Week 5: Interactive Visuals

Author

Robert W. Walker

Published

February 13, 2023

Meeting Date: February 13, 2023.

Last updated: 2023-04-10 13:53:03

Timezone: America/Los_Angeles

Data from Our World in data, example sankey

Class Plan

AMA
A Review of Nice Tables?
Interactive Graphics in R
- Network data and interaction with networkD3.
- Plotly and ggplotly
- Crosstalk related tools
- ggiraph

Slides:

Week 5 Slides

The Site I am Building

My github main page

Readings:

Quarto Documentation [if not already done]
The gt docs link
The flextable book

Resources

Tables

Interactive Graphics

Homework

The fifth assignment consists of adding at two interactive graphics.

Deliverables: an email containing the URLs for the interactive visualization posts.

Network Visualization

The Data

Code

library(tidyverse)
library(jsonlite)
URL <- paste0("https://cdn.rawgit.com/christophergandrud/networkD3/","master/JSONdata/energy.json")
Energy <- fromJSON(URL)
Links <- Energy$links 
pivot_wider(Links, id_cols=source, names_from = target, values_from = value) %>% knitr::kable()

source	1	2	3	4	5	9	12	13	14	16	17	18	19	20	21	22	24	26	15	28	30	31	32	33	37	42	41	11
0	124.729	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
1	NA	0.597	26.862	280.322	81.144	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
6	NA	35.000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
7	NA	NA	NA	35.000	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
8	NA	NA	NA	NA	NA	11.606	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
10	NA	NA	NA	NA	NA	63.965	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
9	NA	NA	NA	75.571	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
11	NA	NA	NA	NA	NA	NA	10.639	22.505	46.184	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
15	NA	NA	56.691	NA	NA	NA	342.165	40.858	113.726	104.453	27.14	37.797	4.412	7.863	90.008	93.494	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
23	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	40.719	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
25	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	82.233	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
5	NA	NA	1.401	NA	NA	NA	48.580	0.129	NA	NA	NA	NA	2.096	NA	NA	NA	NA	151.891	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
27	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	7.013	NA	NA	NA	NA	NA	NA	NA	NA	NA
17	NA	NA	6.242	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	20.897	NA	NA	NA	NA	NA	NA	NA	NA
28	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	20.897	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
29	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	6.995	NA	NA	NA	NA	NA	NA	NA	NA	NA
2	NA	NA	NA	NA	NA	NA	121.066	NA	NA	NA	NA	135.835	3.640	4.413	NA	NA	NA	NA	NA	NA	128.69	14.458	206.267	33.218	NA	NA	NA	NA
34	4.375	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
24	NA	NA	NA	NA	122.952	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
35	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	839.978	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
36	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	504.287	NA	NA	NA
38	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	107.703	NA	NA	NA
37	NA	611.990	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
39	77.810	NA	NA	56.587	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
40	NA	NA	NA	NA	NA	NA	NA	70.672	193.026	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
41	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	59.901	NA	NA	NA	NA	NA	NA	NA	NA	NA
42	NA	NA	NA	NA	NA	NA	NA	NA	19.263	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
43	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	19.263	59.901	NA
4	NA	NA	NA	NA	NA	NA	46.477	NA	NA	NA	NA	NA	0.882	NA	NA	NA	NA	400.120	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
26	NA	NA	787.129	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	525.531	NA	NA	NA	NA	NA	NA	NA	NA	79.329
44	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	9.452	NA	NA	NA	NA	NA	NA	NA	NA	NA
45	182.010	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
46	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	19.013	NA	NA	NA	NA	NA	NA	NA	NA	NA
47	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	289.366	NA	NA	NA	NA	NA	NA	NA	NA	NA

The Plot

Code

library(networkD3)
# Plot
sankeyNetwork(Links = Energy$links, 
              Nodes = Energy$nodes, 
              Source = "source",
              Target = "target", 
              Value = "value", 
              NodeID = "name",
              units = "TWh", 
              fontSize = 12, 
              nodeWidth = 30)

Stocks and Plotly

The `plotly` package

plotly as a package has far more it can do.

