I am trying to create a swimlane plot of different subjects doses over time. When I run my code the bars are stacked by amount of dose. My issue is that subjects doses vary they could have 5, 10 , 5 in my plot the 5's are stacked together. But I want the represented as they happen over time. In my data set I have the amount of time each patient was on a dose for ordered by when they had the dose. I want by bars stacked by ordering variable called "p" which is numeric is goes 1,2,3,4,5,6 etc which what visit the subject had that dose.
ggplot(dataset,aes(x=diff+1, y=subject)) +
geom_bar(stat="identity", aes(fill=as.factor(EXDOSE))) +
scale_fill_manual(values = dosecol, name="Actual Dose in mg")
I want the bars stacked by my variable "p" not by fill
I tried forcats but that does not work. Unsure how to go about this the data in the dataset is arranged by p for each subject
example data
dataset <- data.frame(subject = c("1002", "1002", "1002", "1002", "1034","1034","1034","1034"),
exdose = c(5,10,20,5,5,10,20,20),
p= c(1,2,3,4,1,2,3,4),
diff = c(3,3,9,7,3,3,4,5)
)
ggplot(dataset,aes(x=diff+1, y=subject)) +
geom_bar(stat="identity", aes(fill=as.factor(exdose)),position ="stack") +
scale_fill_manual(values = dosecol, name="Actual Dose in mg")
If you want to order your stacked bar chart by p you have to tell ggplot2 to do so by mapping p on the group aesthetic. Otherwise ggplot2 will make a guess which by default is based on the categorical variables mapped on any aesthetic, i.e. in your case the fill aes:
Note: I dropped the scale_fill_manual as you did not provide the vector of colors. But that's not important for the issue.
library(ggplot2)
ggplot(dataset, aes(x = diff + 1, y = subject, group = p)) +
geom_col(aes(fill = as.factor(exdose)))
EDIT And to get the right order we have to reverse the order of the stack which could be achieved using position_stack(reverse = TRUE):
Note: To check that we have the right order I added a geom_text showing the p value.
ggplot(dataset, aes(x = diff + 1, y = subject, group = p)) +
geom_col(aes(fill = as.factor(exdose)), position = position_stack(reverse = TRUE)) +
geom_text(aes(label = p), position = position_stack(reverse = TRUE))
Second option would be to convert p to a factor which the order of levels set in the reverse order:
ggplot(dataset, aes(x = diff + 1, y = subject, group = factor(p, rev(sort(unique(p)))))) +
geom_col(aes(fill = as.factor(exdose))) +
geom_text(aes(label = p), position = "stack")
I am trying to create a picture that summarises my data. Data is about prevalence of drug use obtained from different practices form different countries. Each practice has contributed with a different amount of data and I want to show all of this in my picture.
Here is a subset of the data to work on:
gr<-data.frame(matrix(0,36))
gr$drug<-c("a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","a","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b","b")
gr$practice<-c("a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r","a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r")
gr$country<-c("c1","c1","c1","c1","c1","c1","c1","c1","c1","c1","c2","c2","c2","c2","c2","c2","c3","c3","c1","c1","c1","c1","c1","c1","c1","c1","c1","c1","c2","c2","c2","c2","c2","c2","c3","c3")
gr$prevalence<-c(9.14,5.53,16.74,1.93,8.51,14.96,18.90,11.18,15.00,20.10,24.56,22.29,19.41,20.25,25.01,25.87,29.33,20.76,18.94,24.60,26.51,13.37,23.84,21.82,23.69,20.56,30.53,16.66,28.71,23.83,21.16,24.66,26.42,27.38,32.46,25.34)
gr$prop<-c(0.027,0.023,0.002,0.500,0.011,0.185,0.097,0.067,0.066,0.023,0.433,0.117,0.053,0.199,0.098,0.100,0.594,0.406,0.027,0.023,0.002,0.500,0.011,0.185,0.097,0.067,0.066,0.023,0.433,0.117,0.053,0.199,0.098,0.100,0.594,0.406)
gr$low.CI<-c(8.27,4.80,12.35,1.83,7.22,14.53,18.25,10.56,14.28,18.76,24.25,21.72,18.62,19.83,24.36,25.22,28.80,20.20,17.73,23.15,21.06,13.12,21.79,21.32,22.99,19.76,29.60,15.41,28.39,23.25,20.34,24.20,25.76,26.72,31.92,24.73)
gr$high.CI<-c(10.10,6.37,22.31,2.04,10.00,15.40,19.56,11.83,15.74,21.52,24.87,22.86,20.23,20.68,25.67,26.53,29.86,21.34,20.21,26.10,32.79,13.63,26.02,22.33,24.41,21.39,31.48,17.98,29.04,24.43,22.01,25.12,27.09,28.05,33.01,25.95)
The code I wrote is this
p<-ggplot(data=gr, aes(x=factor(drug), y=as.numeric(gr$prevalence), ymax=max(high.CI),position="dodge",fill=practice,width=prop))
colour<-c(rep("gray79",10),rep("gray60",6),rep("gray39",2))
p + theme_bw()+
geom_bar(stat="identity",position = position_dodge(0.9)) +
labs(x="Drug",y="Prevalence") +
geom_errorbar(ymax=gr$high.CI,ymin=gr$low.CI,position=position_dodge(0.9),width=0.25,size=0.25,colour="black",aes(x=factor(drug), y=as.numeric(gr$prevalence), fill=practice)) +
ggtitle("Drug usage by country and practice") +
scale_fill_manual(values = colour)+ guides(fill=F)
The figure I obtain is this one where bars are all on top of each other while I want them "dodge".
