Data Analysis > Descriptive Statistics > Frequency table for a Continuous data

Frequency table for a Continuous data 

Now we will construct a frequency table income column.

1. Select lower limit of class interval i.e. 0-500, 500-1000, 1000-1500, 1500+

        breaks <- c(0, 500, 1000, 1500)  # Income ranges

2. Specify all the income data to the class interval. data[[5]] is the income column

        income_bins <- cut(data[[5]], breaks, right = TRUE, include.lowest = TRUE)

3. Construct Frequency table

         income_freq_table <- table(income_bins) 

         print(income_freq_table)

 

4. To make frequency table to general dataFrame:

        income_df <- as.data.frame(income_freq_table) 

        colnames(income_df) <- c("Income Range", "Frequency")

        print(income_df)

 

 

5. For a better bar plot:

        barplot(income_freq_table, main = "Income Distribution", 

                    col = "steelblue", xlab = "Income Range", 

                    ylab = "Frequency", las = 2)

 

6. Histogram: For histogram original data will be input and bins also.

        hist(data[[5]], breaks = breaks, col = "lightblue",

               main = "Income Histogram", xlab = "Income")

 

 What is an Ogive?

An ogive is a cumulative frequency graph used to show the number of observations below a certain value. It helps to get the idea how data accumulates over a range.

How much observations are bellow 1000. The answer is 12.

Constructing Ogive for income data 

1. Load the package:

        library(dplyr) # For data manipulation

2. Load the data:

        setwd("/Users/mdfazlulkarimpatwary/documents/Rtraining/")

        data=read.csv(“Cheat.csv”, header=TRUE)

3. Above is a tibble (dataFrame), extract income data which is in column 5

        income <- data[[5]] # Extract as numeric vector

4. Define bin breaks

         breaks <- seq(min(income, na.rm = TRUE),

                              max(income, na.rm = TRUE), by = 300)

        Here na.rm = TRUE indicates to remove null values

5. Categorize income into bins

         income_bins <- cut(income, breaks = breaks,

                                       include.lowest = TRUE, right = TRUE)

Here,

include.lowest = TRUE means  lowest value is included in the first interval.

right = TRUE means intervals are right-closed ((a, b])

6. Constructing frequency table:

        freq_table <- table(income_bins) 

7. Construction Cumulative frequency

         cum_freq <- cumsum(freq_table)

8. Convert Bin Range, Frequency and Cumulative Frequency to data frame with headings:

        ogive_data <- data.frame( Bin = names(freq_table),

             Frequency = as.numeric(freq_table),

             Cumulative_Frequency = as.numeric(cum_freq))

         print(ogive_data)

 

9. Convert bin labels to midpoints for plotting

     bin_midpoints <- (head(breaks, -1) + tail(breaks, -1)) / 2  

    plot(bin_midpoints, cum_freq, type = "o", col = "blue", pch = 16,

            xlab = "Income", ylab = "Cumulative Frequency",

            main = "Ogive for Income Data")

# Add grid for better readability

    grid()

 

We can use package ggplot2 also:

1. First install the package:

        install.packages("ggplot2")

2. Prepare a datFrame for this using mid point of Bin Range and cumulative frequency

     ogive_df <- data.frame(Bin = bin_midpoints, 

                       Cumulative_Frequency = cum_freq)

 3. Plot construction:

ggplot(ogive_df, aes(x = Bin, y = Cumulative_Frequency)) + 

   geom_line(color = "blue", size = 1) + 

   geom_point(color ="red", size = 2) + 

   labs(title = "Ogive for Income Data", 

   x = "Income", y = "Cumulative Frequency") +

   theme_minimal()

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Frequency Table and related Graphs-2

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Histogram of a Continuous Variable

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