I have a data frame(t1) and I want to illustrate the shares of companies in relation to their size
I added a Dummy variable in order to make a filled barplot and not 3:
t1$row <- 1
The size of companies are separated in medium, small and micro:
f_size <- factor(t1$size,
ordered = TRUE,
levels = c("medium", "small", "micro"))
The plot is build up with the economic_theme:
ggplot(t1, aes(x = "Size", y = prop.table(row), fill = f_size)) +
geom_col() +
geom_text(aes(label = as.numeric(f_size)),
position = position_stack(vjust = 0.5)) +
theme_economist(base_size = 14) +
scale_fill_economist() +
theme(legend.position = "right",
legend.title = element_blank()) +
theme(axis.title.y = element_text(margin = margin(r = 20))) +
ylab("Percentage") +
xlab(NULL)
How can I modify my code to get the share for medium, small and micro in the middle of the three filled parts in the barplot?
Thanks in advance!
Your question isn't quite clear to me and I suggest you re-phrase it for clarity. But I believe you're trying to get the annotations to be accurately aligned on the Y-axis. For this use, pre-calculate the labels and then use annotate
library(data.table)
library(ggplot2)
set.seed(3432)
df <- data.table(
cat= sample(LETTERS[1:3], 1000, replace = TRUE)
, x= rpois(1000, lambda = 5)
)
tmp <- df[, .(pct= sum(x) / sum(df[,x])), cat][, cumsum := cumsum(pct)]
ggplot(tmp, aes(x= 'size', y= pct, fill= cat)) + geom_bar(stat='identity') +
annotate('text', y= tmp[,cumsum] - 0.15, x= 1, label= as.character(tmp[,pct]))
But this is a poor decision graphically. Stacked bar charts, by definition sum to 100%. Rather than labeling the components with text, just let the graphic do this for you via the axis labels:
ggplot(tmp, aes(x= cat, y= pct, fill= cat)) + geom_bar(stat='identity') + coord_flip() +
scale_y_continuous(breaks= seq(0,1,.05))
Related
I am using the windrose function posted here: Wind rose with ggplot (R)?
I need to have the percents on the figure showing on the individual lines (rather than on the left side), but so far I have not been able to figure out how. (see figure below for depiction of goal)
Here is the code that makes the figure:
p.windrose <- ggplot(data = data,
aes(x = dir.binned,y = (..count..)/sum(..count..),
fill = spd.binned)) +
geom_bar()+
scale_y_continuous(breaks = ybreaks.prct,labels=percent)+
ylab("")+
scale_x_discrete(drop = FALSE,
labels = waiver()) +
xlab("")+
coord_polar(start = -((dirres/2)/360) * 2*pi) +
scale_fill_manual(name = "Wind Speed (m/s)",
values = spd.colors,
drop = FALSE)+
theme_bw(base_size = 12, base_family = "Helvetica")
I marked up the figure I have so far with what I am trying to do! It'd be neat if the labels either auto-picked the location with the least wind in that direction, or if it had a tag for the placement so that it could be changed.
I tried using geom_text, but I get an error saying that "aesthetics must be valid data columns".
Thanks for your help!
One of the things you could do is to make an extra data.frame that you use for the labels. Since the data isn't available from your question, I'll illustrate with mock data below:
library(ggplot2)
# Mock data
df <- data.frame(
x = 1:360,
y = runif(360, 0, 0.20)
)
labels <- data.frame(
x = 90,
y = scales::extended_breaks()(range(df$y))
)
ggplot(data = df,
aes(x = as.factor(x), y = y)) +
geom_point() +
geom_text(data = labels,
aes(label = scales::percent(y, 1))) +
scale_x_discrete(breaks = seq(0, 1, length.out = 9) * 360) +
coord_polar() +
theme(axis.ticks.y = element_blank(), # Disables default y-axis
axis.text.y = element_blank())
#teunbrand answer got me very close! I wanted to add the code I used to get everything just right in case anyone in the future has a similar problem.
