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I'm very new to R - but have been developing SAS-programs (and VBA) for some years. Well, the thing is that I have 4 lines of R-code (scripts?) that I would like to repeat 44 times. Two times for each of 22 different train stations, indicating whether the train is in- or out-going. The four lines of code are:
dataGL_FLIin <- subset( dataGL_all, select = c(Tidsinterval, Dag, M.ned, Ugenr.,Kode, Ugedag, FLIin))
names(dataGL_FLIin)[names(dataGL_FLIin)=='FLIin'] <- 'GL_Antal'
dataGL_FLIin$DIR<-"IN"
dataGL_FLIin$STATION<-"FLI
To avoid repeating the 4 lines 44 times I need 2 "macro variables" (yes, I'm aware, that this is a SAS-thing only, sorry). One "macro variable" indicating the train station and one indicating the direction. In the example above the train station is FLI and the direction is in. Below the same 4 lines are demonstrated for the train station FBE, this time in out-going direction.
dataGL_FBEout <- subset( dataGL_all, select = c(Tidsinterval, Dag, M.ned, Ugenr.,Kode, Ugedag, FBEout))
names(dataGL_FBEout)[names(dataGL_FBEout)=='FBEout'] <- 'GL_Antal'
dataGL_FBEout$DIR<-"OUT"
dataGL_FBEout$STATION<-"FBE"
I have looked many places and tried many combinations of R-functions and R-lists, but I can't make it work. Quite possible I'm getting it all wrong. I apologize in advance if the question is (too) stupid, but will however be very grateful for any help on the matter.
Pls. notice that I, in the end, want 44 different data-frames created:
1) dataGL_FLIin
2) dataGL_FBEout
3) Etc. ...
ADDED: 2 STATION 2 DIRECTIONS EXAMPLE OF MY PROBLEM
'The one data frame I have'
Date<-c("01-01-15 04:00","01-01-15 04:20","01-01-15 04:40")
FLIin<-c(96,39,72)
FLIout<-c(173,147,103)
FBEin<-c(96,116,166)
FBEout<-c(32,53,120)
dataGL_all<-data.frame(Date, FLIin, FLIout, FBEin, FBEout)
'The four data frames I would like'
GL_antal<-c(96,39,72)
Station<-("FLI")
Dir<-("IN")
dataGL_FLIin<-data.frame(Date, Station, Dir, GL_antal)
GL_antal<-c(173,147,103)
Station<-("FLI")
Dir<-("OUT")
dataGL_FLIout<-data.frame(Date, Station, Dir, GL_antal)
GL_antal<-c(96,116,166)
Station<-("FBE")
Dir<-("IN")
dataGL_FBEin<-data.frame(Date, Station, Dir, GL_antal)
GL_antal<-c(32,53,120)
Station<-("FBE")
Dir<-("OUT")
dataGL_FBEout<-data.frame(Date, Station, Dir, GL_antal)
Thanks,
lars
With your example, it is now clearer what you want and I give it a second try. I use dataGL_all as defined in your question and the define
stations <- rep(c("FLI","FBE"),each=2)
directions <- rep(c("in","out"),times=length(stations)/2)
You could also extract the stations and directions from your data frame. Using your example, the following would work
stations <- substr(names(dataGL_all)[-1],1,3)
directions <- substr(names(dataGL_all)[-1],4,6)
Then, I define the function that will work on the data:
dataGLfun <- function(station,direction) {
name <- paste0(station,direction)
dataGL <- dataGL_all[,c("Date", name)]
names(dataGL)[names(dataGL)==name] <- 'GL_Antal'
dataGL$DIR<-direction
dataGL$STATION<-station
dataGL
}
And now I apply this function to all stations with both directions:
dataGL <- mapply(dataGLfun,stations,directions,SIMPLIFY=FALSE)
names(dataGL) <- paste0(stations,directions)
Now, you can get the data frames for each combination of station and direction. For instance, the two examples in your question, you get with dataGL$FLIin and dataGL$FBEout. The reason that there is a $ instead of a _ is that I did not actually create a separate variable for each data frame. Instead, I created a list, where each element of the list is one of the data frames. This has the advantage that it will be easier to do something to all the data frames later. With your solution, you would have to type all the various variable names, but if the data frames are in a list, you can work with them using functions like lapply.
If you prefer to have many different variables, you could do the following
for (i in seq_along(stations)) {
assign(paste0("dataGL_",stations[i],directions[i]), dataGLfun(stations[i],directions[i]))
}
However, in my opinion, this is not how you should solve this problem in R.
