Housekeeping

  • Final grading in this course:

    • adheres to Whitman grading policy, but is fairly gentle.

    • takes into account assignments, course project, and class particpation.

  • Quiz 2 will be during Week 11 and will combine previous skills with material from weeks 6 through 10

    • It will be similar to Quiz 1 but may have more questions and more steps in multi-step tasks.

  • If you have questions about your quiz, please let me know.

HW 4 is posted and is due on Wednesday, 3/4/26.

  • HW 4 - Part 1 was due already but the grace period is extended until tonight 2/24 and is required in order for you to complete this course.

  • There are no office hours today, Tuesday, 2/24.

BUA 455 Group Dashboard Project

Group Assignments

  • Complete HW 4 - Part 1 TODAY, 2/24! (This should only take 5 min.)

  • Note: If you do not complete this Survey, I will not put you in a project group and you can not pass this class.

  • Groups of 3 or 4 will be determined and posted (Hopefully by Monday)

  • If you have a request to work with someone, include that information in your survey (Not required).

  • Wednesday, 2/25, is the last day I will accept any group requests.

  • I cannot guarantee that requests will be honored, but I will try.

  • I control group assignments to maintain some balance in skill level among groups.

BUA 455 Group Dashboard Project Information

Upcoming Dates

  • Groups assigned by Monday, 3/2 at the latest.

  • Thu. 3/26 at 5:00 PM: Draft Proposals Due - NO GRACE PERIOD

    • Proposals should be in bullet point format and include links to data sources

    • It should take me 5 minutes to read your proposed ideas and check your data.

  • Proposal Meetings:

    • Recommended but not required: Come with questions and be prepared to answer my questions (5-15 min. per groups)

    • Meetings will take place outside of class. See sign-up sheet when it is posted.

  • Wed. 3/25/26: HW 5 - Part 1 Due

  • Tue. 4/7: Quiz 2

  • Thu. 4/9: Final Proposals Due

    • Not much longer than draft proposal and also in bullet point.

    • Questions and issues discussed during meeting should be addressed.

Reminders about HW 4

  • In Chunk 6 (Part 5), the chunk header in the the template appears as follows:

  • The eval=F prevents this chunk from being evaluated when it is knit.

  • eval=F was included in the template because original code was incomplete.

  • Remember to remove the text eval=F

  • Other helpful chunk header options for dashboard: echo=F, include=F

  • Chunk options can also be included as fences:

  • NOTE: If two chunks have the SAME name or label, the file will not render.

Quarto Output Formats

  • So far, all Quarto files in this course have been rendered as HTML (.html) files or slides

    • All slides for this course are created in Quarto.

  • Other common formats are Word documents, PDF documents, Powerpoint Slides, and dashboards

  • We will use the dashboard (next slide) format in HW 5 and in your projects.

  • Groups will also write their two project memos in Quarto and publish them as word documents.

    • Writing the memos in Quarto files simplifies formatting R, RStudio and packages citations.

Quarto Dashboards

  • REQUIRED: Download the latest version of Quarto here

    • You will not be able to complete HW 5 without having Quarto installed on your computer.

  • Quarto Dashboard is a new feature of Quarto that is extremely flexible and straightforward to use.

  • The Quarto Dashboard Gallery includes example dashboards made with R, Python, and other langaugages.

    • In this course I will provide a simple template for HW 5 that can be used to build your dashboard.

    • Once you understand how to add pages, rows, column, tabsets, and modify as needed you are welcome to tailor the template to your project.

    • A Quarto dashboard is a flexible blank canvas that you can tailor to your project and future endeavors.

Types of Time Series Data in R

  • In recent weeks, we have worked with Box Office Mojo and Bureau of Labor Statistics Data

  • These datasets are time series data.

  • They all include a date variable and another quantitative variable that changes at each time period.

  • So far we have worked with data in an R format called a tibble.

  • Two common data formats in R, tibble and data.frame are needed for creating ggplots of time series.

    • tibble is the more modern format and is more compatible with tidyverse commands to manage data.

  • Today, we’ll discuss a third data format, xts that can be used specifically for time series data.

Importing Stock Data as xts using tidyquant Package

  • Yahoo Finance, the Federal Reserve Bank, the Wall Street Journal, and others are excellent data sources that can be directly imported into R.

    • The default for getsymbols in the tidyquant package is Yahoo Finance.

