R/clean_v3.R
In rafaelsommer1/emocempr: Functions for analyzing EMOCEMP data
Defines functions
clean_v3
Documented in
clean_v3
#'Takes the raw dataset from qualtrics and clean it
#'@description The raw data need to be exported from qualtrics EMOCEMP V3 in .csv with the numeric values option checked
#' @import dplyr
#' @import stringr
#' @importFrom lubridate dmy
#' @importFrom utils read.csv
#'@param datafile a V3 .csv file exported from qualtrics with numeric data option
#'@return a data.frame with cleaned data which may be used to merge with other visits
#'@export
#'@author R.C.S
clean_v3 <- function(datafile){
# Read input
emocemp_messy <- read.csv(datafile,
skip = 1,
encoding = "UTF-8",
stringsAsFactors = FALSE)
# Exclude irrelevant rows and columns from table
emocempv3 <- emocemp_messy[-c(1),-c(1:17)]
emocempv3 <- emocempv3[,-79]
# -------------------------------------------------------------------------------- #
# Rename
colnames(emocempv3) <- c("data_visita",
"id_centro",
"id_paciente",
"peso",
"altura",
"imc",
"tabagismo",
"sf_piramidal",
"sf_cerebelar",
"sf_sensitivo",
"sf_tronco",
"sf_visual",
"sf_vesical_intestinal",
"sf_cerebral",
"conv_sf_visual",
"conv_sf_vesical_intestinal",
"deambula",
"edss",
"data_cerebro_rm_v3",
"data_orbitas_rm_v3",
"data_medula_rm_v3",
"rm_encefalo",
"rm_encefalo_realce",
"rm_encefalo_coment",
"rm_orbita",
"rm_orbita_realce_text",
"rm_medula",
"rm_medula_realce_text",
"rm_medula_coment",
"rm_outros_texto",
"medicacoes_cont",
"criterio_diag_v3",
"criterio_diag_outros_v3_text",
"fan_titulo_padrao",
"fr",
"vhs",
"anti-ssa",
"anti-ssb",
"vit_d",
"vit_b12",
"outros_labs",
"dmd_v3",
"ifn_b1a_im_dose",
"ifn_b1a_sc22_dose",
"ifn_b1a_sc44_dose",
"ifn_b1b_dose",
"acetato_glati_dose",
"teriflunomida_dose",
"fumarato_dose",
"fingolimod_dose",
"natalizumba_dose",
"alentuzumab_dose",
"rituximab_dose",
"azatioprina_dose",
"outros_dose",
"efeitos_adversos",
"efeitos_adversos_texto",
"observacoes",
"novo_surto_v3",
"novas_sindromes_clinicas",
"nob_data",
"nou_data",
"mielite_p_data",
"mielite_t_data",
"adem_data",
"romboencefalite_data",
"outras_data",
"lcr_cel",
"lcr_dif",
"lcr_prot",
"lcr_boc",
"lcr_igg_i",
"tto_fa_v3",
"mpiv_dose",
"corticoide_vo_dose",
"igiv_dose",
"plex_dose")
# -------------------------------------------------------------------------------- #
# Multiple choice variables
print("Dumming multiple choice variables")
# Implement the cleaning function
clean <- function(var,database,n,names){
df <- dummie_mcv(names = names,
var = var,
n = n,
dataf = database)
database <- database[, names(database) != var]
database <- cbind(database, df)
return(database)
}
# -------------------------------------------------------------------------------- #
print("Clinical Data")
# Clinical syndromes
name_clinical <- c("neurite_b",
"neurite_u",
"mielite_p",
"mielite_t",
"adem" ,
"romboencefalite",
"outras",
"nenhumas_das_sindromes")
emocempv3 <- clean("novas_sindromes_clinicas",emocempv3,8,name_clinical)
# -------------------------------------------------------------------------------- #
print("Brain Data")
# Brain imaging
name_brm <- c("1_lesao_perpendicular_cc_brm",
"apenas_bem_delimitadas_brm",
"5_ou_mais_t2_brm",
"2_ou_mais_peri_brm",
"1_ou_mais_tronco_brm" ,
"black_holes_brm",
"lesoes_difusas_bilat_brm",
"1_ou_mais_justa_brm",
"1_ou_mais_peri_brm",
"1_ou_mais_infra_brm",
"realce_anel_inc_brm",
"outros_realces_brm",
"nenhuma_acima_brm",
"nao_realizada_brm",
"outras_brm")
emocempv3 <- clean("rm_encefalo",emocempv3,15,name_brm)
print("Done")
# -------------------------------------------------------------------------------- #
print("Spinal Data")
# Spinal imaging
name_srm <- c("mielite_t_srm",
"mielite_centromed_srm",
"mielite_perif_srm",
"mielite_menos_3seg_srm",
"mielite_mais_3seg_srm" ,
"mielite_cerv_srm",
"mielite_dors_srm",
"mielite_lombo_srm",
