How do I write a function in r to do cacluations on a record?

Question

In C# I am used to the concept of a data set and a current record. It would be easy for me to write a complicated calc-price function with conditions on the current record.

I am having trouble understanding how to do this in r.

I tried the following

   train <- read.csv("Train.csv" )
   df <- as.data.frame.matrix(train)
   v = c(  df$Fuel.Type ,df$No.Gears)
   names(v ) <- c( "FuelType" ,"NoGears")
   df$FEType = FEType( v)

Where the my function is defined as

FEType <- function(v    ){
  ret="Low"
  if (v["FuelType"]=='G') {
    ret ="High"
  }
  return(ret)
}

This is not working how I expected and when I examine v I see that it contains aggregate totals rather than the current row I expected.

Where am I going wrong?

In the question here I see some hints in the last paragraph.

To reproduce the problem, indicating what I want to do, I have

IsPretty <-function(PetalWidth){
  if (PetalWidth  >0.3) return("Y")
  return("N")
}

df <- iris
df$Pretty = IsPretty(df$Petal.Width)
    

This gives the error

the condition has length > 1 and only the first element will be used

Which led me to look into vectors. But I am not confident that is the right direction.

[Update]

I am used to thinking of tables and current records. Thus I was thinking that

df$Pretty = IsPretty(df$Petal.Width)

would have the effect of adding a column to my data frame with the calculated isPretty property

Why can I not include if conditions in my calculation?

Answer 1

Vectorization is one of the most fundamental (and unusual) things you'll need to get used to in R. Many (most?) R operations are vectorized. But a few things aren't - and if(){}else{} is one of the non-vectorized things. It's used for control flow (whether or not to run a code block) not for vector operations. ifelse() is a separate function that is used for vectors, where the first argument is a "test", and the 2nd and 3rd arguments are the "if yes" and "if no" results. The test is a vector, and the returned value is the appropriate yes/no result for each item in test. The result will be the same length as the test .

So we would write your IsPretty function like this:

IsPretty <- function(PetalWidth){
  return(ifelse(PetalWidth > 0.3, "Y", "N"))
}

df <- iris
df$Pretty = IsPretty(df$Petal.Width)

Contrast to an if(){...}else{...} block where the test condition is of length one, and arbitrary code can be run in the ... - may return a bigger result than the test, or a smaller result, or no result - might modify other objects... You can do anything inside if(){}else() , but the test condition must have length 1.

You could use your IsPretty function one row at a time - it will work fine for any one row. So we could put it in a loop as below, checking one row at time, giving if() one test at a time, assigning results one at a time. But R is optimized for vectorization, and this will be noticeably slower and is a bad habit.

IsPrettyIf <-function(PetalWidth){
  if (PetalWidth  >0.3) return("Y")
  return("N")
}

for(i in 1:nrow(df)) {
  df$PrettyLoop[i] = IsPrettyIf(df$Petal.Width[i])
}

A benchmark below shows that the vectorized version is 50x faster. This is such a simple case and such small data that it doesn't much matter, but on larger data, or with more complex operations the difference between vectorized and non-vectorized code can be minutes vs days.

microbenchmark::microbenchmark(
  loop = {
    for(i in 1:nrow(df)) {
      df$PrettyLoop[i] = IsPrettyIf(df$Petal.Width[i])
    }
  },
  vectorized = {
    df$Pretty = IsPretty(df$Petal.Width)    
  }
)
Unit: microseconds
       expr    min     lq     mean median      uq     max neval
       loop 3898.9 4365.6 5880.623 5442.3 7041.10 11344.6   100
 vectorized   47.7   59.6  112.288   67.4   83.85  1819.4   100

This is a common bump for R learners - you can find many questions on Stack Overflow where people are using if(){}else{} when they need ifelse() or vice versa. Why can't ifelse return vectors? is a FAQ coming from the opposite side of the problem.

---

What goes on in your attempt?

df <- iris

## The condition has length equal to the number of rows in the data frame
df$Petal.Width > 0.3
#>   [1] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
#>  [13] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
## ... truncated


## R warns us that only the first value (which happens to be FALSE) is used
result = if(df$Petal.Width > 0.3) {"Y"} else {"N"}
#> Warning in if (df$Petal.Width > 0.3) {: the condition has length > 1 and only
#> the first element will be used

## So the result is a single "N"
result  
#> [1] "N"

length(result)
#> [1] 1


## R "recycles" inputs that are of insufficient length
## so we get a full column of "N"
df$Pretty = result
head(df)
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species Pretty
#> 1          5.1         3.5          1.4         0.2  setosa      N
#> 2          4.9         3.0          1.4         0.2  setosa      N
#> 3          4.7         3.2          1.3         0.2  setosa      N
#> 4          4.6         3.1          1.5         0.2  setosa      N
#> 5          5.0         3.6          1.4         0.2  setosa      N
#> 6          5.4         3.9          1.7         0.4  setosa      N

^{Created on 2020-11-08 by the reprex package (v0.3.0)}

Answer 2

For my own notes on Gregor's answer

IsPrettyIf <-function(row){
 ret ="N"  
 if(row$Petal.Width > 0.3) { ret="Y"}
 return(ret)
}

 
df <- iris
df$PrettyLoop ="" # add a column and initialize all the cells to be empty
for(i in 1:5) {
  df$PrettyLoop[i] = IsPrettyIf(df[i,])
  cat("Row",i, "is Pretty?",df$PrettyLoop[i],"\n")
}

The bit that trips me up is that row$PrettyLoop is like a cell and df$PrettyLoop is like a column, thinking with the spreadsheet analogy.