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!
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So far I have imported a data set with columns labeled with things people are afraid of "snakes, heights, spiders, etc.) and each row is a number (1,2,3) representing a different person. So each person rated how much each thing scared them using 1-5.
I cleared the data so the NAs are gone. Now every time I go to do calculations in the data set, it tells me
Error: object 'fear.of.public.speaking' not found
I hope this has not been answered, but when I search for a solution to my problem I am not getting any results.
I have a data.frame of 2000+ observations and 20+ columns. Each row represents a different observation and each column represents a different facet of data for that observation. My objective is to iterate through the data.frames and select observations which match criteria (eg. I am trying to pick out observations that are in certain states). After this, I need to subtract or add time to convert it to its appropriate time zone (all of the times are in CST). What I have so far is an exorbitant amount of subsetting commands that pick out the rows that are of the state being checked against. When I try to write a for loop I can only get one value returned, not the whole row.
I was wondering if anyone had any suggestions or knew of any functions that could help me. I've tried just about everything, but I really don't want to have to go through each state of observations and modify the time. I would prefer a loop that could easily go through the data, select rows based on their state, subtract or add time, and then place the row back into its original data.frame (replacing the old value).
I appreciate any help.
I am currently attempting to implement a trading idea that I have been playing around with. It consists of 50+ securities and has a strategy very similar to this one. (Current package I am using is quantmod).
http://www.r-bloggers.com/backtesting-a-simple-stock-trading-strategy/
For those who aren't interested in clicking, it is a strategy that will look at the pass X days( in his case 200 ) and enter a position depending on the peak reached in the stock. I understand how to do this strategy for my idea, but I cannot grasp how to aggregate my data into one summary.
Is there a way I can consolidate the summary for all the positions I have entered into one larger portfolio summary and chart that against the S&P 500?
Any advice on where I can find resources or being lead to the information. I have looked at portfolio analysis package for R and I do not believe that will be much help to me.
Thank you in advance.
Edit: In the link, at the bottom, there are 3 indexes that are FTSE, N225, DJIA. Could i combine those 3 summaries to show the same output as below, BUT combined
FTSE:
Me Index
Cumulative Return 3.56248582 3.8404476
Annual Return 0.05667121 0.0589431
Annualized Sharpe Ratio 0.45907768 0.3298633
Win % 0.53216374 0.5239884
Annualized Volatility 0.12344579 0.1786895
Maximum Drawdown -0.39653398 -0.5256991
Max Length Drawdown 1633.00000 2960.0000
Could I get that same output but for the 3 securities data combined? Is there a effective way of doing that. Thank you so much. Happy holidays
It's a little unclear to me what you mean by "combine" in this case. If you want a single column representing the combined returns from all three exchanges as if they were a single unified market, that's really tricky, because the exchanges trade in different currencies (British pounds; U.S. dollars, Japanese Yen, etc.). The underlying analysis would have to be modified substantially to take into account fluctuating daily foreign exchange rates.
I suspect that this is NOT want you want. Rather, you are simply asking how to take three sequential two-column outputs and turn them into a single parallel six-column output.
If that is indeed what you want, then you need to rewrite the testStrategy() function shown near the bottom of the link. As it's currently written, that function takes three inputs: an index name myStock (with allowed values of FTSE, DJIA, or N225), and two integer values, nHold and nHigh. You would need to change it so that it instead accepts five inputs; e.g., myStockA, myStockB and myStockC, plus the two integer values already mentioned. Then each of the lines currently referring to myStock would have to be replicated three times. Finally, the two cbind() lines that you see at the bottom would have to be modified so that instead of merging the data together into only two columns, you include all six.
For a good intro tutorial on how to write and modify your own R functions, please see this. To understand how to use the cbind() function, which you will have to call with six rather than two inputs, please see this.
I'm using TraMineR to analyze sets of sequences. Each coherent set of sequences may contain 100 work processes from a single project for a single period of time. Using TraMineR I can easily calculate descriptive statistics for each sequence, however I'm more interested in descriptive statistics of the sequence object itself - subsuming all the smaller sequences within.
