Lendo os dados
# u <- "2017 11 05 - Dados pré-Tx para R.csv"
# da <- read.csv(file = u,
# na.string = c("?", "9999"))
# str(da)
# Ler da planilha xls onde as variáveis não estão codificadas.
u <- "2017 11 05 - Descrição I Var. Vanessa H..xlsx"
da <- read.xls(xls = u,
sheet = 1,
verbose = TRUE,
na.string = c("", "?", "9999"),
skip = 1)
## Using perl at /usr/bin/perl
## Using perl at /usr/bin/perl
##
## Converting xls file
## "2017 11 05 - Descrição I Var. Vanessa H..xlsx"
## to csv file
## "/tmp/RtmpHQBTAn/file3366259e73ac.csv"
## ...
##
## Executing ' '/usr/bin/perl' '/usr/lib/R/site-library/gdata/perl/xls2csv.pl' '2017 11 05 - Descrição I Var. Vanessa H..xlsx' '/tmp/RtmpHQBTAn/file3366259e73ac.csv' '1' '...
##
## 0
##
## Done.
##
## Reading csv file "/tmp/RtmpHQBTAn/file3366259e73ac.csv" ...
## Done.
str(da)
## 'data.frame': 178 obs. of 68 variables:
## $ ID : Factor w/ 178 levels "NTXC01","NTXC02",..: 112 113 114 115 116 117 118 119 120 121 ...
## $ Pop : Factor w/ 3 levels "NTXC","NTXS",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ Pop_Art1 : Factor w/ 2 levels "Not selected",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ Pop_Art3 : Factor w/ 2 levels "Not selected",..: 2 2 2 2 1 1 2 1 1 1 ...
## $ Pop_Ana : Factor w/ 1 level "Selected": 1 1 1 1 1 1 1 1 1 1 ...
## $ Model7 : int 2 3 4 1 3 4 2 5 2 1 ...
## $ Age : int 55 34 24 20 47 48 53 25 69 54 ...
## $ Gender : Factor w/ 2 levels "F","M": 2 2 2 2 2 2 2 1 2 1 ...
## $ Weight : int 53 88 65 62 79 74 84 42 78 66 ...
## $ BMI : Factor w/ 4 levels "Normal weight",..: 1 3 1 1 3 1 3 1 1 3 ...
## $ Height : int 164 174 170 164 163 175 168 150 193 150 ...
## $ N_pre_Tx : int 0 0 0 0 0 0 0 1 0 0 ...
## $ Re_Tx : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 2 1 1 ...
## $ N_pre_blood_tx : int 1 0 0 0 2 0 3 16 0 2 ...
## $ Multip : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 2 ...
## $ N_pregn : int 0 0 0 0 0 0 0 0 0 3 ...
## $ Abortions : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ N_abortions : int 0 0 0 0 0 0 0 0 0 0 ...
## $ PRA_30 : Factor w/ 2 levels "high PRA","low PRA": 2 1 1 2 2 1 2 1 1 2 ...
## $ DSAtotal : Factor w/ 2 levels "no total DSA",..: 1 1 2 1 1 2 1 2 1 1 ...
## $ DSA_classI : Factor w/ 2 levels "No DSA classI",..: 1 2 2 1 2 1 1 1 1 2 ...
## $ DSA_classII : Factor w/ 2 levels "No DSA classII",..: 1 1 1 1 1 1 1 2 1 1 ...
## $ MHC_I : Factor w/ 2 levels "no Ab anti-MHC-I",..: 2 2 2 1 2 2 2 2 2 2 ...
## $ MHC_II : Factor w/ 2 levels "no Ab anti-MHC-II",..: 1 2 1 2 1 2 1 2 1 1 ...
## $ Hd_CAPD : Factor w/ 4 levels "CAPD","Hd","Hd plus CAPD",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ Time_dialysis : int 51 13 15 0 83 54 48 12 96 96 ...
## $ Ischemia : Factor w/ 40 levels "00:00:00.00",..: 2 17 2 2 33 28 2 2 37 20 ...
## $ HLA_MM : Factor w/ 7 levels "1 Mismatch","2 Mismatches",..: 3 5 2 6 6 3 4 3 4 4 ...
## $ D_typeI : Factor w/ 2 levels "Alive donor",..: 1 2 1 1 2 2 1 1 2 2 ...
## $ D_typeII : Factor w/ 5 levels "Alive related donor 1 (brothers and parents) DVR1",..: 1 5 1 2 5 5 4 4 5 5 ...
## $ D_gender : Factor w/ 2 levels "F","M": 1 1 1 1 2 2 1 2 2 1 ...
## $ D_age : int 44 58 33 49 60 31 48 25 27 49 ...
## $ Diff_age : int 11 24 9 29 13 17 5 0 42 5 ...
## $ Diff_weight : int 22 8 10 15 4 36 21 21 8 4 ...
## $ Diff_height : int 6 9 15 3 12 15 12 21 3 15 ...
## $ Ind_ATG : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 2 1 1 ...
## $ B_Hypert : Factor w/ 2 levels "no","yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ B_DM : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ B_DLP : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ B_GNC : Factor w/ 2 levels "no","yes": 2 2 2 1 2 2 1 1 1 2 ...
