The data I am working on is a clustering data, with multiple observations within one group, I generated a caterpillar plot and want labelling for each group(zipid), not every line, my current graph and code look like this:
text = hosp_new[,c("zipid")]
ggplot(hosp_new, aes(x = id, y = oe, colour = zipid, shape = group)) +
# theme(panel.grid.major = element_blank()) +
geom_point(size=1) +
scale_shape_manual(values = c(1, 2, 4)) +
geom_errorbar(aes(ymin = low_ci, ymax = high_ci)) +
geom_smooth(method = lm, se = FALSE) +
scale_linetype_manual(values = linetype) +
geom_segment(aes(x = start_id, xend = end_id, y = region_oe, yend = region_oe, linetype = "4", size = 1.2)) +
geom_ribbon(aes(ymin = region_low_ci, ymax = region_high_ci), alpha=0.2, linetype = "blank") +
geom_hline(aes(yintercept = 1, alpha = 0.2, colour = "red", size = 1), show.legend = "FALSE") +
scale_size_identity() +
scale_x_continuous(name = "hospital id", breaks = seq(0,210, by = 10)) +
scale_y_continuous(name = "O:E ratio", breaks = seq(0,7, by = 1)) +
geom_text(aes(label = text), position = position_stack(vjust = 10.0), size = 2)
Caterpillar plot:
Each color represents a region, I just want one label/per region, but don't know how to delete the duplicated labels in this graph.
Any idea?
The key is to have geom_text return only one value for each zipid, rather than multiple values. If we want each zipid label located in the middle of its group, then we can use the average value of id as the x-coordinate for each label. In the code below, we use stat_summaryh (from the ggstance package) to calculate that average id value for the x-coordinate of the label and return a single label for each zipid.
library(ggplot2)
theme_set(theme_bw())
library(ggstance)
# Fake data
set.seed(300)
dat = data.frame(id=1:100, y=cumsum(rnorm(100)),
zipid=rep(LETTERS[1:10], c(10, 5, 20, 8, 7, 12, 7, 10, 13,8)))
ggplot(dat, aes(id, y, colour=zipid)) +
geom_segment(aes(xend=id, yend=0)) +
stat_summaryh(fun.x=mean, aes(label=zipid, y=1.02*max(y)), geom="text") +
guides(colour=FALSE)
You could also use faceting, as mentioned by #user20650. In the code below, panel.spacing.x=unit(0,'pt') removes the space between facet panels, while expand=c(0,0.5) adds 0.5 units of padding on the sides of each panel. Together, these ensure constant spacing between tick marks, even across facets.
ggplot(dat, aes(id, y, colour=zipid)) +
geom_segment(aes(xend=id, yend=0)) +
facet_grid(. ~ zipid, scales="free_x", space="free_x") +
guides(colour=FALSE) +
theme_classic() +
scale_x_continuous(breaks=0:nrow(dat),
labels=c(rbind(seq(0,100,5),'','','',''))[1:(nrow(dat)+1)],
expand=c(0,0.5)) +
theme(panel.spacing.x = unit(0,"pt"))
Related
I conducted some interviews and I wanted to create box plots with ggplot based on these interviews. I managed to create the box plots but I do not manage to include the outliers in the box plot. I have only a few observations and therefore I want the outliers to be part of the box plot.
This is the code that I have so far:
data_insurances_boxplot_merged <- ggplot(data_insurances_merged, aes(x = value, y = func, fill = group)) +
stat_boxplot(geom = "errorbar", width = 0.3, position = position_dodge(width = 0.75)) +
geom_boxplot() +
stat_summary(fun.y = mean, geom = "point", shape = 20, size = 3, color = "red",
position = position_dodge2(width = 0.75,
preserve = "single")) +
scale_x_continuous(breaks = seq(1, 7, 1), limits = c(1, 7)) +
scale_fill_manual(values = c("#E6645E", "#EF9C9D")) +
labs(x = "",
y = "", title = "") +
theme_light(base_size = 12) +
theme(legend.title = element_blank())
data_insurances_boxplot_merged
And this is the box plot that is generated:
Does anyone know how to achieve this?
I'm trying to create a plot like this image below where the individual data lines are in between the box plots. Image to create in R ggplot2
The closest I am getting is something like this:
Image using ggplot2 but it looks a bit cluttered with the lines/points behind.
data1 %>%
ggplot(aes(Time,Trait)) +
geom_line(aes(group=ID), position = "identity")+
geom_point(aes(group=ID), shape=21, colour="black", size=2, position = "identity")+
geom_boxplot(width=.5,position = position_dodge(width=0.9), fill="white") +
stat_summary(fun.data= mean_cl_boot, geom = "errorbar", width = 0.1, position = position_dodge(width = .9)) +
stat_summary(fun = mean, geom = "point", shape = 18, size=3, position = "identity")+
facet_wrap(~Cond) +
theme_classic()
Any tips would be greatly appreciated!
