[r] Adding a column to a data.frame

I have the data.frame below. I want to add a column that classifies my data according to column 1 (h_no) in that way that the first series of h_no 1,2,3,4 is class 1, the second series of h_no (1 to 7) is class 2 etc. such as indicated in the last column.

h_no  h_freq  h_freqsq
1     0.09091 0.008264628 1
2     0.00000 0.000000000 1
3     0.04545 0.002065702 1
4     0.00000 0.000000000 1  
1     0.13636 0.018594050 2
2     0.00000 0.000000000 2
3     0.00000 0.000000000 2
4     0.04545 0.002065702 2
5     0.31818 0.101238512 2
6     0.00000 0.000000000 2
7     0.50000 0.250000000 2 
1     0.13636 0.018594050 3 
2     0.09091 0.008264628 3
3     0.40909 0.167354628 3
4     0.04545 0.002065702 3

I believe that using "cbind" is the simplest way to add a column to a data frame in R. Below an example:

    myDf = data.frame(index=seq(1,10,1), Val=seq(1,10,1))
    newCol= seq(2,20,2)
    myDf = cbind(myDf,newCol)

In addition to Roman's answer, something like this might be even simpler. Note that I haven't tested it because I do not have access to R right now.

# Note that I use a global variable here
# normally not advisable, but I liked the
# use here to make the code shorter
index <<- 0
new_column = sapply(df$h_no, function(x) {
  if(x == 1) index = index + 1
  return(index)
})

The function iterates over the values in n_ho and always returns the categorie that the current value belongs to. If a value of 1 is detected, we increase the global variable index and continue.

Approach based on identifying number of groups (x in mapply) and its length (y in mapply)

mytb<-read.table(text="h_no  h_freq  h_freqsq group
1     0.09091 0.008264628 1
2     0.00000 0.000000000 1
3     0.04545 0.002065702 1
4     0.00000 0.000000000 1  
1     0.13636 0.018594050 2
2     0.00000 0.000000000 2
3     0.00000 0.000000000 2
4     0.04545 0.002065702 2
5     0.31818 0.101238512 2
6     0.00000 0.000000000 2
7     0.50000 0.250000000 2 
1     0.13636 0.018594050 3 
2     0.09091 0.008264628 3
3     0.40909 0.167354628 3
4     0.04545 0.002065702 3", header=T, stringsAsFactors=F)
mytb$group<-NULL

positionsof1s<-grep(1,mytb$h_no)

mytb$newgroup<-unlist(mapply(function(x,y) 
  rep(x,y),                      # repeat x number y times
  x= 1:length(positionsof1s),    # x is 1 to number of nth group = g1:g3
  y= c( diff(positionsof1s),     # y is number of repeats of groups g1 to penultimate (g2) = 4, 7
        nrow(mytb)-              # this line and the following gives number of repeat for last group (g3)
          (positionsof1s[length(positionsof1s )]-1 )  # number of rows - position of penultimate group (g2) 
      ) ) )
mytb

Easily: Your data frame is A

b <- A[,1]
b <- b==1
b <- cumsum(b)

Then you get the column b.

You can add a column to your data using various techniques. The quotes below come from the "Details" section of the relevant help text, [[.data.frame.

Data frames can be indexed in several modes. When [ and [[ are used with a single vector index (x[i] or x[[i]]), they index the data frame as if it were a list.

my.dataframe["new.col"] <- a.vector
my.dataframe[["new.col"]] <- a.vector

The data.frame method for $, treats x as a list

my.dataframe$new.col <- a.vector

When [ and [[ are used with two indices (x[i, j] and x[[i, j]]) they act like indexing a matrix

my.dataframe[ , "new.col"] <- a.vector

Since the method for data.frame assumes that if you don't specify if you're working with columns or rows, it will assume you mean columns.

