I have created a time series using zoo. It has daily values for a long period of time (40 years). I can easily plot it, but what I want is to create a time series with monthly (mean) values from this original time series and then plot it as monthly values.
I thought the package lubridate could be a good option for this and maybe there is an easy way but I don't see how. I'm a beginner in R. Has somebody a tip here?
You can use apply.monthly() from the xts package.
library(xts)
data(sample_matrix)
x <- as.xts(sample_matrix, dateFormat = "Date")
(m <- apply.monthly(x, mean))
# Open High Low Close
# 2007-01-31 50.21140 50.31528 50.12072 50.22791
# 2007-02-28 50.78427 50.88091 50.69639 50.79533
# 2007-03-31 49.53185 49.61232 49.40435 49.48246
# 2007-04-30 49.62687 49.71287 49.53189 49.62978
# 2007-05-31 48.31942 48.41694 48.18960 48.26699
# 2007-06-30 47.47717 47.57592 47.38255 47.46899
You might also want to convert your index from Date to yearmon, which you can do like this:
index(m) <- as.yearmon(index(m))
m
# Open High Low Close
# Jan 2007 50.21140 50.31528 50.12072 50.22791
# Feb 2007 50.78427 50.88091 50.69639 50.79533
# Mar 2007 49.53185 49.61232 49.40435 49.48246
# Apr 2007 49.62687 49.71287 49.53189 49.62978
# May 2007 48.31942 48.41694 48.18960 48.26699
# Jun 2007 47.47717 47.57592 47.38255 47.46899
You can use aggregate.zoo as shown in the examples:
x2a <- aggregate(x, as.Date(as.yearmon(time(x))), mean)
if you want to stick to zoo.
Related
Both maxMATX and maxZIM return no observation, which I am very confused about.
Here is the code
library(tseries)
\#teries have all the Financial Data , hence we need to load it
data.ZIM\<- get.hist.quote("ZIM")
data.MATX\<- get.hist.quote("MATX")
data.ZIM\<-data.ZIM\[Sys.Date()-0:364\]
data.MATX\<-data.MATX\[Sys.Date()-0:364\]
head(data.ZIM)
head(data.MATX)
min(data.ZIM$Close)
max(data.ZIM$Close)
minZIM=data.ZIM\[data.ZIM$Close==24.34\]
maxZIM=data.ZIM\[data.ZIM$Close==88.62\]
data.ZIM\[data.ZIM$Close==88.62\]
minZIM
maxZIM
min(data.MATX$Close)
max(data.MATX$Close)
minMATX=data.MATX\[data.MATX$Close==60.07,\]
maxMATX=data.MATX\[data.MATX$Close==121.47,\]
minMATX
maxMATX
I was trying to extract the data from Tseries and I have faced difficulty when trying to print the row (or specifically I was trying to find the date of which the 52 weeks low and high was happening ).
Use which.min and which.max to find indexes of minimum and maximum close and use those to look up the time.
library(tseries)
data.ZIM <- get.hist.quote("ZIM", start = Sys.Date() - 364)
tmin <- time(data.ZIM)[which.min(data.ZIM$Close)]; tmin
## [1] "2021-03-31"
data.ZIM[tmin]
## Open High Low Close
## 2021-03-31 24.75 24.99 24.15 24.34
I am learning time series analysis with R and came across these 2 functions while learning. I do understand that the output of both of these is a periodic data defined by the frequency of period and the only difference I can see is the OHLC output option in the to.period().
Other than the OHLC when a particular of these functions is to be used?
to.period and all the to.minutes, to.weekly, to.quarterly are indeed meant for OHLC data.
If you take the function to.period it will take the open from the first day of the period, the close of the last day of the period and the highest high / lowest low of the specified period. These functions work very well together with the quantmod / tidyquant / quantstrat packages. See code example 1.
If you give the to.period non-OHLC data, but a timeseries with 1 data column, you still get a sort of OHLC back. See code example 2.
Now period.apply is is more interesting. Here you can supply your own functions to be applied on the data. Especially in combination with endpoints this can be a powerful function in timeseries data if you want to aggregate your function to different time periods. The index is mostly specified with endpoints, since with endpoints you can create the index you need to get to higher time levels (from day to week / etc etc). See code example 3 and 4.
