--- title: "BUA - HW 5 - Part 1" format: dashboard --- ```{r setup, warning=F, message=F, echo=F, include=F} # this line specifies options for default options for all R Chunks knitr::opts_chunk$set(echo=T) # suppress scientific notation options(scipen=100) # install helper package that loads and installs other packages, if needed if (!require("pacman")) install.packages("pacman", repos = "http://lib.stat.cmu.edu/R/CRAN/") # install and load required packages # pacman should be first package in parentheses and then list others pacman::p_load(pacman,tidyverse, magrittr, tidyquant, ggthemes, RColorBrewer, highcharter, kableExtra) # verify packages #p_loaded() ``` ```{r data import, warning=F, message=F, echo=F, include=F} # import data and do prelim cleaning nflx <- read_csv("data/netflix_titles.csv", show_col_types=F) |> select(show_id, type, date_added, release_year, rating, listed_in) |> # useful variables (subjective) separate(listed_in, sep=", ", into=c("g1", "g2", "g3")) |> # separate out genre listings # will result in lots of NAs mutate(date_added = mdy(date_added), # convert data_added to a date var year_added = year(date_added)) |> filter(!is.na(year_added)) |> filter(!rating %in% c("NR", "UR", "66 min", "74 min", "84 min")) ``` ```{r simplify ratings and release year data, warning=F, message=F, echo=F, include=F} # create min_age variable from ratings data nflx <- nflx |> mutate(min_age = ifelse(rating %in% c("G", "TV-G", "TV-Y"), 0, NA), min_age = ifelse(rating %in% c("PG", "TV-PG", "TV-Y7", "TV-Y7-FV"), 7, min_age), min_age = ifelse(rating %in% c("PG-13", "TV-14"), 13, min_age), min_age = ifelse(rating %in% c("NC-17", "TV-MA", "R"), 17, min_age)) # create release_period variable from release year nflx <- nflx |> mutate(release_period = ifelse(release_year <= 1980, "1925-1980", NA), release_period = ifelse(release_year > 1980 & release_year <= 2000, "1981-2000", release_period), release_period = ifelse(release_year > 2000 & release_year <= 2005, "2001-2005", release_period), release_period = ifelse(release_year > 2005 & release_year <= 2010, "2006-2010", release_period), release_period = ifelse(release_year > 2010 & release_year <= 2015, "2011-2015", release_period), release_period = ifelse(release_year > 2015, "2016-2021", release_period)) ``` ```{r genre data cleaning, warning=F, message=F, echo=F, include=F} # iterative cleaning, simplifying and filtering of genre information # helpful to create new dataset for this step nflx <- nflx |> select(show_id, type, year_added, release_period, min_age, g1, g2, g3) |> pivot_longer(cols=c("g1","g2","g3"), names_to="g", values_to="genre") |> select(!g) |> mutate(genre=tolower(genre), genre = str_trim(genre), genre = gsub(" movies", "", genre), genre = gsub("movies", "", genre), genre = gsub(" tv shows", "", genre), genre = gsub("tv shows", "", genre), genre = gsub("tv ", "", genre), genre = gsub(" tv", "", genre), genre = ifelse(genre == "drama", "dramas", genre), genre = ifelse(genre %in% c("stand-up comedy", "stand-up comedy & talk shows"), "comedies", genre), genre = ifelse(genre %in% c("documentaries", "docuseries"), "docs", genre), genre = ifelse(genre %in% c("children & family", "kids'"), "kids", genre), genre = gsub("action & adventure", "action_adventr", genre))|> filter(genre %in% c("action_adventr", "comedies", "docs", "dramas", "international", "kids")) # table(nflx$type, nflx$genre) ``` ```{r summarizing dataset by relevant variables, warning=F, message=F, echo=F, include=F} nflx_wide <- nflx |> group_by(type, release_period, year_added, min_age, genre) |> summarize(num = n()) |> pivot_wider(id_cols=c(type, release_period, year_added, min_age), names_from = genre, values_from = num) nflx_wide[is.na(nflx_wide)] <- 0 # example dashboard will be created using cleaned and managed TV data nflx_tv <- nflx_wide |> filter(type == "TV Show") # for hw 5 you will create a dataset of just movies: nflx_mv <- nflx_wide |> filter(type == "Movie") ``` # Nextflix and AMC Stock Values ```{r import stock data, warning=F, message=F, echo=F, include=F} getSymbols("NFLX", from = Sys.Date()-7, to = Sys.Date()) NFLX_mr <- NFLX |> fortify.zoo() |> as_tibble(.name_repair = "minimal") |> rename("date" = "Index") |> filter(date==max(date)) getSymbols("AMC", from = Sys.Date()-7, to = Sys.Date()) AMC_mr <- AMC |> fortify.zoo() |> as_tibble(.name_repair = "minimal") |> rename("date" = "Index") |> filter(date==max(date)) getSymbols("AMC", from = "2013-01-01", to = "2022-12-31") getSymbols("NFLX", from = "2013-01-01", to = "2022-12-31") ``` ## Row Value boxes show the present day values of Netflix and AMC stocks. The interactive Highchart plots show the trend over time for the range of data examined in the Netflix plots on the following pages. Stock data was downloaded from [Yahoo Finance](https://finance.yahoo.com/). ## Row {height="20%"} ```{r} #| content: valuebox #| title: "Last Update" list( color = "green", value = stamp("Sat. Jan. 1, 1999", quiet = T)(NFLX_mr$date) ) ``` ```{r} #| content: valuebox #| title: "Netflix Adjusted Close" list( color = "red", value = NFLX_mr$NFLX.Adjusted |> round(2) ) ``` ```{r} #| content: valuebox #| title: "AMC Adjusted Close" list( color = "blue", value = AMC_mr$AMC.Adjusted |> round(2) ) ``` ## Row {height="75%"} ```{r pg1 nflx stock trends, warning=F, message=F, echo=F} # create nflx and amzn plots of adjusted, high and low for this time frame (NFLX_fts <- hchart(NFLX$NFLX.Adjusted, name="NFLX Adj.", color="red") |> hc_add_series(NFLX$NFLX.High, name="NFLX Hi." , color="red4") |> hc_add_series(NFLX$NFLX.Low, name="NFLX Lo." , color="lightcoral")) ``` ```{r pg1 amc stock trends, warning=F, message=F, echo=F} (AMC_fts <- hchart(AMC$AMC.Adjusted, name="AMC Adj.", color="blue") |> hc_add_series(AMC$AMC.High, name="AMC Hi." , color="darkblue") |> hc_add_series(AMC$AMC.Low, name="AMC Lo." , color="lightblue")) ``` ## Row {height="5%"} The trend in the two plots appear similar but the y-axis axis scale differs. This plot is an alternative to the [High-Low Candlestick plot](https://jkunst.com/highcharter/articles/stock.html) which can also be created as a highchart. [**This link is to Quarto**](https://quarto.org/){target="_blank"} # Bar Chart of Movie Trends ## Row ### Column {width="70%"} ```{r pg2 nflx mv release period data mgmt, include = F} nflx_mv_plot1 <- nflx_mv |> # reshape data to long format for plot pivot_longer(cols=c("comedies","action_adventr","docs", "dramas","international","kids"), names_to="genre", values_to="n") nflx_mv_plot1 <- nflx_mv_plot1 |> # simplify release period (see instructions) mutate(release_period = ifelse(release_period %in% c("2001-2005", "2006-2010"), "2001-2010", release_period)) nflx_mv_plot1 <- nflx_mv_plot1 |> # add one time period to correct filter statement filter(release_period %in% c("1981-2000", "2001-2010", "2011-2015", "2016-2021")) nflx_mv_plot1 <- nflx_mv_plot1 |> # create factor variable min_ageF from min_age mutate(min_ageF = factor(min_age, levels = c(0, 7, 13, 17))) nflx_mv_plot1 <- nflx_mv_plot1 |> # create genre factor variable, genreF mutate(genreF = factor(genre, # note that order must be corrected levels = c("international","dramas","comedies","docs","kids","action_adventr"), labels = c("Int","Dr","C","Do","K","A/A"))) ``` ```{r pg2 nflx mv release period bar chart, fig.dim = c(10, 5), echo=F} # creates a grouped stacked bar_chart to show proportion in each min_age cat in each bar (nflx_mv_barplot <- nflx_mv_plot1 |> # creates a grouped stacked bar_chart to show proportion in each age_min cat in each bar ggplot() + geom_bar(aes(x=genreF, y=n, fill=min_ageF), stat="identity", position="stack") + theme_classic() + # facet_grid creates a separate panel for each period facet_grid(~release_period) + # adjust colors all at once by changing palette scale_fill_brewer(palette = "Spectral") + # labels axes, titles, caption, and legend labs(x="Genre", y="Number of Movies", fill="Min. Age", title="Release Time Periods of Netflix Movies by Genre", subtitle="I=International D=Drama C=Comedy Do=Documentaries K=Kids A/A=Action/Adventure", caption="Data Source: https://www.kaggle.com/shivamb/netflix-shows")) ``` ### Column {width="30%"} #### Row Number of Netflix Movies from each Genre and Release Period. #### Row ```{r pg2 nflx summary table, echo=F, message=F} nflx_smry1 <- nflx_mv_plot1 |> select(release_period, genreF, n) |> # select variables group_by(release_period, genreF) |> # group and summarize data summarize(n=sum(n)) |> pivot_wider(id_cols = release_period, names_from=genreF, values_from=n) |> # reshape data for table output rename("Release Time Period" = "release_period") kable(nflx_smry1) # print out presentation table ``` #### Row Genres are shown in plot and table in order of prevalence. Information