I have a dataframe that looks as follows:
X = c(6,6.2,6.4,6.6,6.8,5.6,5.8,6,6.2,6.4,6.6,6.8,7,7.2,7.4,7.6,7.8,8,2.8,3,3.2,3.4,3.6,3.8,4,4.2,4.4,4.6,4.8,5)
Y = c(2.2,2.2,2.2,2.2,2.2,2.6,2.6,2.6,2.6,2.6,2.6,2.6,2.6,2.6,2.6,2.6,2.6,2.6,2.8,2.8,2.8,2.8,2.8,2.8,2.8,2.8,2.8,2.8,2.8,2.8)
Value = c(0,0.00683254,0,0.007595654,0.015517884,0,0,0,0,0,0,0,0,0,0.005219395,0,0,0,0,0,0,0,0,0,0,0,0.002892342,0,0.002758141,0)
table = data.frame(X, Y, Value)
I have put together a heatmap in R, based on the following command:
ggplot(data = table, mapping = aes(x = X, y = Y)) +
geom_tile(aes(fill = Value), colour = 'black') +
theme_void() +
scale_fill_gradient2(low = "white", high = "black") + xlab(label = "X") + ylab(label = "Y")
Since there is not a value for every X and Y, it leads to plots that appear as follows.
I am attempting to smoothen the plot and have the following question:
As there are small white spaces between the plotted values, how could one color these white spaces to be the median intensity? Said differently, how would I first create an initial layer with non-zero median 'Value' before plotting the non-zero 'Value' on top (overlayed)?
A sample is shown below, which has been 'smoothed', which looks closer to the desired output.
I'm not sure if it will totally fit your need but from my understanding you have some missing values and combination of X and Y.
So, you can use complete function from tidyr to get all different combinations of X and Y (those without values will be filled with NA) and then by using na.value argument in scale_fill_gradient2 function, you can set the values of these NA values to the same color of the midpoint value:
library(tidyr)
library(dplyr)
library(ggplot2)
table %>% complete(X,Y) %>%
ggplot(aes(x = X, y = Y))+
geom_raster(aes(fill = Value), interpolate = TRUE)+
scale_fill_gradient2(low = "white", mid = "grey",high = "black",
na.value = "grey")
Does it answer your question ?
Related
I am trying to plot a graph in ggplot2 where the x-axis represents month-day combinations, the dots represent y-values for two different groups.
When graphing my original data set using this code,
ggplot(graphing.df, aes(MONTHDAY, y.var, color = GROUP)) +
geom_point() +
ylab(paste0(""))+
scale_x_discrete(breaks = function(x) x[seq(1, length(x), by = 15)])+
theme(legend.text = element_blank(),
legend.title = element_blank()) +
geom_vline(xintercept = which(graphing.df$MONTHDAY == "12-27")[1], col='red', lwd=2)
I get this graph where the vertical line is not showing.
When I tried to create a reproducible example using the following code...
df <- data.frame(MONTHDAY = c("01-01", "01-01", "01-02", "01-02", "01-03", "01-03"),
TYPE = rep(c("A", "B"), 3),
VALUE = sample(1:10, 6, replace = TRUE))
verticle_line <- "01-02"
ggplot(df, aes(MONTHDAY, VALUE, color = TYPE)) +
geom_point() +
#geom_vline(xintercept = which(df$MONTHDAY == verticle_line)[1], col='red', lwd=2)+
geom_vline(xintercept = which(df$MONTHDAY == verticle_line), col='blue', lwd=2)
The vertical line is showing, but now its showing in the wrong place
In my original data set I have two values for each month-day combination (representing each of the two groups). The month-day combination column is a character vector, it is not a factor and does not have levels.
Here is a way. It subsets the data keeping only the rows of interest and plots the vertical line defined by MONTHDAY.
library(ggplot2)
verticle_line <- "01-02"
ggplot(df, aes(MONTHDAY, VALUE, color = TYPE)) +
geom_point() +
geom_vline(data = subset(df, MONTHDAY == verticle_line),
mapping = aes(xintercept = MONTHDAY), color = 'blue', size = 2)
Data
I will repost the data creation code, this time setting the RNG seed in order to make the example reproducible.
set.seed(2020)
df <- data.frame(MONTHDAY = c("01-01", "01-01", "01-02", "01-02", "01-03", "01-03"),
TYPE = rep(c("A", "B"), 3),
VALUE = sample(1:10, 6, replace = TRUE))
The reason your line is not showing up where you expect is because you are setting the value of xintercept= via the output of the which() function. which() returns the index value where the condition is true. So in the case of your reproducible example, you get the following:
> which(df$MONTHDAY == verticle_line)
[1] 3 4
It returns a vector indicating that in df$MONTHDAY, indexes 3 and 4 in that vector are true. So your code below:
geom_vline(xintercept = which(df$MONTHDAY == verticle_line)...
