I have a dataframe and I would like to count the number of rows within each group. I reguarly use the `aggregate` function to sum data as follows:

``````df2 <- aggregate(x ~ Year + Month, data = df1, sum)
``````

Now, I would like to count observations but can't seem to find the proper argument for `FUN`. Intuitively, I thought it would be as follows:

``````df2 <- aggregate(x ~ Year + Month, data = df1, count)
``````

But, no such luck.

Any ideas?

Some toy data:

``````set.seed(2)
df1 <- data.frame(x = 1:20,
Year = sample(2012:2014, 20, replace = TRUE),
Month = sample(month.abb[1:3], 20, replace = TRUE))
``````

## Solution 1

Current best practice (tidyverse) is:

``````require(dplyr)
df1 %>% count(Year, Month)
``````

## Solution 2

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)
``````

## Solution 3

`dplyr` package does this with `count`/`tally` commands, or the `n()` function:

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()
``````

## Solution 4

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)]
``````

## Solution 5

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) )`.

## Solution 6

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)
``````

## Solution 7

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),]
``````

## Solution 8

If you want to include 0 counts for month-years that are missing in the data, you can use a little `table` magic.

``````data.frame(with(df1, table(Year, Month)))
``````

For example, the toy data.frame in the question, df1, contains no observations of January 2014.

``````df1
x Year Month
1   1 2012   Feb
2   2 2014   Feb
3   3 2013   Mar
4   4 2012   Jan
5   5 2014   Feb
6   6 2014   Feb
7   7 2012   Jan
8   8 2014   Feb
9   9 2013   Mar
10 10 2013   Jan
11 11 2013   Jan
12 12 2012   Jan
13 13 2014   Mar
14 14 2012   Mar
15 15 2013   Feb
16 16 2014   Feb
17 17 2014   Mar
18 18 2012   Jan
19 19 2013   Mar
20 20 2012   Jan
``````

The base R `aggregate` function does not return an observation for January 2014.

``````aggregate(x ~ Year + Month, data = df1, FUN = length)
Year Month x
1 2012   Feb 1
2 2013   Feb 1
3 2014   Feb 5
4 2012   Jan 5
5 2013   Jan 2
6 2012   Mar 1
7 2013   Mar 3
8 2014   Mar 2
``````

If you would like an observation of this month-year with 0 as the count, then the above code will return a data.frame with counts for all month-year combinations:

``````data.frame(with(df1, table(Year, Month)))
Year Month Freq
1 2012   Feb    1
2 2013   Feb    1
3 2014   Feb    5
4 2012   Jan    5
5 2013   Jan    2
6 2014   Jan    0
7 2012   Mar    1
8 2013   Mar    3
9 2014   Mar    2
``````

## Solution 9

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)
``````

## Solution 10

A solution using `sqldf` package:

``````library(sqldf)
sqldf("SELECT Year, Month, COUNT(*) as Freq
FROM df1
GROUP BY Year, Month")
``````

## Solution 11

``````library(tidyverse)

df_1 %>%
group_by(Year, Month) %>%
summarise(count= n())
``````

## Solution 12

Using `collapse` package in `R`

``````library(collapse)
library(magrittr)
df %>%
fgroup_by(year, month) %>%
fsummarise(number = fNobs(x))
``````

## Solution 13

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)
``````

## Solution 14

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
>
``````

## Solution 15

There are plenty of wonderful answers here already, but I wanted to throw in 1 more option for those wanting to add a new column to the original dataset that contains the number of times that row is repeated.

``````df1\$counts <- sapply(X = paste(df1\$Year, df1\$Month),
FUN = function(x) { sum(paste(df1\$Year, df1\$Month) == x) })
``````

The same could be accomplished by combining any of the above answers with the `merge()` function.

## Solution 16

If your trying the aggregate solutions above and you get the error:

invalid type (list) for variable

Because you're using date or datetime stamps, try using as.character on the variables:

``````aggregate(x ~ as.character(Year) + Month, data = df, FUN = length)
``````

On one or both of the variables.

## Solution 17

I usually use table function

``````
df <- data.frame(a=rep(1:8,rep(c(1,2,3, 4),2)),year=2011:2021,month=c(1,3:10))

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

``````