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tried to pick up 2 random subjects, but don't know how to do in R
random.subj <- sample(1:max(Data$Id), 2)
rd <- subset(Data$Id, Data$Id==random.subj)
I have a dataset "Data" like
Id
1
1
2
2
3
3
4
4
4
...
Well, in this case random.subj will be a vector of two elements. In that case, doing an equality comparison with == probably isn't want you want because it will just recycle through the shorter list to perform the comparison rather than checking each row for either value as you probably intend.
Also i'm not sure if all your IDs are numerical and sequential. It's better to just take a random sample from the IDs themselves rather than from the index of the IDs.
Fixing the second problem first
random.subj <- sample(Data$Id, 2)
Actually, if you just want two IDs then that's all you need, but if you want the data for those IDs then
rd <- subset(Data, Data$Id %in% random.subj)
is the correct way to extract it.
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I have a vector in R with zeroes,positive and negative numbers.I want to delete two values above and three values below every positive number and zero using for loops. How can I do it?
Here is a step by step solution:
# Generate a vector of random numbers between -10 and 3
v=round(runif(20)*13-10)
# Get positions of elements >= 0
to_delete=which(v>=0)
# Store position of elemets >=0, positions 1 or 2 before, and positions 1, 2, or 3
# after (i.e. the positions of elements we want to delete)
to_delete=c(to_delete, to_delete-1,to_delete-2,to_delete+1,to_delete+2,to_delete+3)
# Filter positions appearing multiple time and sort them
to_delete=sort(unique(to_delete))
# Keep only positions between 1 and length of initial vector
to_delete=to_delete[which(to_delete>0 & to_delete<=length(v))]
# Delete positions from the initial vector
output=v[-to_delete]
# Print outputs
print(v)
print(to_delete)
print(output)
Note that the output vector will be numeric(0) if there are no elements to keeh
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How can I scale(x) only certain columns of a dataframe? I have a dataframe with 7 columns and I want to scale only column 3 and 6. The rest should stay as it is.
We can do this with lapply. Subset the columns of interest, loop through them with lapply, assign the output back to the subset of data. Here, we are using c because the outpuf of scale is a matrix with a single column. Using c or as.vector, it gets converted to vector
df[c(3,6)] <- lapply(df[c(3, 6), function(x) c(scale(x)))
Or another option is mutate_at from dplyr
library(dplyr)
df %>%
mutate_at(c(3,6), funs(c(scale(.))))
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When I submit this code to R:
x <- c(1,2,4)
z <- c(7,6,3)
a <- x * z
I get:
a
[1] 7 12 12
So R just multiples element by element. But the two vectors are not compatible for multiplication because the first one has three columns and the second one does not have three rows.
What is happening internally?
Please note that these are vectors; not tables.
This means they can of course be multiplied with each other and would give the expected result through their inner product.
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I have a table like so:
1 2 3 4 5
a b a c
b b
a a a a
c b c b
Is there a special syntax to use for either Filter or Reduce (or something else entirely?) to get it so only the rows with an 'a' (including blanks) are shown? Likewise, is there a built-in way to count the frequency of 'a' for each column or would I have to loop over those individually?
Can't think of a way to pass rows or columns to Reduce or Filter to achieve the first, although a data.frame might get passed in a column-wise fashion for the second question since it is a list of columns. apply is the usual mechanism for doing row-wise operations, but I can think of quicker methods. For the first, under the assumption it is named X and is either a matrix or a data.frame:
X[ rowSums(X=="a", na.rm-TRUE) > 0 , ]
For the second:
colSums( X == "a")
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Does anybody know the examples on how to run paired ttest in Matlab/R/SAS or Python/Java on many columns (I have 1139 variables) in all combinations or selected respective columns in a loop.
thank you
MATLAB Solution:
If I understand correctly, you're just looking for a way to feed ttest with two different columns from your input matrix everytime. You can get all possible combinations of column pairs using nchoosek:
pairs = nchoosek(1:size(X, 2), 2);
Now you can iterate over these indices, each time invoking ttest with a different pair:
for idx = transpose(pairs)
h = ttest(X(:, idx(1)), X(:, idx(2)));
%// Do something with the result...
end