Vampires, Plots, and Interactivity - Oh My!

READING IN DATA

First we have to find our data sources. We are pretty sure that all the sites referenced are fully legal!

# saving the page url
twilight_url <- "https://archive.org/stream/Book3Eclipse/Book%201%20-%20Twilight_djvu.txt"

# scraping with rvest and selectors
twilight_text <- twilight_url %>%
  read_html() %>%
  html_node(css = "#maincontent > div > pre") %>%
  html_text() 

# making one big charater into actual dataframe
twilight_token <- tibble(text = twilight_text) %>% 
# adding identifier
  mutate(title = "Twilight") %>% 
# tokenizing
  unnest_tokens(input = text,
                output = word,
                token = "words")

# new moon
newmoon_url <- "https://archive.org/stream/Book3Eclipse/Book%202%20-%20New%20Moon_djvu.txt"

newmoon_text <- newmoon_url %>%
  read_html() %>%
  html_node(css = "#maincontent > div > pre") %>%
  html_text() 

newmoon_token <- tibble(text = newmoon_text) %>% 
  mutate(title = "New Moon") %>% 
  unnest_tokens(input = text,
                output = word,
                token = "words")

# eclipse
eclipse_url <- "https://archive.org/stream/Book3Eclipse/Book%203%20-%20Eclipse_djvu.txt"

eclipse_text <- eclipse_url %>%
  read_html() %>%
  html_node(css = "#maincontent > div > pre") %>%
  html_text() 

eclipse_token <- tibble(text = eclipse_text) %>% 
  mutate(title = "Eclipse") %>% 
  unnest_tokens(input = text,
                output = word,
                token = "words")

# breaking dawn
breakingdawn_url <- "https://archive.org/stream/Book3Eclipse/Book%204%20-%20Breaking%20Dawn_djvu.txt"

breakingdawn_text <- breakingdawn_url %>%
  read_html() %>%
  html_node(css = "#maincontent > div > pre") %>%
  html_text() 

breakingdawn_token <- tibble(text = breakingdawn_text) %>% 
  mutate(title = "Breaking Dawn") %>% 
  unnest_tokens(input = text,
                output = word,
                token = "words")

# reading in data from pdf
twilight_script_raw <- pdf_text("scripts/twilight_script.pdf") %>% 
  # making it legible
  strsplit(split = "\n")

# converting to dataframe
twilight_script_token <- twilight_script_raw %>% 
  unlist() %>% 
  as.data.frame() %>%
  # renaming the poorly named autogenerated variable
  rename("text" = ".") %>% 
  # converting "text" from factor to character for tokenizing
  mutate(text = as.character(text)) %>% 
  unnest_tokens(output = word,
                input = text,
                token = "words") %>% 
  # adding an identifying variable for use in larger dataset we are going to create
  mutate(title = "Twilight")

# letsa go
newmoon_script_url <- html("https://www.imsdb.com/scripts/Twilight-New-Moon.html")

# scraping with rvest and selectors 
# used selectorgadget chrome extention (http://selectorgadget.com/) to find path
newmoon_script_text <- newmoon_script_url %>%
  html_nodes("pre") %>%
  html_text() 

# making one big charater into actual dataframe
newmoon_script_token <- tibble(text = newmoon_script_text) %>% 
# adding identifier
  mutate(title = "New Moon") %>% 
# tokenizing
  unnest_tokens(input = text,
                output = word,
                token = "words")

# this whole thing works

eclipse_script_url <- "https://transcripts.foreverdreaming.org/viewtopic.php?f=150&t=9147"

# scraping with rvest and selectors
eclipse_script_text <- eclipse_script_url %>%
  read_html() %>%
  html_node(css = "#p119175 > div > div.postbody") %>%
  html_text() 

# making one big charater into actual dataframe
eclipse_script_token <- tibble(text = eclipse_script_text) %>% 
# adding identifier
  mutate(title = "Eclipse") %>% 
# tokenizing
  unnest_tokens(input = text,
                output = word,
                token = "words")

# first for part 1

# reading in data from pdf
breakingdawn1_script_raw <- pdf_text("scripts/breakingdawn1.pdf") %>% 
  # making it legible
  strsplit(split = "\n")
# converting to dataframe
breakingdawn1_script_token <- breakingdawn1_script_raw %>% 
  unlist() %>% 
  as.data.frame() %>%
  # renaming the poorly named autogenerated variable
  rename("text" = ".") %>% 
  # converting "text" from factor to character for tokenizing
  mutate(text = as.character(text)) %>% 
  unnest_tokens(output = word,
                input = text,
                token = "words") %>% 
  # adding an identifying variable for use in larger dataset we are going to create
  mutate(title = "Breaking Dawn part 1")

