I had to flip the axis of my line, but still need the geom_area to be under the curve. However I cannot figure out how to do so.
This is the line of code I tried
ggplot(PalmBeachWell, aes(x=Date, y=Depth.to.Water.Below.Land.Surface.in.ft.)) +
geom_area(position= "identity", fill='lightblue') +
theme_classic() +
geom_line(color="blue") +
scale_y_reverse()
and here is what i got
One option would be to use a geom_ribbon to fill the area above the curve which after applying scale_y_reverse will result in a fill under the curve.
Using some fake example data based on the ggplot2::economics dataset:
library(ggplot2)
PalmBeachWell <- economics[c("date", "psavert")]
names(PalmBeachWell) <- c("Date", "Depth.to.Water.Below.Land.Surface.in.ft.")
ggplot(PalmBeachWell, aes(x = Date, y = Depth.to.Water.Below.Land.Surface.in.ft.)) +
geom_ribbon(aes(ymin = Depth.to.Water.Below.Land.Surface.in.ft., ymax = Inf),
fill = "lightblue"
) +
geom_line(color = "blue") +
scale_y_reverse() +
theme_classic()
Related
I have a ggplot bar and don't know how to change the scale of the x axis. At the moment it looks like on the image below. However I'd like to reorder the scale of the x axis so that 21% bar is higher than the 7% bar. How could I get the % to the axis? Thanks in advance!
df= data.frame("number" = c(7,21), "name" = c("x","y"))
df
ggplot(df, aes(x=name, y=number)) +
geom_bar(stat="identity", fill = "blue") + xlab("Title") + ylab("Title") +
ggtitle("Title")
Use the prop.table function to in y variable in the geom plot.
ggplot(df, aes(x=name, y=100*prop.table(number))) +
geom_bar(stat="identity", fill = "blue") +
xlab("Stichprobe") + ylab("Paketmenge absolut") +
ggtitle("Menge total")
If you want to have the character, % in the y axis, you can add scale_y_continuous to the plot as below:
library(scales)
ggplot(df, aes(x=name, y=prop.table(number))) +
geom_bar(stat="identity", fill = "blue") +
xlab("Stichprobe") + ylab("Paketmenge absolut") +
ggtitle("Menge total") +
scale_y_continuous(labels=percent)
The only way I am able to duplicate the original plot is, as #sconfluentus noted, for the 7% and 21% to be character strings. As an aside the data frame column names need not be quoted.
df= data.frame(number = c('7%','21%'), name = c("x","y"))
df
ggplot(df, aes(x=name, y=number)) +
geom_bar(stat="identity", fill = "blue") + xlab("Title") + ylab("Title") +
ggtitle("Title")
Changing the numbers to c(0.07, 0.21) and adding, as #Mohanasundaram noted, scale_y_continuous(labels = scales::percent) corrects the situation:
To be pedantic using breaks = c(0.07, 0.21) creates nearly an exact duplicate. See also here.3
Hope this is helpful.
library(ggplot2)
library(scales)
df= data.frame(number = c(0.07,0.21), name = c("KG","MS"))
df
ggplot(df, aes(x=name, y=number)) +
geom_bar(stat="identity", fill = "blue") + xlab("Title") + ylab("Title") +
ggtitle("Title") + scale_y_continuous(labels = scales::percent, breaks = c(.07, .21)))
Consider the following code:
library(ggplot2)
ggplot(diamonds, aes("", price)) + geom_boxplot() + coord_flip()
After flipping the box plot, how can I zoom in to c(0,7000) on price (which is the new x-axis)?
I feel like it has something to do with coord_cartesian(ylim=c(0, 7000)), but this doesn't seem to work in conjunction with coord_flip().
Here is my solution:
ggplot(diamonds, aes("", price)) +
geom_boxplot() +
coord_flip(ylim=c(0, 7000))
Just combine the ylim command as argument in coord_flip().
You can use scale_y_continuous():
library(ggplot2)
ggplot(diamonds, aes("", price)) +
geom_boxplot() +
coord_flip() +
scale_y_continuous(limits = c(0, 7000))
Remember that coord_flip() just rotates the plot, hence you call scale_ on the y axis, which is what you specify price as. I usually like to call it last for that reason: to help limit confusion over which axis is which!
I think you need to manually compute the boxplot statistics and plot these.
# Compute summary statistics with max (y100) set to cutoff (7000)
df <- data.frame(x = 1,
y0 = min(diamonds$price),
y25 = quantile(diamonds$price, 0.25),
y50 = median(diamonds$price),
y75 = quantile(diamonds$price, 0.75),
y100 = 7000
)
ggplot(df, aes(x)) +
geom_boxplot(aes(ymin = y0, lower = y25, middle = y50, upper = y75, ymax = y100),
stat = "identity") +
coord_flip()
Below code works well and it labels the barplot correctly, However, if I try geom_text for a histogram I fail since geom_text requires a y-component and a histogram's y component is not part of the original data.
