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Plotting two variables as lines using ggplot2 on the same graph
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The solution with ggplot in this question worked really well for my data. However, I am trying to add a legend and everything that I tried does not work...
For example, in the ggplot example in the above question, how I can add a legend to show that the red curve is related to "Ocean" and the green curve is related to "Soil"? Yes, I want to add text that I will define and it is not related to any other variable in my data.frame.
The example below is some of my own data...
Rate Probability Stats
1.0e-04 1e-04 891.15
1.0e-05 1e-04 690
...
etc (it's about 400 rows). And I have two data frames similar to the above one.
So My code is
g <- ggplot(Master1MY, aes(Probability))
g <- g + geom_point(aes(y=Master1MY$Stats), colour="red", size=1)
g <- g + geom_point(aes(y=Transposon1MY$Stats), colour="blue", size=1)
g + labs(title= "10,000bp and 1MY", x = "Probability", y = "Stats")
The plot looks like
I just want a red and blue legend saying "Master" and "Transposon"
Thanks!
In ggplot it is generally most convenient to keep the data in a 'long' format. Here I use the function melt from the reshape2 package to convert your data from wide to long format. Depending how you specify different aesthetics (size, shape, colour et c), corresponding legends will appear.
library(ggplot2)
library(reshape2)
# data from the example you were referring to, in a 'wide' format.
x <- seq(-2, 2, 0.05)
ocean <- pnorm(x)
soil <- pnorm(x, 1, 1)
df <- data.frame(x, ocean, soil)
# melt the data to a long format
df2 <- melt(data = df, id.vars = "x")
# plot, using the aesthetics argument 'colour'
ggplot(data = df2, aes(x = x, y = value, colour = variable)) + geom_line()
Edit, set name and labels of legend
# Manually set name of the colour scale and labels for the different colours
ggplot(data = df2, aes(x = x, y = value, colour = variable)) +
geom_line() +
scale_colour_discrete(name = "Type of sample", labels = c("Sea water", "Soil"))
Edit2, following new sample data
Convert your data, assuming its organization from your update, to a long format. Again, I believe you make your ggplot life easier if you keep your data in a long format. I relate every step with the simple example data which I used in my first answer. Please note that there are many alternative ways to rearrange your data. This is one way, based on the small (non-reproducible) parts of your data you provided in the update.
# x <- seq(-2, 2, 0.05)
# Master1MY$Probability
Probability <- 1:100
# ocean <- pnorm(x)
# Master1MY$Stats
Master1MY <- rnorm(100, mean = 600, sd = 20)
# soil <- pnorm(x,1,1)
# Transposon1MY$Stats
Transposon1MY <- rnorm(100, mean = 100, sd = 10)
# df <- data.frame(x, ocean, soil)
df <- data.frame(Probability, Master1MY, Transposon1MY)
# df2 <- melt(df, id.var = "x")
df2 <- melt(df, id.var = "Probability")
# default
ggplot(data = df2, aes(x = Probability, y = value, col = variable)) +
geom_point()
# change legend name and labels, see previous edit using 'scale_colour_discrete'
# set manual colours scale using 'scale_colour_manual'.
ggplot(data = df2, aes(x = Probability, y = value, col = variable)) +
geom_point() +
scale_colour_manual(values = c("red","blue"), name = "Type of sample", labels = c("Master", "Transposon"))
Related
The grouping variable for creating a geom_violin() plot in ggplot2 is expected to be discrete for obvious reasons. However my discrete values are numbers, and I would like to show them on a continuous scale so that I can overlay a continuous function of those numbers on top of the violins. Toy example:
library(tidyverse)
df <- tibble(x = sample(c(1,2,5), size = 1000, replace = T),
y = rnorm(1000, mean = x))
ggplot(df) + geom_violin(aes(x=factor(x), y=y))
This works as you'd imagine: violins with their x axis values (equally spaced) labelled 1, 2, and 5, with their means at y=1,2,5 respectively. I want to overlay a continuous function such as y=x, passing through the means. Is that possible? Adding + scale_x_continuous() predictably gives Error: Discrete value supplied to continuous scale. A solution would presumably spread the violins horizontally by the numeric x values, i.e. three times the spacing between 2 and 5 as between 1 and 2, but that is not the only thing I'm trying to achieve - overlaying a continuous function is the key issue.
