This calculator creates a structured table showing how often values appear in your dataset. It displays the frequency (count), cumulative frequency, percentage (relative frequency), and cumulative percentage (cumulative relative frequency) of each unique value, helping you identify patterns and distributions in your data. You can choose how categories are sorted (alphabetically, by weekday, by month, numerically, or in a custom order you define) to best represent your data. A bar chart is also included to better understand your data's distribution.
Related tools you might find useful:
- Histogram - Grouped frequency table for numeric data with ranges (0-10, 11-20, etc.)
- Contingency table - Two way frequency table between two categorical variables
- Pivot table - Custom aggregations (count, sum, etc.) for multiple categorical variables and a numeric variable
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What is a Frequency Table?
Definition
A frequency table (also called a frequency distribution table) is a statistical tool that organizes data by displaying how often each distinct value or category occurs in a dataset. It provides a structured way to summarize data distribution through counts, percentages, and cumulative frequencies.
When to Use Frequency Tables
Categorical Data:
- Survey responses (Yes/No/Maybe)
- Product categories
- Geographic regions
- Educational levels
Discrete Numerical Data:
- Number of children in families
- Days of the week
- Exam grades (A, B, C, D, F)
- Rating scales (1-5 stars)
Types of Frequency Tables
Simple Frequency Table
Shows frequency and relative frequency for a single variable.
Two-Way Frequency Table
Shows relationship between two categorical variables.
Grouped Frequency Table
Groups continuous data into intervals or ranges.
Cumulative Frequency Table
Includes running totals to show cumulative distribution.
Understanding Frequency Table Components
Frequency (Count)
The number of times each value appears in your dataset.
Relative Frequency (%)
The percentage each value represents of the total dataset.
Formula: (Frequency ÷ Total) × 100
Cumulative Frequency
Running total of frequencies up to and including the current value.
Cumulative Percentage
Running total of relative frequencies, useful for percentiles.
How to Make a Frequency Table (Step-by-Step)
Manual Method
- Collect your data: Gather all values you want to analyze
- Identify unique values: List all distinct categories or values
- Count frequencies: Tally how many times each value appears
- Calculate percentages: Divide each frequency by total and multiply by 100
- Add cumulative columns: Calculate running totals if needed
- Sort appropriately: Order by frequency, alphabetically, or logically
Example: Survey Responses
Raw data: Red, Blue, Red, Green, Blue, Blue, Red, Green, Blue, Red
Academic Example: Final Exam Scores Distribution
Distribution of final exam scores for 100 students in a statistics course.
Score Distribution
| Score Range | Grade | Frequency | Percentage | Cumulative % |
|---|---|---|---|---|
| 90-100 | A | 15 | 15.0% | 15.0% |
| 80-89 | B | 25 | 25.0% | 40.0% |
| 70-79 | C | 35 | 35.0% | 75.0% |
| 60-69 | D | 20 | 20.0% | 95.0% |
| Below 60 | F | 5 | 5.0% | 100.0% |
Grade Distribution
Key Insights:
- Mode: Grade C (Most common grade)
- 40% of students received grades B or higher
- Only 5% of students failed the exam
- The distribution shows a roughly normal curve
This frequency table helps instructors understand the overall class performance, identify areas for improvement, and compare with expected grade distributions.
Creating Frequency Tables in Different Tools
Examples of how to create frequency tables in R, Python, and Excel.
R (tidyverse)
# Enhanced R frequency table with multiple sorting options
library(tidyverse)
data <- c("Red", "Blue", "Red", "Green", "Blue", "Blue", "Red", "Green", "Blue", "Red")
# quick and simple way
table(data)
# create comprehensive frequency table
freq_table <- data.frame(value = data) |>
count(value, name = "frequency") |>
mutate(
percentage = round(frequency / sum(frequency) * 100, 1),
cumulative_freq = cumsum(frequency),
cumulative_pct = round(cumsum(percentage), 1)
) |>
arrange(desc(frequency)) # Sort by frequency (most common first)
print(freq_table)
# visualization
ggplot(freq_table, aes(x = reorder(value, frequency), y = frequency)) +
geom_col(fill = "steelblue", alpha = 0.8) +
geom_text(aes(label = frequency), hjust = -0.1) +
coord_flip() +
labs(title = "Frequency Distribution",
x = "Category", y = "Frequency") +
theme_minimal()Python (pandas)
import pandas as pd
import matplotlib.pyplot as plt
# Sample data
data = ["Red", "Blue", "Red", "Green", "Blue", "Blue", "Red", "Green", "Blue", "Red"]
df = pd.DataFrame({'color': data})
# Create frequency table
freq_table = df['color'].value_counts().reset_index()
freq_table.columns = ['Category', 'Frequency']
freq_table['Percentage'] = round(freq_table['Frequency'] / len(data) * 100, 1)
freq_table['Cumulative_Freq'] = freq_table['Frequency'].cumsum()
freq_table['Cumulative_Pct'] = freq_table['Percentage'].cumsum()
print(freq_table)
# Simple bar chart
freq_table.plot(x='Category', y='Frequency', kind='bar')
plt.title('Frequency Distribution')
plt.show()Excel
=COUNTIF($A$1:$A$100,B1)Method 1: COUNTIF Function
Use =COUNTIF($A$1:$A$100,B1) to count how many times the value in B1 appears in your data range.
Method 2: Pivot Table
- Select your data range
- Go to Insert → PivotTable
- Drag your category field to Rows
- Drag the same field to Values (it will count automatically)
- To add percentages, right-click a value in the PivotTable, choose Show Values As → % of Column Total