"nenhuma_das_acima_srm",
"nao_realizada_srm",
"realce_srm",
"brigth_spot_srm",
"outras_srm")
emocempv3 <- clean("rm_medula",emocempv3,13,name_srm)
print("Done")
# -------------------------------------------------------------------------------- #
# Orbit imaging
print("Orbital Data")
name_orm <- c("t2_hiper_retrobulbar_orm",
"t2_hiper_quiasma_orm",
"t2_hiper_medportion_orm",
"t2_hiper_mais_2tercos_orm",
"realce_orm" ,
"nenhuma_das_acima_orm",
"nao_realizada_orm",
"outras_orm")
emocempv3 <- clean("rm_orbita",emocempv3,8,name_orm)
print("Done")
# -------------------------------------------------------------------------------- #
print("DMD Data")
# DMD dumming
names_dmd <- c("ifnb_1a_im",
"ifnb_1a_22sc",
"ifnb_1a_44sc",
"ifnb_1b",
"glatiramer",
"teriflunomida",
"fumarato_dimetil",
"fingolimod",
"natalizumab",
"alentuzumab",
"rituximab",
"azatioprina",
"outros_dmds",
"nenhum_dmd")
emocempv3 <- clean("dmd_v3", emocempv3, 14, names_dmd)
print("Done")
# -------------------------------------------------------------------------------- #
print("Acute treatment Data")
# Acute treatment dumming
names_tta <- c("mpiv",
"corticoide_vo",
"igiv",
"plex",
"nenhum_tto_a")
emocempv3 <- clean("tto_fa_v3",emocempv3,5,names_tta)
print("Done")
# -------------------------------------------------------------------------------- #
print("Adverse Events Data")
# Adverse events dumming
names_ae <- c("dor_local_ae",
"lipodistrofia_ae",
"flu_like_ae",
"infec__ae",
"lemp__ae",
"leuco_linfopenia_ae",
"desordem_autoimune_ae",
"infusao_react_ae",
"rubor_facial_ae",
"gastrointestinal_ae",
"elev_transaminases_ae",
"outros")
emocempv3 <- clean("efeitos_adversos", emocempv3, 12, names_ae)
print("Done")
# -------------------------------------------------------------------------------- #
# -------------------------------------------------------------------------------- #
# -------------------------------------------------------------------------------- #
# Cleaning dates
print("Cleaning dates")
dates <- c("data_visita","romboencefalite_data",
"data_cerebro_rm_v3","data_medula_rm_v3",
"data_orbitas_rm_v3","nob_data",
"nou_data","adem_data","outras_data","mielite_p_data",
"mielite_t_data")
for (i in dates) {
emocempv3[[i]] <- suppressWarnings(dmy(emocempv3[[i]]))
}
print("Done")
# -------------------------------------------------------------------------------- #
# -------------------------------------------------------------------------------- #
# -------------------------------------------------------------------------------- #
# Cleaning factors
print("Cleaning factors")
# Smoking status
emocempv3$tabagismo <- as.factor(as.numeric(emocempv3$tabagismo))
levels(emocempv3$tabagismo) <- c("ativo","passivo","ausente")
# -------------------------------------------------------------------------------- #
# Diagnostic Criteria
emocempv3$criterio_diag_v3 <- factor(as.numeric(emocempv3$criterio_diag_v3))
levels(emocempv3$criterio_diag_v3) <- c("em","nmosd","isolated_event",
"outras","cis")
# -------------------------------------------------------------------------------- #
# Relapses
emocempv3$novo_surto_v3 <- as.numeric(emocempv3$novo_surto_v3)
emocempv3$novo_surto_v3 <- as.factor(emocempv3$novo_surto_v3)
levels(emocempv3$novo_surto_v3) <- c("sim","nao")
print("Done")
# -------------------------------------------------------------------------------- #
# -------------------------------------------------------------------------------- #
# -------------------------------------------------------------------------------- #
# Cleaning numeric variables
print("Cleaning numeric variables")
# EDSS
emocempv3$edss <- as.numeric(emocempv3$edss) / 2
emocempv3$edss[emocempv3$edss == 0.5] <- 0
# -------------------------------------------------------------------------------- #
# Weight
emocempv3$peso <- as.numeric(str_replace(emocempv3$peso, ",","."))
