I tried to reproduce the answer given by Roman in this post: The same width of the bars in geom_bar(position = "dodge")
but I couldnot fix my problem. When bars have the same width, the distance between the groups are too big. Same problem when I use facet_grid
My df:
df <- structure(list(discipline = structure(c(2L, 3L, 3L, 2L, 2L, 2L, 4L, 6L, 7L, 3L, 4L, 6L, 8L, 8L, 2L, 2L, 2L, 3L, 3L, 3L), .Label = c("", "Biogeochemistry", "Ecology", "Geochemistry", "Geography", "Management", "Microbiology", "Oceanography"), class = "factor"), focus = structure(c(34L, 55L, 40L, 47L, 54L, 57L, 47L, 19L, 31L, 25L, 23L, 25L, 47L, 52L,13L, 20L, 23L, 16L, 26L, 27L), .Label = c("", "Abiotic measures", "Acidification", "Biogeochemichal budgets", "Biogeochemistry", "Biogeochemistry, discharge", "Blue Carbon", "Chromophoric Dissolved organic matter, river plume", "Coastal anthromes", "Connectivity", "Coral reefs", "Ecology", "Ecosystem Function", "Ecosystem Services", "Embryo plants", "Fisheries", "Food webs", "Global change", "Governance", "Groundwater", "Hidrology", "Integrative Magamenet", "Isotopes", "Land-sea interactions","Land-sea interface", "Land use", "Life history", "Life traits", "Livelihoods", "Management", "Microbial community", "Modelling water quality", "Nitrogen fluxes", "Nutrients", "Parasites", "ph, CO2", "Planning", "Pollutants", "Pollution", "Primary production", "Remote Sensing", "Resilience", "resilience, self-organization", "Restoration",
"Salinization", "Sea level rise", "Sediment flux", "Sediments", "socio land-sea interactions", "Species interaction", "Submarine ground water", "Submarine groundwater", "Subsidies", "Trace metals", "Trophic interactions", "Water quality", "Water resources"), class = "factor"), n = c(39L, 17L, 11L, 9L, 6L, 5L, 5L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L)), row.names = c(NA, -20L), class = c("tbl_df","tbl", "data.frame"))
First I tried with position = position_dodge2(preserve = "single")
ggplot(df, aes(x = (discipline), y = n, fill = reorder(focus, n))) +
geom_bar(position = position_dodge2(width = 0.9, preserve = "single"), stat = "identity") + ylab("N") + theme_classic() + geom_text(aes(label=focus), position = position_dodge2(width = 0.9, preserve = "single"), angle = 90, hjust = -0.1) + theme(legend.position = "none")
Then I used facet_grid
ggplot(df, aes(x = (discipline), y = n, fill = reorder(focus, n))) +
geom_bar(position = "dodge", stat = "identity") + ylab("N") + theme_classic() + geom_text(aes(label=focus), position = position_dodge2(width = 0.9, preserve = "single"), angle = 90, hjust = -0.1) + theme(legend.position = "none") + facet_grid(scales = "free_x", space = "free_x", switch = "x")
Even when width of bars are equal, distance between groups are too big.
What can I do to solve this problem?
Maybe try this. It looks like the issue is with position. If you define position_dodge2() for the bars you can avoid the big bars you got. Here the code:
library(ggplot2)
#Code
ggplot(df, aes(x = (discipline), y = n, fill = reorder(focus, n))) +
geom_bar(position = position_dodge2(0.9,preserve = 'single'),
stat = "identity") + ylab("N") +
theme_classic() +
geom_text(aes(label=focus), position = position_dodge2(width = 0.9, preserve = "single"),
angle = 90, hjust = -0.1) + theme(legend.position = "none") +
facet_grid(scales = "free_x", space = "free_x", switch = "x")
Output:
Whereas, the original code produces this (using position = "dodge"):
Related
I have a sparse plot due to data input
Data input
dframe <- structure(list(value = c(1L, 2L, 3L, 4L, 5L, 8L, 6L, 7L,
10L, 9L, 14L, 15L, 20L, 22L, 24L), level= c(1009L, 103L, 43L,
7L, 5L, 4L, 3L, 3L, 2L, 1L, 1L, 1L, 1L, 1L, 1L)), class = "data.frame", row.names = c(NA,
-15L))
And the plot:
library(ggplot2)
p <- ggplot(data=dframe, mapping = aes(x=value, y=level)) +
geom_col(color = '#032838', fill = 'steelblue', size = 1) +
geom_text(aes(label = level), vjust = -0.4, size = 4, position = position_dodge(0.9))
Is there any alternative plot which will not be so sparse after frequency of 30 in x axis?
