Modérateur : Groupe des modérateurs
Code : Tout sélectionner
library("palmerpenguins")
library("ggplot2")
library("scales")
ggplot(data = penguins) +
aes(x = flipper_length_mm, y = body_mass_g) +
theme_minimal() +
annotation_raster(
raster = matrix(
data = rep(viridis_pal(option = "plasma", alpha = 0.10)(50), each = 50),
nrow = 50
),
xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf
) +
geom_point(aes(colour = species, shape = species), size = 2)
Code : Tout sélectionner
ecm <- structure(list(Freq = c(2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L,
2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 3L, 1L,
1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L,
1L, 3L, 2L, 1L, 1L, 3L, 2L, 2L, 1L, 3L, 2L, 1L, 2L, 1L, 1L, 2L,
3L, 2L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L,
3L, 1L, 2L, 2L, 3L, 2L, 3L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 3L, 2L, 1L, 1L, 2L,
1L, 1L, 1L, 3L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 3L,
1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 4L, 2L, 1L, 1L, 4L, 3L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L,
2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L), date_hm = structure(c(1602591060, 1602591120,
1602591240, 1602591360, 1602591420, 1602591480, 1602591540, 1602591600,
1602591840, 1602591900, 1602592020, 1602592200, 1602592380, 1602592440,
1602592680, 1602592740, 1602592800, 1602592920, 1602592980, 1602593040,
1602593100, 1602593280, 1602593640, 1602593700, 1602593760, 1602593820,
1602593880, 1602594000, 1602594060, 1602594120, 1602594180, 1602594360,
1602594420, 1602594480, 1602594720, 1602594780, 1602594840, 1602595020,
1602595080, 1602595380, 1602595560, 1602595980, 1602596040, 1602596160,
1602596220, 1602596280, 1602596640, 1602596760, 1602597120, 1602597240,
1602597300, 1602597840, 1602597960, 1602598020, 1602598080, 1602598140,
1602598500, 1602598800, 1602599220, 1602599280, 1602599340, 1602599400,
1602599460, 1602599520, 1602599580, 1602599640, 1602599700, 1602599760,
1602599820, 1602599880, 1602600000, 1602600060, 1602600120, 1602600180,
1602600240, 1602600300, 1602600360, 1602600420, 1602600480, 1602600600,
1602600780, 1602600840, 1602601200, 1602601260, 1602601500, 1602601620,
1602601680, 1602601740, 1602601800, 1602601860, 1602602040, 1602602100,
1602602160, 1602602220, 1602602280, 1602602400, 1602602460, 1602602520,
1602602580, 1602602760, 1602602820, 1602602880, 1602602940, 1602603000,
1602603180, 1602603480, 1602603660, 1602603720, 1602603780, 1602603840,
1602603900, 1602603960, 1602604140, 1602604260, 1602604500, 1602604560,
1602604680, 1602604740, 1602605100, 1602605340, 1602605460, 1602605520,
1602605640, 1602605700, 1602605760, 1602606000, 1602606060, 1602606120,
1602606240, 1602606300, 1602606420, 1602606480, 1602606900, 1602607020,
1602607200, 1602607260, 1602607320, 1602607440, 1602607500, 1602607560,
1602607680, 1602607740, 1602607800, 1602607860, 1602607920, 1602607980,
1602608040, 1602608100, 1602608160, 1602608220, 1602608280, 1602608340,
1602608400, 1602608460, 1602608520, 1602608580, 1602608640, 1602608760,
1602608820, 1602608940, 1602609000, 1602609060, 1602609120, 1602609180,
1602609240, 1602609300, 1602609360, 1602609420, 1602609480, 1602609540,
1602609600, 1602609720, 1602609780, 1602609900, 1602610080, 1602610140,
1602610200, 1602610260, 1602611880, 1602611940, 1602612060, 1602612120,
1602612240, 1602612300, 1602612360, 1602612420, 1602612480, 1602612540,
1602612840, 1602612900, 1602613140, 1602613200, 1602613980, 1602614040,
1602614100, 1602614160, 1602614460, 1602614520, 1602614580, 1602614640,
1602614700, 1602614760, 1602614820, 1602614880, 1602614940, 1602615000,
1602615360, 1602615420, 1602615480, 1602615840, 1602615900, 1602616200,
1602616620, 1602616680, 1602616740, 1602616860, 1602616920, 1602617040,
1602617100, 1602617160, 1602617400, 1602617520, 1602617580, 1602618240,
