[r] count number of rows in a data frame in R based on group

I have a data frame in R like this:

  ID   MONTH-YEAR   VALUE
  110   JAN. 2012     1000
  111   JAN. 2012     2000
         .         .
         .         .
  121   FEB. 2012     3000
  131   FEB. 2012     4000
         .           .
         .           .

So, for each month of each year there are n rows and they can be in any order(mean they all are not in continuity and are at breaks). I want to calculate how many rows are there for each MONTH-YEAR i.e. how many rows are there for JAN. 2012, how many for FEB. 2012 and so on. Something like this:

 MONTH-YEAR   NUMBER OF ROWS
 JAN. 2012     10
 FEB. 2012     13
 MAR. 2012     6
 APR. 2012     9

I tried to do this:

n_row <- nrow(dat1_frame %.% group_by(MONTH-YEAR))

but it does not produce the desired output.How can I do that?

Here is another way of using aggregate to count rows by group:

my.data <- read.table(text = '
    month.year    my.cov
      Jan.2000     apple
      Jan.2000      pear
      Jan.2000     peach
      Jan.2001     apple
      Jan.2001     peach
      Feb.2002      pear
', header = TRUE, stringsAsFactors = FALSE, na.strings = NA)

rows.per.group  <- aggregate(rep(1, length(my.data$month.year)),
                             by=list(my.data$month.year), sum)
rows.per.group

#    Group.1 x
# 1 Feb.2002 1
# 2 Jan.2000 3
# 3 Jan.2001 2

library(plyr)
ddply(data, .(MONTH-YEAR), nrow)

This will give you the answer, if "MONTH-YEAR" is a variable. First, try unique(data$MONTH-YEAR) and see if it returns unique values (no duplicates).

Then above simple split-apply-combine will return what you are looking for.

Suppose we have a df_data data frame as below

> df_data
   ID MONTH-YEAR VALUE
1 110   JAN.2012  1000
2 111   JAN.2012  2000
3 121   FEB.2012  3000
4 131   FEB.2012  4000
5 141   MAR.2012  5000

To count number of rows in df_data grouped by MONTH-YEAR column, you can use:

> summary(df_data$`MONTH-YEAR`)

FEB.2012 JAN.2012 MAR.2012 
   2        2        1 

summary function will create a table from the factor argument, then create a vector for the result (line 7 & 8)

Try using the count function in dplyr:

library(dplyr)
dat1_frame %>% 
    count(MONTH.YEAR)

I am not sure how you got MONTH-YEAR as a variable name. My R version does not allow for such a variable name, so I replaced it with MONTH.YEAR.

As a side note, the mistake in your code was that dat1_frame %.% group_by(MONTH-YEAR) without a summarise function returns the original data frame without any modifications. So, you want to use

dat1_frame %>%
    group_by(MONTH.YEAR) %>%
    summarise(count=n())

Using the example data set that Ananda dummied up, here's an example using aggregate(), which is part of core R. aggregate() just needs something to count as function of the different values of MONTH-YEAR. In this case, I used VALUE as the thing to count:

aggregate(cbind(count = VALUE) ~ MONTH.YEAR, 
          data = mydf, 
          FUN = function(x){NROW(x)})

which gives you..

  MONTH.YEAR count
1  FEB. 2012     2
2  JAN. 2012     2
3  MAR. 2012     1

The count() function in plyr does what you want:

library(plyr)

count(mydf, "MONTH-YEAR")

Just for completion the data.table solution:

library(data.table)

mydf <- structure(list(ID = c(110L, 111L, 121L, 131L, 141L), 
                       MONTH.YEAR = c("JAN. 2012", "JAN. 2012", 
                                      "FEB. 2012", "FEB. 2012", 
                                      "MAR. 2012"), 
                       VALUE = c(1000L, 2000L, 3000L, 4000L, 5000L)), 
                  .Names = c("ID", "MONTH.YEAR", "VALUE"), 
                  class = "data.frame", row.names = c(NA, -5L))

setDT(mydf)
mydf[, .(`Number of rows` = .N), by = MONTH.YEAR]

   MONTH.YEAR Number of rows
1:  JAN. 2012              2
2:  FEB. 2012              2
3:  MAR. 2012              1