R/fun2_1_4_RegData_newVarDich.R
In Rapporteket/norspis2: Plots and Tables of Results and Data Quality in NorSpis
Defines functions
fun2_1_4_RegData_newVarDich
Documented in
fun2_1_4_RegData_newVarDich
#' Make dichotomous variables (dich)
#'
#' Output is data, with new variables that ar dichotomous.
#'
#' Note: This function's name when writing aarsrapport 2019 (in 2020) was: make24_data_newVar01
#'
#' @param myInData RegData
#'
#' @return myInData (invisible)
#' @export
#'
#' @examples
fun2_1_4_RegData_newVarDich <- function(myInData) {
###Dicotomous (0-1) variables
#Variables indicating "missing" (REAL missing values are then coded as NA, and those that should not be considered as "null")
#EDEQ60GlobalScore_miss
#EDEQ60GlobalScore_missStart
#EDEQ60GlobalScore_missEnd
#EDEQ60GlobalScore_missStartEnd
#CIA30GlobalScore__miss
#CIA30GlobalScore_missStart
#CIA30GlobalScore_missEnd
#CIA30GlobalScore_missStartEnd
#TODO: Diagnser ICD-10 ...404?
#MedBMI_miss
#MedBMI_missStart
#MedBMI_missEnd
#MedBMI_missEnd_whenU185Start
#MedBMI_missStartEnd
#PT03Utfallsvurd_miss
#PO09Utbytte_miss
#PasOppTilfredshet_miss
#PasOppErfaring_miss
#PasOppTilgjengelighet_miss
#PasOppSikkerhet_miss
#PT01OnsketInvolv_miss
#PT02BleInvol_miss
#PT04KontaktBrukerorg
#PT05OrientertBrukerorg_miss
#BehTiltak_miss (not made yet)
#Other dicotomous variables ()
#PROP_PO10Pasientsikkerhet
#Quality indicators:
#PROP_PO10Pasientsikkerhet
#PROP here means that can be used to make proportions(TODO: rename to either Dic for Dicotomous or 01
#(01 may be misleasing as in can indicate "one" or "first"))
##Variables indicating "missing"
#EDEQ60GlobalScore_miss
myInData <- myInData %>% dplyr::mutate(EDEQ60GlobalScore_miss = dplyr::case_when(
!is.na(EDEQ60GlobalScore) & RegRegtype %in% c(1,2,3,4,5,6) ~ as.character(EDEQ60GlobalScore), #just keeping numeric values as is
is.na(EDEQ60GlobalScore) & RegRegtype %in% c(1,2,3,4,5,6) ~ NA_character_,
TRUE ~ "null")) #this means that RegRegType 98 and 99 are coded as "null" (and exluded from missing calc)
#EDEQ60GlobalScore_missStart
myInData <- myInData %>% dplyr::mutate(EDEQ60GlobalScore_missStart = dplyr::case_when(
!is.na(EDEQ60GlobalScore) & RegRegtype %in% c(1,2,3,4) ~ as.character(EDEQ60GlobalScore), #just keeping numeric values as is
is.na(EDEQ60GlobalScore) & RegRegtype %in% c(1,2,3,4) ~ NA_character_,
TRUE ~ "null"))
#EDEQ60GlobalScore_missEnd
myInData <- myInData %>% dplyr::mutate(EDEQ60GlobalScore_missEnd = dplyr::case_when(
!is.na(EDEQ60GlobalScore) & RegRegtype %in% c(5,6) ~ as.character(EDEQ60GlobalScore), #just keeping numeric values as is
is.na(EDEQ60GlobalScore) & RegRegtype %in% c(5,6) ~ NA_character_,
TRUE ~ "null"))
#CIA30GlobalScore_miss
myInData <- myInData %>% dplyr::mutate(CIA30GlobalScore_miss = dplyr::case_when(