# Create the labels:
x_location <- pi # x location of the labels
# Get the percentage
T_data <- data %>%
dplyr::group_by(dir.binned) %>%
dplyr::summarise(count= n()) %>%
dplyr::mutate(y = count/sum(count))
labels <- data.frame(x = x_location,
y = scales::extended_breaks()(range(T_data$y)))
# Create figure
p.windrose <- ggplot() +
geom_bar(data = data,
aes(x = dir.binned, y = (..count..)/sum(..count..),
fill = spd.binned))+
geom_text(data = labels,
aes(x=x, y=y, label = scales::percent(y, 1))) +
scale_y_continuous(breaks = waiver(),labels=NULL)+
scale_x_discrete(drop = FALSE,
labels = waiver()) +
ylab("")+xlab("")+
coord_polar(start = -((dirres/2)/360) * 2*pi) +
scale_fill_manual(name = "Wind Speed (m/s)",
values = spd.colors,
drop = FALSE)+
theme_bw(base_size = 12, base_family = "Helvetica") +
theme(axis.ticks.y = element_blank(), # Disables default y-axis
axis.text.y = element_blank())
I am trying to create a donut chart using ggplot2 with the following data (example).
library(ggplot2)
library(svglite)
library(scales)
# dataframe
Sex = c('Male', 'Female')
Number = c(125, 375)
df = data.frame(Sex, Number)
df
The code I used to generate donut chart is
ggplot(aes(x= Sex, y = Number, fill = Sex), data = df) +
geom_bar(stat = "identity") +
coord_polar("y") +
theme_void() +
theme (legend.position="top") + # legend position
geom_text(aes(label = percent(Number/sum(Number))), position = position_stack(vjust = 0.75), size = 3) +
ggtitle("Participants by Sex")
The above code generated the following chart. Some how not convinced with the chart.
For our purposes, the following chart would better communicate the message. How do I create a chart like this. Where am I doing wrong in my code? I have googled with out any success.
Thanks in advance for help.
They aren't in the same 'circle' because they have different x values. Imagine it as a normal plot first (i.e. without coord_polar("y")) and this will become clear. What you really want is them set at the same x value and then stacked. Here I set x to 2 because it then makes a nicely sized "donut".
donut <- ggplot(df, aes(x = 2, y = Number, fill = Sex)) +
geom_col(position = "stack", width = 1) +
geom_text(aes(label = percent(Number/sum(Number))), position = position_stack(vjust = 0.75), size = 3) +
xlim(0.5, 2.5) +
ggtitle("Participants by Sex")
donut
donut +
coord_polar("y") +
theme_void() +
theme(legend.position="top")
Here is a dataframe
DF <- data.frame(SchoolYear = c("2015-2016", "2016-2017"),
Value = sample(c('Agree', 'Disagree', 'Strongly agree', 'Strongly disagree'), 50, replace = TRUE))
I have created this graph.
ggplot(DF, aes(x = Value, fill = SchoolYear)) +
geom_bar(position = 'dodge', aes(y = (..count..)/sum(..count..))) +
geom_text(aes(y = ((..count..)/sum(..count..)), label = scales::percent((..count..)/sum(..count..))),
stat = "count", vjust = -0.25, size = 2, position = position_dodge(width = 0.9)) +
scale_y_continuous(labels = percent) +
ylab("Percent") + xlab("Response") +
theme(axis.text.x = element_text(angle = 75, hjust = 1))
Is there a way to make the data for each school year add up to 100%, but not have the data stacked, in the graph?
I know this question is similar to this question Create stacked barplot where each stack is scaled to sum to 100%, but I don't want the graph to be stacked. I can't figure out how to apply the solution in my question to this situation. Also I would prefer not to summarize the data before graphing, as I have to make this graph many times using different data each time and would prefer not to have to summarize the data each time.
I'm not sure how to create the plot that you want without transforming the data. But if you want to re-use the same code for multiple datasets, you can write a function to transform your data and generate the plot at the same time:
plot.fun <- function (original.data) {
newDF <- reshape2::melt(apply(table(original.data), 1, prop.table))
Plot <- ggplot(newDF, aes(x=Value, y=value)) +
geom_bar(aes(fill=SchoolYear), stat="identity", position="dodge") +
geom_text(aes(group=SchoolYear, label=scales::percent(value)), stat="identity", vjust=-0.25, size=2, position=position_dodge(width=0.85)) +
scale_y_continuous(labels=scales::percent) +
ylab("Percent") + xlab("Response") +
theme(axis.text.x = element_text(angle = 75, hjust = 1))
return (Plot)
}
plot.fun(DF)
Big Disclaimer: I would highly recommend you summarize your data before hand and not try to do these calculations within ggplot. That is not what ggplot is meant to do. Furthermore, it not only complicates your code unnecessarily, but can easily introduce bugs/unintended results.
