The harmonized variable is generated by replicating one input variable.

Note:

• One and only one variable can be replicated at a time.

The harmonized variable is generated by recoding values from one input variable.

Notes:

• One and only one variable can be recoded at a time.

• The variable to be recoded must be (partially at the very least) a categorical variable. To recode a continuous variable (to create brackets for example.), use case_when instead.

• If all categories are recoded to the same categories (recode(1 = 1 ; 2 = 2)), Prefer direct_mapping instead.

• Separate each value/code with an equal sign =

• Separate each elements with a semi-colon ; .

• Use ELSE = NA to attribute NA to all of the other values.

If an equal sign already exists in the data, use _= to escape them. Equally, if a semi-colon already exists in the data, use _; to escape them.

recode(
"banana ; apple"  = "fruits"    _;
"salad  ; potatoe" = "veggies"  _;
"bread  ; pasta"   = "carbs"        )

recode(
"1000 (='high')  _= 3  ;
" 500 (='mid')   _= 2  ;
" 200 (='low')   _= 1     )

The values can be gathered using R syntax to recode multiple numerical values.

recode(
0            = "low"   ;
c(1:10)      = "mid"   ;
c(-7, -99)   = NA    )

If the recoding requires more complex codification, use case_when or other instead.

The harmonized variable is generated from one or more if-else conditions, using one or more input variables.

Notes:

• Multiple variables can be used to combine their values using case_when. Separate each of them in input_variables by a semi-colon ;

• If only one variable is used, and is (or seems) a categorical variable, use recode or direct_mapping instead.

• Each statement (“if … equals, greater, is not, …”) can be use in this function. Separate the statement/code with a tilde ~

• Separate each elements with a semi-colon ; .

• Use ELSE ~ NA to attribute NA to all of the other values.

case_when is sensitive to the data type. Each code generated with the statement must have the same data type, including the NA.

case_when(
var_x == 1                 ~ 1L  
var_x != 0 & !is.na(var_y) ~ 0L  
ELSE                       ~ NA_integer_  )

case_when(
var_x == 1                 ~ "1"  
var_x != 0 & !is.na(var_y) ~ "0"  
ELSE                       ~ NA_character_)

If the statement requires more complex codification, use other instead.

The harmonized variable is generated by setting the same value for all observation, not taken from a input variable.

Notes:

• This function does not require any variable. The user must provide __BLANK__ as a placeholder.

• Usually, the harmonized variable is a standardized identifier for the whole dossier when comes the time to aggregate the harmonized datasets into a pooled harmonized dataset.

The harmonized variable is generated by applying an operation to one or more input variables.

Notes:

• Multiple variables can be used to combine their values using case_when. Separate each of them in input_variables by a semi-colon ;

• If the operation (or seems) is simple, prefer case_when, recode or direct_mapping instead.

• The user must have the libraries present on their machine (and loaded) to function with the call of them in the case_when script. To specify the library calling, use double two-point :: in the formula.

lubridate::year(var_x)

• If the operation is requires more complex codification, use other instead.

The harmonized variable is generated from a non-standard or complex processing rule, not covered by other rule categories.

Note:

• This feature is equivalent to launch a local code/function in a R script.

If assignment is needed to modify environment of the user, use double assignation <<- to place the result in the user environment. Carefully make sure you control your environment when using the other function.

my_harmo_var <- runif(20) + ... # complex lines of code

# double assignation to modify the environment.
harmonized_dossier$DATASET$variable_F <<- my_harmo_var

other function can be used to source a code from a different script where complex harmonization processes are written.

source("my_file.R")

These additional features allow the user to handle specific cases. This ensure that the line is completed and there is no missing argument in the function to perform.

Notes:

• __BLANK__ : If no variable is needed to generate the harmonized variable (for example using the rule category paste or other)

• impossible : If the project of research does not collect DataSchema variable or cannot be used to generate DataSchema variable or is unknown.

• undetermined : If the user needs further investigation to harmonize, or future information to be completed, they can use this feature without being blocked in the process.