I have several matrices that I would like to rbind in a single summary one. They are objects product of different functions and they have share a pattern in their names.
What I want to do is to tell R to look for all the objects with that common pattern and then rbind them.
Assuming these matrices exist:
commonname.N1<-matrix(nrow=2,ncol=3)
commonname.N2<-matrix(nrow=2,ncol=3)
commonname.M1<-matrix(nrow=2,ncol=3)
I tried something like this to get them:
mats<-grep(x= ls(pos=1), pattern="commonname.", value=TRUE)
mats
[1] "commonname.N1" "commonname.N2" "commonname.M1"
What I can't figure out is how to tell rbind to use that as argument. Basically I would something that gives the same matrix than what rbind(commonname.N1, commonname.N2, commonname.M1) would do in this example.
I have tried things on the line of
mats<-toString(mats)
rbind(mats2)
but that just creates a matrix with the different objects as names.
A similar question was asked here, but:
mats<-as.list(mats)
do.call(what=rbind, args=as.list(mats))
doesn't do the job.
Sorry if there is something basic I'm missing somewhere, but I can't figure it out and I'm relatively new to R.
Use mget:
do.call(rbind,mget(mats))
Related
This is a question of a general approach in R, I'm trying to find a way into R language but the data types and loop approaches (apply, sapply, etc) are a bit unclear to me.
What is my target:
Query data from API with parameters from a config list with multiple parameters. Return the data as aggregated data.frame.
First I want to define a list of multiple vectors (colums)
site segment id
google.com Googleuser 123
bing.com Binguser 456
How to manage such a list of value groups (row by row)? data.frames are column focused, you cant write a data.frame row by row in an R script. So the only way I found to define this initial config table is a csv, which is really an approach I try to avoid, but I can't find a way to make it more elegant.
Now I want to query my data, lets say with this function:
query.data <- function(site, segment, id){
config <- define_request(site, segment, id)
result <- query_api(config)
return result
}
This will give me a data.frame as a result, this means every time I query data the same columns are used. So my result should be one big data.frame, not a list of similar data.frames.
Now sapply allows to use one parameter-list and multiple static parameters. The mapply works, but it will give me my data in some crazy output I cant handle or even understand exactly what it is.
In principle the list of data.frames is ok, the data is correct, but it feels cumbersome to me.
What core concepts of R I did not understand yet? What would be the approach?
If you have a lapply/sapply solution that is returning a list of dataframes with identical columns, you can easily get a single large dataframe with do.call(). do.call() inputs each item of a list as arguments into another function, allowing you to do things such as
big.df <- do.call(rbind, list.of.dfs)
Which would append the component dataframes into a single large dataframe.
In general do.call(rbind,something) is a good trick to keep in your back pocket when working with R, since often the most efficient way to do something will be some kind of apply function that leaves you with a list of elements when you really want a single matrix/vector/dataframe/etc.
This is my first post, and I think I have looked thoroughly for my answer with no luck, but I might not be typing in the right search terms, since I am relatively new to R. I apologize if this has been answered before and if it has a link would be greatly appreciated.
In essence, I am trying to make a loop that will operate on a set of data frames that I have read into R from .txt files using read.table. I am working with simulated vegetation data organized into many species by site matrices, so it would be best for me if I could create loops that will just operate on the objects I have read in using some functions I have made and then put out new objects into my workspace with a specific naming pattern (e.g. put "_av" on the end of the name of the object operated on when creating a new object).
for convenience sake, lets say I have only four matrices I want to work with, all which contain the phrase "mod" for model. I have read that I can put these data frames into a list of data frames by the following code:
list.mods=lapply(ls(pattern="mod"),get)
This does create a list which I have been having trouble on getting my functions to actually operate on. From what I read this is the best way to make a list of objects you want to operate on.
So lets say that list.mods is now my list of operable matrices - mod1, mod2, mod3, and mod4. Also, lets say I have a function that simply calculates Bray-Curtis dissimilarity as follows:
bc=function(x){
vegdist(x,method="bray")
}
I can use this by typing in:
mod1.bc=bc(mod1)
That works. But it seems like I should be able to apply my list of models to the function bc and have it output the models with a pattern mod1.bc, mod2.bc, mod3.bc, and mod4.bc. I cannot get my list of files to work in the function much less save each operation as a new object with a patterned name.
What am I doing wrong? In the end I might have as many as a hundred models or more and would really appreciate being able to create a list of items that I can run through loops.
Thanks in advance.
You can use lapply again:
new.list.mods <- lapply(list.mods, bc)
This will return a new list in which each element is the result of applying bc to the corresponding element of list.mods.
The 'apply' family of functions in R basically allows you to save typing. If that's easier for you to understand, you can use a 'for loop' instead. Of course you will need to know how to access elements in a list for that. There is a question about that.
How about collecting the names of the models/objects you want into a list:
mod_list <- sapply(ls(pattern = "mod"), as.name)
and then looping over them with your function:
output_list <- lapply(eval(mod_list), bc)
With this approach you avoid creating the potentially large and redundant list.mods object in your example. Also, I think this will result in conveniently named lists.
