Trying to make some plots with ggplot2 and cannot figure out how colour works as defined in aes. Struggling with errors of aesthetic length.
I've tried defining colours in either main ggplot call aes to give legend, but also in geom_line aes.
# Define dataset:
number<-rnorm(8,mean=10,sd=3)
species<-rep(c("rose","daisy","sunflower","iris"),2)
year<-c("1995","1995","1995","1995","1996","1996","1996","1996")
d.flowers<-cbind(number,species,year)
d.flowers<-as.data.frame(d.flowers)
#Plot with no colours:
ggplot(data=d.flowers,aes(x=year,y=number))+
geom_line(group=species) # Works fine
#Adding colour:
#Defining aes in main ggplot call:
ggplot(data=d.flowers,aes(x=year,y=number,colour=factor(species)))+
geom_line(group=species)
# Doesn't work with data size 8, asks for data of size 4
ggplot(data=d.flowers,aes(x=year,y=number,colour=unique(species)))+
geom_line(group=species)
# doesn't work with data size 4, now asking for data size 8
The first plot gives
Error: Aesthetics must be either length 1 or the same as the data (4): group
The second gives
Error: Aesthetics must be either length 1 or the same as the data (8): x, y, colour
So I'm confused - when given aes of length either 4 or 8 it's not happy!
How could I think about this more clearly?
Here are #kath's comments as a solution. It's subtle to learn at first but what goes inside or outside the aes() is key. Some more info here - When does the aesthetic go inside or outside aes()? and lots of good googleable "ggplot aesthetic" centric pages with lots of examples to cut and paste and try.
library(ggplot2)
number <- rnorm(8,mean=10,sd=3)
species <- rep(c("rose","daisy","sunflower","iris"),2)
year <- c("1995","1995","1995","1995","1996","1996","1996","1996")
d.flowers <- data.frame(number,species,year, param1, param2)
head(d.flowers)
#number species year
#1 8.957372 rose 1995
#2 7.145144 daisy 1995
#3 9.864917 sunflower 1995
#4 7.645287 iris 1995
#5 4.996174 rose 1996
#6 8.859320 daisy 1996
ggplot(data = d.flowers, aes(x = year,y = number,
group = species,
colour = species)) + geom_line()
#note geom_point() doesn't need to be grouped - try:
ggplot(data = d.flowers, aes(x = year,y = number, colour = species)) + geom_point()
Related
So my first ggplot2 box plot was just one big stretched out box plot, the second one was correct but I don't understand what changed and why the second one worked. I'm new to R and ggplot2, let me know if you can, thanks.
#----------------------------------------------------------
# This is the original ggplot that didn't work:
#----------------------------------------------------------
zSepalFrame <- data.frame(zSepalLength, zSepalWdth)
zPetalFrame <- data.frame(zPetalLength, zPetalWdth)
p1 <- ggplot(data = zSepalFrame, mapping = aes(x=zSepalWdth, y=zSepalLength, group = 4)) + #fill = zSepalLength
geom_boxplot(notch=TRUE) +
stat_boxplot(geom = 'errorbar', width = 0.2) +
theme_classic() +
labs(title = "Iris Data Box Plot") +
labs(subtitle ="Z Values of Sepals From Iris.R")
p1
#----------------------------------------------------------
# This is the new ggplot box plot line that worked:
#----------------------------------------------------------
bp = ggplot(zSepalFrame, aes(x=factor(zSepalWdth), y=zSepalLength, color = zSepalWdth)) + geom_boxplot() + theme(legend.position = "none")
bp
This is what the ggplot box plot looked like
I don't have your precise dataset, OP, but it seems to stem from assigning a continuous variable to your x axis, when boxplots require a discrete variable.
A continuous variable is something like a numeric column in a dataframe. So something like this:
x <- c(4,4,4,8,8,8,8)
Even though the variable x only contains 4's and 8's, R assigns this as a numeric type of variable, which is continuous. It means that if you plot this on the x axis, ggplot will have no issue with something falling anywhere in-between 4 or 8, and will be positioned accordingly.