Remember to use matrix functions with period.apply if you have more than 1 column of data since xts is basicly a matrix and an index. See code example 5.
More info on this data.camp course.
library(xts)
data(sample_matrix)
zoo.data <- zoo(rnorm(31)+10,as.Date(13514:13744,origin="1970-01-01"))
# code example 1
to.quarterly(sample_matrix)
sample_matrix.Open sample_matrix.High sample_matrix.Low sample_matrix.Close
2007 Q1 50.03978 51.32342 48.23648 48.97490
2007 Q2 48.94407 50.33781 47.09144 47.76719
# same as to.quarterly
to.period(sample_matrix, period = "quarters")
sample_matrix.Open sample_matrix.High sample_matrix.Low sample_matrix.Close
2007 Q1 50.03978 51.32342 48.23648 48.97490
2007 Q2 48.94407 50.33781 47.09144 47.76719
# code example 2
to.period(zoo.data, period = "quarters")
zoo.data.Open zoo.data.High zoo.data.Low zoo.data.Close
2007-03-31 9.039875 11.31391 7.451139 10.35057
2007-06-30 10.834614 11.31391 7.451139 11.28427
2007-08-19 11.004465 11.31391 7.451139 11.30360
# code example 3 using base standard deviation in the chosen period
period.apply(zoo.data, endpoints(zoo.data, on = "quarters"), sd)
2007-03-31 2007-06-30 2007-08-19
1.026825 1.052786 1.071758
# self defined function of summing x + x for the period
period.apply(zoo.data, endpoints(zoo.data, on = "quarters"), function(x) sum(x + x) )
2007-03-31 2007-06-30 2007-08-19
1798.7240 1812.4736 993.5729
# code example 5
period.apply(sample_matrix, endpoints(sample_matrix, on = "quarters"), colMeans)
Open High Low Close
2007-03-31 50.15493 50.24838 50.05231 50.14677
2007-06-30 48.47278 48.56691 48.36606 48.45318
I intend to perform a time series analysis on my data set. I have imported the data (monthly data from January 2015 till December 2017) from a csv file and my codes in RStudio appear as follows:
library(timetk)
library(tidyquant)
library(timeSeries)
library(tseries)
library(forecast)
mydata1 <- read.csv("mydata.csv", as.is=TRUE, header = TRUE)
mydata1
date pkgrev
1 1/1/2015 39103770
2 2/1/2015 27652952
3 3/1/2015 30324308
4 4/1/2015 35347040
5 5/1/2015 31093119
6 6/1/2015 20670477
7 7/1/2015 24841570
mydata2 <- mydata1 %>%
mutate(date = mdy(date))
mydata2
date pkgrev
1 2015-01-01 39103770
2 2015-02-01 27652952
3 2015-03-01 30324308
4 2015-04-01 35347040
5 2015-05-01 31093119
6 2015-06-01 20670477
7 2015-07-01 24841570
class(mydata2)
[1] "data.frame"
It is when running this piece of code that things get a little weird (for me at least):
mydata2_ts <- ts(mydata2, start=c(2015,1), freq=12)
mydata2_ts
date pkgrev
Jan 2015 16436 39103770
Feb 2015 16467 27652952
Mar 2015 16495 30324308
Apr 2015 16526 35347040
May 2015 16556 31093119
Jun 2015 16587 20670477
Jul 2015 16617 24841570
I don't really understand the values in the date column! It seems the dates have been converted into numeric format.
class(mydata2_ts)
[1] "mts" "ts" "matrix"
Now, running the following codes give me an error:
stlRes <- stl(mydata2_ts, s.window = "periodic")
Error in stl(mydata2_ts, s.window = "periodic") :
only univariate series are allowed
What is wrong with my process?
The reason that you got this error is because you tried to feed a data set with two variables (date + pkgrev) into STL's argument, which only takes a univariate time series as a proper argument.