I have 75 matrices that I want to search through. The matrices are named a1r1, a1r2, a1r3, a1r4, a1r5, a2r1,...a15r5, and I have a list with all 75 of those names in it; each matrix has the same number of rows and columns. Inside some nested for loops, I also have a line of code that, for the first matrix looks like this:
total <- (a1r1[row,i]) + (a1r1[row,j]) + (a1r1[row,k])
(i, j, k, and row are all variables that I am looping over.) I would like to automate this line so that the for loops would fully execute using the first matrix in the list, then fully execute using the second matrix and so on. How can I do this?
(I'm an experienced programmer, but new to R, so I'm willing to be told I shouldn't use a list of the matrix names, etc. I realize too that there's probably a better way in R than for loops, but I was hoping for sort of quick and dirty at my current level of R expertise.)
Thanks in advance for the help.
Here The R way to do this :
lapply(ls(pattern='a[0-9]r[0-9]'),
function(nn) {
x <- get(nn)
sum(x[row,c(i,j,k)])
})
ls will give a list of variable having a certain pattern name
You loop through the resulted list using lapply
get will transform the name to a varaible
use multi indexing with the vectorized sum function
It's not bad practice to build automatically lists of names designating your objects. You can build such lists with paste, rep, and sequences as 0:10, etc. Once you have a list of object names (let's call it mylist), the get function applied on it gives the objects themselves.
I have a list of several data.frames. Each data.frame has several columns.
By using
mean(mylist$first_dataframe$a
I can get the mean for a in this one data.frame.
However I do not know how to calculate over all the data.frames stored in my list or how for specific data.frames.
I could use a loop but I was told that
apply() and its variations are better
I tried using several solutions I found via search but somehow it just doesn't work.
I assume I need to use
unlist()
Could you provide an example of how to calculate e.g. a mean for a data structure like mine.
A list with several data.frames containing several columns.
Update:
I'm sorry for the confusion. I wanted the grand mean for a specific column in all dataframes.
Thanks to Thomas for providing a working solution for calculating a grand mean for a specific column in all dataframes and to psychometriko for providing a useful solution for calculating means over all columns in all dataframes (& even for the case when not numeric data is involved).
Thanks!
Is this what you are looking for?
set.seed(42)
mylist <- list(a=data.frame(foo=rnorm(10),
bar=rnorm(10)),
b=data.frame(foo=rnorm(10),
bar=rnorm(10)),
c=data.frame(foo=rnorm(10),
bar=rnorm(10)))
sapply(do.call("rbind",mylist),mean)
foo bar
0.1163340 -0.1696556
Note: do.call("rbind",mylist) returns something similar to what you referred to above with the unlist function, and then sapply, as referred to by Roland in his answer, just calls the function mean on each component (column) of the data.frame that results from the above do.call function.
Edit: In response to the question of how to deal with non-numeric data.frame components, the below solution admittedly isn't very elegant and I'm sure better ones exist, but here's the first thing I was able to think of:
set.seed(42)
mylist <- list(a=data.frame(rand=rnorm(10),
lets=sample(LETTERS,10,replace=TRUE)),
b=data.frame(rand=rnorm(10),
lets=sample(LETTERS,10,replace=TRUE)),
c=data.frame(rand=rnorm(10),
lets=sample(LETTERS,10,replace=TRUE)))
sapply(do.call("rbind",mylist),function(x) {
if (is.numeric(x)) mean(x)
})
$rand
[1] -0.02470602
$lets
NULL
This basically just creates a custom function that first tests whether each component is numeric and, if it is, returns the mean. If it isn't, it skips it.
The whole do.call('rbind', List) thing can be quite slow and prone to mishaps. If there is only one column you need the mean for, the best way is:
mean(sapply(mylist, function(X) X$rand))
It's about 10x faster the the do.call method.
I want to sort a data.frame by multiple columns, ideally using base R without any external packages (though if necessary, so be it). Having read How to sort a dataframe by column(s)?, I know I can accomplish this with the order() function as long as I either:
Know the explicit names of each of the columns.
Have a separate object representing each individual column by which to sort.
But what if I only have one vector containing multiple column names, of length that's unknown in advance?
Say the vector is called sortnames.
data[order(data[, sortnames]), ] won't work, because order() treats that as a single sorting argument.
data[order(data[, sortnames[1]], data[, sortnames[2]], ...), ] will work if and only if I specify the exact correct number of sortname values, which I won't know in advance.
Things I've looked at but not been totally happy with:
eval(parse(text=paste("data[with(data, order(", paste(sortnames, collapse=","), ")), ]"))). Maybe this is fine, but I've seen plenty of hate for using eval(), so asking for alternatives seemed worthwhile.
I may be able to use the Deducer library to do this with sortData(), but like I said, I'd rather avoid using external packages.
If I'm being too stubborn about not using external packages, let me know. I'll get over it. All ideas appreciated in advance!
You can use do.call:
data<-data.frame(a=rnorm(10),b=rnorm(10))
data<-data.frame(a=rnorm(10),b=rnorm(10),c=rnorm(10))
sortnames <- c("a", "b")
data[do.call("order", data[sortnames]), ]
This trick is useful when you want to pass multiple arguments to a function and these arguments are in convenient named list.