    • Data format is xts which we will cover today

#|label: importing data from yahoo finance
#|output: false

# download data from Netflix, Amazon, Disney
# time series starts day after from date specified
# time series ends day before to date specified
 
getSymbols("NFLX", from = "2016-01-01", to = "2026-02-01")
[1] "NFLX"
getSymbols("AMZN", from = "2016-01-01", to = "2026-02-01")
[1] "AMZN"
getSymbols("DIS", from = "2016-01-01", to = "2026-02-01")
[1] "DIS"

Example of hchart for One Stock

hchart in the highcharter package is one way to plot xts data

This chunk not compatible with published slides or published html file but this code will work in a published dashboard (see posted examples).

(hc_nflx <- hchart(NFLX$NFLX.Adjusted, name="Adjusted", color="green") |>   # plot adj. close
  hc_add_series(NFLX$NFLX.High, name="High" , color="blue") |>             # add daily high
  hc_add_series(NFLX$NFLX.Low, name="Low" , color="red"))                   # add daily low

R code for Multi-Panel hcharts display

  • Stocks can be shown in separate plots that can be shown side by side or in one stacked column

  • The command hw_grid is used to display them and ncol indicates how many columns.

nflx_plt <- hchart(NFLX$NFLX.Adjusted, name="Adjusted", color="green") |>
  hc_add_series(NFLX$NFLX.High, name="High" , color="darkgreen") |>
  hc_add_series(NFLX$NFLX.Low, name="Low" , color="lightgreen")

amzn_plt <- hchart(AMZN$AMZN.Adjusted, name="Adjusted", color="blue") |>
  hc_add_series(AMZN$AMZN.High, name="High" , color="darkblue") |>
  hc_add_series(AMZN$AMZN.Low, name="Low" , color="lightblue")

dis_plt <- hchart(DIS$DIS.Adjusted, name="Adjusted", color="mediumpurple") |>
  hc_add_series(DIS$DIS.High, name="High" , color="purple4") |>
  hc_add_series(DIS$DIS.Low, name="Low" , color="plum")

Multi-Panel hcharts Display

This chunk not compatible with published slides or published html file but this code will work in a published dashboard (see posted examples).

#|label: display of hcharts
hw_grid(nflx_plt, amzn_plt, dis_plt, ncol=3)

💥 Week 7 In-class Exercises - Q1 💥

Poll Everywhere - My User Name: penelopepoolereisenbies685

In the example above, we use the hw_grid command to create a multi-plot composition of hcharts.

Previously, we covered another command to create a composition of non-interactive ggplots of tibble data.


What is that other command?

Hints:

This very useful command is in the gridExtra package which is loaded.

If gridExtra is loaded in R, start typing grid in the console, and the command and others will appear.

💥 Week 7 In-class Exercises - Q2 💥

Poll Everywhere - My User Name: penelopepoolereisenbies685

  1. Use provided exampled of getSymbols code to write code to import the stock time series for Apple (AAPL)

    • Use these dates: from = “2017-01-01”, to = “2026-02-21”

  2. Open the imported xts file by clicking on it in the Global Environment

  3. Sort the AAPL.Adjusted column by clicking on it.

  4. Answer Question:

    • On what recent date, was Apple (AAPL) report it’s highest adjusted closing value?
#|label: import aapl data

More Information about xts

  • When these stock datasets are imported, they are in xts format.

  • xts stands for Extensible Time Series which means they are self-aware.

  • The key feature is that date is NOT a variable, but instead the dates become row IDs.

    • Any dataset with a date variable can be converted to an xts dataset.

    • Any xts dataset can be converted a tibble or data.frame (two common R data formats).

#|label: examine xts data
head(NFLX)
           NFLX.Open NFLX.High NFLX.Low NFLX.Close NFLX.Volume NFLX.Adjusted
2016-01-04    10.900    11.000   10.521     10.996   207948000        10.996
2016-01-05    11.045    11.058   10.585     10.766   176646000        10.766
2016-01-06    10.529    11.791   10.496     11.768   330457000        11.768
2016-01-07    11.636    12.218   11.229     11.456   336367000        11.456
2016-01-08    11.633    11.772   11.110     11.139   180671000        11.139
2016-01-11    11.213    11.679   11.120     11.497   219204000        11.497

Merging xts datasets using merge

  • Converting xts to a tibble or dataframe (R data formats) is required if you want to create a ggplot or use other methods covered previously

  • A good first step is to create a merged xts dataset of the desired variables.

