After reading a dataset:

dataset <- read.csv("forR.csv")
  • How can I get R to give me the number of cases it contains?
  • Also, will the returned value include of exclude cases omitted with na.omit(dataset)?
  • 3
    $\begingroup$ I also recommend taking a look at str() as it provides other useful details about your object. Can often explain why a column isn't behaving as it should (factor instead of numeric, etc). $\endgroup$
    – Chase
    Commented Dec 8, 2010 at 13:45
  • 3
    $\begingroup$ Please read the R guide of Owen first (cran.r-project.org/doc/contrib/Owen-TheRGuide.pdf), and if possible, Introduction to R (cran.r-project.org/doc/manuals/R-intro.pdf). Both are on the official website of R. You're incredibly lucky you actually get an answer. On the r-help list one would redirect you to the manual in less elegant terms. No offense meant. $\endgroup$
    – Joris Meys
    Commented Dec 8, 2010 at 15:19
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    $\begingroup$ @Joris - Point taken (without offence), but it was my impression that SE sites were designed to foster problem/solution learning in a way not afforded by manuals. Additionally, this question will now be available for other beginners. Thanks for the links though. $\endgroup$
    – Tom Wright
    Commented Dec 8, 2010 at 15:42
  • 2
    $\begingroup$ I disagree with your assertion that this question will be helpful for other beginners, especially if they don't skim the manual. They will just create a duplicate question. $\endgroup$ Commented Dec 8, 2010 at 21:01
  • 9
    $\begingroup$ And, four years later, this is the second hit I got on Google trying to find an answer to this question. No need for me to create a duplicate (@JoshuaUlrich). $\endgroup$
    – Richard
    Commented Dec 26, 2014 at 6:58

2 Answers 2


dataset will be a data frame. As I don't have forR.csv, I'll make up a small data frame for illustration:

dataset <- data.frame(A = sample(c(NA, 1:100), 1000, rep = TRUE),
                      B = rnorm(1000))

> head(dataset)
   A           B
1 26  0.07730312
2 37 -0.29686864
3 57 -1.18324224
4 91  0.01129269
5 20  0.99160104
6 90  1.59396745

To get the number of cases, count the number of rows using nrow() or NROW():

> nrow(dataset)
[1] 1000
> NROW(dataset)
[1] 1000

To count the data after omitting the NA, use the same tools, but wrap dataset in na.omit():

> NROW(na.omit(dataset))
[1] 993

The difference between NROW() and NCOL() and their lowercase variants (ncol() and nrow()) is that the lowercase versions will only work for objects that have dimensions (arrays, matrices, data frames). The uppercase versions will work with vectors, which are treated as if they were a 1 column matrix, and are robust if you end up subsetting your data such that R drops an empty dimension.

Alternatively, use complete.cases() and sum it (complete.cases() returns a logical vector [TRUE or FALSE] indicating if any observations are NA for any rows.

> sum(complete.cases(dataset))
[1] 993


  1. Run dim(dataset) to retrieve both n and k, you can also use nrow(df) and ncol(df) (and even NROW(df) and NCOL(df) -- variants are needed for other types too).

  2. If you transform e.g. via dataset <- na.omit(dataset), then the cases are gone and are not counted. But if you do e.g. summary(dataset) the NA cases are accounted for.


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