!is.na(CIA30GlobalScore) & RegRegtype %in% c(1,2,3,4,5,6) ~ as.character(CIA30GlobalScore), #just keeping numeric values as is
is.na(CIA30GlobalScore) & RegRegtype %in% c(1,2,3,4,5,6) ~ NA_character_,
TRUE ~ "null")) #this means that RegRegType 98 and 99 are coded as "null" (and exluded from missing calc)
#CIA30GlobalScore_missStart
myInData <- myInData %>% dplyr::mutate(CIA30GlobalScore_missStart = dplyr::case_when(
!is.na(CIA30GlobalScore) & RegRegtype %in% c(1,2,3,4) ~ as.character(CIA30GlobalScore), #just keeping numeric values as is
is.na(CIA30GlobalScore) & RegRegtype %in% c(1,2,3,4) ~ NA_character_,
TRUE ~ "null"))
#CIA30GlobalScore_missEnd
myInData <- myInData %>% dplyr::mutate(CIA30GlobalScore_missEnd = dplyr::case_when(
!is.na(CIA30GlobalScore) & RegRegtype %in% c(5,6) ~ as.character(CIA30GlobalScore), #just keeping numeric values as is
is.na(CIA30GlobalScore) & RegRegtype %in% c(5,6) ~ NA_character_,
TRUE ~ "null"))
#MedBMI_miss
myInData <- myInData %>% dplyr::mutate(MedBMI_miss = dplyr::case_when(
!is.na(MedBMI) & RegRegtype %in% c(1,2,3,4,5,6) ~ as.character(MedBMI), #just keeping numeric values as is
is.na(MedBMI) & RegRegtype %in% c(1,2,3,4,5,6) ~ NA_character_,
TRUE ~ "null")) #this means that RegRegType 98 and 99 are coded as "null" (and exluded from missing calc)
#MedBMI_missStart
myInData <- myInData %>% dplyr::mutate(MedBMI_missStart = dplyr::case_when(
!is.na(MedBMI) & RegRegtype %in% c(1,2,3,4) ~ as.character(MedBMI), #just keeping numeric values as is
is.na(MedBMI) & RegRegtype %in% c(1,2,3,4) ~ NA_character_,
TRUE ~ "null"))
#MedBMI_missEnd
myInData <- myInData %>% dplyr::mutate(MedBMI_missEnd = dplyr::case_when(
!is.na(MedBMI) & RegRegtype %in% c(5,6) ~ as.character(MedBMI), #just keeping numeric values as is
is.na(MedBMI) & RegRegtype %in% c(5,6) ~ NA_character_,
TRUE ~ "null"))
#### Mads: please check edits below #########################################
#PT03Utfallsvurd_miss
myInData <- myInData %>%
dplyr::mutate(PT03Utfallsvurd_miss =
dplyr::case_when(
#just keeping numeric values as is
PT03Utfallsvurd %in% c(1, 2, 3, 4, 5) ~ PT03Utfallsvurd,
PasientAlder >= 16 &
# 9 missing is coded as missing
PT03Utfallsvurd == 9 &
RegRegtype %in% c(5, 6) |
is.na(PT03Utfallsvurd) ~ NA_integer_,
TRUE ~ NA_integer_
)
)
#PO09Utbytte_miss
myInData <- myInData %>%
dplyr::mutate(PO09Utbytte_miss =
dplyr::case_when(
PO09Utbytte %in% c(0, 1, 2, 3, 4) ~ PO09Utbytte,
PO09Utbytte %in% c(9, 99) &
is.na(PO09Utbytte) &
# 9 and 99 ("ikke aktuelt", "ikke besvart") is also coded as missing
RegRegtype %in% c(5, 6) ~ NA_integer_,
TRUE ~ NA_integer_
)
)
#PasOppTilfredshet_miss
myInData <- myInData %>%
dplyr::mutate(PasOppTilfredshet_miss =
dplyr::case_when(
!is.na(PasOppTilfredshet) ~ PasOppTilfredshet,
is.na(PasOppTilfredshet) & RegRegtype %in% c(5, 6) ~ NA_real_,