If this isn't possible, alternative visualisation suggestions are welcome. I know I could replace violins with a simple scatter plot to give a rough sense of density as a function of y for a given x.
The functionality to plot violin plots on a continuous scale is directly built into ggplot.
The key is to keep the original continuous variable (instead of transforming it into a factor variable) and specify how to group it within the aesthetic mapping of the geom_violin() object. The width of the groups can be modified with the cut_width argument, depending on the data at hand.
library(tidyverse)
df <- tibble(x = sample(c(1,2,5), size = 1000, replace = T),
y = rnorm(1000, mean = x))
ggplot(df, aes(x=x, y=y)) +
geom_violin(aes(group = cut_width(x, 1)), scale = "width") +
geom_smooth(method = 'lm')
By using this approach, all geoms for continuous data and their varying functionalities can be combined with the violin plots, e.g. we could easily replace the line with a loess curve and add a scatter plot of the points.
ggplot(df, aes(x=x, y=y)) +
geom_violin(aes(group = cut_width(x, 1)), scale = "width") +
geom_smooth(method = 'loess') +
geom_point()
More examples can be found in the ggplot helpfile for violin plots.
Try this. As you already guessed, spreading the violins by numeric values is the key to the solution. To this end I expand the df to include all x values in the interval min(x) to max(x) and use scale_x_discrete(drop = FALSE) so that all values are displayed.
Note: Thanks #ChrisW for the more general example of my approach.
library(tidyverse)
set.seed(42)
df <- tibble(x = sample(c(1,2,5), size = 1000, replace = T), y = rnorm(1000, mean = x^2))
# y = x^2
# add missing x values
x.range <- seq(from=min(df$x), to=max(df$x))
df <- df %>% right_join(tibble(x = x.range))
#> Joining, by = "x"
# Whatever the desired continuous function is:
df.fit <- tibble(x = x.range, y=x^2) %>%
mutate(x = factor(x))
ggplot() +
geom_violin(data=df, aes(x = factor(x, levels = 1:5), y=y)) +
geom_line(data=df.fit, aes(x, y, group=1), color = "red") +
scale_x_discrete(drop = FALSE)
#> Warning: Removed 2 rows containing non-finite values (stat_ydensity).
Created on 2020-06-11 by the reprex package (v0.3.0)
I have timeseries of 4 simulated variables, with its 4 observed variables (observed variables have less data than simulated variables) as attached in the following link:
https://www.dropbox.com/s/sumgi6pqmjx70dl/nutrients2.csv?dl=0
I used the following code, The data is stored in "data 2" object
data2 <- read.table("C:/Users/Downloads/nutrients2.csv", header=T, sep=",")
library(lubridate)
data2$Date <- dmy(data2$Date)
library(reshape2)
data2 <- melt(data2, id=c("Date","Type"))
seg2 <- ggplot(data = data2, aes(x = Date, y = value, group = Type, colour = Type)) +
geom_line() +
facet_wrap(~ variable, scales = "free")
seg2
This give the plot (all variables in line)
Plot obtained
I need the observed data in points instead of interrupted lines, like this example
Plot desired
How to get a plot like this in ggplot, (simulated variables in line and observed variables in points or dots)?
One possible solution is to subset your dataset for geom_line and geom_point in order to use only sim and obs data respectively.
