I have a dataframe df
df<-structure(list(X = c(540.857881018969, 179.756453922596, 375.597673455575,
-24.7802579900034, 435.072227193852, 316.91305374488, 385.910199259729,
118.282069554042, 47.0931945371692, 46.8541543075746, -1.34917072989697,
-7.85914134272889, -74.1514974421857, -14.1349095526425, -180.103770130757,
-138.258618377921, 189.011576650288, 69.2480232224876, 42.356103377609,
-330.116265944969, 12.2073132306264, 82.0963304851313, -76.2883430762099,
13.7563014075505, 286.364096895997, 238.289414874803, 192.304857815893,
196.85181686938, 545.585335607164, 380.551416666209, 285.533878238757,
349.173312050698, 234.149820388793, 182.680602233473, 123.508718623119,
-59.3666813922188, 309.812271637758, 378.745487723212, 383.286192402579,
407.250999438653, 968.521681377846, 168.221917301957, 308.817578621267,
187.43921153459, 219.229124870899, 273.756349813256, 330.797398870288,
310.938292903419, 285.256002320225, 404.534158133551, 298.161606939762,
96.4609965966811, 237.259654760246, 410.903483047603, 453.107209854966,
476.020674679612, 390.865258755938, -148.590524527974, 109.883635942258,
78.869912902272, 782.478670456904, 245.257050602357, 308.573221705999,
568.136911803784, 163.617826469925, 210.109579924637, 175.657357919781,
175.760684810558, -254.181869342232, 288.452497381572), Y = c(436.783385497984,
55.1825021383702, 526.4133417369, 560, 391.49284084118, -519.814235572849,
11.5525291214872, 162.441016515717, 39.0395567645998, -70.4910326673707,
17.1155716306239, -106.326129257097, -94.9308303585276, -66.4285516217351,
-144.929052323413, -220.613145695315, 157.129576861289, 44.1257786633602,
46.8326830295943, -146.719591499443, 30.8043649939355, -4.10548956954153,
-108.258462657337, 90.3369144331664, 126.866108251153, 42.9489971246803,
690.903947139159, -45.4886732113082, 483.932040393885, 618.930183215125,
590.754048774834, 82.1480000555981, 76.8863707484328, 404.007940533033,
202.629066249886, -46.9675149230141, 557.939170770813, 333.76992898155,
300.979565786038, 224.256197650044, 148.719307398695, 201.195892312115,
466.727302447427, 552.762670615377, 595.145436977735, 481.359543363331,
467.379381521489, 279.980726677847, 545.324660883673, 444.812610935212,
308.198167469197, -638.973101716489, 292, 331.193419647713, 181.896345832773,
629.214319321327, -176.181996958815, 214, 59.1716887350485, -77.1223124726675,
-186.42650026083, 279.123776521767, 515.533437888983, 595.091753601562,
367.15020653978, 713.607404187601, 268.681242669467, 239.81099676255,
91.8453621773515, 246.686055020047), A = c(98.5, 77, 63.2222222222222,
97, 52.5, 3.5, 15.5, 71, 161.833333333333, 153.5, 73, 39, 40,
23, 14, 5.5, 78, 129.5, 73.5, 4, 100, 10, 3, 30, 65.5, 198, 26,
45.5, 20, 42.5, 111.5, 44, 68.5, 102.5, 39.1111111111111, 83.8,
136, 28.6666666666667, 31.5, 56.5, 101, 39.25, 108.5, 52.1666666666667,
54.5, 9.5, 13, 8.5, 8, 52.1428571428571, 66.5, 1, 42, 45, 106,
19, 202.571428571429, 200, 36.6, 83.75, 2, 33, 21.2, 69, 67.5,
14, 83, 16, 4, 99)), .Names = c("X", "Y", "A"), row.names = c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,
29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L,
42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L,
55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L,
68L, 69L, 71L), class = "data.frame", na.action = structure(c(70L,
72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L,
85L, 86L, 87L, 88L, 89L, 90L), .Names = c("70", "72", "73", "74",
"75", "76", "77", "78", "79", "80", "81", "82", "83", "84", "85",
"86", "87", "88", "89", "90"), class = "omit"))
I am plotting Y against X with ggplot in a simple scatterplot. However I also want to add a color scheme and a legend based on the variable A. The idea is to have four classes (e.g. 0-50, 50-100, 100-150 and 150-200) in the legend.
I only managed to get a gradient legend so far as such:
library(ggplot2)
ggplot(df, aes(x=X, y=Y, colour=A))+
geom_point(shape=3)+
geom_abline(slope=1)+
theme_bw(base_size = 14, base_family = "Helvetica") +
theme(panel.grid = element_line(colour="grey", size=0.5),
axis.text.x = element_text(hjust = 1),
legend.position="bottom",
legend.box="horizontal",
legend.key = element_blank(),
legend.text=element_text(size=12))+
xlab("Predicted")+
ylab("Observed")+
scale_colour_gradient(name= "Stand age", low= "#67a9cf", high ="#ef8a62")+
guides(colour = guide_colourbar(title.position="top", title.hjust = 0.5),
size = guide_legend(title.position="top", title.hjust = 0.5))
In addition, I would like to have the legend box in the lower right corner inside the panel. Anyone knows how to do it?
