I have vector that contains the stock market returns. I would like to add a normal distribution to the plot in highcharts. I have tried using dnorm() but cant seem to figure out how to plot it on the graph. I have made a work around by creating a vector using rnorm(). That said, I have to use 20k points in order for it to remove the bumps. This is time consuming.
Here is my current workaround
library(quantmod)
library(highcharter)
library(tidyverse)
getSymbols("SPY")
returns = na.omit(coredata(ROC(Ad(SPY))))
r = rnorm(20000, mean = 0, sd = sd(returns))
hchart(
density(returns), type = "area",
name = "SPY"
) %>%
hc_add_series(
density(r), type = "area",
name = "Normal"
)
Related
I have probably a simple R plotly question but I spent about one hour reading questions in stackoverflow and I really can't find what I need. I have a dataframe (I will share a screenshot) with different columns used to create a multiple lines plotly chart.
This is the code I use to create the plot:
plot_ly(data = df_final, x=~TENOR, y=~RATE) %>% add_trace(type='scatter',mode='lines', color=~LINE_NAME, colors = ~LINE_COL) %>%
layout(title=paste0("Market data"),
xaxis=list(title='Term (years)'),
yaxis=list(title='Yield'))
it works amazing but I would like to have the option to choose if some lines will have to be dashed, dots, or solid lines as well as their width.
I would need / want to specify this information inside the dataframe and choose the dataframe column that has such information (i.e. see the column "LINE_STYLE_FACTOR" in my attached dataframe).
I checked Multiple line chart using plotly r and Plotly r, line style by variable but I can't find how to do what I need.
The solution has to use plotly and not other charting solutions.
Thanks
At least for the line types (dash vs line), you can you 'linetype':
library(dplyr)
library(plotly)
df = data.frame(xVals = rep(1:10,2),
yVals = c(1:10, 2:11),
myColor = c(rep('Red', 10), rep('Blue', 10)),
myType = c(rep('solid', 10), rep('dot', 10)),
myName = c(rep('FirstName', 10), rep('SecondName', 10)))
plot_ly(df,
x = ~xVals,
y = ~yVals,
color = ~I(myColor),
name = ~myName,
type = 'scatter',
mode = 'lines',
linetype = ~I(myType)
)
I made some interactive heatmaps using leaflet (particularly the addHeatmap() command from the leaflet.extras package) and shiny. Having created a desired map, I would like to add a legend to it.
What I am interested in is a "rgb" legend, based on density values deduced by addHeatmap() from pure long/lat coords.
What I need is something like this map - https://www.patrick-wied.at/static/heatmapjs/example-legend-tooltip.html - unfortunately I have no knowledge of JS and can't rewrite this code in terms of R/include the right fragment of JS code for my problem.
What I tried so far is the addLegend() command, which does not give the desired result, as in this case I would need to specify a variable for which the legend would be prepared. I also tried to extract the color range and assigned values from created leaflet object, however with no success.
Here's link to full data to run the reproducible example on:
https://drive.google.com/file/d/1h3jL_PU6DGTtdIWBK02Tt37R7IB2ArH9/view
And here's top 20 records:
structure(list(latitude = c(30.309522, 30.24429616, 30.30038194,
30.27752338, 30.23294081, 30.23038507,
30.34285933, 30.24962237, 30.26594744,
30.20515821, 30.22363485, 30.2759184,
30.28283226, 30.33816909, 30.26611565,
30.18835401, 30.26704789, 30.27456699,
30.19237135, 30.1925213),
longitude = c(-97.73171047, -97.77446858, -97.77885789,
-97.71919076, -97.58937812, -97.76581095,
-97.73598704, -97.72215443, -97.74144275,
-97.8782895, -97.78329845, -97.71321066,
-97.70820152, -97.82413058, -97.7327258,
-97.81606795, -97.68989589, -97.7580592,
-97.7816127, -97.73138523)),
.Names = c("latitude", "longitude"), row.names =
c(NA, 20L), class = "data.frame")
Here's an example code, which I'd like to extend by the mentioned functionality:
library(magrittr)
library(leaflet)
library(leaflet.extras)
data <- read.csv('DATA.csv')
leaflet(data) %>%
addTiles(group="OSM") %>%
addHeatmap(group="heat", lng = ~longitude, lat = ~latitude, max=.5, blur = 60)
And here is the result of that code (on whole dataset):
https://i.stack.imgur.com/6VFNC.jpg
So to sum up what I would like to do: based on such picture I would like to extract the range of the drawn colors along with values assigned to them, and draw legend using that information.
Is there something I am missing? It looks like a pretty simple issue, but I've been struggling to find a solution for past few hours.
Thanks in advance for any help!
EDIT: extended the reproducible example.
Your sample data does not have any values to it to actually map a density overlay.
You can specify the number of bins with colorBin() and then specify those bins with your pal function. You can set the bins differently depending on your needs at the data_values distributions. The help section of colorBin() is helpful in identifying the correct parameters for your needs.
bins <- c(0,1,2,3,4)
pal <- colorBin("Spectral", domain = data_value, bins = bins, na.color = "transparent")
m <-leaflet() %>%
addTiles() %>%
addHeatmap(lng= long_cords, lat = lat_cords, intensity = data_value,
blur = 20, max = 400, radius = 15, cellSize = 3) %>%
addLegend(pal = pal, values = data_value,
title="Heat map legend")
You'll have to play around with the addHeatmap arguments to get an the right transparency and density settings.
