Still very new to using rCharts and R, so please excuse me if the question sounds very stupid!
I'm trying to plot a time series chart using Quarter labels along the x-axis, simple example:
quarters <- c("Q413","Q313","Q213","Q13")
values <- c("120","40","60","80")
testing = data.frame(quarters,values)
tfrPlot <- nPlot(x="quarter", y="values", data = testing, type = "lineChart")
But this doesn't plot the graph and instead generates value between -1 and 1 on the x-axis. I made sure the quarters were factors as well, so I don't know how to proceed.
The error is primarily caused by a typo x="quarter which should be x=quarters, but even with this we will have errors. nvd3 with lineChart expects y to be numeric or continuous, so values<-as.numeric(c("120","40","60","80")) will also be required. Then one last thing the date conversion from R to Javascript in rCharts is still not optimal. One way to force it to work would be to pass the date in numeric form and then tell nvd3 how to handle it. Here is an example:
quarters <- as.Date(c("2013-03-31", "2013-06-30", "2013-09-30", "2013-12-31"))
values <- as.numeric(c("120","40","60","80"))
testing = data.frame(quarters,values)
tfrPlot <- nPlot(x="quarters", y="values", data = testing, type = "lineChart")
tfrPlot$xAxis(
tickFormat =
"#! function(d) {
return d3.time.format('%b %Y')(new Date(d * 24 * 60 * 60 * 1000))
} !#"
)
tfrPlot
You will probably agree this is more painful than it should be, and we are working on a much better way to handle this.
Related
<Disclaimer(s) - (1) This is my first post, so please be gentle, specifically regarding formatting and (2) I did try to dig as much as I could on this topic before posting the question here>
I have a simple data vector containing returns of 40 portfolios on the same day:
Year Return
Now -17.39862061
Now -12.98954582
Now -12.98954582
Now -12.86928749
Now -12.37044334
Now -11.07007504
Now -10.68971539
Now -10.07578182
Now -9.984867096
Now -8.764036179
Now -8.698093414
Now -8.594026566
Now -8.193638802
Now -7.818599701
Now -7.622627735
Now -7.535216808
Now -7.391239166
Now -7.331315517
Now -5.58059597
Now -5.579797268
Now -4.525201797
Now -3.735909224
Now -2.687532902
Now -2.65363884
Now -2.177522898
Now -1.977644682
Now -1.353205681
Now -0.042584345
Now 0.096564181
Now 0.275416046
Now 0.638839543
Now 1.959529042
Now 3.715519428
Now 4.842819691
Now 5.475946426
Now 6.380955219
Now 6.535937309
Now 8.421762466
Now 8.556800842
Now 10.39185524
I am trying to plot these returns to compare versus other days (so the rest of my history e.g.). I tried to use stat_density_ridges as per the code block below
ggplot(data = data.plot, aes(x = Return, y = Year, fill = factor(..quantile..))) +
stat_density_ridges(geom = "density_ridges_gradient",calc_ecdf = TRUE,
quantiles = c(0.025, 0.5, 0.975),
quantile_lines = TRUE)
As you can see - the "year" in this case is the same i.e. there is no height parameter, yet I get a nice ridg(y) chart. While the chart is beautiful to behold, and very very awesome, I am at a loss to determine how the plotting function is computing the density in this case, specially the height.
This is the output chart I get (I have omitted the formatting code here since it doesn't make a difference to my question):
Portfolio Return Distribution Plots - US versus Europe
I tried digging into the code of the function itself, but came up with a total blank. The documentation didn't help (except perhaps give me a hint that the function plots continous distributions).
Any help, or guidance, or even a nudge in the right direction would be extremely helpful.
I'm trying to convert some simple data into a form I thought ggplot2 would accept.
I snag some simple stock data and now I just want to plot, later I want to plot say a 10-day moving average or a 30-day historical volatility period to go with it, which is I'm using ggplot.
I thought it would work something like this line of pseudocode
ggplot(maindata)+geom_line(moving average)+geom_line(30dayvol)
library(quantmod)
library(ggplot2)
start = as.Date("2008-01-01")
end = as.Date("2019-02-13")
start
tickers = c("AMD")
getSymbols(tickers, src = 'yahoo', from = start, to = end)
closing_prices = as.data.frame(AMD$AMD.Close)
ggplot(closing_prices, aes(y='AMD.Close'))
But I can't even get this to work. The problem of course appears to be that I don't have an x-axis. How do I tell ggplot to use the index column as a. Can this not work? Do I have to create a new "date" or "day" column?
This line for instance using the Regular R plot function works just fine
plot.ts(closing_prices)
This works without requiring me to enter a hard x-axis, and produces a graph, however I haven't figured out how to layer other lines onto this same graph, evidently ggplot is better so I tried that.
Any advice?
as.Date(rownames(df)) will get you the rownames and parse it as a date. You also need to specify a geom_line()
library(quantmod)
library(ggplot2)
start = as.Date("2008-01-01")
end = as.Date("2019-02-13")
start
tickers = c("AMD")
getSymbols(tickers, src = 'yahoo', from = start, to = end)
closing_prices = as.data.frame(AMD$AMD.Close)
ggplot(closing_prices, aes(x = as.Date(rownames(closing_prices)),y=AMD.Close))+
geom_line()
Edit
Thought it would be easier to explain in the answers as opposed to the comments.
ggplot and dplyr have two methods of evaluation. Standard and non standard evaluation. Which is why in ggplot you have both aes and aes_(). The former being non standard evaluation and the later being standard evaluation. In addition there is also aes_string() which is also standard evaluation.