library(RColorBrewer)
library (ggplot)
vecP <- c(0, 50, 100, 150, 200, 250)
labP <- c("0-50", "50-100", "100-150", "150-200", "200-250")
df$Age <- cut(df$A, breaks=vecP, labels =labP)
colorsP <- brewer.pal(length(labP), "RdBu")
ggplot(df, aes(x=X, y=Y, colour=Age))+
geom_point(shape=1)+
geom_abline(slope=1)+
theme_bw(base_size = 14, base_family = "Helvetica") +
theme(panel.grid = element_line(colour="grey", size=0.5),
axis.text.x = element_text(hjust = 1),
legend.position=c(1, 0),
legend.direction = "vertical",
legend.justification = c(1,0),
legend.key = element_blank())+
xlab("Predicted")+
ylab("Observed")+
scale_fill_manual(name = "Stand age", values = setNames(colorsP, labP),breaks=rev(labP),labels=rev(labP))
library(ggplot2)
plt <- ggplot(df, aes(x=X, y=Y, colour=A))+
geom_point(shape=3)+
geom_abline(slope=1)+
theme_bw(base_size = 14, base_family = "Helvetica") +
theme(panel.grid = element_line(colour="grey", size=0.5),
axis.text.x = element_text(hjust = 1),
legend.position=c(1, 0),
legend.direction = "horizontal",
legend.justification = c(1,0))+
xlab("Predicted")+
ylab("Observed")+
scale_colour_gradient(name= "Stand age", low= "#67a9cf", high ="#ef8a62")+
guides(colour = guide_colourbar(title.position="top", title.hjust = 0.5),
size = guide_legend(title.position="top", title.hjust = 0.5))
Just changed,
legend.position = c(1,0)
legend.direction = "horizontal"
legend.justification = c(1,0)
Related
Sorry if this is a duplicate question but I cannot seem to find the answer to my question anywhere. I have two plots and I would like to overlay plot two on plot one so that they form one plot. Is this possible? I will attach how both plots look separately. They are both facetted by the same variable which is by location and are on the same x and y-axis scale so theoretically should be possible.
Thank you.
## Plot one
Proxy<-read.csv("ALLRSL.csv",header=T)
p1<-ggplot()+
geom_ribbon(data=Proxy,aes(x=YEAR,ymin=LOWER,ymax=UPPER,fill=SITE),alpha=.5)+
geom_line(data=Proxy,aes(x=YEAR,y=RSL,col=SITE))+
facet_wrap(~ SITE,ncol= 1)+
scale_fill_manual(values=c("#4E193D","#342955","#4E617E","#97B4CB"))+
scale_color_manual(values=c("#4E193D","#342955","#4E617E","#97B4CB"))+
theme_classic()+
xlim(1900, 2020)+
theme(panel.grid.major.x = element_blank())+
theme(panel.grid.minor.x = element_blank())+
theme(panel.grid.minor.y = element_blank())+
theme(panel.grid.major.y = element_blank())+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
theme(
strip.background = element_blank(),
strip.text.x = element_blank()
)+
theme(legend.position="none")
p1
plot two
tgsm<-read.csv("tgsm.csv",header=T)
tgsm<-na.omit(tgsm)
tglonger<-pivot_longer(tgsm, cols=c(-Year),names_to="Site", values_to = "value")
p2<-ggplot()+
geom_point(data=tglonger,aes(x=Year,y=value,col=Site),alpha=.7,size=1)+
facet_wrap(~Site,ncol=1)+
theme_classic()+
xlim(1900,2020)+
scale_color_manual(values=c("#4E193D","#342955","#4E617E","#97B4CB"))+
theme(panel.grid.major.x = element_blank())+
theme(panel.grid.minor.x = element_blank())+
theme(panel.grid.minor.y = element_blank())+
theme(panel.grid.major.y = element_blank())+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())+
theme(
strip.background = element_blank(),
strip.text.x = element_blank()
)+
theme(legend.position="none")
p2
Data
Proxy <- structure(list(RSL = c(-0.305251214, -0.306414006, -0.307194187,
-0.308202139, -0.309150572, -0.309679123), UPPER = c(-0.182716456,
-0.186724068, -0.189331305, -0.193118273, -0.197069799, -0.20118809
), LOWER = c(-0.416725663, -0.413606073, -0.411131729, -0.408930899,
-0.406531588, -0.404478981), YEAR = 1820:1825, SITE = structure(c(1L,
1L, 1L, 1L, 1L, 1L), .Label = c("Little Swanport", "Lutregala",
"Tarra", "Wapengo"), class = "factor")), row.names = c(NA, 6L
), class = "data.frame")
tgsm <- structure(list(Year = 1993:1998, Lg2002 = c(-0.001164223, -0.002229453,
-0.002734792, -0.002977787, -0.002786098, -0.002026994), Wap2002 = c(-0.002531348,
-0.002051993, -0.001468704, -0.001182162, -0.001027132, -0.00020881
), Tar2002 = c(-0.029020612, -0.024330561, -0.019927593, -0.015682528,
-0.012907219, -0.009784772), LSP2002 = c(-0.034514531, -0.030171621,
-0.026095138, -0.021952898, -0.018480702, -0.014531318)), na.action = structure(c(`1` = 1L,
