Plot de caja GGPLOT
ggplot(data = data, aes(agea, y = " ")) + geom_boxplot(fill = "dark orange", alpha = 0.7)
Diana H
ggplot(data = data, aes(agea, y = " ")) + geom_boxplot(fill = "dark orange", alpha = 0.7)ggplot_box_legend <- function(family = "serif"){
# Create data to use in the boxplot legend:
set.seed(100)
sample_df <- data.frame(parameter = "test",
values = sample(500))
# Extend the top whisker a bit:
sample_df$values[1:100] <- 701:800
# Make sure there's only 1 lower outlier:
sample_df$values[1] <- -350
# Function to calculate important values:
ggplot2_boxplot <- function(x){
quartiles <- as.numeric(quantile(x,
probs = c(0.25, 0.5, 0.75)))
names(quartiles) <- c("25th percentile",
"50th percentile\n(median)",
"75th percentile")
IQR <- diff(quartiles[c(1,3)])
upper_whisker <- max(x[x < (quartiles[3] + 1.5 * IQR)])
lower_whisker <- min(x[x > (quartiles[1] - 1.5 * IQR)])
upper_dots <- x[x > (quartiles[3] + 1.5*IQR)]
lower_dots <- x[x < (quartiles[1] - 1.5*IQR)]
return(list("quartiles" = quartiles,
"25th percentile" = as.numeric(quartiles[1]),
"50th percentile\n(median)" = as.numeric(quartiles[2]),
"75th percentile" = as.numeric(quartiles[3]),
"IQR" = IQR,
"upper_whisker" = upper_whisker,
"lower_whisker" = lower_whisker,
"upper_dots" = upper_dots,
"lower_dots" = lower_dots))
}
# Get those values:
ggplot_output <- ggplot2_boxplot(sample_df$values)
# Lots of text in the legend, make it smaller and consistent font:
update_geom_defaults("text",
list(size = 3,
hjust = 0,
family = family))
# Labels don't inherit text:
update_geom_defaults("label",
list(size = 3,
hjust = 0,
family = family))
# Create the legend:
# The main elements of the plot (the boxplot, error bars, and count)
# are the easy part.
# The text describing each of those takes a lot of fiddling to
# get the location and style just right:
explain_plot <- ggplot() +
stat_boxplot(data = sample_df,
aes(x = parameter, y=values),
geom ='errorbar', width = 0.3) +
geom_boxplot(data = sample_df,
aes(x = parameter, y=values),
width = 0.3, fill = "lightgrey") +
geom_text(aes(x = 1, y = 950, label = "500"), hjust = 0.5) +
geom_text(aes(x = 1.17, y = 950,
label = "Number of values"),
fontface = "bold", vjust = 0.4) +
theme_minimal(base_size = 5, base_family = family) +
geom_segment(aes(x = 2.3, xend = 2.3,
y = ggplot_output[["25th percentile"]],
yend = ggplot_output[["75th percentile"]])) +
geom_segment(aes(x = 1.2, xend = 2.3,
y = ggplot_output[["25th percentile"]],
yend = ggplot_output[["25th percentile"]])) +
geom_segment(aes(x = 1.2, xend = 2.3,
y = ggplot_output[["75th percentile"]],
yend = ggplot_output[["75th percentile"]])) +
geom_text(aes(x = 2.4, y = ggplot_output[["50th percentile\n(median)"]]),
label = "Interquartile\nrange", fontface = "bold",
vjust = 0.4) +
geom_text(aes(x = c(1.17,1.17),
y = c(ggplot_output[["upper_whisker"]],
ggplot_output[["lower_whisker"]]),
label = c("Largest value within 1.5 times\ninterquartile range above\n75th percentile",
"Smallest value within 1.5 times\ninterquartile range below\n25th percentile")),
fontface = "bold", vjust = 0.9) +
geom_text(aes(x = c(1.17),
y = ggplot_output[["lower_dots"]],
label = "Outside value"),
vjust = 0.5, fontface = "bold") +
geom_text(aes(x = c(1.9),
y = ggplot_output[["lower_dots"]],
label = "-Value is >1.5 times and"),
vjust = 0.5) +
geom_text(aes(x = 1.17,
y = ggplot_output[["lower_dots"]],
label = "<3 times the interquartile range\nbeyond either end of the box"),
vjust = 1.5) +
geom_label(aes(x = 1.17, y = ggplot_output[["quartiles"]],
label = names(ggplot_output[["quartiles"]])),
vjust = c(0.4,0.85,0.4),
fill = "white", label.size = 0) +
ylab("") + xlab("") +
theme(axis.text = element_blank(),
axis.ticks = element_blank(),
panel.grid = element_blank(),
aspect.ratio = 4/3,
plot.title = element_text(hjust = 0.5, size = 10)) +
coord_cartesian(xlim = c(1.4,3.1), ylim = c(-600, 900)) +
labs(title = "EXPLANATION")
return(explain_plot)
}
ggplot_box_legend()