R/scrapeVarCross.R
In jsakaluk/dySEM: Dyadic Structural Equation Modeling
Defines functions
scrapeVarCross
Documented in
scrapeVarCross
#' A Variable Name-Scraping and Indexing Function for cross-sectional data
#'
#' This function scrapes the names of indicator variables in a
#' wide-format data set used for dyadic analyses of two latent variables (LV; X and Y),
#' and indexes which indicators correspond to which partner, for which LV.
#' It is used primarily to guide the syntax-writing of the other dySEM functions.
#' @param dat input data frame of indicators of a particular LV
#' @param x_order input character for order of (S)tem, (P)artner number, and (I)tem number
#' when creating variable names. Defaults to "spi" (Qualtrics-friendly), but can alternatively take "sip" or "psi"
#' @param x_stem input character stem of indicator variables for LV X
#' @param x_delim1 optional character to separate stem from partner number (spi) or item number (sip)
#' @param x_delim2 optional character to separate stem/partner number (spi) or stem/item number (sip) from
#' from final element of variable name
#' @param x_item_num defaults to scrape all items that match the stem with any digits that follow.
#' Will be updated to allow particular range of values, to make more sub-scale friendly.
#' @param distinguish_1 input character used as the identifier for the first partner
#' @param distinguish_2 input character used as the identifier for the first partner
#' @param y_order optional character for order of (S)tem, (P)artner number, and (I)tem number
#' when creating variable names. Defaults to "spi" (Qualtrics-friendly), but can alternatively take "sip" or "psi". This and other Y-arguments
#' only necessary if there is a latent Y variable to model
#' @param y_stem optional input character stem of indicator variables for LV X
#' @param y_delim1 optional character to separate stem from partner number (spi) or item number (sip)
#' @param y_delim2 optional character to separate stem/partner number (spi) or stem/item number (sip) from
#' from final element of variable name
#' @param y_item_num defaults to scrape all items that match the stem with any digits that follow.
#' Will be updated to allow particular range of values, to make more sub-scale friendly.
#' @param var_list optional named list of indicator variable information, if more than one LV is to be scripted (e.g., a dyadic CFA with multiple sub-scales from the same measure). If supplied, this list *must* contain the following elements: "stem" (a vector of stems), "delim1" (a vector of delimiting characters), and"delim2" (a vector of subsequently delimiting characters). Optionally may include numeric vectors "min_num" and "max_num" if indicators for different LVs share the same stem and must be separated by range of item numbers within a measure.
#' @param var_list_order optional character for order of (S)tem, (P)artner number, and (I)tem number for any of the indicator variables of a multi-LV model (i.e., this functionality assumes the same ordering of elements throughout)
#' @param var_list_item_num optional character for item number of any of the indicator variables of a multi-LV model
#' @param covs_order optional character for order of (S)tem, (P)artner number, and (I)tem number for any
#' covariate(s). Defaults to NULL. This and other covariate arguments only necessary if there are covariates to be scripted in your model(s).
#' @param covs_stem optional input character stem(s) of indicator variables for covariate(s). Can accept a single stem (e.g., "anx"), or a vector of stems (e.g., c("anx", "dep")).
#' Defaults to NULL.
#' @param covs_delim1 optional character to separate stem from partner number (spi) or item number (sip) for covariate(s). Defaults to NULL.
