I have a ts object: 240 monthly observations stating from January 2000:
data <- runif(240)
data_ts <- ts(data,
start = c(2000, 1),
frequency = 12)
And I want to create a boxplot per year out of my data_ts.
I know how to create a boxplot per month:
boxplot(data_ts ~ cycle(data_ts))
But I don't know how to create a boxplot per year, that is, a boxplot of the observations of each year (a boxplot of year 2000, a boxplot of 2001, and so on).
Any idea?
Thanks!
The year is given as shown:
year <- as.integer(time(data_ts))
boxplot(data_ts ~ year)
I use the window() function to subset the years, and a for() loop to iterate each year and create a boxplot(). The title() function adds the title to the plot, and png() and dev.off() work together to save the image to disk:
getwd() # print location files will be saved too.
for (i in 2010:2012) { # small loop for testing)
png(file=paste("boxplot_",i,".png",sep="")) # create a png
boxplot(window(x=data_ts, start=c(i, 1), end=c(i, 12))) # boxplot, of yearly data.
title(i) # add the year as a title to the plot
dev.off() # save the png
}
Maybe this also helps:
data <- runif(240)
data_ts <- ts(data,
start = c(2000, 1),
frequency = 12)
frame<-data.frame(values=as.matrix(data_ts), date=lubridate::year(zoo::as.Date(data_ts)))
library(ggplot2)
ggplot(frame,aes(y=values,x=date,group=date))+
geom_boxplot()
It is not the most elegant solution though as it uses both the zoo and lubridate packages to convert the date into a year that ggplot understands.
Related
Hi I try desperately to plot several time series with a 12 months moving average.
Here is an example with two time series of flower and seeds densities. (I have much more time series to work on...)
#datasets
taxon <- c(rep("Flower",36),rep("Seeds",36))
density <- c(seq(20, 228, length=36),seq(33, 259, length=36))
year <- rep(c(rep("2000",12),rep("2001",12),rep("2002",12)),2)
ymd <- c(rep(seq(ymd('2000-01-01'),ymd('2002-12-01'), by = 'months'),2))
#dataframe
df <- data.frame(taxon, density, year, ymd)
library(forecast)
#create function that does a Symmetric Weighted Moving Average (2x12) of the monthly log density of flowers and seeds
ma_12 <- function(x) {
ts_x <- ts(x, freq = 12, start = c(2000, 1), end = c(2002, 12)) # transform to time-series object as it is necessary to run the ma function
return(ma(log(ts_x + 1), order = 12, centre = T))
}
#trial of the function
ma_12(df[df$taxon=="Flower",]$density) #works well
library(ggplot2)
#Trying to plot flower and seeds log density as two time series
ggplot(df,aes(x=year,y=density,colour=factor(taxon),group=factor(taxon))) +
stat_summary(fun.y = ma_12, geom = "line") #or geom = "smooth"
#Warning message:
#Computation failed in `stat_summary()`:
#invalid time series parameters specified
Function ma_12 works correctly. The problem comes when I try to plot both time-series (Flower and Seed) using ggplot. I cannot define both taxa as different time series and apply a moving average on them. Seems that it has to do with "stat_summary"...
Any help would be more than welcome! Thanks in advance
Note: The following link is quite useful but can not directly help me as I want to apply a specific function and plot it in accordance to the levels of one group variable. For now, I can't find any solution. Any way, thank you to suggest me this.
Multiple time series in one plot
This is what you need?
f <- ma_12(df[df$taxon=="Flower", ]$density)
s <- ma_12(df[df$taxon=="Seeds", ]$density)
f <- cbind(f,time(f))
s <- cbind(s,time(s))
serie <- data.frame(rbind(f,s),
taxon=c(rep("Flower", dim(f)[1]), rep("Seeds", dim(s)[1])))
serie$density <- exp(serie$f)
library(lubridate)
serie$time <- ymd(format(date_decimal(serie$time), "%Y-%m-%d"))
library(ggplot2)
ggplot() + geom_point(data=df, aes(x=ymd, y=density, color=taxon, group=taxon)) +
geom_line(data=serie, aes(x= time, y=density, color=taxon, group=taxon))
R could be amazingly powerful and frustrating at the same time. This makes teaching R to non-statisticians (business students in my case) rather challenging. Let me illustrate this with a simple task.