I also obtain the following warning:
ymax not defined: adjusting position using y instead
Warning message:
position_dodge requires non-overlapping x intervals
Ideally I would get each bar near one another, with their error bars in the middle of its bar, all organised by country.
Also should I be concerned about the warning (which I clearly do not fully understand)?
I hope this makes sense. I hope I am close enough, but I don't seem to be going anywhere, some help would be greatly appreciated.
Thank you
ggplot's geom_bar() accepts the width parameter, but doesn't line them up neatly against one another in dodged position by default. The following workaround references the solution here:
library(dplyr)
# calculate x-axis position for bars of varying width
gr <- gr %>%
group_by(drug) %>%
arrange(practice) %>%
mutate(pos = 0.5 * (cumsum(prop) + cumsum(c(0, prop[-length(prop)])))) %>%
ungroup()
x.labels <- gr$practice[gr$drug == "a"]
x.pos <- gr$pos[gr$drug == "a"]
ggplot(gr,
aes(x = pos, y = prevalence,
fill = country, width = prop,
ymin = low.CI, ymax = high.CI)) +
geom_col(col = "black") +
geom_errorbar(size = 0.25, colour = "black") +
facet_wrap(~drug) +
scale_fill_manual(values = c("c1" = "gray79",
"c2" = "gray60",
"c3" = "gray39"),
guide = F) +
scale_x_continuous(name = "Drug",
labels = x.labels,
breaks = x.pos) +
labs(title = "Drug usage by country and practice", y = "Prevalence") +
theme_classic()
There is a lot of information you are trying to convey here - to contrast drug A and drug B across countries using the barplots and accounting for proportions, you might use the facet_grid function. Try this:
colour<-c(rep("gray79",10),rep("gray60",6),rep("gray39",2))
gr$drug <- paste("Drug", gr$drug)
p<-ggplot(data=gr, aes(x=factor(practice), y=as.numeric(prevalence),
ymax=high.CI,ymin = low.CI,
position="dodge",fill=practice, width=prop))
p + theme_bw()+ facet_grid(drug~country, scales="free") +
geom_bar(stat="identity") +
labs(x="Practice",y="Prevalence") +
geom_errorbar(position=position_dodge(0.9), width=0.25,size=0.25,colour="black") +
ggtitle("Drug usage by country and practice") +
scale_fill_manual(values = colour)+ guides(fill=F)
The width is too small in the C1 country and as you indicated the one clinic is quite influential.
Also, you can specify your aesthetics with the ggplot(aes(...)) and not have to reset it and it is not needed to include the dataframe objects name in the aes function within the ggplot call.
I am trying to make some changes to my plot, but am having difficulty doing so.
(1) I would like warm, avg, and cold to be filled in as the colors red, yellow, and blue, respectively.
(2) I am trying to make the y-axis read "Count" and have it be horizontally written.
