Intermediate programming with R

Defensive programming with stopifnot

As we saw in the debugging section, working functions can still produce unexpected errors. And tracking down the cause of the errors can be difficult even with a debugging tool. This is why the concept of defensive programming is important. Defensive programming encourages us to frequently check conditions and throw an error if something is wrong. These checks are referred to as assertion statements because we want to assert some condition is TRUE before proceeding. This makes it easier to debug because we have a better idea of where the error originated. Recall the uninformative error we received from sum_calc_stat: “Error in apply(df_sub, 1, mean): dim(X) must have a positive length”, and the problem was actually introduced at the step before during the subsetting. While it will involve more work when initially writing the code, it will save time in the future when others (including your future self) use the code.

Checking conditions with stopifnot

To create an error, we can use the function stop. For example, if a variable x needed to be a character vector, we could check for this condition with an if statement and throw an error if the condition was violated.

x <- 1
if (!is.character(x)) {
  stop("x must be a character vector.")
}

Error in eval(expr, envir, enclos): x must be a character vector.

If we had multiple conditions to check, it would take many lines of code to check all of them. Luckily R provides the convenience function stopifnot. We can list as many requirements that should evaluate to TRUE, and stopifnot throws an error if it finds one that is FALSE.

stopifnot(is.character(x))

Error: is.character(x) is not TRUE

If x also had to have a length of one, we can add that condition.

stopifnot(length(x) == 1, is.character(x))

Error: is.character(x) is not TRUE

Listing these conditions also serves a secondary purpose as documentation for the code.

Let’s try out defensive programming by adding assertions to check the input to our function mean_metric_per_var.

mean_metric_per_var <- function(metric, variable) {
  if (!is.factor(variable)) {
    variable <- as.factor(variable)
  }
  variable <- droplevels(variable)
  result <- numeric(length = length(levels(variable)))
  names(result) <- levels(variable)
  for (v in levels(variable)) {
    result[v] <- mean(metric[variable == v], na.rm = TRUE)
  }
  return(result)
}

We want to assert the following:

• metric is a numeric vector
• metric and variable have the same length

mean_metric_per_var <- function(metric, variable) {
  stopifnot(is.numeric(metric),
            length(metric) == length(variable))
  if (!is.factor(variable)) {
    variable <- as.factor(variable)
  }
  variable <- droplevels(variable)
  result <- numeric(length = length(levels(variable)))
  names(result) <- levels(variable)
  for (v in levels(variable)) {
    result[v] <- mean(metric[variable == v], na.rm = TRUE)
  }
  return(result)
}

It still works when given proper input.

mean_metric_per_var(counts_raw$backtweetsCount, counts_raw$year)

       2003        2004        2005        2006        2007        2008 
0.000000000 0.009578544 0.054976303 0.016170763 0.040122277 0.047532408 
       2009        2010 
0.351047202 0.704338789 

But fails instantly if given improper input.

# Metric is a factor instead of numeric
mean_metric_per_var(counts_raw$journal, counts_raw$year)

Error: is.numeric(metric) is not TRUE

# The variable vector is shorter than metric
mean_metric_per_var(counts_raw$backtweetsCount, counts_raw$year[1:20])

Error: length(metric) == length(variable) is not TRUE

We can also check the output. Let’s add an assertion to ensure that the result does not contain any NAs.

mean_metric_per_var <- function(metric, variable) {
  stopifnot(is.numeric(metric),
            length(metric) == length(variable))
  if (!is.factor(variable)) {
    variable <- as.factor(variable)
  }
  variable <- droplevels(variable)
  result <- numeric(length = length(levels(variable)))
  names(result) <- levels(variable)
  for (v in levels(variable)) {
    result[v] <- mean(metric[variable == v], na.rm = TRUE)
    stopifnot(!is.na(result[v]))
  }
  return(result)
}

Send warnings

In general, if something is wrong, we want to produce an error as quickly as possible to make it easier to debug. However there are situations where it is appropriate to issue a warning to the user without stopping the code. For example, when a non-factor is passed to variable in mean_metric_per_var, we automatically convert it to a factor. Since the user could have simply passed in the wrong vector, we should issue a warning. This alerts the user to double check their input. To do this we use the function warning.

mean_metric_per_var <- function(metric, variable) {
  stopifnot(is.numeric(metric),
            length(metric) == length(variable))
  if (!is.factor(variable)) {
    warning("variable was automatically converted to a factor.")
    variable <- as.factor(variable)
  }
  variable <- droplevels(variable)
  result <- numeric(length = length(levels(variable)))
  names(result) <- levels(variable)
  for (v in levels(variable)) {
    result[v] <- mean(metric[variable == v], na.rm = TRUE)
    stopifnot(!is.na(result[v]))
  }
  return(result)
}

Now when we use a non-factor, the function warns us that it was converted to a factor.

mean_metric_per_var(counts_raw$backtweetsCount, counts_raw$year)

Warning in mean_metric_per_var(counts_raw$backtweetsCount, counts_raw
$year): variable was automatically converted to a factor.

       2003        2004        2005        2006        2007        2008 
0.000000000 0.009578544 0.054976303 0.016170763 0.040122277 0.047532408 
       2009        2010 
0.351047202 0.704338789 

Challenge

calc_sum_stat <- function(df, cols) {
  df_sub <- df[, cols, drop = FALSE]
  sum_stat <- apply(df_sub, 1, mean)
  return(sum_stat)
}

# Empty data frame
sum_stat <- calc_sum_stat(data.frame(), c("wosCountThru2010", "f1000Factor"))
# Non-character cols
sum_stat <- calc_sum_stat(counts_raw, 1:3)
# Bad column names
sum_stat <- calc_sum_stat(counts_raw, c("a", "b"))
# Issue warning since only one column
sum_stat <- calc_sum_stat(counts_raw, "mendeleyReadersCount")
# NA output
sum_stat <- calc_sum_stat(counts_raw, c("wosCountThru2010", "facebookLikeCount"))