Graficas_CR
Margarita Valdes
6/28/2021
Cargar librerias
library(ggplot2)
library(patchwork)
library(dplyr)
library(readr)leer archivos
all_selected <- read_csv("All_rounds_selected.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## X1 = col_double(),
## Ronda = col_character(),
## Fecha = col_date(format = ""),
## ID = col_character(),
## Clave = col_character(),
## IgG = col_character(),
## valor_IgG = col_double(),
## Edad = col_double(),
## Sexo = col_character(),
## Riesgo = col_character(),
## Area = col_character(),
## Delegación = col_character(),
## Vacunado = col_character(),
## Vacuna = col_character(),
## Diagnostico_COVID = col_character(),
## Metodo = col_character(),
## Fecha_diagnostico = col_character(),
## dias_postinfeccion = col_double(),
## Factores_riesgo = col_character()
## )all_selected## # A tibble: 262 x 19
## X1 Ronda Fecha ID Clave IgG valor_IgG Edad Sexo Riesgo Area
## <dbl> <chr> <date> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
## 1 1 D0 2021-01-13 Anny N… CR20… nega… 0.0392 23 Feme… Inter… Soco…
## 2 2 D0 2021-01-13 Omar M… CR20… nega… 0.0422 23 Masc… Alto Soco…
## 3 3 D0 2021-01-13 Pedro … CR20… nega… 0.0768 30 Masc… Inter… Soco…
## 4 4 D0 2021-01-13 Eduard… CR20… nega… 0.0552 25 Masc… Alto Soco…
## 5 5 D0 2021-01-13 Mauric… CR20… nega… 0.0758 37 Masc… Inter… Soco…
## 6 6 D0 2021-01-13 Edgar … CR20… nega… 0.0958 45 Masc… Inter… Soco…
## 7 7 D0 2021-01-13 Diana … CR20… nega… 0.0528 24 Feme… Inter… Soco…
## 8 8 D0 2021-01-13 Daniel… CR20… nega… 0.0828 40 Masc… Alto Soco…
## 9 9 D0 2021-01-13 Juan P… CR20… nega… 0.0362 24 Masc… Alto Soco…
## 10 10 D0 2021-01-13 Rene H… CR20… nega… 0.0368 45 Masc… Alto Soco…
## # … with 252 more rows, and 8 more variables: Delegación <chr>, Vacunado <chr>,
## # Vacuna <chr>, Diagnostico_COVID <chr>, Metodo <chr>,
## # Fecha_diagnostico <chr>, dias_postinfeccion <dbl>, Factores_riesgo <chr>Graficas de muestreo
muestras_area <- ggplot(data=all_selected, aes(x= Fecha, fill= Area)) +
geom_histogram(colour="grey") +
theme(legend.spacing.x= unit(10, 'cm')) +
theme_bw(base_size = 12) +
labs(title="Toma de muestras", y="Número de Muestras", x="")
muestras_area
# ggsave(plot = muestras_area,
# filename = "tomademuestras_por_area.png",
# width = 7,
# height = 4,
# dpi = 300)order_riesgo <- c("Alto", "Intermedio", "Bajo")
Toma_riesgo <- ggplot(all_selected,
aes(x= Fecha,
fill= factor(Riesgo, levels = order_riesgo))) +
geom_histogram(colour="grey") +
theme(legend.spacing.x= unit(10, 'cm')) +
scale_fill_manual(values = c("orangered", "royalblue1", "springgreen")) +
theme_bw(base_size = 12) +
labs(title="Riesgo", y="Número de Muestras", x="", fill= "")
Toma_riesgo
# ggsave(plot = Toma_riesgo,
# filename = "tomademuestras_por_Riesgo.png",
# width = 7,
# height = 4,
# dpi = 300) Pie Charts
num_sexo <- all_selected %>%
filter(Ronda != "D0") %>%
group_by(Sexo) %>%
summarise(n = n())
num_sexo## # A tibble: 2 x 2
## Sexo n
## <chr> <int>
## 1 Femenino 56
## 2 Masculino 92sexo_Pie <- num_sexo %>%
ggplot() +
geom_col(aes(x= 1, y= n, # para que funcione como pie chart debes poner x=1
fill= factor(Sexo)),
position = "fill") +
coord_polar(theta = "y") +
labs(title= "", fill="Sexo") +
theme_bw() +
theme(plot.title = element_text(size = 22, hjust = 0.5)) +
theme(axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank())
sexo_Pie
Porcentaje de muestras por delegación
num_delegacion <- all_selected %>%
filter(Ronda != "D0") %>%