I am trying to create (vector) objects in R. Thereby, I want to achieve that I don't specify a priori the name of the object. For example if I have a list of length 3, I want to create the objects p1 to p3 and if I have a list of length 10, the objects p1to p10 have to be created. The length should be arbitrary and not a priori determined.
Thanks for your help!
I guess the proper way of doing that is to consider a list p = list() and then you can use p[[i]] with i as big as you wish without having specified any length.
Then once your list is filled up, you can rename it: names(p) = paste0("p",c(1:length(p)))
Finally, if you want to get all the pi variables directly accessible, you add attach(p)
This is kind of a hack but you can do the following
short_list <- list(rnorm(10),rnorm(20),1:3)
long_list <- c(short_list,short_list )
paste0("p",seq_along(short_list))
mapply(assign, paste0("p",seq_along(short_list)), short_list, MoreArgs = list(envir = .GlobalEnv))
result:
> p3
[1] 1 2 3
you can do the same with long_list
I dont see a statistical model you will need this. Better start working with lists like short_list or data.frame's directly.
PS If you just want to use it for glm you probably want to learn formula's in R.
glm(y~., data=your_data) takes all columns in your data-frame that are not named y as regressor. Maybe this helps.
assign (and maybe also attach) are often a sign that you have not yet arrived at an "Rish" version of the code.
Considering that you need this for modeling: if your $p_1 \cdot p_n$ are of the same type, you can put them into a matrix (inside a column of a data.frame; for modeling they anyways need to be of same length):
df$matrix <- p.matrix
If you directly create the data.frame, you need to make sure the matrix is not expanded to data.frame columns:
df <- data.frame (matrix = I (matrix), ...)
Then glm (y ~ matrix, ...) will work.
For examples of this technique see e.g. packages pls or hyperSpec or the pls paper in the Journal of Statistical Software.
I am trying to run some Monte Carlo simulations on animal position data. So far, I have sampled 100 X and Y coordinates, 100 times. This results in a list of 200. I then convert this list into a dataframe that is more condusive to eventual functions I want to run for each sample (kernel.area).
Now I have a data frame with 200 columns, and I would like to perform the kernel.area function using each successive pair of columns.
I can't reproduce my own data here very well, so I've tried to give a basic example just to show the structure of the data frame I'm working with. I've included the for loop I've tried so far, but I am still an R novice and would appreciate any suggestions.
# generate dataframe representing X and Y positions
df <- data.frame(x=seq(1:200),y=seq(1:200))
# 100 replications of sampling 100 "positions"
resamp <- replicate(100,df[sample(nrow(df),100),])
# convert to data frame (kernel.area needs an xy dataframe)
df2 <- do.call("rbind", resamp[1:2,])
# xy positions need to be in columns for kernel.area
df3 <- t(df2)
#edit: kernel.area requires you have an id field, but I am only dealing with one individual, so I'll construct a fake one of the same length as the positions
id=replicate(100,c("id"))
id=data.frame(id)
Here is the structure of the for loop I've tried (edited since first post):
for (j in seq(1,ncol(df3)-1,2)) {
kud <- kernel.area(df3[,j:(j+1)],id=id,kern="bivnorm",unin=c("m"),unout=c("km2"))
print(kud)
}
My end goal is to calculate kernel.area for each resampling event (ie rows 1:100 for every pair of columns up to 200), and be able to combine the results in a dataframe. However, after running the loop, I get this error message:
Error in df[, 1] : incorrect number of dimensions
Edit: I realised my id format was not the same as my data frame, so I change it and now have the error:
Error in kernelUD(xy, id, h, grid, same4all, hlim, kern, extent) :
id should have the same length as xy
First, a disclaimer: I have never worked with the package adehabitat, which has a function kernel.area, which I assume you are using. Perhaps you could confirm which package contains the function in question.
I think there are a couple suggestions I can make that are independent of knowledge of the specific package, though.
The first lies in the creation of df3. This should probably be
df3 <- t(df2), but this is most likely correct in your actual code
and just a typo in your post.
The second suggestion has to do with the way you subset df3 in the
loop. j:j+1 is just a single number, since the : has a higher
precedence than + (see ?Syntax for the order in which
mathematical operations are conducted in R). To get the desired two
columns, use j:(j+1) instead.
EDIT:
When loading adehabitat, I was warned to "Be careful" and use the related new packages, among which is adehabitatHR, which also contains a function kernel.area. This function has slightly different syntax and behavior, but perhaps it would be worthwhile examining. Using adehabitatHR (I had to install from source since the package is not available for R 2.15.0), I was able to do the following.
library(adehabitatHR)
for (j in seq(1,ncol(df3)-1,2)) {
kud <-kernelUD(SpatialPoints(df3[,j:(j+1)]),kern="bivnorm")
kernAr<-kernel.area(kud,unin=c("m"),unout=c("km2"))
print(kernAr)
}
detach(package:adehabitatHR, unload=TRUE)
This prints something, and as is mentioned in a comment below, kernelUD() is called before kernel.area().
