Data Visualization

Assignments

Assignment 1

Assignment 2

Assignment 3

Assignment 4(Hackathon)

data(iris)

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.1     ✔ stringr   1.5.2
✔ ggplot2   4.0.0     ✔ 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

# 1. Divide the dataset into three rectangles based on species.
# The average of Petal.Length and Petal.Width is the length and width.
# Draw three rectangles arranged horizontally.

#1

plot_data <- iris %>%
  mutate(
    sepal_length_group = cut(
      Sepal.Length,
      breaks = c(4, 5.5, 7.0, 8.0),
      labels = c("Small (4.0-5.5)", "Medium (5.6-7.0)", "Large (7.1-8.0)"),
      include.lowest = TRUE
    )
  ) %>%
  group_by(sepal_length_group) %>%
  summarise(
    count = n(),
    avg_petal_length = mean(Petal.Length)
  ) %>%
  mutate(
    xmax = cumsum(count),
    xmin = xmax - count,
    x_label_pos = (xmin + xmax) / 2
  )

ggplot(plot_data, aes(ymin = 0)) +
  geom_rect(
    aes(
      xmin = xmin,
      xmax = xmax,
      ymax = avg_petal_length,
      fill = sepal_length_group
    ),
    color = "white"
  ) +
  scale_x_continuous(
    breaks = plot_data$x_label_pos,
    labels = plot_data$sepal_length_group,
    expand = c(0, 0)
  ) +
  scale_fill_viridis_d(option = "D", direction = -1) +
  labs(
    title = "Average Petal Length by Sepal Length Group",
    subtitle = "Column width is proportional to the number of flowers in each group",
    x = "Count of Flowers in Group",
    y = "Average Petal Length (cm)",
    fill = "Sepal Length Group"
  ) +
  # Apply a clean theme
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold", size = 18),
    legend.position = "bottom",
    panel.grid.major.x = element_blank(), # Remove vertical grid lines
    panel.grid.minor.x = element_blank()
  )

# 2. table with embedded charts
iris_long <- iris %>%
  pivot_longer(cols = -Species, names_to = "Measurement", values_to = "Value")

ggplot(iris_long, aes(x = Value, fill = Species)) +
  geom_histogram(color = "white", bins = 15) +
  facet_grid(Species ~ Measurement, scales = "free") +
  scale_fill_manual( #coloring each species
    values = c(
      "setosa" = "steelblue", 
      "versicolor" = "orange",   
      "virginica" = "seagreen"     
    ) 
    ) + #labels
      labs(
        title = "Distribution of Iris Measurements by Species",
        x = "Measurement Value (cm)",
        y = "Count"
      ) +
  theme_bw() +
    theme(
      plot.title = element_text(hjust = 0.5, face = "bold"),
      strip.text.x = element_text(face = "bold"),
      strip.text.y = element_text(face = "bold"),
      panel.border = element_rect(color = "grey80", fill = NA),
      legend.position = "bottom"
    )

# 3. Extract setona and versicolor from species.
# Then create df_2 and df_3. Draw a bar plot using petal.width: p1 p2.
# Finally, use gridExtra to combine the plots.'
library("gridExtra")


Attaching package: 'gridExtra'

The following object is masked from 'package:dplyr':

    combine

df_2 <- subset(iris, Species %in% "setosa")
df_3 <- subset(iris, Species %in% "versicolor")
df_2$id <- 1:nrow(df_2)
df_3$id <- 1:nrow(df_3)



p1 = ggplot(df_2, aes(x = factor(id), y = Petal.Width)) +
  geom_bar(stat = "identity", fill = 'red', color = "black") +
  coord_flip() +
  labs(title = "setosa") +
  theme(
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank() #this was by GPT
  )


p2 = ggplot(df_3, aes(x = factor(id), y = Petal.Width)) +
  geom_bar(stat = "identity", fill = "blue", color = "black") +
  coord_flip() +
  labs(title = "versicolor")+
  theme(
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank() #this was by GPT
  )



gridExtra::grid.arrange(p1, p2, ncol = 2)

