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# Stacked Bar Chart

# Load necessary libraries
library(plotly)

## Loading required package: ggplot2

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

# Create or modify 'cyl4' column
mtcars$cyl4 <- ifelse(mtcars$cyl == 4, "4 cylinder", "Not 4 cylinder")


# Modify am variable to have descriptive labels
mtcars$am <- factor(mtcars$am, levels = c(0, 1), labels = c("Automatic", "Manual"))

# Create a table
mtcars_table <- with(mtcars, table(am, cyl4))


# Create a data frame from the table
df <- as.data.frame(mtcars_table)
names(df) <- c("Transmission", "FourCyl", "Count")

# Create interactive plot
fig <- df %>%
  plot_ly(x = ~Transmission, y = ~Count, color = ~FourCyl,
          type = "bar", split = ~FourCyl) %>%
  layout(barmode = 'stack', title="Car Specs",
         xaxis=list(title="Transmission"),
         yaxis=list(title="Number of Cars"))

fig

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels

# Histogram with Skewed Data
# Load necessary libraries
library(ggplot2)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

# Load diamonds dataset
data(diamonds)

# Calculate median and IQR
median_price <- median(diamonds$price)
IQR_price <- IQR(diamonds$price)
Q1_price <- quantile(diamonds$price, 0.25) # First Quartile (Q1)
Q3_price <- quantile(diamonds$price, 0.75) # Third Quartile (Q3)

# Create histogram
ggplot(diamonds, aes(x = price)) +
  geom_histogram(aes(y = ..density..), binwidth = 500, fill = "steelblue", alpha = 0.7) +
  
  # Add vertical lines for Median and IQR
  geom_vline(aes(xintercept = median_price), color="blue", linetype="dashed", size=1) +
  geom_vline(aes(xintercept = Q1_price), color="green", linetype="dashed", size=1) +
  geom_vline(aes(xintercept = Q3_price), color="green", linetype="dashed", size=1) +
  
  labs(
    title ="Histogram with Median and Interquartile Range",
    subtitle = "Price data from 'diamonds'",
    x = "Price ($)",
    y = "Density"
  ) +
  
  # Adding text to denote Median and IQR 
  annotate("text", x = c(Q1_price -1000 , Q3_price +2000 ,median_price),
           y=c(.00001,.00001,.00002),
           label=c(paste("Q1 =", round(Q1_price)),
                   paste("Q3 =", round(Q3_price)),
                   paste("Median =", round(median_price))),
           colour=c("green","green","blue")) +
  
  theme_minimal() # For prettier theme

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

# Histogram with Normal Data
# Load necessary libraries
library(ggplot2)
library(dplyr)

# Calculate mean and standard deviation of mpg
mean_mpg <- mean(mtcars$mpg)
sd_mpg <- sd(mtcars$mpg)

# Create histogram
ggplot(mtcars, aes(x = mpg)) +
  geom_histogram(aes(y = ..density..), binwidth = 1, fill = "steelblue", alpha = 0.7) +
  
  # Add lines for Mean and Mean ± SD
  geom_vline(aes(xintercept = mean_mpg), color="blue", linetype="dashed", size=1) +
  geom_vline(aes(xintercept = mean_mpg - sd_mpg), color="red", linetype="dashed", size=1) +
  geom_vline(aes(xintercept = mean_mpg + sd_mpg), color="red", linetype="dashed", size=1) +
  
  labs(
    title ="Histogram with Mean and Standard Deviation",
    subtitle = "Miles Per Gallon ('mpg') from 'mtcars'",
    x = "Miles Per Gallon",
    y = "Density"
  ) +
  
  # Adding text to denote Mean and SD 
  annotate("text", x = c(mean_mpg - sd_mpg , mean_mpg + sd_mpg ,mean_mpg),
           y=c(.02,.02,.03),
           label=c(paste("Mean - SD =", round(mean_mpg - sd_mpg, digits=2)),
                   paste("Mean + SD =", round(mean_mpg + sd_mpg, digits=2)),
                   paste("Mean =", round(mean_mpg, digits=2))),
           colour=c("red","red","blue")) +
  
  theme_minimal() # For prettier theme

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