Presentation 6 Jose Salazar

knitr::include_graphics("C:/Users/Jose Salazar/OneDrive/Pictures/Jose_Salazar_R for Data Science Analysis and Visualization.jpg")

This introductory course for R covered the basics when it came to the R language. Mostly utilized for analyzing of data, R for Data Science covered the fundamentals of R, to include installation, setting up RStudio, and using code packages. It also allowed for the student to practice and get experience with models, visualization, and statistical analysis. At the end of the course, I felt that I had a good understanding of R’s capabilities. It allowed me to go back a rewatch videos to understand basic coding needed for my work in this course. It’s a great class for anyone who needs an introduction to programming. I enjoyed the data visualization as it was very fulfilling to see your data inputs in a graph or chart. It verified that you were inputting the information in correctly.

# Load the necessary libraries
library(tidyverse)

── Attaching core tidyverse packages ───────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     ── Conflicts ─────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors

# Use the mtcars dataset
data("mtcars")

# Group by the number of cylinders and calculate averages
average_mpg_weight <- mtcars %>%
  group_by(cyl) %>%
  summarize(
    average_mpg = mean(mpg, na.rm = TRUE),
    average_weight = mean(wt, na.rm = TRUE)
  )

# Display the results
print(average_mpg_weight)

NA