diff options
Diffstat (limited to '')
| -rw-r--r-- | admin/survey/modules/mod_kakovost/R/gen.usability.matrix.R | 181 |
1 files changed, 0 insertions, 181 deletions
diff --git a/admin/survey/modules/mod_kakovost/R/gen.usability.matrix.R b/admin/survey/modules/mod_kakovost/R/gen.usability.matrix.R deleted file mode 100644 index a2b1465..0000000 --- a/admin/survey/modules/mod_kakovost/R/gen.usability.matrix.R +++ /dev/null @@ -1,181 +0,0 @@ -gen.usability.matrix <- function(dsa, survey.str){
- #define special values to detect
- #order of this values is important:
- # in case of conflicts @ chk.t types of questions the order sets the priporty of which values to keep
- special.v <- c(-1, -3, -5, -96, -97, -98, -99, -4, -2)
-
- #define which variables belong to checkbox-like* questions
- #(* i.e.: check for special values @ ANY variable per question/item ID)
- # 2: normal checkbox
- # 16: multicheckbox
- # 17: ranking
- chkbox.t <- c(2, 16, 17)
-
- ##all other variables belong to normal** questions
- #(** i.e.: check for special values @ each variable per question/item ID)
- #if there are no normal questions, create 0 matrix, otherwise...
- if(nrow(survey.str[!(tip %in% chkbox.t),])==0){
- m.n <- matrix(0, nrow = nrow(dsa), ncol=length(special.v)+1)
- }else{
- #create list of all normal questions
- c.n <- colnames(dsa)[which(colnames(dsa) %in% survey.str[!(tip %in% chkbox.t), variable])]
-
- #...count all non-special values for each variable
- #... + count each special value for each variable
- m.n <- cbind(rowSums(sapply(dsa[, c.n, with=FALSE], function(x){!(x %in% special.v)})),
- sapply(special.v, function(x){as.integer(rowSums(dsa[, c.n, with=FALSE]==x, na.rm=TRUE))}))
- }
-
- ##procedure for tip:2
- #only run if there is an at least one tip:2 variable
- if(survey.str[, any(tip==2)]){
- #get list of all unique tip:2 question ids
- q.2 <- unique(survey.str[tip==2, question.id])
- #get list of all corresponding variables for each q.2 id
- c.2 <- lapply(q.2, function(x){colnames(dsa)[which(colnames(dsa) %in% survey.str[question.id==x & tip==2, variable])]})
-
- #(do this for each instance in c.2):
- #for each set of variables:
- # check if any variable contains at least one non-special value
- # + (for each special value) check if any variable contains at least special value
- m.2 <- lapply(c.2, function(x){
- cbind(apply(dsa[, x, with=FALSE], 1, function(q){any(!(q %in% special.v))}),
- sapply(special.v, function(y){
- apply(dsa[, x, with=FALSE], 1, function(q){any(q==y)})
- })
- )
- })
-
- # (do this for each instance in c.2)
- # if multiple special values per respondent exist, keep only the first one
- m.2 <- lapply(m.2, function(x){
- if(any(rowSums(x)>1)){
- p <- x[rowSums(x)>1,]
- for(i in 1:nrow(p)){
- a <- p[i,]
- f <- TRUE
- for(j in 1:length(a)){
- print(j)
- if(a[j] & f){
- f <- FALSE
- }else if(a[j] & !f){
- a[j] <- FALSE
- }
- }
- p[i,] <- a
- }
- x[rowSums(x)>1,] <- p
- }else{x}
- })
-
-
- #add to m.n
- m.n <- m.n + Reduce('+', m.2)
- }
-
- ##procedure for tip:16
- #only run if there is an at least one tip:16 variable
- if(survey.str[, any(tip==16)]){
- #get list of all unique tip:16 item ids
- q.16 <- unique(survey.str[tip==16, item.id])
-
- #get list of all corresponding variables for each q.16 id
- c.16 <- lapply(q.16, function(x){colnames(dsa)[which(colnames(dsa) %in% survey.str[item.id==x & tip==16, variable])]})
- #(do this for each special value):
- #for each set of variables, check if any variable contains at least one special value
- # m.16 <- sapply(special.v, function(x){
- # rowSums(sapply(c.16, function(y){
- # apply(dsa[, y, with=FALSE], 1, function(q){any(q==x)})
- # }))
- # })
-
- #(do this for each instance in c.16):
- #for each set of variables:
- # check if any variable contains at least one non-special value
- # + (for each special value) check if any variable contains at least special value
- m.16 <- lapply(c.16, function(x){
- cbind(apply(dsa[, x, with=FALSE], 1, function(q){any(!(q %in% special.v))}),
- sapply(special.v, function(y){
- apply(dsa[, x, with=FALSE], 1, function(q){any(q==y)})
- })
- )
- })
-
- # (do this for each instance in c.16)
- # if multiple special values per respondent exist, keep only the first one
- m.16 <- lapply(m.16, function(x){
- if(any(rowSums(x)>1)){
- p <- x[rowSums(x)>1,]
- for(i in 1:nrow(p)){
- a <- p[i,]
- f <- TRUE
- for(j in 1:length(a)){
- print(j)
- if(a[j] & f){
- f <- FALSE
- }else if(a[j] & !f){
- a[j] <- FALSE
- }
- }
- p[i,] <- a
- }
- x[rowSums(x)>1,] <- p
- }else{x}
- })
-
- m.n <- m.n + Reduce('+', m.16)
- }
-
- ##procedure for tip:17
- #only run if there is an at least one tip:17 variable
- if(survey.str[, any(tip==17)]){
- #get list of all unique tip:17 question ids
- q.17 <- unique(survey.str[tip==17, question.id])
-
- #get list of all corresponding variables for each q.17 id
- c.17 <- lapply(q.17, function(x){colnames(dsa)[which(colnames(dsa) %in% survey.str[question.id==x & tip==17, variable])]})
-
- #similiar procedure as for tip:2 and tip:16....
- m.17 <- lapply(c.17, function(x){
- cbind(apply(dsa[, x, with=FALSE], 1, function(q){any(!(q %in% special.v))}),
- sapply(special.v, function(y){
- apply(dsa[, x, with=FALSE], 1, function(q){any(q==y)})
- })
- )
- })
-
- #... the only difference is that we are checking for all rowsums > 0, not > 1
- m.17 <- lapply(m.17, function(x){
- if(any(rowSums(x)>1)){
- p <- x[rowSums(x)>0,]
- for(i in 1:nrow(p)){
- a <- p[i,]
- f <- TRUE
- for(j in 1:length(a)){
- if(a[j] & f){
- f <- FALSE
- }else if(a[j] & !f){
- a[j] <- FALSE
- }
- }
- p[i,] <- a
- }
- x[rowSums(x)>0,] <- p
- }else{x}
- })
-
- m.n <- m.n + Reduce('+', m.17)
- }
-
- m.n <- cbind(m.n, rowSums(m.n))
-
- if(all(m.n[, ncol(m.n)][1]==m.n[, ncol(m.n)])){
- m.n <- as.data.table(m.n)
- m.n[, recnum:=dsa$recnum]
- setnames(m.n, colnames(m.n)[-length(colnames(m.n))], c("va", "v1", "v3", "v5", "v96", "v97", "v98", "v99", "v4", "v2", "allqs"))
- setcolorder(m.n, c("recnum", colnames(m.n)[-length(colnames(m.n))]))
- return(m.n)
- }else{
- print("not all rowsums equal!")
- }
-}
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