For my data the average normally lies between 8,000 and 10,000 and I want to indicate this range on my bar chart below,
I want to show to red lines from y=10,000 and y=8,000 and potentially shade the area in between them, if possible.
Bar chart attachted
Monthly_accidents2 %>%
ggplot(aes(x=Month,y=Traffic_Accidents))+
geom_bar(stat ="identity",fill = "#97B3C6")+
geom_text(aes(label = Traffic_Accidents), vjust = 0.5, colour = "white")+
ylim(0,12000)+
#coord_flip()+
theme_dark()+
labs(x=NULL,
y="Number of traffic accidents",
title = " Traffic Accidents throughout the year")
Thanks for any possible help in advance.
I tried creating a data set and adding the two lines but it didn't work.
For adding the two lines, 'geom_hline' makes it very straightforward. And for the shaded area, you can use 'geom_rect', though I'm guessing your 'Month' variable is factor so it requires a little bit of faffing to convert it to numeric first then adjust so the shaded area covers all of your bars :)
You didn't provide any sample data so I haven't run this but it should work.
Monthly_accidents2 %>%
ggplot(aes(x=Month,y=Traffic_Accidents))+
geom_bar(stat ="identity",fill = "#97B3C6")+
geom_text(aes(label = Traffic_Accidents), vjust = 0.5, colour = "white")+
ylim(0,12000)+
geom_hline(yintercept = c(8000, 10000), colour = 'red')+
geom_rect(aes(xmin = min(as.integer(Monthly_accidents2$Month)) - 0.5,
xmax = max(as.integer(Monthly_accidents2$Month)) + 0.5,
ymin = 8000, ymax = 10000), alpha = 0.2, fill = 'darkred')+
#coord_flip()+
theme_dark()+
labs(x=NULL,
y="Number of traffic accidents",
title = " Traffic Accidents throughout the year")
One option to achieve your desired result would be to use geom_hline to add some horizontal lines and annotate to add a shaded rectangle:
Using some fake example data:
Monthly_accidents2 <- data.frame(
Month = factor(month.abb, month.abb),
Traffic_Accidents = 1000 * seq_len(12)
)
library(ggplot2)
base <- ggplot(Monthly_accidents2, aes(x = Month, y = Traffic_Accidents)) +
geom_col(fill = "#97B3C6") +
geom_text(aes(label = Traffic_Accidents), vjust = 0.5, colour = "white") +
ylim(0, 12000) +
theme_dark() +
labs(
x = NULL,
y = "Number of traffic accidents",
title = "Traffic Accidents throughout the year"
) +
theme(plot.title = element_text(hjust = .5))
base +
geom_hline(yintercept = c(8000, 10000), color = "red") +
annotate(geom = "rect", ymin = 8000, ymax = 10000, xmin = -Inf, xmax = Inf, fill = "red", alpha = .2)
add the following to your plot:
+
geom_hline(aes(yintercept = c(8000, 10000), color = "red"))
For the lines
edit:
See stefan's more complete answer.
Related
I am trying to create 3 layers of rectangles each with different color on top of each other to get something like below image:
Data:
library(tidyverse)
df_vaccination <- data.frame(type = c('Population', 'First.Dose.Administered', 'Second.Dose.Administered'),
count = c(1366400000, 952457943, 734608556))
Code tried:
df_vaccination %>%
ggplot()+
geom_rect(aes(xmin = 0, ymin = 0, xmax = count, ymax = 0,
size = 10, lineend = 'round',
alpha = 0.5, fill = type)) +
scale_fill_manual(values = c("#d8b365", "orange", "#5ab4ac")) +
theme_clean() +
scale_x_continuous(labels = unit_format(scale = 1e-7, unit = "Cr")) +
guides(color = guide_legend(order = 1),
size = FALSE,
alpha = FALSE)
Result I am getting is blank plot when I am using geom_rect() & scale_fill_manual(). I am not sure why am I getting blank rectangle:
Convert type column to ordered factor so that largest number plots first, then use geom_col with x = 1. This will make the bars to plot on top of each other, lastly flip the coordinates:
df_vaccination$type <- factor(df_vaccination$type, levels = df_vaccination$type)
ggplot(df_vaccination, aes(x = 1, y = count, fill = type))+
geom_col() +
scale_fill_manual(values = c("#d8b365", "orange", "#5ab4ac")) +
coord_flip() +
theme_void()
I would like to create a raincloud plot. I have successfully done it. But I would like to know if instead of the density curve, I can put a histogram (it's better for my dataset).
