YouTipR Func Cor
# R cor() Function - Calculating Correlation Coefficient
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The R cor() function is used to calculate the correlation coefficient between two or more variables.
The correlation coefficient measures the degree of linear correlation between variables, with a value range of -1 to 1. A value of 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation.
The syntax format of the cor() function is as follows:
cor(x, y = NULL, method = c("pearson", "kendall", "spearman"))
**Parameter Description:**
* **x** Input numeric vector or matrix.
* **y** Optional, the second vector or matrix.
* **method** Correlation coefficient type: pearson (default, linear correlation), kendall, spearman (rank correlation).
## Example
# Create Two Datasets
height <-c(160, 165, 170, 175, 180)# Height (cm)
weight <-c(55, 60, 65, 70, 80)# Weight (kg)
# Calculate Correlation Coefficient
r <-cor(height, weight)
print(paste("Correlation Coefficient Between Height and Weight:", round(r, 3)))
# Calculate the Correlation Coefficient Matrix for Multiple Variables
sleep_hours <-c(7, 6.5, 8, 7.5, 6)
df<-data.frame(height, weight, sleep_hours)
print("Correlation Coefficient Matrix:")
print(round(cor(df), 3))
Executing the above code outputs the following result:
"Correlation Coefficient Between Height and Weight: 0.993" "Correlation Coefficient Matrix:" height weight sleep_hours height 1.000 0.993 -0.784 weight 0.993 1.000 -0.729 sleep_hours -0.784 -0.729 1.000
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