When and why did you learn R?
I moved over to R after college when I lost access to Stata (a statistics software). At the time, I was working as a data analyst and I needed to use software that was free to use. My primary motivation came after I saw examples of data visualization and statistical modeling in R; it was beyond the capabilities of any other software I had used in the past.
What are the benefits of R over other languages?
Compared to other statistical software programs, R is great because it is free and open-source. Open-source languages tend to be well supported with online documentation, which made it much easier for me to look up code examples. Compared to other programming languages, R has built-in functionality for statistics and a wide ecosystem of packages. This means you can do virtually any type of statistical analysis you can imagine in R. You might’ve heard about Python for data science. As a data scientist, it’s beneficial to learn a little bit of both R and Python, but choose whichever language works best for you to get started.
Keywords: time series, decisions, analysis, forecasting
# Load necessary libraries
library(ggplot2)
library(forecast)
# Generate a sample time series data
set.seed(123)
time_series <- ts(rnorm(100), start = 1)
# Visualize the time series
autoplot(time_series, main = "Sample Time Series Data") +
ylab("Value") +
xlab("Time")
# Perform a simple time series decomposition
decomposition <- decompose(t