Let's say you are working with a monthly time series dataset. Most business data are usually plotted as monthly time series. We would like to plot the data such that the x-axis depicts a combination of month and year. For instance, January 2017 could be depicted as 2017-01. It should be straightforward with the plot command. Not true.
Data Generation
Let's illustrate this with an example. I'll generate a random time series of monthly data for 120 observations representing 10 years of information starting in January 2007 and ending in December 2017. Here's the code.
set.seed(1234)
x <- rnorm(120)
d <-.07
y <- cumsum(x+d)*-1
Since we have not declared the data as time series, plotting it with the plot command would not return the intended labels for the x-axis. See the code and the chart below.
plot(y, type="l")
Now there should be an option in the plot or the plot.ts command to display the time series specific x-axis. I couldn't find one. So here's the workaround.
Declare the data set to be time series.
Use tsp and seq to generate the required x-axis labels.
Plot the chart but suppress x-axis.
Use the axis command to add the custom x-axis labels.
Add an extra step to draw a vertical line at 2012.
Here's the code.
my.ts <- ts(y, start=c(2007, 1), end=c(2017, 12), frequency=12)
tsp = attributes(my.ts)$tsp
dates = seq(as.Date("2007-01-01"), by = "month", along = my.ts)
plot(my.ts, xaxt = "n", main= "Plotting outcome over time",
ylab="outcome", xlab="time")
axis(1, at = seq(tsp[1], tsp[2], along = my.ts), labels = format(dates, "%Y-%m"))
abline(v=2012, col="blue", lty=2, lwd=2)
The result is charted below.
This is a workable solution for most data scientists. But if your audience comprises business students or professionals there are too many lines of code to write.
Question: Is it possible to plot a time series variable (object) using the plot command with the format option controlling how the x-axis will be displayed?
--
ggplot2 package has the scale_x_date function for plotting time series in desired scales, labels, breaks and limits (day, month, year formats).
All you need is date class object and values y. For eg.
dates = seq(as.Date("01-01-2007", format = "%d-%m-%Y"), length.out = 120, by = "month")
df <- data.frame(dates, y)
# use the format you need in your plot using scale_x_date
library(ggplot2)
ggplot(df, aes(dates, y)) + geom_line() + scale_x_date(date_labels = "%b-%Y") +
geom_vline(xintercept = as.Date("01-01-2012", format = "%d-%m-%Y"), linetype = 'dotted', color = 'blue')
I think the question boils down to wanting a pre-written function for the custom axis you have in mind. Note that plot(my.ts) does give a plot with ticks every month and labels every year which to me looks better than the plot shown in the question but if you want a custom axis since R is a programming language you can certainly write a simple function for that and from then on it's just a matter of calling that function.
For example, to get you started here is a function that accepts a frequency 12 ts object. It draws an X axis with ticks for each month labelling the years and each every'th month where the every argument can be a divisor of 12. The default is 3 so a label for every third month is shown (except Jan which is shown as the year). len is the number of letters of the month shown and can be 1, 2 or 3. 1 means show Jul as J, 2 means Ju and 3 means Jul. The default is 1.
xaxis12 <- function(ser, every = 3, len = 1) {
tt <- time(ser)
axis(side = 1, at = tt, labels = FALSE)
is.every <- cycle(ser) %in% seq(1, 12, every)[-1]
month.labs <- substr(month.abb[cycle(ser)][is.every], 1, len)
axis(side = 1, at = tt[is.every], labels = month.labs,
cex.axis = 0.7, tcl = -0.75)
is.jan <- cycle(ser) == 1
year.labs <- sprintf("'%02d", as.integer(tt)[is.jan] %% 100)
axis(side = 1, at = tt[is.jan], labels = year.labs,
cex.axis = 0.7, tcl = -1)
}
# test
plot(my.ts, xaxt = "n")
xaxis12(my.ts)
Gabor is spot-on. It really just depends on what you want, and what you are willing to dig up or alter. Here is a simple alternative using a newer and less-well-known package that is excellent for plotting xts types:
## alternative
library(rtsplot) # load the plotting package
library(xts) # load the xts time-series container package
xx <- as.xts(my.ts) # create an xts object
rtsplot(xx, main= "Plotting outcome over time")
rtsplot.x.highlight(xx, which(index(xx)=="Jan 2012"), 1)
As you can see, the plotting then is two calls -- rtsplot has lots of nice defaults. Below is a screenshot as I am lazy, the plot window does of course not have a title bar...