# then for part 2

# reading in data from pdf
breakingdawn2_script_raw <- pdf_text("scripts/breakingdawn2.pdf") %>% 
  # making it legible
  strsplit(split = "\n")
# converting to dataframe
breakingdawn2_script_token <- breakingdawn2_script_raw %>% 
  unlist() %>% 
  as.data.frame() %>%
  # renaming the poorly named autogenerated variable
  rename("text" = ".") %>% 
  # converting "text" from factor to character for tokenizing
  mutate(text = as.character(text)) %>% 
  unnest_tokens(output = word,
                input = text,
                token = "words") %>% 
  # adding an identifying variable for use in larger dataset we are going to create
  mutate(title = "Breaking Dawn part 2")

# making dataset of all the albums
soundtrack_names <- data.frame(artist = c("Various Artists", 
                                          "Various Artists", 
                                          "Chop Shop Records", 
                                          "Various Artists",
                                          "Various Artists"),
                               album = c("Twilight (Original Motion Picture Soundtrack)",
                                         "The Twilight Saga: New Moon (Original Motion Picture Soundtrack)",
                                         "The Twilight Saga: Eclipse (Original Motion Picture Soundtrack)",
                                         "The Twilight Saga: Breaking Dawn, Pt. 1 (Original Motion Picture Soundtrack)",
                                         "The Twilight Saga: Breaking Dawn – Part 2 (Original Motion Picture Soundtrack)"))

# reading in albums from genius, so cache = TRUE
soundtracks <- soundtrack_names %>%
  add_genius(artist, album) %>%
  mutate(album = factor(album),
         album = fct_inorder(album))

# tokenizing for sentiment analysis
soundtracks_token <- soundtracks %>%
  unnest_tokens(output = word,
                input = lyric,
                token = "words")

ADDING SENTIMENTS

Now that we have datasets for each title in each medium, we are ready to add sentiments! We do this with sentiment lexicons.

# reading in bing lexicon
bing <- get_sentiments("bing")

# making the dataset with sentiments
twilight_bing <- twilight_token %>%
  inner_join(bing, by = "word")

# rest of the books

# making all the individual senitimented datasests
# starting with New Moon because already did twilight
newmoon_bing <- newmoon_token %>%
  # adding bing sentiment
  inner_join(bing, by = "word") %>% 
  # summarizing to just ratio, keeping title
  group_by(title) %>%
  count(sentiment) %>% 
  mutate(prop = n/sum(n))
# repeating for rest of books
eclipse_bing <- eclipse_token %>%
  inner_join(bing, by = "word") %>% 
  group_by(title) %>%
  count(sentiment) %>% 
  mutate(prop = n/sum(n))
breakingdawn_bing <- breakingdawn_token %>%
  inner_join(bing, by = "word") %>% 
  group_by(title) %>%
  count(sentiment) %>% 
  mutate(prop = n/sum(n))

# making saga dataset for bing lexicon
saga_bing <- bind_rows(twilight_bing,
                       newmoon_bing,
                       eclipse_bing,
                       breakingdawn_bing)


# SOUNDTRACKS

soundtracks_bing <- soundtracks_token %>%
  inner_join(bing, by = "word")

# BOOKS

# reading in lexicon
nrc <- get_sentiments("nrc")

# making the dataset with new sentiments
twilight_nrc <- twilight_token %>%
  inner_join(nrc, by = "word") %>% 
# then wrangle into format useful for graphing
  group_by(title) %>% 
  count(sentiment) %>% 
  mutate(prop = n/sum(n))
# repeat for remaining books

# new moon
newmoon_nrc <- newmoon_token %>%
  inner_join(nrc, by = "word") %>% 
  group_by(title) %>%
  count(sentiment) %>% 
  mutate(prop = n/sum(n))
# eclipse
eclipse_nrc <- eclipse_token %>%
  inner_join(nrc, by = "word") %>% 
  group_by(title) %>% 
  count(sentiment) %>% 
  mutate(prop = n/sum(n))
# breaking dawn 
breakingdawn_nrc <- breakingdawn_token %>%
  inner_join(nrc, by = "word") %>% 
  group_by(title) %>% 
  count(sentiment) %>% 
  mutate(prop = n/sum(n))