Code

library(plotly); library(magrittr)
library(tidyquant)
# Use tidyquant to get the data
INTC <- tq_get("INTC")
# Slice off the most recent 90 days
INTC.tail.90 <- tail(INTC, 90)
INTC.tail <- INTC.tail.90
# Create a counter of days
INTC.tail$ID <- seq.int(nrow(INTC.tail))
# Round the prices to 2 digits
INTC.tail %<>% mutate(close = round(close, digits=2))

Now we want a function to create the dataset for each stage of the animation. There are a few ways to do this but most involve writing a function to create them. This example function comes from the plotly documents.

Code

# This is in the example for plotly paths
# First a supporting function: getLevels takes input x
getLevels <- function (x) {
  # if x is a factor
    if (is.factor(x)) 
  # grab the levels of x
        levels(x)
  # if x is not a factor, sort unique values of x
    else sort(unique(x))
}
# Two inputs, the data and the variable to form the splits along the x-axis
accumulate_by <- function(dat, var) {
  # This handles linking variables to their environment
  var <- lazyeval::f_eval(var, dat)
  # get the levels of the given variable using the function above
  lvls <- getLevels(var)
  # use lapply, tidy would use map to iterate over the levels in `lvls` and column bind the data with frame denotes by lvls[[x]]
  dats <- lapply(seq_along(lvls), function(x) {
   cbind(dat[var %in% lvls[seq(1, x)], ], frame = lvls[[x]])
  })
  # bind the rows together
  dplyr::bind_rows(dats)
}
# Invoke the function on our ID variable
INTC.tail <- INTC.tail %>% accumulate_by(~ID)
# Create a figure of ID and close for each frame value using plotly's version of a line plot: type:scatter-mode:lines
# The rest is standard plotly
fig <- INTC.tail %>% plot_ly(
  x = ~ID, 
  y = ~close, 
  frame = ~frame,
  type = 'scatter', 
  mode = 'lines', 
  # This is short for fill to zero on the y-axis
  fill = 'tozeroy',
  fillcolor='rgba(73, 26, 201, 0.5)',
  line = list(color = 'rgb(73, 26, 201)'),
  text = ~paste("Date: ", date, "<br>Close: $", close), 
  hoverinfo = 'text'
)
# Add the layout; one title and two axes
# I also mess with the margin to keep the figure from being cut off.
fig <- fig %>% layout(
  title ="Intel Stock Closing Price: Last 90 Days",
  yaxis = list(
    title = "Close", 
    range = c(0,50), 
    zeroline = F,
    tickprefix = "$"
  ),
  xaxis = list(
    title = "Day", 
    range = c(0,90), 
    zeroline = F, 
    showgrid = F
  ),
  margin = list(t=120) # adjust the plot margin to avoid cutting off letters
) 
# Animate the figure with 100 frames
fig <- fig %>% animation_opts(
  frame = 100, # transition time 100 ms
  transition = 0, # duration of smooth transition in ms
  redraw = FALSE # redraw the plot at each transition?
)
fig <- fig %>% animation_slider(
  currentvalue = list(
    prefix = "Day "
  )
)
fig

An easier plotly for these data because they are OHLC

A link to a stand-alone document with all the data.

Code

# basic example of ohlc charts
# custom colors
i <- list(line = list(color = '#000000')) # black
d <- list(line = list(color = '#FF0000')) # red
# Create the figure
fig.2 <- INTC.tail.90 %>%
  plot_ly(x = ~date, type="ohlc",
          open = ~open, close = ~close,
          high = ~high, low = ~low,
          increasing = i, decreasing = d)
fig.2

Human Rights Data from Fariss

I will load the necessary libraries for this before loading the data.