TRUE ~ NA_real_
)
)
#PasOppErfaring_miss
myInData <- myInData %>%
dplyr::mutate(PasOppErfaring_miss =
dplyr::case_when(
!is.na(PasOppErfaring) ~ PasOppErfaring,
is.na(PasOppErfaring) & RegRegtype %in% c(5, 6) ~ NA_real_,
TRUE ~ NA_real_
)
)
#PasOppTilgjengelighet_miss
myInData <- myInData %>%
dplyr::mutate(PasOppTilgjengelighet_miss =
dplyr::case_when(
!is.na(PasOppTilgjengelighet) ~ PasOppTilgjengelighet,
is.na(PasOppTilgjengelighet) & RegRegtype %in% c(5, 6) ~ NA_real_,
TRUE ~ NA_real_
)
)
#PasOppSikkerhet_miss
myInData <- myInData %>%
dplyr::mutate(PasOppSikkerhet_miss =
dplyr::case_when(
!is.na(PasOppSikkerhet) ~ PasOppSikkerhet,
is.na(PasOppSikkerhet) & RegRegtype %in% c(5, 6) ~ NA_real_,
TRUE ~ NA_real_
)
)
#### Mads: please check edits abow ##########################################
#PT01OnsketInvolv_miss
myInData <- myInData %>% dplyr::mutate(PT01OnsketInvolv_miss = dplyr::case_when(
PT01OnsketInvolv %in% c(0,1) ~ PT01OnsketInvolv, #just keeping numeric values
PasientAlder >= 16 & PT01OnsketInvolv == 9 & is.na(PT01OnsketInvolv) & RegRegtype %in% c(5,6) ~ NA_character_,
TRUE ~ "null"))
#PT02BleIvolv_miss
myInData <- myInData %>% dplyr::mutate(PT02BleInvolv_miss = dplyr::case_when(
PT02BleInvolv %in% c(0,1) ~ PT02BleInvolv, #just keeping numeric values
PasientAlder >= 16 & PT02BleInvolv %in% c(9,"null") & RegRegtype %in% c(5,6) ~ NA_character_,
TRUE ~ "null"))
#PT04KontaktBrukerorg_miss
myInData <- myInData %>% dplyr::mutate(PT04KontaktBrukerorg_miss = dplyr::case_when(
PT04KontaktBrukerorg %in% c(0,1) ~ PT04KontaktBrukerorg, #just keeping numeric values
PasientAlder >= 16 & PT04KontaktBrukerorg %in% c(9,"null") & RegRegtype %in% c(5,6) ~ NA_character_,
TRUE ~ "null"))
#PT05OrientertBrukerorg_miss
myInData <- myInData %>% dplyr::mutate(PT05OrientertBrukerorg_miss = dplyr::case_when(
PT05OrientertBrukerorg %in% c(0,1) ~ PT05OrientertBrukerorg, #just keeping numeric values
PasientAlder >= 16 & PT05OrientertBrukerorg %in% c(9,"null") & RegRegtype %in% c(5,6) ~ NA_character_,
TRUE ~ "null"))
##Other dicotomous variables
#PO10Pasientsikkerhet...Values in raw data: (0,1,2,3,4,9,99,'null)
#1,2,3)
myInData$PROP_PO10Pasientsikkerhet <- myInData$PO10Pasientsikkerhet
#1)
myInData$PROP_PO10Pasientsikkerhet <- dplyr::recode(myInData$PO10Pasientsikkerhet,
"0"="1",
"1"="0",
"2"="0",
"3"="0",
"4"="0")#recode function is "questioning" (see ?recode)
myInData$PROP_PO10Pasientsikkerhet <- replace(myInData$PROP_PO10Pasientsikkerhet,
myInData$PROP_PO10Pasientsikkerhet %in%
c("9",
"99"),
NA)
#as.numeric to enable calculations (this also removes "null" values (where there are any left) and introduces NAs)
myInData$PROP_PO10Pasientsikkerhet <- as.numeric(myInData$PROP_PO10Pasientsikkerhet)
#2)