`2` = 2L, `3` = 3L, `4` = 4L, `5` = 5L, `6` = 6L, `7` = 7L, `8` = 8L,
`9` = 9L, `10` = 10L, `11` = 11L, `12` = 12L, `13` = 13L, `14` = 14L,
`15` = 15L, `16` = 16L, `17` = 17L, `18` = 18L, `19` = 19L, `20` = 20L,
`21` = 21L, `22` = 22L, `23` = 23L, `24` = 24L, `25` = 25L, `26` = 26L,
`27` = 27L, `28` = 28L, `29` = 29L, `30` = 30L, `31` = 31L, `32` = 32L,
`33` = 33L, `34` = 34L, `35` = 35L, `36` = 36L, `37` = 37L, `38` = 38L,
`39` = 39L, `40` = 40L, `41` = 41L, `42` = 42L, `43` = 43L, `44` = 44L,
`45` = 45L, `46` = 46L, `47` = 47L, `48` = 48L, `49` = 49L, `50` = 50L,
`51` = 51L, `52` = 52L, `53` = 53L, `54` = 54L, `55` = 55L, `56` = 56L,
`57` = 57L, `58` = 58L, `59` = 59L, `60` = 60L, `61` = 61L, `62` = 62L,
`63` = 63L, `64` = 64L, `65` = 65L, `66` = 66L, `67` = 67L, `68` = 68L,
`69` = 69L, `70` = 70L, `71` = 71L, `72` = 72L, `73` = 73L, `74` = 74L,
`75` = 75L, `76` = 76L, `77` = 77L, `78` = 78L, `79` = 79L, `80` = 80L,
`81` = 81L, `82` = 82L, `83` = 83L, `84` = 84L, `85` = 85L, `86` = 86L,
`87` = 87L, `88` = 88L, `89` = 89L, `90` = 90L, `91` = 91L, `92` = 92L,
`93` = 93L, `94` = 94L, `95` = 95L, `96` = 96L, `97` = 97L, `98` = 98L,
`99` = 99L, `100` = 100L, `101` = 101L, `102` = 102L, `103` = 103L,
`104` = 104L, `105` = 105L, `106` = 106L, `107` = 107L, `108` = 108L,
`109` = 109L, `110` = 110L, `111` = 111L, `112` = 112L, `113` = 113L,
`114` = 114L, `115` = 115L, `116` = 116L, `117` = 117L, `118` = 118L,
`119` = 119L, `120` = 120L, `121` = 121L, `122` = 122L, `123` = 123L,
`124` = 124L, `125` = 125L, `126` = 126L, `127` = 127L, `128` = 128L,
`129` = 129L, `130` = 130L, `131` = 131L, `132` = 132L, `133` = 133L,
`134` = 134L, `135` = 135L, `136` = 136L, `137` = 137L, `138` = 138L,
`139` = 139L, `140` = 140L, `141` = 141L, `142` = 142L, `143` = 143L,
`144` = 144L, `145` = 145L, `146` = 146L, `147` = 147L, `148` = 148L,
`149` = 149L, `150` = 150L, `151` = 151L, `152` = 152L, `153` = 153L,
`154` = 154L, `155` = 155L, `156` = 156L, `157` = 157L, `183` = 183L
), class = "omit"), row.names = 158:163, class = "data.frame")
See plot one how you can do that with patchwork.
However. Conceptually, I am guessing you want to add a sort of prediction to some historic values or so. I personally would put everything in one data frame and plot this. If there is a too large gap between the two time points, you can facet by timepoints (as in my suggestion).
The plots look a bit different than your plot because you only provided data for one Site in Proxy (so I filtered the other for what I thought is the equivalent, it will work nonetheless, because the faceting remains) - and I removed all those theme elements that are not relevant to the problem.
Plot one - combining plots.
library(tidyverse)
library(patchwork)
tgsm<-na.omit(tgsm)
tglonger <-
pivot_longer(tgsm, cols=c(-Year), names_to="SITE", values_to = "RSL") %>%
filter(SITE == "LSP2002") %>%
rename(YEAR = Year)
p1 <- ggplot() +
geom_ribbon(data = Proxy, aes(x = YEAR, ymin = LOWER, ymax = UPPER, fill = SITE), alpha = .5) +
geom_line(data = Proxy, aes(x = YEAR, y = RSL, col = SITE)) +
facet_wrap(~SITE) +
coord_cartesian(xlim = c(1800, 1830), ylim = c(-1, 0)) +
theme_classic() +
theme(
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
strip.background = element_blank(),
strip.text.x = element_blank(),
legend.position = "none"
)
p2 <- ggplot() +
geom_point(data = tglonger, aes(x = YEAR, y = RSL, col = SITE), alpha = .7, size = 1) +
facet_wrap(~SITE) +
coord_cartesian(xlim = c(1990, 2000), ylim = c(-1, 0)) +
theme_classic() +
## only one call to theme!!
theme(
## this is where the theme call is different to above
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.line.y = element_blank(),
strip.background = element_blank(),
strip.text.x = element_blank(),
legend.position = "none",
)
p1 + p2
Suggestion for an alternative visualisation
df_new <-
bind_rows(time1 = Proxy, time2 = tglonger, .id = "timevar") %>%
mutate(SITE = "LSP2002")
ggplot(df_new)+
geom_point(aes(x=YEAR,y=RSL))+
facet_grid(SITE~timevar, scales = "free_x")+
theme(legend.position="none") +
theme(panel.spacing = unit(.5, "lines"))
You can also use this data frame in order to create a list of plots, and then stitch it together with patchwork. This approach doesn't allow to change individual plots though.
