I am absolutely new to pari/gp. After watching a few videos on YouTube and looking through some online pdf tutorial files, 5 hours later I am still at a loss on how to write the iterated function below. I was using Excel but needed something that can handle very large numbers and was introduced to pari. The Excel formula is very simple and goes like this for b = 7 and c = 9/7:
(1) with numbers 1 to 10^5 in Column A;
(2) type the formula =IF(MOD(A1,7)=0,A1/7,CEILING(A1*9/7,1)) in B1; and
(3) drag B1 down to A100000 then drag the entire column horizontally as far as needed.
Iterated Function
Any help would be greatly appreciated.
Related
I have a model code written in Matlab, and I am trying to translate the code to R. I am almost done, however, I did not manage to convert some simple codes.
These are below:
Assume that I have a row of cells (lets say 50), and the first 10 cells are saturated to water. The rest are under saturated. The below code finds the last saturated cell in the row.
idx_sat_last = find(Exc(t,:)>0, 1, 'last' );
If a cell is saturated, it creates an excess water, so Excess(t,:) > 0 statement is understandable. However, I do not understand rest of the code.
The 2nd code is below. The story of the code is:
If the cell is saturated it creates an excess, else it is a deficit. I do not understand the "includenan" statement.
InSurf(t+1,j)=min(Excess,Deficit(j),'includenan');
Is there anyone who knows how to translate these codes to R?
Thanks in advance..
Maybe you can try this (given a list Exc)
idx_sat_last <- tail(which(Exc[[t]]>0),1)
I just hope to learn how to make a simple statistical summary of the random numbers fra row 1 to 5 in R. (as shown in picture).
And then assign these rows to a single variable.
enter image description here
Hope you can help!
When you type something like 3 on a single line and ask R to "run" it, it doesn't store that anywhere -- it just evaluates it, meaning that it tries to make sense out of whatever you've typed (such as 3, or 2+1, or sqrt(9), all of which would return the same value) and then it more or less evaporates. You can think of your lines 1 through 5 as behaving like you've used a handheld scientific calculator; once you type something like 300 / 100 into such a calculator, it just shows you a 3, and then after you have executed another computation, that 3 is more or less permanently gone.
To do something with your data, you need to do one of two things: either store it into your environment somehow, or to "pipe" your data directly into a useful function.
In your question, you used this script:
1
3
2
7
6
summary()
I don't think it's possible to repair this strategy in the way that you're hoping -- and if it is possible, it's not quite the "right" approach. By typing the numbers on individual lines, you've structured them so that they'll evaluate individually and then evaporate. In order to run the summary() function on those numbers, you will need to bind them together inside a single vector somehow, then feed that vector into summary(). The "store it" approach would be
my_vector <- c(1, 3, 7, 2, 6)
summary(my_vector)
The importance isn't actually the parentheses; it's the function c(), which stands for concatenate, and instructs R to treat those 5 numbers as a collective object (i.e. a vector). We then pass that single object into my_vector().
To use the "piping" approach and avoid having to store something in the environment, you can do this instead (requires R 4.1.0+):
c(1, 3, 7, 2, 6) |> summary()
Note again that the use of c() is required, because we need to bind the five numbers together first. If you have an older version of R, you can get a slightly different pipe operator from the magrittr library instead that will work the same way. The point is that this "binding" part of the process is an essential part that can't be skipped.
Now, the crux of your question: presumably, your data doesn't really look like the example you used. Most likely, it's in some separate .csv file or something like that; if not, hopefully it is easy to get it into that format. Assuming this is true, this means that R will actually be able to do the heavy lifting for you in terms of formatting your data.
As a very simple example, let's say I have a plain text file, my_example.txt, whose contents are
1
3
7
2
6
In this case, I can ask R to parse this file for me. Assuming you're using RStudio, the simplest way to do this is to use the File -> Import Dataset part of the GUI. There are various options dealing with things such as headers, separators, and so forth, but I can't say much meaningful about what you'd need to do there without seeing your actual dataset.
When I import that file, I notice that it does two things in my R console:
my_example <- read.table(...)
View(my_example)
The first line stores an object (called a "data frame" in this case) in my environment; the second shows a nice view of how it's rendered. To get the summary I wanted, I just need to extract the vector of numbers I want, which I see from the view is called V1, which I can do with summary(my_example$V1).
This example is probably not helpful for your actual data set, because there are so many variations on the theme here, but the theme itself is important: point R at a file, as it to render an object, then work with that object. That's the approach I'd recommend instead of typing data as lines within an R script, as it's much faster and less error-prone.
Hopefully this will get you pointed in the right direction in terms of getting your data into R and working with it.
I have two vectors in my dataset Vs = s1 to s10 and Vt= t1 to t10.
They describe two pictures and I want to know for each case what the correlation is.
However there is no such a function Cor(Vs, Vt) because Vectors are apparently not usable in the standard functions. There is even no mean(Vs)!
I tried to write syntax but failed also because the problem of missing variables (implementing pairwise deletion seems complex).
Any hint is welcome.
Is it possible to ask a question that is only seen by SPSS experts?
calculating the correlation in the present structure is probably feasible but would be pretty complex. I suggest restructuring the data, then all becomes easy:
The code assumes you have some line ID in the data, called lineNum.