Label an "ordinary" bar plot (geom_bar(stat = "identity") works well:
ggplot(csub, aes(x = Year, y = Anomaly10y, fill = pos)) +
geom_bar(stat = "identity", position = "identity") +
geom_text(aes(label = Anomaly10y,vjust=1.5))
My Problem: How to get the correct y and label (indicated by ?) for geom_text, to put labels on top of the histogram bars
ggplot(csub,aes(x = Anomaly10y)) +
geom_histogram()
geom_text(aes(label = ?, vjust = 1.5))
geom_text requires x, y and labels. However, y and labels are not in the original data, but generated by the geom_histogram function. How can I extract the necessary data to position labels on a histogram?
geom_histogram() is just a fancy wrapper to stat_bin so you can all that yourself with the bars and text that you like. Here's an example
#sample data
set.seed(15)
csub<-data.frame(Anomaly10y = rpois(50,5))
And then we plot it with
ggplot(csub,aes(x=Anomaly10y)) +
stat_bin(binwidth=1) + ylim(c(0, 12)) +
stat_bin(binwidth=1, geom="text", aes(label=..count..), vjust=-1.5)
to get
Ok to make it aesthetically appealing here is the solution:
set.seed(15)
csub <- data.frame(Anomaly10y = rpois(50, 5))
Now Plot it
csub %>%
ggplot(aes(Anomaly10y)) +
geom_histogram(binwidth=1) +
stat_bin(binwidth=1, geom='text', color='white', aes(label=..count..),
position=position_stack(vjust = 0.5))
resultant plot will be
Below code works well and it labels the barplot correctly, However, if I try geom_text for a histogram I fail since geom_text requires a y-component and a histogram's y component is not part of the original data.
Label an "ordinary" bar plot (geom_bar(stat = "identity") works well:
ggplot(csub, aes(x = Year, y = Anomaly10y, fill = pos)) +
geom_bar(stat = "identity", position = "identity") +
geom_text(aes(label = Anomaly10y,vjust=1.5))
My Problem: How to get the correct y and label (indicated by ?) for geom_text, to put labels on top of the histogram bars
ggplot(csub,aes(x = Anomaly10y)) +
geom_histogram()
geom_text(aes(label = ?, vjust = 1.5))
geom_text requires x, y and labels. However, y and labels are not in the original data, but generated by the geom_histogram function. How can I extract the necessary data to position labels on a histogram?
geom_histogram() is just a fancy wrapper to stat_bin so you can all that yourself with the bars and text that you like. Here's an example
#sample data
set.seed(15)
csub<-data.frame(Anomaly10y = rpois(50,5))
And then we plot it with
ggplot(csub,aes(x=Anomaly10y)) +
stat_bin(binwidth=1) + ylim(c(0, 12)) +
stat_bin(binwidth=1, geom="text", aes(label=..count..), vjust=-1.5)
to get
Ok to make it aesthetically appealing here is the solution:
set.seed(15)
csub <- data.frame(Anomaly10y = rpois(50, 5))
Now Plot it
csub %>%
ggplot(aes(Anomaly10y)) +
geom_histogram(binwidth=1) +
stat_bin(binwidth=1, geom='text', color='white', aes(label=..count..),
position=position_stack(vjust = 0.5))
resultant plot will be
In a ggplot boxplot, it is easy to use jitter to add the raw data points with varying degrees of jitter. With zero jitter the following code
dat <- data.frame(group=c('a', 'b', 'c'), values = runif(90))
ggplot(dat, aes(group, values)) +
geom_boxplot(outlier.size = 0) +
geom_jitter(position=position_jitter(width=0), aes(colour=group), alpha=0.7) +
ylim(0, 1) + stat_summary(fun.y=mean, shape=3, col='red', geom='point') +
opts(legend.position = "right") + ylab("values") + xlab("group")
produces the plot below.
Is it possible to use zero jitter but add an offset such that the points are in a line but shifted left by 25% of the box width? I tried geom_point with dodge but this generated a jitter.
If we convert group to numeric and then add an offset, you seem to get your desired output. There is probably a more effective / efficient way, but give this a whirl:
ggplot(dat, aes(group, values)) +
geom_boxplot(outlier.size = 0) +
geom_point(aes(x = as.numeric(group) + .25, colour=group), alpha=0.7) +
ylim(0, 1) + stat_summary(fun.y=mean, shape=3, col='red', geom='point') +
opts(legend.position = "right") + ylab("values") + xlab("group")