To solve this problem, you could create a univariate ts object without the date variable. In your case, you need to use mydata2$pkgrev (or mydata2["pkgrev"] after mydata2 is converted into a dataframe) instead of mydata2 in your code mydata2_ts <- ts(mydata2, start=c(2015,1), freq=12). The ts object is already supplied with the temporal information as you specified start date and frequency in the argument.
If you would like to create a new dataframe with both the ts object and its corresponding date variable, I would suggest you to use the following code:
mydata3 = cbind(as.Date(time(mydata2_ts)), mydata2_ts)
mydata3 = as.data.frame(mydata3)
However, for the purpose of STL decompostion, the input of the first argument should be a ts object, i.e., mydata2_ts.
I'm trying to do a zoo merge between stock prices from selected trading days and observations about those same stocks (we call these "Nx observations") made on the same days. Sometimes do not have Nx observations on stock trading days and sometimes we have Nx observations on non-trading days. We want to place an "NA" where we do not have any Nx observations on trading days but eliminate Nx observations where we have them on non-trading day since without trading data for the same day, Nx observations are useless.
The following SO question is close to mine, but I would characterize that question as REPLACING missing data, whereas my objective is to truly eliminate observations made on non-trading days (if necessary, we can change the process by which Nx observations are taken, but it would be a much less expensive solution to leave it alone).
merge data frames to eliminate missing observations
The script I have prepared to illustrate follows (I'm new to R and SO; all suggestions welcome):
# create Stk_data data.frame for use in the Stack Overflow question
Date_Stk <- c("1/2/13", "1/3/13", "1/4/13", "1/7/13", "1/8/13") # dates for stock prices used in the example
ABC_Stk <- c(65.73, 66.85, 66.92, 66.60, 66.07) # stock prices for tkr ABC for Jan 1 2013 through Jan 8 2013
DEF_Stk <- c(42.98, 42.92, 43.47, 43.16, 43.71) # stock prices for tkr DEF for Jan 1 2013 through Jan 8 2013
GHI_Stk <- c(32.18, 31.73, 32.43, 32.13, 32.18) # stock prices for tkr GHI for Jan 1 2013 through Jan 8 2013
Stk_data <- data.frame(Date_Stk, ABC_Stk, DEF_Stk, GHI_Stk) # create the stock price data.frame
# create Nx_data data.frame for use in the Stack Overflow question
Date_Nx <- c("1/2/13", "1/4/13", "1/5/13", "1/6/13", "1/7/13", "1/8/13") # dates for Nx Observations used in the example
ABC_Nx <- c(51.42857, 51.67565, 57.61905, 57.78349, 58.57143, 58.99564) # Nx scores for stock ABC for Jan 1 2013 through Jan 8 2013
DEF_Nx <- c(35.23809, 36.66667, 28.57142, 28.51778, 27.23150, 26.94331) # Nx scores for stock DEF for Jan 1 2013 through Jan 8 2013
GHI_Nx <- c(7.14256, 8.44573, 6.25344, 6.00423, 5.99239, 6.10034) # Nx scores for stock GHI for Jan 1 2013 through Jan 8 2013
Nx_data <- data.frame(Date_Nx, ABC_Nx, DEF_Nx, GHI_Nx) # create the Nx scores data.frame
# create zoo objects & merge
z.Stk_data <- zoo(Stk_data, as.Date(as.character(Stk_data[, 1]), format = "%m/%d/%Y"))
z.Nx_data <- zoo(Nx_data, as.Date(as.character(Nx_data[, 1]), format = "%m/%d/%Y"))
z.data.outer <- merge(z.Stk_data, z.Nx_data)
The NAs on Jan 3 2013 for the Nx observations are fine (we'll use the na.locf) but we need to eliminate the Nx observations that appear on Jan 5 and 6 as well as the associated NAs in the Stock price section of the zoo objects.
I've read the R Documentation for merge.zoo regarding the use of "all": that its use "allows
intersection, union and left and right joins to be expressed". But trying all combinations of the
following use of "all" yielded the same results (as to why would be a secondary question).
z.data.outer <- zoo(merge(x = Stk_data, y = Nx_data, all.x = FALSE)) # try using "all"
While I would appreciate comments on the secondary question, I'm primarily interested in learning how to eliminate the extraneous Nx observations on days when there is no trading of stocks. Thanks. (And thanks in general to the community for all the great explanations of R!)