# 4 Column Chart
# getting means of Petal length and width for each species
# and mean sepal length and sepal width
iris_means <- iris %>%
  group_by(Species) %>%
  summarise(
    mean_sepal_length = mean(Sepal.Length),
    mean_sepal_width = mean(Sepal.Width),
    mean_petal_length = mean(Petal.Length),
    mean_petal_width = mean(Petal.Width)
  ) %>%
  pivot_longer(
    cols = -Species,
    names_to = "Measurement",
    values_to = "MeanValue"
    )

ggplot(iris_means, aes(x = Measurement, y = MeanValue, fill = Species)) +
  geom_col(position = position_dodge(width = 0.8)) + 
  labs(title = "Mean Iris Measurements by Species",
       x = "Measurement", y = "Mean Value") + 
  theme_minimal(base_size = 12) +
  scale_fill_manual(values = c("steelblue", "orange", "seagreen"))

Class coding competition

library(ggplot2)
mpg <- as.data.frame(mpg)
#2seater, compact, midsize, minivan, pickup, subcompact, suv scatterplots in one view
ggplot(mpg, aes(x=displ, y=hwy)) +
  geom_point(color = "black") +
  facet_wrap(~ class) +
  labs(x="displ",
       y="hwy") +
  theme_gray()

#improving the chart
ggplot(mpg, aes(x=displ, y=hwy)) +
  geom_point(color = "blue", size=2, alpha=0.3) +
  geom_smooth(method = "lm", se = FALSE, color = "#E65100", linewidth = 0.8) +
  facet_wrap(~ class) +
  labs(title="Engine Displacement vs Highway MPG by Vehicle Class",
       x="Engine Displacement (liters)",
       y="Highway Miles per Gallon (MPG)") +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size=16, face="bold"),
    axis.title.x = element_text(size=12),
    axis.title.y = element_text(size=12)
  )

`geom_smooth()` using formula = 'y ~ x'

Assignment 5

# GPT was used for picking colors and family.
# GPT was used for adjusting the format of the code.
library(ggplot2)
library(scales)   # for alpha()


Attaching package: 'scales'

The following object is masked from 'package:purrr':

    discard

The following object is masked from 'package:readr':

    col_factor

data(iris)
color1 <- "#18A3A380"
color2 <- "#FF4D8DCC"
color3 <- "#7A7A7A"
color4 <- "#000000"
base_family <- "sans"

# custom theme used across plots
theme1 <- function() {
  theme_minimal(base_family = base_family) +
    theme(
      text        = element_text(family = base_family, colour = color4),
      plot.title  = element_text(face = "bold", colour = color4, size = 13),
      axis.title  = element_text(colour = color4),
      axis.text   = element_text(colour = color3),
      panel.grid.major = element_line(color = scales::alpha(color3, 0.3), linetype = "dotted"),
      panel.grid.minor = element_blank()
    )
}


Histo <- function(){
  hist(iris$Sepal.Length,
       main="Distribution of Sepal Length (iris)",
       col=color1, border=color3)
}

Bar1 <- function(){
  barplot(table(iris$Species),
          col=c(color1,color2,color3),
          border=color4,
          main="Count by Species",
          xlab="Species", ylab="Count")
}

Bar2 <- function(){
  barplot(table(iris$Species),
          horiz=TRUE,
          col=c(color1,color2,color3),
          border=color4,
          main="Count by Species (Horizontal)",
          xlab="Count", ylab="Species")
}

Pie <- function(){
  pie(table(iris$Species),
      col=c(color1,color2,color3),
      main="Species Composition",
      clockwise=TRUE)
}

Box <- function(){
  boxplot(Sepal.Length~Species, data=iris,
          col=c(color1,color2,color3),
          main="Sepal Length by Species",
          xlab="Species", ylab="Sepal Length (cm)")
}

