attributes.to.long

Start with a wide-form dataframe reported about alters using network method questions and convert it into a long-form dataset. For example, after a network survey of out-migrants, there might be variables about sex and age of each emigre reported to be connected to each respondent. In a study that encountered a maximum of 3 reported emigres across all respondents, this wide-form dataframe might look like:

resp.id	resp.d.hat	emage.1	emage.2	emage.3	emsex.1	emsex.2	emsex.3	1
100	24	NA	NA	M	NA	NA	2	110
NA	NA	NA	NA	NA	NA	3	140	33

The attributes.to.long function could convert that into a long-form dataframe that looks like this:

degree	age	sex
100	24	M
140	33	F
140	23	M
140	53	F
	...

(Note that we make no guarantees about the order in which the reshaped data will be returned.)

Usage

attributes.to.long(
  df,
  attribute.prefix,
  ego.vars = NULL,
  keep.na = FALSE,
  idvar = NULL,
  sep = "\\.|_",
  varname.first = TRUE
)

Arguments

df

the wide-form dataset to convert

attribute.prefix

a vector whose entries have the prefixes of the names of variables in the dataframe data that pertain to each alter. if you'd like these to be re-named in the long form data, then the variable names you'd like to use in the long form should be the names of each entry in this vector. in the example above, we would use attribute.prefix=c("age"="emage", "sex"="emsex"). see regexp, below, to understand how these prefixes are used to match columns of the dataset; by default, we assume that the variables match <either '.' or '_'>.

ego.vars

if not NULL, the names of columns in the dataset that refer to the egos and so should not be converted to long-form. you can specify that they should be renamed in the same way as with attribute.prefix. in the example above, we would use ego.vars=c("degree"="resp.d.hat").

keep.na

if FALSE, remove columns in the resulting dataset that are all NAs

idvar

the index or name of the variable in the data that has the respondent id. if NULL, then new ids which correspond to the rows in data are created.

sep

a regular expression that the wide-form variable names are split around. (eg, for "var_01", sep="_"; for "var.01" is is "\.")

varname.first

TRUE if the text before the separator is the variable name (eg var_01), and FALSE otherwise

Value

a long-form dataframe with the attributes reported for all of the alters. the dataframe will include an alter.id variable which is formed using .

TODO

• should follow the se / nse pattern in, eg, the kp functions; interim workaround – eg as.data.frame(df)[,idvar] – for now

• for now, this converts any factors into characters. this is obviously not ideal. eventually, data type should be preserved...

• handle the case of "" more effectively. Right now, we assume that all structural missings (eg, I only report one alter, though there are three columns for me to do so) are NA

• look at the code in the middle of the function that's commented out and be sure we know that the order of the rows will be the same, to that we can cbind them together.

Examples

if (FALSE) { # \dontrun{
   ## TODO add example
} # }