ls_p <-
df_new %>%
split(., .$timevar) %>%
map(~{ggplot(.x)+
geom_point(aes(x=YEAR,y=RSL))+
coord_cartesian(ylim = c(-0.4,0))+
facet_grid(~SITE, scales = "free_x")+
theme(legend.position="none") +
theme(panel.spacing = unit(.5, "lines"))})
library(patchwork)
wrap_plots(ls_p)
I am creating a scatterplot using ggplot. I am able to create a scatterplot using the following code.
ggplot(df2, aes(x = date, y = mean, color = NULL)) +
geom_point(position = "jitter") +
labs(title = "ShotSpotter incidents around July 4th",
x = "Day of year", y = "Mean daily gunshots") +
labs(fill = "Treatment Status") +
geom_segment(aes(x = "07-01", xend = "07-01", y = 0, yend = 50), colour = "red")
I would like to change the labels on the x-axis so that they are easier to read. When I try to do so using scale_x_discrete(), most of the datapoints disappear except for those corresponding to the values now labeled on the x-axis.
ggplot(df2, aes(x = date, y = mean, color = NULL)) +
geom_point(position = "jitter") +
labs(title = "ShotSpotter incidents around July 4th",
x = "Day of year", y = "Mean daily gunshots") +
labs(fill = "Treatment Status") +
geom_segment(aes(x = "07-01", xend = "07-01", y = 0, yend = 50), colour = "red") +
scale_x_discrete(limits = c("05-01", "06-01", "07-01", "08-01", "09-01"),
labels = c("May 1", "June 1", "July 1", "Aug 1", "Sept 1"))
How can I keep the labels from the 2nd graph and include all the datapoints shown in the 1st?
Data using dput():
structure(list(date = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L,
34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L,
47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L,
60L, 61L, 62L, 63L, 64L, 68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L,
76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L,
89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L,
101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L,
112L, 113L, 114L, 115L, 116L, 117L, 118L, 119L, 120L, 121L, 122L,
123L, 124L), .Label = c("05-01", "05-02", "05-03", "05-04", "05-05",
"05-06", "05-07", "05-08", "05-09", "05-10", "05-11", "05-12",
"05-13", "05-14", "05-15", "05-16", "05-17", "05-18", "05-19",
"05-20", "05-21", "05-22", "05-23", "05-24", "05-25", "05-26",
"05-27", "05-28", "05-29", "05-30", "05-31", "06-01", "06-02",
"06-03", "06-04", "06-05", "06-06", "06-07", "06-08", "06-09",
"06-10", "06-11", "06-12", "06-13", "06-14", "06-15", "06-16",
"06-17", "06-18", "06-19", "06-20", "06-21", "06-22", "06-23",
"06-24", "06-25", "06-26", "06-27", "06-28", "06-29", "06-30",
"07-01", "07-02", "07-03", "07-04", "07-05", "07-06", "07-07",
"07-08", "07-09", "07-10", "07-11", "07-12", "07-13", "07-14",
"07-15", "07-16", "07-17", "07-18", "07-19", "07-20", "07-21",
"07-22", "07-23", "07-24", "07-25", "07-26", "07-27", "07-28",
"07-29", "07-30", "07-31", "08-01", "08-02", "08-03", "08-04",
"08-05", "08-06", "08-07", "08-08", "08-09", "08-10", "08-11",
"08-12", "08-13", "08-14", "08-15", "08-16", "08-17", "08-18",
"08-19", "08-20", "08-21", "08-22", "08-23", "08-24", "08-25",
"08-26", "08-27", "08-28", "08-29", "08-30", "08-31", "09-01"
), class = "factor"), mean = c(13, 15, 16.5, 17.6666666666667,
14.5, 13.3333333333333, 11.8333333333333, 13, 13, 14.3333333333333,
13.8333333333333, 15.5, 11.1666666666667, 15, 12.5, 15.6666666666667,
14.5, 10.5, 11.6666666666667, 17.5, 14.5, 13, 14.6666666666667,
15.6666666666667, 21.3333333333333, 30.6666666666667, 18.5, 17.5,
13.5, 18.5, 13.3333333333333, 14.5, 14.8333333333333, 9.66666666666667,
15.8333333333333, 13.5, 20.5, 16.1666666666667, 15.1666666666667,
14.8333333333333, 15.3333333333333, 14.1666666666667, 14.5, 13.6666666666667,
20.1666666666667, 17.8333333333333, 22.3333333333333, 15.8333333333333,
15.5, 16.1666666666667, 15, 20, 20.8333333333333, 20.8333333333333,
25, 21.1666666666667, 18.1666666666667, 27, 19.5, 19.5, 19.6666666666667,
25.6666666666667, 36.8333333333333, 46.6666666666667, 40.5, 21.3333333333333,
16.3333333333333, 18, 20.1666666666667, 22.6666666666667, 16.8333333333333,