Here is a hypothesis: you could zoom in on the part of the plot where the data are more sparse. An example with ggforce
library(ggforce)
#transform your data to be plotted by geom_histogram (or geom_density)
df <- data.frame(value=rep(dframe$value,dframe$level))
ggplot() +
geom_histogram(aes(x=value),dplyr::mutate(df, z = F),bins = 25,color = '#032838', fill = 'steelblue') +
geom_histogram(aes(x=value),dplyr::mutate(df, z = T),bins =50,color = '#032838', fill = 'steelblue') +
facet_zoom(xlim = c(5, 25),ylim=c(0,10), horizontal = F,zoom.data = z,zoom.size=0.5)+
theme(zoom.y = element_blank(), validate = FALSE)
which give you:
you can play with the bins argument to find the perfect solution for you.
N.B. I remove the geom_text part since you did not provide the Users variable
Why not just take the logarithm of your level data? That would be the standard thing to do in such a situation. Consider:
p <- ggplot(data=dframe, mapping = aes(x=value, y=log(level))) +
geom_col(color = '#032838', fill = 'steelblue', size = 1)
This is what I want to achieve:
I'm trying to replicate the theme of these graph using ggplot, I searched online for articles and question to show me how to assign these plots the right size and position and also to assign the tight dot shape, and I found few articles that discussed changing position, I tried the following:
d1<-read.csv("./data/games.csv")
library(ggplot2)
library(dplyr)
d1 %>%
filter(winner != "draw") %>%
ggplot(aes(x=cream_rating, y=charcoal_rating, color = winner, shape = winner)) +
# Map winner on color. Add some transparency in case of overplotting
geom_point(alpha = 0.2, na.rm = TRUE) +
# Just a guess to add the cross: Add geom_pints with one variable fixed on its mean
# Should "draw"s be colored or dropped?
scale_color_manual(values = c(cream = "seagreen4", charcoal = "chocolate3")) +
scale_shape_manual(values = c(cream = 16, charcoal = 17)) +
ggtitle("Rating of Cream vs Charcoal") +
xlab("rating of cream") + ylab("rating of charcoal")+ theme_classic() + theme(plot.title = element_text(hjust = 0.5))
p.1<-ggplot(d1, aes(x=cream_rating, y=charcoal_rating)) +
# Map winner on color. Add some transparency in case of overplotting
geom_point(aes(color = winner), alpha = 0.2) +
# Add the cross: Add geom_pints with one variable fixed on its mean
geom_point(aes(y = mean(charcoal_rating), color = winner), alpha = 0.2) +
scale_shape_manual(values=c(16, 17)) +
# "draw"s should be dropped and removed from the title
scale_color_manual(values = c(cream = "seagreen4", charcoal = "chocolate3", draw = NA)) +
ggtitle("Rating of Cream vs Charcoal") +
xlab("rating of cream") + ylab("rating of charcoal") + theme_classic() + theme(plot.title = element_text(hjust = 0.5))
p.01<-ggplot(d1, aes(x=cream_rating, y=charcoal_rating)) +
# Map winner on color. Add some transparency in case of overplotting
geom_density2d(aes(color = winner), alpha = 0.2) +
# Add the cross: Add geom_pints with one variable fixed on its mean
scale_shape_manual(values=c(16, 17, 0)) +
# "draw"s should be dropped and removed from the title
scale_color_manual(values = c(cream = "seagreen4", charcoal = "chocolate3", draw = "blue")) +
ggtitle("Rating of Cream vs Charcoal") +
xlab("rating of cream") + ylab("rating of charcoal") + theme_classic() + theme(plot.title = element_text(hjust = 0.5))
plot.1<-p.1
plot.02<-ggExtra::ggMarginal(p.01, type = "density",
margins = 'both',
size = 5,
groupColour = TRUE,
groupFill = TRUE
)
plot.02
require(gridExtra)
plot.1<-p.1
plot.2<-ggExtra::ggMarginal(p.1, type = "histogram")
grid.arrange(plot.1, plot.2, ncol=3)
plot.02
library(cowplot)
theme_set(theme_cowplot())
plot.1<-p.1