## $ B_PN : Factor w/ 2 levels "no","yes": 2 1 1 1 2 1 1 2 1 1 ...
## $ B_PKD : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 2 1 ...
## $ B_Hypert_nephro: Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 2 1 1 1 ...
## $ B_CMV : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ B_Chagas : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ B_Toxoplasma : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ B_EBV : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ B_HBV : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ B_HCV : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ B_Others : Factor w/ 2 levels "no","yes": 1 1 1 1 1 2 1 2 2 2 ...
## $ B_G_other : Factor w/ 2 levels "no","yes": 1 1 1 1 1 2 1 2 2 2 ...
## $ B_G_virus : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ B_G_parasitic : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ DRC : Factor w/ 3 levels "DRC-3A ou -3B",..: 3 3 3 2 3 3 3 2 2 3 ...
## $ Clin_rej : Factor w/ 2 levels "Developed clinical rejection",..: 2 1 1 1 2 1 1 2 1 1 ...
## $ Diff_Lost : int 0 0 0 1 0 0 0 0 0 0 ...
## $ Thrombosis : int 0 0 0 0 0 0 0 0 0 0 ...
## $ RA : int 0 0 0 0 0 0 0 0 0 0 ...
## $ RAC1 : int 0 1 2 0 0 0 0 0 5 3 ...
## $ RAC2 : int 0 7 0 0 0 0 0 0 0 0 ...
## $ CAN : int 0 0 0 0 0 0 44 0 0 0 ...
## $ Nephro_DM : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Nephrotoxicity : int 0 0 0 0 0 1 0 0 0 0 ...
## $ PNA1 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ PNA2 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ GNC : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Graft_lost : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Death : int 0 0 0 0 0 0 0 0 0 0 ...
Análise exploratória
Variáveis antropométricas
ggplot(data = da,
mapping = aes(color = Gender, x = Age)) +
stat_ecdf(geom = "step") +
labs(x = "Idade (anos)",
y = "Frequência relativa acumulada")
ggplot(data = da,
mapping = aes(color = Gender, x = Weight)) +
geom_density() +
geom_rug() +
labs(x = "Massa (kg)",
y = "Densidade")
ggplot(data = da,
mapping = aes(color = Gender, y = Weight, x = Height)) +
geom_point() +
geom_smooth(method = "loess") +
labs(x = "Altura (cm)",
y = "Massa (kg)")
# xtabs(~BMI + Gender, data = da)
# dput(levels(da$BMI)[c(4, 1, 3, 2)])
da$BMI <- factor(da$BMI,
levels = c("Underweight",
"Normal weight",
"Overweight",
"Obesity"))
ggplot(data = da,
mapping = aes(fill = Gender, x = BMI)) +
geom_bar(position = "dodge", col = "gray50") +
labs(x = "Classe para índice de massa corporal",
y = "Frequência absoluta")
Pacientes transplantados (receptores)
#-----------------------------------------------------------------------
# dput(levels(da$Pop)[c(2, 1, 3)])
da$Pop <- factor(da$Pop,
levels = c("NTXS", "NTXC", "TXR"))
ggplot(data = da,
mapping = aes(x = Pop)) +
geom_bar(position = "dodge", col = "gray50") +
labs(x = "Tipo de paciente segundo transplante e doença renal",
y = "Frequência absoluta")
#-----------------------------------------------------------------------
# Apenas nos pacientes transplantados (Pop == TXR)
# Quantos transplantes anteriores.
ggplot(data = subset(da, Pop == "TXR"),
mapping = aes(x = N_pre_Tx)) +
geom_bar(position = "dodge", col = "gray50") +
labs(x = "Número de transplantes prévios",
y = "Frequência absoluta")
ggplot(data = subset(da, Pop == "TXR"),
mapping = aes(x = Time_dialysis)) +
geom_density(na.rm = TRUE) +
geom_rug() +
labs(x = "Tempo em diálise prá-transplante (meses)",
y = "Frequência absoluta")
