I am using the extract function in a loop. See below.
for (i in 1:length(list_shp_Tanzania)){
LU_Mod2000<- extract(x=rc_Mod2000_LC, y=list_shp_Tanzania[[i]], fun=maj)
}
Where maj function is:
maj <- function(x){
y <- as.numeric(names(which.max(table(x))))
return(y)
}
I was expecting to get i outputs, but I get only one output once the loop is done. Somebody knows what I am doing wrong. Thanks.
One solution in this kind of situation is to create a list and then assign the result of each iteration to the corresponding element of the list:
LU_Mod2000 <- vector("list", length(list_shp_Tanzania))
for (i in 1:length(list_shp_Tanzania)){
LU_Mod2000[[i]] <- extract(x=rc_Mod2000_LC, y=list_shp_Tanzania[[i]], fun=maj)
}
Do not do
LU_Mod2000 <- c(LU_Mod2000, extract(x=rc_Mod2000_LC, y=list_shp_Tanzania[[i]], fun=maj))
inside the loop. This will create unnecessary copies and will take long to run. Use the list method, and after the loop, convert the list of results to the desired format (usually using do.call(LU_Mod2000, <some function>))
Alternatively, you could substitute the for loop with lapply, which is what many people seem to prefer
LU_Mod2000 <- lapply(list_shp_Tanzania, function(z) extract(x=rc_Mod2000_LC, y=z, fun=maj))
Related
I am trying to create a vector or list of values based on the output of a function performed on individual elements of a column.
library(hpoPlot)
xyz_hpo <- c("HP:0003698", "HP:0007082", "HP:0006956")
getallancs <- function(hpo_col) {
for (i in 1:length(hpo_col)) {
anc <- get.ancestors(hpo.terms, hpo_col[i])
output <- list()
output[[length(anc) + 1]] <- append(output, anc)
}
return(anc)
}
all_ancs <- getallancs(xyz_hpo)
get.ancestors outputs a character vector of variable length depending on each term. How can I loop through hpo_col adding the length of each ancs vector to the output vector?
Welcome to Stack Overflow :) Great job on providing a minimal reproducible example!
As mentioned in the comments, you need to move the output <- list() outside of your for loop, and return it after the loop. At present it is being reset for each iteration of the loop, which is not what you want. I also think you want to return a vector rather than a list, so I have changed the type of output.
Also, in your original question, you say that you want to return the length of each anc vector in the loop, so I have changed the function to output the length of each iteration, rather than the whole vector.
getallancs <- function(hpo_col) {
output <- numeric()
for (i in 1:length(hpo_col)) {
anc <- get.ancestors(hpo.terms, hpo_col[i])
output <- append(output, length(anc))
}
return(output)
}
If you are only doing this for a few cases, such as your example, this approach will be fine, however, this paradigm is typically quite slow in R and it's better to try and vectorise this style of calculation if possible. This is especially important if you are running this for a large number of elements where computation will take more than a few seconds.
For example, one way the function above could be vectorised is like so:
all_ancs <- sapply(xyz_hpo, function(x) length(get.ancestors(hpo.terms, x)))
If in fact you did mean to output the whole vector of anc, not just the lengths, the original function would look like this:
getallancs <- function(hpo_col) {
output <- character()
for (i in 1:length(hpo_col)) {
anc <- get.ancestors(hpo.terms, hpo_col[i])
output <- c(output, anc)
}
return(output)
}
Or a vectorised version could be
all_ancs <- unlist(lapply(xyz_hpo, function(x) get.ancestors(hpo.terms, x)))
Hope that helps. If it solves your problem, please mark this as the answer.
Lets say I have 5 databases, named data1-data5. I basically want to create a loop that prints the first 10 rows of the data. In my naïve mind, the code should look something like this:
for (i in 1:5){
print(head(data[i]))
}
That does not work. What's the proper way to do this? How do I define [i] as the "indexing" variable for the different databases?
Another way would be to use get function:
for (i in 1:5){
tmp <- get(paste0("data", i))
## Assigns the data to the variable tmp - just like tmp <- data1/data2/data3 etc
print(head(tmp))
}
It would be better to put these objects in a list and use [[ to reference them. But if you must use separate names for the objects, then you need to parse them and evaluate the resulting expressions.