plot.2<-ggExtra::ggMarginal(p.1, type = "histogram")
plot.02
plot_grid(plot.1, plot.2, plot.02, labels = "AUTO")
cowplot::plot_grid(plot.1, plot.2, plot.02, labels = "AUTO")
library(magrittr)
library(multipanelfigure)
figure1 <- multi_panel_figure(columns = 2, rows = 1, panel_label_type = "none")
# show the layout
figure1
figure1 %<>%
fill_panel(plot.1, column = 1, row = 1) %<>%
fill_panel(plot.2, column = 2, row = 1) %<>%
fill_panel(plot.02, column= 3, row = 1) %<>%
figure1
This is my data set structure:
structure(list(rated = c(FALSE, TRUE, TRUE, TRUE, TRUE, FALSE,
TRUE, FALSE, TRUE, TRUE), turns = c(13L, 16L, 61L, 61L, 95L,
5L, 33L, 9L, 66L, 119L), victory_status = structure(c(3L, 4L,
2L, 2L, 2L, 1L, 4L, 4L, 4L, 2L), .Label = c("draw", "mate", "outoftime",
"resign"), class = "factor"), winner = structure(c(2L, 1L, 2L,
2L, 2L, 3L, 2L, 1L, 1L, 2L), .Label = c("charcoal", "cream",
"draw"), class = "factor"), increment_code = structure(c(3L,
7L, 7L, 5L, 6L, 1L, 1L, 4L, 2L, 1L), .Label = c("10+0", "15+0",
"15+2", "15+30", "20+0", "30+3", "5+10"), class = "factor"),
cream_rating = c(1500L, 1322L, 1496L, 1439L, 1523L, 1250L,
1520L, 1413L, 1439L, 1381L), charcoal_rating = c(1191L, 1261L,
1500L, 1454L, 1469L, 1002L, 1423L, 2108L, 1392L, 1209L)), row.names = c(NA,
10L), class = "data.frame")
Thanks to #Stefan's suggestion (which was great help) in getting me this far.
I need to plot a ribbon around a hline in a graph with barplots divided in facets. The x axis is non continuous and even though I have tried different solutions like making x numeric for geom_ribbon, I can't find a solution.
toplot=structure(list(size = c(10L, 10L, 10L, 10L, 30L, 30L, 30L, 30L,
50L, 50L, 50L, 50L, 100L, 100L, 100L, 100L), density = structure(c(2L,
3L, 4L, 5L, 2L, 3L, 4L, 5L, 2L, 3L, 4L, 5L, 2L, 3L, 4L, 5L), .Label = c("control",
"low", "medium", "high", "extreme"), class = "factor"), mean = c(0.649495617453177,
0.595030456501759, 0.671853292620394, 0.772710452129729, 0.208287258947775,
0.113070097194118, 0.138593272196695, 0.106836463449531, 0.142217123599047,
0.291860533054406, 0.187033701620647, 0.12045308442074, 0, 0.0000389132497170763,
0.00251973356226341, 0), sd = c(0.0472308191904496, 0.0716594048000388,
0.0857233139528986, 0.0534307204561747, 0.0481240616513752, 0.0390094013972726,
0.0412224562146842, 0.0278742510208481, 0.0233346723409426, 0.0559831409664118,
0.0494588911471589, 0.0270924698136921, 0, 0.000218839700404029,
0.00550243848896909, 0), period = structure(c(1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), class = "factor", .Label = "final")), .Names = c("size",
"density", "mean", "sd", "period"), row.names = c(2L, 3L, 4L,
5L, 7L, 8L, 9L, 10L, 12L, 13L, 14L, 15L, 17L, 18L, 19L, 20L), class = "data.frame")
contr=structure(list(size = c(10L, 30L, 50L, 100L), density = structure(c(1L,
1L, 1L, 1L), .Label = c("control", "low", "medium", "high", "extreme"
), class = "factor"), mean = c(0.640615964924125, 0.231731093831607,
0.122309113981835, 0.0053438272624331), sd = c(0.04503167947312,
0.0406874041671366, 0.0173288744394121, 0.00181433175554796),
period = c("final", "final", "final", "final")), .Names = c("size",
"density", "mean", "sd", "period"), row.names = c(1L, 6L, 11L,
16L), class = "data.frame")
and the code that I have
p <- ggplot(data=toplot,aes(x=period,y=mean,fill=density)) +
geom_bar(stat='identity',position = 'dodge') +
facet_grid(~size) +
geom_hline(data = contr, aes(yintercept = mean,linetype = "control"),size=1.2) +
scale_linetype_manual(name = "",values=2)
I would like to draw a ribbon around the horizontal control line but it's not working. This doesn't draw anything and changes the fill.
