I'm attempting to create a graph of various strains preferred light level for growth. My dataset is named "D" and my x axis should be "strain" and my y axis "light". I've tried the code
geom_point(data = D, aes(strain, light))
but when I plot this, the argument
mapping: x = ~strain, y = ~light
geom_point: na.rm = FALSE
stat_identity: na.rm = FALSE
position_identity"
appears instead of a plot. I then tried
ggplot(D, aes(strain, light))
but then a graph shows up but without anything plotted. Any ideas for why this might be?
You just need to reformat your code:
ggplot(D, aes(x = strain, y = light)) +
geom_point()
This will get you to a basic plot, and then you can look at adding layers onto it.
You need to connect the ggplot() call via + to the geom_point() call like this: ggplot(D, aes(strain, light)) + geom_point()
Related
Hi I am trying to code for a scatter plot for three variables in R:
Race= [0,1]
YOI= [90,92,94]
ASB_mean = [1.56, 1.59, 1.74]
Antisocial <- read.csv(file = 'Antisocial.csv')
Table_1 <- ddply(Antisocial, "YOI", summarise, ASB_mean = mean(ASB))
Table_1
Race <- unique(Antisocial$Race)
Race
ggplot(data = Table_1, aes(x = YOI, y = ASB_mean, group_by(Race))) +
geom_point(colour = "Black", size = 2) + geom_line(data = Table_1, aes(YOI,
ASB_mean), colour = "orange", size = 1)
Image of plot: https://drive.google.com/file/d/1E-ePt9DZJaEr49m8fguHVS0thlVIodu9/view?usp=sharing
Data file: https://drive.google.com/file/d/1UeVTJ1M_eKQDNtvyUHRB77VDpSF1ASli/view?usp=sharing
Can someone help me understand where I am making mistake? I want to plot mean ASB vs YOI grouped by Race. Thanks.
I am not sure what is your desidered output. Maybe, if I well understood your question I Think that you want somthing like this.
g_Antisocial <- Antisocial %>%
group_by(Race) %>%
summarise(ASB = mean(ASB),
YOI = mean(YOI))
Antisocial %>%
ggplot(aes(x = YOI, y = ASB, color = as_factor(Race), shape = as_factor(Race))) +
geom_point(alpha = .4) +
geom_point(data = g_Antisocial, size = 4) +
theme_bw() +
guides(color = guide_legend("Race"), shape = guide_legend("Race"))
and this is the output:
#Maninder: there are a few things you need to look at.
First of all: The grammar of graphics of ggplot() works with layers. You can add layers with different data (frames) for the different geoms you want to plot.
The reason why your code is not working is that you mix the layer call and or do not really specify (and even mix) what is the scatter and line visualisation you want.
(I) Use ggplot() + geom_point() for a scatter plot
The ultimate first layer is: ggplot(). Think of this as your drawing canvas.
You then speak about adding a scatter plot layer, but you actually do not do it.
For example:
# plotting antisocal data set
ggplot() +
geom_point(data = Antisocial, aes(x = YOI, y = ASB, colour = as.factor(Race)))
will plot your Antiscoial data set using the scatter, i.e. geom_point() layer.
Note that I put Race as a factor to have a categorical colour scheme otherwise you might end up with a continous palette.
(II) line plot
In analogy to above, you would get for the line plot the following:
# plotting Table_1
ggplot() +
geom_line(data = Table_1, aes(x = YOI, y = ASB_mean))
I save showing the plot of the line.
(III) combining different layers
# putting both together
ggplot() +
geom_point(data = Antisocial, aes(x = YOI, y = ASB, colour = as.factor(Race))) +
geom_line(data = Table_1, aes(x = YOI, y = ASB_mean)) +
## this is to set the legend title and have a nice(r) name in your colour legend
labs(colour = "Race")
This yields:
That should explain how ggplot-layering works. Keep an eye on the datasets and geoms that you want to use. Before working with inheritance in aes, I recommend to keep the data= and aes() call in the geom_xxxx. This avoids confustion.
You may want to explore with geom_jitter() instead of geom_point() to get a bit of a better presentation of your dataset. The "few" points plotted are the result of many datapoints in the same position (and overplotted).
Moving away from plotting to your question "I want to plot mean ASB vs YOI grouped by Race."
I know too little about your research to fully comprehend what you mean with that.
I take it that the mean ASB you calculated over the whole population is your reference (aka your Table_1), and you would like to see how the Race groups feature vs this population mean.
One option is to group your race data points and show them as boxplots for each YOI.
This might be what you want. The boxplot gives you the median and quartiles, and you can compare this per group against the calculated ASB mean.