# -------------------------------------------------------------------------------- #
# Height
altura_i <- str_detect(emocempv3$altura, "^\\d{2}")
emocempv3$altura <- as.numeric(str_replace(emocempv3$altura,",","."))
altura_i[is.na(altura_i)] <- FALSE
emocempv3$altura[altura_i] <- emocempv3$altura[altura_i] / 100
# -------------------------------------------------------------------------------- #
# IMC
emocempv3$imc <- as.numeric(str_replace(emocempv3$imc,",","."))
# -------------------------------------------------------------------------------- #
# Cleaning LCR strings
# Input NAs
emocempv3$lcr_boc[!str_detect(emocempv3$lcr_boc,"")] <- NA
emocempv3$lcr_cel[!str_detect(emocempv3$lcr_cel,"")] <- NA
emocempv3$lcr_prot[!str_detect(emocempv3$lcr_prot,"")] <- NA
emocempv3$lcr_igg_i[!str_detect(emocempv3$lcr_igg_i,"")] <- NA
# Clean strings
# Protein
emocempv3$lcr_prot <- str_extract(emocempv3$lcr_prot,
"^\\d{1,3}[\\. ,]?\\d{0,3}$")
# Cel
emocempv3$lcr_cel <- str_extract(emocempv3$lcr_cel,
"^\\d{1,3}[\\. ,]?\\d{0,3}")
emocempv3$lcr_cel <- as.numeric(str_replace(emocempv3$lcr_cel,
",","."))
return(emocempv3)
}
rafaelsommer1/emocempr documentation built on Aug. 31, 2020, 4:38 p.m.
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Defines functions clean_v3
Documented in clean_v3
#'Takes the raw dataset from qualtrics and clean it
#'@description The raw data need to be exported from qualtrics EMOCEMP V3 in .csv with the numeric values option checked
#' @import dplyr
#' @import stringr
#' @importFrom lubridate dmy
#' @importFrom utils read.csv
#'@param datafile a V3 .csv file exported from qualtrics with numeric data option
#'@return a data.frame with cleaned data which may be used to merge with other visits
#'@export
#'@author R.C.S
clean_v3 <- function(datafile){
# Read input
emocemp_messy <- read.csv(datafile,
skip = 1,
encoding = "UTF-8",
stringsAsFactors = FALSE)
# Exclude irrelevant rows and columns from table
emocempv3 <- emocemp_messy[-c(1),-c(1:17)]
emocempv3 <- emocempv3[,-79]
# -------------------------------------------------------------------------------- #
# Rename
colnames(emocempv3) <- c("data_visita",
"id_centro",
"id_paciente",
"peso",
"altura",
"imc",
"tabagismo",
"sf_piramidal",
"sf_cerebelar",
"sf_sensitivo",
"sf_tronco",
"sf_visual",
"sf_vesical_intestinal",
"sf_cerebral",
"conv_sf_visual",
"conv_sf_vesical_intestinal",
"deambula",
"edss",
"data_cerebro_rm_v3",
"data_orbitas_rm_v3",
"data_medula_rm_v3",
"rm_encefalo",
"rm_encefalo_realce",
"rm_encefalo_coment",
"rm_orbita",
"rm_orbita_realce_text",
"rm_medula",
"rm_medula_realce_text",
"rm_medula_coment",
"rm_outros_texto",
"medicacoes_cont",
"criterio_diag_v3",
"criterio_diag_outros_v3_text",
"fan_titulo_padrao",
"fr",
"vhs",
"anti-ssa",
"anti-ssb",
"vit_d",
"vit_b12",
"outros_labs",
"dmd_v3",
"ifn_b1a_im_dose",
"ifn_b1a_sc22_dose",
"ifn_b1a_sc44_dose",
"ifn_b1b_dose",
"acetato_glati_dose",
"teriflunomida_dose",
"fumarato_dose",
"fingolimod_dose",
"natalizumba_dose",
"alentuzumab_dose",
"rituximab_dose",
"azatioprina_dose",
"outros_dose",
"efeitos_adversos",
"efeitos_adversos_texto",
"observacoes",
"novo_surto_v3",
"novas_sindromes_clinicas",
"nob_data",
"nou_data",
"mielite_p_data",
"mielite_t_data",
"adem_data",
"romboencefalite_data",
"outras_data",
"lcr_cel",
"lcr_dif",
"lcr_prot",
"lcr_boc",
"lcr_igg_i",
"tto_fa_v3",