The other type of variable is called discrete, which would be something like this:
y <- c("Green", "Green", "Flags", "Flags", "Cars")
The variable y contains only characters. It must be discrete, since there is no such thing as something between "Green" and "Cars". If plotted on an x axis, ggplot will group things as either being "Green", "Flags", or "Cars".
The cool thing is that you can change a continuous variable into a discrete one. One way to do that is to factorize or force R to consider a variable as a factor. If you typed factor(x), you get this:
[1] 4 4 4 8 8 8 8
Levels: 4 8
The values in x are the same, but now there is no such thing as a number between 4 and 8 when x is a factor - it would just add another level.
That is in short why your box plot changes. Let's demonstrate with the iris dataset. First, an example like yours. Notice that I'm assigning x=Sepal.Length. In the iris dataset, Sepal.Length is numeric, so continuous.
ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width)) +
geom_boxplot()
This is similar to yours. The reason is that the boxplot is drawn by grouping according to x and then calculating statistics on those groups. If a variable is continuous, there are no "groups", even if data is replicated (like as in x above). One way to make groups is to force the data to be discrete, as in factor(Sepal.Length). Here's what it looks like when you do that:
ggplot(iris, aes(x=factor(Sepal.Length), y=Sepal.Width)) +
geom_boxplot()
The other way to have this same effect would be to use the group= aesthetic, which does what you might think: it groups according to that column in the dataset.
ggplot(iris, aes(x=Sepal.Length), y=Sepal.Width, group=Sepal.Length)) +
geom_boxplot()
I am trying to show different growing season lengths by displaying crop planting and harvest dates at multiple regions.
My final goal is a graph that looks like this:
which was taken from an answer to this question. Note that the dates are in julian days (day of year).
My first attempt to reproduce a similar plot is:
library(data.table)
library(ggplot2)
mydat <- "Region\tCrop\tPlanting.Begin\tPlanting.End\tHarvest.Begin\tHarvest.End\nCenter-West\tSoybean\t245\t275\t1\t92\nCenter-West\tCorn\t245\t336\t32\t153\nSouth\tSoybean\t245\t1\t1\t122\nSouth\tCorn\t183\t336\t1\t153\nSoutheast\tSoybean\t275\t336\t1\t122\nSoutheast\tCorn\t214\t336\t32\t122"
# read data as data table
mydat <- setDT(read.table(textConnection(mydat), sep = "\t", header=T))
# melt data table
m <- melt(mydat, id.vars=c("Region","Crop"), variable.name="Period", value.name="value")
# plot stacked bars
ggplot(m, aes(x=Crop, y=value, fill=Period, colour=Period)) +
geom_bar(stat="identity") +
facet_wrap(~Region, nrow=3) +
coord_flip() +
theme_bw(base_size=18) +
scale_colour_manual(values = c("Planting.Begin" = "black", "Planting.End" = "black",
"Harvest.Begin" = "black", "Harvest.End" = "black"), guide = "none")
However, there's a few issues with this plot:
Because the bars are stacked, the values on the x-axis are aggregated and end up too high - out of the 1-365 scale that represents day of year.
I need to combine Planting.Begin and Planting.End in the same color, and do the same to Harvest.Begin and Harvest.End.
Also, a "void" (or a completely uncolored bar) needs to be created between Planting.Begin and Harvest.End.
Perhaps the graph could be achieved with geom_rect or geom_segment, but I really want to stick to geom_bar since it's more customizable (for example, it accepts scale_colour_manual in order to add black borders to the bars).
Any hints on how to create such graph?