This is my code if it can be usefull
ATSC <- ggplot(data = data, aes(y = atsc, x = numlecteur, fill = numlecteur)) +
geom_flat_violin(position = position_nudge(x = .2, y = 0), alpha = .5) +
geom_point(aes(y = atsc, color = numlecteur), position = position_jitter(width = .15), size = .5, alpha = 0.8) +
geom_point(data = sumld, aes(x = numlecteur, y = mean), position = position_nudge(x = 0.25), size = 2.5) +
geom_errorbar(data = sumld, aes(ymin = lower, ymax = upper, y = mean), position = position_nudge(x = 0.25), width = 0) +
guides(fill = FALSE) +
guides(color = FALSE) +
scale_color_brewer(palette = "Spectral") +
scale_y_continuous(breaks=c(0,2,4,6,8,10), labels=c("0","2","4","6","8","10"))+
scale_fill_brewer(palette = "Spectral") +
coord_flip() +
theme_bw() +
expand_limits(y=c(0, 10))+
xlab("Lecteur") + ylab("Age total sans check")+
raincloud_theme
I think we can maybe put the "geom_histogram()" but it doesn't work
Thank you in advance for your help !
(sources : https://peerj.com/preprints/27137v1.pdf
https://neuroconscience.wordpress.com/2018/03/15/introducing-raincloud-plots/)
This is actually not quite easy. There are a few challenges.
geom_histogram is "horizontal by nature", and the custom geom_flat_violin is vertical - as are boxplots. Therefore the final call to coord_flip in that tutorial. In order to combine both, I think best is switch x and y, forget about coord_flip, and use ggstance::geom_boxploth instead.
Creating separate histograms for each category is another challenge. My workaround to create facets and "merge them together".
The histograms are scaled way bigger than the width of the points/boxplots. My workaround scale via after_stat function.
How to nudge the histograms to the right position above Boxplot and points - I am converting the discrete scale to a continuous by mapping a constant numeric to the global y aesthetic, and then using the facet labels for discrete labels.
library(tidyverse)
my_data<-read.csv("https://data.bris.ac.uk/datasets/112g2vkxomjoo1l26vjmvnlexj/2016.08.14_AnxietyPaper_Data%20Sheet.csv")
my_datal <-
my_data %>%
pivot_longer(cols = c("AngerUH", "DisgustUH", "FearUH", "HappyUH"), names_to = "EmotionCondition", values_to = "Sensitivity")
# use y = -... to position boxplot and jitterplot below the histogram
ggplot(data = my_datal, aes(x = Sensitivity, y = -.5, fill = EmotionCondition)) +
# after_stat for scaling
geom_histogram(aes(y = after_stat(count/100)), binwidth = .05, alpha = .8) +
# from ggstance
ggstance::geom_boxploth( width = .1, outlier.shape = NA, alpha = 0.5) +
geom_point(aes(color = EmotionCondition), position = position_jitter(width = .15), size = .5, alpha = 0.8) +
# merged those calls to one
guides(fill = FALSE, color = FALSE) +
# scale_y_continuous(breaks = 1, labels = unique(my_datal$EmotionCondition))
scale_color_brewer(palette = "Spectral") +
scale_fill_brewer(palette = "Spectral") +
# facetting, because each histogram needs its own y
# strip position = left to fake discrete labels in continuous scale
facet_wrap(~EmotionCondition, nrow = 4, scales = "free_y" , strip.position = "left") +
# remove all continuous labels from the y axis
theme(axis.title.y = element_blank(), axis.text.y = element_blank(),
axis.ticks.y = element_blank())
Created on 2021-04-15 by the reprex package (v1.0.0)
I'm having a problem with gganimate where it does not fill the geom_sf points I am using all of the time. A static version of the plot I'm using works fine:
precincts$margingroup <- cut(precincts$margin,
breaks = breaks, labels = c(1:37))
pointfig <- ggmap(myMap) +
geom_sf(data=centroids, aes(fill=precincts$margin,group=precincts$margingroup), size=precincts$dotsize, pch=21, alpha=1, inherit.aes = FALSE) +
scale_fill_gradient2(midpoint = 0, low='darkmagenta',
min = 'white',
high='orange',
limits = c(-50,50),
oob = scales::squish) +
geom_shadowtext(mapping = aes(x = longitude, y = latitude, label = name, vjust=vjust),
data = places, size = 5, fontface = "bold") +
labs(fill='Fidesz Margin, %',caption = "Data from valasztas.hu") +
ggtitle("Borsod-Abaúj-Zemplén 6th District By-Election by Precinct") +
theme_void() +
theme(legend.box.just = "center") +
theme(plot.title = element_text(size = 12, face = "bold", vjust=4)) +
theme(plot.margin = unit(c(1,1,1,1), "cm")) +
coord_sf()
But when I try to animate it I get an issue with fill. I want the points to appear in order of margin (but I had to group them so that there were fewer than 50 states).