How can I plot a very large data set in R?
I'd like to use a boxplot, or violin plot, or similar. All the data cannot be fit in memory. Can I incrementally read in and calculate the summaries needed to make these plots? If so how?
In supplement to my comment to Dmitri answer, a function to calculate quantiles using ff big-data handling package:
ffquantile<-function(ffv,qs=c(0,0.25,0.5,0.75,1),...){
stopifnot(all(qs<=1 & qs>=0))
ffsort(ffv,...)->ffvs
j<-(qs*(length(ffv)-1))+1
jf<-floor(j);ceiling(j)->jc
rowSums(matrix(ffvs[c(jf,jc)],length(qs),2))/2
}
This is an exact algorithm, so it uses sorting -- and thus may take a lot of time.
Problem is you can't load all data into the memory. So you could do sampling of the data, as indicated earlier by #Marek. On such a huge datasets, you get essentially the same results even if you take only 1% of the data. For the violin plot, this will give you a decent estimate of the density. Progressive calculation of quantiles is impossible, but this should give a very decent approximation. It is essentially the same as the "randomized method" described in the link #aix gave.
If you can't subset the date outside of R, it can be done using connections in combination with sample(). Following function is what I use to sample data from a dataframe in text format when it's getting too big. If you play a bit with the connection, you could easily convert this to a socketConnection or other to read it from a server, a database, whatever. Just make sure you open the connection in the correct mode.
Good, take a simple .csv file, then following function samples a fraction p of the data:
sample.df <- function(f,n=10000,split=",",p=0.1){
con <- file(f,open="rt",)
on.exit(close(con,type="rt"))
y <- data.frame()
#read header
x <- character(0)
while(length(x)==0){
x <- strsplit(readLines(con,n=1),split)[[1]]
}
Names <- x
#read and process data
repeat{
x <- tryCatch(read.table(con,nrows=n,sep=split),error = function(e) NULL )
if(is.null(x)) {break}
names(x) <- Names
nn <- nrow(x)
id <- sample(1:nn,round(nn*p))
y <- rbind(y,x[id,])
}
rownames(y) <- NULL
return(y)
}
An example of the usage :
#Make a file
Df <- data.frame(
X1=1:10000,
X2=1:10000,
X3=rep(letters[1:10],1000)
)
write.csv(Df,file="test.txt",row.names=F,quote=F)
# n is number of lines to be read at once, p is the fraction to sample
DF2 <- sample.df("test.txt",n=1000,p=0.2)
str(DF2)
#clean up
unlink("test.txt")
All you need for a boxplot are the quantiles, the "whisker" extremes, and the outliers (if shown), which is all easily precomputed. Take a look at the boxplot.stats function.
You should also look at the RSQLite, SQLiteDF, RODBC, and biglm packages. For large datasets is can be useful to store the data in a database and pull only pieces into R. The databases can also do sorting for you and then computing quantiles on sorted data is much simpler (then just use the quantiles to do the plots).
There is also the hexbin package (bioconductor) for doing scatterplot equivalents with very large datasets (probably still want to use a sample of the data, but works with a large sample).
You could put the data into a database and calculate the quantiles using SQL. See : http://forge.mysql.com/tools/tool.php?id=149
This is an interesting problem.
Boxplots require quantiles. Computing quantiles on very large datasets is tricky.
The simplest solution that may or may not work in your case is to downsample the data first, and produce plots of the sample. In other words, read a bunch of records at a time, and retain a subset of them in memory (choosing either deterministically or randomly.) At the end, produce plots based on the data that's been retained in memory. Again, whether or not this is viable very much depends on the properties of your data.
Alternatively, there exist algorithms that can economically and approximately compute quantiles in an "online" fashion, meaning that they are presented with one observation at a time, and each observation is shown exactly once. While I have some limited experience with such algorithms, I have not seen any readily-available R implementations.
The following paper presents a brief overview of some relevant algorithms: Quantiles on Streams.
You could make plots from manageable sample of your data. E.g. if you use only 10% randomly chosen rows then boxplot on this sample shouldn't differ from all-data boxplot.
If your data are on some database there you be able to create some random flag (as I know almost every database engine has some kind of random number generator).
