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)])
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
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")
I know there are many other answers, but here is another way to do it using the sort and rle functions. The function rle stands for Run Length Encoding. It can be used for counts of runs of numbers (see the R man docs on rle), but can also be applied here.
test.data = rep(c(1, 2, 2, 2), 25)
rle(sort(test.data))
## Run Length Encoding
## lengths: int [1:2] 25 75
## values : num [1:2] 1 2
If you capture the result, you can access the lengths and values as follows:
## rle returns a list with two items.
result.counts <- rle(sort(test.data))
result.counts$lengths
## [1] 25 75
result.counts$values
## [1] 1 2
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
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)
}
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
length(unique(df$col)) is the most simple way I can see.
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.
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]
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.
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))
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
Source: Stackoverflow.com