# r - Count unique values

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Let's say I have:

``````v = rep(c(1,2, 2, 2), 25)
``````

Now, I want to count the number of times each unique value appears. `unique(v)` returns what the unique values are, but not how many they are.

``````> unique(v)
[1] 1 2
``````

I want something that gives me

``````length(v[v==1])
[1] 25
length(v[v==2])
[1] 75
``````

but as a more general one-liner :) Something close (but not quite) like this:

``````#<doesn't work right> length(v[v==unique(v)])
``````

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Perhaps table is what you are after?

``````dummyData = rep(c(1,2, 2, 2), 25)

table(dummyData)
# dummyData
#  1  2
# 25 75

## or another presentation of the same data
as.data.frame(table(dummyData))
#    dummyData Freq
#  1         1   25
#  2         2   75
``````

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If you have multiple factors (= a multi-dimensional data frame), you can use the `dplyr` package to count unique values in each combination of factors:

``````library("dplyr")
data %>% group_by(factor1, factor2) %>% summarize(count=n())
``````

It uses the pipe operator `%>%` to chain method calls on the data frame `data`.

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It is a one-line approach by using `aggregate`.

``````> aggregate(data.frame(count = v), list(value = v), length)

value count
1     1    25
2     2    75
``````

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table() function is a good way to go, as Chase suggested. If you are analyzing a large dataset, an alternative way is to use .N function in datatable package.

Make sure you installed the data table package by

``````install.packages("data.table")
``````

Code:

``````# Import the data.table package
library(data.table)

# Generate a data table object, which draws a number 10^7 times
# from 1 to 10 with replacement
DT<-data.table(x=sample(1:10,1E7,TRUE))

# Count Frequency of each factor level
DT[,.N,by=x]
``````

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To get an un-dimensioned integer vector that contains the count of unique values, use `c()`.

``````dummyData = rep(c(1, 2, 2, 2), 25) # Chase's reproducible data
c(table(dummyData)) # get un-dimensioned integer vector
1  2
25 75

str(c(table(dummyData)) ) # confirm structure
Named int [1:2] 25 75
- attr(*, "names")= chr [1:2] "1" "2"
``````

This may be useful if you need to feed the counts of unique values into another function, and is shorter and more idiomatic than the `t(as.data.frame(table(dummyData))[,2]` posted in a comment to Chase's answer. Thanks to Ricardo Saporta who pointed this out to me here.

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This works for me. Take your vector `v`

`length(summary(as.factor(v),maxsum=50000))`

Comment: set maxsum to be large enough to capture the number of unique values

or with the `magrittr` package

`v %>% as.factor %>% summary(maxsum=50000) %>% length`

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If you need to have the number of unique values as an additional column in the data frame containing your values (a column which may represent sample size for example), plyr provides a neat way:

``````data_frame <- data.frame(v = rep(c(1,2, 2, 2), 25))

library("plyr")
data_frame <- ddply(data_frame, .(v), transform, n = length(v))
``````

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Also making the values categorical and calling `summary()` would work.

``````> v = rep(as.factor(c(1,2, 2, 2)), 25)
> summary(v)
1  2
25 75
``````

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You can try also a `tidyverse`

``````library(tidyverse)
dummyData %>%
as.tibble() %>%
count(value)
# A tibble: 2 x 2
value     n
<dbl> <int>
1     1    25
2     2    75
``````

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If you want to run unique on a data.frame (e.g., train.data), and also get the counts (which can be used as the weight in classifiers), you can do the following:

``````unique.count = function(train.data, all.numeric=FALSE) {
# first convert each row in the data.frame to a string
train.data.str = apply(train.data, 1, function(x) paste(x, collapse=','))
# use table to index and count the strings
train.data.str.t = table(train.data.str)
# get the unique data string from the row.names
train.data.str.uniq = row.names(train.data.str.t)
weight = as.numeric(train.data.str.t)
# convert the unique data string to data.frame
if (all.numeric) {
train.data.uniq = as.data.frame(t(apply(cbind(train.data.str.uniq), 1,
function(x) as.numeric(unlist(strsplit(x, split=","))))))
} else {
train.data.uniq = as.data.frame(t(apply(cbind(train.data.str.uniq), 1,
function(x) unlist(strsplit(x, split=",")))))
}
names(train.data.uniq) = names(train.data)
list(data=train.data.uniq, weight=weight)
}
``````

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``````count_unique_words <-function(wlist) {
ucountlist = list()
unamelist = c()
for (i in wlist)
{
if (is.element(i, unamelist))
ucountlist[[i]] <- ucountlist[[i]] +1
else
{
listlen <- length(ucountlist)
ucountlist[[i]] <- 1
unamelist <- c(unamelist, i)
}
}
ucountlist
}

expt_counts <- count_unique_words(population)
for(i in names(expt_counts))
cat(i, expt_counts[[i]], "\n")
``````