For example, to get state frequencies, I run:
seqstatd(sequences.sts)
However, this gives me the state frequencies for each sequence within my sequence object. I want to access the frequencies of states across all sequences inside of my sequence object. How can I accomplish this?
I am not sure to understand your question since seqstatd() returns the cross-sectional frequencies at each successive position, and NOT the state frequencies for each sequence. The latter is returned by seqistatd().
Assuming you refer to the outcome of seqistatd() you would get the mean time spent in each state with seqmeant(sequence.sts).
For other summaries you can use the apply function. For instance, you get the variance of the time spent in each state with
tab <- seqistatd(mvad.seq)
vart <- apply(tab,2,var)
head(vart)
Hope this helps.
I want to analyse angles in movement of animals. I have tracking data that has 10 recordings per second. The data per recording consists of the position (x,y) of the animal, the angle and distance relative to the previous recording and furthermore includes speed and acceleration.
I want to analyse the speed an animal has while making a particular angle, however since the temporal resolution of my data is so high, each turn consists of a number of minute angles.
I figured there are two possible ways to work around this problem for both of which I do not know how to achieve such a thing in R and help would be greatly appreciated.
The first: Reducing my temporal resolution by a certain factor. However, this brings the disadvantage of losing possibly important parts of the data. Despite this, how would I be able to automatically subsample for example every 3rd or 10th recording of my data set?
The second: By converting straight movement into so called 'flights'; rule based aggregation of steps in approximately the same direction, separated by acute turns (see the figure). A flight between two points ends when the perpendicular distance from the main direction of that flight is larger than x, a value that can be arbitrarily set. Does anyone have any idea how to do that with the xy coordinate positional data that I have?
It sounds like there are three potential things you might want help with: the algorithm, the math, or R syntax.
The algorithm you need may depend on the specifics of your data. For example, how much data do you have? What format is it in? Is it in 2D or 3D? One possibility is to iterate through your data set. With each new point, you need to check all the previous points to see if they fall within your desired column. If the data set is large, however, this might be really slow. Worst case scenario, all the data points are in a single flight segment, meaning you would check the first point the same number of times as you have data points, the second point one less, etc. The means n + (n-1) + (n-2) + ... + 1 = n(n-1)/2 operations. That's O(n^2); the operating time could have quadratic growth with respect to the size of your data set. Hence, you may need something more sophisticated.
The math to check whether a point is within your desired column of x is pretty straightforward, although maybe more sophisticated math could help inform a better algorithm. One approach would be to use vector arithmetic. To take an example, suppose you have points A, B, and C. Your goal is to see if B falls in a column of width x around the vector from A to C. To do this, find the vector v orthogonal to C, then look at whether the magnitude of the scalar projection of the vector from A to B onto v is less than x. There is lots of literature available for help with this sort of thing, here is one example.
I think this is where I might start (with a boolean function for an individual point), since it seems like an R function to determine this would be convenient. Then another function that takes a set of points and calculates the vector v and calls the first function for each point in the set. Then run some data and see how long it takes.
I'm afraid I won't be of much help with R syntax, although it is on my list of things I'd like to learn. I checked out the manual for R last night and it had plenty of useful examples. I believe this is very doable, even for an R novice like myself. It might be kind of slow if you have a big data set. However, with something that works, it might also be easier to acquire help from people with more knowledge and experience to optimize it.
Two quick clarifying points in case they are helpful:
The above suggestion is just to start with the data for a single animal, so when I talk about growth of data I'm talking about the average data sample size for a single animal. If that is slow, you'll probably need to fix that first. Then you'll need to potentially analyze/optimize an algorithm for processing multiple animals afterwards.
I'm implicitly assuming that the definition of flight segment is the largest subset of contiguous data points where no "sub" flight segment violates the column rule. That is to say, I think I could come up with an example where a set of points satisfies your rule of falling within a column of width x around the vector to the last point, but if you looked at the column of width x around the vector to the second to last point, one point wouldn't meet the criteria anymore. Depending on how you define the flight segment then (e.g. if you want it to be the largest possible set of points that meet your condition and don't care about what happens inside), you may need something different (e.g. work backwards instead of forwards).