updating documentation

Author: edwindj

R/locate-errors.R

@@ -2,7 +2,8 @@
 #'
 #' Find out which fields in a data.frame are "faulty" using validation rules
 #' This method returns found errors, according to the specified method `x`.
-#' Use method [replace_errors()], to automatically remove these errors.
+#' Use method [replace_errors()], to automatically remove these errors. Use
+#' `[base::set.seed()]` beforehand to make the function call reproducible.
 #' `
 #'
 #' Use an `Inf` `weight` specification to fixate variables that can not be changed.

examples/locate_errors.R

@@ -6,6 +6,9 @@ data <- data.frame( profit   = 755
                   , cost     = 125
                   , turnover = 200
                   )
+
+# use set.seed to maake results reproducible
+set.seed(42)
 le <- locate_errors(data, rules)
 
 print(le)
@@ -29,13 +32,17 @@ v_logical <- validator( citizen %in% c(TRUE, FALSE)
                       )
 
 data <- data.frame(voted = TRUE, citizen = FALSE)
+
+set.seed(42)
 locate_errors(data, v_logical, weight=c(2,1))$errors
 
 # try a condinational rule
 v <- validator( married %in% c(TRUE, FALSE)
               , if (married==TRUE) age >= 17
               )
 data <- data.frame( married = TRUE, age = 16)
+
+set.seed(42)
 locate_errors(data, v, weight=c(married=1, age=2))$errors
 
 
@@ -52,9 +59,12 @@ weight <- read.csv(text=
        2,   1
 ", strip.white = TRUE)
 
+set.seed(42)
 locate_errors(data, v, weight = weight)$errors
 
 # fixate / exclude a variable from error localiziation
 # using an Inf weight
 weight <- c(age = Inf)
+
+set.seed(42)
 locate_errors(data, v, weight = weight)$errors

man/locate_errors.Rd

@@ -121,7 +121,9 @@ rules <- validator( r1 = age > 0
 With `validate::confront` we can see that rule `r2` is violated (record 2).
 
 ```{r}
-summary(confront(d, rules))
+d |> 
+  confront(rules) |> 
+  summary()
 ```
 
 
@@ -143,7 +145,7 @@ With `replace_errors` we can remove the errors (which still need to be imputed).
 
 ```{r}
 d_fixed <- replace_errors(d, le)
-summary(confront(d_fixed, rules))
+d_fixed |> confront(rules) |>summary()
 ```
 In which `replace_errors` set all faulty values to `NA`. 
 
@@ -153,9 +155,10 @@ d_fixed
 
 ### Weights
 
-`locate_errors` allows for supplying weigths for the variables. 
+`locate_errors` allows for supplying weights for the variables. 
 It is common that the quality of the observed variables differs.
-When we have more trust in `age` we can give it more weight so it chooses
+When we have more trust in `age` because it was retrieved from the
+official population register, we can give it more weight so it chooses
 income when it has to decide between the two (record 2):
 
 ```{r}
@@ -184,12 +187,12 @@ For example given the rule:
 and the following data:
 
 ```{r, echo=FALSE}
-d <- data.frame(age = 4, married = TRUE)
-d
+d2 <- data.frame(age = 4, married = TRUE)
+d2
 ```
 Then either `age` or `married` can be considered faulty.
 When no weights are specified, `locate_errors` will randomly choose one of the two.
-It does this by adding a small amount of random noise to the weights internally. 
+It does this by adding internally a small amount of random noise to the weights. 
 To make sure that the results are reproducible, it is good practice to use `set.seed`
 before calling `locate_errors`.
 
@@ -210,6 +213,8 @@ indicates the time spent to find a solution for each record. This can
 be restricted using the argument `timeout` (s).
 
 ```{r}
-# duration is in seconds. 
+# restrict time per record to max 30 seconds
+le <- locate_errors(d, rules, timeout=30)
+# duration is in seconds.
 le$duration
 ```