For presentation purposes, I highlighted the line by increasing its size and linetype. You can play around with the colours, etc. to give you the aesthetics you aim for.
Please note, that for the grouped boxplot, you also have to treat your integer variable YOI, I coerced into a categorical factor. Boxplot works with fill for the body (colour sets only the outer line). In this setup, you also need to supply a group value to geom_line() (I just assigned it to 1, but that is arbitrary - in other contexts you can assign another variable here).
ggplot() +
geom_boxplot(data = Antisocial, aes(x = as.factor(YOI), y = ASB, fill = as.factor(Race))) +
geom_line(data = Table_1, aes(x = as.factor(YOI), y = ASB_mean, group = 1)
, size = 2, linetype = "dashed") +
labs(x = "YOI", fill = "Race")
Hope this gets you going!
I am trying to get a boxplot with 3 different tools in each dataset size like the one below:
ggplot(data1, aes(x = dataset, y = time, color = tool)) + geom_boxplot() +
labs(x = 'Datasets', y = 'Seconds', title = 'Time') +
scale_y_log10() + theme_bw()
But I need to transform x-axis to log scale. For that, I need to numericize each dataset to be able to transform them to log scale. Even without transforming them, they look like the one below:
ggplot(data2, aes(x = dataset, y = time, color = tool)) + geom_boxplot() +
labs(x = 'Datasets', y = 'Seconds', title = 'Time') +
scale_y_log10() + theme_bw()
I checked boxplot parameters and grouping parameters of aes, but could not resolve my problem. At first, I thought this problem is caused by scaling to log, but removing those elements did not resolve the problem.
What am I missing exactly? Thanks...
Files are in this link. "data2" is the numericized version of "data1".
Your question was a tough cookie, but I learned something new from it!
Just using group = dataset is not sufficient because you also have the tool variable to look out for. After digging around a bit, I found this post which made use of the interaction() function.
This is the trick that was missing. You want to use group because you are not using a factor for the x values, but you need to include tool in the separation of your data (hence using interaction() which will compute the possible crosses between the 2 variables).
# This is for pretty-printing the axis labels
my_labs <- function(x){
paste0(x/1000, "k")
}
levs <- unique(data2$dataset)
ggplot(data2, aes(x = dataset, y = time, color = tool,
group = interaction(dataset, tool))) +
geom_boxplot() + labs(x = 'Datasets', y = 'Seconds', title = 'Time') +
scale_x_log10(breaks = levs, labels = my_labs) + # define a log scale with your axis ticks
scale_y_log10() + theme_bw()
This plots
I have two data frames: one I am using to create the bars in a barchart and a second that I am using to create a shaded "target region" behind the bars using geom_rect.
Here is example data:
test.data <- data.frame(crop=c("A","B","C"), mean=c(6,4,12))
target.data <- data.frame(crop=c("ONE","TWO"), mean=c(31,12), min=c(24,9), max=c(36,14))
I start with the means of test.data for the bars and means of target.data for the line in the target region:
library(ggplot2)
a <- ggplot(test.data, aes(y=mean, x=crop)) + geom_hline(aes(yintercept = mean, color = crop), target.data) + geom_bar(stat="identity")
a
So far so good, but then when I try to add a shaded region to display the min-max range of target.data, there is an issue. The shaded region appears just fine, but somehow, the crops from target.data are getting added to the x-axis. I'm not sure why this is happening.
b <- a + geom_rect(aes(xmin=-Inf, xmax=Inf, ymin=min, ymax=max, fill = crop), data = target.data, alpha = 0.5)
b
How can I add the geom_rect shapes without adding those extra names to the x-axis of the bar-chart?
This is a solution to your question, but I'd like to better understand you problem because we might be able to make a more interpretable plot. All you have to do is add aes(x = NULL) to your geom_rect() call. I took the liberty to change the variable 'crop' in add.data to 'brop' to minimize any confusion.
test.data <- data.frame(crop=c("A","B","C"), mean=c(6,4,12))
add.data <- data.frame(brop=c("ONE","TWO"), mean=c(31,12), min=c(24,9), max=c(36,14))
ggplot(test.data, aes(y=mean, x=crop)) +
geom_hline(data = add.data, aes(yintercept = mean, color = brop)) +
geom_bar(stat="identity") +
geom_rect(data = add.data, aes(xmin=-Inf, xmax=Inf, x = NULL, ymin=min, ymax=max, fill = brop),
alpha = 0.5, show.legend = F)
In ggplot calls all of the aesthetics or aes() are inherited from the intial call:
ggplot(data, aes(x=foo, y=bar)).