Given that, it appears that what you want is doable (without summarizing first). A very hacky way to get what you want by doing the calculations within ggplot would be:
#Store factor values
fac <- unique(DF$SchoolYear)
ggplot(DF, aes(x = Value, fill = SchoolYear)) +
geom_bar(position = 'dodge', aes(y = (..count..)/stats::ave(..count.., get("fac", globalenv()), FUN = sum))) +
geom_text(aes(y = (..count..)/stats::ave(..count.., get("fac", globalenv()), FUN = sum), label = scales::percent((..count..)/stats::ave(..count.., get("fac", globalenv()), FUN = sum))),
stat = "count", vjust = -0.25, size = 2, position = position_dodge(width = 0.9)) +
scale_y_continuous(labels = percent) +
ylab("Percent") + xlab("Response") +
theme(axis.text.x = element_text(angle = 75, hjust = 1))
This takes the ..count.. variable and divides it by the sum within it's respective group using stats::ave. Note this can be messed up extremely easily.
Finally, we check to see the plot is in fact giving us what we want.
#Check to see we have the correct values
d2 <- DF
d2 <- setDT(d2)[, .(count = .N), by = .(SchoolYear, Value)][, percent := count/sum(count), by = SchoolYear]
I am trying to plot two flows and one rainfall data in one graph. I have broke it up into top and bottom parts as shown in the following pic. Here I have two issues with this plots and spent ages but cannot solve it.
Why the observed flow always in black, even I have set it up as blue? Did I accidentally used some other arguments to overwrite it?
The most importantly is, how do I able to add a legend for the bottom plot? I tried many different codes but they don't seem to work for me.
x = data.frame(date = Date, rain = Obs_rain, obsflow = Obs_flow,simflow=Sim_flow)
g.top <- ggplot(x, aes(x = date, y = rain, ymin=0, ymax=rain)) +
geom_linerange() +
scale_y_continuous(trans = "reverse") +
theme_bw() +
theme(plot.margin = unit(c(1,5,-30,6),units="points"),
axis.title.y = element_text(vjust =0.3)) +
labs(x = "Date",y = "Rain(mm)")
g.bottom <- ggplot(x, aes(x = date, y = obsflow, ymin=0, ymax=obsflow), colour = "blue",size=0.5) +
geom_linerange() + #plot flow
geom_linerange(aes(y = simflow, ymin=0, ymax=simflow), colour = "red", size =0.5)+
labs(x = "Date", y = "River flow (ML/day)") +
theme_classic() +
theme(plot.background = element_rect(fill = "transparent"),
plot.margin = unit(c(2,0,1,1),units="lines"))
grid.arrange(g.top,g.bottom, heights = c(1/5, 4/5))
Update:
I have resolved the issue with blue line colour. I accidently put arguments in the wrong place. But I'm still struggling with the legend.
g.bottom <- ggplot(x, aes(x = date, y = obsflow, ymin=0, ymax=obsflow)) +
geom_linerange(colour = "blue",size=0.5) + #plot flow
As an explanation of what #pierre means... turn your data from "wide" to "long" format using reshape2::melt, so that the flow type for each date is in one column flow_type, and the value is another (flow_val). Then you specify flow_type as the grouping variable with which to assign colour:
require(reshape2)
x.melted <- melt(x, id.vars = c("date", "rain"), variable.name="flow_type",
value.name="flow_val")
g.bottom <- ggplot(x.melted, aes(x = date),size=0.5) +
geom_linerange(aes(ymin=0, ymax=flow_val, colour=flow_type)) + #plot flow
labs(x = "Date", y = "River flow (ML/day)") +
theme_classic() +
theme(plot.background = element_rect(fill = "transparent"),
plot.margin = unit(c(2,0,1,1),units="lines"),
legend.position="bottom") +
scale_colour_manual(guide = guide_legend(title = "Flow Type"),
values = c("obsflow"="blue", "simflow"="red"))
Using ggplot2 1.0.0, I followed the instructions in below post to figure out how to plot percentage bar plots across factors:
Sum percentages for each facet - respect "fill"
test <- data.frame(
test1 = sample(letters[1:2], 100, replace = TRUE),
test2 = sample(letters[3:8], 100, replace = TRUE)
)
library(ggplot2)
library(scales)
ggplot(test, aes(x= test2, group = test1)) +
geom_bar(aes(y = ..density.., fill = factor(..x..))) +
facet_grid(~test1) +
scale_y_continuous(labels=percent)
However, I cannot seem to get a label for either the total count or the percentage above each of the bar plots when using geom_text.
