I am working with some observation data and have run into a bit of an issue beyond my current capabilities. I surveyed different polygons (the column "PolygonID" in the screenshot) for lizards two times during a survey season. I want to determine the total search effort (shown in the column "Effort") for each individual polygon within each survey round. Problem is, the software I was using to collect the data sometimes creates unnecessary repeats for polygons within a survey round. There is an example of this in the screenshot for the rows with PolygonID P3.
Most of the time it does not affect the effort calculations because the start and end time for the rows (the fields used to calculate effort) are the same, and I know how to filter the dataset so it only shows one line per polygon per survey, but I have reason to be concerned there might be some lines where the software glitched and assigned incorrect start and end times for one of the repeat lines. Is there a way I can test whether start and end time match for any such repeats with R, rather than manually going through all the data?
Thank you!
To create a ts-object in R, one has to specify a data frame, a start date and the frequency of the time series.
When searching the internet (e.g. Role of frequency parameter in ts), I get the impression that by choosing the frequency, one can emphasise whatever periodic pattern one believes is the most important in the data. However, I doubt that this is actually true. My impression is that it is solely used to compute the dates of the time series on-the-fly. E.g. when I set the start date “2015-08-01”, R automatically transforms it into a decimal date and I get something like 2015.58. If I now choose a frequency of 365 (or 365.25), R divides one unit by 365 and assigns this fraction to each day as one unit ahead, so the entry 366 days later is exactly 2016.58. However, if I choose frequency=7, the fraction assigned to each day is 1/7th, so the date assigned to the 8th day after my start date corresponds to a decimal number between 2016 and 2017. So the only choice for a data set with 365 entries per year is 365, isn’t it? And it is only used to actually create the time series?
Otherwise, if I choose the xts-class, an xts-object is built from a vector and a matrix where the vector has to be created in advance. So here there is no need to compute the date on-the-fly using a start date and a frequency and that is the reason why no frequency has to be assigned at all.
In both cases I can apply forecasting packages to either ts or xts objects (such as ARIMA, ets, stl, bats, bats etc) without specifying anything else so this shows that the frequency is actually not used for anything else. Or am I missing something here?
Thanks in advance for your comments!
What I have is data in a tab delimited txt file in the following format (http://pastebin.com/XN3y9Wek):
Date Time Flow (L/h)
...
6/10/15 05:19:05 -0.175148624605041
6/10/15 05:34:05 -0.170297042615798
...
7/10/15 07:34:08 -0.033833540932291
7/10/15 07:49:08 -0.0256913011453011
...
The data currently ranges from 6/10/15 till 22/11/15. Measurements occur approximately every 15 minutes, but sometimes there is data loss which means that there are not the same amount of data points for every day. There are also periods where there is a larger gap (for example evening 16/11 -> morning 17/11) due to logger malfunction.
From this data I would like to create a similar figure like this one, as it offers a very nice seasonal representation of a large amount of data (my full dataset spans over several years):
Its similar to the style of a Hovmöller diagram. I have tried experimenting with R and the lattice package, but I struggle with the data gaps I have in my datasets and the irregular data points per day.
Any help you can offer me, an R beginner, would be greatly appreciated!
(If it would be possible in PHP or Javascript, feel free to post this as well)
I have time series data that I'm trying to analyse in R. It was provided as a CSV from excel, which I subsequently read as a data.frame all. Let's say it has two columns: all$date and all$people, representing the count of people on a particular date. The frequency is hence daily.
Being from Excel, the dates are integers representing the number of days since 1900-01-01.
I could read the data as people = ts(all$people, start=c(all$date[1], 1), frequency=365); but that gives a silly start value of almost 40000 because the data starts in 2006. The start parameter doesn't take a date object, according to ?ts, so I can't just use as.Date():
ts - ...
start: the time of the first observation. Either a single number
or a vector of two integers, which specify a natural time unit and
a (1-based) number of samples into the time unit. See the examples
for the use of the second form.
I could of course set start=1, but it's a bit painful to figure out what season we're in when the plot tells me interesting things are happening around day 2100. (To be clear, setting frequency=365 does tell me what year we're in, but isn't useful more precise dates). Is there a useful way of expressing the date in ts in a human-readable form so that I don't have to keep calling as.Date() to understand when the interesting features are happening?
All,
I'm looking to download stock data either from Yahoo or Google on 15 - 60 minute intervals for as much history as I can get. I've come up with a crude solution as follows:
library(RCurl)
tmp <- getURL('https://www.google.com/finance/getprices?i=900&p=1000d&f=d,o,h,l,c,v&df=cpct&q=AAPL')
tmp <- strsplit(tmp,'\n')
tmp <- tmp[[1]]
tmp <- tmp[-c(1:8)]
tmp <- strsplit(tmp,',')
tmp <- do.call('rbind',tmp)
tmp <- apply(tmp,2,as.numeric)
tmp <- tmp[-apply(tmp,1,function(x) any(is.na(x))),]
Given the amount of data I'm looking to import, I worry that this could be computationally expensive. I also don't for the life of me, understand how the time stamps are coded in Yahoo and Google.
So my question is twofold--what's a simple, elegant way to quickly ingest data for a series of stocks into R, and how do I interpret the time stamping on the Google/Yahoo files that I would be using?