#|label: merge xts stock data 

# data are merged by matching dates
nflx_amzn_dis <- merge(NFLX$NFLX.Adjusted,
                       AMZN$AMZN.Adjusted,
                       DIS$DIS.Adjusted) 
head(nflx_amzn_dis)
           NFLX.Adjusted AMZN.Adjusted DIS.Adjusted
2016-01-04        10.996       31.8495     94.92046
2016-01-05        10.766       31.6895     93.00326
2016-01-06        11.768       31.6325     92.50550
2016-01-07        11.456       30.3970     91.71281
2016-01-08        11.139       30.3525     91.48238
2016-01-11        11.497       30.8870     92.09995

Converting xts datasets to tibble format

  • There are a few ways to convert an xts to a tibble.

  • In the code below I show the conversion and then I rename the the new date variable as date

# converting data to a tibble requires a couple lines of code 
# I prefer to rename the index as date 
nflx_amzn_dis_tibble <- nflx_amzn_dis |> 
  fortify.zoo() |> as_tibble(.name_repair = "minimal") |>
  rename("date" = "Index") 
head(nflx_amzn_dis_tibble)
# A tibble: 6 × 4
  date       NFLX.Adjusted AMZN.Adjusted DIS.Adjusted
  <date>             <dbl>         <dbl>        <dbl>
1 2016-01-04          11.0          31.8         94.9
2 2016-01-05          10.8          31.7         93.0
3 2016-01-06          11.8          31.6         92.5
4 2016-01-07          11.5          30.4         91.7
5 2016-01-08          11.1          30.4         91.5
6 2016-01-11          11.5          30.9         92.1

Converting tibble datasets to xts

  • Any dataset with a date formatted variable can be converted to an xts dataset
  • This means that we can create a hchart or dygraph (next topic) for any dataset with a date variable.
#|label: convert tibble to xts
exp_imp <- read_csv("data/export_import_tidy.csv", show_col_types=F)
exp_imp_xts <- xts(x=exp_imp[,2:3], order.by=exp_imp$date) # order.by must be a date variable
#|label: hchart code export import xts
exp_imp_hchart <- hchart(exp_imp_xts$exp_indx, 
                         name="Export Price Index", color="blue") |>
   hc_add_series(exp_imp_xts$imp_indx, 
                 name="Import Price Index" , color="red")

Export Import HighChart (hchart)

#|label: display of hchart
exp_imp_hchart

Dygraphs - An Alternative to hchart

  • dygraph is a more flexible alternative to hchart.

    • Straightforward to modify, add reference lines and shaded regions
    • Both dygraph and hchart allow viewer to interactively select date range

Here is the dataset we will use:

#|label: dataset for dygraphs example
three_stocks <- merge(AMZN$AMZN.Adjusted, DIS$DIS.Adjusted, NFLX$NFLX.Adjusted) 
names(three_stocks) <- c("AMZN.adj", "DIS.adj", "NFLX.adj")
head(three_stocks, 3) # print first three rows only
           AMZN.adj  DIS.adj NFLX.adj
2016-01-04  31.8495 94.92046   10.996
2016-01-05  31.6895 93.00326   10.766
2016-01-06  31.6325 92.50550   11.768

Basic unformatted plot of three stocks with the range selector option

#|label: dygraph with range selector
(dy3 <- dygraph(three_stocks, main="Streaming Company Stock Trends") |>
  dySeries("AMZN.adj", label="AMZN", color= "green") |>
  dySeries("DIS.adj", label="DIS", color= "red") |>
  dySeries("NFLX.adj", label="NFLX", color= "blue") |>
  dyRangeSelector())

Two useful formatting options (shown below) to make the plot more readable are: Removing the the grid lines Formatting the axis labels

#|label: dygraph with axes labeled and gridlines removed
(dy3 <- dy3 |>
  dyAxis("y", label = "Adjusted Close", drawGrid = FALSE) |>
  dyAxis("x", label = "Date", drawGrid = FALSE))

Vertical lines can be added at specific dates and can be labeled and formatted.