Second thing is how large is your dataset? For boxplot you need two columns: value variable and group variable. This example:
N <- 1e6
x <- rnorm(N)
b <- sapply(1:100, function(i) paste(sample(letters,40,TRUE),collapse=""))
g <- factor(sample(b,N,TRUE))
boxplot(x~g)
needs 100MB of RAM. If N=1e7 then it uses <1GB of RAM (which is still manageable to modern machine).
Perhaps you can think about using disk.frame to summarise the data down first before running the plotting?
The problem with R (and other languages like Python and Julia) is that you have to load all your data into memory to plot it. As of 2022, the best solution is to use DuckDB (there is an R connector), it allows you to query very large datasets (CSV, parquet, among others), and it comes with many functions to compute summary statistics. The idea is to use DuckDB to compute those statistics, load such statistics into R/Python/Julia, and plot.
Computing a boxplot with SQL + R
You need a bunch of statistics to plot a boxplot. If you want a complete reference, you can look at matplotlib's code. The code is in Python, but the code is pretty straightforward, so you'll get it even if you don't know Python.
The most critical piece are percentiles; you can compute those in DuckDB like this (just change the placeholders):
SELECT
percentile_disc(0.25) WITHIN GROUP (ORDER BY "{{column}}") AS q1,
percentile_disc(0.50) WITHIN GROUP (ORDER BY "{{column}}") AS med,
percentile_disc(0.75) WITHIN GROUP (ORDER BY "{{column}}") AS q3,
AVG("{{column}}") AS mean,
COUNT(*) AS N
FROM "{{path/to/data.parquet}}"
You need some other statistics to create the boxplot with all its details. For full implementation, check this (note: it's written in Python). I had to implement this for a package I wrote called JupySQL, which allows plotting very large datasets in Jupyter by leveraging SQL engines such as DuckDB.
Once you compute the statistics, you can use R to generate the boxplot.
In a problem, I have a set of vectors. Each vector has sensor readings but are of different lengths. I'd like to compute the same descriptive statistics on each of these vectors. My question is, how should I store them in R. Using c() concatenates the vectors. Using list() seems to cause functions like mean() to misbehave. Is a data frame the right object?
What is the best practice for applying the same function to vectors if different sizes? Supposing the data resides in a SQL server, how should it be imported?
Vectors of different sizes should be combined into a list: a data.frame expects each column to be the same length.
Use lapply to fetch your data. Then use lapply again to get the descriptive statistics.
x <- lapply(ids, sqlfunction)
stats <- lapply(x, summary)
Where sqlfunction is some function you created to query your database. You can collapse the stats list into a data.frame by calling do.call(rbind, stats) or by using plyr:
library(plyr)
x <- llply(ids, sqlfunction)
stats <- ldply(x, summary)
Most plotting and regression functions expect data to be in a "long" format: numeric values in one column and grouping or covariate values in others. The stack function will accept irregular length lists, and tapply or aggregate will allow functions to work over irregular length category variables:
dlist <- list(a=1:2, b=13:15, cc= 5:1)
s.dfrm <- stack(dlist)
s.dfrm
values ind
1 1 a
2 2 a
3 13 b
4 14 b
5 15 b
6 5 cc
7 4 cc
8 3 cc
9 2 cc
10 1 cc
tapply(s.dfrm$values, s.dfrm$ind, mean)
a b cc
1.5 14.0 3.0
"What is the best practice for applying the same function to vectors if different sizes? Supposing the data resides in a SQL server, how should it be imported?"
As suggested by Shane, lappy is your definite choice here. You can, of course, use it with custom functions as well – in case you feel summary does not provide enough information.
For the SQL part: There are packages around for most relational DBMS: RPostgreSQL, RMySQL, ROracle and there´s RODBC as a general one. If you speak of MS SQL server, I am not sure if there is a specific package, but RODBC should do the job. I don´t know if you are married to MS SQL server stuff, but if it´s an option for you to run your own local database for R – RMySQL is really easy to set up.
In general, by using database packages you use wrappers like dbListTable, or dbReadTable which simply turns a table into a R data.frame.
If you really want to import the data you could use .csv exports of your database and use read.table or read.csv depending on what fits your needs. But I suggest to directly connect to the database – it´s not that difficult even if you haven´t done it before and it's more fun.
EDIT: I don´t use MS, but others done it before maybe the mailing list post helps
I would tend to import this into a data frame and not a list. Each of your individual vectors is likely differentiated by one or more meaningful variables. Let's say you wanted to keep track of the time that the data was collected and location it was collected from. In a data frame you would have one column that was all of the vectors concatenated together but they would each be differentiated by values in the time and location columns. To get each individual vector mean then tapply() might be the tool of choice.
tapply(df$y, list(df$time, df$location), mean)
Or, perhaps aggregate() would be even better, depending on the number of variables and your future needs.