#' d_score function
#'
#' This function takes dataframe and appends the results of analyses at the left end of the original dataframe.
#' @param df Dataframe that is not formatted, downloaded from Qualtrics.
#' @param var Name of the column serves as unique identifier in dataset.
#' @param file_type csv or xml
#' @keywords dospert
#' @export
#' @examples
#' csvdata <- read_csv("data/raw_data/DOSPERT_test.csv")
#' csvScore <- d_score(csvdata, "uid", file_type = "csv")
#' xmldata <- xmlToDataFrame("data/raw_data/DOSPERT_test.xml", stringsAsFactors = F)
#' xmlScore <- d_score(xmldata, "uid", file_type = "xml")
# new_dscore --------------------------------------------------------------
d_score <- function(df, var, file_type = "csv"){
# component functions
# uniqR_csv
# uniqR_xml
# for .csv, use the identified variable as unique_id and remove the first row
uniqR_csv <- function(df, var) {
df <- df %>% rename_(unique_id = as.symbol(var))
df <- df[-1, ]
}
# for .xml, use the identified variable as unique_id
uniqR_xml <- function(df, var){
df <- df %>% rename_(unique_id = as.symbol(var))
}
wideformat <- function(df, var, file_type){
if(file_type == "csv"){
df <- uniqR_csv(df, var)
} else if (file_type == "xml") {
df <- uniqR_xml(df, var)
}
return(df)
}
d_clean <- function(df, var, file_type) {
# take dataframe and subset only the columns of responses
# to risk- questions and make sure that they are in numeric
# format. Then, link unique_id and responses back as identifier.
# df should be contain only rows of responses
# column names of risk-responses should start with first three
# characters of the domain followed by RT, RB, RP
# df should have a column named 'unique_id', uniquely identifying the
# survey taker.
# full.panel separates benefit/taking/perception questions and
# merge them by unique_id, domain and question number
# and add a numeric id by unique_id
full.panel <- function(df){
# make sure factors are correctly converted into numeric format
# used in [selectcol] function
fac_friendly <- function(x) {
x <- as.numeric(as.character(x))
}
selectcol <- function(df){
x<- df %>% select(
starts_with("finR"), starts_with("heaR"),
starts_with("safR"), starts_with("recR"),
starts_with("ethR"), starts_with("socR"))
x <- x %>% mutate_each(funs(fac_friendly))
## making sure correct type of conversion
dat <- data.frame(select(df, unique_id), x)
return(dat)
}
# panelform function is used in [full.panel] function
# changes input df into longform and identifies the question
# domain, number and type (e.g. fin 6 RT, 6th question in
# finance domain, of Risk Taking)
panelform <- function(df, var) {
df <- df %>% select( unique_id, contains(paste0(var, "_")))
df <- melt(df, id="unique_id", value.name = var)
df <- mutate(df, domain = substr(variable, 1, 3), Qnumber=substr(variable, 7, 7))
df <- select(df, -variable)
return(df)
}
df <- selectcol(df)
benefit <- panelform(df, "RB")
taking <- panelform(df, "RT")
perception <- panelform(df, "RP")
df <- Reduce(function(x,y) merge(x, y, by=c("unique_id", "domain", "Qnumber"),
all=TRUE), list(taking, perception, benefit))
df <- mutate(df, id=as.numeric(factor(unique_id)))
return(df)
}
df <- dplyr:: tbl_df(df)
if (file_type == "csv") {
df <- uniqR_csv(df, var)
dat <- full.panel(df)
dat$sum <- rowSums(cbind(dat$RT, dat$RP, dat$RB))
dat_group <- dat %>% group_by(id) %>% dplyr::mutate(group_sum = sum(sum)) %>%
filter(!is.na(group_sum)) %>% select(-sum, -group_sum)
dat <- ungroup(dat_group)
return(dat)
} else if (file_type == "xml"){
df <- uniqR_xml(df, var)
dat <- full.panel(df)
dat$sum <- rowSums(cbind(dat$RT, dat$RP, dat$RB))
dat_group <- dat %>% group_by(id) %>% dplyr::mutate(group_sum = sum(sum)) %>%
filter(!is.na(group_sum)) %>% select(-sum, -group_sum)
dat <- ungroup(dat_group)
return(dat)
}
}
format.result <- function(df){
result <- attr(df, "split_labels")
for ( i in 1: dim(result)[1] ){
result$int[i] <- df[[i]]$coefficients[1]
result$RB[i] <- df[[i]]$coefficients[2]
result$RP[i] <- df[[i]]$coefficients[3]
}
result <- merge(result, idlist, by="id") %>%
select(unique_id, domain, int, RB, RP)
return(result)
}
if(file_type != "csv" & file_type != "xml"){
print("file_type should be either .csv or .xml")
} else{
wide_temp <- wideformat(df, var, file_type)
clean_df <- d_clean(df, var, file_type)
reg <- dlply(clean_df, c("id", "domain"), function(data) lm(RT ~ RB + RP, data = data))
domainlist <- distinct(select(clean_df, domain))
idlist <- unique(select(clean_df, unique_id, id))
reg_result <- format.result(reg)
split <- split(reg_result, reg_result$domain)
list_df <- list()
# see if fin.int -> fin_int
for (i in 1:nrow(domainlist)){
temp <- as.data.frame(split[i])[, -2]
names(temp) <- ifelse(stringr::str_detect(names(temp), "unique_id"),
"unique_id", names(temp))
assign(paste0("coef_", domainlist[i, ]), temp)
colnames(temp)[2] <- paste0(domainlist[i, ], "_int")
colnames(temp)[3] <- paste0(domainlist[i, ], "_RB")
colnames(temp)[4] <- paste0(domainlist[i, ], "_RP")
list_df[[i]] <- temp
}
full_coef <- Reduce(function(x,y) merge(x, y, all = TRUE, by = "unique_id"), list_df)
df <- merge(wide_temp, full_coef, by = "unique_id")
return(df)
}
}
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