#|label: dygraph with event lines
(dy3 <- dy3 |>
  dyEvent("2020-3-12", label = "Theaters Closed", labelLoc = "bottom") |>
  dyEvent("2021-6-15", label = "Restrictions End", labelLoc = "bottom", strokePattern = "solid"))

Alternatively, it may be helpful to shade plot for a specific time range.

#|label:  dygraph with shaded region
(dy3 <- dy3 |>
  dyShading(from = "2020-3-12", to = "2021-6-15", axis = "x", color = "lightgrey"))

Review: bls_tidy Function - Labor Data

  • Before using our function on new data, we ALWAYS examine the .csv files

  • The number of rows to skip for these three labor datasets is 11.

bls_tidy <- function(data_file, skip_num, var_name){
  read_csv(data_file, skip = skip_num, show_col_types = F) |> 
  pivot_longer(cols = Jan:Dec,                      
               names_to = "month", 
               values_to = "value") |>
  filter(!is.na(value)) |>                    
  rename({{var_name}} := "value")                             
}

labor_force <- bls_tidy("data/bls_civ_lf.csv", skip_num=11, var_name="lf")
unemp <- bls_tidy("data/bls_civ_unemp.csv", skip_num=11, var_name="unemp")
emp <- bls_tidy("data/bls_civ_emp.csv", skip_num=11, var_name="emp")

head(unemp)
# A tibble: 6 × 3
   Year month unemp
  <dbl> <chr> <dbl>
1  2014 Jan   10202
2  2014 Feb   10349
3  2014 Mar   10380
4  2014 Apr    9702
5  2014 May    9859
6  2014 Jun    9460

Joining More than Two Datasets

  • Last Week and in HW 4 we covered joining TWO datasets.

  • The commands we covered (there are 4) all have the same limitation: datasets must be joined two at a time.

Joining with Piping

#|label: joining 3 datasets with pipes
# with piping
lf_all <- labor_force |>
  full_join(emp) |>
  full_join(unemp) |>
  write_csv("data/labor_tidy.csv") #export
head(lf_all)
# A tibble: 6 × 5
   Year month     lf    emp unemp
  <dbl> <chr>  <dbl>  <dbl> <dbl>
1  2014 Jan   155352 145150 10202
2  2014 Feb   155483 145134 10349
3  2014 Mar   156028 145648 10380
4  2014 Apr   155369 145667  9702
5  2014 May   155684 145825  9859
6  2014 Jun   155707 146247  9460

Joining without Piping

#|label: joining 3 datasets without pipes
lf_all <- full_join(labor_force, emp) 
lf_all <- full_join(lf_all, unemp) 
head(lf_all)
# A tibble: 6 × 5
   Year month     lf    emp unemp
  <dbl> <chr>  <dbl>  <dbl> <dbl>
1  2014 Jan   155352 145150 10202
2  2014 Feb   155483 145134 10349
3  2014 Mar   156028 145648 10380
4  2014 Apr   155369 145667  9702
5  2014 May   155684 145825  9859
6  2014 Jun   155707 146247  9460

Review: Dates and Plot Data

  • Chunk below includes code that is similar to Parts 3 and 4 of HW 4.

  • BONUS: Code modified to show how to get ‘End of Month’ (eom) date.

#|label: dates and data mod for plot
lf_plt <- lf_all |>
  mutate(date_som = ym(paste(Year, month)),         # create som date var
         date = ceiling_date(date_som, "month")-1,  # create eom month date var
         empM = (emp/1000) |> round(2),             # convert counts to millions
         unempM = (unemp/1000) |> round(2)) |>
  select(date, empM, unempM) |>                     # select vars and reshape
  pivot_longer(cols=empM:unempM, names_to = "type", values_to = "count") |>
  mutate(type = factor(type,                        # create factor var for plot
                       levels = c("unempM", "empM"),
                       labels = c("Unemployed", "Employed"))) 

head(lf_plt, 4) # examine first 8 rows
# A tibble: 4 × 3
  date       type       count
  <date>     <fct>      <dbl>
1 2014-01-31 Employed   145. 
2 2014-01-31 Unemployed  10.2
3 2014-02-28 Employed   145. 
4 2014-02-28 Unemployed  10.4

