Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
Physics
Chemistry
Biology
Mathematics
English
Economics
Psychology
Social Studies
Fashion Studies
Legal Studies
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
How to find the count of each category in a data.table object column in R?
To find the count of each category in a data.table object column in R, we can follow the below steps −
First of all, create a data.table object.
Then, use summarise function of dplyr package after grouping along with n.
Example
Create the data.table object
Let’s create a data.table object as shown below −
library(data.table)
factor<-sample(c("very low","low","medium","high","very high"),25,replace=TRUE)
response<-rnorm(25)
DT<-data.table(factor,response)
DTOutput
On executing, the above script generates the below output(this output will vary on your system due to randomization) −
factor response 1: very high -0.14064701 2: high -0.36029663 3: low -0.50825291 4: very high -2.07959316 5: high 0.25578922 6: very high 1.17946264 7: low 1.32803179 8: medium -0.23736325 9: medium -0.07440274 10: very low 0.32669673 11: very low 0.54953485 12: high -0.93197732 13: very high -0.77787846 14: very low 0.63808636 15: very high 0.76008767 16: very high -0.38712279 17: very high -0.64259955 18: high 0.30249791 19: high 0.16984229 20: very low -0.24732679 21: high -0.47456705 22: low 0.08789350 23: low 0.32541943 24: medium -0.27170782 25: high -0.09860207 factor response
Find the count of each category in data.table object
Using summarise function of dplyr package after grouping along with n to find the count of each category in factor column of data.table object DT −
library(data.table)
factor<-sample(c("very low","low","medium","high","very high"),25,replace=TRUE)
response<-rnorm(25)
DT<-data.table(factor,response)
library(dplyr)
DT %>% group_by(factor) %>% summarise(count=n())Output
# A tibble: 5 x 2 factor count <chr> <int> 1 high 7 2 low 4 3 medium 3 4 very high 7 5 very low 4
Updated on: 09-Nov-2021
216 Views
Advertisements