I don't think this is something you can do with a geom_bar or geom_col. A more general approach would be to use geom_rect to draw rectangles. To do this, we need to reshape the data a bit
plotdata <- mydat %>%
dplyr::mutate(Crop = factor(Crop)) %>%
tidyr::pivot_longer(Planting.Begin:Harvest.End, names_to="period") %>%
tidyr::separate(period, c("Type","Event")) %>%
tidyr::pivot_wider(names_from=Event, values_from=value)
# Region Crop Type Begin End
# <chr> <fct> <chr> <int> <int>
# 1 Center-West Soybean Planting 245 275
# 2 Center-West Soybean Harvest 1 92
# 3 Center-West Corn Planting 245 336
# 4 Center-West Corn Harvest 32 153
# 5 South Soybean Planting 245 1
# ...
We've used tidyr to reshape the data so we have one row per rectangle that we want to draw and we've also make Crop a factor. We can then plot it like this
ggplot(plotdata) +
aes(ymin=as.numeric(Crop)-.45, ymax=as.numeric(Crop)+.45, xmin=Begin, xmax=End, fill=Type) +
geom_rect(color="black") +
facet_wrap(~Region, nrow=3) +
theme_bw(base_size=18) +
scale_y_continuous(breaks=seq_along(levels(plotdata$Crop)), labels=levels(plotdata$Crop))
The part that's a bit messy here that we are using a discrete scale for y but geom_rect prefers numeric values, so since the values are factors now, we use the numeric values for the factors to create ymin and ymax positions. Then we need to replace the y axis with the names of the levels of the factor.
If you also wanted to get the month names on the x axis you could do something like
dateticks <- seq.Date(as.Date("2020-01-01"), as.Date("2020-12-01"),by="month")
# then add this to you plot
... +
scale_x_continuous(breaks=lubridate::yday(dateticks),
labels=lubridate::month(dateticks, label=TRUE, abbr=TRUE))
I'm currently trying to develop a surface plot that examines the results of the below data frame. I want to plot the increasing values of noise on the x-axis and the increasing values of mu on the y-axis, with the point estimate values on the z-axis. After looking at ggplot2 and ggplotly, it's not clear how I would plot each of these columns in surface or 3D plot.
df <- "mu noise0 noise1 noise2 noise3 noise4 noise5
1 1 0.000000 0.9549526 0.8908646 0.919630 1.034607
2 2 1.952901 1.9622004 2.0317115 1.919011 1.645479
3 3 2.997467 0.5292921 2.8592976 3.034377 3.014647
4 4 3.998339 4.0042379 3.9938346 4.013196 3.977212
5 5 5.001337 4.9939060 4.9917115 4.997186 5.009082
6 6 6.001987 5.9929932 5.9882173 6.015318 6.007156
7 7 6.997924 6.9962483 7.0118066 6.182577 7.009172
8 8 8.000022 7.9981131 8.0010066 8.005220 8.024569
9 9 9.004437 9.0066182 8.9667536 8.978415 8.988935
10 10 10.006595 9.9987245 9.9949733 9.993018 10.000646"
Thanks in advance.
Here's one way using geom_tile(). First, you will want to get your data frame into more of a Tidy format, where the goal is to have columns:
mu: nothing changes here
noise: need to combine your "noise0", "noise1", ... columns together, and
z: serves as the value of the noise and we will apply the fill= aesthetic using this column.
To do that, I'm using dplyr and gather(), but there are other ways (melt(), or pivot_longer() gets you that too). I'm also adding some code to pull out just the number portion of the "noise" columns and then reformatting that as an integer to ensure that you have x and y axes as numeric/integers:
# assumes that df is your data as data.frame
df <- df %>% gather(key="noise", value="z", -mu)
df <- df %>% separate(col = "noise", into=c('x', "noise"), sep=5) %>% select(-x)
df$noise <- as.integer(df$noise)
Here's an example of how you could plot it, but aesthetics are up to you. I decided to also include geom_text() to show the actual values of df$z so that we can see better what's going on. Also, I'm using rainbow because "it's pretty" - you may want to choose a more appropriate quantitative comparison scale from the RColorBrewer package.
