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Is there an easy way to do this in r with a matrix, similar to negative indexing for data.frames?
for example, I can remove the n-th row of the matrix mat as follows:
mat = rbind(mat[1:(n-1),],mat[(n+1):nrow(mat)])
but are there faster and/or simpler ways to do this?
-Paul
Negative indexing works for matrices, i.e. mat[-n,]
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I’m new to R and I’m trying to add 15 to every figure in my dataset for a specific column and was wondering how it’s possible to this. Any help would be much appreciated, thanks.
Asssuming you have a data.frame df with a column col that you want to increase:
df$col <- df$col + 15
No loop required, the fundamental objects in R are vectors.
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Suppose we have a vector [1:10] of players, I want to generate all possible roommates for these playes (not combn(10, 2))
Can you help me?
Thank you
You can iterate over combn(with different ks)
x= 1:10
lapply(1:length(x), function(k) combn(x,k))
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Looking for other way than ifelse.
How to create NewColumn like this:
As displayed in your picture, you want to paste together two columns. Assuming your dataframe is called df, you can do:
df$NewColumn <- paste(df$Column2,"",df$Column1)
Which will get you the outcome in the picture.
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For one of my projects I would like to create several random matrices, which have full rank. Does anybody know a quick way to do this in R or has an idea how to proceed?
You are overwhelmingly likely to get a full-rank matrix if you generate a matrix with iid elements, with no additional constraints:
library(Matrix)
set.seed(101)
r <- replicate(1000,rankMatrix(matrix(rnorm(10000),100)))
table(r) ## all values are equal to 100
(Someone who spent more time on the math might be able to prove that the set of reduced-rank matrices within this space of matrices actually has measure 0 ...)
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I have data that contains 14 columns of predictors and 1 column of solution variable(y).
I wanted to know if there are any inbuilt functions to normalize and denormalize data in R.
Thank you.
normDataWithin of package {Rmisc} can be used: http://www.inside-r.org/packages/cran/Rmisc/docs/normDataWithin
Else following methods can be used:
(variable-mean)/sd . Following code can be used for a data.frame:
mydata$myNormalizedVar<-(mydata$myvar-mean(mydata$myvar))/sd(myvar)
log (log10), log2, and square root (sqrt)
Normal quantile normalization or normal quantile transformation. Try:
quantNorm = function(x){qnorm(rank(x,ties.method = "average")/(length(x)+1))}
hist(quantNorm(1:10000),100)