Reformatting
Reformatting.RmdIntroduction
Reformatting in dunlin consists in replacing
predetermined values by another in particular variables for selected
tables of a data set stored.
This is performed in two steps:
A Reformatting Map (
ruleobject) is created which specifies the correspondence between the old and the new valuesThe reformatting itself is performed with the
dunlin::reformat()function.
The Formatting Map Structure
The Reformatting Map is a rule object inheriting from
character. Its names are the new values to be used, and its
values are the old values to be used.
This rule will replace “a” with “A”, replace “c” or “d” with “B”.
Calling reformat
reformat is a generic supports reformatting of
character or factor. Reformatting for other
types of variables is meaningless. reformat will also
preserve the attributes of the original data, e.g. the data type or
labels will be unchanged.
An example of reformatting character can be
r <- rule(A = "a", B = c("c", "d"))
reformat(c("a", "c", "d", NA), r)
#> [1] A B B <NA>
#> Levels: A BWe can see that the NA values are not changed.
Now we test the factor reformatting:
r <- rule(A = "a", B = c("c", "d"))
reformat(factor(c("a", "c", "d", NA)), r)
#> [1] A B B <NA>
#> Levels: A BThe NA values are also not changed. However, if we
including reformatting for the NA, there is something
different:
r <- rule(A = "a", C = NA, B = c("c", "d"))
reformat(factor(c("a", "c", "d", NA)), r)
#> [1] A B B C
#> Levels: A B CBy default, the level replacing NA is set as the last
one. This can be changed by setting na_last = FALSE.
r <- rule(A = "a", C = NA, B = c("c", "d"))
reformat(factor(c("a", "c", "d", NA)), r, na_last = FALSE)
#> [1] A B B C
#> Levels: A C BFor list of data.frames, the
format argument is actually a nested list of rule. The
first layer indicates the table names, the second layer indicates the
variables in that table. Reformatting is only available for columns of
characters or factors, reformatting columns of another types will result
in a warning.
Example
df1 <- data.frame(
"char" = c("", "b", NA, "a", "k", "x"),
"fact" = factor(c("f1", "f2", NA, NA, "f1", "f1"), levels = c("f2", "f1")),
"logi" = c(NA, FALSE, TRUE, NA, FALSE, NA)
)
df2 <- data.frame(
"char" = c("a", "b", NA, "a", "k", "x"),
"fact" = factor(c("f1", "f2", NA, NA, "f1", "f1"))
)
db <- list(df1 = df1, df2 = df2)
attr(db$df1$char, "label") <- "my label"
rule_map <- list(
df1 = list(
char = rule("Empty" = "", "B" = "b", "Not Available" = NA),
fact = rule("F1" = "f1"),
logi = rule()
),
df2 = list(
char = rule("Empty" = "", "A" = "a", "Not Available" = NA)
)
)
res <- reformat(db, rule_map, na_last = TRUE)
res
#> $df1
#> char fact logi
#> 1 Empty F1 NA
#> 2 B f2 FALSE
#> 3 Not Available <NA> TRUE
#> 4 a <NA> NA
#> 5 k F1 FALSE
#> 6 x F1 NA
#>
#> $df2
#> char fact
#> 1 A f1
#> 2 b f2
#> 3 Not Available <NA>
#> 4 A <NA>
#> 5 k f1
#> 6 x f1