- 26th Jan 2023
- 06:03 am
Whether you're working with large datasets or small samples, R programming can help you create clear and effective visualizations that can help you communicate your findings.
Another benefit of R programming is its flexibility. It can be used for a wide range of statistical analyses, including descriptive statistics, inferential statistics, and machine learning. This flexibility makes it a great tool for students and researchers in the field of Applied Statistics, such as STAT500.
In conclusion, R programming is a powerful tool for applied statistics and data analysis. Its extensive libraries and packages, emphasis on visualization, and flexibility make it a valuable tool for researchers and students in the field of Applied Statistics like STAT500. Its open-source nature and strong community support make it an accessible and widely used option for data analysis.
R Programming Code:
GlobalIndicators <- read.csv("AppliedStatistics_Assignment2_GlobalIndicators(5).csv")
work_data <- GlobalIndicators[c("Country", "Region" ,"GDPGrowth" , "PopulationGrowth")]
work_data <- subset(work_data, Country!= "Libya")
model1<- lm(GDPGrowth ~ Region, data = work_data)
summary(aov(model1))
library(stargazer)
stargazer(model1, type = "text")
library(ggplot2)
ggplot(work_data, aes(x = Region, y = GDPGrowth, fill = Region)) + geom_boxplot()
work_data <- subset(work_data, Country!= "Libya")
model3 <- lm(GDPGrowth ~ PopulationGrowth, work_data)
summary(model3)
work_data <- work_data[complete.cases(work_data),]
ggplot(work_data, aes(x = PopulationGrowth, y = GDPGrowth, col = Region)) + geom_point()