group_by(Delegación) %>%
summarise(n = n())
num_delegacion## # A tibble: 4 x 2
## Delegación n
## <chr> <int>
## 1 Cuautla 10
## 2 Cuernavaca 107
## 3 Jojutla 26
## 4 <NA> 5data <- num_delegacion %>%
arrange(desc(Delegación)) %>%
mutate(prop = n/ sum(num_delegacion$n) *100) %>%
mutate(ypos = cumsum(prop)- 0.5*prop )
delegacion_Pie <- num_delegacion %>%
ggplot() +
geom_col(aes(x= 1, y= n, # para que funcione como pie chart debes poner x=1
fill= factor(Delegación)),
position = "fill") +
#geom_text(aes(x = n, y = ypos, label = n), color = "white", size=6) +
coord_polar(theta = "y", start = 0) +
labs(title= "Porcentaje de muestras por Delegación", fill="") +
scale_fill_manual(values=c("lightgreen","skyblue","violet"), na.value = "azure2") +
theme_bw() +
theme(plot.title = element_text(size = 22, hjust = 0.5)) +
theme(axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank())
delegacion_Pie
vacunados <- all_selected %>%
filter(Vacunado == "SÃ")
Toma_dosis <- ggplot(data= all_selected, aes(x= Fecha, fill= Vacuna)) +
geom_histogram(colour="grey") +
theme(legend.spacing.x= unit(10, 'cm')) +
theme_bw(base_size = 12) +
labs(title="Vacuna", y="Número de Muestras", x="", fill= "")
Toma_dosis 
# ggsave(plot = Toma_dosis,
# filename = "tomademuestras_por_Vacuna.png",
# width = 7,
# height = 4,
# dpi = 300) Anticuerpos <- all_selected %>%
ggplot(aes(x = Ronda, y = valor_IgG)) +
geom_jitter(aes(shape= Diagnostico_COVID, colour= Vacunado)) +
theme(legend.spacing.x= unit(10, 'cm')) +
theme_bw(base_size = 12) +
labs(title="Anticuerpos de tipo IgG",
y="Absorbancia",
x="Dosis",
colour= "Vacunado",
shape= "Diagnosticado vs COVID-19") +
geom_boxplot(alpha=0.4, outlier.shape = NA)
#
Anticuerpos
# ggsave(plot = Anticuerpos,
# filename = "Anticuerpos_IgG_boxplot_CR.png",
# width = 7,
# height = 4,
# dpi = 300)Anticuerpos1 <- all_selected %>%
ggplot(aes(x = Ronda, y = valor_IgG)) +
geom_jitter(aes(shape= Diagnostico_COVID, colour= Vacunado)) +
geom_boxplot(alpha=0.4, outlier.shape = NA) +
facet_wrap(.~ Sexo) +
theme(legend.spacing.x= unit(10, 'cm')) +
theme_bw(base_size = 12) +
labs(title="Anticuerpos de tipo IgG",
y="Absorbancia",
x="Dosis",
colour= "Vacunado",
shape= "Diagnosticado vs COVID-19")
Anticuerpos1
# ggsave(plot = Anticuerpos1,
# filename = "Anticuerpos_IgG_boxplot_CR_vacunado_sexo.png",
# width = 7,
# height = 4,
# dpi = 300
# )Anticuerpos2 <- all_selected %>%
ggplot(aes(x = Ronda, y = valor_IgG)) +
geom_jitter(aes(shape= Diagnostico_COVID, colour= Edad)) +
theme(legend.spacing.x= unit(10, 'cm')) +
theme_bw(base_size = 12) +
labs(title="Anticuerpos de tipo IgG",
y="Absorbancia",
x="Dosis",
colour= "Edad",
shape= "Diagnosticado vs COVID-19") +
#scale_y_log10() +
geom_boxplot(alpha=0.4, outlier.shape = NA)
Anticuerpos2
# ggsave(plot = Anticuerpos2,
# filename = "Anticuerpos_IgG_boxplot_CR_edad.png",
# width = 7,
# height = 4,
# dpi = 300)Anticuerpos3 <- all_selected %>%
ggplot(aes(x = Ronda, y = valor_IgG)) +
geom_jitter(aes(shape= Diagnostico_COVID, colour= Factores_riesgo)) +
geom_boxplot(alpha=0.4, outlier.shape = NA) +
theme_bw(base_size = 12) +
theme(legend.text = element_text(size= 8)) +
labs(title="Anticuerpos de tipo IgG",
y="Absorbancia",
x="Dosis",
colour= "Factores de Riesgo",
shape= "Diagnosticado vs COVID-19")
Anticuerpos3
# ggsave(plot = Anticuerpos3,
# filename = "Anticuerpos_IgG_boxplot_FR_CR.png",
# width = 7,
# height = 4,
# dpi = 300,
# scale = 1.5)