One option to achieve your desired result would be to make use of continuous x scale. Doing so makes it possible to shift the box plots to the left or to right and vice versa for the points and lines:
Making use of some random data to mimic your real data set.
data1$Time1 <- as.numeric(factor(data1$Time, levels = c("Pre", "Post")))
data1$Time_box <- data1$Time1 + .1 * ifelse(data1$Time == "Pre", -1, 1)
data1$Time_lp <- data1$Time1 + .1 * ifelse(data1$Time == "Pre", 1, -1)
library(ggplot2)
ggplot(data1, aes(x = Time_box, y = Trait)) +
geom_line(aes(x = Time_lp, group=ID), position = "identity")+
geom_point(aes(x = Time_lp, group=ID), shape=21, colour="black", size=2, position = "identity")+
geom_boxplot(aes(x = Time_box, group=Time1), width=.25, fill="white") +
stat_summary(fun.data = mean_cl_boot, geom = "errorbar", width = 0.1) +
stat_summary(fun = mean, geom = "point", shape = 18, size=3, position = "identity") +
scale_x_continuous(breaks = c(1, 2), labels = c("Pre", "Post")) +
facet_wrap(~Cond) +
theme_classic()
DATA
set.seed(42)
data1 <- data.frame(
ID = rep(1:10, 4),
Time = rep(c("Pre", "Post"), each = 10),
Trait = runif(40),
Cond = rep(c("MBSR", "SME"), each = 20)
)
EDIT If you want to two boxplots side by side it's basically the same. However in that case you have to map the interaction of Time1 and the variable mapped on fill on the group aesthetic in geom_boxplot (and probably the error bars as well):
library(ggplot2)
set.seed(42)
data1 <- data.frame(
ID = rep(1:10, 4),
Time = rep(c("Pre", "Post"), each = 10),
Fill = rep(c("Fill1", "Fill2"), each = 5),
Trait = runif(40),
Cond = rep(c("MBSR", "SME"), each = 20)
)
ggplot(data1, aes(x = Time_box, y = Trait)) +
geom_line(aes(x = Time_lp, group=ID, color = Fill), position = "identity")+
geom_point(aes(x = Time_lp, group=ID, fill = Fill), shape=21, colour="black", size=2, position = "identity")+
geom_boxplot(aes(x = Time_box, group=interaction(Time1, Fill) , fill = Fill), width=.25) +
stat_summary(fun.data = mean_cl_boot, geom = "errorbar", width = 0.1) +
stat_summary(fun = mean, geom = "point", shape = 18, size=3, position = "identity") +
scale_x_continuous(breaks = c(1, 2), labels = c("Pre", "Post")) +
facet_wrap(~Cond) +
theme_classic()
I'm having a hard time dealing with this plot.
The height of values in ANI>96 making it hard to read the red and blue percentage text.
I failed to break the y-axis by looking at answers from other posts in StackOverflow.
Any suggestions?
Thanks.
library(data.table)
library(ggplot2)
dt <- data.table("ANI"= sort(c(seq(79,99),seq(79,99))), "n_pairs" = c(5, 55, 13, 4366, 6692, 59568, 382873, 397996, 1104955, 282915,
759579, 261170, 312989, 48423, 120574, 187685, 353819, 79468, 218039, 66314, 41826, 57668, 112960, 81652, 28613,
64656, 21939, 113656, 170578, 238967, 610234, 231853, 1412303, 5567, 4607268, 5, 14631942, 0, 17054678, 0, 3503846, 0),
"same/diff" = rep(c("yes","no"), 21))
for (i in 1:nrow(dt)) {
if (i%%2==0) {
next
}
total <- dt$n_pairs[i] + dt$n_pairs[i+1]
dt$total[i] <- total
dt$percent[i] <- paste0(round(dt$n_pairs[i]/total *100,2), "%")
dt$total[i+1] <- total
dt$percent[i+1] <- paste0(round(dt$n_pairs[i+1]/total *100,2), "%")
}
ggplot(data=dt, aes(x=ANI, y=n_pairs, fill=`same/diff`)) +
geom_text(aes(label=percent), position=position_dodge(width=0.9), hjust=0.75, vjust=-0.25) +
geom_bar(stat="identity") + scale_x_continuous(breaks = dt$ANI) +
labs(x ="ANI", y = "Number of pairs", fill = "Share one common species taxonomy?") +
theme_classic() + theme(legend.position="bottom")
Here is the list of major changes I made:
I reduced the y axis by zooming into the chart with coord_cartesian (which is called by coord_flip).
coord_flip shouuld also improve the readability of the chart by switching x and y. I don't know if the switch is a desirable output for you.
Also now position_dodge, works as expected: two bars next to each other with the labels on top (on the left in this case).
I set geom_bar before geom_text so that the text is always in front of the bars in the chart.
