How to Create Frequency Table by Group using Dplyr in R Last Updated : 02 Jun, 2022 Comments Improve Suggest changes Like Article Like Report In this article, we will be looking at the approach to creating a frequency table group with its working examples in the R programming language. Create Frequency Table by Group using dplyr package: In this approach to create the frequency table by group, the user first needs to import and install the dplyr package in the working console, and then the user needs to call the group_by() and the summarize() function from the dplyr() package, here the group_by() function is responsible for to make groups of the data frames. Group_by() function alone doesn't give any output so it should be followed by summarise() function with an appropriate action to perform. This works similar to GROUP BY used in SQL and pivot table in excel. Syntax to install and import the dplyr package in the R console:install.package('dplyr') library(dplyr)Example 1: In this example, we have created a data frame of two attributes, first and second each containing 6 entities and further with the provided syntax and the call of the group_by() and the summarize() function passed with the attribute name and the data frame used to get the frequency table accordingly in the Rn language. R # Import the required library library(dplyr) # Created data frame df <- data.frame(first=c(1,1,1,2,2,2), second=c('a', 'a', 'b', 'a', 'c', 'd')) # calculate frequency df %>% group_by(first,second) %>% summarize(Freq=n()) Output: first second Freq <dbl> <chr> <int> 1 1 a 2 2 1 b 1 3 2 a 1 4 2 c 1 5 2 d 1Example 2:Â In this example, we are using the in-built data frame of the R named:- "ToothGrowth" and will be creating the frequency table of the supp and the dose attribute of this data frame using the given syntax with the call of the group_by() and the summarize() function passed with the required parameters in the R language. R # Import the required library library(dplyr) # Created data frame data("ToothGrowth") df<-ToothGrowth # calculate frequency df %>% group_by(supp , dose) %>% summarize(Freq=n()) Output: supp dose Freq <fct> <dbl> <int> 1 OJ 0.5 10 2 OJ 1 10 3 OJ 2 10 4 VC 0.5 10 5 VC 1 10 6 VC 2 10 Comment More infoAdvertise with us Next Article How to Create Frequency Table by Group using Dplyr in R G geetansh044 Follow Improve Article Tags : R Language R Dplyr Similar Reads How to Use dplyr to Generate a Frequency Table in R The frequency table in R is used to create a table with a respective count for both the discrete values and the grouped intervals. It indicates the counts of each segment of the table. It is helpful for constructing the probabilities and drawing an idea about the data distribution. The dplyr package 3 min read How to Create Frequency Tables in Python? In this article, we are going to see how to Create Frequency Tables in Python Frequency is a count of the number of occurrences a particular value occurs or appears in our data. A frequency table displays a set of values along with the frequency with which they appear. They allow us to better unders 3 min read Group by function in R using Dplyr Group_by() function belongs to the dplyr package in the R programming language, which groups the data frames. Group_by() function alone will not give any output. It should be followed by summarise() function with an appropriate action to perform. It works similar to GROUP BY in SQL and pivot table i 2 min read How to Create a Relative Frequency Histogram in R? In this article, we will discuss how to create a Relative Frequency Histogram in the R programming language. Relative Frequency Histogram helps us to visualize the relative frequencies of values in a dataset. This shows how often a certain value is present in the dataset. A relative frequency histog 3 min read How to Create a Lag Variable Within Each Group in R? Creating lag variables within groups is a common task in time series and panel data analysis. It involves generating a new variable that contains the value of an existing variable from a previous period or row within each group. This process is crucial for tasks such as time series forecasting, pane 5 min read Like