I have this data
enter image description here
and i am trying to sort by job number and get the beginning date minus the end date to see hw many days or months the job took. i get the answer in the picture which is not right. any idea?
X2019Data$dateDiff<-ave(as.numeric(X2019Data$date), X2019Data$JOBNUMBER_I, FUN=function(x) c(abs(diff(x)),0))
I have not worked with SPSS (.sav) files before and am trying to work with some data files provided to me by importing them into R. I did not receive any explanation of the files, and because communication is difficult I am trying to figure out as much as I can on my own.
Here's my first question. This is what the Date field looks like in an R data frame after import:
> dataset2$Date[1:4]
[1] 13608172800 13608259200 13608345600 13608345600
I don't know what dates the data is supposed to be for, but I found that if I divide the above numbers by 10, that seems to give a reasonable date (in February 2013). Can anyone confirm this is indeed what the above represents?
My second question is regarding another column called Begin_time. Here's what that looks like:
> dataset2$Begin_time[1:4]
[1] 29520 61800 21480 55080
Any idea what this is representing? I want to believe this is some representation of time of day because the records are for wildlife observations, but I haven't got more info than that to try to guess. I noticed that if I take the difference between End_Time and Begin_time I get numbers like 120 and 180, which seems like minutes to me (3 hours seems reasonable to observe a wild animal), but the absolute numbers are far greater than the number of minutes in a day (1440), so that leaves me puzzled. Is this some time keeping format from SPSS? If so, what's the logic?
Unfortunately, I don't have access to SPSS, so any help would be much appreciated.
I had the same problem and this function is a good solution:
pss2date <- function(x) as.Date(x/86400, origin = "1582-10-14")
This is where I found the answer:
http://scs.math.yorku.ca/index.php/R:_Importing_dates_from_SPSS
Dates in SPSS Statistics are represented as floating point doubles holding the number of seconds since Oct 1, 1582. If you use the SPSS R plugin apis, they can be automatically converted to R dates, but any proper converter should be able to do this for you.
I have an Excel table which contains thousands of incident tickets. Each tickets typically carried over few hours or few days, and I usually calculate the total duration by substracting opening date and time from closing date and time.
However I would like to take into account and not count the out of office hours (night time), week-ends and holidays.
I have therefore created two additional reference tables, one which contains the non-working hours (eg everyday after 7pm until 7am in the morning, saturday and sunday all day, and list of public holidays).
Now I need to find some sort of VB macro that would automatically calculate each ticket "real duration" by removing from the total ticket time any time that would fall under that list.
I had a look around this website and other forums, however I could not find what I am looking for. If someone can help me achieve this, I would be extremely grateful.
Best regards,
Alex
You can use the NETWORKDAYS function to calculate the number of working days in the interval. Actually you seem to be perfectly set up for it: it takes start date, end date and a pointer to a range of holidays. By default it counts all days non-weekend.
For calculating the intraday time, you will need some additional magic. assuming that tickets are only opened and closed in bussines hours, it would look like this:
first_day_hrs := dayend - ticketstart
last_day_hrs := ticketend - daystart
inbeetween_hrs := (NETWORKDAYS(ticketstart, ticketend, rng_holidays) - 2) * (dayend - daystart)
total_hrs := first_day_hrs + inbetween_hrs + last_day_hrs
Of course the names should in reality refer to Excel cells. I recommend using lists and/or names.
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)
Basically I want to know why as.Date(200322,format="%Y%W") gives me NA. While we are at it, I would appreciate any advice on a data structure for repeated cross-section (aka pseudo-panel) in R.
I did get aggregate() to (sort of) work, but it is not flexible enough - it misses data on columns when I omit the missed values, for example.
Specifically, I have a survey that is repeated weekly for a couple of years with a bunch of similar questions answers to which I would like to combine, average, condition and plot in both dimensions. Getting the date conversion right should presumably help me towards my goal with zoo package or something similar.
Any input is appreciated.
Update: thanks for string suggestion, but as you can see in your own example, %W part doesn't work - it only identifies the year while setting the current day while I need to set a specific week (and leave the day blank).
Use a string as first argument in as.Date() and select a specific weekday (format %w, value 0-6). There are seven possible dates in each week, therefore strptime needs more information to select a unique date. Otherwise the current day and month are returned.
> as.Date(paste("200947", "0", sep="-"), format="%Y%W-%w")
[1] "2009-11-22"