I have a continuous variable y measured on different dates. I need to make boxplots with a box showing the distribution of y for each 5 year interval.
Sample data:
rdob <- as.Date(dob, format= "%m/%d/%y")
ggplot(data = data, aes(x=rdob, y=ageyear)) + geom_boxplot()
#Warning message:
#Continuous x aesthetic -- did you forget aes(group=...)?
This image is the first one I tried. What I want is a box for every five year interval, instead of a box for every year.
Here is a way to pull out the year in base R:
format(as.Date("2008-11-03", format="%Y-%m-%d"), "%Y")
Simply wrap your date vector in a format() and add the "%Y". To get this to be integer, you can use as.integer.
You could also take a look at the year function in the lubridate package which will make this extraction a little bit more straightforward.
One method to get 5 year intervals is to use cut to create a factor variable that creates levels at selected break points. Unless you have dozens of years your best bet would be to set the break points manually:
df$myTimeInterval <- cut(df$years, breaks=c(1995, 2000, 2005, 2010, 2015))
Here's an example taking Dave2e's suggestion of using cut on date intervals along with ggplot's group aesthetic mapping:
library(ggplot2)
n <- 1000
## Randomly sample birth dates and dummy up an effect that trends upward with DOB
dobs <- sample(seq(as.Date('1970/01/01'), Sys.Date(), by="day"), n)
effect <- rnorm(n) + as.numeric(as.POSIXct(dobs)) / as.numeric(as.POSIXct(Sys.Date()))
data <- data.frame(dob=dobs, effect=effect)
## boxplot w/ DOB binned to 5 year intervals
ggplot(data=data, aes(x=dob, y=effect)) + geom_boxplot(aes(group=cut(dob, "5 year")))
library(lubridate)
year=year(rdob)
I have a daily time series about number of visitors on the web site. my series start from 01/06/2014 until today 14/10/2015 so I wish to predict number of visitor for in the future. How can I read my series with R? I'm thinking:
series <- ts(visitors, frequency=365, start=c(2014, 6))
if yes,and after runing my time series model arimadata=auto.arima() I want to predict visitor's number for the next 6o days, how can i do this?
h=..?
forecast(arimadata,h=..),
the value of h shoud be what ?
thanks in advance for your help
The ts specification is wrong; if you are setting this up as daily observations, then you need to specify what day of the year 2014 is June 1st and specify this in start:
## Create a daily Date object - helps my work on dates
inds <- seq(as.Date("2014-06-01"), as.Date("2015-10-14"), by = "day")
## Create a time series object
set.seed(25)
myts <- ts(rnorm(length(inds)), # random data
start = c(2014, as.numeric(format(inds[1], "%j"))),
frequency = 365)
Note that I specify start as c(2014, as.numeric(format(inds[1], "%j"))). All the complicated bit is doing is working out what day of the year June 1st is:
> as.numeric(format(inds[1], "%j"))
[1] 152
Once you have this, you're effectively there:
## use auto.arima to choose ARIMA terms
fit <- auto.arima(myts)
## forecast for next 60 time points
fore <- forecast(fit, h = 60)
## plot it
plot(fore)
That seems suitable given the random data I supplied...
You'll need to select appropriate arguments for auto.arima() as suits your data.
Note that the x-axis labels refer to 0.5 (half) of a year.
Doing this via zoo
This might be easier to do via a zoo object created using the zoo package:
## create the zoo object as before
set.seed(25)
myzoo <- zoo(rnorm(length(inds)), inds)
Note you now don't need to specify any start or frequency info; just use inds computed earlier from the daily Date object.