# putting all the books togther
saga_nrc <- bind_rows(twilight_nrc,
                      newmoon_nrc,
                      eclipse_nrc,
                      breakingdawn_nrc) %>% 
  # removing n because we never use it
  select(-n)


# MOVIES

# twilight
twilight_script_nrc <- twilight_script_token %>%
  inner_join(nrc, by = "word") %>% 
  group_by(title) %>% 
  count(sentiment) %>% 
  mutate(prop = n/sum(n))
# new moon
newmoon_script_nrc <- newmoon_script_token %>%
  inner_join(nrc, by = "word") %>% 
  group_by(title) %>%
  count(sentiment) %>% 
  mutate(prop = n/sum(n))
# eclipse
eclipse_script_nrc <- eclipse_script_token %>%
  inner_join(nrc, by = "word") %>% 
  group_by(title) %>% 
  count(sentiment) %>% 
  mutate(prop = n/sum(n))
# breaking dawn part 1
breakingdawn1_script_nrc <- breakingdawn1_script_token %>%
  inner_join(nrc, by = "word") %>% 
  group_by(title) %>% 
  count(sentiment) %>% 
  mutate(prop = n/sum(n))
# breaking dawn part 2
breakingdawn2_script_nrc <- breakingdawn2_script_token %>%
  inner_join(nrc, by = "word") %>% 
  group_by(title) %>% 
  count(sentiment) %>% 
  mutate(prop = n/sum(n))

# putting all the movies togther
films_nrc <- bind_rows(twilight_script_nrc,
                       newmoon_script_nrc,
                       eclipse_script_nrc,
                       breakingdawn1_script_nrc,
                       breakingdawn2_script_nrc) %>% 
  select(-n)


# SOUNDTRACKS

soundtracks_nrc <- soundtracks_token %>% 
  # adding sentiment
  inner_join(nrc, by = "word") %>% 
  # reducing to proportions for graphing
  group_by(album) %>%
  count(sentiment) %>% 
  mutate(prop = n/sum(n)) %>% 
  ungroup() %>% 
  select(-n) %>% 
  # standardizing names
  mutate(album = case_when(
    album == 'Twilight (Original Motion Picture Soundtrack)' ~ "Twilight",
    album == 'The Twilight Saga: New Moon (Original Motion Picture Soundtrack)' ~ "New Moon",
    album == 'The Twilight Saga: Eclipse (Original Motion Picture Soundtrack)' ~ "Eclipse",
    album == 'The Twilight Saga: Breaking Dawn, Pt. 1 (Original Motion Picture Soundtrack)' ~ "Breaking Dawn part 1",
    album == 'The Twilight Saga: Breaking Dawn – Part 2 (Original Motion Picture Soundtrack)' ~ "Breaking Dawn part 2"
  )) %>% 
  rename(title = album)

AGRESSIVE WRANGLING

We will also want to compare sentiments across mediums, i.e. between books and movies, etc. To do this we have to put together the datasets that we already have. Our only problem is with Breaking Dawn, which has two movies and two soundtracks (Breaking Dawn part 1 and Breaking Dawn part 2) but only one book. Using our extensive knowlege of the Twilight books and movies, we will manually split the book into sections that map to the content of the movie. This will be done by using stringr functions on the scraped text, then replacing the single Breaking Dawn book with our split versions.

# splitting book:
# parts 1+2 -> movie 1
# part 3 -> movie 2

# splitting the big character (whole book) into the two parts
breakingdawn_split <- str_split(breakingdawn_text, "BOOK THREE", 2)

# making into more useful dataframe
breakingdawn_split <- breakingdawn_split[[1]]

# splitting into parts one and two
breakingdawn1_book <- breakingdawn_split[1]

breakingdawn2_book <- breakingdawn_split[2]

# tokenizing them separately
breakingdawn1_token <- tibble(text = breakingdawn1_book) %>% 
  mutate(title = "Breaking Dawn part 1") %>% 
  unnest_tokens(input = text,
                output = word,
                token = "words")
breakingdawn2_token <- tibble(text = breakingdawn2_book) %>% 
  mutate(title = "Breaking Dawn part 2") %>% 
  unnest_tokens(input = text,
                output = word,
                token = "words")

# adding sentiments
breakingdawn1_nrc <- breakingdawn1_token %>%
  inner_join(nrc, by = "word") %>% 
  group_by(title) %>% 
  count(sentiment) %>% 
  mutate(prop = n/sum(n))

breakingdawn2_nrc <- breakingdawn2_token %>%
  inner_join(nrc, by = "word") %>% 
  group_by(title) %>% 
  count(sentiment) %>% 
  mutate(prop = n/sum(n))

# replacing single breaking dawn with breaking dawn 1 and 2
saga_nrc <- saga_nrc %>% 
  filter(title != "Breaking Dawn") %>% 
  bind_rows(breakingdawn1_nrc,
            breakingdawn2_nrc) %>% 
  select(-n)

After splitting the Breaking Dawn book into equivalent parts we can add the movie and soundtrack sentiments to the dataset. This gives us a dataset which can be used to make comparisons across mediums.

