I have the following data frame data frame and I am plotting the average (Accuracy) per level. But I want to also the individual data points with shapes (e.g.Accuracy1, Accuracy2, Accuracy3 etc) on the line. Anyone who could help me? Thanks
ggplot(data=Accuracy_means, aes(x=Effort_Level, y=Accuracy,
group=1)) +
geom_errorbar(aes(ymin=Accuracy-se, ymax=Accuracy+se), width=.05, size=1) +
geom_line(size=1)+
geom_hline(yintercept=c(-0.5,0.5), linetype="dashed", colour="black", size=0.5)+
ylim(0,1)+
coord_fixed(ratio = 2.5)+
theme_classic()
It's not clear if you want to change the line type. If so, here is an approach using gather from tidyr.
library(tidyverse)
Accuracy_means %>%
gather(key = accuracy_vars, value = values, -Effort_Level, -Accuracy, -se) %>%
ggplot(aes(x=Effort_Level,
y=values)) +
geom_errorbar(aes(ymin=Accuracy-se, ymax=Accuracy+se), colour = "red", width =0.05, size = 0.5) +
geom_line(aes(linetype = accuracy_vars), size=1) +
geom_line(aes(y = Accuracy), colour = "red")+
coord_fixed(ratio = 2.5)+
theme_classic()
Related
library(ggplot2)
x <- data.frame(Specimen=c("A","B","C","D"), Value=rep(0.5,4),
Type=c("c1","c1","c2","c2"), Treatment=factor(rep("A", 4)),
bar=c("hot", "cold", "cold", "cold"))
list2env(split(x, x$Type), envir = .GlobalEnv)
p1 <- ggplot() +
geom_bar(data=c1, aes(x = Treatment, y = Value, fill = Specimen, colour=bar),
stat="identity", position="fill", width=0.5) +
scale_fill_manual("",values=c("gold", "green"))+
scale_color_manual("",values=c("gray40","black")) +
scale_y_continuous(expand = c(0, 0),labels = scales::percent) +
theme(legend.position = "bottom") +
coord_flip()
p2 <- ggplot() +
geom_bar(data=c2, aes(x = Treatment, y = Value, fill = Specimen),
stat="identity", position="fill", col="gray40", width=0.5) +
scale_fill_manual("",values=c("red", "blue"))+
scale_y_continuous(expand = c(0, 0),labels = scales::percent) +
theme(legend.position = "bottom",
axis.text.y=element_blank()) +
xlab("")+
coord_flip()
library(cowplot)
plot_grid(p1,p2, nrow=1, align="v")
In this example, i had to shut down the guide for color, as i couldnt combine it with the guide for fill, despite following the guidelines proposed in this question.
After turning off the guide for col in p1 (guide=F), the legends now appear to be differently drawn (one with col="gray40", the other without any border, as the col-guide is set to false):
]1
How to combine the two legends in p1?
fill and color are mapped to two different varaibles, it's only by chance that in this (trivial) case "A" is always "hot" and "B" is always "cold".
You can map both fill and color to Specimen or bar, but different variable will always result in different legends.
An alternative may be to create an interaction between the two varaibles:
library(ggplot2)
ggplot() +
geom_col(data=c1, aes(x = Treatment,
y = Value,
fill = interaction(Specimen, bar, sep = '-'),
color = interaction(Specimen, bar, sep = '-')),
position="fill", width=0.5) +
scale_fill_manual("",values=c("gold", "green")) +
scale_color_manual("",values=c("gray40", "black")) +
scale_y_continuous(expand = c(0, 0),labels = scales::percent) +
theme(legend.position = "bottom") +
coord_flip()
Created on 2018-05-08 by the reprex package (v0.2.0).
I am actually trying to do a graph with ggplot2 but I'd like to add some options (colors, legend...).
Here is my code :
ggplot(FINAL, aes(x = as.factor(gender), y=overtreatment)) +
stat_summary(fun.y="mean", geom="bar") +
facet_grid(. ~ treatment) +
theme_grey() +
xlab("Treatment") +
ylab("OT") +
scale_fill_grey() +
theme(strip.background = element_rect(colour = "black", fill = "white"))
And here the actual output.
Could you please indicate me how to change the name of 1 and 2 (without changing in it the dataframe) and how to add colours to this ?
I tried this
ggplot(FINAL, aes(x = as.factor(gender), y=overtreatment, colour=Treatment))
But it applies the color only to the outline of the figure.
To change the color of the bars you need fill = Treatment.
To change the labels on the x axis you need scale_x_discrete(labels = your_labels). See here.
So your code will look like:
ggplot(FINAL, aes(x = as.factor(gender), y=overtreatment, fill= Treatment)) +
scale_x_discrete(labels = your_labels) +
...
Following guides like ggplot Donut chart I am trying to draw small gauges, doughnuts with a label in the middle, with the intention to put them later on on a map.
If the value reaches a certain threshold I would like the fill of the doughnut to change to red. Is it possible to achieve with if_else (it would be most natural but it does not work).
library(tidyverse)
df <- tibble(ID=c("A","B"),value=c(0.7,0.5)) %>% gather(key = cat,value = val,-ID)
ggplot(df, aes(x = val, fill = cat)) + scale_fill_manual(aes,values = c("red", "yellow"))+
geom_bar(position="fill") + coord_polar(start = 0, theta="y")
ymax <- max(df$val)
ymin <- min(df$val)
p2 = ggplot(df, aes(fill=cat, y=0, ymax=1, ymin=val, xmax=4, xmin=3)) +
geom_rect(colour="black",stat = "identity") +
scale_fill_manual(values = if_else (val > 0.5, "red", "black")) +
geom_text( aes(x=0, y=0, label= scales::percent (1-val)), position = position_dodge(0.9))+
coord_polar(theta="y") +
xlim(c(0, 4)) +
theme_void() +
theme(legend.position="none") +
scale_y_reverse() + facet_wrap(facets = "ID")
Scale fill manual values= if else.... this part does not work, the error says: Error in if_else(val > 0.5, "red", "black") : object 'val' not found. Is it my error, or some other solution exists?