If you don't, you'll need to create one using the first line.
compute lineNum=$casenum. /* this is only necessary if you don't have some other line ID.
varstocases /mame V_s from S1 to S10 /make V_t from V1 to V10 /index=pairNum(V_s).
sort cases by lineNum.
split file by lineNum.
correlations V_s with V_t. /* you can edit the code here to add features to the analysis.
split file off.
That's it. Now the results will appear in the output window - one correlation for each of the original lines. If you need to import the correlations back to the original data you can do that by using OMS control to capture the results into a new dataset and then matching it back to the original file.
Sorry if this is difficult to understand - I don't have enough karma to add a picture so I will do the best I can to describe this! Using XLConnect package within R to read & write from/to Excel spreadsheets.
I am working on a project in which I am trying to take columns of data out of many workbooks and concatenate them together into rows of a new workbook based on which workbook they came from (each workbook is data from a consecutive business day). The snag is that the data that I seek is only a small part (10 rows X 3 columns) of each workbook/worksheet and is not always located in the same place within the worksheet due to sloppiness on behalf of the person who originally created the spreadsheets. (e.g. I can't just start at cell A2 because the dataset that starts at A2 in one workbook might start at B12 or C3 in another workbook).
I am wondering if it is possible to search for a cell based on its contents (e.g. a cell containing the title "Table of Arb Prices") and return either the index or reference formula to be able to access that cell.
Also wondering if, once I reference that cell based on its contents, if there is a way to adjust that formula to get to where I know another cell is compared to that one. For example if a cell with known contents is always located 2 rows above and 3 columns to the left of the cell where I wish to start collecting data, is it possible for me to take that first reference formula and increment it by 2 rows and 3 columns to get the reference formula for the cell I want?
Thanks for any help and please advise me if you need further information to be able to understand my questions!
You can just read the entire worksheet in as a matrix with something like
library(XLConnect)
demoExcelFile <- system.file("demoFiles/mtcars.xlsx", package = "XLConnect")
mm <- as.matrix(readWorksheetFromFile(demoExcelFile, sheet=1))
class(mm)<-"character" # convert all to character
Then you can search for values and get the row/colum
which(mm=="3.435", arr.ind=T)
# row col
# [1,] 23 6
Then you can offset those and extract values from the matrix how ever you like. In the end, when you know where you want to read from, you can convert to a cleaner data frame with
read.table(text=apply(mm[25:27, 6:8],1,paste, collapse="\t"), sep="\t")
Hopefully that gives you a general idea of something you can try. It's hard to be more specific without knowing exactly what your input data looks like.
First time poster here, so I'll try and make myself as clear as possible on the help I need. I'm fairly new to R, and this is my first real independent programming experience.
I have stock tick data for about 2.5 years, each day has its own file. The files are .txt and consist of approximately 20-30 million rows, and averaging I guess 360mb each. I am working one file at a time for now. I don't need all the data these files contain, and I was hoping that I could use the programming to minimize my files a bit.
Now my problem is that I am having some difficulties with writing the proper code so R understands what I need it to do.
Let me first show you some of the data so you can get an idea of the formatting.
M977
R 64266NRE1VEW107 FI0009653869 2EURXHEL 630 1
R 64516SSA0B 80SHB SE0002798108 8SEKXSTO 40 1
R 645730BBREEW750 FR0010734145 8EURXHEL 640 1
R 64655OXS1C 900SWE SE0002800136 8SEKXSTO 40 1
R 64663OXS1P 450SWE SE0002800219 8SEKXSTO 40 1
R 64801SSIEGV LU0362355355 11EURXCSE 160 1
M978
Another snip of data:
M732
D 3547742
A 3551497B 200000 67110 02800
D 3550806
D 3547743
A 3551498S 250000 69228 09900
So as you can see each line begins with a letter. Each letter denotes what the line means. For instance R means order book directory message, M means milliseconds after last second, H means stock trading action message. There are 14 different letters used in total.
I have used the readLines function to import the data into R. This however seems to take a very long time for R to process when I want to work with the data.
Now I would like to write some sort of If function that says if the first letter is R then from offset 1 to 4 the code means Market Segment Identifier etc., and have R add columns to these so I can work with the data in a more structured fashion.
What is the best way of importing such data, and also creating some form of structure - i.e. use unique ID information in the line of data to analyze 1 stock at a time for instance.
You can try something like this :
options(stringsAsFactors = FALSE)
f_A <- function(line,tab_A){
values <- unlist(strsplit(line," "))[2:5]
rbind(tab_A,list(name_1=as.character(values[1]),name_2=as.numeric(values[2]),name_3=as.numeric(values[3]),name_4=as.numeric(values[4])))
}
tab_A <- data.frame(name_1=character(),name_2=numeric(),name_3=numeric(),name_4=numeric(),stringsAsFactors=F)
for(i in readLines(con="/home/data.txt")){
switch(strsplit(x=i,split="")[[1]][1],M=cat("1\n"),R=cat("2\n"),D=cat("3\n"),A=(tab_A <- f_A(i,tab_A)))
}
And replace cat() by different functions that add values to each type of data.frame. Use the pattern of the function f_A() to construct others functions and same things for the table structure.
You can combine your readLines() command with regular expressions. To get more information about regular expressions, look at the R help site for grep()
> ?grep
So you can go through all the lines, check for each line what it means, and then handle or store the content of the line however you like. (Regular Expressions are also useful to split the data within one line...)