I am trying to create hexbins where the x-axis is a date using the hexbin function in the hexbin package in R. When I feed in my data, it seems to convert the dates into a numeric, which gets displayed on the x-axis. I want it force the x-axis to be a date.
#Create Hex Bins
hbin <- hexbin(xData$Date, xData$YAxis, xbins = 80)
#Plot using rBokeh
figure() %>%
ly_hexbin(hbin)
This gives me:
Here's a brute force approach using the underlying grid plotting package. The axes are ugly; maybe someone with better grid skills than I could pretty them up.
# make some data
x = seq.Date(as.Date("2015-01-01"),as.Date("2015-12-31"),by='days')
y = sample(x)
# make the plot and capture the plot
p <- plot(hexbin(x,y),yaxt='n',xaxt='n')
# calculate the ticks
x_ticks_date <-
x_ticks <- axTicks(1, log = FALSE, usr = as.numeric(range(x)),
axp=c(as.numeric(range(x)) ,5))
class(x_ticks_date) <- 'Date'
y_ticks_date <-
y_ticks <- axTicks(1, log = FALSE, usr = as.numeric(range(y)),
axp=c(as.numeric(range(y)) ,5))
class(y_ticks_date) <- 'Date'
# push the ticks to the view port.
pushViewport(p$plot.vp#hexVp.off)
grid.xaxis(at=x_ticks, label = format(y_ticks_date))
grid.yaxis(at=y_ticks, label = format(y_ticks_date))
Using the package manipulate in Rstudio, I'm trying to create a scatterplot in which I can select among several data frames using a picker and then using sliders I control the columns I'd like to plot for each axis. For example, using these two datasets: mtcars and iris.
library(manipulate)
manipulate(
plot(dataset[, xaxis] ~ dataset[, yaxis],
dataset,
xlab = colnames(dataset)[xaxis],
ylab = colnames(dataset)[yaxis],
main = title),
xaxis = slider(1, 10),
yaxis = slider(1, 10),
dataset = picker("mtcars" = mtcars, "iris" = iris),
title = picker("mtcars", "iris")
)
It works ok, however, I'm struggling with two questions:
How to change dynamically the title of the plot based on the selected dataset (mtcars or iris) instead of manually using another picker as I do in the example above. I'm unable to get the name of the selected data frame and pass it as a character title.
How can I determine dynamically the max argument of the sliders, instead of hardcoding in the sliders from 1 to 10. For example mtcars has 11 columns and iris 5. Or better still, select the columns for each axis by name. I've tried many different ways but I think the problem is that I can't pass variables used in a control (dataset) to others (sliders). For example, this generates an error:
xaxis = slider(1,as.numeric(dim(dataset)[2]))
I took a different approach, after reading an example included in Learning RStudio for R Statistical Computing by Mark P. J. Van der Loo. I wrote a function that solves my problems:
library(manipulate)
scatterplot <- function(dataset){
vars <- as.list(names(dataset))
name <- sys.call()[[2]]
manipulate(
plot(dataset[, xaxis] ~ dataset[, yaxis],
xlab = colnames(dataset)[xaxis],
ylab = colnames(dataset)[yaxis],
main = as.character(name)),
xaxis = slider(1, as.numeric(dim(dataset)[2]), initial = 1),
yaxis = slider(1, as.numeric(dim(dataset)[2]), initial = 2)
)
}
scatterplot(mtcars)
I've been trying to write out an R script that will plot the date-temp series for a set of locations that are identified by a Deployment_ID.
Ideally, each page of the output pdf would have the name of the Deployment_ID (check), a graph with proper axes (check) and correct scaling of the x-axis to best show the date-temp series for that specific Deployment_ID (not check).
At the moment, the script makes a pdf that shows each ID over the full range of the dates in the date column (i.e. 1988-2010), instead of just the relevant dates (i.e. just 2005), which squishes the scatterplot down into uselessness.
I'm pretty sure it's something to do with how you define xlim, but I can't figure out how to have R access the date min and the date max for each factor as it draws the plots.
Script I have so far:
#Get CSV to read data from, change the file path and name
data <- read.csv(file.path("C:\Users\Person\Desktop\", "SampleData.csv"))
#Make Date real date - must be in yyyy/mm/dd format from the csv to do so
data$Date <- as.Date(data$Date)
#Call lattice to library, note to install.packages(lattice) if you don't have it
library(lattice)
#Make the plots with lattice, this takes a while.
dataplot <- xyplot(data$Temp~data$Date|factor(data$Deployment_ID),
data=data,
stack = TRUE,
auto.key = list(space = "right"),
layout = c(1,1),
ylim = c(-10,40)
)
#make the pdf
pdf("Dataplots_SampleData.pdf", onefile = TRUE)
#print to the pdf? Not really sure how this works. Takes a while.
print(dataplot)
dev.off()
Use the scales argument. give this a try
dataplot <- xyplot(data$Temp~data$Date|factor(data$Deployment_ID),
data=data,
stack = TRUE,
auto.key = list(space = "right"),
layout = c(1,1),
scales= list( relation ="free")
)