Step-by-Step Guide for Calculating CV in R
Now we will explain Step-by-Step for Calculate the Coefficient of Variation in R Programming Language.
Step 1: Load the Data
Now we will load the dataset for Calculate the Coefficient of Variation in R and for this we will use Weather History dataset.
Dataset : Weather History
data <- read.csv("C:\\Users\\Tonmoy\\Downloads\\Dataset\\weatherHistory.csv")
head(data)
Output:
Formatted.Date Summary Precip.Type Temperature..C.
1 2006-04-01 00:00:00.000 +0200 Partly Cloudy rain 9.472222
2 2006-04-01 01:00:00.000 +0200 Partly Cloudy rain 9.355556
3 2006-04-01 02:00:00.000 +0200 Mostly Cloudy rain 9.377778
4 2006-04-01 03:00:00.000 +0200 Partly Cloudy rain 8.288889
5 2006-04-01 04:00:00.000 +0200 Mostly Cloudy rain 8.755556
6 2006-04-01 05:00:00.000 +0200 Partly Cloudy rain 9.222222
Apparent.Temperature..C. Humidity Wind.Speed..km.h. Wind.Bearing..degrees.
1 7.388889 0.89 14.1197 251
2 7.227778 0.86 14.2646 259
3 9.377778 0.89 3.9284 204
4 5.944444 0.83 14.1036 269
5 6.977778 0.83 11.0446 259
6 7.111111 0.85 13.9587 258
Visibility..km. Loud.Cover Pressure..millibars. Daily.Summary
1 15.8263 0 1015.13 Partly cloudy throughout the day.
2 15.8263 0 1015.63 Partly cloudy throughout the day.
3 14.9569 0 1015.94 Partly cloudy throughout the day.
4 15.8263 0 1016.41 Partly cloudy throughout the day.
5 15.8263 0 1016.51 Partly cloudy throughout the day.
6 14.9569 0 1016.66 Partly cloudy throughout the day.
Step 2: Calulate Mean and Standard Deviation
Now we will calculate Mean and Standard Deviation for calculating Coefficient of Variation in R.
mean_temp <- mean(data$Temperature..C., na.rm = TRUE)
mean_temp
sd_temp <- sd(data$Temperature..C., na.rm = TRUE)
sd_temp
Output:
[1] 11.93268
[1] 9.551546
Step 3: Calculate the Coefficient of Variation
Now we will calculate the Coefficient of Variation.
cv_temp <- (sd_temp / mean_temp) * 100
print(cv_temp)
Output:
[1] 80.04528
The output 80.04528 indicates that the Coefficient of Variation for the Temperature (C) column in the dataset is about 80.04528%. This means the standard deviation is 80.04528% of the mean temperature. A higher CV indicates greater variability relative to the mean.
Limitations and Considerations
- Sensitivity to Mean: CV is not defined when the mean is zero, and it can be misleading for datasets with a mean close to zero.
- Comparability: CV is useful for comparing variability between datasets, but only if they have the same unit of measurement.
- Outliers: Presence of outliers can significantly affect the mean and standard deviation, thereby distorting the CV.
How to Calculate the Coefficient of Variation in R
The Coefficient of Variation (CV) is a standardized measure of dispersion in a dataset. It is defined as the ratio of the standard deviation to the mean, and it is usually expressed as a percentage. The CV is particularly valuable in statistics because it allows for the comparison of variability between datasets with different units or vastly different means, providing a relative measure of variability.