Code

library(tidyverse)
library(magrittr)
library(DT)
load(url("https://github.com/robertwwalker/robertwwalker.github.io/raw/main/posts/HumanRightsTable/data/HumanRightsProtectionScores_v4.01.RData"))
HR.Data <- x
rm(x)
HR.Data.Selection <- HR.Data %>% 
  mutate(country_name = as.factor(country_name)) %>%
  select(country_name, theta_mean, theta_sd) %>%
  group_by(country_name) %>%
  summarise(Mean = mean(theta_mean),
            SD = mean(theta_sd)) %>% 
  mutate(Mean = round(Mean, digits=2),
         SD = round(SD, digits=2))

To link the data selection and the visualization, I need one line of code necessary to share the data.

Code

# Get bscols onto rows with widths
# https://stackoverflow.com/questions/72064781/in-package-crosstalk-are-there-have-function-bsrows-and-rowscols-mixed
library(crosstalk); library(plotly)
shared <- SharedData$new(HR.Data.Selection)
bscols(widths = c(12, 12),
  plot_ly(shared, x = ~Mean, y=~SD, type = "scatter"),
  datatable(shared)
)


# Pizza [ggiraph]

::: {.cell}

```{.r .cell-code}
library(tidyverse)
library(ggthemes)
library(ggiraph)
library(ggthemes)
library(patchwork)
library(cowplot)
library(rayshader)
library(emojifont)
library(gridExtra)
library(extrafont)

:::

Pizza Ratings

A #tidyTuesday on pizza shop ratings data. The data come from a variety of sources; it is price, ratings, and similar data for pizza restaurants. The actual contents vary depending on the data source. I will begin by loading the data and summarizing what data seem to be available so that we can figure out what we can do with it. Let’s see what we have; NB: there are three datasets, I chose one.

Code

pizza_datafiniti <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-10-01/pizza_datafiniti.csv")

Code

summary(pizza_datafiniti)

     name             address              city             country         
 Length:10000       Length:10000       Length:10000       Length:10000      
 Class :character   Class :character   Class :character   Class :character  
 Mode  :character   Mode  :character   Mode  :character   Mode  :character  
                                                                            
                                                                            
                                                                            
   province            latitude       longitude        categories       
 Length:10000       Min.   :21.42   Min.   :-157.80   Length:10000      
 Class :character   1st Qu.:34.42   1st Qu.:-104.80   Class :character  
 Mode  :character   Median :40.12   Median : -82.91   Mode  :character  
                    Mean   :38.37   Mean   : -90.06                     
                    3rd Qu.:40.91   3rd Qu.: -75.19                     
                    Max.   :64.85   Max.   : -71.95                     
 price_range_min  price_range_max
 Min.   : 0.000   Min.   : 7.00  
 1st Qu.: 0.000   1st Qu.:25.00  
 Median : 0.000   Median :25.00  
 Mean   : 4.655   Mean   :27.76  
 3rd Qu.: 0.000   3rd Qu.:25.00  
 Max.   :50.000   Max.   :55.00

I will use this data; it contains some Oregon pizzarias.

Code

pizza_datafiniti %>% filter(province=="OR")

# A tibble: 122 × 10
   name             address city  country province latitude longitude categories
   <chr>            <chr>   <chr> <chr>   <chr>       <dbl>     <dbl> <chr>     