p + geom_ribbon(data=contr, aes(ymin = mean - sd, ymax = mean + sd),fill='grey')
and this also messes up the facets
p + geom_ribbon(data=contr, aes(x=1:4, ymin = mean - sd, ymax = mean + sd),fill='grey')
I have also tried to use group=size to match the facet command but nothing happens.
Either I am using the wrong geom or I am missing how to structure the data. I tried to use this http://mjskay.github.io/tidybayes/reference/geom_lineribbon.html but it doesn't exist in ggplot2
Objects like geom_ribbon expect a series of x and y values, so that points can be connected via lines. The main problem here is that your x-axis has only 1 value ('final'), so there's nothing to connect. You can get around the problem with geom_rect, which only needs values for the upper-right and lower-left corners. We simply use -Inf and Inf for the xmin and xmax values, so that the rectangle spans the full width of each facet:
p <- ggplot(data=toplot,aes(x=period,y=mean,fill=density)) +
geom_bar(stat='identity',position = 'dodge') +
facet_grid(~size) +
geom_rect(data = contr, aes(ymin = mean - sd, ymax = mean + sd), xmin = -Inf, xmax = Inf, alpha = 0.25, fill = 'black') +
geom_hline(data = contr, aes(yintercept = mean,linetype = "control"),size=1.2) +
scale_linetype_manual(name = "",values=2)
The geom_rect() approach is nice. You could do something similar with geom_crossbar():
p <- ggplot(data=toplot,aes(x=period,y=mean,fill=density)) +
geom_bar(stat='identity',position = 'dodge') +
facet_grid(~size) +
geom_crossbar(data = contr,
aes(ymin = (mean - 2*sd),
ymax=(mean + 2*sd), linetype = "control"),
size=.2, alpha=.5, width=1, fill='darkgrey') +
scale_linetype_manual(name = "",values=2)
p + theme_minimal()
Something like this. Modify the size=7 value to change the thickness of the line; and alpha=0.2 to edit transparency.
p <- ggplot(data=toplot,aes(x=period,y=mean,fill=density)) +
geom_bar(stat='identity',position = 'dodge') +
facet_grid(~size) +
geom_hline(data = contr, aes(yintercept = mean),size=7,alpha=0.2) +
geom_hline(data = contr, aes(yintercept = mean,linetype = "control"),size=1.2) +
scale_linetype_manual(name = "",values=2)
Using ggplot2, I am plotting percentage values for 15 species across three sites (each species occurs in each site). The data points associated with site 'C' are my reference points.
Now, instead of plotting sites 'A' and 'B' as points, I would like to display them using vertical lines or column-like structures. As such, these data points should be extended as vertical lines to the top or bottom side of the site 'C' points (green colour), i.e. to the top where values are larger than the reference value and bottom for smaller values.
Specifically, I would hope for a red line from a red point to the green point and a blue line from the blue point to the green point. The red line should ideally have the same width as the red point (and same for blue). The line should also be offset as are the red and blue points (relative to the green point), so that lines do not overlap. Finally, the line should not go to the center but the edge of a point.