# renaming to book proportion to keep track of which prop is which
big_nrc <- saga_nrc %>% 
  rename(book = prop) %>% 
  # adding movie script proportion
  left_join(films_nrc, by = c("title", "sentiment")) %>% 
  rename(movie = prop) %>% 
  # adding soundtrack proportion
  left_join(soundtracks_nrc, by = c("title", "sentiment")) %>% 
  rename(soundtrack = prop) %>% 
  # giving an order to the titles
  ungroup() %>% 
  mutate(title = fct_inorder(title))

This dataset is not in the tidy format. Most plotly graphs will require a tidy dataset, so we will reformat using the tidyr package.

big_nrc_tidy <- big_nrc %>% 
  pivot_longer(cols = c(book, movie, soundtrack),
               names_to = "prop_type",
               values_to = "prop")

It’s important to note that the only way this data joining process worked is because we used summarized versions of our datasets. Trying to join this many datasets is pretty hard on the computer if the datasets are really big, so trying to combine the tokenized verisons of all the movies, books, and soundtracks would probably not be a great idea.

FIRST EXAMPLE

An example of simple graph we can make with the bing dataset is the ratio of positive to negative words in twilight. This is how it might be done with ggplot2:

# how many of each sentiment
twilight_bing <- twilight_bing %>% 
  group_by(title) %>% 
  count(sentiment) %>% 
  # finding proportion
  mutate(prop = n/sum(n))

  # graphing 
  ggplot(twilight_bing, 
         mapping = aes(x = sentiment,
                       y = prop)) +
  geom_col(width = 0.7,
           color = "black",
           fill = c("#B6BE9C", "#5E747F")) +
  theme_bw() +
  # coord_polar("y", start = 0) +
  scale_y_continuous(limits = c(0, 0.65),
                     breaks = seq(0, 0.65, .05)) +
  theme(
    plot.title = element_text(size = 17, 
                              color = "black",
                              hjust = 0.5),
    plot.subtitle = element_text(size = 12,
                                 hjust = 0.5,
                                 lineheight = 0.5)) +
  labs(title = "Ratio of Positive to Negative Sentiments",
       subtitle = "Twilight",
       x = "Sentiment",
       y = "Proportion of the Book")

We can also graph this with plotly. If we are just interested in making a generic plot, we can just specify the x and y variables and plotly will use the geom that it thinks is best. These variables must be specified within the plot_ly() function, but no other information is needed besides the dataset.

# graphing
twilight_bing %>% 
  plot_ly(x = ~sentiment,
          y = ~prop)

Note the ~ before each variable (x = ~sentiment, y = ~prop). This needs to happen before variables in all aspects of plotly.

Plotly chose to use a bar plot to display the data that we gave it. This means that the biggest difference between the plotly plot and ggplot plot is the interactivity mentioned earlier, i.e. popups and viewing control. Note that these options were added automatically without any need for additonal code.

HOW DOES PLOTLY WORK

Plotly does a lot of work in JavaScript behind the scenes to create the interactive graphs in our output. That said, you dont need to know much of anything about JavaScript to use plotly. If you want to know more about how plotly makes graphs, check out the Interactive web-based data visualization with R, plotly, and shiny! This is a fantastic comprehensive resource that has a lot of great material on shiny. You could consider starting with section 2.2 - Intro to plotly.js, then moving on to other relevent sections.

PLAYING WITH PLOTLY

If we so desire, we can change the way plotly displays the content of the graph (geom) by specifying the geom that we want and adhering to its imput requirements. For example, with a pie chart, we cannot specify x or y variables, but instead just use values. We specify the geom for the graph with type = .

We can also remove the popup if it will not tell us anyting interesting, as well as modify other aspects of the graph with the layout() function.

We will demonstrate this with the Bing sentiments from the Twilight movie soundtrack! First we have to wrangle our big soundtrack dataset into shape then we can get right into graphing!