Code for Polished Area Plot for Slides

  • Useful for data that sum to a whole: Employed + Unemployed = Total Labor Force
lf_area_plt_slides <- lf_plt |>
  ggplot() +
  geom_area(aes(x=date, y=count, fill=type)) +
  theme_classic() +
  theme(legend.position="bottom") +
  scale_fill_manual(values=c("red", "blue")) + 
  scale_x_date(date_breaks = "year", date_labels = "%Y") +
  labs(x="Date", y = "Number of Peolple (Millions)", fill="",
       title="Total Labor Force: Employed and Unemployed ", 
       subtitle="Jan. 2014 - June 2024",
       caption="Data Source:www.bls.gov") + 
  theme(plot.title = element_text(size = 20),                    
        plot.subtitle = element_text(size = 15),
        axis.title = element_text(size=18),
        axis.text = element_text(size=15),
        plot.caption = element_text(size = 10),
        legend.text = element_text(size = 12),
        panel.border = element_rect(colour = "lightgrey", fill=NA, linewidth=2),
        plot.background = element_rect(colour = "darkgrey", fill=NA, linewidth=2))

Area Plot Formatted for Slides

Area Plot for HTML, Documents and Export

  • Additional formatting in previous slides can always be added

  • Plot exported using ggsave which by default exports last plot created

#|label: simpler plot code with ggsave export 

lf_area_plt <- lf_plt |>
  ggplot() +
  geom_area(aes(x=date, y=count, fill=type)) +
  theme_classic() +
  theme(legend.position="bottom") +
  scale_fill_manual(values=c("red", "blue")) + 
  scale_x_date(date_breaks = "year", date_labels = "%Y") +
  labs(x="Date", y = "Number of Peolple (Millions)", fill="",
       title="Total Labor Force: Employed and Unemployed ", 
       subtitle="Jan. 2014 - Jun. 2024",
       caption="Data Source:www.bls.gov") + 
  theme(plot.title = element_text(size = 20),                    
        plot.subtitle = element_text(size = 15),
        axis.title = element_text(size=18),
        axis.text = element_text(size=15),
        plot.caption = element_text(size = 10),
        legend.text = element_text(size = 12))
ggsave("img/labor_force_area_plot.png", width=6,height=4)

Exported Plot

  • Looks fine in HTML notes but not slides
  • May be fine in Word Document or Dashboard
  • If not, previous code shows additional options for formatting

Week 7 In-class Exercise

In this exercise we will:

  1. Import labor_tidy.csv and convert variables to millions and round to 2 decimal places and select two variables. (Review)

    • OPTIONAL: use provided example to create an END of Month (eom) date variable and use that.
#|label: import labor_tidy and modify variables
labor_new <- read_csv("data/labor_tidy.csv", show_col_types=F) |>
  mutate(date = ym(paste(Year,month)),
         lfM = (lf/1000) |> round(2),
         empM = (emp/1000) |> round(2))|>
  select(date, lfM, empM)
  1. Convert labor_new to an xts format, labor_xts
#|label: create labor_xts

In-class Exercise Cont’d

  1. Create an unformatted hchart OR a dygraph with two variables
    • Plot lfM and empM and save it as labor_hc or labor_dy
#|label: create and display labor hchart or dygraph
# (labor_hc <- hchart())  or   (labor_dy <- dygraph())

Basic hchart

Basic dygraph

In-class Exercise - Final Steps

  1. Submit screenshots of plot from Viewer pane.

  2. Save R code in the prvided text file. In the R project folder I have saved an text file for your work (Updated February 2026).

    • Copy and paste code into provided text file and rename the file with your name, e.g. Week_7_In_Class_Penelope_Pooler.txt

    • text file should include:

      • code I provided to import and modify data

      • tibble to xts conversion of labor dataset

      • hchart OR dygraph plot code with comments

    • Submit final text file on Blackboard (counts towards class participation for Week 7)

    • Due by Friday 2/27. No late submission accepted for In-class Exercises.

Quarto, R Markdown files, R Scripts, and text files

  • Quarto, Markdown files, and R Scripts are ‘smart’, i.e. aware of where they are located.

  • Code from text files can be run in console.

    • A better option is to change the text file to an R script with .R extension.

  • If code from any ‘smart’ file does not run because an import file cannot be found, check your working directory.

  • To check working directory: getwd()

  • To set working directory (not recommended for data management):

    • Click Session > Set Working Directory > To Source File Location

NOTES:

  • R users and developers do not recommend setting working directories within code which would have to be changed for each laptop.

  • Whenever possible, use R Projects and ‘smart’ files that are aware of their respective file location.