13.8333333333333, 14.5, 14.1666666666667, 16.5, 15.1666666666667,
15.1666666666667, 13.3333333333333, 13.3333333333333, 12.6666666666667,
12.8333333333333, 12.3333333333333, 16.5, 19.6666666666667, 16.3333333333333,
10.6666666666667, 13.1666666666667, 17.5, 10.3333333333333, 15.5,
12.1666666666667, 14.3333333333333, 13.8333333333333, 11.6666666666667,
13, 10.6666666666667, 17.5, 19.3333333333333, 12.6666666666667,
12.5, 12.5, 13.5, 15.8333333333333, 13.5, 15.6666666666667, 16.3333333333333,
14.5, 13.8333333333333, 14.3333333333333, 11, 13.3333333333333,
23.8333333333333, 14.1666666666667, 13.5, 13.3333333333333, 13.3333333333333,
14, 10.6666666666667, 14.3333333333333, 13.3333333333333, 13.1666666666667
)), class = "data.frame", row.names = c(NA, -121L))
df2$date <- as.Date(paste0("2000-", as.character(df2$date)))
ggplot(df2, aes(x = date, y = mean, color = NULL)) +
geom_point(position = "jitter") +
labs(title = "ShotSpotter incidents around July 4th",
x = "Day of year", y = "Mean daily gunshots") +
labs(fill = "Treatment Status") +
geom_segment(aes(x = as.Date("2000-07-01"), xend = as.Date("2000-07-01"), y = 0, yend = 50), colour = "red") +
scale_x_date(labels = scales::date_format("%b-%d"))
This required the two changes: as.Date (I'm supposing 2000 year just for something), and changing geom_segment so that x and xend are Date objects.
We can use
library(dplyr)
library(lubridate)
library(ggplot2)
df2 %>%
mutate(date = mdy(date, truncated = 2)) %>%
ggplot(aes(x = date, y = mean, color = NULL)) +
geom_point(position = "jitter") +
labs(title = "ShotSpotter incidents around July 4th",
x = "Day of year", y = "Mean daily gunshots") +
labs(fill = "Treatment Status") +
geom_segment(aes(x = mdy('07-01', truncated = 2),
xend = mdy('07-01', truncated = 2), y=0, yend = 50), colour = 'red') +
scale_x_date(labels = scales::date_format("%b-%d"))
-output
I am using gganimate and I need 4 elements in the plot area:
A geom_line()
A geom_point()
A geom_abline() with static slope and color, varying intercept
Another geom_abline() with varying slope, color, and intercept
The first 3 elements are animated as I wanted:
where only geom_line() is gradually revealed, with geom_line() and the first geom_abline() showing only the data of that frame
But when I add the fourth layer this way (I need manual color palette), the past geom_abline() does not disappear:
I tried adding the argument transition_reveal(keep_last = F), and also trying to define different rules for different layers using multiple shadow_mark()'s, but nothing works so far.
Anyway to make sure the fourth layer only shows the current frame?
Current code:
temp_ani <-
ggplot(tab_temp, aes(tot, new_loess)) +
geom_line(size = 1.25) +
geom_point(size = 2) +
geom_abline(aes(slope = ref_log10m, intercept = ref_log10c), size = 1.1, linetype = "dashed", alpha = 0.3) +
geom_abline(aes(slope = trend_log10m, intercept = trend_log10c, color = ID_factor), size = 1.1, linetype = "dashed", alpha = 0.7,
show.legend = F) +
scale_color_manual(values = tab_temp[, trend_color]) +
transition_reveal(t) +
scale_x_continuous(name = "tot", trans = "log10", limits = c(24, 786.5)) +
scale_y_continuous(name = "new_loess", trans = "log10", limits = c(21, 601.7)) +
theme_classic() +
theme(axis.title = element_text(size = 18),
axis.text = element_text(size = 16))
animate(temp_ani)
example data (generated by dput):
tab_temp <-
structure(list(tot = c(24L, 26L, 26L, 36L, 42L, 49L, 50L, 53L,
56L, 56L, 57L, 60L, 62L, 65L, 69L, 69L, 69L, 75L, 81L, 85L, 91L,
93L, 94L, 95L, 100L, 101L, 101L, 105L, 105L, 108L, 110L, 115L,
116L, 121L, 130L, 132L, 138L, 142L, 149L, 158L, 168L, 193L, 209L,
257L, 274L, 318L, 357L, 387L, 411L, 454L, 519L, 583L, 642L, 683L,
715L),
new = c(22L, 21L, 21L, 28L, 34L, 41L, 40L, 41L, 43L, 42L,
42L, 44L, 45L, 44L, 45L, 43L, 43L, 39L, 39L, 36L, 41L, 40L, 38L,
39L, 43L, 41L, 39L, 40L, 36L, 39L, 41L, 40L, 35L, 36L, 39L, 39L,
44L, 47L, 49L, 57L, 67L, 88L, 104L, 149L, 164L, 203L, 241L, 266L,