#Making missing variable ("ikke besvart" and every observation with end registration where patient is above 15 years)
#myInData[which(myInData$MISSING_PO10Pasientsikkerhet%in% c("0","1","2","3","4","9")), ]$MISSING_PO10Pasientsikkerhet <- "0"
#myInData[which(myInData$MISSING_PO10Pasientsikkerhet %in% c("99")), ]$MISSING_PO10Pasientsikkerhet <- "1"
#3) Making a "ikke aktuelt" variable:
#myInData[which(as.numeric(myInData$UAKTUELL_PO10Pasientsikkerhet)%in%c("0","1","2","3","4","99")),]$UAKTUELL_PO10Pasientsikkerhet <-"0"
#myInData[which(as.numeric(myInData$UAKTUELL_PO10Pasientsikkerhet) %in% c("9")), ]$UAKTUELL_PO10Pasientsikkerhet <- "1"
#myInData$MISSING_PO10Pasientsikkerhet <- as.numeric(myInData$MISSING_PO10Pasientsikkerhet)
#myInData$UAKTUELL_PO10Pasientsikkerhet <- as.numeric(myInData$UAKTUELL_PO10Pasientsikkerhet)
##Quality indicators (more precisely MISSING for QIs)
#QI1: Already OK
#TODO to get missing: table(is.na(myInData$EDEQ60GlobalScore))[1]/ (table(is.na(myInData$EDEQ60GlobalScore))[2] + table(is.na(myInData$EDEQ60GlobalScore))[1])
#TODO OR USE: sum(is.na(myInData$EDEQ60GlobalScore))
#QI5: P09 utbytte (0-1)
myInData$PROP_PO09Utbytte <- dplyr::recode(myInData$PO09Utbytte,
"0"="0",
"1"="0",
"2"="1",
"3"="1",
"4"="1")#regarding recode function, be aware that it is "questioning" (see ?recode)
myInData$PROP_PO09Utbytte <- replace(myInData$PROP_PO09Utbytte,
myInData$PROP_PO09Utbytte %in%
c("9",
"99"),
NA)
myInData$PROP_PO09Utbytte <- as.numeric(myInData$PROP_PO09Utbytte)
#QI4: PT03 utfall (0-1)
myInData$PROP_PT03Utfallsvurd <- dplyr::recode(myInData$PT03Utfallsvurd,
"1"="1",#Ikke noe problem lenger
"2"="1",#Klar bedring
"3"="1",#Noe bedring
"4"="0",#Uendret
"5"="0") #Forverret
myInData$PROP_PT03Utfallsvurd <- replace(myInData$PROP_PT03Utfallsvurd,
myInData$PROP_PT03Utfallsvurd %in%
c("9"),
NA)
myInData$PROP_PT03Utfallsvurd <- as.numeric(myInData$PROP_PT03Utfallsvurd) #need to be numeric, also introduces NAs where "null"
#PT03 utfall MISSING (0-1)
myInData$MISSING_PT03Utfallsvurd <- dplyr::recode(myInData$PT03Utfallsvurd,
"1"="0",#Ikke noe problem lenger
"2"="0",#Klar bedring
"3"="0",#Noe bedring
"4"="0",#Uendret
"5"="0") #Forverret
myInData$MISSING_PT03Utfallsvurd <- replace(myInData$MISSING_PT03Utfallsvurd,
myInData$MISSING_PT03Utfallsvurd == "9",
1)
myInData$MISSING_PT03Utfallsvurd <- as.numeric(myInData$MISSING_PT03Utfallsvurd) #need to be numeric, also introduces NAs where "null"
#PT03 utfall UAKTUELL (0-1) (EMPTY - there is no such category on this variable)
### END: Quality indicators ###
myInData
}
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Defines functions fun2_1_4_RegData_newVarDich
Documented in fun2_1_4_RegData_newVarDich
#' Make dichotomous variables (dich)
#'
#' Output is data, with new variables that ar dichotomous.