# making dataset of only words with sentiments from the twilight soundtrack only
soundtracks_bing_twilight <- soundtracks_bing %>% 
  # subsetting the dataset to just twilight
  filter(
    album == "Twilight (Original Motion Picture Soundtrack)"
  ) %>% 
  # getting proportion of positive to negative
  count(sentiment) %>% 
  mutate(prop = n/sum(n))

  # graphing with plotly
plot_ly(data = soundtracks_bing_twilight,
        # not x or y
        values = ~prop,
        labels = ~sentiment,
        type = "pie")

It is worth noting that graphs can generally be made in a few ways with plotly(). The first is using a type = in a plot_ly() function as done above with type = “pie”. Another is using add_() in a new line, as done below with add_pie(). We use both ways of creating graphs in this post. The following code would produce the exact same graph as the chunk above.

plot_ly(data = soundtracks_bing_twilight) %>% 
  add_pie(values = ~prop, labels = ~sentiment)

This is a perfectly acceptable pie chart, but its not perfect. In this case the content of the interactive popup would be better to just statically show inside the graph itself, as well as the content of the legend. We can do both of these things pretty easily - the content of the popup can be moved onto the graph itself with textposition = “inside”. The popup can then be removed with hoverinfo = “none”, and the legend removed with showlegend = FALSE. We also do not need the option to zoom on such a simple plot, so we will remove the option with the config() function. We can also add a title with the layout() function.

plot_ly(data = soundtracks_bing_twilight,
        values = ~prop,
        labels = ~sentiment,
        type = "pie",
        # moving text inside the pie
        textposition = "inside",
        textinfo = "label+percent",
        # removing popup
        hoverinfo = "none",
        # removing legend
        showlegend = FALSE) %>% 
  # adding title
  layout(title = "positive/negative ratio in the twilight movie soundtrack") %>% 
  # removing options to zoom
  config(modeBarButtonsToRemove = c("zoomIn2d", "zoomOut2d"))

GGPLOTLY

This is the second way to create a plot in plotly - making a graph with ggplot2, then simply converting it into a plotly graph with code>ggplotly() of the Tidyverse. This is a super easy way to add interactivity to a plot. The compatability of plotly with the ubiquitous ggplot2 makes it much more accessable, as you effectively just need to know ggplot2 to be able to use the basic features of a plotly graph.

For example, we can make and store a graph of the NRC sentiments from the Twilight book with ggplot2:

# graphing, storing the graph as a variable to use in ggplotly()
twilight_nrc_graph <- ggplot(data = twilight_nrc,
                             mapping = aes(x = sentiment,
                                           y = prop,
                                           fill = sentiment)) +
  geom_col(width = 0.7,
           color = "black") +
  coord_flip() +
  theme_bw() +
  theme(
    plot.title = element_text(size = 17, 
                              color = "black",
                              hjust = 0.5),
    plot.subtitle = element_text(size = 12,
                                 hjust = 0.5,
                                 lineheight = 0.5),
    legend.position = "none") +
  labs(title = "NRC Sentiments in Twilight",
       x = "Sentiment",
       y = "Proportion of the Book")

twilight_nrc_graph

Then just specify the stored ggplot object (graph) in ggplotly() and the graph will be made into a plotly graph.

By default, the automatically generated pop-up shows all the information used to create the graphical representation that is shown on the graph. In this example, those would be sentiment and proportion, because those were our x and y variables. We can also specify the information contained in the popup as we did below.

# this is the graph we just made
twilight_nrc_graph %>% 
  # adding interactivity with ggplotly()
  ggplotly(tooltip = "prop")
# note modified tooltip

As you can see we added a popup showing the exact proportion of the book made up by each sentiment, which keeps our axis looking clean while still providing precise information.

TOOLTIPS AND LEGENDS

Almost everything about a plotly plot can be modified in one way or another. One of the most important aspects to have control over is the pop-ups/tooltips. These can be changed either with the simple hoverinfo = , as done with the pie chart earier, or with more complicated and more customizable hovertemplate = . We use the second option below to reformat what is displayed in the popup of the graph.

The layout() function that we used earier can be used to change the appearence of the graph. Note that many of the inputs of the layout() function must be in list format, such as xaxis = , yaxis = , and legend = shown below. We changed the appearence of the legend rather easily by making a list of the wanted attributes, then imputting it within the layout() function.

We wil show these features with a simple line graph that would be much too complicated to be useful in ggplot2, but is parsed much more easily using the interactive elements of plotly. That said, Plotly has its limits as well, so we will graph just a few sentiments rather than all of them, because too many lines is too many lines.