Scat <- function(){
  plot(iris$Petal.Length, iris$Sepal.Length,
       main="Sepal vs Petal Length",
       xlab="Petal Length (cm)", ylab="Sepal Length (cm)",
       pch=19, col=color1)
}

library(gridExtra)

par(mfrow=c(2,3), mar=c(4,4,2.5,1), family="sans")
Histo(); Bar1(); Bar2(); Pie(); Box(); Scat()

draw6 <- function(){
  par(mfrow=c(2,3), mar=c(4,4,2.5,1), family=base_family)
  Histo(); Bar1(); Bar2(); Pie(); Box(); Scat()
}

save_plot <- function(fmt, file){
  switch(fmt,
    pdf  = pdf(file, width=10, height=7, family=base_family),
    jpg  = jpeg(file, width=2400, height=1600, res=300, quality=95),
    svg  = svg(file, width=2400, height=1600),
    tiff = tiff(file, width=2400, height=1600, res=300),
    bmp  = bmp(file, width=2400, height=1600, res=300), # cannot find bmg, and was told it might be .bmp by GPT
  )
  draw6(); invisible(dev.off())
}

save_plot("pdf",  "/Users/buttegg/Documents/IAmTryingToUseQuartoDoSthCool/buttegggggggg.github.io/baseR_6plots.pdf")
save_plot("jpg",  "/Users/buttegg/Documents/IAmTryingToUseQuartoDoSthCool/buttegggggggg.github.io/baseR_6plots.jpg")
save_plot("svg",  "/Users/buttegg/Documents/IAmTryingToUseQuartoDoSthCool/buttegggggggg.github.io/baseR_6plots.svg")
save_plot("tiff", "/Users/buttegg/Documents/IAmTryingToUseQuartoDoSthCool/buttegggggggg.github.io/baseR_6plots.tiff")
save_plot("bmp",  "/Users/buttegg/Documents/IAmTryingToUseQuartoDoSthCool/buttegggggggg.github.io/baseR_6plots.bmp")

ggHisto <- ggplot(iris, aes(x=Sepal.Length)) +
  geom_histogram(fill=color1, color=color3, bins=20) +
  labs(title="Distribution of Sepal Length (iris)") +
  theme1()

ggBar1 <- ggplot(iris, aes(x=Species, fill=Species)) +
  geom_bar(color=color4) +
  scale_fill_manual(values=c(color1,color2,color3)) +
  labs(title="Count by Species") +
  theme1() + ggplot2::theme(legend.position="none")

ggBar2 <- ggplot(iris, aes(x=Species, fill=Species)) +
  geom_bar(color=color4) + coord_flip() +
  scale_fill_manual(values=c(color1,color2,color3)) +
  labs(title="Count by Species (Horizontal)") +
  theme1() + ggplot2::theme(legend.position="none")

df <- as.data.frame(prop.table(table(iris$Species)))
colnames(df) <- c("Species","prop")
ggPie <- ggplot(df, aes(x="", y=prop, fill=Species)) +
  geom_col(width=1, color=NA) + coord_polar(theta="y") +
  scale_fill_manual(values=c(color1,color2,color3)) +
  labs(title="Species Composition") +
  theme1() + ggplot2::theme(axis.text=ggplot2::element_blank(),
                            axis.title=ggplot2::element_blank(),
                            panel.grid=ggplot2::element_blank(),
                            legend.position="right")

ggBox <- ggplot(iris, aes(x=Species, y=Sepal.Length, fill=Species)) +
  geom_boxplot(color=color4) +
  scale_fill_manual(values=c(color1,color2,color3)) +
  labs(title="Sepal Length by Species") +
  theme1() + ggplot2::theme(legend.position="none")

ggScat <- ggplot(iris, aes(x=Petal.Length, y=Sepal.Length)) +
  geom_point(color=color1, size=2) +
  labs(title="Sepal vs Petal Length") +
  theme1()

gridExtra::grid.arrange(ggHisto, ggBar1, ggBar2, ggPie, ggBox, ggScat, ncol = 3)

combo <- gridExtra::arrangeGrob(ggHisto, ggBar1, ggBar2, ggPie, ggBox, ggScat, ncol = 3)
outdir <- "/Users/buttegg/Documents/IAmTryingToUseQuartoDoSthCool/buttegggggggg.github.io"
formats <- c("pdf", "jpg", "svg", "tiff", "bmp")

for (fmt in formats) {
  outpath <- file.path(outdir, paste0("ggplot_6plots.", fmt))
  ggplot2::ggsave(
    filename = outpath,
    plot = combo,
    width = 10, height = 7, dpi = 300
  )
}