The all argument of merge.zoo must be (quoting from the help file):
logical vector having the same length as the number of "zoo" objects to be merged
(otherwise expanded)
and you want to keep all rows from the first argument but not the second so its value should be c(TRUE, FALSE).
merge(z.Stk_data, z.Nx_data, all = c(TRUE, FALSE))
The reason for the change in all syntax for merge.zoo relative to merge.data.frame is that merge.zoo can merge any number of arguments whereas merge.data.frame only handles two so the syntax had to be extended to handle that.
Also note that %Y should have been %y in the question's code.
I hope I have understood your desired output correctly ("NAs on Jan 3 2013 for the Nx observations are fine"; "eliminate [...] observations that appear on Jan 5 and 6"). I don't quite see the need for zoo in the merging step.
merge(Stk_data, Nx_data, by.x = "Date_Stk", by.y = "Date_Nx", all.x = TRUE)
# Date_Stk ABC_Stk DEF_Stk GHI_Stk ABC_Nx DEF_Nx GHI_Nx
# 1 1/2/13 65.73 42.98 32.18 51.42857 35.23809 7.14256
# 2 1/3/13 66.85 42.92 31.73 NA NA NA
# 3 1/4/13 66.92 43.47 32.43 51.67565 36.66667 8.44573
# 4 1/7/13 66.60 43.16 32.13 58.57143 27.23150 5.99239
# 5 1/8/13 66.07 43.71 32.18 58.99564 26.94331 6.10034
I have a question which might be trivial for most of you guys. I tried a lot, didn't come to a solution, so I would be glad if somebody could give me a hint. The starting point is a weekly xts-time series.
Month Week Value Goal
Dec 2011 W50 a a
Dec 2011 W51 b mean(a,b)
Dec 2011 W52 c mean(a,b,c)
Dec 2011 W53 d mean(a,b,c,d)
Jan 2012 W01 e e
Jan 2012 W02 f mean(e,f)
Jan 2012 W03 g mean(e,f,g)
Jan 2012 W04 h mean(e,f,g,h)
Feb 2012 W05 i i
Feb 2012 W06 j mean(i,j)
Please excuse the Excel notation, but I think it makes it pretty clear what I want to do: I want to calculate a left sided moving average for the column "Value" but just for the respective month, as it is displayed in the column Goal. I experimented with apply.monthly() and period.apply(). But it didn't get me what I want. Can sombody of you give me a hint how to solve the problem? Just a hint which function I should use would be already enough!
Thank you very much!
Best regards,
Andreas
apply.monthly will not work because it only assigns one value to the endpoint of the period, whereas you want to assign many values to each monthly period.
You can do this pretty easily by splitting your xts data by month, applying a cumulative mean function to each, and rbind'ing the list back together.
library(quantmod)
# Sample data
getSymbols("SPY")
spy <- to.weekly(SPY)
# Cumulative mean function
cummean <- function(x) cumsum(x)/seq_along(x)
# Expanding average calculation
spy$EA <- do.call(rbind, lapply(split(Cl(spy),'months'), cummean))
I hope I got your question right. but is it that what you are looking for:
require(plyr)
require(PerformanceAnalytics)
ddply(data, .(Week), summarize, Goal=apply.fromstart(Value,fun="mean"))
this should work - though a reproducible expample would have been nice.
here's what it does.
df <- data.frame(Week=rep(1:5, each=5), Value=c(1:25)*runif(25)) #sample data
require(plyr)
require(PerformanceAnalytics)
df$Goal <- ddply(df, .(Week), summarize, Goal=apply.fromstart(Value,FUN="mean"))[,2]
outcome:
Week Value Goal
1 1 0.7528037 0.7528037
2 1 1.9622622 1.3575330
3 1 0.3367802 1.0172820
4 1 2.5177284 1.3923936
of course you may obtain further info via the help: ?ddply or ?apply.fromstart.