1602618300, 1602618480, 1602618540, 1602618660, 1602619080, 1602619320,
1602619380, 1602619440, 1602619740, 1602620160, 1602620220, 1602620700,
1602620760, 1602621240, 1602621300, 1602622380, 1602622440, 1602623040,
1602623520, 1602624900, 1602624960, 1602625380, 1602625440, 1602625500,
1602626160, 1602626520, 1602627060, 1602627120, 1602627240, 1602627360,
1602627480, 1602628080, 1602628140, 1602628500, 1602628560, 1602628680,
1602628740, 1602628800, 1602628860, 1602628920, 1602635220, 1602636540,
1602636840, 1602636900, 1602636960, 1602638100, 1602638460, 1602638520,
1602638760, 1602639240, 1602639300, 1602649620, 1602654480, 1602656520,
1602660660, 1602660720, 1602661380, 1602661560, 1602661620, 1602661860,
1602661920, 1602669180, 1602669240, 1602669300, 1602671160, 1602671220,
1602671280, 1602671340, 1602671400, 1602671460, 1602671520, 1602671580,
1602671640, 1602671700, 1602671760, 1602671880, 1602672000, 1602672180,
1602672900, 1602672960, 1602673020, 1602673080, 1602673140, 1602673560,
1602675420, 1602675480, 1602675540, 1602675720, 1602675780, 1602676080,
1602676260, 1602676440, 1602676500, 1602676800, 1602676920, 1602677400,
1602677460, 1602677580, 1602678000, 1602678540, 1602678600, 1602678660,
1602678720, 1602679140, 1602679260, 1602679320, 1602679380, 1602679440,
1602679500, 1602679560, 1602679620, 1602679680, 1602679740, 1602680100,
1602680520, 1602680580, 1602680700, 1602680760, 1602680820, 1602681000,
1602681120, 1602681720, 1602681780, 1602681840, 1602682260, 1602682320,
1602682560, 1602682680, 1602682980, 1602683040, 1602683100, 1602683160,
1602683220, 1602683280, 1602683340, 1602683400, 1602683640, 1602684060,
1602684120, 1602684180, 1602684840, 1602684960, 1602685020, 1602685080,
1602685140, 1602685200, 1602685500, 1602685560, 1602685620, 1602685680,
1602685740, 1602685800, 1602685860, 1602685920, 1602686220, 1602687000,
1602687180, 1602687240, 1602687480, 1602687540, 1602687720, 1602687900,
1602687960, 1602688080, 1602688140, 1602688260, 1602688440, 1602688500,
1602688560, 1602688620, 1602688680, 1602688800, 1602688860, 1602688920,
1602689100, 1602689160, 1602689220, 1602689280, 1602690120, 1602690180,
1602690360, 1602690420, 1602690480, 1602690960, 1602691020, 1602691260,
1602691320, 1602691620, 1602691920, 1602691980, 1602692040, 1602692100,
1602692160, 1602692340, 1602692400, 1602692460, 1602692520, 1602692640,
1602693240, 1602693300, 1602693360, 1602693780, 1602693840, 1602694320,
1602694380, 1602694860, 1602694920, 1602695040, 1602695100, 1602695520,
1602695700, 1602695820, 1602695880, 1602695940, 1602696000, 1602696240,
1602698580, 1602698640, 1602698700, 1602699660, 1602700080, 1602700140,
1602700200, 1602700260, 1602700320, 1602701760, 1602701880, 1602702300,
1602702480, 1602702540, 1602702720, 1602703320, 1602703740, 1602703800,
1602703860, 1602704880, 1602705480, 1602705540, 1602706560, 1602707280,
1602707340, 1602707400, 1602707760, 1602708000, 1602708060, 1602708120,
1602709320, 1602709380, 1602709560, 1602709620, 1602709680, 1602710160,
1602710220, 1602712500), tzone = "UTC", class = c("POSIXct",
"POSIXt")), illumination = c(127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 169L, 169L, 169L, 211L, 254L, 254L, 254L, 254L, 254L, 254L,
254L, 254L, 254L, 254L, 254L, 254L, 254L, 254L, 254L, 254L, 254L,
254L, 254L, 254L, 254L, 254L, 254L, 254L, 254L, 254L, 254L, 254L,
254L, 254L, 254L, 254L, 254L, 254L, 254L, 169L, 169L, 169L, 169L,
169L, 169L, 169L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L,
127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L, 127L)), row.names = c(NA,
-478L), class = "data.frame")
Code : Tout sélectionner
ecm %>% ggplot(.,aes(x = date_hm, y=Freq))+
geom_line()+
scale_x_datetime(breaks = scales::breaks_width("30 min"))+ # valeur d'unité possible : Unit can be of one "sec", "min", "hour", "day", "week", "month", "year".