I set scale_y_continuous to change the labels of the y axis (in the chart the x axis because of the switch) to improve the readability of the zeros.
ggplot(data=dt, aes(x = ANI, y = n_pairs, fill = `same/diff`)) +
geom_bar(stat = "identity", position = position_dodge2(width = 1), width = 0.8) +
geom_text(aes(label = percent), position = position_dodge2(width = 1), hjust = 0, size = 3) +
scale_x_continuous(breaks = dt$ANI) +
scale_y_continuous(labels = scales::comma) +
labs(x ="ANI", y = "Number of pairs", fill = "Share one common species taxonomy?") +
theme_classic() +
theme(legend.position = "bottom") +
coord_flip(ylim = c(0, 2e6))
EDIT
Like this columns and labels are stacked but labels never overlap.
ggplot(data=dt, aes(x = ANI, y = n_pairs, fill = `same/diff`)) +
geom_bar(stat = "identity", width = 0.8) +
geom_text(aes(label = percent,
hjust = ifelse(`same/diff` == "yes", 1, 0)),
position = "stack", size = 3) +
scale_x_continuous(breaks = dt$ANI) +
scale_y_continuous(labels = scales::comma) +
labs(x ="ANI", y = "Number of pairs", fill = "Share one common species taxonomy?") +
theme_classic() +
theme(legend.position = "bottom") +
coord_flip(ylim = c(0, 2e6))
Alternatively, you can avoid labels overlapping with check_overlap = TRUE, but sometimes one of the labels will not be shown.
ggplot(data=dt, aes(x = ANI, y = n_pairs, fill = `same/diff`)) +
geom_bar(stat = "identity", width = 0.8) +
geom_text(aes(label = percent), hjust = 1, position = "stack", size = 3, check_overlap = TRUE) +
scale_x_continuous(breaks = dt$ANI) +
scale_y_continuous(labels = scales::comma) +
labs(x ="ANI", y = "Number of pairs", fill = "Share one common species taxonomy?") +
theme_classic() +
theme(legend.position = "bottom") +
coord_flip(ylim = c(0, 2e6))
I have a graph made in ggplot that looks like this:
I wish to have the numeric labels at each of the bars to be grounded/glued to the x axis where y <= 0.
This is the code to generate the graph as such:
ggplot(data=df) +
geom_bar(aes(x=row, y=numofpics, fill = crop, group = 1), stat='identity') +
geom_point(data=df, aes(x = df$row, y=df$numofparcels*50, group = 2), alpha = 0.25) +
geom_line(data=df, aes(x = df$row, y=df$numofparcels*50, group = 2), alpha = 0.25) +
geom_text(aes(x=row, y=numofpics, label=bbch)) +
geom_hline(yintercept=300, linetype="dashed", color = "red", size=1) +
scale_y_continuous(sec.axis= sec_axis(~./50, name="Number of Parcels")) +
scale_x_discrete(name = c(),breaks = unique(df$crop), labels = as.character(unique(df$crop)))+
labs(x=c(), y="Number of Pictures")
I've tried vjust and experimenting with position_nudge for the geom_text element, but every solution I can find changes the position of each element of the geom_text respective to its current position. As such everything I try results in situation like this one:
How can I make ggplot ground the text to the bottom of the x axis where y <= 0, possibly with the possibility to also introduce a angle = 45?
Link to dataframe = https://drive.google.com/file/d/1b-5AfBECap3TZjlpLhl1m3v74Lept2em/view?usp=sharing
As I said in the comments, just set the y-coordinate of the text to 0 or below, and specify the angle : geom_text(aes(x=row, y=-100, label=bbch), angle=45)
I'm behind a proxy server that blocks connections to google drive so I can't access your data. I'm not able to test this, but I would introduce a new label field in my dataset that sets y to be 0 if y<0:
df <- df %>%
mutate(labelField = if_else(numofpics<0, 0, numofpics)
I would then use this label field in my geom_text call:
geom_text(aes(x=row, y=labelField, label=bbch), angle = 45)
Hope that helps.
You can simply define the y-value in geom_text (e.g. -50)
ggplot(data=df) +
geom_bar(aes(x=row, y=numofpics, fill = crop, group = 1), stat='identity') +
geom_point(data=df, aes(x = df$row, y=df$numofparcels*50, group = 2), alpha = 0.25) +
geom_line(data=df, aes(x = df$row, y=df$numofparcels*50, group = 2), alpha = 0.25) +
geom_text(aes(x=row, y=-50, label=bbch)) +
geom_hline(yintercept=300, linetype="dashed", color = "red", size=1) +
scale_y_continuous(sec.axis= sec_axis(~./50, name="Number of Parcels")) +
scale_x_discrete(name = c(),breaks = unique(df$crop), labels =
as.character(unique(df$crop)))+
labs(x=c(), y="Number of Pictures")
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")