I will try to answer timestamp question first. Please note this is my interpretation and I could be wrong.
Using the link in your example https://www.google.com/finance/getprices?i=900&p=1000d&f=d,o,h,l,c,v&df=cpct&q=AAPL I get following data :
EXCHANGE%3DNASDAQ
MARKET_OPEN_MINUTE=570
MARKET_CLOSE_MINUTE=960
INTERVAL=900
COLUMNS=DATE,CLOSE,HIGH,LOW,OPEN,VOLUME
DATA=
TIMEZONE_OFFSET=-300
a1357828200,528.5999,528.62,528.14,528.55,129259
1,522.63,528.72,522,528.6499,2054578
2,523.11,523.69,520.75,522.77,1422586
3,520.48,523.11,519.6501,523.09,1130409
4,518.28,520.579,517.86,520.34,1215466
5,518.8501,519.48,517.33,517.94,832100
6,518.685,520.22,518.63,518.85,565411
7,516.55,519.2,516.55,518.64,617281
...
...
Note the first value of first column a1357828200, my intuition was that this has something to do with POSIXct. Hence a quick check :
> as.POSIXct(1357828200, origin = '1970-01-01', tz='EST')
[1] "2013-01-10 14:30:00 EST"
So my intuition seems to be correct. But the time seems to be off. Now we have one more info in the data. TIMEZONE_OFFSET=-300. So if we offset our timestamps by this amount we should get :
as.POSIXct(1357828200-300*60, origin = '1970-01-01', tz='EST')
[1] "2013-01-10 09:30:00 EST"
Note that I didn't know which day data you had requested. But quick check on google finance reveals, those were indeed price levels on 10th Jan 2013.
Remaining values from first column seem to be some sort of offset from first row value.
So downloading and standardizing the data ended up being more much of a bear than I figured it would--about 150 lines of code. The problem is that while Google provides the past 50 training days of data for all exchange-traded stocks, the time stamps within the days are not standardized: an index of '1,' for example could either refer to the first of second time increment on the first trading day in the data set. Even worse, stocks that only trade at low volumes only have entries where a transaction is recorded. For a high-volume stock like APPL that's no problem, but for low-volume small caps it means that your series will be missing much if not the majority of the data. This was problematic because I need all the stock series to lie neatly on to of each other for the analysis I'm doing.
Fortunately, there is still a general structure to the data. Using this link:
https://www.google.com/finance/getprices?i=1800&p=1000d&f=d,o,h,l,c,v&df=cpct&q=AAPL
and changing the stock ticker at the end will give you the past 50 days of trading days on 1/2-hourly increment. POSIX time stamps, very helpfully decoded by #geektrader, appear in the timestamp column at 3-week intervals. Though the timestamp indexes don't invariably correspond in a convenient 1:1 manner (I almost suspect this was intentional on Google's part) there is a pattern. For example, for the half-hourly series that I looked at the first trading day of ever three-week increment uniformly has timestamp indexes running in the 1:15 neighborhood. This could be 1:13, 1:14, 2:15--it all depends on the stock. I'm not sure what the 14th and 15th entries are: I suspect they are either daily summaries or after-hours trading info. The point is that there's no consistent pattern you can bank on.The first stamp in a training day, sadly, does not always contain the opening data. Same thing for the last entry and the closing data. I found that the only way to know what actually represents the trading data is to compare the numbers to the series on Google maps. After days of futiley trying to figure out how to pry a 1:1 mapping patter from the data, I settled on a "ballpark" strategy. I scraped APPL's data (a very high-volume traded stock) and set its timestamp indexes within each trading day as the reference values for the entire market. All days had a minimum of 13 increments, corresponding to the 6.5 hour trading day, but some had 14 or 15. Where this was the case I just truncated by taking the first 13 indexes. From there I used a while loop to essentially progress through the downloaded data of each stock ticker and compare its time stamp indexes within a given training day to the APPL timestamps. I kept the overlap, gap-filled the missing data, and cut out the non-overlapping portions.
Sounds like a simple fix, but for low-volume stocks with sparse transaction data there were literally dozens of special cases that I had to bake in and lots of data to interpolate. I got some pretty bizarre results for some of these that I know are incorrect. For high-volume, mid- and large-cap stocks, however, the solution worked brilliantly: for the most part the series either synced up very neatly with the APPL data and matched their Google Finance profiles perfectly.
There's no way around the fact that this method introduces some error, and I still need to fine-tune the method for spare small-caps. That said, shifting a series by a half hour or gap-filling a single time increment introduces a very minor amount of error relative to the overall movement of the market and the stock. I am confident that this data set I have is "good enough" to allow me to get relevant answers to some questions that I have. Getting this stuff commercially costs literally thousands of dollars.
Thoughts or suggestions?
Why not loading the data from Quandl? E.g.
library(Quandl)
Quandl('YAHOO/AAPL')
Update: sorry, I have just realized that only daily data is fetched with Quandl - but I leave my answer here as Quandl is really easy to query in similar cases
For the timezone offset, try:
as.POSIXct(1357828200, origin = '1970-01-01', tz=Sys.timezone(location = TRUE))
(The tz will automatically adjust according to your location)