These are the core packages used in R to recreate some of the basic data visualization examples we explored, such as bar charts, pie charts, and pivot-style summaries. Each of these tools plays a specific role from data import and cleaning to aggregation and plotting.
While R provides a powerful and flexible framework for building reproducible and visualizations, it’s important to note the trade-offs in terms of efficiency and ease of use. For basic visual tasks, such as creating pie charts, bar graphs, or pivot tables, Excel is often faster and more intuitive, especially for users familiar with its drag-and-drop interface.
As a visualizations become more complex or need to be automated and repeated (e.g., across multiple datasets or time periods), R offers a more scalable and programmatic solution. The ability to script your workflow means you can quickly regenerate visuals without manually repeating steps: a major advantage over Excel for larger projects or reporting pipelines.
In short, Excel excels at speed and simplicity for one-off tasks, while R shines in flexibility, automation, and reproducibility for more advanced or recurring analysis and visualization tasks.
#install.packages("ggplot2")
#install.packages("dplyr")
#install.packages("tidyr")
#install.packages("readxl")
library(readxl)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(tidyr)
library(ggplot2)Warning: package 'ggplot2' was built under R version 4.3.3Before beginning your data visualization work in R, it’s important to ensure that R can locate and access the data file you want to work with. This typically involves setting the correct file path.
In the code below, you’ll need to replace the example file path with the actual path to your file. (MUST DO!)
For example, if your file is located on your Desktop in a folder named “ExcelFiles”, and the file is named “restaurant.xlsx”, the full file path might look like:
On Windows:
"C:/Users/YourName/Desktop/ExcelFiles/restaurant.xlsx"On Mac:
"/Users/YourName/Desktop/ExcelFiles/restaurant.xlsx"Be sure to:
What I would recommend is right click option mac Use forward slashes / instead of backslashes in the file path
Check that the file name is spelled correctly and includes the .xlsx extension
Ensure the file is not open in Excel when R is trying to read it
Once you’ve set the correct file path, you can use the readxl package to import the data into R for analysis and visualization.
If the readxl package is not already installed on your computer, you’ll need to install it first using install.packages(“readxl”). After it’s installed, you load the package with library(readxl).
# Fix the below script to find file you downloaded from Lamaku
#restaurants <- read_excel("~/Chapter 03/restaurant.xlsx")
head(restaurants)# A tibble: 6 × 4
Restaurant `Quality Rating` `Meal Price ($)` `Wait Time (min)`
<dbl> <chr> <dbl> <dbl>
1 1 Good 18 5
2 2 Very Good 22 6
3 3 Good 28 1
4 4 Excellent 38 74
5 5 Very Good 33 6
6 6 Good 28 5Files (Bottom-Right):
Go through the folder path to where the data is and click on the data to reveal the import option.
Then the following screen will pop up.
Import the data and you will notice the code needed will run in the console. Which you can copy and paste for later or at least take note of because it will show you what the manual code should have looked at.
# Summarize number of restaurants by quality rating
restaurant_counts <- restaurants %>%
group_by(`Quality Rating`) %>%
summarise(Count = n(), .groups = "drop")
# Create pie chart
ggplot(restaurant_counts, aes(x = "", y = Count, fill = `Quality Rating`)) +
geom_col(width = 1, color = "white") +
coord_polar(theta = "y") +
theme_void() + # removes axes and background
labs(title = "Number of Restaurants by Quality Rating",
fill = "Quality Rating") +
theme(plot.title = element_text(hjust = 0.5))
# Create bar plot
ggplot(restaurant_counts, aes(x = `Quality Rating`, y = Count, fill = `Quality Rating`)) +
geom_bar(stat = "identity") +
labs(
title = "Number of Restaurants by Quality Rating",
x = "Quality Rating",
y = "Number of Restaurants"
) +
theme_minimal() +
theme(legend.position = "none", plot.title = element_text(hjust = 0.5))
This is what we created in Excel using pivot
# Step 1: Bin the Meal Price into $10 bins
restaurants_binned <- restaurants %>%
mutate(
PriceBin = cut(
`Meal Price ($)`,
breaks = seq(0, max(`Meal Price ($)`) + 10, by = 10),
include.lowest = TRUE,
right = FALSE # e.g., [10,20)
)
)
# Step 2: Count of restaurants by Quality Rating and Price Bin
pivot_table <- restaurants_binned %>%
group_by(`Quality Rating`, PriceBin) %>%
summarise(RestaurantCount = n(), .groups = "drop") %>%
pivot_wider(names_from = PriceBin, values_from = RestaurantCount, values_fill = 0)
# View the result
print(pivot_table, width = Inf)# A tibble: 3 × 5
`Quality Rating` `[10,20)` `[20,30)` `[30,40)` `[40,50]`
<chr> <int> <int> <int> <int>
1 Excellent 2 14 28 22
2 Good 42 40 2 0
3 Very Good 34 64 46 6median(restaurants$`Meal Price ($)`)[1] 25mean(restaurants$`Meal Price ($)`)[1] 25.89667A z-score measures how many standard deviations a value is from the mean.
# Calculate z-scores for meal price
restaurants$Meal_Z <- scale(restaurants$`Meal Price ($)`)
head(restaurants$Meal_Z) [,1]
[1,] -0.8502343
[2,] -0.4195542
[3,] 0.2264659
[4,] 1.3031661
[5,] 0.7648160
[6,] 0.2264659# 90th percentile of meal price
quantile(restaurants$`Meal Price ($)`, probs = 0.90)90%
38 # 90th percentile of meal price
quantile(restaurants$`Meal Price ($)`, probs = c(0, 0.25, 0.5, 0.75, 1)) 0% 25% 50% 75% 100%
10 19 25 32 48 cor(restaurants$`Meal Price ($)`, restaurants$`Wait Time (min)`,)[1] 0.4628228