Reduces down to this:
geom_vline(xintercept = c(3,4)...
Your MONTHDAY axis is not formatted as a date, but treated as a discrete axis of character vectors. In this case xintercept=c(3,4) applied to a discrete axis draws two vertical lines at x intercepts equivalent to the 3rd and 4th discrete position on that axis: in other words, "01-03" and... some unknown 4th position that is not observable within the axis limits.
How do you fix this? Just take out which():
ggplot(df, aes(MONTHDAY, VALUE, color = TYPE)) +
geom_point() +
geom_vline(xintercept = verticle_line, col='blue', lwd=2)
We can get the corresponding values of 'MONTHDAY' after subsetting
ggplot(df, aes(MONTHDAY, VALUE, color = TYPE)) +
geom_point() +
geom_vline(xintercept = df$MONTHDAY[df$MONTHDAY == verticle_line],
col='blue', lwd=2)
I'm plotting correlations in ggpairs and am splitting the data based on a filter.
The density plots are normalising themselves on the number of data points in each filtered group. I would like them to normalise on the total number of data points in the entire data set. Essentially, I would like to be able to have the sum of the individual density plots be equal to the density plot of the entire dataset.
I know this probably breaks the definition of "density plot", but this is a presentation style I'd like to explore.
In plain ggplot, I can do this by adding y=..count.. to the aesthetic, but ggpairs doesn't accept x or y aesthetics.
Some sample code and plots:
set.seed(1234)
group = as.numeric(cut(runif(100),c(0,1/2,1),c(1,2)))
x = rnorm(100,group,1)
x[group == 1] = (x[group == 1])^2
y = (2 * x) + rnorm(100,0,0.1)
data = data.frame(group = as.factor(group), x = x, y = y)
#plot of everything
data %>%
ggplot(aes(x)) +
geom_density(color = "black", alpha = 0.7)
#the scaling I want
data %>%
ggplot(aes(x,y=..count.., fill=group)) +
geom_density(color = "black", alpha = 0.7)
#the scaling I get
data %>%
ggplot(aes(x, fill=group)) +
geom_density(color = "black", alpha = 0.7)
data %>% ggpairs(., columns = 2:3,
mapping = ggplot2::aes(colour=group),
lower = list(continuous = wrap("smooth", alpha = 0.5, size=1.0)),
diag = list(continuous = wrap("densityDiag", alpha=0.5 ))
)
Are there any suggestions that don't involve reformatting the entire dataset?
I am not sure I understand the question but if the densities of both groups plus the density of the entire data is to be plotted, it can easily be done by
Getting rid of the grouping aesthetics, in this case, fill.
Placing another call to geom_density but this time with inherit.aes = FALSE so that the previous aesthetics are not inherited.
And then plot the densities.
library(tidyverse)
data %>%
ggplot(aes(x, y=..count.., fill = group)) +
geom_density(color = "black", alpha = 0.7) +
geom_density(mapping = aes(x, y = ..count..),
inherit.aes = FALSE)
I have 'elevation' as my y-axis and I want it as a discrete variable (in other words I want the space between each elevation to be equal and not relative to the numerical differences). My x-axis is 'time' (julian date).
