Tesla 2022 stock analysis

library(tidyquant)

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library(ggplot2)
library(dplyr)

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symbol <- "TSLA"
start_date <- "2022-01-01"
end_date <- "2022-12-31"

tsla_data <- tq_get(symbol, from = start_date, to = end_date)

head(tsla_data)

## # A tibble: 6 × 8
##   symbol date        open  high   low close    volume adjusted
##   <chr>  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
## 1 TSLA   2022-01-03  383.  400.  379.  400. 103931400     400.
## 2 TSLA   2022-01-04  397.  403.  374.  383. 100248300     383.
## 3 TSLA   2022-01-05  382.  390.  360.  363.  80119800     363.
## 4 TSLA   2022-01-06  359   363.  340.  355.  90336600     355.
## 5 TSLA   2022-01-07  360.  360.  337.  342.  84164700     342.
## 6 TSLA   2022-01-10  333.  353.  327.  353.  91815000     353.

ggplot(tsla_data, aes(x = date, y = adjusted)) +
  geom_line(color = "blue") +
  labs(title = "Tesla Stock Price in 2022",
       x = "Date",
       y = "Adjusted Closing Price") +
  theme_minimal()

tsla_monthly_returns <- tsla_data %>%
  tq_transmute(select = adjusted,
               mutate_fun = periodReturn,
               period = "monthly",
               type = "log")

ggplot(tsla_monthly_returns, aes(x = date, y = monthly.returns)) +
  geom_col(fill = "red") +
  labs(title = "Tesla Monthly Returns in 2022",
       x = "Date",
       y = "Log Return") +
  theme_minimal()