
# Load required libraries
library(tidyverse)
# Load the data
data <- read.csv("data.csv")
# Calculate the correlation between the sale rate and economic growth
correlation <- cor(data$sale_rate, data$economic_growth)
# Print the correlation coefficient
print(correlation)
# Plot the data to visualize the relationship
ggplot(data, aes(x = sale_rate, y = economic_growth)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
ggtitle("Correlation between Sale Rate and Economic Growth") +
xlab("Sale Rate") +
ylab("Economic Growth")
# Interpret the results
if (correlation > 0) {
print("There is a positive correlation between the sale rate and economic growth.")
} else if (correlation < 0) {
print("There is a negative correlation between the sale rate and economic growth.")
} else {
print("There is no correlation between the sale rate and economic growth.")
}
This code will load the required packages and load the data, then calculate the correlation coefficient between the sale rate and economic growth. It will print the correlation coefficient, create a scatterplot of the data with a fitted line to visualize the relationship, and interpret the results based on the value of the correlation coefficient.
#CHATGPT #R코딩 #R데이터분석 #데이터분석 #기초자료 #인공지능코딩 #코딩
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