#'
#' Note: This function's name when writing aarsrapport 2019 (in 2020) was: make24_data_newVar01
#'
#' @param myInData RegData
#'
#' @return myInData (invisible)
#' @export
#'
#' @examples
fun2_1_4_RegData_newVarDich <- function(myInData) {
###Dicotomous (0-1) variables
#Variables indicating "missing" (REAL missing values are then coded as NA, and those that should not be considered as "null")
#EDEQ60GlobalScore_miss
#EDEQ60GlobalScore_missStart
#EDEQ60GlobalScore_missEnd
#EDEQ60GlobalScore_missStartEnd
#CIA30GlobalScore__miss
#CIA30GlobalScore_missStart
#CIA30GlobalScore_missEnd
#CIA30GlobalScore_missStartEnd
#TODO: Diagnser ICD-10 ...404?
#MedBMI_miss
#MedBMI_missStart
#MedBMI_missEnd
#MedBMI_missEnd_whenU185Start
#MedBMI_missStartEnd
#PT03Utfallsvurd_miss
#PO09Utbytte_miss
#PasOppTilfredshet_miss
#PasOppErfaring_miss
#PasOppTilgjengelighet_miss
#PasOppSikkerhet_miss
#PT01OnsketInvolv_miss
#PT02BleInvol_miss
#PT04KontaktBrukerorg
#PT05OrientertBrukerorg_miss
#BehTiltak_miss (not made yet)
#Other dicotomous variables ()
#PROP_PO10Pasientsikkerhet
#Quality indicators:
#PROP_PO10Pasientsikkerhet
#PROP here means that can be used to make proportions(TODO: rename to either Dic for Dicotomous or 01
#(01 may be misleasing as in can indicate "one" or "first"))
##Variables indicating "missing"
#EDEQ60GlobalScore_miss
myInData <- myInData %>% dplyr::mutate(EDEQ60GlobalScore_miss = dplyr::case_when(
!is.na(EDEQ60GlobalScore) & RegRegtype %in% c(1,2,3,4,5,6) ~ as.character(EDEQ60GlobalScore), #just keeping numeric values as is
is.na(EDEQ60GlobalScore) & RegRegtype %in% c(1,2,3,4,5,6) ~ NA_character_,
TRUE ~ "null")) #this means that RegRegType 98 and 99 are coded as "null" (and exluded from missing calc)
#EDEQ60GlobalScore_missStart
myInData <- myInData %>% dplyr::mutate(EDEQ60GlobalScore_missStart = dplyr::case_when(
!is.na(EDEQ60GlobalScore) & RegRegtype %in% c(1,2,3,4) ~ as.character(EDEQ60GlobalScore), #just keeping numeric values as is
is.na(EDEQ60GlobalScore) & RegRegtype %in% c(1,2,3,4) ~ NA_character_,
TRUE ~ "null"))
#EDEQ60GlobalScore_missEnd
myInData <- myInData %>% dplyr::mutate(EDEQ60GlobalScore_missEnd = dplyr::case_when(
!is.na(EDEQ60GlobalScore) & RegRegtype %in% c(5,6) ~ as.character(EDEQ60GlobalScore), #just keeping numeric values as is
is.na(EDEQ60GlobalScore) & RegRegtype %in% c(5,6) ~ NA_character_,
TRUE ~ "null"))
#CIA30GlobalScore_miss
myInData <- myInData %>% dplyr::mutate(CIA30GlobalScore_miss = dplyr::case_when(
!is.na(CIA30GlobalScore) & RegRegtype %in% c(1,2,3,4,5,6) ~ as.character(CIA30GlobalScore), #just keeping numeric values as is
is.na(CIA30GlobalScore) & RegRegtype %in% c(1,2,3,4,5,6) ~ NA_character_,