#' @param covs_delim2 optional character to separate stem/partner number (spi) or stem/item number (sip) from
#' @return a list, referred in short-hand as a "dvn" (dyad variable names list) containing variable names for p1, p2, # of items per LV,
#'characters distinguishing partners, and total number of indicators
#' @family variable-scraping functions
#' @export
#' @examples
#' dvnx <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
#' x_delim2="_", distinguish_1="1", distinguish_2="2")
#' dvnxy <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
#' x_delim2="_", distinguish_1="1", distinguish_2="2",
#' y_order="spi", y_stem="com", y_delim1 = ".", y_delim2="_")
scrapeVarCross <- function(dat, x_order = "spi", x_stem, x_delim1=NULL, x_delim2=NULL, x_item_num="\\d+", distinguish_1="1", distinguish_2="2",
y_order=NULL, y_stem=NULL, y_delim1=NULL, y_delim2=NULL, y_item_num="\\d+",
var_list = NULL, var_list_order = NULL, var_list_item_num="\\d+",
covs_order = NULL, covs_stem = NULL, covs_delim1 = NULL, covs_delim2 = NULL){
if(!is.null(var_list)){
if(var_list_order == "sip"){
x1vars = list()
for(i in 1:length(var_list$lvnames)){
x1VarNames <- sipExtractor(dat = dat, stem = var_list$stem[i],
delim1= var_list$delim1[i], item_num = var_list_item_num,
delim2 = var_list$delim2[i], distinguish = distinguish_1)
#subset to variables in range, if range is supplied (otherwise merely supply all counted variables matching)
if(all(c("min_num", "max_num") %in% names(var_list))){
x1VarNames <- x1VarNames[var_list$min_num[i]:var_list$max_num[i]]
}
x1vars[[var_list$lvnames[i]]] <- x1VarNames
}
x2vars = list()
for(i in 1:length(var_list$lvnames)){
x2VarNames <- sipExtractor(dat = dat, stem = var_list$stem[i],
delim1= var_list$delim1[i], item_num = var_list_item_num,
delim2 = var_list$delim2[i], distinguish = distinguish_2)
#subset to variables in range, if range is supplied (otherwise merely supply all counted variables matching)
if(all(c("min_num", "max_num") %in% names(var_list))){
x2VarNames <- x2VarNames[var_list$min_num[i]:var_list$max_num[i]]
}
x2vars[[var_list$lvnames[i]]] <- x2VarNames
}
num_x1_var <- sum(lengths(x1vars))# count total number of items in x1
num_x2_var <- sum(lengths(x2vars))# count total number of items in x2
dist_1 <- distinguish_1
dist_2 <- distinguish_2
tot_var <- num_x1_var + num_x2_var# count total number of items in x1 and x2
if(num_x1_var == num_x2_var){#return list unless number of x1 and x2 items dont match
varlist <- list(p1xvarnames = x1vars,
p2xvarnames = x2vars,
xindper = num_x1_var,
dist1 =dist_1,
dist2 = dist_2,
indnum = tot_var)
return(varlist)
}else if(num_x1_var != num_x2_var){
stop("scrapeVarCross() cannot detect a similar number of ", x_stem, " items for P1 and P2")
}
}
else if(var_list_order == "spi"){
x1vars = list()
for(i in 1:length(var_list$lvnames)){
x1VarNames <- spiExtractor(dat = dat, stem = var_list$stem[i],
delim1= var_list$delim1[i], item_num = var_list_item_num,
delim2 = var_list$delim2[i], distinguish = distinguish_1)
#subset to variables in range, if range is supplied (otherwise merely supply all counted variables matching)
if(all(c("min_num", "max_num") %in% names(var_list))){
x1VarNames <- x1VarNames[var_list$min_num[i]:var_list$max_num[i]]
}
x1vars[[var_list$lvnames[i]]] <- x1VarNames
}
x2vars = list()
for(i in 1:length(var_list$lvnames)){
x2VarNames <- spiExtractor(dat = dat, stem = var_list$stem[i],
delim1= var_list$delim1[i], item_num = var_list_item_num,
delim2 = var_list$delim2[i], distinguish = distinguish_2)
#subset to variables in range, if range is supplied (otherwise merely supply all counted variables matching)
if(all(c("min_num", "max_num") %in% names(var_list))){
x2VarNames <- x2VarNames[var_list$min_num[i]:var_list$max_num[i]]
}