ggtitle("Wheel occupation")
Code : Tout sélectionner
ecm <- structure(list(Freq = c(2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L,
2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 3L, 1L,
1L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L,
1L, 3L, 2L, 1L, 1L, 3L, 2L, 2L, 1L, 3L, 2L, 1L, 2L, 1L, 1L, 2L,
3L, 2L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L,
3L, 1L, 2L, 2L, 3L, 2L, 3L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 3L, 2L, 1L, 1L, 2L,
1L, 1L, 1L, 3L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 3L,
1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 4L, 2L, 1L, 1L, 4L, 3L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L,
2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 1L), date_hm = structure(c(1602591060, 1602591120,
1602591240, 1602591360, 1602591420, 1602591480, 1602591540, 1602591600,
1602591840, 1602591900, 1602592020, 1602592200, 1602592380, 1602592440,
1602592680, 1602592740, 1602592800, 1602592920, 1602592980, 1602593040,
1602593100, 1602593280, 1602593640, 1602593700, 1602593760, 1602593820,
1602593880, 1602594000, 1602594060, 1602594120, 1602594180, 1602594360,
1602594420, 1602594480, 1602594720, 1602594780, 1602594840, 1602595020,
1602595080, 1602595380, 1602595560, 1602595980, 1602596040, 1602596160,
1602596220, 1602596280, 1602596640, 1602596760, 1602597120, 1602597240,
1602597300, 1602597840, 1602597960, 1602598020, 1602598080, 1602598140,
1602598500, 1602598800, 1602599220, 1602599280, 1602599340, 1602599400,
1602599460, 1602599520, 1602599580, 1602599640, 1602599700, 1602599760,
1602599820, 1602599880, 1602600000, 1602600060, 1602600120, 1602600180,
1602600240, 1602600300, 1602600360, 1602600420, 1602600480, 1602600600,
1602600780, 1602600840, 1602601200, 1602601260, 1602601500, 1602601620,
1602601680, 1602601740, 1602601800, 1602601860, 1602602040, 1602602100,
1602602160, 1602602220, 1602602280, 1602602400, 1602602460, 1602602520,
1602602580, 1602602760, 1602602820, 1602602880, 1602602940, 1602603000,
1602603180, 1602603480, 1602603660, 1602603720, 1602603780, 1602603840,
1602603900, 1602603960, 1602604140, 1602604260, 1602604500, 1602604560,
1602604680, 1602604740, 1602605100, 1602605340, 1602605460, 1602605520,
1602605640, 1602605700, 1602605760, 1602606000, 1602606060, 1602606120,
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Code : Tout sélectionner
library(ggplot2)
library(scales)
ggplot(ecm) +
theme_minimal() +
aes(x = date_hm, y = Freq) +
geom_ribbon(mapping = aes(ymin = -Inf, ymax = Inf, fill = factor(illumination))) +
geom_line()+
scale_x_datetime(
# breaks = breaks_width("30 min")
) +
labs(title = "Wheel occupation")
Code : Tout sélectionner
library(data.table)
library(ggplot2)
library(scales)
ecm <- setDT(ecm)
ecm[
i = illumination != shift(illumination, n = 1, fill = 0),
j = fill_group := 1:.N
]
ecm[j = fill_group := nafill(fill_group, type = "locf")]
ggplot(ecm) +
theme_minimal() +
aes(x = date_hm, y = Freq) +
geom_ribbon(
mapping = aes(
ymin = -Inf,
ymax = Inf,
fill = factor(illumination),
group = fill_group
)
) +
geom_line(alpha = 0) +
# scale_x_datetime(breaks = breaks_width("30 min")) +
labs(title = "Wheel occupation")
Code : Tout sélectionner
library(data.table)
library(ggplot2)
library(scales)
ggplot(setDT(ecm)) +
theme_minimal() +
aes(x = date_hm, y = Freq) +
geom_rect(
data = ~ .x[
i = illumination != shift(illumination, n = 1, fill = 0),
j = fill_group := 1:.N
][
j = fill_group := nafill(fill_group, type = "locf")
][
j = list(xmin = min(date_hm), xmax = max(date_hm), illumination = unique(illumination)),
by = "fill_group"
][
j = xmax := shift(xmin, n = 1, type = "lead", fill = max(xmax))
],
mapping = aes(
xmin = xmin,
xmax = xmax,
ymin = -Inf,
ymax = Inf,
fill = factor(illumination)
),
inherit.aes = FALSE
) +
geom_line(alpha = 0) +
# scale_x_datetime(breaks = breaks_width("30 min")) +
labs(title = "Wheel occupation")
Code : Tout sélectionner
Erreur : Aesthetics can not vary with a ribbon
Run `rlang::last_error()` to see where the error occurred.
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