I have a faceted plot wherein I'd like to have the Y-axis labels and the associated values appear in descending order of values (and thereby changing the order of the labels) for each facet. What I have is this, but the order of the labels (and the corresponding values) is the same for each facet.
ggplot(rf,
aes(x = revenues,
y = reorder(AgencyName, revenues))) +
geom_point(stat = "identity",
aes(color = AgencyName),
show.legend = FALSE) +
xlab(NULL) +
ylab(NULL) +
scale_x_continuous(label = scales::comma) +
facet_wrap(~year, ncol = 3, scales = "free_y") +
theme_minimal()
Can someone point me to the solution?
The functions reorder_within and scale_*_reordered from the tidytext package might come in handy.
reorder_within recodes the values into a factor with strings in the form of "VARIABLE___WITHIN". This factor is ordered by the values in each group of WITHIN.
scale_*_reordered removes the "___WITHIN" suffix when plotting the axis labels.
Add scales = "free_y" in facet_wrap to make it work as expected.
Here is an example with generated data:
library(tidyverse)
# Generate data
df <- expand.grid(
year = 2019:2021,
group = paste("Group", toupper(letters[1:8]))
)
set.seed(123)
df$value <- rnorm(nrow(df), mean = 10, sd = 2)
df %>%
mutate(group = tidytext::reorder_within(group, value, within = year)) %>%
ggplot(aes(value, group)) +
geom_point() +
tidytext::scale_y_reordered() +
facet_wrap(vars(year), scales = "free_y")
I'm working now in a statistics project and recently started with R. I have some problems with the visualization. I found a lot of different tutorials about how to add percentage labels in pie charts, but after one hour of trying I still don't get it. Maybe something is different with my data frame so that this doesn't work?
It's a data frame with collected survey answers, so I'm not allowed to publish them here. The column in question (geschäftliche_lage) is a factor with three levels ("Gut", "Befriedigend", "Schlecht"). I want to add percentage labels for each level.
I used the following code in order to create the pie chart:
dataset %>%
ggplot(aes(x= "", fill = geschäftliche_lage)) +
geom_bar(stat= "count", width = 1, color = "white") +
coord_polar("y", start = 0, direction = -1) +
scale_fill_manual(values = c("#00BA38", "#619CFF", "#F8766D")) +
theme_void()
This code gives me the desired pie chart, but without percentage labels. As soon as a I try to add percentage labels, everything is messed up. Do you know a clean code for adding percentage labels?
If you need more information or data, just let me know!
Greetings
Using mtcars as example data. Maybe this what your are looking for:
library(ggplot2)
ggplot(mtcars, aes(x = "", fill = factor(cyl))) +
geom_bar(stat= "count", width = 1, color = "white") +
geom_text(aes(label = scales::percent(..count.. / sum(..count..))), stat = "count", position = position_stack(vjust = .5)) +
coord_polar("y", start = 0, direction = -1) +
scale_fill_manual(values = c("#00BA38", "#619CFF", "#F8766D")) +
theme_void()
Created on 2020-05-25 by the reprex package (v0.3.0)
I created a bar chart using geom_bar with "Group" on the x-axis (Female, Male), and "Values" on the y-axis. Group is further subdivided into "Session" such that there is "Session 1" and "Session 2" for both Male and Female (i.e. four bars in total).
Since all participants participated in Session 1 and 2, I overlayed a dotplot (geom_dot) over each of the four bars, to represent the individual data.
I am now trying to connect the observations for all participants ("PID"), between session 1 and 2. In other words, there should be lines connecting several sets of two-points on the "Male" portion of the x-axis (i.e. per participant), and "Female portion".
I tried this with "geom_line" (below) but to no avail (instead, it created a single vertical line in the middle of "Male" and another in the middle of "Female"). I'm not too sure how to fix this.
See code below:
ggplot(data_foo, aes(x=factor(Group),y=Values, colour = factor(Session), fill = factor(Session))) +
geom_bar(stat = "summary", fun.y = "mean", position = "dodge") +
geom_dotplot(binaxis = "y", stackdir = "center", dotsize = 1.0, position = "dodge", fill = "black") +
geom_line(aes(group = PID), colour="dark grey") +
labs(title='My Data',x='Group',y='Values') +
theme_light()
Sample data (.txt)
data_foo <- readr::read_csv("PID,Group,Session,Values
P1,F,1,14
P2,F,1,13
P3,F,1,16
P4,M,1,18
P5,F,1,20
P6,M,1,27
P7,M,1,19
P8,M,1,11
P9,F,1,28
P10,F,1,20
P11,F,1,24
P12,M,1,10
P1,F,2,26
P2,F,2,21
P3,F,2,19
P4,M,2,13
P5,F,2,26
P6,M,2,15
P7,M,2,23
P8,M,2,23
P9,F,2,30
P10,F,2,21
P11,F,2,11
P12,M,2,19")
The trouble you have is that you want to dodge by several groups. Your geom_line does not know how to split the Group variable by session. Here are two ways to address this problem. Method 1 is probably the most "ggploty way", and a neat way of adding another grouping without making the visualisation too overcrowded. for method 2 you need to change your x variable
1) Use facet
2) Use interaction to split session for each Group. Define levels for the right bar order
I have also used geom_point instead, because geom_dot is more a specific type of histogram.