x2vars[[var_list$lvnames[i]]] <- x2VarNames
}
num_x1_var <- sum(lengths(x1vars))# count total number of items in x1
num_x2_var <- sum(lengths(x2vars))# count total number of items in x2
dist_1 <- distinguish_1
dist_2 <- distinguish_2
tot_var <- num_x1_var + num_x2_var# count total number of items in x1 and x2
if(num_x1_var == num_x2_var){#return list unless number of x1 and x2 items dont match
varlist <- list(p1xvarnames = x1vars,
p2xvarnames = x2vars,
xindper = num_x1_var,
dist1 =dist_1,
dist2 = dist_2,
indnum = tot_var)
return(varlist)
}else if(num_x1_var != num_x2_var){
stop("scrapeVarCross() cannot detect a similar number of ", x_stem, " items for P1 and P2")
}
}
else if(var_list_order == "psi"){
x1vars = list()
for(i in 1:length(var_list$lvnames)){
x1VarNames <- psiExtractor(dat = dat, stem = var_list$stem[i],
delim1= var_list$delim1[i], item_num = var_list_item_num,
delim2 = var_list$delim2[i], distinguish = distinguish_1)
#subset to variables in range, if range is supplied (otherwise merely supply all counted variables matching)
if(all(c("min_num", "max_num") %in% names(var_list))){
x1VarNames <- x1VarNames[var_list$min_num[i]:var_list$max_num[i]]
}
x1vars[[var_list$lvnames[i]]] <- x1VarNames
}
x2vars = list()
for(i in 1:length(var_list$lvnames)){
x2VarNames <- psiExtractor(dat = dat, stem = var_list$stem[i],
delim1= var_list$delim1[i], item_num = var_list_item_num,
delim2 = var_list$delim2[i], distinguish = distinguish_2)
#subset to variables in range, if range is supplied (otherwise merely supply all counted variables matching)
if(all(c("min_num", "max_num") %in% names(var_list))){
x2VarNames <- x2VarNames[var_list$min_num[i]:var_list$max_num[i]]
}
x2vars[[var_list$lvnames[i]]] <- x2VarNames
}
num_x1_var <- sum(lengths(x1vars))# count total number of items in x1
num_x2_var <- sum(lengths(x2vars))# count total number of items in x2
dist_1 <- distinguish_1
dist_2 <- distinguish_2
tot_var <- num_x1_var + num_x2_var# count total number of items in x1 and x2
if(num_x1_var == num_x2_var){#return list unless number of x1 and x2 items dont match
varlist <- list(p1xvarnames = x1vars,
p2xvarnames = x2vars,
xindper = num_x1_var,
dist1 =dist_1,
dist2 = dist_2,
indnum = tot_var)
return(varlist)
}else if(num_x1_var != num_x2_var){
stop("scrapeVarCross() cannot detect a similar number of ", x_stem, " items for P1 and P2")
}
}
}
else if(is.null(y_order)){
if(x_order == "sip"){
x1vars <- sipExtractor(dat, x_stem, x_delim1, x_item_num, x_delim2, distinguish_1)
x2vars <- sipExtractor(dat, x_stem, x_delim1, x_item_num, x_delim2, distinguish_2)
}else if(x_order == "spi"){
x1vars <- spiExtractor(dat, x_stem, x_delim1, x_item_num, x_delim2, distinguish_1)
x2vars <- spiExtractor(dat, x_stem, x_delim1, x_item_num, x_delim2, distinguish_2)
}else if(x_order == "psi"){
x1vars <- psiExtractor(dat, x_stem, x_delim1, x_item_num, x_delim2, distinguish_1)
x2vars <- psiExtractor(dat, x_stem, x_delim1, x_item_num, x_delim2, distinguish_2)
}
num_x1_var <- length(x1vars)
num_x2_var <- length(x2vars)
dist_1 <- distinguish_1
dist_2 <- distinguish_2
tot_var <- num_x1_var + num_x2_var
if(num_x1_var == num_x2_var){
varlist <- list(p1xvarnames = x1vars,
p2xvarnames = x2vars,
xindper = num_x1_var,
dist1 =dist_1,
dist2 = dist_2,
indnum = tot_var)
return(varlist)
}else if(num_x1_var != num_x2_var){
stop("scrapeVarCross() cannot detect a similar number of ", x_stem, " items for P1 and P2")
}
}
else if(!is.null(y_order)){
if(x_order == "sip"){
x1vars <- sipExtractor(dat, x_stem, x_delim1, x_item_num, x_delim2, distinguish_1)
x2vars <- sipExtractor(dat, x_stem, x_delim1, x_item_num, x_delim2, distinguish_2)
}else if(x_order == "spi"){
x1vars <- spiExtractor(dat, x_stem, x_delim1, x_item_num, x_delim2, distinguish_1)