For this purpose I have offset points for 'A' and 'B' and also reduced their size to half of the reference point size.
library(ggplot2)
MyData$species <- as.character(MyData$species)
MyData$species <- factor(MyData$species, levels=unique(MyData$species))
pos <- position_dodge(width=0.21)
cols <- c("C" = "darkgreen", "B" = "blue", "A" = "red")
tiff(file = "MyData.tiff", height=10, width=10, units="in", res=300, compression="lzw")
ggplot(data = MyData, aes(x=species, y=value, group=site, colour=site)) +
geom_point(data=subset(MyData, site=="C"), size = 4, shape=15, alpha=1, position=pos) +
geom_line(data=subset(MyData, site=="C"), size = 2, lwd=2, alpha=0.4, show_guide=FALSE) +
geom_point(data=subset(MyData, site!="C"), size = 1.8, shape=15, alpha=1, position = pos) +
scale_colour_manual(values = cols) +
xlab("Species") +
ylab("Value (%)") +
scale_y_continuous(expand=c(0.01,0.01),
limits=c(0.0,100),
breaks=c(0,20,40,60,80,100),
labels=c("0","20","40","60","80","100")) +
theme_bw() +
theme(legend.position="none") +
theme(axis.title.x = element_text(vjust=0.1,face="bold", size=16),
axis.text.x = element_text(vjust=0.4, size=14, angle=90, hjust=1.0)) +
theme(axis.title.y = element_text(vjust=0.1,face="bold", size=16),
axis.text.y = element_text(face="bold", size=14, angle=0)) +
theme(panel.grid.minor=element_blank(), panel.grid.major=element_blank()) +
theme(panel.border = element_rect(size=1, color = "black")) +
theme(plot.margin = unit(c(0.3,0.4,0.3,0.3),"lines"))
dev.off()
This is my current plot. So basically, I would like to replace the red and blue points with lines that extend to the green points (without overlapping them).
Many thanks in advance for any advice on an elegant solution for this.
This is a dput() of my dataset.
structure(list(site = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L), .Label = c("A", "B", "C"), class = "factor"),
species = structure(c(13L, 11L, 2L, 14L, 1L, 9L, 12L, 10L,
6L, 8L, 15L, 7L, 3L, 4L, 5L, 13L, 11L, 2L, 14L, 1L, 9L, 12L,
10L, 6L, 8L, 15L, 7L, 3L, 4L, 5L, 13L, 11L, 2L, 14L, 1L,
9L, 12L, 10L, 6L, 8L, 15L, 7L, 3L, 4L, 5L), .Label = c("Species 1",
"Species 10", "Species 11", "Species 12", "Species 13", "Species 14",
"Species 15", "Species 2", "Species 3", "Species 4", "Species 5",
"Species 6", "Species 7", "Species 8", "Species 9"), class = "factor"),
value = c(2, 3.25, 3.53, 4.31, 4.59, 5.26, 6.02, 6.42, 6.6,
7.26, 8.89, 12.45, 35.62, 72.42, 73.55, 1.36, 2.36, 2.17,
10.34, 6.84, 1.88, 5.09, 7.35, 3.87, 10.55, 6.6, 14.64, 39.57,
88.06, 64.54, 5.03, 12.34, 5.42, 3.63, 5.16, 6.04, 3, 8.94,
3.28, 7.64, 6.25, 21.96, 39.35, 78.55, 47.35)), .Names = c("site",
"species", "value"), class = "data.frame", row.names = c(NA,
-45L))
You can try geom_linerange() for the lines from points A/B to point C.
Define the ymin/ymax values for each site/species, & reorder site such that A / B lines drop down to each side of point C:
library(dplyr)
MyData <- MyData %>%
group_by(species) %>%
mutate(value.C = value[site == "C"]) %>%
rowwise() %>%
mutate(value.min = min(value, value.C),
value.max = max(value, value.C)) %>%
ungroup() %>%
mutate(site = factor(site, levels = c("A", "C", "B")))
Plot:
# vary dodge width such that the lines drop to the edge of point C
# for your chosen dimensions (for mine 0.5 was about right)
pos <- position_dodge(width = 0.5)
ggplot(data = MyData,
aes(x = species, y = value,
ymin = value.min, ymax = value.max,
group = site, colour = site, size = site)) +
geom_linerange(size = 1.8, alpha = 0.4, position = pos) +
geom_line(data = subset(MyData, site == "C"),
size = 2, lwd = 2, alpha = 0.4) +
geom_point(data = subset(MyData, site == "C"),
size = 4, shape = 15, position = pos) +
scale_color_manual(values = cols) +
theme_classic() +
theme(legend.position = "none")
