Loop in R: How to Save the Outputs

Loop in R: how to save the outputs?

Store each boolean matrix as an item in a list:

result <- vector("list",49)
for (i in 1:49)
{
   result[[i]] <- T1>F[i] # I used print to see the results in the screen
}

#Print the first matrix on screen
result[[1]]

How to loop over on different files and save the output with filename in R?

How about putting all your names in a single vector, called names, like this:

names<-c("TRYFG","RTDFE",...)

and then feeding each one to a function that reads the files, merges them, and returns the rows

f<-function(n) {
    fs = paste0(n,c("_filter", "_data"),".txt")
    C = merge(
        read.delim(fs[1],sep="\t", header=F),
        read.delim(fs[2],sep="\t", header=F), by="XYZ")
    data.frame(Samples=n,Common=nrow(C))
}

Then just call call this function f on each of the values in names, row binding the result together

do.call(rbind, lapply(names, f))

An easy way to create the vector names is like this:

p = "_(filter|data).txt"
names = unique(gsub(p,"",list.files(pattern = p)))

R: Saving the Results of a Loop

You can use expand.grid to create all possible combinations of vec1, vec2 and vec3 and save con$overall[1] on each iteration in the dataframe.

library(caret)
library(rpart)

#generate data

a = rnorm(1000, 10, 10)
b = rnorm(1000, 10, 5)
c = rnorm(1000, 5, 10)
group <- sample( LETTERS[1:2], 1000, replace=TRUE, prob=c(0.5,0.5))
group_1 <- 1:1000

#put data into a frame
d = data.frame(a,b,c, group, group_1)
d$group = as.factor(d$group)

e <- d
vec1 <- sample(200:300, 5)
vec2 <- sample(400:500,5)
vec3 <- sample(700:800,5)
z <- 0
df <- expand.grid(vec1, vec2, vec3)
df$Accuracy <- NA

for (i in seq_along(vec1)) { 
  for (j in seq_along(vec2)) {
    for (k in seq_along(vec3)) {
      # d <- e
      d$group_2 = as.integer(ifelse(d$group_1 < vec1[i] , 0, ifelse(d$group_1 >vec1[i]  & d$group_1 < vec2[j] , 1, ifelse(d$group_1 >vec2[j]  & d$group_1 < vec3[k] , 2,3))))
      
      d$group_2 = as.factor(d$group_2)
      
      fitControl <- trainControl(## 10-fold CV
        method = "repeatedcv",
        number = 2,
        ## repeated ten times
        repeats = 1)
      
      TreeFit <- train(group_2 ~ ., data = d[,-5],
                       method = "rpart",
                       trControl = fitControl)
      
      pred <- predict(
        TreeFit,
        d[,-5])
      
      con <- confusionMatrix(
        d$group_2,
        pred) 
      
      #update results into table
      #final_table[i,j] = con$overall[1]
      z <- z + 1
      df$Accuracy[z] <- con$overall[1]
    }
  }
}

head(df)

#  Var1 Var2 Var3 Accuracy
#1  300  492  767    0.299
#2  202  492  767    0.299
#3  232  492  767    0.299
#4  293  492  767    0.376
#5  231  492  767    0.299
#6  300  435  767    0.331

How to save the for loop output as data.frame in R?

Here is a solution that is most similar to your code.

The points are using the initialisation (indexing ...[NULL, ]) and the function rbind()

output <- mtcars[NULL,]
for (i in seq_len(nrow(mtcars))) { 
  if (i  <= 30) {
        next
  }
  # ...
  output <- rbind(output, mtcars[i, ]) 
}

loop to run through multiple datasets and save each output in R

Have you considered changing your code to a function? You can store all data.frames in a named list, then apply the function on each data.frame in the list and then collect the results.

library(tidyverse)
library(broom)

# changing your code to a function
my_function <- function(.df){
  
  nls.mon <- nls(wt~A*(1-exp(k*(t0-age))), 
                 data=.df,
                 start = list(A=253.6,k=.03348,t0=32.02158))
  
  nls.von <- nls(wt ~A*(1-(1/3)*exp(k*(t0-age)))^3, 
                 data=.df,
                 start= list(A=253.6,k=.03348,t0=32.02158))
  
  # I slightly edited this part of your code
  df <- bind_rows(
    tidy(nls.mon) %>% mutate(model = "nls.mon"),
    tidy(nls.von) %>% mutate(model = "nls.von")
  ) %>%
    select(model, term, estimate) %>%
    pivot_wider(names_from = "term", values_from = "estimate")

  return(df)
}

# reading in data, the path to the data needs to be changed
df1 <- read_csv(r"{C:\Users\novot\Downloads\sample.csv}") %>%
  select(-1)

df2 <- df1 %>%
  filter(sex == "M")

# using map to apply the created function to each member of the list
df_out <- list("df1" = df1, "df2" = df2) %>%
  map(
    ~my_function(.x)
  ) %>%
  bind_rows(.id = "dataframe")

df_out
#> # A tibble: 4 x 5
#>   dataframe model       A      k    t0
#>   <chr>     <chr>   <dbl>  <dbl> <dbl>
#> 1 df1       nls.mon  248. 0.0135  2.09
#> 2 df1       nls.von  246. 0.0222 32.9 
#> 3 df2       nls.mon  248. 0.0135  2.09
#> 4 df2       nls.von  246. 0.0222 32.9

R: Saving model output from for loop - list is blank

Your problem is (as said by Pkumar in the comments) that you are assigning your loop to your variable. If you for example try running the code segment

x <- list()
for(i in 1:10){
  x[[i]] <- rnorm(i)
}
x # List of length 10, containing vectors of length 1:10

x <- for(i in 1:10){
  x[[i]] <- rnorm(i)
}
x # NULL

The for-loop itself does not return a value, it simply iterates. So if you instead ran

Models <- list()
for(i in 1:nlayers(Y_Vals)){
  s <- stack(Y_Vals[[i]], r1)
  df <- data.frame(na.omit(values(s)))
  names(df) <- c("response_var", "pred_var")
  m <- glm(response_var~pred_var, data=df, family=binomial)
  Models[[i]] <- m
}

you would see the result you expect. Note that iteratively extending a list is inefficient however. So to be more efficient, you could instead start with Models <- vector('list', nlayers(Y_Vals)), this will increase your performance slightly for larger datasets.

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