mydata2<- data.frame(
"Elevation" = c(rep(c(1200),10),rep(c(1325.5),10),rep(c(1350.75),10), rep(c(1550.66),10)),
"Sex" = c(rep(c("F","M"),20)),
"Type" = c(rep(c("emerge","emerge","endhet","endhet","immerge","immerge","melt","melt", "storpor","storpor"),4)),
"mean" = c(rep(c(104,100,102,80,185,210,84,84,188,208,104,87,101,82, 183,188,83,83,190,189),2))
"se"=c(rep(c(.1,.01,.2,.02,.03),4)))
mydata2$Sex<-factor(mydata2$Sex))
mydata2$Type<-factor(mydata2$Type))
mydata2$Elevation<-factor(mydata2$Elevation))
at<-ggplot(mydata2, aes(y = mean, x = Elevation,color=Type, group=Sex)) +
geom_pointrange(aes(ymin = mean-se, ymax = mean+se),
position=position_jitter(width=0.2,height=.1),
linetype='solid') +
facet_grid(Sex~season,scales = "free")+
coord_flip()
at
Ideally, I would like each 'type' to be separated vertically. When I jitter or dodge only those that are close separate and not evenly. Is there a way to force each 'type' to be slightly shifted so they are all on their own line? I tried to force it by giving each type a slightly different 'elevation' but then I end up with a messy y-axis (I can't figure out a way to keep the point but not display all the tick marks with a discrete scale).
Thank you for your help.
If you want to use a numerical value as a discrete value, you should use as.factor. In your example, try to use x = as.factor(Elevation).
Additionally, I will suggest to use position = position_dodge() to get points from different conditions corresponding to the same elevation to be plot side-by-side
ggplot(mydata2, aes(y = mean, x = as.factor(Elevation),color=Type, group=Sex)) +
geom_pointrange(aes(ymin = mean-se, ymax = mean+se),
position=position_dodge(),
linetype='solid') +
facet_grid(Sex~season,scales = "free")+
coord_flip()
EDIT with example data provided by the OP
Using your dataset, I was not able to get range being plot with your point. So, I create two variable Lower and Upper using dplyr package.
Then, I did not pass your commdnas facotr(...) you provided in your question but instead, I used as.factor(Elevation) and position_dodge(0.9) for the plotting to get the following plot:
library(tidyverse)
mydata2 %>% mutate(Lower = mean-se*100, Upper = mean+se*100) %>%
ggplot(., aes( x = as.factor(Elevation), y = mean, color = Type))+
geom_pointrange(aes(ymin = Lower, ymax = Upper), linetype = "solid", position = position_dodge(0.9))+
facet_grid(Sex~., scales = "free")+
coord_flip()
Does it look what you are looking for ?
Data
Your dataset provided contains few errors (too much parenthesis), so I correct here.
mydata2<- data.frame(
"Elevation" = c(rep(c(1200),10),rep(c(1325.5),10),rep(c(1350.75),10), rep(c(1550.66),10)),
"Sex" = rep(c("F","M"),20),
"Type" = rep(c("emerge","emerge","endhet","endhet","immerge","immerge","melt","melt", "storpor","storpor"),4),
"mean" = rep(c(104,100,102,80,185,210,84,84,188,208,104,87,101,82, 183,188,83,83,190,189),2),
"se"=rep(c(.1,.1,.2,.05,.03),4))
I really struggle to set the correct legend for a geom_point plot with loess regression, while there is 2 data set used
I got a data set, who is summarizing activity over a day, and then I plot on the same graph, all the activity per hours and per days recorded, plus a regression curve smoothed with a loess function, plus the mean of each hours for all the days.
To be more precise, here is an example of the first code, and the graph returned, without legend, which is exactly what I expected:
# first graph, which is given what I expected but with no legend
p <- ggplot(dat1, aes(x = Hour, y = value)) +
geom_point(color = "darkgray", size = 1) +
geom_point(data = dat2, mapping = aes(x = Hour, y = mean),
color = 20, size = 3) +
geom_smooth(method = "loess", span = 0.2, color = "red", fill = "blue")
and the graph (in grey there is all the data, per hours, per days. the red curve is the loess regression. The blue dots are the means for each hours):
When I tried to set the legend I failed to plot one with the explanation for both kind of dots (data in grey, mean in blue), and the loess curve (in red). See below some example of what I tried.