For your example, this should work:

# make some fake data
your.df <- data.frame(no = c(1:4, 1:7, 1:5), h_freq = runif(16), h_freqsq = runif(16))

# find where one appears and 
from <- which(your.df$no == 1)
to <- c((from-1)[-1], nrow(your.df)) # up to which point the sequence runs

# generate a sequence (len) and based on its length, repeat a consecutive number len times
get.seq <- mapply(from, to, 1:length(from), FUN = function(x, y, z) {
            len <- length(seq(from = x[1], to = y[1]))
            return(rep(z, times = len))
         })

# when we unlist, we get a vector
your.df$group <- unlist(get.seq)
# and append it to your original data.frame. since this is
# designating a group, it makes sense to make it a factor
your.df$group <- as.factor(your.df$group)


   no     h_freq   h_freqsq group
1   1 0.40998238 0.06463876     1
2   2 0.98086928 0.33093795     1
3   3 0.28908651 0.74077119     1
4   4 0.10476768 0.56784786     1
5   1 0.75478995 0.60479945     2
6   2 0.26974011 0.95231761     2
7   3 0.53676266 0.74370154     2
8   4 0.99784066 0.37499294     2
9   5 0.89771767 0.83467805     2
10  6 0.05363139 0.32066178     2
11  7 0.71741529 0.84572717     2
12  1 0.10654430 0.32917711     3
13  2 0.41971959 0.87155514     3
14  3 0.32432646 0.65789294     3
15  4 0.77896780 0.27599187     3
16  5 0.06100008 0.55399326     3

Data.frame[,'h_new_column'] <- as.integer(Data.frame[,'h_no'], breaks=c(1, 4, 7))

If I understand the question correctly, you want to detect when the h_no doesn't increase and then increment the class. (I'm going to walk through how I solved this problem, there is a self-contained function at the end.)

Working

We only care about the h_no column for the moment, so we can extract that from the data frame:

> h_no <- data$h_no

We want to detect when h_no doesn't go up, which we can do by working out when the difference between successive elements is either negative or zero. R provides the diff function which gives us the vector of differences:

> d.h_no <- diff(h_no)
> d.h_no
 [1]  1  1  1 -3  1  1  1  1  1  1 -6  1  1  1

Once we have that, it is a simple matter to find the ones that are non-positive:

> nonpos <- d.h_no <= 0
> nonpos
 [1] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
[13] FALSE FALSE

In R, TRUE and FALSE are basically the same as 1 and 0, so if we get the cumulative sum of nonpos, it will increase by 1 in (almost) the appropriate spots. The cumsum function (which is basically the opposite of diff) can do this.

> cumsum(nonpos)
 [1] 0 0 0 1 1 1 1 1 1 1 2 2 2 2

But, there are two problems: the numbers are one too small; and, we are missing the first element (there should be four in the first class).

The first problem is simply solved: 1+cumsum(nonpos). And the second just requires adding a 1 to the front of the vector, since the first element is always in class 1:

 > classes <- c(1, 1 + cumsum(nonpos))
 > classes
  [1] 1 1 1 1 2 2 2 2 2 2 2 3 3 3 3

Now, we can attach it back onto our data frame with cbind (by using the class= syntax, we can give the column the class heading):

 > data_w_classes <- cbind(data, class=classes)

And data_w_classes now contains the result.

Final result

We can compress the lines together and wrap it all up into a function to make it easier to use:

classify <- function(data) {
   cbind(data, class=c(1, 1 + cumsum(diff(data$h_no) <= 0)))
}

Or, since it makes sense for the class to be a factor:

classify <- function(data) {
   cbind(data, class=factor(c(1, 1 + cumsum(diff(data$h_no) <= 0))))
}

You use either function like:

> classified <- classify(data) # doesn't overwrite data
> data <- classify(data) # data now has the "class" column

(This method of solving this problem is good because it avoids explicit iteration, which is generally recommend for R, and avoids generating lots of intermediate vectors and list etc. And also it's kinda neat how it can be written on one line :) )