Project 6

Certificate

Certificate

Summary of Concepts Learned

In Prof. Paulsons course going over R for Data Science: Analysis and Visualization I learned several different useful applications for R Studio, such as: - Data Wrangling to filter, sort, change, and summarize datasets - Data Visualization with various graphs, charts, and histograms - Data Importation using CSV from external sources to analyzing it within R Studio - Replicating data through R Notebooks and RMarkdown - How to tidy datasets, utilizing ‘tidyr’ - How to explore real world datasets using RStudio

Finding statistics of a dataset using internal parameters

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   3.5.2     ✔ tibble    3.3.0
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.1.0     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

data("mtcars")

# groups data by cylinders, find mpg/weight
mtcars_summary <- mtcars %>%
  group_by(cyl) %>%
  summarise(
    avg_mpg = mean(mpg),
    avg_wt = mean(wt)
  )

print(mtcars_summary)

## # A tibble: 3 × 3
##     cyl avg_mpg avg_wt
##   <dbl>   <dbl>  <dbl>
## 1     4    26.7   2.29
## 2     6    19.7   3.12
## 3     8    15.1   4.00

Using ChatGPT to determine the spread of EV registration across the United States

library(tidyverse)
ev_data <- read_csv("EV_Cleaned.csv")

## Rows: 51 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): State
## dbl (1): Registration Count
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Get the top 3 states by registration count
top3 <- ev_data %>%
  arrange(desc(`Registration Count`)) %>%
  slice(1:3)

# Calculate total national registrations
total_national <- sum(ev_data$`Registration Count`)

# Calculate 'Other States' total
others_total <- total_national - sum(top3$`Registration Count`)

# Create final data frame for plotting
pie_data <- top3 %>%
  select(State, `Registration Count`) %>%
  add_row(State = "Other States", `Registration Count` = others_total)

# Create pie chart
pie(pie_data$`Registration Count`,
    labels = paste0(pie_data$State, " (", round(100 * pie_data$`Registration Count` / sum(pie_data$`Registration Count`), 1), "%)"),
    main = "Top 3 States vs Rest of US - EV Registrations (2023)",
    col = rainbow(4))