ggplot(df, aes(x=noise, y=mu, fill=z)) + theme_bw() +
geom_tile() +
geom_text(aes(label=round(z, 2))) +
scale_fill_gradientn(colors = rainbow(5))
EDIT: To answer OP's follow up, yes, you can also showcase this via plotly. Here's a direct transition:
p <- plot_ly(
df, x= ~noise, y= ~mu, z= ~z,
type='mesh3d', intensity = ~z,
colors= colorRamp(rainbow(5))
)
p
Static image here:
A much more informative way to show this particular set of information is to see the variation of df$z as it relates to df$mu by creating df$delta_z and then using that to plot. (you can also plot via ggplot() + geom_tile() as above):
df$delta_z <- df$z - df$mu
p1 <- plot_ly(
df, x= ~noise, y= ~mu, z= ~delta_z,
type='mesh3d', intensity = ~delta_z,
colors= colorRamp(rainbow(5))
)
Giving you this (static image here):
ggplot accepts data in the long format, which means that you need to melt your dataset using, for example, a function from the reshape2 package:
dfLong = melt(df,
id.vars = "mu",
variable.name = "noise",
value.name = "meas")
The resulting column noise contains entries such as noise0, noise1, etc. You can extract the numbers and convert to a numeric column:
dfLong$noise = with(dfLong, as.numeric(gsub("noise", "", noise)))
This converts your data to:
mu noise meas
1 1 0 1.0000000
2 2 0 2.0000000
3 3 0 3.0000000
...
As per ggplot documentation:
ggplot2 can not draw true 3D surfaces, but you can use geom_contour(), geom_contour_filled(), and geom_tile() to visualise 3D surfaces in 2D.
So, for example:
ggplot(dfLong,
aes(x = noise
y = mu,
fill = meas)) +
geom_tile() +
scale_fill_gradientn(colours = terrain.colors(10))
Produces:
I have scripted a ggplot compiled from two separate data frames, but as it stands there is no legend as the colours aren't included in aes. I'd prefer to keep the two datasets separate if possible, but can't figure out how to add the legend. Any thoughts?
I've tried adding the colours directly to the aes function, but then colours are just added as variables and listed in the legend instead of colouring the actual data.
Plotting this with base r, after creating the plot I would've used:
legend("top",c("Delta 18O","Delta 13C"),fill=c("red","blue")
and gotten what I needed, but I'm not sure how to replicate this in ggplot.
The following code currently plots exactly what I want, it's just missing the legend... which ideally should match what the above line would produce, except the "18" and "13" need superscripted.
Examples of an old plot using base r (with a correct legend, except lacking superscripted 13 and 18) and the current plot missing the legend can be found here:
Old: https://imgur.com/xgd9e9C
New, missing legend: https://imgur.com/eGRhUzf
Background data
head(avar.data.x)
time av error
1 1.015223 0.030233604 0.003726832
2 2.030445 0.014819145 0.005270609
3 3.045668 0.010054801 0.006455241
4 4.060891 0.007477541 0.007453974
5 5.076113 0.006178282 0.008333912
6 6.091336 0.004949045 0.009129470
head(avar.data.y)
time av error
1 1.015223 0.06810001 0.003726832
2 2.030445 0.03408136 0.005270609
3 3.045668 0.02313839 0.006455241
4 4.060891 0.01737148 0.007453974
5 5.076113 0.01405144 0.008333912
6 6.091336 0.01172788 0.009129470
The following avarn function produces a data frame with three columns and several thousand rows (see header above). These are then graphed over time on a log/log plot.
avar.data.x <- avarn(data3$"d Intl. Std:d 13C VPDB - Value",frequency)
avar.data.y <- avarn(data3$"d Intl. Std:d 18O VPDB-CO2 - Value",frequency)
Create allan deviation plot
ggplot()+
geom_line(data=avar.data.y,aes(x=time,y=sqrt(av)),color="red")+
geom_line(data=avar.data.x,aes(x=time,y=sqrt(av)),color="blue")+
scale_x_log10()+
scale_y_log10()+
labs(x=expression(paste("Averaging Time ",tau," (seconds)")),y="Allan Deviation (per mil)")
The above plot is only missing a legend to show the name of the two plotted datasets and their respective colours. I would like the legend in the top centre of the graph.