anim <- pointfig + transition_states(precincts$margingroup,
transition_length = 1,
state_length = 0.45, wrap = FALSE) +
shadow_wake(wake_length = 0.1, alpha = TRUE)
This results in (small version for SO but you get the idea!):
The colours which appear towards the end are whole the whole thing should look. Any help would be hugely appreciated, thanks!
I am making a set of scorecards where I am generating a set of graphs that show the distribution of responses from a survey and also where the response for a specific company falls. I need to modify the formatting of a graph, a stacked barchart, and add a few features I’ve outlined below. I’ve already spent a few hours getting my chart to where it is now and would appreciate your help with the features I outline below.
Data is
Data<-data.frame(Reviewed = c("Annually", "Annually", "Hourly", "Monthly", "Weekly","Monthly","Weekly","Other","Other","Monthly","Weekly"),Company=c("a","b","c","d","e","f","g","h","i","j","k"),Question="Q1")
So far I’ve developed this
ggplot(Data, aes(x="Question", fill=Reviewed)) + geom_bar(position='fill' ) +
coord_flip()
I would like to do the following:
Order the variables so they are arranged on plot as follows: Annually,Monthly,Weekly,Hourly,Other
Express the y axis in terms of percent. I.e. 0.25 turns into 25%
Move y-axis directly underneath the bar.
Remove the legend but move the terms underneath the respective part of the graph on a diagonal slant.
Add a black line that cuts down the 50% mark
Add a dot in at the midpoint of the stack for the value of company “e”.
Remove gray background
This is what I'm hoping the finished graph will look like.
There's a lot to unpack here, so I'll break it down bit by bit:
Order the variables so they are arranged on plot as follows: Annually,Monthly,Weekly,Hourly,Other
Assign "Reviewed" as an ordered factor. I'm reversing the order here since it wants to plot the "lowest" factor first (to the left).
Data$Reviewed <- factor(Data$Reviewed,
levels = rev(c('Annually', 'Monthly', 'Weekly', 'Hourly', 'Other')),
ordered = T)
ggplot(Data, aes(x="Question", fill=Reviewed)) + geom_bar(position='fill' ) +
coord_flip()
Express the y axis in terms of percent. I.e. 0.25 turns into 25%
Use scale_y_continuous(labels = scales::percent) to adjust the labels. I believe that the scales was pulled in when you installed ggplot2.
ggplot(Data, aes(x="Question", fill=Reviewed)) +
geom_bar(position = 'fill') +
scale_y_continuous(labels = scales::percent) +
coord_flip()
Move y-axis directly underneath the bar.
Remove gray background
These are done all at once by adding expand = F to coord_flip.
ggplot(Data, aes(x="Question", fill=Reviewed)) +
geom_bar(position = 'fill') +
scale_y_continuous(labels = scales::percent) +
coord_flip(expand = F)
Remove the legend...
Add theme(legend.position = 'none').
ggplot(Data, aes(x="Question", fill=Reviewed)) +
geom_bar(position = 'fill') +
scale_y_continuous(labels = scales::percent) +
coord_flip(expand = F) +
theme(legend.position = 'none')
but move the terms underneath the respective part of the graph on a diagonal slant.
This is tougher and takes a good amount of fiddling.
Use geom_text to make the labels
Calculate the position along the bar using the 'count' stat
Move the labels to the bottom of the plot by providing a fake x coordinate
Align the labels in the center of the bars using position_stack, and make them abut the x axis using hjust.
Add angle.
Use clip = 'off' in coord_flip to make sure that these values are not cut out since they're outside the plotting area.
Fiddle with the x limits to crop out empty plotting area.