# + other theme-related settings...
You can add geom_line to draw the vertical lines
library(ggplot2)
MyData$species <- as.character(MyData$species)
MyData$species <- factor(MyData$species, levels=unique(MyData$species))
pos <- position_dodge(width=0.21)
cols <- c("C" = "darkgreen", "B" = "blue", "A" = "red")
windows()
ggplot(data = MyData, aes(x=species, y=value, group=site, colour=site)) +
geom_point(data=subset(MyData, site=="C"), size = 4, shape=15, alpha=1, position=pos) +
geom_line(data=subset(MyData, site=="C"), size = 2, lwd=2, alpha=0.4, show_guide=FALSE) +
geom_point(data=subset(MyData, site!="C"), size = 1.8, shape=15, alpha=1, position = pos) +
geom_line(aes(group = species)) + #New code Added
scale_colour_manual(values = cols) +
xlab("Species") +
ylab("Value (%)") +
scale_y_continuous(expand=c(0.01,0.01),
limits=c(0.0,100),
breaks=c(0,20,40,60,80,100),
labels=c("0","20","40","60","80","100")) +
theme_bw() +
theme(legend.position="none") +
theme(axis.title.x = element_text(vjust=0.1,face="bold", size=16),
axis.text.x = element_text(vjust=0.4, size=14, angle=90, hjust=1.0)) +
theme(axis.title.y = element_text(vjust=0.1,face="bold", size=16),
axis.text.y = element_text(face="bold", size=14, angle=0)) +
theme(panel.grid.minor=element_blank(), panel.grid.major=element_blank()) +
theme(panel.border = element_rect(size=1, color = "black")) +
theme(plot.margin = unit(c(0.3,0.4,0.3,0.3),"lines"))
R version 3.1.1 (2014-07-10) Platform: i386-w64-mingw32/i386 (32-bit)
I am working on a barplot with ggplot2. The aim is to have a combination of a stacked and dodged barplot for the data. My problem is to include a legend, which includes both layers or shows them separately.
Data:
df <- structure(list(year = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L,
1L, 2L, 3L, 4L, 5L, 6L, 7L), .Label = c("2008", "2009", "2010",
"2011", "2012", "2013", "2014"), class = "factor"), product = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("a",
"b"), class = "factor"), total = c(1663L, 1344L, 1844L, 444L,
1336L, 897L, 655L, 3433L, 3244L, 2044L, 3344L, 1771L, 1410L,
726L), partial = c(1663L, 1344L, 1844L, 444L, 949L, 302L, 5L,
3433L, 3244L, 2044L, 3344L, 1476L, 1158L, 457L)), .Names = c("year",
"product", "total", "partial"), row.names = c(NA, -14L), class = "data.frame")
The plan was, to plot two geom_bar layers to combine dodge and stacked. The first layer is the total amount, the second layer is the partial amount. The alpha value for the first layer is reduced to see the difference between the two layers. So far it worked.
Example:
ggplot(df, aes(x = year))+
geom_bar(aes(y = total, fill = product), alpha= 0.3, stat = "identity", position = "dodge", width = 0.3)+
geom_bar(aes(y = partial, fill = product), alpha= 1, stat = "identity", position = "dodge", width = 0.3)
Now the legend is not sufficiant. It shows the colour of fill = product and is not sensitive to the alpha value of the first layer.
My approach was to use scale_fill_manual and manually add a new lable with a new colour, which did not worked.
My idea:
ggplot(df, aes(x = year))+
geom_bar(aes(y = total, fill = product), alpha= 0.3, stat = "identity", position = "dodge", width = 0.3)+
geom_bar(aes(y = partial, fill = product), alpha= 1, stat = "identity", position = "dodge", width = 0.3)+
scale_fill_manual(name = "",
values=c("red", "black","blue"),
labels=c("a","b","test"))
Thank you for any help on my problem!
Try to use different fill values for total and partial data.
Quick and dirty solution:
ggplot(df, aes(x = year))+
geom_bar(aes(y = total, fill = factor(as.numeric(product))), alpha= 0.3, stat = "identity", position = "dodge", width = 0.3) +
geom_bar(aes(y = partial, fill = factor(as.numeric(product) * 3)), alpha= 1, stat = "identity", position = "dodge", width = 0.3) +
scale_fill_manual(name = "", values=c("red", "black","blue", "green"), labels=c("A","B","Partial A", "Partial B"))
Tested on
R x64 3.2.2