I also realize my code is not optimal, initially gather waited for more variables to be included in the plot, but I failed to stack one variable on top of the other. Now one variable should be enough to indicate the percentage of completion. I realise my code is redundant for the purpose. Can you help me out?
A solution for the color problem is to first create a variable in the data and then use that to map the color in the plot:
df <- tibble(ID=c("A","B"),value=c(0.7,0.5)) %>% gather(key = cat,value = val,-ID) %>%
mutate(color = if_else(val > 0.5, "red", "black"))
p2 = ggplot(df, aes(fill=color, y=0, ymax=1, ymin=val, xmax=4, xmin=3)) +
geom_rect(colour="black",stat = "identity") +
scale_fill_manual(values = c(`red` = "red", `black` = "black")) +
geom_text( aes(x=0, y=0, label= scales::percent (1-val)), position = position_dodge(0.9))+
coord_polar(theta="y") +
xlim(c(0, 4)) +
theme_void() +
theme(legend.position="none") +
scale_y_reverse() + facet_wrap(facets = "ID")
The result would be:
I know that this question has been asked before but the solutions don't seem to work for me.
What I want to do is represent my median, mean, upper and lower quantiles on a histogram in different colours and then add a legend to the plot. This is what I have so far and I have tried to use scale_color_manual and scale_color_identity to give me a legend. Nothing seems to be working.
quantile_1 <- quantile(sf$Unit.Sales, prob = 0.25)
quantile_2 <- quantile(sf$Unit.Sales, prob = 0.75)
ggplot(aes(x = Unit.Sales), data = sf) +
geom_histogram(color = 'black', fill = NA) +
geom_vline(aes(xintercept=median(Unit.Sales)),
color="blue", linetype="dashed", size=1) +
geom_vline(aes(xintercept=mean(Unit.Sales)),
color="red", linetype="dashed", size=1) +
geom_vline(aes(xintercept=quantile_1), color="yellow", linetype="dashed", size=1)
You need to map the color inside the aes:
ggplot(aes(x = Sepal.Length), data = iris) +
geom_histogram(color = 'black', fill = NA) +
geom_vline(aes(xintercept=median(iris$Sepal.Length),
color="median"), linetype="dashed",
size=1) +
geom_vline(aes(xintercept=mean(iris$Sepal.Length),
color="mean"), linetype="dashed",
size=1) +
scale_color_manual(name = "statistics", values = c(median = "blue", mean = "red"))
I've created a grouped boxplot and added three specific geom_hlines to the plot. However, I want to set the hline colors to fill=factor(Training.Location), rather than trying to match the colors manually with a color palette. Is there a way to do this?
ggplot(aes(x=factor(CumDes),y=Mn_Handle), data=NH_C) +
geom_boxplot( aes(fill=factor(Training.Location))) +
geom_point( aes(color=factor(Training.Location)),
position=position_dodge(width=0.75) ) +
theme(axis.ticks = element_blank(), axis.text.x = element_blank()) +
coord_cartesian(ylim = c(0, 2000)) +
geom_hline(yintercept=432, linetype="dashed", lwd=1.2) +
geom_hline(yintercept=583, linetype="dashed", lwd=1.2) +
geom_hline(yintercept=439, linetype="dashed", lwd=1.2)
This is the sort of thing that seems easiest with a new dataset. I'm not sure how you are calculating the values you are using for the horizontal lines, but often times I want to calculate these from the original dataset and use some sort of aggregation function/package for that.
Here is a modified example from the help page for geom_hline.
Make the dataset to give to geom_hline, including the values for the horizontal lines as well as the grouping variable.
mean_wt = data.frame(cyl = c(4, 6, 8), wt = c(2.28, 3.11, 4.00))
Then just plot with the new dataset for that layer, using whatever aesthetic you wish with the grouping variable.
ggplot(mtcars, aes(x = factor(vs), wt) ) +
geom_boxplot(aes(fill = factor(cyl))) +
geom_point(aes(color = factor(cyl)), position = position_dodge(.75)) +
geom_hline(data = mean_wt, aes(yintercept = wt, color = factor(cyl)) )
Here's a somewhat hackish solution (I had to improvise on the data, feel free to improve)
# install.packages("ggplot2", dependencies = TRUE)
library(ggplot2)
col <- c("#CC6666", "#9999CC", "#66CC99")
ggplot(mtcars, aes(x = factor(cyl), y=mpg)) +
geom_boxplot(aes(fill=gear)) +
geom_point( aes(color=factor(gear)),
position=position_dodge(width=0.75) ) +
scale_colour_manual(values= col) +
theme(axis.ticks = element_blank(), axis.text.x = element_blank()) + coord_cartesian(ylim = c(8, 35)) +
geom_hline(yintercept=12, linetype="dashed", lwd=1.2, color=col[1]) +
geom_hline(yintercept=18, linetype="dashed", lwd=1.2, color=col[2]) +
geom_hline(yintercept=28, linetype="dashed", lwd=1.2, color=col[3])