Adjust the plot margin in theme to make sure everything can be seen.
ggplot(Data, aes(x="Question", fill=Reviewed)) +
geom_bar(position = 'fill') +
geom_text(aes(label = Reviewed, x = 0.45,
y = stat(..count../sum(..count..))), stat = 'count',
position = position_stack(0.5),
hjust = 0,
angle = 45) +
scale_y_continuous(labels = scales::percent) +
coord_flip(xlim = c(0.555, 1.4), clip = 'off',expand = F) +
theme(plot.margin = margin(0, 0, 35, 10),
legend.position = 'none')
Add a black line that cuts down the 50% mark
Use geom_hline(yintercept = 0.5); remember that it's a "horizontal" line since the coordinates are flipped.
ggplot(Data, aes(x="Question", fill=Reviewed)) +
geom_bar(position = 'fill') +
geom_text(aes(label = Reviewed, x = 0.45,
y = stat(..count../sum(..count..))), stat = 'count',
position = position_stack(0.5),
hjust = 0,
angle = 45) +
geom_hline(yintercept = 0.5) +
scale_y_continuous(labels = scales::percent) +
coord_flip(xlim = c(0.555, 1.4), clip = 'off',expand = F) +
theme(plot.margin = margin(0, 0, 20, 10),
legend.position = 'none')
Add a dot in at the midpoint of the stack for the value of company “e”.
This is pretty hack-y. Using the same y values as in geom_text, use geom_point to plot a point for every value of Reviewed, then use position_stack(0.5) to nudge them to the center of the bar. Then use scale_color_manual to only color "Weekly" values (which is the corresponding value of Reviewed for Company "e"). I'm sure there's a way to do this more programmatically.
ggplot(Data, aes(x="Question", fill=Reviewed)) +
geom_bar(position = 'fill') +
geom_text(aes(label = Reviewed, x = 0.45,
y = stat(..count../sum(..count..))), stat = 'count',
position = position_stack(0.5),
hjust = 0,
angle = 45) +
geom_hline(yintercept = 0.5) +
geom_point(aes(y = stat(..count../sum(..count..)),
color = Reviewed), stat = 'count',
position = position_stack(0.5), size = 5) +
scale_color_manual(values = 'black', limits = 'Weekly') +
scale_y_continuous(labels = scales::percent) +
coord_flip(xlim = c(0.555, 1.4), clip = 'off',expand = F) +
theme(plot.margin = margin(0, 0, 20, 10),
legend.position = 'none')
This is what I'm hoping the finished graph will look like.
Prettying things up:
ggplot(Data, aes(x="Question", fill = Reviewed)) +
geom_bar(position = 'fill') +
geom_text(aes(label = Reviewed, x = 0.45,
y = stat(..count../sum(..count..))), stat = 'count',
position = position_stack(0.5),
hjust = 0,
angle = 45) +
geom_hline(yintercept = 0.5) +
geom_point(aes(y = stat(..count../sum(..count..)),
color = Reviewed), stat = 'count',
position = position_stack(0.5), size = 5) +
scale_color_manual(values = 'black', limits = 'Weekly') +
scale_y_continuous(labels = scales::percent) +
coord_flip(xlim = c(0.555, 1.4), clip = 'off', expand = F) +
labs(x = NULL, y = NULL) +
theme_minimal() +
theme(plot.margin = margin(0, 0, 35, 10),
legend.position = 'none')
I wanted to comment on the following doubt.
Using this code:
Plot<-data.frame(Age=c(0,0,0,0,0),Density=c(0,0,0,0,0),Sensitivity=c(0,0,0,0,0),inf=c(0,0,0,0,0),sup=c(0,0,0,0,0),tde=c(0,0,0,0,0))
Plot[1,]<-c(1,1,0.857,0.793,0.904,0.00209834)
Plot[2,]<-c(1,2,0.771 ,0.74,0.799,0.00348286)
Plot[3,]<-c(1,3,0.763 ,0.717,0.804,0.00577784)
Plot[4,]<-c(1,4,0.724 ,0.653,0.785,0.00504161)
Plot[5,]<-c(2,1,0.906,0.866,0.934,0.00365742)
Plot[6,]<-c(2,2,0.785 ,0.754,0.813,0.00440399)
Plot[7,]<-c(2,3,0.660,0.593,0.722,0.00542849)
Plot[8,]<-c(2,4,0.544,0.425,0.658,0.00433052)
names(Plot)<-c("Age","Mammographyc density","Sensitivity","inf","sup","tde")
Plot$Age<-c("50-59","50-59","50-59","50-59","60-69","60-69","60-69","60-69")
Plot$Density<-c("Almost entirely fat","Scattered fibroglandular density","Heterogeneously dense","Extremely dense","Almost entirely fat","Scattered fibroglandular density","Heterogeneously dense","Extremely dense")
levels(Plot$Age)<-c("50-59","60-69")
