So I have this graph:
But I would like to add appropriate labels to the x axis so each column has a label. For this I used scale_x_discrete(limit = as.character(2008:2017)) however doing so gives this result:
As you can see the labels are all squashed to one side and the data bars to the other.
Am I doing something wrong? or is this bug?
Here is my code:
# First image
ggplot(data_melt, aes(x = year, y = value, fill = variable)) +
geom_bar(position = "fill", stat = "identity") +
theme(legend.position = "none")
# Second image
ggplot(data_melt, aes(x = year, y = value, fill = variable)) +
geom_bar(position = "fill", stat = "identity") +
theme(legend.position = "none") +
scale_x_discrete(limit = as.character(2008:2017))
Your labeling problem is due to the variable type and it has not nothing to do with the axis scale. Try changing the variable, in the x axis, from numerical to categorical:
data_melt$year <- as.factor(data_melt$year)
Related
I want to plot the data shown below as a grouped bar_plot.
I tried position = "dodge" or position = "dodge2" but it didn't work. Ι also tried position = position_dodge()
It kinda works if i use geom_bar instead of geom_col and remove the y=overlap_percent:
p3 <- ggplot(data = comp_coors, aes(x = species_code, fill = mirna_form)) +
geom_bar(position = "dodge2") + theme_classic()
p3
but i would like the y_axis to have the overlap_percent.
Another attempt which ends in a stacked barplot is:
p2 <- ggplot(data = comp_coors, aes(x = species_code, y = overlap_percent, fill = mirna_form)) +
geom_bar(stat = "identity") + theme_classic()
p2
Finally by using geom_col, it returns this which it doesn't make sense, at least to me:
p4 <- ggplot(data = comp_coors, aes(x = species_code, y = overlap_percent, fill = mirna_form)) +
geom_col(position = "dodge") + theme_classic()
p4
The data that i want to plot :
comp_coors <- data.table( species = c("aae","cel", "dme","hsa", "mdo"),
mirna_form = c("mature", "precursor"),
overlap_percent = c(100.0, 100.0, 88.0, 95.5, 91.7, 100.0, 96.6, 98.4),
overlapping_attribute = c("ID=MIMAT0014285;Alias=MIMAT0014285", "ID=MI0000043;Alias=MI0000043;Name=cel-mir-72", "ID=MIMAT0000401;Alias=MIMAT0000401;Name=dme-miR-", "ID=MI0000791;Alias=MI0000791;Name=hsa-mir-383", "ID=MI0005331;Alias=MI0005331;Name=mdo-let-7g")
)
Try using species as a factor and add stat = "identity" like this:
ggplot(data = comp_coors, aes(x = factor(species), y = overlap_percent, fill = mirna_form)) +
geom_bar(position = "dodge", stat = "identity") + theme_classic() + labs(x = "Species", y = "Overlap percent")
Output:
A grouped barplot with overlap_percent on y-axis right.
I have this data.frame:
df <- data.frame(id = c("A","A","B","B","C","C"),
age = rep(c("young", "old"), 3),
value = c(20,15,7,5,2,6))
I'd like to plot it using ggplot2's geom_bar such that the bars are first separated (dodged) by age (but with no gaps between them) and then separated by id (along the x axis, with gaps), and are colored by id.
I'm only familiar with setting either the aes(x) argument to id and the fill argument to age:
ggplot(df, aes(x = id, y = value)) +
geom_bar(aes(fill = age), position = "dodge", stat = "identity") +
theme_minimal()
Or the opposite - the aes(x) argument to age and the fill argument to id:
ggplot(df, aes(x = age, y = value)) +
geom_bar(aes(fill = id), position = "dodge", stat = "identity") +
theme_minimal()
But what I want is the plot to look like the first one above but only filled by id rather than by age.
There's probably a combination position and/or statvalues that get that. Any idea?
You may use the group argument in aes:
ggplot(df, aes(x = id, y = value)) +
geom_bar(aes(group = age, fill = id), position = "dodge", stat = "identity") +
theme_minimal()
Goal is to achieve ordered categories on y axis.
y1 -> y2 -> y3.
