I'm looking to get a count for the following data frame:
> Santa
Believe Age Gender Presents Behaviour
1 FALSE 9 male 25 naughty
2 TRUE 5 male 20 nice
3 TRUE 4 female 30 nice
4 TRUE 4 male 34 naughty
of the number of children who believe. What command would I use to get this?
(The actual data frame is much bigger. I've just given you the first four rows...)
Thanks!
A one-line solution with data.table
could be
library(data.table)
setDT(x)[,.N,by=Believe]
Believe N
1: FALSE 1
2: TRUE 3
using sqldf
fits here:
library(sqldf)
sqldf("SELECT Believe, Count(1) as N FROM Santa
GROUP BY Believe")
You can do summary(santa$Believe)
and you will get the count for TRUE
and FALSE
DPLYR makes this really easy.
x<-santa%>%
count(Believe)
If you wanted to count by a group; for instance, how many males v females believe, just add a group_by
:
x<-santa%>%
group_by(Gender)%>%
count(Believe)
I think of this as a two-step process:
subset the original data frame according to the filter supplied (Believe==FALSE); then
get the row count of this subset
For the first step, the subset function is a good way to do this (just an alternative to ordinary index or bracket notation).
For the second step, i would use dim or nrow
One advantage of using subset: you don't have to parse the result it returns to get the result you need--just call nrow on it directly.
so in your case:
v = nrow(subset(Santa, Believe==FALSE)) # 'subset' returns a data.frame
or wrapped in an anonymous function:
>> fnx = function(fac, lev){nrow(subset(Santa, fac==lev))}
>> fnx(Believe, TRUE)
3
Aside from nrow, dim will also do the job. This function returns the dimensions of a data frame (rows, cols) so you just need to supply the appropriate index to access the number of rows:
v = dim(subset(Santa, Believe==FALSE))[1]
An answer to the OP posted before this one shows the use of a contingency table. I don't like that approach for the general problem as recited in the OP. Here's the reason. Granted, the general problem of how many rows in this data frame have value x in column C? can be answered using a contingency table as well as using a "filtering" scheme (as in my answer here). If you want row counts for all values for a given factor variable (column) then a contingency table (via calling table and passing in the column(s) of interest) is the most sensible solution; however, the OP asks for the count of a particular value in a factor variable, not counts across all values. Aside from the performance hit (might be big, might be trivial, just depends on the size of the data frame and the processing pipeline context in which this function resides). And of course once the result from the call to table is returned, you still have to parse from that result just the count that you want.
So that's why, to me, this is a filtering rather than a cross-tab problem.
sum(Santa$Believe)
Source: Stackoverflow.com