Tukukkk

Following @Joshua's suggestion, here's one way you might count the number of observations in your df dataframe where Year = 2007 and Month = Nov (assuming they are columns):

nrow(df[,df$YEAR == 2007 & df$Month == "Nov"])

and with aggregate, following @GregSnow:

aggregate(x ~ Year + Month, data = df, FUN = length)

We can also use dplyr.

First, some data:

df <- data.frame(x = rep(1:6, rep(c(1, 2, 3), 2)), year = 1993:2004, month = c(1, 1:11))

Now the count:

library(dplyr)

count(df, year, month)

#piping

df %>% count(year, month)

We can also use a slightly longer version with piping and the n() function:

df %>% 

  group_by(year, month) %>%

  summarise(number = n())

or the tally function:

df %>% 

  group_by(year, month) %>%

  tally()

An old question without a data.table solution. So here goes...

Using .N

library(data.table)

DT <- data.table(df)

DT[, .N, by = list(year, month)]

The simple option to use with aggregate is the length function which will give you the length of the vector in the subset. Sometimes a little more robust is to use function(x) sum( !is.na(x) ).

Create a new variable Count with a value of 1 for each row:

df1["Count"] <-1

Then aggregate dataframe, summing by the Count column:

df2 <- aggregate(df1[c("Count")], by=list(year=df1$year, month=df1$month), FUN=sum, na.rm=TRUE)

An alternative to the aggregate() function in this case would be table() with as.data.frame(), which would also indicate which combinations of Year and Month are associated with zero occurrences

df<-data.frame(x=rep(1:6,rep(c(1,2,3),2)),year=1993:2004,month=c(1,1:11))



myAns<-as.data.frame(table(df[,c("year","month")]))

And without the zero-occurring combinations

myAns[which(myAns$Freq>0),]

For my aggregations I usually end up wanting to see mean and "how big is this group" (a.k.a. length).
So this is my handy snippet for those occasions;

agg.mean <- aggregate(columnToMean ~ columnToAggregateOn1*columnToAggregateOn2, yourDataFrame, FUN="mean")

agg.count <- aggregate(columnToMean ~ columnToAggregateOn1*columnToAggregateOn2, yourDataFrame, FUN="length")

aggcount <- agg.count$columnToMean

agg <- cbind(aggcount, agg.mean)

A sql solution using sqldf package:

library(sqldf)

sqldf("SELECT Year, Month, COUNT(*) as Freq

       FROM df1

       GROUP BY Year, Month")

Considering @Ben answer, R would throw an error if df1 does not contain x column. But it can be solved elegantly with paste:

aggregate(paste(Year, Month) ~ Year + Month, data = df1, FUN = NROW)

Similarly, it can be generalized if more than two variables are used in grouping:

aggregate(paste(Year, Month, Day) ~ Year + Month + Day, data = df1, FUN = NROW)

You can use by functions as by(df1$Year, df1$Month, count) that will produce a list of needed aggregation.

The output will look like,

df1$Month: Feb

     x freq

1 2012    1

2 2013    1

3 2014    5

--------------------------------------------------------------- 

df1$Month: Jan

     x freq

1 2012    5

2 2013    2

--------------------------------------------------------------- 

df1$Month: Mar

     x freq

1 2012    1

2 2013    3

3 2014    2

>