281L, 322L, 381L, 441L, 493L, 525L, 547L),
t = 1:55,
new_loess = c(20.4718824559981,
21.7890308359201, 21.7890308359201, 29.230438378301, 34.1819985150492,
39.5079831823911, 40.0755079303131, 41.4099675429299, 42.370529571607,
42.370529571607, 42.8201044925118, 43.8252925663542, 44.3125327660056,
44.5559375847947, 43.3892881478697, 43.3892881478697, 43.3892881478697,
40.6214119319565, 38.4905820730386, 37.9720281923537, 39.2781675720742,
39.6852276451529, 39.9358369585547, 40.1843988106463, 39.903890056883,
39.8282043659178, 39.8282043659178, 39.5872985292962, 39.5872985292962,
39.1703139824658, 38.8033157618615, 37.6950388015054, 37.5718391291946,
37.3871340774544, 38.9365752067175, 39.8424253760653, 42.8908232704117,
45.2358046101902, 49.8927271106793, 56.9673605996446, 65.3666082472634,
88.2333199821777, 103.004387148103, 148.220525358668, 163.942873635871,
204.371579248934, 238.230045236249, 264.200021490433, 285.078351674107,
323.515728645106, 381.682707746736, 438.213805930832, 489.137706154487,
523.921712190664, 550.558894652666),
ID_factor = structure(1:55, .Label = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24",
"25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35",
"36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46",
"47", "48", "49", "50", "51", "52", "53", "54", "55"), class = "factor"),
ref_log10m = c(NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1),
ref_log10c = c(NA, -0.0767354344741067, -0.0767354344741067,
-0.0904671721067551, -0.0894518394244057, -0.09351121980074,
-0.096090968882282, -0.107170979497638, -0.12112413490539,
-0.12112413490539, -0.124227132918044, -0.136426426370142,
-0.145865115840727, -0.16400776974052, -0.201466565659624,
-0.201466565659624, -0.201466565659624, -0.266306248714822,
-0.323130540124996, -0.349955131070593, -0.364890173692428,
-0.369854072675033, -0.371765062973866, -0.373666129954109,
-0.398984764767179, -0.404130646875006, -0.404130646875006,
-0.423633432928999, -0.423633432928999, -0.440466702695637,
-0.452523847307499, -0.484413645662749, -0.489595535070859,
-0.510063194866713, -0.523585603228512, -0.520228163317121,
-0.507514703991367, -0.496806024856726, -0.475149025512369,
-0.443030988676679, -0.409953330914954, -0.339924688141996,
-0.307290563603587, -0.239024775067351, -0.223058019569702,
-0.192006619096867, -0.17567168290562, -0.165778116414225,
-0.15887758283895, -0.147160452867149, -0.133464873299646,
-0.123982498866305, -0.118103885501835, -0.115154306972075,
-0.113502258448684),
trend_log10m = c(NA, 0.779013043966055,
0, 0.902838605051119, 1.01516626395308, 0.939364077071771,
0.705975457374834, 0.562156421073622, 0.41648278860048, 0,
0.596322729780414, 0.452366794075921, 0.337191122084057,
0.115926899508026, -0.444292873406387, 0, 0, -0.790549373381475,
-0.700116895372846, -0.281394397911645, 0.495820425082167,
0.474249915989644, 0.588583559834837, 0.586342537885251,
-0.136567884586587, -0.190797733928004, 0, -0.156205385180462,
0, -0.375890288287152, -0.513019327466591, -0.651879927019231,
-0.378107511884984, -0.116780389727616, 0.566005001030829,
1.50635698066896, 1.6585525574429, 1.86295886106405, 2.03632300098696,
2.26097387810847, 2.24108569839695, 2.16233918241475, 1.94348336584128,
1.76030953589316, 1.57398365165524, 1.48010286623756, 1.32513096747946,
1.28232849305659, 1.26407605433669, 1.27114179598159, 1.23567759206272,
1.18776534049548, 1.14041338808179, 1.10970817963632, 1.08307872430731
),
trend_log10c = c(NA, 0.235955218563125, 1.33823791349671,
0.0607453498302259, -0.114070466624236, 0.00897537944146443,
0.403447909576491, 0.647792138304764, 0.898973667615296,
1.62706389210081, 0.584579635667037, 0.837348243495363, 1.04214800866147,
1.43874016239004, 2.45437007136201, 1.63738252507763, 1.63738252507763,
3.09108352150306, 2.92151708503656, 2.1223914715644, 0.622818482734461,
0.665076002900152, 0.440012174544851, 0.444433997374107,
1.874151004406, 1.98261070308883, 1.60019072690764, 1.91327651912479,