# Convertendo para horas.
da$IschemiaH <-
sapply(strsplit(x = as.character(da$Ischemia), split = ":"),
FUN = function(x) {
sum(as.numeric(x) * c(60, 1, 1/60))/60
})
ggplot(data = subset(da, Pop == "TXR"),
mapping = aes(x = IschemiaH)) +
geom_density(na.rm = TRUE) +
geom_rug() +
labs(x = "Tempo de isquemia fria do órgão transplantado (horas)",
y = "Frequência absoluta")
Doadores
#-----------------------------------------------------------------------
# Sobre os doadores.
ggplot(data = subset(da, Pop == "TXR"),
mapping = aes(x = D_typeI)) +
geom_bar() +
labs(x = "Condição do doador",
y = "Frequência absoluta")
ggplot(data = subset(da, Pop == "TXR"),
mapping = aes(x = D_typeII)) +
geom_bar() +
labs(x = "Condição do doador",
y = "Frequência absoluta") +
coord_flip()
ggplot(data = subset(da, Pop == "TXR"),
mapping = aes(x = D_age, color = D_gender)) +
geom_density() +
geom_rug() +
labs(x = "Idade do doador (anos)",
y = "Densidade")
grid.arrange(
ggplot(data = subset(da, Pop == "TXR"),
mapping = aes(x = Age - D_age, color = Gender)) +
geom_density() +
geom_rug() +
geom_vline(xintercept = 0) +
labs(x = "Diferença de idade entre transplantado e doador (anos)",
y = "Densidade"),
#
ggplot(data = subset(da, Pop == "TXR"),
mapping = aes(x = Diff_weight, color = Gender)) +
geom_density() +
geom_rug() +
labs(x = "Diferença absoluta de peso entre transplantados (kg)",
y = "Densidade"),
#
ggplot(data = subset(da, Pop == "TXR"),
mapping = aes(x = Diff_height, color = Gender)) +
geom_density() +
geom_rug() +
labs(x = "Diferença de altura entre transplantado (cm)",
y = "Densidade")
)
Doenças de base
#-----------------------------------------------------------------------
# Sobre as doenças de base.
dbas <- melt(data = da,
id.vars = c("Gender", "Age", "Pop"),
measure.vars = grep("^B_[^G]", names(da), value = TRUE),
value.name = "valor",
variable.name = "doenca")
str(dbas)
## 'data.frame': 2314 obs. of 5 variables:
## $ Gender: Factor w/ 2 levels "F","M": 2 2 2 2 2 2 2 1 2 1 ...
## $ Age : int 55 34 24 20 47 48 53 25 69 54 ...
## $ Pop : Factor w/ 3 levels "NTXS","NTXC",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ doenca: Factor w/ 13 levels "B_Hypert","B_DM",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ valor : chr "yes" "yes" "yes" "yes" ...
ftable(xtabs(~doenca + valor + Pop, data = dbas))
## Pop NTXS NTXC TXR
## doenca valor
## B_Hypert no 67 5 5
## yes 12 26 62
## B_DM no 77 20 55
## yes 2 11 12
## B_DLP no 77 21 61
## yes 2 10 6
## B_PN no 79 29 62
## yes 0 2 5
## B_PKD no 79 30 60
## yes 0 2 7
## B_Hypert_nephro no 79 26 57
## yes 0 5 10
## B_CMV no 0 29 65
## yes 0 2 2
## B_Chagas no 79 16 66
## yes 0 0 1
## B_Toxoplasma no 78 10 65
## yes 1 8 2
## B_EBV no 69 7 66
## yes 10 11 1
## B_HBV no 79 16 66
## yes 0 10 1
## B_HCV no 79 24 66
## yes 0 1 1
## B_Others no 71 11 40
## yes 8 20 27
ggplot(data = dbas, mapping = aes(x = valor)) +
geom_bar(position = "dodge") +
labs(x = "Resposta",
y = "Frequência absoluta") +
facet_wrap(facets = doenca ~ Pop,
ncol = 3,
# labeller = "label_both",
drop = FALSE)
Desfechos
#-----------------------------------------------------------------------
# Desfechos: perda do enxerto e morte.
ggplot(data = subset(da, Pop == "TXR"),
mapping = aes(x = Graft_lost == 0)) +
geom_bar() +
labs(x = "Perda do enxerto",
y = "Frequência absoluta")
ggplot(data = subset(da, Pop == "TXR"),
mapping = aes(x = Death == 0)) +
geom_bar() +
labs(x = "Morte do transplantado",
y = "Frequência absoluta")