Proceed as before
## use auto.arima to choose ARIMA terms
fit <- auto.arima(myts)
## forecast for next 60 time points
fore <- forecast(fit, h = 60)
The plot though will cause an issue as the x-axis is in days since the epoch (1970-01-01), so we need to suppress the auto plotting of this axis and then draw our own. This is easy as we have inds
## plot it
plot(fore, xaxt = "n") # no x-axis
Axis(inds, side = 1)
This only produces a couple of labeled ticks; if you want more control, tell R where you want the ticks and labels:
## plot it
plot(fore, xaxt = "n") # no x-axis
Axis(inds, side = 1,
at = seq(inds[1], tail(inds, 1) + 60, by = "3 months"),
format = "%b %Y")
Here we plot every 3 months.
Time Series Object does not work well with creating daily time series. I will suggest you use the zoo library.
library(zoo)
zoo(visitors, seq(from = as.Date("2014-06-01"), to = as.Date("2015-10-14"), by = 1))
Here's how I created a time series when I was given some daily observations with quite a few observations missing. #gavin-simpson gave quite a big help. Hopefully this saves someone some grief.
The original data looked something like this:
library(lubridate)
set.seed(42)
minday = as.Date("2001-01-01")
maxday = as.Date("2005-12-31")
dates <- seq(minday, maxday, "days")
dates <- dates[sample(1:length(dates),length(dates)/4)] # create some holes
df <- data.frame(date=sort(dates), val=sin(seq(from=0, to=2*pi, length=length(dates))))
To create a time-series with this data I created a 'dummy' dataframe with one row per date and merged that with the existing dataframe:
df <- merge(df, data.frame(date=seq(minday, maxday, "days")), all=T)
This dataframe can be cast into a timeseries. Missing dates are NA.
nts <- ts(df$val, frequency=365, start=c(year(minday), as.numeric(format(minday, "%j"))))
plot(nts)
series <- ts(visitors, frequency=365, start=c(2014, 152))
152 number is 01-06-2014 as it start from 152 number because of frequency=365
To forecast for 60 days, h=60.
forecast(arimadata , h=60)
I want to produce seasonal boxplots for a lot of different time series. I hope that the code below clearly illustrates what I want to do.
My question is now, how to do this in the most elegant way with as few lines of code as possible. I can create an new object for each month with the function "subset" and then plot it, but this seems to be not very elegant. I tried to use the "split" function, but I don't know, how to proceed from there.
Please tell me if my question is not clearly stated or edit it to make it clearer.
Any direct help or linkage to other websites/posts is greatly appreciated. Thanks for your time.
Here is the code:
## Create Data
Time <- seq(as.Date("2003/8/6"), as.Date("2011/8/5"), by = "2 weeks")
data <- rnorm(209, mean = 15, sd = 1)
DF <- data.frame(Time = Time, Data = data)
DF[,3] <- as.numeric(format(DF$Time, "%m"))
colnames(DF)[3] <- "Month"
## Create subsets
Jan <- subset(DF, Month == 1)
Feb <- subset(DF, Month == 2)
Mar <- subset(DF, Month == 3)
Apr <- subset(DF, Month == 4)
## Create boxplot
months <- c("Jan", "Feb", "Mar", "Apr")
boxplot(Jan$Data, Feb$Data, Mar$Data, Apr$Data, ylab = "Data", xlab = "Months", names = months)
## Try with "split" function
DF.split <- split(DF, DF$Month)
head(DF.split)
Using 'ggplot2' (and #James' month names, thanks!):
DF$month <- factor(strftime(DF$Time,"%b"),levels=month.abb)
ggplot(DF, aes(x=,month, y=Data)) +
geom_boxplot()
(BTW: note that in 'ggplot2' "The upper and lower "hinges" correspond to the first and third quartiles (the 25th and 7th percentiles). This differs slightly from the method used by the boxplot function, and may be apparent with small samples." - see documentation)
You are better off picking out the month names directly with the "%b" format and using an ordered factor and the formula interface for boxplot:
DF$month <- factor(strftime(DF$Time,"%b"),levels=month.abb)
boxplot(Data~month,DF)
To set months as ordered factor in any locale settings use a trick which can be found in help page for ?month.abb:
Sys.setlocale("LC_TIME", "German_Germany")
DF$month <- factor(format(DF$Time, "%b"), levels=format(ISOdate(2000, 1:12, 1), "%b"))
And you could plot it in lattice as well:
require(lattice)
bwplot(Data~month, DF, pch="|") # set pch to nice line instead of point