1.59755586614094, 2.35730129445125, 2.63614274028582, 2.91961175246926,
2.35544952785373, 1.81595066271427, 0.393855239776779, -1.59399557640782,
-1.91673754895948, -2.35414232320695, -2.72727194089704,
-3.21548014224428, -3.17175285497492, -2.99651750205615,
-2.49630999086779, -2.07131990960092, -1.62228698944198,
-1.39342905195201, -1.00562316966424, -0.896362653634025,
-0.849130617820197, -0.867599348834218, -0.773368978244672,
-0.643279196748284, -0.512319390951289, -0.426113442696479,
-0.350634363184159),
trend_color = c(NA, "#a7ad06", "#00b51b",
"#c1ac03", "#d7a800", "#c9ac02", "#97ae08", "#78af0c", "#59b110",
"#00b51b", "#80af0b", "#61b00f", "#48b212", "#19b418", "#00a980",
"#00b51b", "#00b51b", "#009fcf", "#00a1bb", "#00ad5b", "#6ab00e",
"#65b00e", "#7eaf0b", "#7daf0b", "#00b13a", "#00b047", "#00b51b",
"#00b13f", "#00b51b", "#00aa71", "#00a790", "#00a3b0", "#00aa71",
"#00b236", "#79af0c", "#eb5400", "#f13a00", "#f91700", "#fb0006",
"#df002b", "#e20028", "#eb001b", "#fd0a00", "#f52900", "#ee4900",
"#ea5900", "#e37300", "#e27b00", "#e17e00", "#e17d00", "#e08300",
"#de8b00", "#dc9300", "#da9800", "#d99d00")),
row.names = c(NA, -55L), class = c("data.table", "data.frame"))
Assuming that tab_temp$trend_color is the same as tab_ani$trend_color (which you don't provide), I got your desired result by moving the color argument out of the aesthetics in the second geom_abline and setting color equal to trend_color. Then the scale_color_manual is unnecessary:
temp_ani <-
ggplot(tab_temp, aes(tot, new_loess)) +
geom_line(size = 1.25) +
geom_point(size = 2) +
geom_abline(aes(slope = ref_log10m, intercept = ref_log10c), size = 1.1, linetype = "dashed", alpha = 0.3) +
geom_abline(aes(slope = trend_log10m, intercept = trend_log10c), color = tab_temp$trend_color, size = 1.1, linetype = "dashed", alpha = 0.7,
show.legend = F) +
transition_reveal(t) +
scale_x_continuous(name = "tot", trans = "log10", limits = c(24, 786.5)) +
scale_y_continuous(name = "new_loess", trans = "log10", limits = c(21, 601.7)) +
theme_classic() +
theme(axis.title = element_text(size = 18),
axis.text = element_text(size = 16))
Below there is a code producing a histogram using ggplot:
ggplot(res, aes(x=TOPIC,y=count)) +
scale_y_continuous(expand=c(0,0)) +
geom_bar(stat='identity') +
theme(axis.title.x=element_blank()) +
theme_bw(base_size = 16) +
ggtitle("Peer-reviewed papers per topic")
res<-structure(list(TOPIC = structure(c(20L, 18L, 21L, 3L, 9L, 4L,
7L, 8L, 17L, 27L, 29L, 2L, 24L, 30L, 16L, 28L, 10L, 22L, 12L,
5L, 1L, 19L, 6L, 25L, 11L, 23L, 14L, 15L, 13L, 26L), .Label = c("ANA",
"BEH", "BOUND", "CC", "DIS", "EVO", "HAB", "HABP", "HARV", "HWC",
"ISSUE", "METH", "MINOR", "PA", "PEFF", "PHYS", "POLL", "POPABU",
"POPGEN", "POPSTAT", "POPTR", "PRED", "PROT", "REPEC", "RESIMP",
"REV", "SHIP", "TEK", "TOUR", "ZOO"), class = "factor"), count = c(9,
7, 13, 5, 23, 35, 27, 5, 118, 0, 9, 22, 29, 46, 27, 12, 9, 44,
70, 40, 24, 19, 26, 2, 4, 17, 4, 10, 86, 31)), .Names = c("TOPIC",
"count"), row.names = c(NA, -30L), class = "data.frame")
The topics have names like "Population status" (POPSTAT), "Population trend" (POPTREND) etc.
There are issues I need help to solve:
1. The theme(axis.title.x=element_blank()) does not remove the x axis title.
2. The scale_y_continuous(expand=c(0,0)) puts the bar on the level of
the x-axis, which I need, but I need some expansion on the upper part
of the y-axis. How do I achieve that?
3. My labels from the data frame are abbreviations. How do I replace
these with new tick labels?
4. I need to turn the x-axis labels 90°.
Use labs(x = NULL)
Do you mean ylim? Using this, you can set the limits of the y-axis. Or use scale_y_continuous(limits = c(0, <upperLimit>), expand = c(0, 0))
Use a discrete scale with labels: scale_x_discrete(labels = <axis-labels>, limits = c(1:length(axis-labels))
theme(axis.text.x = element_text(angle = 90))
I have a dataset that I want to plot with ggplot using geom_histogram (data below). I am having issues with trying to get a gradient colour scheme.
