Displaying 4 results from an estimated 4 matches for "scale_y_discrete".
Did you mean:
scale_x_discrete
2013 Apr 14
1
Problem plotting continuous and discrete series in ggplot with facet
...39;))
mcsm <- melt(data.frame(date=economics$date, q=quarters(economics$date)),
id='date')
mcsm$value <- factor(mcsm$value)
ggplot(subm, aes(date, value, col=variable, group=1)) + geom_line() +
facet_grid(variable~., scale='free_y') + geom_step(data=mcsm, aes(date,
value)) + scale_y_discrete(breaks=levels(mcsm$value))
If I leave out scale_y_discrete, R complains that I'm trying to combine
discrete value with continuous scale. If I include scale_y_discreate my
continuous series miss their scale.
Is there any neat way of solving this issue ? I also see that the legend is
alphabetic...
2011 Nov 08
3
ggplot2 reorder factors for faceting
...lt;- hp2 + scale_fill_gradient2(name=NULL, low="#0571B0", mid="#F7F7F7", high="#CA0020", midpoint=0, breaks=NULL, labels=NULL, limits=NULL, trans="identity")
# set up text (size, colour etc etc)
hp2 <- hp2 + labs(x = "Time", y = "") + scale_y_discrete(expand = c(0, 0)) + opts(axis.ticks = theme_blank(), axis.text.x = theme_text(size = 10, angle = 360, hjust = 0, colour = "grey25"), axis.text.y = theme_text(size=10, colour = 'gray25'))
hp2 <- hp2 + theme_bw()
In the resulting plot I would like infections infA and infC plott...
2019 Jul 18
4
Gráfico tiempos de supervivencia
...r(sample(c(0,1), 10, replace=T))
)
library(ggplot2)
ggplot( data = DATOS ) +
geom_point( aes(x = TIEMPO, y = ID , shape = DEF, color = DEF), size = 5
) +
geom_segment( aes( x = 0, y = ID, xend = TIEMPO, yend = ID ) ) +
guides(colour = FALSE) +
labs(shape = 'LEGEND') +
scale_y_discrete() +
theme_minimal()
#-----------------
E incluso puedes reproducirlo usando fuentes parecidas a la de los comics
con el paquete "xkcd".
Saludos,
Carlos Ortega
www.qualityexcellence.es
El jue., 18 jul. 2019 a las 13:05, Griera-yandex (<griera en yandex.com>)
escribió:
> H...
2019 Jul 18
3
Gráfico tiempos de supervivencia
Hola, te vale esto? Es forma estandar de representar graficos supervivencia
Basado en esto:
https://rviews.rstudio.com/2017/09/25/survival-analysis-with-r/
set.seed(20)
DATOS <- data.frame (
ID = c (1:10)
, TIEMPO = sample(1:40, 10, replace=F)
, DEF = sample(0:1, 10, replace=T)
)
DATOS
library(survival)
DATOS$DEF <- as.numeric(DATOS$DEF)
DATOS$TIEMPO <-