Recode with Table is responsible for recoding values of a dataset based on the specifications in variable_details.
rec_with_table(
data,
variables = NULL,
database_name = NULL,
variable_details = NULL,
else_value = NA,
append_to_data = FALSE,
log = FALSE,
notes = TRUE,
var_labels = NULL,
custom_function_path = NULL,
attach_data_name = FALSE
)A dataframe containing the variables to be recoded. Can also be a list of dataframes
character vector containing variable names to recode or a variables csv containing additional variable info
String, the name of the dataset containing the variables to be recoded. Can also be a vector of strings if data is a list
A dataframe containing the specifications (rules) for recoding.
Value (string, number, integer, logical or NA) that is used to replace any values that are outside the specified ranges (no rules for recoding).
Logical, if TRUE (default), recoded variables
will be appended to the data.
Logical, if FALSE (default), a log of recoding will
not be printed.
Logical, if FALSE (default), will not print the
content inside the `Note`` column of the variable being recoded.
labels vector to attach to variables in variables
path to location of the function to load
to attach name of database to end table
a dataframe that is recoded according to rules in variable_details.
The variable_details dataframe needs the following variables to function:
name of new (mutated) variable that is recoded
type the variable is being recoded to cat = categorical, cont = continuous
name of dataframe with original variables to be recoded
name of variable to be recoded
variable type of start variable. cat = categorical or factor variable cont = continuous variable (real number or integer)
Value to recode to
Value/range being recoded from
Each row in variable_details comprises one category in a newly transformed variable. The rules for each category the new variable are a string in recFrom and value in recTo. These recode pairs are the same syntax as sjmisc::rec(), except in sjmisc::rec() the pairs are a string for the function attribute rec =, separated by '='. For example in rec_w_table variable_details$recFrom = 2; variable_details$recTo = 4 is the same as sjmisc::rec(rec = "2=4"). the pairs are obtained from the RecFrom and RecTo columns
each recode pair is row. see above example or PBC-variableDetails.csv
multiple old values that should be recoded into a new single value may be separated with comma, e.g. recFrom = "1,2"; recTo = 1
a value range is indicated by a colon, e.g. recFrom= "1:4"; recTo = 1 (recodes all values from 1 to 4 into 1)
for double vectors (with fractional part), all values within the specified range are recoded; e.g. recFrom = "1:2.5'; recTo = 1 recodes 1 to 2.5 into 1, but 2.55 would not be recoded (since it's not included in the specified range)
minimum and maximum values are indicates by min (or lo) and max (or hi), e.g. recFrom = "min:4"; recTo = 1 (recodes all values from minimum values of x to 4 into 1)
all other values, which have not been specified yet, are indicated by else, e.g. recFrom = "else"; recTo = NA (recode all other values (not specified in other rows) to "NA")
the "else"-token can be combined with copy, indicating that all remaining, not yet recoded values should stay the same (are copied from the original value), e.g. recFrom = "else"; recTo = "copy"
NA values are allowed both as old and new value, e.g. recFrom "NA"; recTo = 1. or "recFrom = "3:5"; recTo = "NA" (recodes all NA into 1, and all values from 3 to 5 into NA in the new variable)
# NOT RUN {
library(cchsflow)
bmi2001 <- rec_with_table(
data = cchs2001_p, c(
"HWTGHTM",
"HWTGWTK", "HWTGBMI_der"
)
)
head(bmi2001)
bmi2011_2012 <- rec_with_table(
data = cchs2011_2012_p, c(
"HWTGHTM",
"HWTGWTK", "HWTGBMI_der"
)
)
tail(bmi2011_2012)
combined_bmi <- bind_rows(bmi2001, bmi2011_2012)
head(combined_bmi)
tail(combined_bmi)
# }
Run the code above in your browser using DataLab