x2vars <- spiExtractor(dat, x_stem, x_delim1, x_item_num, x_delim2, distinguish_2)
}else if(x_order == "psi"){
x1vars <- psiExtractor(dat, x_stem, x_delim1, x_item_num, x_delim2, distinguish_1)
x2vars <- psiExtractor(dat, x_stem, x_delim1, x_item_num, x_delim2, distinguish_2)
}
if(y_order == "sip"){
y1vars <- sipExtractor(dat, y_stem, y_delim1, y_item_num, y_delim2, distinguish_1)
y2vars <- sipExtractor(dat, y_stem, y_delim1, y_item_num, y_delim2, distinguish_2)
}else if(y_order == "spi"){
y1vars <- spiExtractor(dat, y_stem, y_delim1, y_item_num, y_delim2, distinguish_1)
y2vars <- spiExtractor(dat, y_stem, y_delim1, y_item_num, y_delim2, distinguish_2)
}else if(y_order == "psi"){
y1vars <- psiExtractor(dat, y_stem, y_delim1, y_item_num, y_delim2, distinguish_1)
y2vars <- psiExtractor(dat, y_stem, y_delim1, y_item_num, y_delim2, distinguish_2)
}
num_x1_var <- length(x1vars)
num_x2_var <- length(x2vars)
dist_1 <- distinguish_1
dist_2 <- distinguish_2
num_y1_var <- length(y1vars)
num_y2_var <- length(y2vars)
tot_var <- num_x1_var + num_x2_var + num_y1_var + num_y2_var
if(num_x1_var == num_x2_var && num_y1_var == num_y2_var){
varlist <- list(p1xvarnames = x1vars,
p2xvarnames = x2vars,
xindper = num_x1_var,
dist1 = dist_1,
dist2 = dist_2,
p1yvarnames = y1vars,
p2yvarnames = y2vars,
yindper =num_y1_var,
indnum = tot_var)
return(varlist)
}else if(num_x1_var != num_x2_var || num_y1_var != num_y2_var){
stop("dvn() cannot detect a similar number of ", x_stem, "or", y_stem, " items for P1 and P2")
}
}
}
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Defines functions scrapeVarCross
Documented in scrapeVarCross
#' A Variable Name-Scraping and Indexing Function for cross-sectional data
#'
#' This function scrapes the names of indicator variables in a
#' wide-format data set used for dyadic analyses of two latent variables (LV; X and Y),
#' and indexes which indicators correspond to which partner, for which LV.
#' It is used primarily to guide the syntax-writing of the other dySEM functions.
#' @param dat input data frame of indicators of a particular LV
#' @param x_order input character for order of (S)tem, (P)artner number, and (I)tem number
#' when creating variable names. Defaults to "spi" (Qualtrics-friendly), but can alternatively take "sip" or "psi"
#' @param x_stem input character stem of indicator variables for LV X
#' @param x_delim1 optional character to separate stem from partner number (spi) or item number (sip)
#' @param x_delim2 optional character to separate stem/partner number (spi) or stem/item number (sip) from
#' from final element of variable name
#' @param x_item_num defaults to scrape all items that match the stem with any digits that follow.
#' Will be updated to allow particular range of values, to make more sub-scale friendly.
#' @param distinguish_1 input character used as the identifier for the first partner
#' @param distinguish_2 input character used as the identifier for the first partner
#' @param y_order optional character for order of (S)tem, (P)artner number, and (I)tem number
#' when creating variable names. Defaults to "spi" (Qualtrics-friendly), but can alternatively take "sip" or "psi". This and other Y-arguments
#' only necessary if there is a latent Y variable to model
#' @param y_stem optional input character stem of indicator variables for LV X
#' @param y_delim1 optional character to separate stem from partner number (spi) or item number (sip)
#' @param y_delim2 optional character to separate stem/partner number (spi) or stem/item number (sip) from
#' from final element of variable name
#' @param y_item_num defaults to scrape all items that match the stem with any digits that follow.
#' Will be updated to allow particular range of values, to make more sub-scale friendly.
