ggplot Showcase

library(tidyverse)
library(png)

path <- "C:/Users/Justin Pons/Fall 2023/DA 6233 Data Visualization/DA-6233-2023/Homework 2"
setwd(path)

d1 <- read_csv("tech_stocks_csv.zip", show_col_types = FALSE) |>  
  filter(sale > 0) |>  
  mutate(conm = stringr::str_to_title(conm),
         datadate = lubridate::ymd(datadate))

Question 1

d1_1 <- d1 |> 
  group_by(conm) |> 
  summarize(avg_sale = mean(sale), .groups = "drop") |> 
  arrange(desc(avg_sale))

plot_1 <- d1_1 |> ggplot(aes(reorder(conm, avg_sale), avg_sale))

plot_1 <- plot_1 +
  geom_bar(stat = "identity") +
  labs(x = "Company", y = "Average Sale in $ Millions") + 
  scale_y_continuous(labels = scales::label_dollar(prefix = "$")) +
  coord_flip() +
  theme_minimal()

plot_1

Question 2

plot_2 <- plot_1

plot_2 <- plot_2 +
  geom_text(aes(label = round(avg_sale/1000)),hjust = -.2, size = 3)

plot_2

Question 3

d1_3 <- d1 |> mutate(debt_ratio =
                       (dlc + replace_na(dltt, 0)) / at, rnd_int = xrd / sale) |> 
  select(debt_ratio, rnd_int)

plot_3 <- d1_3 |> ggplot(aes(x=debt_ratio,y=rnd_int))

plot_3 <- plot_3 +
  geom_point(size = 3.5,fill = 'darkgray',shape=21) +
  labs(x = 'Debt Ratio',y = "R&D to Sales Ratio") +
  geom_smooth(method='lm',formula=y~x) +
  theme_minimal()

plot_3

Question 4

d1_4 <- d1 |> 
  filter(tic %in% c("AAPL", "TSLA", "META", "MSFT", "AMZN", "NVDA")) |> 
  mutate(pr_margin = oibdp / sale,
         fyear = as.Date(paste(fyear, "12", "31", sep = "-")))

plot_4 <- d1_4 |> ggplot(aes(x=fyear,y=pr_margin))

plot_4 <- plot_4 +
  geom_line() +
  geom_hline(yintercept=0,color="red") +
  labs(x = "Fiscal Year", y = "Profit Margin") +
  scale_y_continuous(labels = scales::percent) +
  facet_wrap(~conm) +
  theme_minimal() +
  theme(strip.text.x = element_text(hjust = -0.005))

plot_4

Question 5

d1_5 <- d1 |>  
  filter(conm == "Tesla Inc") |>  
  mutate(mkt_val = prcc_f * cshpri) |>  # Create market value
  select(conm, datadate, mkt_val, sale) |>  
  pivot_longer(cols = c(mkt_val, sale), 
               names_to = "fin_var", 
               values_to = "fin_value")

plot_5 <- d1_5 |> ggplot(aes(x = datadate,y = fin_value, color = fin_var)) +
  geom_line(linewidth = 1.25) +
  labs(y = NULL,x = 'Date',color='Financial Variable') + 
  ggtitle("Tesla's Sales and Market Value (Million USD)") +
  scale_y_continuous(labels = scales::label_dollar(prefix = "$")) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5, face = 'bold',size = 16),
        legend.key = element_blank(),
        legend.position="top",
        legend.box="horizontal"
        )

plot_5

Question 6

d1_6 <- d1 |> filter(tic %in% c("NVDA","META"))

plot_6 <- d1_6 |> ggplot(aes(x=as.factor(fyear),y=oibdp,fill=conm)) +
  geom_col() +
  facet_wrap(~conm,ncol=1) +
  labs(x = 'Fiscal Year',y = 'Profit in $ Millions',fill=NULL) +
  scale_fill_manual(values = c("#5cc9f5", "#b131a2")) +
  theme_minimal() +
  theme(legend.position = "top",
        strip.text.x = element_text(hjust = -0.005,size=12)
        )

plot_6

Question 7

d1_7 <- d1 |> filter(tic %in% c("META"))

plot_7 <- d1_7 |> ggplot(aes(x=as.factor(fyear),y=oibdp,fill=conm)) +
  labs(x = 'Fiscal Year',y = 'Profit in $ Millions') +
  geom_col(show.legend=FALSE) +
  scale_fill_manual(values = c("#5cc9f5")) +
  theme_minimal() +
  annotation_raster(readPNG("mark-zuckerberg-celebrity-mask.png"),
                    xmin = 1, xmax = 3, 
                    ymin = 30000, ymax = 40000, interpolate = T)

plot_7