 1 Coburg Pizza Co… 1710 C… Spri… US      OR           44.1     -123. Restauran…
 2 Coburg Pizza Co… 1710 C… Spri… US      OR           44.1     -123. Restauran…
 3 Coburg Pizza Co… 1710 C… Spri… US      OR           44.1     -123. Restauran…
 4 Coburg Pizza Co… 1710 C… Spri… US      OR           44.1     -123. Restauran…
 5 Coburg Pizza Co… 1710 C… Spri… US      OR           44.1     -123. Restauran…
 6 Coburg Pizza Co… 1710 C… Spri… US      OR           44.1     -123. Restauran…
 7 Coburg Pizza Co… 1710 C… Spri… US      OR           44.1     -123. Restauran…
 8 Coburg Pizza Co… 1710 C… Spri… US      OR           44.1     -123. Restauran…
 9 Coburg Pizza Co… 1710 C… Spri… US      OR           44.1     -123. Restauran…
10 Coburg Pizza Co… 1710 C… Spri… US      OR           44.1     -123. Restauran…
# ℹ 112 more rows
# ℹ 2 more variables: price_range_min <dbl>, price_range_max <dbl>

For the first plot, let me show what is going on in Oregon.

Code

pizzaData <- pizza_datafiniti %>% 
  filter(province=="OR") %>% 
  group_by(name) %>% 
  mutate(Tcount = n(), 
         PriceAvg = mean(price_range_max - price_range_min)) %>% 
  ungroup()
p <- pizzaData %>% ggplot(.) +
  aes(x=reorder(name,PriceAvg), 
      fill=PriceAvg) + 
  geom_bar() + 
  coord_flip() + 
  labs(x="Pizza Restaurant", 
       y="Count", 
       title="Oregon Pizzerias", 
       caption = "data from #tidyTuesday; sorted by Average Price")
p

Now I want a map.

Code

pizzaData$group <- 44
states <- map_data("state")
OR.df <- subset(states, region == "oregon")
OR_base <- ggplot(data = OR.df, mapping = aes(x = long, y = lat, group = group)) +
geom_polygon(color = "black", fill = "gray") + labs(title="Oregon Pizzerias")
OR_base

Code

OR.Pizza <- OR_base + geom_point(data = pizzaData, aes(y=latitude, x=longitude, group=group), color = "red")
OR.Pizza

Combine them into one picture

Code

grid.arrange(p,OR.Pizza, ncol=2)

Now to use a girafe to put this together interactively.

Code

theme_set(theme_minimal())
pizzaData <- pizzaData %>% mutate(name = str_remove(name, "['``]"))
# Build the barplot
gg1 <- ggplot(pizzaData, aes(x=reorder(name,PriceAvg), fill=PriceAvg)) +
  geom_bar_interactive(aes(x = name, tooltip = name, data_id = name))  + 
  coord_flip() + scale_fill_viridis_c() + theme(axis.text=element_text(size=8)) +
  labs(x="Pizza Restaurant", y="Count", caption = "data from #tidyTuesday", sub="sorted by Average Price") 
# Build the map
gg2 <- ggplot(data = OR.df, mapping = aes(x = long, y = lat, group = group)) +
  geom_polygon(fill=gray(0.95)) + 
  geom_text_interactive(data = pizzaData, aes(y=latitude, x=longitude, label=emoji('pizza'), tooltip = name, data_id = name), family='EmojiOne', size=2, color="orange") + 
  scale_color_viridis_c(guide=FALSE) +
  theme_nothing()
# Bind them together with girafe
a <- girafe( code = print(gg1 / gg2 + plot_annotation(title="Pizza in Oregon")))
library(widgetframe)
frameWidget(a, width = "100%", height = "100%")

The formatting of this is quite off. For some reason, the frame doesn’t control scroll and overplots.

Rayshader

And a mini-rayshader. This is a complete lift and replace of the vignette after calculating the Price Midpoint.

Code

states <- map_data("state")
PDF <- pizza_datafiniti %>% mutate(Price.Midpoint = (price_range_min + price_range_max / 2))
mtplot <- ggplot(data = states, mapping = aes(x = long, y = lat)) +
