Last updated: 2026-03-12 02:08:48.443933
Timezone: America/Los_Angeles
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# A tibble: 60 × 3
country field percent_women
<chr> <chr> <dbl>
1 Japan Health sciences 0.24
2 Chile Health sciences 0.43
3 United Kingdom Health sciences 0.45
4 United States Health sciences 0.46
5 Mexico Health sciences 0.46
6 Denmark Health sciences 0.47
7 EU28 Health sciences 0.48
8 France Health sciences 0.48
9 Canada Health sciences 0.49
10 Australia Health sciences 0.5
# ℹ 50 more rows# 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)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 = PercentWomen, 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=6)) +
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 = country, fill = percent_women, , data_id = country, tooltip=percent_women) +
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(scatterD3)
# 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
# A d3 visualization
scatterD3(Bonds, x=Expense.Ratio, y=Return.2009)Without the bootstrap formatting of the webpage, it works much better.
knitr::write_bib(names(sessionInfo()$otherPkgs), file="bibliography.bib")