TRUE ~ "null")) #this means that RegRegType 98 and 99 are coded as "null" (and exluded from missing calc)
#CIA30GlobalScore_missStart
myInData <- myInData %>% dplyr::mutate(CIA30GlobalScore_missStart = dplyr::case_when(
!is.na(CIA30GlobalScore) & RegRegtype %in% c(1,2,3,4) ~ as.character(CIA30GlobalScore), #just keeping numeric values as is
is.na(CIA30GlobalScore) & RegRegtype %in% c(1,2,3,4) ~ NA_character_,
TRUE ~ "null"))
#CIA30GlobalScore_missEnd
myInData <- myInData %>% dplyr::mutate(CIA30GlobalScore_missEnd = dplyr::case_when(
!is.na(CIA30GlobalScore) & RegRegtype %in% c(5,6) ~ as.character(CIA30GlobalScore), #just keeping numeric values as is
is.na(CIA30GlobalScore) & RegRegtype %in% c(5,6) ~ NA_character_,
TRUE ~ "null"))
#MedBMI_miss
myInData <- myInData %>% dplyr::mutate(MedBMI_miss = dplyr::case_when(
!is.na(MedBMI) & RegRegtype %in% c(1,2,3,4,5,6) ~ as.character(MedBMI), #just keeping numeric values as is
is.na(MedBMI) & RegRegtype %in% c(1,2,3,4,5,6) ~ NA_character_,
TRUE ~ "null")) #this means that RegRegType 98 and 99 are coded as "null" (and exluded from missing calc)
#MedBMI_missStart
myInData <- myInData %>% dplyr::mutate(MedBMI_missStart = dplyr::case_when(
!is.na(MedBMI) & RegRegtype %in% c(1,2,3,4) ~ as.character(MedBMI), #just keeping numeric values as is
is.na(MedBMI) & RegRegtype %in% c(1,2,3,4) ~ NA_character_,
TRUE ~ "null"))
#MedBMI_missEnd
myInData <- myInData %>% dplyr::mutate(MedBMI_missEnd = dplyr::case_when(
!is.na(MedBMI) & RegRegtype %in% c(5,6) ~ as.character(MedBMI), #just keeping numeric values as is
is.na(MedBMI) & RegRegtype %in% c(5,6) ~ NA_character_,
TRUE ~ "null"))
#### Mads: please check edits below #########################################
#PT03Utfallsvurd_miss
myInData <- myInData %>%
dplyr::mutate(PT03Utfallsvurd_miss =
dplyr::case_when(
#just keeping numeric values as is
PT03Utfallsvurd %in% c(1, 2, 3, 4, 5) ~ PT03Utfallsvurd,
PasientAlder >= 16 &
# 9 missing is coded as missing
PT03Utfallsvurd == 9 &
RegRegtype %in% c(5, 6) |
is.na(PT03Utfallsvurd) ~ NA_integer_,
TRUE ~ NA_integer_
)
)
#PO09Utbytte_miss
myInData <- myInData %>%
dplyr::mutate(PO09Utbytte_miss =
dplyr::case_when(
PO09Utbytte %in% c(0, 1, 2, 3, 4) ~ PO09Utbytte,
PO09Utbytte %in% c(9, 99) &
is.na(PO09Utbytte) &
# 9 and 99 ("ikke aktuelt", "ikke besvart") is also coded as missing
RegRegtype %in% c(5, 6) ~ NA_integer_,
TRUE ~ NA_integer_
)
)
#PasOppTilfredshet_miss
myInData <- myInData %>%
dplyr::mutate(PasOppTilfredshet_miss =
dplyr::case_when(
!is.na(PasOppTilfredshet) ~ PasOppTilfredshet,
is.na(PasOppTilfredshet) & RegRegtype %in% c(5, 6) ~ NA_real_,
TRUE ~ NA_real_
)
)