# second graph, which is given what I expected + the legend for the loess that
# I wanted but with not the dot legend
p <- ggplot(dat1, aes(x = Hour, y = value)) +
geom_point(color = "darkgray", size = 1) +
geom_point(data = dat2, mapping = aes(x = Hour, y = mean),
color = "blue", size = 3) +
geom_smooth(method = "loess", span = 0.2, aes(color = "red"), fill = "blue") +
scale_color_identity(name = "legend model", guide = "legend",
labels = "loess regression \n with confidence interval")
I obtained the good legend for the curve only
and another trial :
# I tried to combine both date set into a single one as following but it did not
# work at all and I really do not understand how the legends works in ggplot2
# compared to the normal plots
A <- rbind(dat1, dat2)
p <- ggplot(A, aes(x = Heure, y = value, color = variable)) +
geom_point(data = subset(A, variable == "data"), size = 1) +
geom_point(data = subset(A, variable == "Moy"), size = 3) +
geom_smooth(method = "loess", span = 0.2, aes(color = "red"), fill = "blue") +
scale_color_manual(name = "légende",
labels = c("Data", "Moy", "loess regression \n with confidence interval"),
values = c("darkgray", "royalblue", "red"))
It appears that all the legend settings are mixed together in a "weird" way, the is a grey dot covering by a grey line, and then the same in blue and in red (for the 3 labels). all got a background filled in blue:
If you need to label the mean, might need to be a bit creative, because it's not so easy to add legend manually in ggplot.
I simulate something that looks like your data below.
dat1 = data.frame(
Hour = rep(1:24,each=10),
value = c(rnorm(60,0,1),rnorm(60,2,1),rnorm(60,1,1),rnorm(60,-1,1))
)
# classify this as raw data
dat1$Data = "Raw"
# calculate mean like you did
dat2 <- dat1 %>% group_by(Hour) %>% summarise(value=mean(value))
# classify this as mean
dat2$Data = "Mean"
# combine the data frames
plotdat <- rbind(dat1,dat2)
# add a dummy variable, we'll use it later
plotdat$line = "Loess-Smooth"
We make the basic dot plot first:
ggplot(plotdat, aes(x = Hour, y = value,col=Data,size=Data)) +
geom_point() +
scale_color_manual(values=c("blue","darkgray"))+
scale_size_manual(values=c(3,1),guide=FALSE)
Note with the size, we set guide to FALSE so it will not appear. Now we add the loess smooth, one way to introduce the legend is to introduce a linetype, and since there's only one group, you will have just one variable:
ggplot(plotdat, aes(x = Hour, y = value,col=Data,size=Data)) +
geom_point() +
scale_color_manual(values=c("blue","darkgray"))+
scale_size_manual(values=c(3,1),guide=FALSE)+
geom_smooth(data=subset(plotdat,Data="Raw"),
aes(linetype=line),size=1,alpha=0.3,
method = "loess", span = 0.2, color = "red", fill = "blue")
I have got a data frame with several 1000 rows in the form of
group = c("gr1","gr1","gr1","gr1","gr1","gr1","gr1","gr1","gr1","gr1","gr2","gr2","gr2","gr2","gr2","gr2","gr2","gr2","gr2","gr2","gr3","gr3","gr3","gr3","gr3","gr3","gr3","gr3","gr3","gr3")
pos = c(1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10)
color = c(2,2,2,2,3,3,2,2,3,2,1,2,2,2,1,1,1,1,1,1,2,2,2,2,2,2,1,1,2,2)
df = data.frame(group, pos, color)
and would like to make a kind of heatmap in which one axes has a continuous scale (position). The color column is categorical. However due to the large amount of data points I want to use binning, i.e. use it as a continuous variable.
This is more or less how the plot should look like:
I can't think of a way to create such a plot using ggplot2/R. I have tried several geometries, e.g. geom_point()
ggplot(data=df, aes(x=strain, y=pos, color=color)) +
geom_point() +
scale_colour_gradientn(colors=c("yellow", "black", "orange"))
Thanks for your help in advance.
Does this help you?
library(ggplot2)
group = c("gr1","gr1","gr1","gr1","gr1","gr1","gr1","gr1","gr1","gr1","gr2","gr2","gr2","gr2","gr2","gr2","gr2","gr2","gr2","gr2","gr3","gr3","gr3","gr3","gr3","gr3","gr3","gr3","gr3","gr3")
pos = c(1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10,1,2,3,4,5,6,7,8,9,10)
color = c(2,2,2,2,3,3,2,2,3,2,1,2,2,2,1,1,1,1,1,1,2,2,2,2,2,2,1,1,2,2)
df = data.frame(group, pos, color)
ggplot(data = df, aes(x = group, y = pos)) + geom_tile(aes(fill = color))
Looks like this
Improved version with 3 color gradient if you like
library(scales)
ggplot(data = df, aes(x = group, y = pos)) + geom_tile(aes(fill = color))+ scale_fill_gradientn(colours=c("orange","black","yellow"),values=rescale(c(1, 2, 3)),guide="colorbar")