Then, if you pass shape = Type in your aes, you can remove dots for sim data in your legend by using scale_shape_manual:
(NB: I used melt function from data.table package because I found it more efficient for big dataset than the melt function reshape2)
library(lubridate)
df$Date <- dmy(df$Date)
library(data.table)
dt.m <- melt(setDT(df),measure = list(c("Nitrate","Ammonium","DIP","Chla")), value.name = "Values", variable.name = "Element")
library(ggplot2)
ggplot(dt.m, aes(x = Date, y = Values, group = Type, color = Type, shape = Type))+
geom_line(data = subset(dt.m, Type == "sim"))+
geom_point(data = subset(dt.m, Type == "obs"))+
scale_shape_manual(values = c(16,NA))+
facet_wrap(~Element, scales = "free")
So, I have a fairly large dataset (Dropbox: csv file) that I'm trying to plot using geom_boxplot. The following produces what appears to be a reasonable plot:
require(reshape2)
require(ggplot2)
require(scales)
require(grid)
require(gridExtra)
df <- read.csv("\\Downloads\\boxplot.csv", na.strings = "*")
df$year <- factor(df$year, levels = c(2010,2011,2012,2013,2014), labels = c(2010,2011,2012,2013,2014))
d <- ggplot(data = df, aes(x = year, y = value)) +
geom_boxplot(aes(fill = station)) +
facet_grid(station~.) +
scale_y_continuous(limits = c(0, 15)) +
theme(legend.position = "none"))
d
However, when you dig a little deeper, problems creep in that freak me out. When I labeled the boxplot medians with their values, the following plot results.
df.m <- aggregate(value~year+station, data = df, FUN = function(x) median(x))
d <- d + geom_text(data = df.m, aes(x = year, y = value, label = value))
d
The medians plotted by geom_boxplot aren't at the medians at all. The labels are plotted at the correct y-axis value, but the middle hinge of the boxplots are definitely not at the medians. I've been stumped by this for a few days now.
What is the reason for this? How can this type of display be produced with correct medians? How can this plot be debugged or diagnosed?
The solution to this question is in the application of scale_y_continuous. ggplot2 will perform operations in the following order:
Scale Transformations
Statistical Computations
Coordinate Transformations
In this case, because a scale transformation is invoked, ggplot2 excludes data outside the scale limits for the statistical computation of the boxplot hinges. The medians calculated by the aggregate function and used in the geom_text instruction will use the entire dataset, however. This can result in different median hinges and text labels.
The solution is to omit the scale_y_continuous instruction and instead use:
d <- ggplot(data = df, aes(x = year, y = value)) +
geom_boxplot(aes(fill = station)) +
facet_grid(station~.) +
theme(legend.position = "none")) +
coord_cartesian(y = c(0,15))
This allows ggplot2 to calculate the boxplot hinge stats using the entire dataset, while limiting the plot size of the figure.
How can I make the x-axis display the text in the "xaxisTitles" vector?
Here is my code you can run:
require(ggplot2)
require(reshape)
xaxisTitles<- cbind("a","b","c","d","e","f","g","h","j","k")
df <- data.frame(time = 1:10,
a = cumsum(rnorm(10)),
b = cumsum(rnorm(10)),
c = cumsum(rnorm(10)))
df <- melt(df , id = 'time', variable_name = 'series')
# plot on same grid, each series colored differently --
# good if the series have same scale
ggplot(df, aes(time,value)) + geom_line(aes(colour = series))+ theme(axis.text.x = xaxisTitles)
I am getting the error:
Error in (function (el, elname) :
Element axis.text.x must be a element_text object.
The reason you are getting the error is that theme(...) is used to set the appearance of the axis text (e.g., color, font family, font face, size, orientation, etc.), but not the values of the text. To do that, as #SteveReno points out, you have to use scale_x_discrete(...).
require(ggplot2)
require(reshape)
set.seed(321)
# xaxisTitles<- cbind("a","b","c","d","e","f","g","h","j","k")
xaxisTItles<- letters[1:10] # easier way to do this...
df <- data.frame(time = 1:10,
a = cumsum(rnorm(10)),
b = cumsum(rnorm(10)),
c = cumsum(rnorm(10)))
df <- melt(df , id = 'time', variable_name = 'series')
# plot on same grid, each series colored differently --
# good if the series have same scale
ggplot(df, aes(time,value)) +
geom_line(aes(colour = series))+
scale_x_discrete(labels=xaxisTitles)+
theme(axis.text.x=element_text(face="bold",colour="red",size=14))
The best way to do this is to make the time variable a factor rather than a numeric vector, as long as you remember to adjust the group aesthetic:
df$time = factor(xaxisTitles[df$time])
ggplot(df, aes(time, value)) + geom_line(aes(colour = series, group=series))
(If you don't add the group=series argument, it won't know that you want to connect lines across the factor on the x axis).