(3) In the legend, I would like the title to be Temperatures, rather than variable
Any help making these changes would be much appreciated along with other suggestions to make the plot look nicer.
df <- read.table(textConnection(
'Statistic Warm Avg Cold
Homers(Away) 1.151 1.028 .841
Homers(Home) 1.202 1.058 .949'), header = TRUE)
library(ggplot2)
library(reshape2)
df <- melt(df, id = 'Statistic')
ggplot(
data = df,
aes(
y = value,
x = Statistic,
group = variable,
shape = variable,
fill = variable
)
) +
geom_bar(stat = "identity")
You are on the right lines by trying to reshape the data into long format. My preference is to use gather from the tidyr package for that. You can also create the variable names Temperatures and Count in the gather step.
The next step is to turn the 3 classes of temperature into a factor, ordered from cold, through average, to warm.
Now you can plot. You want position = "dodge" to get the bars side by side, since it makes no sense to stack the values in a single bar. Fill colours you specify using scale_fill_manual.
You rotate the y-axis title by manipulating axis.title.y.
So putting all of that together (plus a black/white theme):
library(dplyr)
library(tidyr)
library(ggplot2)
df %>%
gather(Temperatures, Count, -Statistic) %>%
mutate(Temperatures = factor(Temperatures, c("Cold", "Avg", "Warm"))) %>%
ggplot(aes(Statistic, Count)) +
geom_col(aes(fill = Temperatures), position = "dodge") +
scale_fill_manual(values = c("blue", "yellow", "red")) +
theme_bw() +
theme(axis.title.y = element_text(angle = 0, vjust = 0.5))
Result:
I'd question whether Count is a sensible variable name in this case.
You are almost there. To map specific colors to specific factor levels you can use scale_fill_manual and create your own scale:
scale_fill_manual(values=c("Warm"="red", "Avg"="yellow", "Cold"="blue")) +
Changing the y axis legend is also easy in ggplot:
ylab("Count") +
And to change the legend title you can use:
labs(fill='TEMPERATURE') +
Giving us:
ggplot(df, aes(y = value, x = Statistic, group= variable, fill = variable)) +
geom_bar(stat = "identity") +
scale_fill_manual(values=c("Warm"="red", "Avg"="yellow", "Cold"="blue")) +
labs(fill='TEMPERATURE') +
ylab("Count") +
xlab("") +
theme_bw() +
theme(axis.title.y = element_text(angle = 0, vjust = 0.5))
If I want to order the bars in a ggplot2 barchart from largest to smallest, then I'd usually update the factor levels of the bar category, like so
one_group <- data.frame(
height = runif(5),
category = gl(5, 1)
)
o <- order(one_group$height, decreasing = TRUE)
one_group$category <- factor(one_group$category, levels = one_group$category[o])
p_one_group <- ggplot(one_group, aes(category, height)) +
geom_bar(stat = "identity")
p_one_group
If have have several groups of barcharts that I'd like in different facets, with each facet having bars ordered from largest to smallest (and different x-axes) then the technique breaks down.
Given some sample data
two_groups <- data.frame(
height = runif(10),
category = gl(5, 2),
group = gl(2, 1, 10, labels = letters[1:2])
)
and the plotting code
p_two_groups <- ggplot(two_groups, aes(category, height)) +
geom_bar(stat = "identity") +
facet_grid(. ~ group, scales = "free_x")
p_two_groups
what do I need to do to get the bar ordering right?
If it helps, an equivalent problem to solve is: how do I update factor levels after I've done the faceting?
here is a hack:
two_groups <- transform(two_groups, category2 = factor(paste(group, category)))
two_groups <- transform(two_groups, category2 = reorder(category2, rank(height)))
ggplot(two_groups, aes(category2, height)) +
geom_bar(stat = "identity") +
facet_grid(. ~ group, scales = "free_x") +
scale_x_discrete(labels=two_groups$category, breaks=two_groups$category2)
make UNIQUE factor variable for all entries (category2)
reorder the variable based on the height
plot on the variable: aes(x=category2)
re-label the axis using original value (category) for the variable (category2) in scale_x_discrete.
Here is a hack to achieve what you want. I was unable to figure out how to get the category values below the tick marks. So if someone can help fix that, it would be wonderful. Let me know if this works
# add a height rank variable to the data frame
two_groups = ddply(two_groups, .(group), transform, hrank = rank(height));
# plot the graph
p_two_groups <- ggplot(two_groups, aes(-hrank, height)) +
geom_bar(stat = "identity") +
facet_grid(. ~ group, scales = "free_x") +
opts(axis.text.x = theme_blank()) +
geom_text(aes(y = 0, label = category, vjust = 1.5))