2014-schedule-scores.shtml" bos <- "http://www.baseball-reference.com/teams/BOS/2014-schedule-scores.shtml" mylist <- list(bal, bos) a <- lapply(mylist, readHTMLTable) b <- lapply(a, function(x) x[["team_schedule"]][, c("R", "RA")]) c <- as.numeric(as.character(b)) When I run this

The dataframe named 'temp' (below) has three columns (1) Canopy Index; (2) Under_tree; and (3) Open_Canopy. The columns Under_tree and Open_Canopy are factors with 5 levels each. data(temp) Canopy_index Under_tree Open_Canopy 1 75 Undergrowth Grass 2 85 Litter Grass 3 75 Litter Grass 4 35 Litter Grass 5 85 Undergrowth Grass The dataframe 'temp' was reformatted to be in long format named df.melt (below) to produce a barplot where the y-axis is denoted as Canopy_index and the x-axis represents the factor Topography (3rd column) which has five levels grouped by two conditions (2nd column - Under