on [grouped and stacked bar charts](https://www.r-graph-gallery.com/stacked-barplot.html) Information on plots with [facets](https://ggplot2.tidyverse.org/reference/facet_grid.html) Bonus link for [multi-panel plots](http://www.sthda.com/english/wiki/ggplot2-facet-split-a-plot-into-a-matrix-of-panels) A link to more information about [Quarto Dashboards](https://quarto.org/docs/gallery/#dashboards){target="_blank"} # Netflix Movie Added Each Year ## Row ### Column {width="75%"} ```{r pg3 nflx mv area plot data mgmt, include=F} nflx_mv_plot2 <- nflx_mv |> # start with nflx_tv rowwise() |> mutate(total = sum(c_across(comedies:kids))) |> # sum all genres rowwise (columns 5 through 10) ungroup() |> select(year_added, min_age, total) |> # keep only the 3 columns needed for plot filter(year_added >= 2013) |> group_by(year_added, min_age) |> # summarize by year and age_min category summarize(total=sum(total, na_rm=T)) |> mutate(min_ageF = factor(min_age, levels=c(0,7,13,17))) ``` ```{r pg3 nflx mv area plot, fig.dim = c(10, 6), echo=F} # area (proportion) plot code begins here # plot code is incomplete (see instructions) (nflx_mv_area_plot <- nflx_mv_plot2 |> ggplot() + # shows stacked areas (proportions) attributed to each category geom_area(aes(x=year_added, y=total, fill=min_ageF)) + theme_classic() + theme(legend.position = "bottom") + # move legend to bottom scale_x_continuous(breaks=seq(2013, 2021, 1)) + # modifies x axis so each year is shown (See HW 4) scale_fill_brewer(palette = "Spectral") + # modifies color palette labs(x="Year", y="Number of Movies", fill="Min. Age", # format and add plot labels title="Number of Netflix Movies Added Each Year", subtitle="2013 - 2021", caption="Data Source: https://www.kaggle.com/shivamb/netflix-shows")) ``` ### Column {width="25%"} #### Row This plot does not include genre information (shown on Page 2). Further analyses would benefit from having data that differentiates between Netflix original content and Netflix content purchased from other sources. #### Row Information on [Area plots](https://www.r-graph-gallery.com/136-stacked-area-chart) Information on [R Color Options](https://www.r-graph-gallery.com/38-rcolorbrewers-palettes.html) # About This dashboard was created using [Quarto](https://quarto.org/) in [RStudio](https://posit.co/), and the [R Language and Environment](https://cran.r-project.org/). The dataset used to create this dashboard was downloaded from [Yahoo Finance](https://finance.yahoo.com/) and [Kaggle](https://www.kaggle.com/) ## Row **Software Citations** ## Row **Software Citations** Arnold J (2024). *ggthemes: Extra Themes, Scales and Geoms for 'ggplot2'*. R package version 5.1.0, https://github.com/jrnold/ggthemes, . Bache S, Wickham H (2025). _magrittr: A Forward-Pipe Operator for R_. doi:10.32614/CRAN.package.magrittr , R package version 2.0.4, . Dancho M, Vaughan D (2025). _tidyquant: Tidy Quantitative Financial Analysis_. doi:10.32614/CRAN.package.tidyquant , R package version 1.0.11, . Kunst J (2022). *highcharter: A Wrapper for the 'Highcharts' Library*. R package version 0.9.4, . Neuwirth E (2022). *RColorBrewer: ColorBrewer Palettes*. R package version 1.1-3, . Posit team (2026). _RStudio: Integrated Development Environment for R_ (Version 2026.1.1.403). Posit Software, PBC, Boston, MA. . Quarto Development Team. (2026). Quarto Publishing System (Version 1.8.27). . R Core Team (2025). _R: A Language and Environment for Statistical Computing_ (Version 4.5.2). R Foundation for Statistical Computing, Vienna, Austria. . Rinker, T. W. & Kurkiewicz, D. (2017). pacman: Package Management for R. version 0.5.0. Buffalo, New York. . Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” *Journal of Open Source Software*, *4*(43), 1686. doi:10.21105/joss.01686 . Xie Y (2025). _knitr: A General-Purpose Package for Dynamic Report Generation in R_. R package version 1.50, . Yihui Xie (2015) Dynamic Documents with R and knitr. 2nd edition. Chapman and Hall/CRC. ISBN 978-1498716963 Yihui Xie (2014) knitr: A Comprehensive Tool for Reproducible Research in R. In Victoria Stodden, Friedrich Leisch and Roger D. Peng, editors, Implementing Reproducible Computational Research. Chapman and Hall/CRC. ISBN 978-1466561595 Zhu H (2024). *kableExtra: Construct Complex Table with 'kable' and Pipe Syntax*. R package version 1.4.0, https://github.com/haozhu233/kableExtra, .