  geom_polygon(color = "black", fill = "gray") + 
  geom_point(data=PDF, aes(x = longitude, y = latitude, color = Price.Midpoint)) + 
  scale_color_viridis_c() + theme_minimal()
plot_gg(mtplot, width = 3.5, multicore = TRUE, windowsize = c(1200, 1200), 
        zoom = 0.5, phi = 35, theta = 30, sunangle = 15, soliddepth = -20)
render_snapshot(clear = TRUE)

Addendum: the function

Borrowed from Ted Laderas.

Code

library(DT)
items <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-05/items.csv')
datatable(head(items))

A clever function

Ted Laderas [@laderast on Twitter] wrote a function to present the ten most expensive items in a category for the items dataset. You can find his repo for this here.. I will change rows 3 and 9. Line 3 adds the new argument to the function and line 9 carries the variable defined in the argument into top_n.

Ted’s Original

Code

library(ggimage); library(gt)
library(tidyverse)
most_expensive <- function(category_name=NULL, price_category=buy_value){
  if(!is.null(category_name)){
    items <- items %>%
      filter(category == category_name)
  }
items %>% 
  top_n(10, {{price_category}}) %>%
  arrange(desc({{price_category}})) %>%
  select(name, sell_value, buy_value, category, image=image_url) %>%
  gt() %>%
   text_transform(
    locations = cells_body(vars(image)),
    fn = function(x) {
      web_image(
        url = x,
        height = 50
      )
    }
  )
}

My Modification

Code

library(ggimage); library(gt)
library(tidyverse)
most_expensive <- function(category_name=NULL, price_category=buy_value, n=10){
  if(!is.null(category_name)){
    items <- items %>%
      filter(category == category_name)
  }
items %>% 
  top_n(n, {{price_category}}) %>%
  arrange(desc({{price_category}})) %>%
  select(name, image=image_url, sell_value, buy_value, category) %>%
  gt() %>%
   text_transform(
    locations = cells_body(vars(image)),
    fn = function(x) {
      web_image(
        url = x,
        height = 50
      )
    }
  )
}

Making a Table

Code

most_expensive() %>%  
  tab_header(title = "Most Expensive Items in Animal Crossing By Buy Price") %>%  
  cols_label(
    name = "Item",
    sell_value = "Sale Price",
    buy_value = "Buy Price",
    category = "Item Type",
    image = "Picture"
  ) %>%   tab_spanner(
    label = "Prices",
    columns = c(buy_value, sell_value)
  )

Item	Picture	Prices		Item Type
Most Expensive Items in Animal Crossing By Buy Price
Item	Picture	Buy Price	Sale Price	Item Type
Royal Crown		1200000	300000	Hats
Crown		1000000	250000	Hats
Gold Armor		320000	80000	Dresses
Golden Casket		320000	80000	Furniture
Grand Piano		260000	65000	Furniture
Golden Toilet		240000	60000	Furniture
Blue Steel Staircase		228000	NA	Furniture
Iron Bridge		228000	NA	Furniture
Red Steel Staircase		228000	NA	Furniture
Red Zen Bridge		228000	NA	Furniture
Zen Bridge		228000	NA	Furniture

The Question

Code

most_expensive("Hats") %>%  tab_header(title = "Most Expensive Items in Animal Crossing By Buy Price") %>%  cols_label(
    name = "Item",
    sell_value = "Sale Price",
    buy_value = "Buy Price",
    category = "Item Type",
    image = "Picture"
) %>%   tab_spanner(
    label = "Prices",
    columns = c(buy_value, sell_value)
)

Item	Picture	Prices		Item Type
Most Expensive Items in Animal Crossing By Buy Price
Item	Picture	Buy Price	Sale Price	Item Type
Royal Crown		1200000	300000	Hats
