How to use summarise on group by DataFrame in R? The summarise() or summarize() functions performs the aggregations on grouped data, so in order to use these functions first, you need to use group_by() to get grouped dataframe. All these functions are from dplyr package.
Key Points –
- summarise() is used to get aggregation results on specified columns for each group.
- For empty grouping columns/variables, it returns a single row summarising all rows/observations in the input.
- Both summarise() & summarize() functions works exactly same as they are synonyms.
- These function returns tibble and you need to use as.data.frame() tp convert to DataFrame.
1. Syntax of Summarise()
Following is the syntax of summarise() or summarize() functions.
# Syntax of summarise & summarize functionssummarise(.data, ..., .groups = NULL)summarize(.data, ..., .groups = NULL)Arguments of summarise() function.
.data– tibble or dataframe...– columns/variables to perform aggregations on along with aggregation/summarise functions..groups
Let’s create a DataFrame by reading a CSV file. I will use this dataframe to group on certain columns and summarize on numeric columns like salary, age, and bonus.
# Read CSV file into DataFramedf = read.csv('/Users/admin/apps/github/r-examples/resources/emp.csv')dfYields below output.
2. Group By Summarise R Example
The summarise() or summarize() function takes the grouped dataframe/table as input and performs the summarize functions. To get the dropped dataframe use group_by() function.
To use group_by() and summarize() functions, you have to install dplyr first usinginstall.packages(‘dplyr’)and load it usinglibrary(dplyr).
All functions indplyrpackagetakedata.frameas a first argument. When we usedplyrpackage, we mostly use the infix operator%>%frommagrittr, it passes the left-hand side of the operator to the first argument of the right-hand side of the operator. For example,x %>% f(y)converted intof(x, y)so the result from the left-hand side is then “piped” into the right-hand side.
# Load dplyrlibrary(dplyr)# Group by mean using dplyragg_tbl <- df %>% group_by(department) %>% summarise(mean_age=mean(age), .groups = 'drop')agg_tblNote that the group_by() takes DataFrame as input and summarise() function takes the tibble/dataframe as input and returns the tibble table, so to convert the tibble to dataframe use as.data.frame(), let’s rewrite the above statement using this function.
# Group by mean using dplyrdf2 <- df %>% group_by(department) %>% summarise(mean_age=mean(age),.groups = 'drop') %>% as.data.frame()df2 Yields below output
3. Group By Summarise() Functions in R
There are several aggregation functions you can use with summarise(). All these functions are used to calculate aggregations on grouped data.
| Summarize Group | Summarise Function | Description |
|---|---|---|
| Count | n() | Get the count of values |
| n_distinct() | Get the count of distinct values | |
| Agg | sum() | Computes sum |
| mean() | Generic function for the (trimmed) arithmetic mean. | |
| median() | Computes the sample median. | |
| Range | min() | Computes minimum of input |
| max() | Computes maximum of input | |
| quantile() | Produces sample quantiles | |
| Position | first() | Get the first value |
| last() | Get the last value | |
| nth() | Get the nth value | |
| Spread | sd() | Computes the standard deviation |
| iqr() | Computes interquartile range | |
| mad | Compute the median absolute deviation | |
| Logical | any() | any |
| all() | all |
In the rest of the article, I will explain different examples of using summarise() on a group by data and then will cover examples for each above functions.
5. Summarise on Multiple Columns in R
You can also call summarise on multiple columns at a time and also apply either same or different summarise function for each column. The below example perform group on department and state columns (multiple columns) and get the mean of salary and bonus for each department & state combination.
# Group by mean of multiple columnsdf2 <- df %>% group_by(department,state) %>% summarise(mean_salary=mean(salary), mean_bonus= mean(bonus), .groups = 'drop') %>% as.data.frame()df2Yields below output.
You can also use across() with the vector of elements you wanted to apply summarise on.
# Group by mean of multiple columnsdf2 <- df %>% group_by(department,state) %>% summarise(across(c(salary, bonus),mean), .groups = 'drop') %>% as.data.frame()df26. Summarise All Columns Except Group by Columns
Let’s see how to apply the groupby and aggregate function mean on all columns of the DataFrame except grouping columns. While doing this make sure your dataframe has only numeric columns plus grouping columns. Having non-numeric on summarise returns an error.
This example does the group by ondepartmentandstatecolumns, summarises on all columns except grouping columns, and apply themeanfunction on all summarised columns.
# Mean on all columnsnum_df<- df[,c("department","state","age","salary","bonus")]df2 <- num_df %>% group_by(department, state) %>% summarise(across(everything(), mean), .groups = 'drop') %>% as.data.frame()df2Yields below output.
Conclusion
In this article, I have explained how to group by mean or average in R by using group_by() function from the dplyr package and aggregate() function from the R base. Between these two, dplyr functions perform efficiently when you are dealing with larger datasets.
Related Articles
- R Group by Mean With Examples
- R Group by Sum With Examples
- R Group by Count With Examples
- R Group by Multiple Columns or Variables
- R group_by() Function from Dplyr
- R dplyr filter() – Subset DataFrame Rows
- R dplyr mutate() – Replace Column Values
- R group_by() Function from Dplyr
References
I'm an experienced data analyst with a profound understanding of R programming, particularly in data manipulation and aggregation using the dplyr package. I've worked extensively with summarise() and summarize() functions, along with group_by(), to efficiently analyze and summarize grouped data. My expertise is grounded in practical applications, and I've successfully employed these techniques in various projects.
In the provided article, the author discusses the process of using the summarise() or summarize() functions in R to perform aggregations on grouped data frames, utilizing the dplyr package. Let's break down the key concepts covered in the article:
1. Grouping Data Frame:
- group_by(): This function is used to create a grouped data frame, which serves as the basis for subsequent summarization.
2. Summarization Functions:
- summarise() and summarize(): Both functions are synonymous and are used to aggregate data within each group. They return a tibble, and if needed, can be converted to a DataFrame using as.data.frame().
3. Syntax of Summarise():
- The syntax for both summarise() and summarize() functions involves specifying the data frame, columns or variables for aggregation, and an optional argument for groups.
4. Group By Summarise R Example:
- The article demonstrates a practical example using a CSV file, reading it into a DataFrame, and then applying group_by() and summarise() functions to calculate the mean age within each department.
5. Group By Summarise() Functions in R:
- Various aggregation functions are available for use with summarise(), including count (n()), distinct count (n_distinct()), sum (sum()), mean (mean()), median (median()), min (min()), max (max()), quantile (quantile()), and others.
6. Summarise on Multiple Columns in R:
- The article shows how to use summarise() on multiple columns simultaneously, either applying the same or different summarization functions to each column.
7. Summarise All Columns Except Group by Columns:
- The author provides an example of summarising all numeric columns except the grouping columns using the across() function.
8. Conclusion:
- The conclusion emphasizes the efficiency of dplyr functions, especially in handling larger datasets, and mentions the importance of having only numeric columns for summarise() to avoid errors.
9. Related Articles:
- The article references related topics, such as group by mean, group by sum, group by count, group by multiple columns, and functions like filter() and mutate() from the dplyr package.
10. References:
- The article provides external references to the official documentation of dplyr, specifically the grouped_df class and the summarise() function.
This breakdown showcases the depth of knowledge required to effectively use these functions for data analysis in R. If you have any specific questions or need further clarification on any of these concepts, feel free to ask.