Here's an example you can emulate. For brevity, it prints the values of numerical objects rather than the heads of "databases."
data1 <- 1; data2 <- 2; data3 <- 3
for (i in 1:3) {
print(eval(parse(text=paste0("data", i))))
}
I am trying to concatenate strings using mapply function in R. However, I want one of the strings to be variable in mapply function. I have a snippet of my code below:
strings<-data.frame(x=c("dsf","sdf","sdf"))
strings2<-data.frame(extension=c(".csv",".json",".xml"))
for (i in 1:3)
{
strings_concat<-mapply(function(string1,string2) paste0(string1,string2),strings$x,strings2$extension[i])%>%
data.frame()%>%
unlist()%>%
data.frame()
#dosomething with strings_concat
}
But this is giving me the last iteration only
strings_concat
dsf.xml
sdf.xml
sdf.xml
bust instead, the desired output is as follows:
strings_concat
dsf.csv
sdf.csv
sdf.csv
dsf.json
sdf.json
sdf.json
dsf.xml
sdf.xml
sdf.xml
At every iteration, i want to combine strings_concat with another dataframe and save it. Can anyone help me if there is an easy way to do this in R?
Perhaps, outer is a better option here :
strings_concat <- c(outer(strings$x, strings2$extension, paste0))
strings_concat
#[1] "dsf.csv" "sdf.csv" "sdf.csv" "dsf.json" "sdf.json" "sdf.json"
# "dsf.xml" "sdf.xml" "sdf.xml"
You can add it in a data.frame :
df <- data.frame(strings_concat)
If you want to add some additional steps at each iteration you can use lapply :
lapply(strings2$extension, function(x) {
strings_concat <- paste0(strings$x, x)
#do something with strings_concat
})
All you should need to do is make sure you are continually augmenting your dataset. So I think this should do the trick:
strings<-data.frame(x=c("dsf","sdf","sdf"))
strings2<-data.frame(extension=c(".csv",".json",".xml"))
# We are going to keep adding things to results
results = NULL
for (i in 1:3)
{
strings_concat<-mapply(function(string1,string2) paste0(string1,string2),strings$x,strings2$extension[i])%>%
data.frame()%>%
unlist()%>%
data.frame()
# Here is where we keep adding things to results
results = rbind(results, strings_concat)
}
print(results)
Caution: not in front a computer with R so this code is untested
I currently have a for loop as below and it does not run as fast as I would like it to.
library(dplyr)
DF<-data.frame(Name=c('Bob','Joe','Sally')) #etc
PrimaryResult <- Function1(DF)
ResultsDF<-Function2(PrimaryResult)
for(i in 1:9)
{
Filtered<-filter(DF,Name!=PrimaryResult[i,2])
NextResult <- Function1(Filtered)
ResultsDF<-rbind(ResultsDF,Function2(NextResult))
}
The code takes an initial result of Function1 (which is a list of names) and tries it again with each name in the initial result being excluded individually to provide alternative results. These are returned as a one row data frame via Function2 and appended to the Results data frame.
How can I make this faster?
It seems like your main problem is the appending results from function 2 each iteration with rbind. This is classically slow because you are telling R to rewrite a bunch of information at each time step and R does not really know how large of a vector you are going to end up with.
Try making your results into a list vector. I don't really know what your functions do so I can't really assist with that part.
results_list <- vector("list", 10)
results_list[[1]] <- Function2(PrimaryResult)
for(i in 1:9){
Filtered<-filter(DF,Name!=PrimaryResult[i,2])
NextResult <- Function1(Filtered)
results_list[[i+1]]<-rbind(results_list[[i]],Function2(NextResult))
}
This is not perfect, but it should speed things up a bit.
So, I built a function called sort.song.
My goal with this function is to randomly sample the rows of a data.frame (DATA) and then filter it out (DATA.NEW) to analyse it. I want to do it multiple times (let's say 10 times). By the end, I want that each object (mantel.something) resulted from this function to be saved in my workspace with a name that I can relate to each cycle (mantel.something1, mantel.somenthing2...mantel.something10).
I have the following code, so far:
sort.song<-function(DATA){
require(ade4)
for(i in 1:10){ # Am I using for correctly here?
DATA.NEW <- DATA[sample(1:nrow(DATA),replace=FALSE),]
DATA.NEW <- DATA.NEW[!duplicated(DATA.NEW$Point),]
coord.dist<-dist(DATA.NEW[,4:5],method="euclidean")
num.notes.dist<-dist(DATA.NEW$Num_Notes,method="euclidean")
songdur.dist<-dist(DATA.NEW$Song_Dur,method="euclidean")
hfreq.dist<-dist(DATA.NEW$High_Freq,method="euclidean")
lfreq.dist<-dist(DATA.NEW$Low_Freq,method="euclidean")
bwidth.dist<-dist(DATA.NEW$Bwidth_Song,method="euclidean")
hfreqlnote.dist<-dist(DATA.NEW$HighFreq_LastNote,method="euclidean")
mantel.numnotes[i]<<-mantel.rtest(coord.dist,num.notes.dist,nrepet=1000)
mantel.songdur[i]<<-mantel.rtest(coord.dist,songdur.dist,nrepet=1000)
mantel.hfreq[i]<<-mantel.rtest(coord.dist,hfreq.dist,nrepet=1000)
mantel.lfreq[i]<<-mantel.rtest(coord.dist,lfreq.dist,nrepet=1000)
mantel.bwidth[i]<<-mantel.rtest(coord.dist,bwidth.dist,nrepet=1000)
mantel.hfreqlnote[i]<<-mantel.rtest(coord.dist,hfreqlnote.dist,nrepet=1000)
}
}
Could someone please help me to do it the right way?