levels(Plot$Density)<-c("Almost entirely fat","Scattered fibroglandular density","Heterogeneously dense","Extremely dense")
pd <- position_dodge(0.2) #
Plot$Density <- reorder(Plot$Density, 1-Plot$Sensitivity)
ggplot(Plot, aes(x = Density, y = 100*Sensitivity, colour=Age)) +
geom_errorbar(aes(ymin = 100*inf, ymax = 100*sup), width = .1, position = pd) +
geom_line(position = pd, aes(group = Age), linetype = c("dashed")) +
geom_point(position = pd, size = 4)+
scale_y_continuous(expand = c(0, 0),name = 'Sensitivity (%)',sec.axis = sec_axis(~./5, name = 'Breast cancer detection rate (per 1000 mammograms)', breaks = c(0,5,10,15,20),
labels = c('0‰',"5‰", '10‰', '15‰', '20‰')), limits = c(0,100)) +
geom_line(position = pd, aes(x = Density, y = tde * 5000, colour = Age, group = Age), linetype = c("dashed"), data = Plot) +
geom_point(shape=18,aes(x = Density, y = tde * 5000, colour = Age, group = Age), position = pd, size = 4) +
theme_light() +
scale_color_manual(name="Age (years)",values = c("50-59"= "grey55", "60-69" = "grey15")) +
theme(legend.position="bottom") + guides(colour = guide_legend(), size = guide_legend(),
shape = guide_legend())
I have made the following graph,
in which the axis on the left is the scale of the circles and the axis on the right is the scale of the diamonds. The fact is that I would like to have a legend approximately like this:
But it is impossible for me, I have tried suggestions of other threads like scale_shape and different commands in guides but I have not got success. I just want to make clear the difference in what shape and color represent.
Would someone know how to help me?
Best regards,
What you should do is a panel plot to avoid the confusion of double axes:
library(dplyr)
library(tidyr)
Plot %>%
gather(measure, Result, Sensitivity, tde) %>%
ggplot(aes(x = Density, y = Result, colour=Age)) +
geom_errorbar(aes(ymin = inf, ymax = sup), width = .1, position = pd,
data = . %>% filter(measure == "Sensitivity")) +
geom_line(aes(group = Age), position = pd, linetype = "dashed") +
geom_point(position = pd, size = 4)+
# scale_y_continuous(expand = c(0, 0), limits = c(0, 1)) +
scale_y_continuous(labels = scales::percent) +
facet_wrap(~measure, ncol = 1, scales = "free_y") +
theme_light() +
scale_color_manual(name="Age (years)",values = c("50-59"= "grey55", "60-69" = "grey15")) +
theme(legend.position="bottom")
But to do what you asked, you problem is that you have only 1 non-positional aesthetic mapped so you cannot get more than one legend. To force a second legend, you need to add a second mapping. It can be a dummy mapping that has no effect, as below we map alpha but then manually scale both levels to 100%. This solution is not advisable because, as you have done in your example of a desired legend, it is easy to mix up the mappings and have your viz tell a lie by mislabeling which points are sensitivity and which are detection rate.
ggplot(Plot, aes(x = Density, y = 100*Sensitivity, colour=Age, alpha = Age)) +
geom_errorbar(aes(ymin = 100*inf, ymax = 100*sup), width = .1, position = pd) +
geom_line(position = pd, aes(group = Age), linetype = c("dashed")) +
geom_point(position = pd, size = 4)+
scale_y_continuous(expand = c(0, 0),name = 'Sensitivity (%)',sec.axis = sec_axis(~./5, name = 'Breast cancer detection rate (per 1000 mammograms)', breaks = c(0,5,10,15,20),
labels = c('0‰',"5‰", '10‰', '15‰', '20‰')), limits = c(0,100)) +
geom_line(position = pd, aes(x = Density, y = tde * 5000, colour = Age, group = Age), linetype = c("dashed"), data = Plot) +
geom_point(shape=18,aes(x = Density, y = tde * 5000, colour = Age, group = Age), position = pd, size = 4) +
theme_light() +
scale_color_manual(name="Age (years)",values = c("50-59"= "grey55", "60-69" = "grey15")) +
scale_alpha_manual(values = c(1, 1)) +
guides(alpha = guide_legend("Sensitivity"),
color = guide_legend("Detection Rate", override.aes = list(shape = 18))) +
theme(legend.position="bottom")