# Use this R-Chunk to import all your datasets!


tickers <- "KR"

getSymbols(tickers)
## [1] "KR"

Background

  1. Build the library(dygraphs) plot that shows the Kroger (KR) stock price performance over five years.
  2. Imagine that you invested $10,000 in Kroger about two years ago on April 5th. Make a graph with dygraph that shows performance dyRebased() to $10,000.
  3. At two or more time points, or intervals, where the price had a significant shift annotate the graphic with a note of the reason why.
  4. Create an .Rmd file with one to two paragraphs summarizing your graphics and the choices you made in the data presentation.
  5. Push your .Rmd, .md, and .html file into your git repository.
  6. Publish your assignment via Rstudio Connect at shiny.byui.edu

Data Wrangling

# Use this R-Chunk to clean & wrangle your data!

Data Visualization

# Use this R-Chunk to plot & visualize your data!


# This code make a call to apply a function to KR, but still cant get on how it works completelly.
# Positive thing is that works both for one or more tickers.


#---------------------- UPDATE how it works ------------------
#Duck typing with quantmod
# the cl after function retrieves from tockers "KR" and from KR the column that have cl
# that is KR.close I try with Ad and work as well

closePrices <- do.call(merge, lapply(tickers, function(x) Cl(get(x))))

dateWindow <- c("2021-04-05", "2023-06-21")

dygraph(closePrices, main = "Value of $10,000 invested in KR on April 5th 2021", group = "stock") %>%
  dyRebase(value = 10000) %>%
  dyRangeSelector(dateWindow = dateWindow) %>% 
  dyShading(from = "2022-3-3", to = "2022-6-15", color = "#CCEBD6") %>%
  dyShading(from = "2022-10-17", to = "2023-3-24", color = "#FFE6E6") %>% 
  dyAnnotation("2022-4-8", text = "H", tooltip = "Stock price soars after Q4 results") %>%
  dyAnnotation("2022-10-17", text = "L", tooltip = "Less favorable pricing environment in 2022-2023")
dygraph(closePrices, main = "Percent variation of KR shares", group = "stock") %>%
  dyRebase(percent = TRUE) %>%
  dyRangeSelector(dateWindow = dateWindow) %>% 
  dyEvent("2022-4-8", "High", labelLoc = "bottom") %>%
  dyEvent("2022-10-17", "Low", labelLoc = "bottom") %>% 
  dyLegend(show = "follow") 
dygraph(closePrices, main = "Price per share", group = "stock") %>%
  dyRangeSelector(dateWindow = dateWindow) %>% 
  dyLegend(show = "follow")

Conclusions

  • It took me a while to understand how quantmod get the data and the functions that are applied in the construction of the straw broom charts in dygraphs. I think that after experimenting with the results of the functions, I will gain a better understanding of how rebase works. I believe that if had more time will be interesting to get the same data with tidyqant and graph accordingly.
  • These are powerful packages that integrate a lot of functions in the back and allow us to use even CSS to style the results. Hope to explore that side also in the future.
  • In this graphs I experimented a bit with different annotations and related functions and didn’t pay much attention to the consistency of the messages, and visual renders, since this is a first approach to new packages.