#' @param var_list optional named list of indicator variable information, if more than one LV is to be scripted (e.g., a dyadic CFA with multiple sub-scales from the same measure). If supplied, this list *must* contain the following elements: "stem" (a vector of stems), "delim1" (a vector of delimiting characters), and"delim2" (a vector of subsequently delimiting characters). Optionally may include numeric vectors "min_num" and "max_num" if indicators for different LVs share the same stem and must be separated by range of item numbers within a measure.
#' @param var_list_order optional character for order of (S)tem, (P)artner number, and (I)tem number for any of the indicator variables of a multi-LV model (i.e., this functionality assumes the same ordering of elements throughout)
#' @param var_list_item_num optional character for item number of any of the indicator variables of a multi-LV model
#' @param covs_order optional character for order of (S)tem, (P)artner number, and (I)tem number for any
#' covariate(s). Defaults to NULL. This and other covariate arguments only necessary if there are covariates to be scripted in your model(s).
#' @param covs_stem optional input character stem(s) of indicator variables for covariate(s). Can accept a single stem (e.g., "anx"), or a vector of stems (e.g., c("anx", "dep")).
#' Defaults to NULL.
#' @param covs_delim1 optional character to separate stem from partner number (spi) or item number (sip) for covariate(s). Defaults to NULL.
#' @param covs_delim2 optional character to separate stem/partner number (spi) or stem/item number (sip) from
#' @return a list, referred in short-hand as a "dvn" (dyad variable names list) containing variable names for p1, p2, # of items per LV,
#'characters distinguishing partners, and total number of indicators
#' @family variable-scraping functions
#' @export
#' @examples
#' dvnx <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
#' x_delim2="_", distinguish_1="1", distinguish_2="2")
#' dvnxy <- scrapeVarCross(dat = commitmentQ, x_order = "spi", x_stem = "sat.g", x_delim1 = ".",
#' x_delim2="_", distinguish_1="1", distinguish_2="2",
#' y_order="spi", y_stem="com", y_delim1 = ".", y_delim2="_")
scrapeVarCross <- function(dat, x_order = "spi", x_stem, x_delim1=NULL, x_delim2=NULL, x_item_num="\\d+", distinguish_1="1", distinguish_2="2",
y_order=NULL, y_stem=NULL, y_delim1=NULL, y_delim2=NULL, y_item_num="\\d+",
var_list = NULL, var_list_order = NULL, var_list_item_num="\\d+",
covs_order = NULL, covs_stem = NULL, covs_delim1 = NULL, covs_delim2 = NULL){
if(!is.null(var_list)){
if(var_list_order == "sip"){
x1vars = list()
for(i in 1:length(var_list$lvnames)){
x1VarNames <- sipExtractor(dat = dat, stem = var_list$stem[i],
delim1= var_list$delim1[i], item_num = var_list_item_num,
delim2 = var_list$delim2[i], distinguish = distinguish_1)
#subset to variables in range, if range is supplied (otherwise merely supply all counted variables matching)
if(all(c("min_num", "max_num") %in% names(var_list))){
x1VarNames <- x1VarNames[var_list$min_num[i]:var_list$max_num[i]]
}
x1vars[[var_list$lvnames[i]]] <- x1VarNames
}
x2vars = list()
for(i in 1:length(var_list$lvnames)){
x2VarNames <- sipExtractor(dat = dat, stem = var_list$stem[i],
delim1= var_list$delim1[i], item_num = var_list_item_num,
delim2 = var_list$delim2[i], distinguish = distinguish_2)
#subset to variables in range, if range is supplied (otherwise merely supply all counted variables matching)
if(all(c("min_num", "max_num") %in% names(var_list))){