"mpiv_dose",
"corticoide_vo_dose",
"igiv_dose",
"plex_dose")
# -------------------------------------------------------------------------------- #
# Multiple choice variables
print("Dumming multiple choice variables")
# Implement the cleaning function
clean <- function(var,database,n,names){
df <- dummie_mcv(names = names,
var = var,
n = n,
dataf = database)
database <- database[, names(database) != var]
database <- cbind(database, df)
return(database)
}
# -------------------------------------------------------------------------------- #
print("Clinical Data")
# Clinical syndromes
name_clinical <- c("neurite_b",
"neurite_u",
"mielite_p",
"mielite_t",
"adem" ,
"romboencefalite",
"outras",
"nenhumas_das_sindromes")
emocempv3 <- clean("novas_sindromes_clinicas",emocempv3,8,name_clinical)
# -------------------------------------------------------------------------------- #
print("Brain Data")
# Brain imaging
name_brm <- c("1_lesao_perpendicular_cc_brm",
"apenas_bem_delimitadas_brm",
"5_ou_mais_t2_brm",
"2_ou_mais_peri_brm",
"1_ou_mais_tronco_brm" ,
"black_holes_brm",
"lesoes_difusas_bilat_brm",
"1_ou_mais_justa_brm",
"1_ou_mais_peri_brm",
"1_ou_mais_infra_brm",
"realce_anel_inc_brm",
"outros_realces_brm",
"nenhuma_acima_brm",
"nao_realizada_brm",
"outras_brm")
emocempv3 <- clean("rm_encefalo",emocempv3,15,name_brm)
print("Done")
# -------------------------------------------------------------------------------- #
print("Spinal Data")
# Spinal imaging
name_srm <- c("mielite_t_srm",
"mielite_centromed_srm",
"mielite_perif_srm",
"mielite_menos_3seg_srm",
"mielite_mais_3seg_srm" ,
"mielite_cerv_srm",
"mielite_dors_srm",
"mielite_lombo_srm",
"nenhuma_das_acima_srm",
"nao_realizada_srm",
"realce_srm",
"brigth_spot_srm",
"outras_srm")
emocempv3 <- clean("rm_medula",emocempv3,13,name_srm)
print("Done")
# -------------------------------------------------------------------------------- #
# Orbit imaging
print("Orbital Data")
name_orm <- c("t2_hiper_retrobulbar_orm",
"t2_hiper_quiasma_orm",
"t2_hiper_medportion_orm",
"t2_hiper_mais_2tercos_orm",
"realce_orm" ,
"nenhuma_das_acima_orm",
"nao_realizada_orm",
"outras_orm")
emocempv3 <- clean("rm_orbita",emocempv3,8,name_orm)
print("Done")
# -------------------------------------------------------------------------------- #
print("DMD Data")
# DMD dumming
names_dmd <- c("ifnb_1a_im",
"ifnb_1a_22sc",
"ifnb_1a_44sc",
"ifnb_1b",
"glatiramer",
"teriflunomida",
"fumarato_dimetil",
"fingolimod",
"natalizumab",
"alentuzumab",
"rituximab",
"azatioprina",
"outros_dmds",
"nenhum_dmd")
emocempv3 <- clean("dmd_v3", emocempv3, 14, names_dmd)
print("Done")
# -------------------------------------------------------------------------------- #
print("Acute treatment Data")
# Acute treatment dumming
names_tta <- c("mpiv",
"corticoide_vo",
"igiv",
"plex",
"nenhum_tto_a")
emocempv3 <- clean("tto_fa_v3",emocempv3,5,names_tta)
print("Done")
# -------------------------------------------------------------------------------- #
print("Adverse Events Data")
# Adverse events dumming
names_ae <- c("dor_local_ae",
"lipodistrofia_ae",
"flu_like_ae",
"infec__ae",
"lemp__ae",
"leuco_linfopenia_ae",
"desordem_autoimune_ae",
"infusao_react_ae",
"rubor_facial_ae",
"gastrointestinal_ae",
"elev_transaminases_ae",
"outros")
emocempv3 <- clean("efeitos_adversos", emocempv3, 12, names_ae)
print("Done")