#PasOppErfaring_miss
myInData <- myInData %>%
dplyr::mutate(PasOppErfaring_miss =
dplyr::case_when(
!is.na(PasOppErfaring) ~ PasOppErfaring,
is.na(PasOppErfaring) & RegRegtype %in% c(5, 6) ~ NA_real_,
TRUE ~ NA_real_
)
)
#PasOppTilgjengelighet_miss
myInData <- myInData %>%
dplyr::mutate(PasOppTilgjengelighet_miss =
dplyr::case_when(
!is.na(PasOppTilgjengelighet) ~ PasOppTilgjengelighet,
is.na(PasOppTilgjengelighet) & RegRegtype %in% c(5, 6) ~ NA_real_,
TRUE ~ NA_real_
)
)
#PasOppSikkerhet_miss
myInData <- myInData %>%
dplyr::mutate(PasOppSikkerhet_miss =
dplyr::case_when(
!is.na(PasOppSikkerhet) ~ PasOppSikkerhet,
is.na(PasOppSikkerhet) & RegRegtype %in% c(5, 6) ~ NA_real_,
TRUE ~ NA_real_
)
)
#### Mads: please check edits abow ##########################################
#PT01OnsketInvolv_miss
myInData <- myInData %>% dplyr::mutate(PT01OnsketInvolv_miss = dplyr::case_when(
PT01OnsketInvolv %in% c(0,1) ~ PT01OnsketInvolv, #just keeping numeric values
PasientAlder >= 16 & PT01OnsketInvolv == 9 & is.na(PT01OnsketInvolv) & RegRegtype %in% c(5,6) ~ NA_character_,
TRUE ~ "null"))
#PT02BleIvolv_miss
myInData <- myInData %>% dplyr::mutate(PT02BleInvolv_miss = dplyr::case_when(
PT02BleInvolv %in% c(0,1) ~ PT02BleInvolv, #just keeping numeric values
PasientAlder >= 16 & PT02BleInvolv %in% c(9,"null") & RegRegtype %in% c(5,6) ~ NA_character_,
TRUE ~ "null"))
#PT04KontaktBrukerorg_miss
myInData <- myInData %>% dplyr::mutate(PT04KontaktBrukerorg_miss = dplyr::case_when(
PT04KontaktBrukerorg %in% c(0,1) ~ PT04KontaktBrukerorg, #just keeping numeric values
PasientAlder >= 16 & PT04KontaktBrukerorg %in% c(9,"null") & RegRegtype %in% c(5,6) ~ NA_character_,
TRUE ~ "null"))
#PT05OrientertBrukerorg_miss
myInData <- myInData %>% dplyr::mutate(PT05OrientertBrukerorg_miss = dplyr::case_when(
PT05OrientertBrukerorg %in% c(0,1) ~ PT05OrientertBrukerorg, #just keeping numeric values
PasientAlder >= 16 & PT05OrientertBrukerorg %in% c(9,"null") & RegRegtype %in% c(5,6) ~ NA_character_,
TRUE ~ "null"))
##Other dicotomous variables
#PO10Pasientsikkerhet...Values in raw data: (0,1,2,3,4,9,99,'null)
#1,2,3)
myInData$PROP_PO10Pasientsikkerhet <- myInData$PO10Pasientsikkerhet
#1)
myInData$PROP_PO10Pasientsikkerhet <- dplyr::recode(myInData$PO10Pasientsikkerhet,
"0"="1",
"1"="0",
"2"="0",
"3"="0",
"4"="0")#recode function is "questioning" (see ?recode)
myInData$PROP_PO10Pasientsikkerhet <- replace(myInData$PROP_PO10Pasientsikkerhet,
myInData$PROP_PO10Pasientsikkerhet %in%
c("9",
"99"),
NA)
#as.numeric to enable calculations (this also removes "null" values (where there are any left) and introduces NAs)
myInData$PROP_PO10Pasientsikkerhet <- as.numeric(myInData$PROP_PO10Pasientsikkerhet)