Crown		1000000	250000	Hats
Gold Helmet		200000	50000	Hats
Blue Rose Crown		48000	12000	Hats
Gold Rose Crown		48000	12000	Hats
Snowperson Head		28000	7000	Hats
Knight's Helmet		15000	3750	Hats
Dark Cosmos Crown		13440	3360	Hats
Chic Rose Crown		11520	2880	Hats
Purple Hyacinth Crown		11520	2880	Hats
Purple Pansy Crown		11520	2880	Hats
Purple Windflower Crown		11520	2880	Hats
Simple Mum Crown		11520	2880	Hats

Fossils?

Code

most_expensive("Fossils", price_category = sell_value, n=65) %>%  tab_header(title = "Most Expensive Items in Animal Crossing By Buy Price") %>%  cols_label(
    name = "Item",
    sell_value = "Sale Price",
    buy_value = "Buy Price",
    category = "Item Type",
    image = "Picture"
) %>%   tab_spanner(
    label = "Prices",
    columns = c(buy_value, sell_value)
)

Item	Picture	Prices		Item Type
Most Expensive Items in Animal Crossing By Buy Price
Item	Picture	Buy Price	Sale Price	Item Type
Brachio Skull		NA	6000	Fossils
T. Rex Skull		NA	6000	Fossils
Brachio Chest		NA	5500	Fossils
Brachio Tail		NA	5500	Fossils
Dimetrodon Skull		NA	5500	Fossils
Right Megalo Side		NA	5500	Fossils
T. Rex Torso		NA	5500	Fossils
Tricera Skull		NA	5500	Fossils
Brachio Pelvis		NA	5000	Fossils
Dimetrodon Torso		NA	5000	Fossils
Diplo Skull		NA	5000	Fossils
Diplo Tail		NA	5000	Fossils
Left Quetzal Wing		NA	5000	Fossils
Right Quetzal Wing		NA	5000	Fossils
Stego Skull		NA	5000	Fossils
T. Rex Tail		NA	5000	Fossils
Tricera Torso		NA	5000	Fossils
Diplo Neck		NA	4500	Fossils
Diplo Pelvis		NA	4500	Fossils
Left Ptera Wing		NA	4500	Fossils
Megacero Skull		NA	4500	Fossils
Plesio Body		NA	4500	Fossils
Plesio Tail		NA	4500	Fossils
Quetzal Torso		NA	4500	Fossils
Right Ptera Wing		NA	4500	Fossils
Stego Torso		NA	4500	Fossils
Tricera Tail		NA	4500	Fossils
Archelon Skull		NA	4000	Fossils
Diplo Chest		NA	4000	Fossils
Diplo Tail Tip		NA	4000	Fossils
Iguanodon Skull		NA	4000	Fossils
Left Megalo Side		NA	4000	Fossils
Pachy Skull		NA	4000	Fossils
Plesio Skull		NA	4000	Fossils
Ptera Body		NA	4000	Fossils
Spino Skull		NA	4000	Fossils
Stego Tail		NA	4000	Fossils
Ankylo Skull		NA	3500	Fossils
Archelon Tail		NA	3500	Fossils
Dunkleosteus		NA	3500	Fossils
Iguanodon Torso		NA	3500	Fossils
Megacero Torso		NA	3500	Fossils
Pachy Tail		NA	3500	Fossils
Parasaur Skull		NA	3500	Fossils
Ankylo Torso		NA	3000	Fossils
Deinony Torso		NA	3000	Fossils
Iguanodon Tail		NA	3000	Fossils
Mammoth Skull		NA	3000	Fossils
Megacero Tail		NA	3000	Fossils
Parasaur Torso		NA	3000	Fossils
Spino Torso		NA	3000	Fossils
Ankylo Tail		NA	2500	Fossils
Deinony Tail		NA	2500	Fossils
Mammoth Torso		NA	2500	Fossils
Ophthalmo Skull		NA	2500	Fossils
Opthalmo Skull		NA	2500	Fossils
Opthalmo Torso		NA	2500	Fossils
Parasaur Tail		NA	2500	Fossils
Sabertooth Skull		NA	2500	Fossils
Spino Tail		NA	2500	Fossils
Acanthostega		NA	2000	Fossils
Anomalocaris		NA	2000	Fossils
Eusthenopteron		NA	2000	Fossils
Ophthalmo Torso		NA	2000	Fossils
Sabertooth Tail		NA	2000	Fossils

Class Plan

Slides:

The Site I am Building

Readings:

Resources

Tables

Interactive Graphics

Homework

Network Visualization

The Data

The Plot

Stocks and Plotly

The plotly package

An easier plotly for these data because they are OHLC

Human Rights Data from Fariss

Pizza Ratings

Rayshader

Addendum: the function

A clever function

Ted’s Original

My Modification

Making a Table

The Question

Fossils?

The `plotly` package