# -------------------------------------------------------------------------------- #
# -------------------------------------------------------------------------------- #
# -------------------------------------------------------------------------------- #
# Cleaning dates
print("Cleaning dates")
dates <- c("data_visita","romboencefalite_data",
"data_cerebro_rm_v3","data_medula_rm_v3",
"data_orbitas_rm_v3","nob_data",
"nou_data","adem_data","outras_data","mielite_p_data",
"mielite_t_data")
for (i in dates) {
emocempv3[[i]] <- suppressWarnings(dmy(emocempv3[[i]]))
}
print("Done")
# -------------------------------------------------------------------------------- #
# -------------------------------------------------------------------------------- #
# -------------------------------------------------------------------------------- #
# Cleaning factors
print("Cleaning factors")
# Smoking status
emocempv3$tabagismo <- as.factor(as.numeric(emocempv3$tabagismo))
levels(emocempv3$tabagismo) <- c("ativo","passivo","ausente")
# -------------------------------------------------------------------------------- #
# Diagnostic Criteria
emocempv3$criterio_diag_v3 <- factor(as.numeric(emocempv3$criterio_diag_v3))
levels(emocempv3$criterio_diag_v3) <- c("em","nmosd","isolated_event",
"outras","cis")
# -------------------------------------------------------------------------------- #
# Relapses
emocempv3$novo_surto_v3 <- as.numeric(emocempv3$novo_surto_v3)
emocempv3$novo_surto_v3 <- as.factor(emocempv3$novo_surto_v3)
levels(emocempv3$novo_surto_v3) <- c("sim","nao")
print("Done")
# -------------------------------------------------------------------------------- #
# -------------------------------------------------------------------------------- #
# -------------------------------------------------------------------------------- #
# Cleaning numeric variables
print("Cleaning numeric variables")
# EDSS
emocempv3$edss <- as.numeric(emocempv3$edss) / 2
emocempv3$edss[emocempv3$edss == 0.5] <- 0
# -------------------------------------------------------------------------------- #
# Weight
emocempv3$peso <- as.numeric(str_replace(emocempv3$peso, ",","."))
# -------------------------------------------------------------------------------- #
# Height
altura_i <- str_detect(emocempv3$altura, "^\\d{2}")
emocempv3$altura <- as.numeric(str_replace(emocempv3$altura,",","."))
altura_i[is.na(altura_i)] <- FALSE
emocempv3$altura[altura_i] <- emocempv3$altura[altura_i] / 100
# -------------------------------------------------------------------------------- #
# IMC
emocempv3$imc <- as.numeric(str_replace(emocempv3$imc,",","."))
# -------------------------------------------------------------------------------- #
# Cleaning LCR strings
# Input NAs
emocempv3$lcr_boc[!str_detect(emocempv3$lcr_boc,"")] <- NA
emocempv3$lcr_cel[!str_detect(emocempv3$lcr_cel,"")] <- NA
emocempv3$lcr_prot[!str_detect(emocempv3$lcr_prot,"")] <- NA
emocempv3$lcr_igg_i[!str_detect(emocempv3$lcr_igg_i,"")] <- NA
# Clean strings
# Protein
emocempv3$lcr_prot <- str_extract(emocempv3$lcr_prot,
"^\\d{1,3}[\\. ,]?\\d{0,3}$")
# Cel
emocempv3$lcr_cel <- str_extract(emocempv3$lcr_cel,
"^\\d{1,3}[\\. ,]?\\d{0,3}")
emocempv3$lcr_cel <- as.numeric(str_replace(emocempv3$lcr_cel,
",","."))
return(emocempv3)
}
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