x2VarNames <- x2VarNames[var_list$min_num[i]:var_list$max_num[i]]
}
x2vars[[var_list$lvnames[i]]] <- x2VarNames
}
num_x1_var <- sum(lengths(x1vars))# count total number of items in x1
num_x2_var <- sum(lengths(x2vars))# count total number of items in x2
dist_1 <- distinguish_1
dist_2 <- distinguish_2
tot_var <- num_x1_var + num_x2_var# count total number of items in x1 and x2
if(num_x1_var == num_x2_var){#return list unless number of x1 and x2 items dont match
varlist <- list(p1xvarnames = x1vars,
p2xvarnames = x2vars,
xindper = num_x1_var,
dist1 =dist_1,
dist2 = dist_2,
indnum = tot_var)
return(varlist)
}else if(num_x1_var != num_x2_var){
stop("scrapeVarCross() cannot detect a similar number of ", x_stem, " items for P1 and P2")
}
}
else if(var_list_order == "spi"){
x1vars = list()
for(i in 1:length(var_list$lvnames)){
x1VarNames <- spiExtractor(dat = dat, stem = var_list$stem[i],
delim1= var_list$delim1[i], item_num = var_list_item_num,
delim2 = var_list$delim2[i], distinguish = distinguish_1)
#subset to variables in range, if range is supplied (otherwise merely supply all counted variables matching)
if(all(c("min_num", "max_num") %in% names(var_list))){
x1VarNames <- x1VarNames[var_list$min_num[i]:var_list$max_num[i]]
}
x1vars[[var_list$lvnames[i]]] <- x1VarNames
}
x2vars = list()
for(i in 1:length(var_list$lvnames)){
x2VarNames <- spiExtractor(dat = dat, stem = var_list$stem[i],
delim1= var_list$delim1[i], item_num = var_list_item_num,
delim2 = var_list$delim2[i], distinguish = distinguish_2)
#subset to variables in range, if range is supplied (otherwise merely supply all counted variables matching)
if(all(c("min_num", "max_num") %in% names(var_list))){
x2VarNames <- x2VarNames[var_list$min_num[i]:var_list$max_num[i]]
}
x2vars[[var_list$lvnames[i]]] <- x2VarNames
}
num_x1_var <- sum(lengths(x1vars))# count total number of items in x1
num_x2_var <- sum(lengths(x2vars))# count total number of items in x2
dist_1 <- distinguish_1
dist_2 <- distinguish_2
tot_var <- num_x1_var + num_x2_var# count total number of items in x1 and x2
if(num_x1_var == num_x2_var){#return list unless number of x1 and x2 items dont match
varlist <- list(p1xvarnames = x1vars,
p2xvarnames = x2vars,
xindper = num_x1_var,
dist1 =dist_1,
dist2 = dist_2,
indnum = tot_var)
return(varlist)
}else if(num_x1_var != num_x2_var){
stop("scrapeVarCross() cannot detect a similar number of ", x_stem, " items for P1 and P2")
}
}
else if(var_list_order == "psi"){
x1vars = list()
for(i in 1:length(var_list$lvnames)){
x1VarNames <- psiExtractor(dat = dat, stem = var_list$stem[i],
delim1= var_list$delim1[i], item_num = var_list_item_num,
delim2 = var_list$delim2[i], distinguish = distinguish_1)
#subset to variables in range, if range is supplied (otherwise merely supply all counted variables matching)
if(all(c("min_num", "max_num") %in% names(var_list))){
x1VarNames <- x1VarNames[var_list$min_num[i]:var_list$max_num[i]]
}
x1vars[[var_list$lvnames[i]]] <- x1VarNames
}
x2vars = list()
for(i in 1:length(var_list$lvnames)){
x2VarNames <- psiExtractor(dat = dat, stem = var_list$stem[i],
delim1= var_list$delim1[i], item_num = var_list_item_num,
delim2 = var_list$delim2[i], distinguish = distinguish_2)
#subset to variables in range, if range is supplied (otherwise merely supply all counted variables matching)
if(all(c("min_num", "max_num") %in% names(var_list))){
x2VarNames <- x2VarNames[var_list$min_num[i]:var_list$max_num[i]]
}
x2vars[[var_list$lvnames[i]]] <- x2VarNames
}
num_x1_var <- sum(lengths(x1vars))# count total number of items in x1
