I'm trying to learn R and I can't figure out how to append to a list.
If this were Python I would . . .
#Python
vector = []
values = ['a','b','c','d','e','f','g']
for i in range(0,len(values)):
vector.append(values[i])
How do you do this in R?
#R Programming
> vector = c()
> values = c('a','b','c','d','e','f','g')
> for (i in 1:length(values))
+ #append value[i] to empty vector
In R, you can try out this way:
X = NULL
X
# NULL
values = letters[1:10]
values
# [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j"
X = append(X,values)
X
# [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j"
X = append(X,letters[23:26])
X
# [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "w" "x" "y" "z"
> vec <- c(letters[1:3]) # vec <- c("a","b","c") ; or just empty vector: vec <- c()
> values<- c(1,2,3)
> for (i in 1:length(values)){
print(paste("length of vec", length(vec)));
vec[length(vec)+1] <- values[i] #Appends value at the end of vector
}
[1] "length of vec 3"
[1] "length of vec 4"
[1] "length of vec 5"
> vec
[1] "a" "b" "c" "1" "2" "3"
Sometimes we have to use loops, for example, when we don't know how many iterations we need to get the result. Take while loops as an example. Below are methods you absolutely should avoid:
a=numeric(0)
b=1
system.time(
{
while(b<=1e5){
b=b+1
a<-c(a,pi)
}
}
)
# user system elapsed
# 13.2 0.0 13.2
a=numeric(0)
b=1
system.time(
{
while(b<=1e5){
b=b+1
a<-append(a,pi)
}
}
)
# user system elapsed
# 11.06 5.72 16.84
These are very inefficient because R copies the vector every time it appends.
The most efficient way to append is to use index. Note that this time I let it iterate 1e7 times, but it's still much faster than c
.
a=numeric(0)
system.time(
{
while(length(a)<1e7){
a[length(a)+1]=pi
}
}
)
# user system elapsed
# 5.71 0.39 6.12
This is acceptable. And we can make it a bit faster by replacing [
with [[
.
a=numeric(0)
system.time(
{
while(length(a)<1e7){
a[[length(a)+1]]=pi
}
}
)
# user system elapsed
# 5.29 0.38 5.69
Maybe you already noticed that length
can be time consuming. If we replace length
with a counter:
a=numeric(0)
b=1
system.time(
{
while(b<=1e7){
a[[b]]=pi
b=b+1
}
}
)
# user system elapsed
# 3.35 0.41 3.76
As other users mentioned, pre-allocating the vector is very helpful. But this is a trade-off between speed and memory usage if you don't know how many loops you need to get the result.
a=rep(NaN,2*1e7)
b=1
system.time(
{
while(b<=1e7){
a[[b]]=pi
b=b+1
}
a=a[!is.na(a)]
}
)
# user system elapsed
# 1.57 0.06 1.63
An intermediate method is to gradually add blocks of results.
a=numeric(0)
b=0
step_count=0
step=1e6
system.time(
{
repeat{
a_step=rep(NaN,step)
for(i in seq_len(step)){
b=b+1
a_step[[i]]=pi
if(b>=1e7){
a_step=a_step[1:i]
break
}
}
a[(step_count*step+1):b]=a_step
if(b>=1e7) break
step_count=step_count+1
}
}
)
#user system elapsed
#1.71 0.17 1.89
FWIW: analogous to python's append():
b <- 1
b <- c(b, 2)
You have a few options:
c(vector, values)
append(vector, values)
vector[(length(vector) + 1):(length(vector) + length(values))] <- values
The first one is the standard approach. The second one gives you the option to append someplace other than the end. The last one is a bit contorted but has the advantage of modifying vector
(though really, you could just as easily do vector <- c(vector, values)
.
Notice that in R you don't need to cycle through vectors. You can just operate on them in whole.
Also, this is fairly basic stuff, so you should go through some of the references.
Some more options based on OP feedback:
for(i in values) vector <- c(vector, i)
What you're using in the python code is called a list in python, and it's tottaly different from R vectors, if i get what you wanna do:
# you can do like this if you'll put them manually
v <- c("a", "b", "c")
# if your values are in a list
v <- as.vector(your_list)
# if you just need to append
v <- append(v, value, after=length(v))
Just for the sake of completeness, appending values to a vector in a for loop is not really the philosophy in R. R works better by operating on vectors as a whole, as @BrodieG pointed out. See if your code can't be rewritten as:
ouput <- sapply(values, function(v) return(2*v))
Output will be a vector of return values. You can also use lapply
if values is a list instead of a vector.
Source: Stackoverflow.com