I would generally recommend to use boxplots for such plots of values like that, because bars are more appropriate for specific measures such as counts.
Method 1: Facets
library(ggplot2)
ggplot(data_foo, aes(x = Session, y = Values, fill = as.character(Session))) +
geom_bar(stat = "summary", fun.y = "mean", position = "dodge") +
geom_line(aes(group = PID)) +
geom_point(aes(group = PID), shape = 21, color = 'black') +
facet_wrap(~Group)
Created on 2020-01-20 by the reprex package (v0.3.0)
Method 2: create an interaction term in your x variable. note that you need to order the factor levels manually.
data_foo <- data_foo %>% mutate(new_x = factor(interaction(Group,Session), levels = c('F.1','F.2','M.1','M.2')))
ggplot(data_foo, aes(x = new_x, y = Values, fill = as.character(Session))) +
geom_bar(stat = "summary", fun.y = "mean", position = "dodge") +
geom_line(aes(group = PID)) +
geom_point(aes(group = PID), shape = 21, color = 'black')
Created on 2020-01-20 by the reprex package (v0.3.0)
But everything gets visually not very compelling.
I suggest doing a few visualization tips to have a more informative chart. For example, I feel like having a differentiation of colors for PID will help us track the changes of each participant for different levels of other variables. Something like:
library(ggplot2)
ggplot(data_foo, aes(x = factor(Session), y = Values, fill = factor(Session))) +
geom_bar(stat = "summary", fun.y = "mean", position = "dodge") +
geom_line(aes(group = factor(PID), colour=factor(PID)), size=2, alpha=0.7) +
geom_point(aes(group = factor(PID), colour=factor(PID)), shape = 21, size=2,show.legend = F) +
theme_bw() +
labs(x='Session',fill='Session',colour='PID')+
theme(legend.position="right") +
facet_wrap(~Group)+
scale_colour_discrete(breaks=paste0('P',1:12))
And we have the following plot:
Hope it helps.
I am trying to get double the space between the groups Automatic and Manual on the x-axis compared to the spaces within these groups. I am using geom_col() and experimted with different arguments, suchs as position_dodge, width and preserve = "single". I can't get this to work. What I am aiming for is a graph such as I have added as an image.
library(ggplot2)
library(ggthemes)
library(plyr)
#dataset
df <- mtcars
df$cyl <- as.factor(df$cyl)
df$am <- as.factor(df$am)
df$am <- revalue(df$am, c("0"="Automatic", "1"="Manual"))
ggplot(df, aes(fill = cyl, x = am, y = mpg)) +
geom_col(position = position_dodge(width = 0.9)) +
theme_bw()
Try using a combination of position=position_dodge(width=...) and width=...
For example:
ggplot(df, aes(fill = cyl, x = am, y = mpg)) +
geom_col(position = position_dodge(width = 0.9), width=0.8) +
theme_bw()
The width() command gives the displayed width of individual bars, while the position(width=) gives the space that is reserved for the bars.
The difference between the two values gives the space between bars within a group, while 1 - position_dodge(width=) gives the space between the groups.
I have a ggplot in which I am using color for my geom_points as a function of one of my columns(my treatment) and then I am using the scale_color_manual to choose the colors.
I automatically get my legend right
The problem is I need to graph some horizontal lines that have to do with the experimental set up, which I am doing with geom_vline, but then I don't know how to manually add a separate legend that doesn't mess with the one I already have and that states what those lines are.
I have the following code
ggplot(dcons.summary, aes(x = meters, y = ymean, color = treatment, shape = treatment)) +
geom_point(size = 4) +
geom_errorbar(aes(ymin = ymin, ymax = ymax)) +
scale_color_manual(values=c("navy","seagreen3"))+
theme_classic() +
geom_vline(xintercept = c(0.23,3.23, 6.23,9.23), color= "bisque3", size=0.4) +
scale_x_continuous(limits = c(-5, 25)) +
labs(title= "Sediment erosion", subtitle= "-5 -> 25 meters; standard deviation; consistent measurements BESE & Control", x= "distance (meters)", y="erosion (cm)", color="Treatment", shape="Treatment")
So I would just need an extra legend beneath the "treatment" one that says "BESE PLOTS LOCATION" and that is related to the gray lines
I have been searching for a solution, I've tried using "scale_linetype_manual" and also "guides", but I'm not getting there
As you provided no reproducible example, I used data from the mtcars dataset.
In addition I modified this similar answer a little bit. As you already specified the color and in addition the fill factor is not working here, you can use the linetype as a second parameter within aes wich can be shown in the legend:
xid <- data.frame(xintercept = c(15,20,30), lty=factor(1))
mtcars %>%
ggplot(aes(mpg ,cyl, col=factor(gear))) +
geom_point() +
geom_vline(data=xid, aes(xintercept=xintercept, lty=lty) , col = "red", size=0.4) +
scale_linetype_manual(values = 1, name="",label="BESE PLOTS LOCATION")
Or without the second data.frame:
ggplot() +
geom_point(data = mtcars,aes(mpg ,cyl, col=factor(gear))) +
geom_vline(aes(xintercept=c(15,20,30), lty=factor(1) ), col = "red", size=0.4)+
scale_linetype_manual(values = 1, name="",label="BESE PLOTS LOCATION")