#2)
#Making missing variable ("ikke besvart" and every observation with end registration where patient is above 15 years)
#myInData[which(myInData$MISSING_PO10Pasientsikkerhet%in% c("0","1","2","3","4","9")), ]$MISSING_PO10Pasientsikkerhet <- "0"
#myInData[which(myInData$MISSING_PO10Pasientsikkerhet %in% c("99")), ]$MISSING_PO10Pasientsikkerhet <- "1"
#3) Making a "ikke aktuelt" variable:
#myInData[which(as.numeric(myInData$UAKTUELL_PO10Pasientsikkerhet)%in%c("0","1","2","3","4","99")),]$UAKTUELL_PO10Pasientsikkerhet <-"0"
#myInData[which(as.numeric(myInData$UAKTUELL_PO10Pasientsikkerhet) %in% c("9")), ]$UAKTUELL_PO10Pasientsikkerhet <- "1"
#myInData$MISSING_PO10Pasientsikkerhet <- as.numeric(myInData$MISSING_PO10Pasientsikkerhet)
#myInData$UAKTUELL_PO10Pasientsikkerhet <- as.numeric(myInData$UAKTUELL_PO10Pasientsikkerhet)
##Quality indicators (more precisely MISSING for QIs)
#QI1: Already OK
#TODO to get missing: table(is.na(myInData$EDEQ60GlobalScore))[1]/ (table(is.na(myInData$EDEQ60GlobalScore))[2] + table(is.na(myInData$EDEQ60GlobalScore))[1])
#TODO OR USE: sum(is.na(myInData$EDEQ60GlobalScore))
#QI5: P09 utbytte (0-1)
myInData$PROP_PO09Utbytte <- dplyr::recode(myInData$PO09Utbytte,
"0"="0",
"1"="0",
"2"="1",
"3"="1",
"4"="1")#regarding recode function, be aware that it is "questioning" (see ?recode)
myInData$PROP_PO09Utbytte <- replace(myInData$PROP_PO09Utbytte,
myInData$PROP_PO09Utbytte %in%
c("9",
"99"),
NA)
myInData$PROP_PO09Utbytte <- as.numeric(myInData$PROP_PO09Utbytte)
#QI4: PT03 utfall (0-1)
myInData$PROP_PT03Utfallsvurd <- dplyr::recode(myInData$PT03Utfallsvurd,
"1"="1",#Ikke noe problem lenger
"2"="1",#Klar bedring
"3"="1",#Noe bedring
"4"="0",#Uendret
"5"="0") #Forverret
myInData$PROP_PT03Utfallsvurd <- replace(myInData$PROP_PT03Utfallsvurd,
myInData$PROP_PT03Utfallsvurd %in%
c("9"),
NA)
myInData$PROP_PT03Utfallsvurd <- as.numeric(myInData$PROP_PT03Utfallsvurd) #need to be numeric, also introduces NAs where "null"
#PT03 utfall MISSING (0-1)
myInData$MISSING_PT03Utfallsvurd <- dplyr::recode(myInData$PT03Utfallsvurd,
"1"="0",#Ikke noe problem lenger
"2"="0",#Klar bedring
"3"="0",#Noe bedring
"4"="0",#Uendret
"5"="0") #Forverret
myInData$MISSING_PT03Utfallsvurd <- replace(myInData$MISSING_PT03Utfallsvurd,
myInData$MISSING_PT03Utfallsvurd == "9",
1)
myInData$MISSING_PT03Utfallsvurd <- as.numeric(myInData$MISSING_PT03Utfallsvurd) #need to be numeric, also introduces NAs where "null"
#PT03 utfall UAKTUELL (0-1) (EMPTY - there is no such category on this variable)
### END: Quality indicators ###
myInData
}
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