Meeting Date: February 20, 2023.
Last updated: 2023-04-10 13:53:21
Timezone: America/Los_Angeles
The sixth assignment consists of a presentation selling yourself.
Deliverables: an email containing the URL to the presentation.
plotly packageplotly as a package has far more it can do.
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.
# 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 "
)
)
figA link to a stand-alone document with all the data.
# 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.2The first two examples come from tidyTuesday on the Economist’s Visualization Errors. It is really great. My complete set of plots appears here.
The data are a small table.
library(ggrepel)
library(tidyverse) # call the tidyverse for %>% and ggplot
library(ggthemes) # Use the economist theme
library(ggiraph)
library(DT)
corbyn <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-16/corbyn.csv")
datatable(corbyn)ggiraph of barplot and lollipop chartI want to plot this in a barplot that is linked with a lollipop chart.
p1.Int <- ggplot(corbyn, aes(x=reorder(political_group,avg_facebook_likes), data_id=political_group, y=avg_facebook_likes, label=avg_facebook_likes, tooltip = paste0(political_group,"<br>",avg_facebook_likes, sep=""))) +
geom_bar_interactive(stat="identity", fill="#DC241f") +
geom_label(fill="#DC241f", color="#FFFFFF", size=3.5) +
labs(x="Associate of the Labour Party", y="Average Facebook Likes") +
hrbrthemes::theme_ipsum_rc() +
coord_flip()
p2.Int <- ggplot(corbyn, aes(x=reorder(political_group,avg_facebook_likes), y=avg_facebook_likes, label=avg_facebook_likes, data_id=political_group, tooltip = paste0(political_group,"<br>",avg_facebook_likes, sep=""))) +
geom_segment_interactive(aes(yend=0, xend=political_group), size=2, color="#DC241f") +
geom_point(size=10, color="#DC241f", alpha=0.8) +
geom_text(color="#FFFFFF", size=3.5, fontface=2) +
labs(x="Associate of the Labour Party", y="Average Facebook Likes") +
hrbrthemes::theme_ipsum_rc() +
coord_flip()
library(patchwork)
GIp1 <- p1.Int / p2.Int
girafe(ggobj=GIp1)I had a simple example for last week. Now I want to make it nicer. The data are essentially a table. Let me show it using pivot_wider.
women_research <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-04-16/women_research.csv")
women_research# There's a spelling error I want to fix, select the rows and columns to fix and reassign the value
women_research[women_research$field=="Women inventores","field"] <- "Women Inventors"
# The label for CS/Maths is too long, also
women_research[women_research$field=="Computer science, maths","field"] <- "CS/Math"
# Make it wider
WIR.Wide <- women_research %>% pivot_wider(., id_cols=country, names_from=field, values_from=percent_women)
# A datatable
datatable(WIR.Wide)WIR.TTip <- WIR.Wide %>% mutate(text.for.tooltip = paste0(country, "<br> Health sciences: ", `Health sciences`, "<br> Physical sciences: ",`Physical sciences`, "<br> Engineering: ",Engineering,"<br> CS/Math: ", `CS/Math`, "<br> Women Inventors: ",`Women Inventors`, sep="")) %>% select(country, text.for.tooltip)
women_research <- women_research %>% left_join(., WIR.TTip)library(ggiraph)
women_research <- women_research %>% group_by(country) %>% mutate(Avg.Women.Percent = mean(percent_women)) %>% ungroup()
women_research$PercentWomen <- as.character(round(women_research$percent_women, 3))
p1 <- ggplot(women_research) +
aes(x = reorder(country,Avg.Women.Percent), fill = country, weight = percent_women, tooltip = text.for.tooltip, data_id = country) +
geom_bar_interactive() +
scale_fill_viridis_d(option = "cividis") +
guides(fill=FALSE) +
coord_flip() +
theme_minimal() +
theme(axis.text.y = element_text(angle = 45, hjust = 1, size=3)) +
labs(y = "Percent of Publications/Patents", x = "Country/Group", fill="Country/Grouping", title = "Publication and Invention by Women Around the World", subtitle = "Percent of Publications or Patents by Women", caption = "source: Economist Visualization Errors") +
facet_wrap(~field)
library(patchwork)
ggp1.changed <- ggplot(women_research) +
aes(x = field, y = reorder(country,Avg.Women.Percent), fill = percent_women, , data_id = country, tooltip=text.for.tooltip) +
geom_tile_interactive(size = 1L) +
scale_fill_viridis_c(option="cividis") +
labs(x = "Field or Occupation", y = "Country", fill = "% Women") +
theme_minimal(base_size = 10)
ggp1.changed <- ggp1.changed + theme(plot.background = element_rect(colour = "whitesmoke"))
comb1 <- p1 / ggp1.changed
girafe(code = print(comb1))Getting this to work wasn’t hard. Getting it to style properly in interaction with the bootstrap frame was a pain. I finally just went with a blank column in two places to make sure that names don’t get cut off.
library(htmltools)
library(crosstalk)
library(DT)
library(d3scatter)
# Load the data
Bonds <- read.csv(url("https://github.com/robertwwalker/DADMStuff/raw/master/BondFunds.csv"), row.names=1)
# Turn characters into factors
Bonds <- Bonds %>% mutate(Fees = as.factor(Fees), Risk = as.factor(Risk), Type = as.factor(Type))
# Create shared data object
shared_bonds <- SharedData$new(Bonds)
# Generate the bootsrap columns as three rows, page is 12 wide
bscols(widths=c(12,12,12),
# A d3 visualization
d3scatter(shared_bonds, ~Expense.Ratio, ~Return.2009, ~Risk),
# The filter checkboxes
list(filter_checkbox("Fees", "Fees", shared_bonds, ~Fees, inline=TRUE),
filter_checkbox("Type", "Type", shared_bonds, ~Type, inline=TRUE),
filter_checkbox("Fees", "Risk", shared_bonds, ~Risk, inline=TRUE)),
# The datatable
datatable(shared_bonds))Without the bootstrap formatting of the webpage, it works much better.
The trick to getting this to display properly was a blank column with width=1.
shared_bonds2 <- SharedData$new(Bonds)
# This was adjusted from above
bscols(widths=c(12,1,11,1,11),
d3scatter(shared_bonds2, ~Expense.Ratio, ~Return.2009, ~Risk),
"", # First blank column
list(filter_checkbox("Fees", "Fees", shared_bonds2, ~Fees, inline=TRUE),
filter_checkbox("Type", "Type", shared_bonds2, ~Type, inline=TRUE),
filter_checkbox("Fees", "Risk", shared_bonds2, ~Risk, inline=TRUE)),
"", # Second blank column
datatable(shared_bonds2))I have used reveal.js since the beginning of the term. There is a slides repository on my github showing how I have done pretty much all of this.
The pptx functionality is pretty limited as is PowerPoint. But it serves many useful purposes for Microsoft shops and that is very common.
If you want to know about beamer just ask. I use $\LaTeX$ for lots of things but that is a whole new [and arguably pretty useless] set of skills unless you need to give highly technical presentations with odd math formatting.
knitr::write_bib(names(sessionInfo()$otherPkgs), file="bibliography.bib")