num_x2_var <- sum(lengths(x2vars))# count total number of items in x2
dist_1 <- distinguish_1
dist_2 <- distinguish_2
tot_var <- num_x1_var + num_x2_var# count total number of items in x1 and x2
if(num_x1_var == num_x2_var){#return list unless number of x1 and x2 items dont match
varlist <- list(p1xvarnames = x1vars,
p2xvarnames = x2vars,
xindper = num_x1_var,
dist1 =dist_1,
dist2 = dist_2,
indnum = tot_var)
return(varlist)
}else if(num_x1_var != num_x2_var){
stop("scrapeVarCross() cannot detect a similar number of ", x_stem, " items for P1 and P2")
}
}
}
else if(is.null(y_order)){
if(x_order == "sip"){
x1vars <- sipExtractor(dat, x_stem, x_delim1, x_item_num, x_delim2, distinguish_1)
x2vars <- sipExtractor(dat, x_stem, x_delim1, x_item_num, x_delim2, distinguish_2)
}else if(x_order == "spi"){
x1vars <- spiExtractor(dat, x_stem, x_delim1, x_item_num, x_delim2, distinguish_1)
x2vars <- spiExtractor(dat, x_stem, x_delim1, x_item_num, x_delim2, distinguish_2)
}else if(x_order == "psi"){
x1vars <- psiExtractor(dat, x_stem, x_delim1, x_item_num, x_delim2, distinguish_1)
x2vars <- psiExtractor(dat, x_stem, x_delim1, x_item_num, x_delim2, distinguish_2)
}
num_x1_var <- length(x1vars)
num_x2_var <- length(x2vars)
dist_1 <- distinguish_1
dist_2 <- distinguish_2
tot_var <- num_x1_var + num_x2_var
if(num_x1_var == num_x2_var){
varlist <- list(p1xvarnames = x1vars,
p2xvarnames = x2vars,
xindper = num_x1_var,
dist1 =dist_1,
dist2 = dist_2,
indnum = tot_var)
return(varlist)
}else if(num_x1_var != num_x2_var){
stop("scrapeVarCross() cannot detect a similar number of ", x_stem, " items for P1 and P2")
}
}
else if(!is.null(y_order)){
if(x_order == "sip"){
x1vars <- sipExtractor(dat, x_stem, x_delim1, x_item_num, x_delim2, distinguish_1)
x2vars <- sipExtractor(dat, x_stem, x_delim1, x_item_num, x_delim2, distinguish_2)
}else if(x_order == "spi"){
x1vars <- spiExtractor(dat, x_stem, x_delim1, x_item_num, x_delim2, distinguish_1)
x2vars <- spiExtractor(dat, x_stem, x_delim1, x_item_num, x_delim2, distinguish_2)
}else if(x_order == "psi"){
x1vars <- psiExtractor(dat, x_stem, x_delim1, x_item_num, x_delim2, distinguish_1)
x2vars <- psiExtractor(dat, x_stem, x_delim1, x_item_num, x_delim2, distinguish_2)
}
if(y_order == "sip"){
y1vars <- sipExtractor(dat, y_stem, y_delim1, y_item_num, y_delim2, distinguish_1)
y2vars <- sipExtractor(dat, y_stem, y_delim1, y_item_num, y_delim2, distinguish_2)
}else if(y_order == "spi"){
y1vars <- spiExtractor(dat, y_stem, y_delim1, y_item_num, y_delim2, distinguish_1)
y2vars <- spiExtractor(dat, y_stem, y_delim1, y_item_num, y_delim2, distinguish_2)
}else if(y_order == "psi"){
y1vars <- psiExtractor(dat, y_stem, y_delim1, y_item_num, y_delim2, distinguish_1)
y2vars <- psiExtractor(dat, y_stem, y_delim1, y_item_num, y_delim2, distinguish_2)
}
num_x1_var <- length(x1vars)
num_x2_var <- length(x2vars)
dist_1 <- distinguish_1
dist_2 <- distinguish_2
num_y1_var <- length(y1vars)
num_y2_var <- length(y2vars)
tot_var <- num_x1_var + num_x2_var + num_y1_var + num_y2_var
if(num_x1_var == num_x2_var && num_y1_var == num_y2_var){
varlist <- list(p1xvarnames = x1vars,
p2xvarnames = x2vars,
xindper = num_x1_var,
dist1 = dist_1,
dist2 = dist_2,
p1yvarnames = y1vars,
p2yvarnames = y2vars,
yindper =num_y1_var,
indnum = tot_var)
return(varlist)
}else if(num_x1_var != num_x2_var || num_y1_var != num_y2_var){
stop("dvn() cannot detect a similar number of ", x_stem, "or", y_stem, " items for P1 and P2")
}
}
}
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