How are these different?
Its easy to see when we explore all the methods,
#Cleaner to read, define every operation in one step
#Non Standard Evaluation
closing_prices%>%
mutate(dates = as.Date(rownames(.)))%>%
ggplot()+
geom_line(aes(x = dates,y = AMD.Close))
#Standard Evaluation
closing_prices%>%
mutate(dates = as.Date(rownames(.)))%>%
ggplot()+
geom_line(aes_(x = quote(dates),y = quote(AMD.Close)))
closing_prices%>%
mutate(dates = as.Date(rownames(.)))%>%
ggplot()+
geom_line(aes_string(x = "dates",y = "AMD.Close"))
Why are there so many different ways of doing the same thing? In most cases its okay to use non standard evaluation. However if we want to wrap these plots in functions and dynamically change the column to plot based on function parametrs passed as strings. It is helpful to plot using the aes_ and aes_string.
I have a partial success with
input = "date,data
1-1-2015,5.5
2-1-2016,1.0
3-1-2016,4.0
4-1-2016,4.0
5-1-2019,3.0"
new = read.csv(text=input)
new$date = as.Date(new$date, "%d-%m-%Y")
new$date = as.numeric(new$date, as.Date("2015-01-01"), units="days") #https://stat.ethz.ch/pipermail/r-help/2008-May/162719.html
plot(density(new$date))
Resulting in working graph, unfortunately x axis is obviously formatted as integers. How can I produce graph with x axis formatted as data?
I expected
new = read.csv(text=input)
new$date = as.Date(new$date, "%d-%m-%Y")
plot(density(new$date))
to work, unfortunately it crashed with Error in density.default(new$date) : argument 'x' must be numeric.
density() wasn't really optimized to work with dates. The easiest fix would probably be to just replace the default axis labeling with date values. Here's how you can do that
plot(density(new$date), xaxt="n")
at<-axTicks(1)
axis(1,at, as.Date(at, origin="1970-01-01"))
I have a monthly time series data set from 2013-07 to 2014-07 and I'm trying to plot a bar chart based on that data set in nPlot from rCharts. The plot looks all right however the ticks of x axis only shows 2013-09, 2013-12, 2014-03, 2014-06. So what can I do to change the ticks and show all the month (e.g. 2013-07, 2013-08, 2013-09, ..., 2014-07).
Suppose my rChart plot is called n1. Do I need to change something in n1$xAxis or n1$chart? If so how do I make this modification.
Thanks if there is any suggestions
To accomplish this you will need a little Javascript. I tried to comment in the code what you might want to change. You could also supply an array of values in the tickValues.
library(rCharts)
data(economics, package = 'ggplot2')
p6 <- nPlot(uempmed ~ date, data = economics, type = 'lineChart')
p6$xAxis(
tickFormat = "#!function(d){return d3.time.format('%b %Y')(new Date(d*60*60*24*1000))}!#"
#for everything
#,tickValues = "#!data[0].values.map(function(v){return v[opts.x]})!#"
#for something potentially more sensible
,tickValues = "#!data[0].values
.map(function(v){
return v[opts.x]
})
.filter(function(v){
return d3.time.format('%m')(new Date(v*60*60*24*1000)) == '12' && +d3.time.format('%Y')(new Date(v*60*60*24*1000)) % 10 == 0
})!#"
)
p6
how can I have a data set of only time intervals (no dates) in R, like the following:
TREATMENT_A TREATMENT_B
1:01:12 0:05:00
0:34:56 1:08:09
and compute mean times, etc, and draw boxplots with time intervals in the y-axis?
I am new to R, and I searched for this but found no example in the net.
Thanks
The chron-package has a 'times' class that supports arithmetic. You could also do all of that with POSIXct objects and format the date-time output to not include the date. I thought axis.POSIXct function has a format argument that should let you have time outputs. However, it does not seem to get dispatched properly, so I needed to construct the axis "by hand."
dft <- data.frame(x= factor( sample(1:2, 100, repl=TRUE)),
y= Sys.time()+rnorm(100)*4000 )
boxplot(y~x, data=dft, yaxt='n')
axis(2, at=seq(from=range(dft$y)[1], to =range(dft$y)[2], by=3000) ,
labels=format.POSIXct(seq(from=range(dft$y)[1], to =range(dft$y)[2], by=3000),
format ="%H:%M:%S") )
There did turn out to be an appropriate method, Axis.POSIXt (to which I thought boxplot should have been turning for plotting, but it did not seem to recognize the class of the 'y' argument):
boxplot(y~x, data=dft, yaxt='n')
Axis(side=2, x=range(dft$y), format ="%H:%M:%S")
Regarding your request for something "simpler", take a look at theis ggplot2 based solution, using the dft dataframe defined above with POSIXct times. (I did try with the chron-times object but got a message saying ggplot did not support that class):
require(ggplot2); p <- ggplot(dft, aes(x,y))
p + geom_boxplot()
Check out the "lubridate" package, and the "hms" function within it.