What is the correct addition to the above code that also preserves the percentage y-axis?
Staying within ggplot, you might try
ggplot(test, aes(x= test2, group=test1)) +
geom_bar(aes(y = ..density.., fill = factor(..x..))) +
geom_text(aes( label = format(100*..density.., digits=2, drop0trailing=TRUE),
y= ..density.. ), stat= "bin", vjust = -.5) +
facet_grid(~test1) +
scale_y_continuous(labels=percent)
For counts, change ..density.. to ..count.. in geom_bar and geom_text
UPDATE for ggplot 2.x
ggplot2 2.0 made many changes to ggplot including one that broke the original version of this code when it changed the default stat function used by geom_bar ggplot 2.0.0. Instead of calling stat_bin, as before, to bin the data, it now calls stat_count to count observations at each location. stat_count returns prop as the proportion of the counts at that location rather than density.
The code below has been modified to work with this new release of ggplot2. I've included two versions, both of which show the height of the bars as a percentage of counts. The first displays the proportion of the count above the bar as a percent while the second shows the count above the bar. I've also added labels for the y axis and legend.
library(ggplot2)
library(scales)
#
# Displays bar heights as percents with percentages above bars
#
ggplot(test, aes(x= test2, group=test1)) +
geom_bar(aes(y = ..prop.., fill = factor(..x..)), stat="count") +
geom_text(aes( label = scales::percent(..prop..),
y= ..prop.. ), stat= "count", vjust = -.5) +
labs(y = "Percent", fill="test2") +
facet_grid(~test1) +
scale_y_continuous(labels=percent)
#
# Displays bar heights as percents with counts above bars
#
ggplot(test, aes(x= test2, group=test1)) +
geom_bar(aes(y = ..prop.., fill = factor(..x..)), stat="count") +
geom_text(aes(label = ..count.., y= ..prop..), stat= "count", vjust = -.5) +
labs(y = "Percent", fill="test2") +
facet_grid(~test1) +
scale_y_continuous(labels=percent)
The plot from the first version is shown below.
This is easier to do if you pre-summarize your data. For example:
library(ggplot2)
library(scales)
library(dplyr)
set.seed(25)
test <- data.frame(
test1 = sample(letters[1:2], 100, replace = TRUE),
test2 = sample(letters[3:8], 100, replace = TRUE)
)
# Summarize to get counts and percentages
test.pct = test %>% group_by(test1, test2) %>%
summarise(count=n()) %>%
mutate(pct=count/sum(count))
ggplot(test.pct, aes(x=test2, y=pct, colour=test2, fill=test2)) +
geom_bar(stat="identity") +
facet_grid(. ~ test1) +
scale_y_continuous(labels=percent, limits=c(0,0.27)) +
geom_text(data=test.pct, aes(label=paste0(round(pct*100,1),"%"),
y=pct+0.012), size=4)
(FYI, you can put the labels inside the bar as well, for example, by changing the last line of code to this: y=pct*0.5), size=4, colour="white"))
I've used all of your code and came up with this. First assign your ggplot to a variable i.e. p <- ggplot(...) + geom_bar(...) etc. Then you could do this. You don't need to summarize much since ggplot has a build function that gives you all of this already. I'll leave it to you for the formatting and such. Good luck.
dat <- ggplot_build(p)$data %>% ldply() %>% select(group,density) %>%
do(data.frame(xval = rep(1:6, times = 2),test1 = mapvalues(.$group, from = c(1,2), to = c("a","b")), density = .$density))
p + geom_text(data=dat, aes(x = xval, y = (density + .02), label = percent(density)), colour="black", size = 3)