How to superscript legend titles?:
ggplot()+
geom_line(data=avar.data.y,aes(x=time,y=sqrt(av),
color =expression(paste("Delta ",18^,"O"))))+
geom_line(data=avar.data.xmod,aes(x=time,y=sqrt(av),
color=expression(paste("Delta ",13^,"C"))))+
scale_color_manual(values = c("blue", "red"),name=NULL) +
scale_x_log10()+
scale_y_log10()+
labs(
x=expression(paste("Averaging Time ",tau," (seconds)")),
y="Allan Deviation (per mil)") +
theme(legend.position = c(0.5, 0.9))
Set color inside the aes and add a scale_color_ function to your plot should do the trick.
ggplot()+
geom_line(data=avar.data.y,aes(x=time,y=sqrt(av), color = "a"))+
geom_line(data=avar.data.x,aes(x=time,y=sqrt(av), color="b"))+
scale_color_manual(
values = c("red", "blue"),
labels = expression(avar.data.x^2, "b")
) +
scale_x_log10()+
scale_y_log10()+
labs(
x=expression(paste("Averaging^2 Time ",tau," (seconds)")),
y="Allan Deviation (per mil)") +
theme(legend.position = c(0.5, 0.9))
You can make better use of ggplot's aesthetics by combining both data sets into one. This is particularly easy when your data frames have the same structure. Here, you could then for example use color.
This way you only need one call to geom_line and it is easier to control the legend(s). You could even make some fancy function to automate your labels. etc.
Also note that white spaces in column names are not great (you're making your own life very difficult) and that you may want to think about automating your avarn calls, e.g. with lapply, which would result in a list of data frames and makes the binding of the data frames even easier.
avar.data.x <- readr::read_table("0 time av error
1 1.015223 0.030233604 0.003726832
2 2.030445 0.014819145 0.005270609
3 3.045668 0.010054801 0.006455241
4 4.060891 0.007477541 0.007453974
5 5.076113 0.006178282 0.008333912
6 6.091336 0.004949045 0.009129470")
avar.data.y <- readr::read_table("0 time av error
1 1.015223 0.06810001 0.003726832
2 2.030445 0.03408136 0.005270609
3 3.045668 0.02313839 0.006455241
4 4.060891 0.01737148 0.007453974
5 5.076113 0.01405144 0.008333912
6 6.091336 0.01172788 0.009129470")
library(tidyverse)
combine_df <- bind_rows(list(a = avar.data.x, b = avar.data.y), .id = 'ID')
ggplot(combine_df)+
geom_line(aes(x = time, y = sqrt(av), color = ID))+
scale_color_manual(values = c("red", "blue"),
labels = c(expression("Delta 18"^"O"), expression("Delta 13"^"C")))
Created on 2019-11-11 by the reprex package (v0.2.1)
Aim
I am trying to change the shape of the geom_point into a cross (so not a "plus/addition" sign, but a 'death' cross).
Attempt
Let say I have the following data:
library(tidyverse)
df <- read.table(text="x y
1 3
2 4
3 6
4 7 ", header=TRUE)
I am able to change the shape using the shape parameter in geom_point into different shapes, like this:
ggplot(data = df, aes(x =x, y=y)) +
geom_point(shape=2) # change shape
However, there is no option to change the shape into a cross.
Question
How do I change the shape of a value into a cross using ggplot in R?
Shape can be set to a unicode character. The below uses the skull and crossbones but you can look up a more suitable symbol.
Note that the final result will depend on the font used to generate the plot.
ggplot(data = df, aes(x =x, y=y)) +
geom_point(shape="\u2620", size = 10)