structure(list(UserID = c(39120L, 39536L, 39550L, 39627L, 39632L,
39709L, 39971L, 39988L, 39990L, 40062L, 40065L, 40065L, 40066L,
40142L, 40142L, 40143L, 40161L, 40193L, 40364L, 40437L, 40439L,
40440L, 40451L, 40453L, 40665L, 40665L, 40668L, 40751L, 40843L,
40843L, 40843L, 40846L, 40846L, 40847L, 40847L, 40850L, 40850L,
40884L, 40884L, 40884L, 40896L, 40900L, 40902L, 40903L, 40905L,
40963L, 40966L, 40966L, 40967L, 40967L, 40969L, 40971L, 40971L,
40985L, 40985L, 41010L, 41079L, 41080L, 41080L, 41081L, 41093L,
41108L, 41110L, 41111L, 41113L, 41114L, 41133L, 41137L, 41138L,
41140L, 41161L, 41162L, 41182L, 41186L, 41260L), sales = c(0.0119,
0.0032, 0.0091, 0.0098, 0.0086, 0.0101, 0.0107, 0.0111, 0.0085,
0.0178, 0.0069, 0.0055, 0.0133, 0.0112, 0.0084, 0.0141, 0.0159,
0.01, 0.0054, 0.0129, 0.011, 0.0116, 0.0099, 0.0134, 0.0046,
0.004, 0.0076, 0.005, 0.0027, 0.0037, 3e-04, 0.022, 0.012, 0.0082,
0.0108, 0.0092, 0.0101, 0.0016, 0.0082, 0.0035, 0.007, 0.0098,
0.0146, 0.0074, 0.005, 0.0152, 0.0046, 0.0032, 0.0028, 0, 0.0179,
0.0185, 0.0095, 0.0401, 0.0163, 0.0085, 0.0099, 0.0064, 0.0067,
0.0052, 0.0191, 0.0118, 0.0054, 0.0111, 0.0065, 0.0124, 0.0047,
0.0111, 0.0063, 0.0072, 0.0062, 0.0091, 0.0066, 0.0169, 0.0071
), salesfromtarget = c(0.214285714285714, -0.673469387755102,
-0.0714285714285714, 0, -0.122448979591837, 0.0306122448979592,
0.0918367346938777, 0.13265306122449, -0.13265306122449, 0.816326530612245,
-0.295918367346939, -0.438775510204082, 0.357142857142857, 0.142857142857143,
-0.142857142857143, 0.438775510204082, 0.622448979591837, 0.0204081632653062,
-0.448979591836735, 0.316326530612245, 0.122448979591837, 0.183673469387755,
0.010204081632653, 0.36734693877551, -0.530612244897959, -0.591836734693878,
-0.224489795918367, -0.489795918367347, -0.724489795918367, -0.622448979591837,
-0.969387755102041, 1.24489795918367, 0.224489795918367, -0.163265306122449,
0.102040816326531, -0.0612244897959184, 0.0306122448979592, -0.836734693877551,
-0.163265306122449, -0.642857142857143, -0.285714285714286, 0,
0.489795918367347, -0.244897959183673, -0.489795918367347, 0.551020408163265,
-0.530612244897959, -0.673469387755102, -0.714285714285714, -1,
0.826530612244898, 0.887755102040817, -0.0306122448979592, 3.09183673469388,
0.663265306122449, -0.13265306122449, 0.010204081632653, -0.346938775510204,
-0.316326530612245, -0.469387755102041, 0.948979591836735, 0.204081632653061,
-0.448979591836735, 0.13265306122449, -0.336734693877551, 0.26530612244898,
-0.520408163265306, 0.13265306122449, -0.357142857142857, -0.26530612244898,
-0.36734693877551, -0.0714285714285714, -0.326530612244898, 0.724489795918367,
-0.275510204081633)), .Names = c("UserID", "sales", "salesfromtarget"
), row.names = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 15L, 16L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L,
28L, 29L, 31L, 32L, 35L, 36L, 37L, 38L, 39L, 41L, 42L, 44L, 45L,
46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 56L, 57L, 58L, 59L,
60L, 61L, 62L, 63L, 64L, 65L, 67L, 69L, 70L, 72L, 77L, 78L, 79L,
80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 93L), class = "data.frame")
Using this code I am trying to use scale_fill_brewer to get a gradient scheme (but it doesn't have to be this specific one, it was just an example of something not working). All of the bars are grey and ideally they would not be grey. Best case scenario I wanted to use something along the lines of scale_fill_gradient(low = "green", high = "blue") (again this doesn't work for me). I feel like I'm missing something fundamental with ggplot2.
require(ggplot2)
require(scales)
require(ggthemes)
ggplot(repex, aes(x = salesfromtarget)) +
geom_histogram(binwidth = .1, alpha = 0.5, colour = "white") +
scale_fill_brewer(palette = "Spectral") +
guides(fill = FALSE) +
theme_solarized() +
ggtitle("Standard Distribution of Sales") + xlab("Sales") + ylab("Frequency") +
theme(plot.title = element_text(size = 13, colour = "black", face = "bold", vjust = 1)) +
theme(axis.title.x = element_text(size = 12, colour = "black", vjust = -.005),
axis.title.y = element_text(size = 12, colour = "black", vjust = 0.2),
axis.text.x = element_text(angle = 45, hjust = 1)) +
scale_x_continuous(breaks = seq(-1.5, 1.5, by = .25),
limits = c(-1.5, 1.5), labels = percent)
You need to define the "fill" variable in the aes() section:
ggplot(repex, aes(x=salesfromtarget, fill=..x..))
+geom_histogram(binwidth=.1)
+scale_fill_gradient("Legend",low = "green", high = "blue")
Since the histogram bars are the count of each x-axis value, if you want to use the original x value you should use "..x..". You can fill with the histogram count using "..count..":
ggplot(repex, aes(x=salesfromtarget, fill=..count..))
+geom_histogram(binwidth=.1)
+scale_fill_gradient("Legend",low = "green", high = "blue")