You can just use scale_x_discrete to set the labels.
ggplot(df, aes(time,value)) + geom_line(aes(colour = series))+ scale_x_discrete(labels= xaxisTitles)
Here's some more helpful info http://docs.ggplot2.org/0.9.3.1/scale_discrete.html
I have a large number of data series that I want to plot using small multiples. A combination of ggplot2 and facet_wrap does what I want, typically resulting a nice little block of 6 x 6 facets. Here's a simpler version:
The problem is that I don't have adequate control over the labels in facet strips. The names of the columns in the data frame are short and I want to keep them that way, but I want the labels in the facets to be more descriptive. I can use facet_grid so that I can take advantage of the labeller function but then there seems to be no straightforward way to specify the number of columns and a long row of facets just doesn't work for this particular task. Am I missing something obvious?
Q. How can I change the facet labels when using facet_wrap without changing the column names? Alternatively, how can I specify the number of columns and rows when using facet_grid?
Code for a simplified example follows. In real life I am dealing with multiple groups each containing dozens of data series, each of which changes frequently, so any solution would have to be automated rather than relying on manually assigning values.
require(ggplot2)
require(reshape)
# Random data with short column names
set.seed(123)
myrows <- 30
mydf <- data.frame(date = seq(as.Date('2012-01-01'), by = "day", length.out = myrows),
aa = runif(myrows, min=1, max=2),
bb = runif(myrows, min=1, max=2),
cc = runif(myrows, min=1, max=2),
dd = runif(myrows, min=1, max=2),
ee = runif(myrows, min=1, max=2),
ff = runif(myrows, min=1, max=2))
# Plot using facet wrap - we want to specify the columns
# and the rows and this works just fine, we have a little block
# of 2 columns and 3 rows
mydf <- melt(mydf, id = c('date'))
p1 <- ggplot(mydf, aes(y = value, x = date, group = variable)) +
geom_line() +
facet_wrap( ~ variable, ncol = 2)
print (p1)
# Problem: we want more descriptive labels without changing column names.
# We can change the labels, but doing so requires us to
# switch from facet_wrap to facet_grid
# However, in facet_grid we can't specify the columns and rows...
mf_labeller <- function(var, value){ # lifted bodily from the R Cookbook
value <- as.character(value)
if (var=="variable") {
value[value=="aa"] <- "A long label"
value[value=="bb"] <- "B Partners"
value[value=="cc"] <- "CC Inc."
value[value=="dd"] <- "DD Company"
value[value=="ee"] <- "Eeeeeek!"
value[value=="ff"] <- "Final"
}
return(value)
}
p2 <- ggplot(mydf, aes(y = value, x = date, group = variable)) +
geom_line() +
facet_grid( ~ variable, labeller = mf_labeller)
print (p2)
I don't quite understand. You've already written a function that converts your short labels to long, descriptive labels. What is wrong with simply adding a new column and using facet_wrap on that column instead?
mydf <- melt(mydf, id = c('date'))
mydf$variableLab <- mf_labeller('variable',mydf$variable)
p1 <- ggplot(mydf, aes(y = value, x = date, group = variable)) +
geom_line() +
facet_wrap( ~ variableLab, ncol = 2)
print (p1)
To change the label names, just change the factor levels of the factor you use in facet_wrap. These will be used in facet_wrap on the strips. You can use a similar setup as you would using the labeller function in facet_grid. Just do something like:
new_labels = sapply(levels(df$factor_variable), custom_labeller_function)
df$factor_variable = factor(df$factor_variable, levels = new_labels)
Now you can use factor_variable in facet_wrap.
Just add labeller = label_wrap_gen(width = 25, multi_line = TRUE) to the facet_wrap() arguments.
Eg.: ... + facet_wrap( ~ variable, ,labeller = label_wrap_gen(width = 25, multi_line = TRUE))
More info: ?ggplot2::label_wrap_gen
Simply add labeller = label_both to the facet_wrap() arguments.
... + facet_wrap( ~ variable, labeller = label_both)