Here's the example:
require(data.table)
require(ggplot2)
dt <- data.table(var = rep("x1", 3),
categ = paste0("y", c(1,2,3)),
value = c(-2,0.5,-1))
ggplot(dt, aes(x = categ, y = value)) +
geom_bar(stat = "identity") +
coord_flip() +
theme_bw()
It seems to be reversed. Here's the one way to achieve desired ordering in ggplot2:
dt$categ <- factor(dt$categ, levels = rev(levels(factor(dt$categ))))
ggplot(dt, aes(x = categ, y = value)) +
geom_bar(stat = "identity") +
coord_flip() +
theme_bw()
Great, now ordering seems to be right. But with some modifications:
ggplot(dt, aes(x = categ, y = value)) +
geom_bar(data = dt[value < 0], stat = "identity", fill = "darkred") +
geom_bar(data = dt[value >= 0], stat = "identity", fill = "darkblue") +
coord_flip() +
theme_bw()
For some reason factor ordering is ignored here. Any clues why?
Solution could be:
# dt is original data without factors
ggplot(dt, aes(categ, value, fill = value >= 0)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = c("darkred", "darkblue")) +
# since we want y1 on top and y3 on bottom we have to apply rev
scale_x_discrete(limits = rev(dt$categ)) +
coord_flip() +
theme_bw()
Trick is to pass dt$categ as limits argument to scale_x_discrete(). In your first plot order is not reversed, this is how it should be as ggplot2 starts putting values from the origin of the axis (0).
I also removed two geom_bar lines that were used in a not-ggplot way.
I came across this question the other day and tried to re-create it for myself. ggplot, facet, piechart: placing text in the middle of pie chart slices
. My data is in a very similar format, but sadly the accepted answer did not help, hence why I am re posting.
I essentially want to create the accepted answer but with my own data, yet the issue I run into is that coord_polar does not support free scale. Using the first answer:
I tried it using the second version of the answer, with the ddplyr version, but I also do not get my desired output. Using the second answer:
Clearly none of these has the desired effect. I would prefer to create one as with size pie charts, but only showed four as an example, follows: .
This I did in excel, but with one legend, and no background grid.
Code
title<-c(1,1,2,2,3,3,4,4,5,5,6,6)
type<-c('A','B','A','B','A','B','A','B','A','B','A','B')
value<-c(0.25,0.75,0.3,0.7,0.4,0.6,0.5,0.5,0.1,0.9,0.15,0.85)
piec<-data.frame(title,type,value)
library(tidyverse)
p1<-ggplot(data = piec, aes(x = "", y = value, fill = type)) +
geom_bar(stat = "identity") +
geom_text(aes(label = value), position = position_stack(vjust = 0.5)) +
coord_polar(theta = "y")
#facet_grid(title ~ ., scales = "free")
p1
piec <- piec %>% group_by(title) %>% mutate(pos=cumsum(value)-0.5*value)
p2<-ggplot(data = piec) +
geom_bar(aes(x = "", y = value, fill = type), stat = "identity") +
geom_text(aes(x = "", y = pos, label = value)) +
coord_polar(theta = "y")
#facet_grid(Channel ~ ., scales = "free")
p2
You don't have to supply different y values for geom_text and geom_bar (use y = value for both of them). Next you have to specify position in geom_text. Finally, remove scales from facets.
library(ggplot2)
title<-c(1,1,2,2,3,3,4,4,5,5,6,6)
type<-c('A','B','A','B','A','B','A','B','A','B','A','B')
value<-c(0.25,0.75,0.3,0.7,0.4,0.6,0.5,0.5,0.1,0.9,0.15,0.85)
piec<-data.frame(title,type,value)
ggplot(piec, aes("", value, fill = type)) +
geom_bar(stat = "identity", color = "white", size = 1) +
geom_text(aes(label = paste0(value * 100, "%")),
position = position_stack(vjust = 0.5),
color = "white", size = 3) +
coord_polar(theta = "y") +
facet_wrap(~ title, ncol = 3) +
scale_fill_manual(values = c("#0048cc", "#cc8400")) +
theme_void()
I created a plot like this;
library("ggplot2")
ggplot(data = diamonds) +
geom_bar(mapping = aes(x = color, y = ..prop.., group = 2)) +
scale_y_continuous(labels=scales::percent) +
facet_grid(~cut)
Now I want to add a legend for the variable "color", also I want to change the colour of the bars. The graph is exactly how I want it to be, and if possible I don't want change the structure of the dataset, just add a legend and change colours.
I could not find example that fit for this "percentage"-style graphics.
ggplot(data = diamonds, aes(x = color, y = ..prop.., group = cut)) +
geom_bar(aes(fill = factor(..x.., labels = LETTERS[seq(from = 4, to = 10 )]))) +
labs(fill = "color") +
scale_y_continuous(labels = scales::percent) +
facet_grid(~ cut)