CODEWITHSUNDEEP
rfor-loopif-statement
user12159477
user12159477

Reputation:

For loop to extract data

I Have data set with these variables (Branch, Item, Sales, Stock) I need to make a for loop to extract a data with the following

The same item which has
1-different branches
2- its sales is higher than the stock

and save the result in data frame The code I used is


trials <- sample_n(Data_with_stock,1000)

for (i in  1:nrow(trials)) 
{
if(trials$sales[i] >  trials$stock[i] & trials$item[i] ==  trials$item[i+1] & trials$branch[i] !=  trials$branch[i+1])

{s <-data.frame( (trials$NAME[i])
  ,(trials$branch[i]))
} 

}

Upvotes: 2

Views: 85

Answers (2)

dpendi
dpendi

Reputation: 329

As you just want to fix your code:

You missed to set one =in your code.

Use:

trials <- sample_n(Data_with_stock,1000)
# next you need first to define s used in your loop
s <- array(NA, dim = c(1,2)) # as you only save 2 things in s per iteration

for (i in  1:nrow(trials)) {
# but I dont get why you compare the second condition.

if(trials$sales[i] >  trials$stock[i] & trials$item[i] ==  trials$item[i] & trials$branch[i] !=  trials$branch[i+1]) {

s[i,] <- cbind(trials$NAME[i], trials$branch[i])
} else {
s[i,] <- NA # just to have no problem with the index i, you can delete the one with na afterwards with na.omit()
}

Upvotes: 1

Rahul Podi Rajagopal
Rahul Podi Rajagopal

Reputation: 41

Suggest you use dplyr library, post installing considering "df" is your dataset, use the below commands for question 1 and 2

Question 1

question_one = df %>% group_by(Item) %>% summarise(No_of_branches = n_distinct(Branch))

items_with_more_than_one_branch = question_one[which(question_one$No_of_branches>1)"Item"]

Question 2: Similarly,

question_two = df %>% group_by(Item) %>% summarise(Stock_Val = sum(Stock), Sales_Val = sum(Sales))

item_with_sales_greater_than_stock = question_two[which(question_two$Sales > question_two$Stock),"Item"]

Couldn't help but solve without dplyr, however suggest, if not used yet, dplyr will always be useful for data crunching

Upvotes: 1

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