I think I'm not assigning the cycles correctly for each mantel.somenthing object.
Many thanks in advance!
The best way to implement what you are trying to do is through a list. You can even make it take two indices, the first for the iterations, the second for the type of analysis.
mantellist <- as.list(1:10) ## initiate list with some values
for (i in 1:10){
...
mantellist[[i]] <- list(numnotes=mantel.rtest(coord.dist,num.notes.dist,nrepet=1000),
songdur=mantel.rtest(coord.dist,songdur.dist,nrepet=1000),
hfreq=mantel.rtest(coord.dist,hfreq.dist,nrepet=1000),
...)
}
return(mantellist)
In this way you can index your specific analysis for each iteration in an intuitive way:
mantellist[[2]][['hfreq']]
mantellist[[2]]$hfreq ## alternative
EDIT by Mohr:
Just for clarification...
So, according to your suggestion the code should be something like this:
sort.song<-function(DATA){
require(ade4)
mantellist <- as.list(1:10)
for(i in 1:10){
DATA.NEW <- DATA[sample(1:nrow(DATA),replace=FALSE),]
DATA.NEW <- DATA.NEW[!duplicated(DATA.NEW$Point),]
coord.dist<-dist(DATA.NEW[,4:5],method="euclidean")
num.notes.dist<-dist(DATA.NEW$Num_Notes,method="euclidean")
songdur.dist<-dist(DATA.NEW$Song_Dur,method="euclidean")
hfreq.dist<-dist(DATA.NEW$High_Freq,method="euclidean")
lfreq.dist<-dist(DATA.NEW$Low_Freq,method="euclidean")
bwidth.dist<-dist(DATA.NEW$Bwidth_Song,method="euclidean")
hfreqlnote.dist<-dist(DATA.NEW$HighFreq_LastNote,method="euclidean")
mantellist[[i]] <- list(numnotes=mantel.rtest(coord.dist,num.notes.dist,nrepet=1000),
songdur=mantel.rtest(coord.dist,songdur.dist,nrepet=1000),
hfreq=mantel.rtest(coord.dist,hfreq.dist,nrepet=1000),
lfreq=mantel.rtest(coord.dist,lfreq.dist,nrepet=1000),
bwidth=mantel.rtest(coord.dist,bwidth.dist,nrepet=1000),
hfreqlnote=mantel.rtest(coord.dist,hfreqlnote.dist,nrepet=1000)
)
}
return(mantellist)
}
You can achieve your objective of repeating this exercise 10 (or more times) without using an explicit for-loop. Rather than have the function run the loop, write the sort.song function to run one iteration of the process, then you can use replicate to repeat that process however many times you desire.
It is generally good practice not to create a bunch of named objects in your global environment. Instead, you can hold of the results of each iteration of this process in a single object. replicate will return an array (if possible) otherwise a list (in the example below, a list of lists). So, the list will have 10 elements (one for each iteration) and each element will itself be a list containing named elements corresponding to each result of mantel.rtest.
sort.song<-function(DATA){
DATA.NEW <- DATA[sample(1:nrow(DATA),replace=FALSE),]
DATA.NEW <- DATA.NEW[!duplicated(DATA.NEW$Point),]
coord.dist <- dist(DATA.NEW[,4:5],method="euclidean")
num.notes.dist <- dist(DATA.NEW$Num_Notes,method="euclidean")
songdur.dist <- dist(DATA.NEW$Song_Dur,method="euclidean")
hfreq.dist <- dist(DATA.NEW$High_Freq,method="euclidean")
lfreq.dist <- dist(DATA.NEW$Low_Freq,method="euclidean")
bwidth.dist <- dist(DATA.NEW$Bwidth_Song,method="euclidean")
hfreqlnote.dist <- dist(DATA.NEW$HighFreq_LastNote,method="euclidean")
return(list(
numnotes = mantel.rtest(coord.dist,num.notes.dist,nrepet=1000),
songdur = mantel.rtest(coord.dist,songdur.dist,nrepet=1000),
hfreq = mantel.rtest(coord.dist,hfreq.dist,nrepet=1000),
lfreq = mantel.rtest(coord.dist,lfreq.dist,nrepet=1000),
bwidth = mantel.rtest(coord.dist,bwidth.dist,nrepet=1000),
hfreqlnote = mantel.rtest(coord.dist,hfreqlnote.dist,nrepet=1000)
))
}
require(ade4)
replicate(10, sort.song(DATA))