# making list with legend styling contents
legend_style <- list(
  font = list(
    family = "sans-serif",
    size = 12,
    color = "#000"),
  bgcolor = "#E2E2E2",
  bordercolor = "#FFFFFF",
  borderwidth = 2)

# initial wrangling
big_nrc_tidy %>% 
  filter(
      sentiment == "joy" |
      sentiment == "positive" |
      sentiment == "trust"
  ) %>% 
  # graphing wrangled dataset
  plot_ly(x = ~title, 
          y = ~prop,
          color = ~sentiment) %>%
  # adding lines
  add_lines(linetype = ~prop_type,
            # changing popup content - type and value only
            # making variable mapped to linetype something we can put in popup
            text = ~prop_type,
            # more customizable way to do popups than hoverinfo
            hovertemplate = paste('<b>Title:</b> %{x}',
                                  '<br><b>Medium:</b> %{text}',
                                  '<br><b>Proportion:</b> %{y}',
                                  # getting rid of secondary textbox
                                  '<extra></extra>')) %>% 
    # adding titles
  layout(title = "Sentiments across titles, shown by medium",
         xaxis = list(title = "Title"),
         yaxis = list(title = "Proportion"),
         # styling legend
         legend = legend_style)

SIDE BY SIDE PLOTS

Plotly has an easy way to show multiple plots side by side with the subplot() function. Plots displayed side by side in this manner retain their interactivity and share a single legend, effectively becoming one graph.

To use subplot(), simply make two separate plotly graphs, store them as variables, then plug them into the function like so:

# making graph 1
graph_1 <- big_nrc_tidy %>% 
  # choosing sentiments
  filter(
    sentiment == "negative" |
      sentiment == "positive" |
      sentiment == "joy" |
      sentiment == "fear"
  ) %>% 
  # just books
  filter(
    prop_type == "book") %>% 
  # making the graph
  plot_ly(x = ~title, 
          y = ~prop,
          color = ~sentiment,
          type = "scatter",
          mode = "lines")

# making graph 2
graph_2 <- big_nrc_tidy %>% 
  # choosing sentiments
  filter(
    sentiment == "negative" |
      sentiment == "positive" |
      sentiment == "joy" |
      sentiment == "fear"
  ) %>% 
  # just books
  filter(
    prop_type == "movie") %>% 
  # making the graph
  plot_ly(x = ~title, 
          y = ~prop,
          color = ~sentiment,
          type = "scatter",
          mode = "lines", 
          # hiding duplicate legend
          showlegend = FALSE)

# making the subplot
subplot(graph_1, 
        graph_2, 
        # standardizing the y axis
        shareY = TRUE)

This line graph uses the NRC sentiments lexicon to show the progression of proportion of words with joy, trust, and positive sentiment associations throughout the movies, starting with Twilight and ending with Breaking Dawn: Part 2. Each line represents a different medium; book, movie, or soundtrack. From this graph, the clearest trend we can see is that of positive sentiments across all mediums, where each medium tracks very closely. The proportion of positive words dips during Twilight: New Moon, which if you’re familiar with the plot does make sense, as Bella is thrown into a deep depression after Edward and his family flee town. However, the trend goes upwards for the remainder of the series. The progression for joyful words is interesting, as there is a fair amount of variation between each medium. The book and movie remain fairly low as far a joy goes, but the soundtrack has a much higher proportion of joyful sentiments than the other two mediums.

ADDITIONAL RESOURCES

As mentioned earlier, the Interactive web-based data visualization with R, plotly, and shiny is a great manual with lots of information about plotly. We reccomend starting with the Overview, then reading the Scattered foundations, then whichever sections are relevant to your project.

Plotly R Open Source Graphing Library has easy-to-follow coding tutorials that can help you make just about any kind of graph, but does not have the in-depth explanations of the previously mentioned guide.

The Plotly cheatsheet from the plotly website is a great tool for refreshing your memory once you know more about plotly, but we wouldn’t reccomend using it to start.

An essential but very confusing resource is the Plotly figure reference, which has a completely comprehensive list of which functions can be used where and what they do. The absolutely comprehensive nature of this resource makes it most useful when you know exactly what you are looking for. We reccomend steering clear of this one until you have gotten your bearings with the first entry on this list, then practiced making graphs with the help of the second entry.

The Plotly Chart Studio is an interesting tool to play around with if you are interesting in creating plotly graphs without coding.