That means that regardless of what layers I add on geom_rect(), geom_hline(), etc. ggplot is looking for 'foo' to assign to x and 'bar' to assign to y, unless you specifically tell it otherwise. So like aeosmith pointed out you can clear all inherited aethesitcs for a layer with inherit.aes = FALSE, or you can knock out single variables at a time by reassigning them as NULL.
I'm iterating through multiple data sets to produce line plots for each set. How can I prevent ggplot from complaining when I use geom_line over one point?
Take, for example, the following data:
mydata = data.frame(
x = c(1, 2),
y = c(2, 2),
group = as.factor(c("foo", "foo"))
)
Creating line graph looks and works just fine because there are two points in the line:
ggplot(mydata, aes(x = x, y = y)) +
geom_point() +
geom_line(aes(group = group))
However, plotting only the fist row give the message:
geom_path: Each group consists of only one observation. Do you need to adjust the group aesthetic?
ggplot(mydata[1,], aes(x = x, y = y)) +
geom_point() +
geom_line(aes(group = group))
Some of my figures will only have one point and the messages cause hangups in the greater script that produces these figures. I know the plots still work, so my concern is avoiding the message. I'd also like to avoid using suppressWarnings() if possible in case another legitimate and unexpected issue arises.
Per an answer to this question: suppressMessages(ggplot()) fails because you have to wrap it around a print() call of the ggplot object--not the ggplot object itself. This is because the warning/message only occurs when the object is drawn.
So, to view your plot without a warning message run:
p <- ggplot(mydata[1,], aes(x = x, y = y)) +
geom_point() +
geom_line(aes(group = group))
suppressMessages(print(p))
I think the following if-else solution should resolve the problem:
if (nrow(mydata) > 1) {
ggplot(mydata, aes(x = x, y = y)) +
geom_point() +
geom_line(aes(group = group))
} else {
ggplot(mydata, aes(x = x, y = y)) +
geom_point()
}
On the community.RStudio.com, John Mackintosh suggests a solution which worked for me:
Freely quoting:
Rather than suppress warnings, change the plot layers slightly.
Facet wrap to create empty plot
Add geom_point for entire data frame
Subset the dataframe by creating a vector of groups with more than one data point, and filtering the original data for those groups. Only
plot lines for this subset.
Details and example code in the followup of the link above.
I'm currently working on a very simple data.frame, containing three columns:
x contains x-coordinates of a set of points,
y contains y-coordinates of the set of points, and
weight contains a value associated to each point;
Now, working in ggplot2 I seem to be able to plot contour levels for these data, but i can't manage to find a way to fill the plot according to the variable weight. Here's the code that I used:
ggplot(df, aes(x,y, fill=weight)) +
geom_density_2d() +
coord_fixed(ratio = 1)
You can see that there's no filling whatsoever, sadly.
I've been trying for three days now, and I'm starting to get depressed.
Specifying fill=weight and/or color = weight in the general ggplot call, resulted in nothing. I've tried to use different geoms (tile, raster, polygon...), still nothing. Tried to specify the aes directly into the geom layer, also didn't work.
Tried to convert the object as a ppp but ggplot can't handle them, and also using base-R plotting didn't work. I have honestly no idea of what's wrong!
I'm attaching the first 10 points' data, which is spaced on an irregular grid:
x = c(-0.13397460,-0.31698730,-0.13397460,0.13397460,-0.28867513,-0.13397460,-0.31698730,-0.13397460,-0.28867513,-0.26794919)
y = c(-0.5000000,-0.6830127,-0.5000000,-0.2320508,-0.6547005,-0.5000000,-0.6830127,-0.5000000,-0.6547005,0.0000000)
weight = c(4.799250e-01,5.500250e-01,4.799250e-01,-2.130287e+12,5.798250e-01,4.799250e-01,5.500250e-01,4.799250e-01,5.798250e-01,6.618956e-01)
any advise? The desired output would be something along these lines:
click
Thank you in advance.
From your description geom_density doesn't sound right.
You could try geom_raster:
ggplot(df, aes(x,y, fill = weight)) +
geom_raster() +
coord_fixed(ratio = 1) +
scale_fill_gradientn(colours = rev(rainbow(7)) # colourmap
Here is a second-best using fill=..level... There is a good explanation on ..level.. here.
# load libraries
library(ggplot2)
library(RColorBrewer)
library(ggthemes)
# build your data.frame
df <- data.frame(x=x, y=y, weight=weight)
# build color Palette
myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")), space="Lab")
# Plot
ggplot(df, aes(x,y, fill=..level..) ) +
stat_density_2d( bins=11, geom = "polygon") +
scale_fill_gradientn(colours = myPalette(11)) +
theme_minimal() +
coord_fixed(ratio = 1)