I am using ggplot and geoms to show my data, but the plot sidebar area just shows a gray box with the x and y axis correctly labeled.
Here is the output image:
The code which made the plot:
ggplot(Wc, aes(y = popsafe, x = rnground)) +
geom_jitter(aes(col = me)) +
geom_smooth(method = "lm", se = FALSE, col = "black")
Looks like your dataset is empty. We don't know what your dataset contains, so here an example with the built-in iris dataset. First a proper plot, using the same geoms and mappings you use:
library(ggplot2)
ggplot(iris, aes(y = Sepal.Length, x = Sepal.Width)) +
geom_jitter(aes(col = Species)) +
geom_smooth(method = "lm", se = FALSE, col = "black")
Now I remove all the data from the dataset and replot:
library(dplyr)
iris_empty <- filter(iris, Sepal.Length < 0)
ggplot(iris_empty, aes(y = Sepal.Length, x = Sepal.Width)) +
geom_jitter(aes(col = Species)) +
geom_smooth(method = "lm", se = FALSE, col = "black")
A simple head(Wc) would confirm whether your dataset actually contains any data.
Related
I am plotting a simple linear model with ggplot2. However, when I increase the size of the line, the ribbon does not change size (understandable). However, how would I scale the ribbon so that it matches the increase in line thickness?
Here is a simple example using the iris dataset:
library(ggplot2)
ggplot(iris, aes(x = Petal.Width, y = Sepal.Length)) +
geom_point() +
stat_smooth(method = "lm", col = "red")
As you can see when you increase size (I'm over exaggerating the size here), then more of the ribbon is covered up.
ggplot(iris, aes(x = Petal.Width, y = Sepal.Length)) +
geom_point() +
stat_smooth(method = "lm", col = "red", size = 5)
Essentially, the ribbon only needs to extend out as much as the additional thickness of the line that is displacing/obscuring the ribbon.
Expected Results
Perhaps like this?
ggplot(iris, aes(x = Petal.Width, y = Sepal.Length)) +
geom_point() +
stat_smooth(method = "lm", col = "red", size = 5,
aes(ymin = after_stat(y - 5*se),
ymax = after_stat(y + 5*se)))
I am interested in doing a plot showing percentages by group.
something like this:
data(iris)
ggplot(iris,
aes(x = Sepal.Length, group = factor(Species), fill = factor(Species))) +
geom_histogram(position = "fill")+theme_bw()
however, I would also like to plot a histogram showing the frequency distribution on top of this graph.
something like the plot below.
ggplot(iris,aes(x = Sepal.Length)) +
geom_histogram()+theme_bw()
Does anyone know how to do this?
Note I know how to do a frequency plot by group: ggplot(iris,aes(x = Sepal.Length, group = factor(Species), fill = factor(Species))) + geom_histogram()+theme_bw(). But this is not what I want. Rather I would like a small frequency distribution at the bottom of the percentage plot presented at the beginning.
Thank you very much
Something like this?
library(gridExtra)
p1 <- ggplot(iris,
aes(x = Sepal.Length,
group = factor(Species),
fill = factor(Species))) +
geom_histogram(position = "fill") +
theme_bw() +
theme(legend.position = "top")
p2 <- ggplot(iris,aes(x = Sepal.Length,
group = factor(Species),
fill = factor(Species))) +
geom_histogram() +
theme_bw() +
theme(legend.position = "none")
grid.arrange(p1, p2,
heights = c(4, 1.5))
Edit: So you are looking for this then? Note that in this case the absolute values of the smaller histogram become meaningless since they were scaled down to be ~25% of the vertical chart range.
ggplot() +
geom_histogram(data = iris,
aes(x = Sepal.Length,
group = factor(Species),
fill = factor(Species)),
position = "fill",
alpha = 1) +
geom_histogram(data = iris,
aes(x = Sepal.Length,
y = ..ncount.. / 4),
alpha = 0.5,
fill = 'black')
I'm trying to plot individual regression lines for all of my experimental subjects (n=40) on the same plot where I show the overall regression line.
I can do the plots separately with ggplot, but I haven't found a way to superpose them on the same graph.
I can illustrate what I did with the iris data frame:
#first plot
ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length)) +
geom_point() +
stat_smooth(method = lm, se = FALSE) +
theme_classic()
# second plot, grouped by species
ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, colour =Species)) +
geom_point() +
stat_smooth(method = lm, se = FALSE) +
theme_classic()
# and I've been trying things like this:
ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length)) +
geom_point() +
stat_smooth(method = lm, se = FALSE) +
theme_classic() +
geom_point(aes(x = Sepal.Width, y = Sepal.Length, colour =Species))) +
stat_smooth(method = lm, se = FALSE) +
theme_classic()
which returns the message "Error: Cannot add ggproto objects together. Did you forget to add this object to a ggplot object?", so I get that this is not the right way to combine them, but what is?
How can I combine both graphs in one?
Thanks in advance!
Repeat the whole data and set Species to be something else ("Together") in example below. Attach the repeated data to the original data and just call the second plot.
d1 = iris
d2 = rbind(d1, transform(d1, Species = "Together"))
ggplot(d2, aes(x = Sepal.Width, y = Sepal.Length, colour =Species)) +
stat_smooth(method = lm, se = FALSE) +
geom_point(data = d1) +
theme_classic()
Similar to #d.b's answer, consider expanding the data frame with rbind, assigning an "All" category for Species and adjust for factor levels (so All shows at top on legend):
new_species_level <- c("All", unique(as.character(iris$Species)))
iris_expanded <- rbind(transform(iris, Species=factor("All", levels=new_species_level)),
transform(iris, Species=factor(Species, levels=new_species_level)))
ggplot(iris_expanded, aes(x=Sepal.Width, y=Sepal.Length, colour=Species)) +
geom_point() +
stat_smooth(method = lm, se = FALSE) +
theme_classic()
I am plotting 2 sets of data on the same plot using ggplot. I have specified the colour for each data set, but there is no legend that comes out when the dot plot is generated.
What can i do to manually add a legend?
# Create an index to hold values of m from 1 to 100
m_index <- (1:100)
data_frame_50 <- data(prob_max_abs_cor_50)
data_frame_20 <- data.frame(prob_max_abs_cor_20)
library(ggplot2)
plot1 <- ggplot(data_frame_50, mapping = aes(x = m_index,
y = prob_max_abs_cor_50),
colour = 'red') +
geom_point() +
ggplot(data_frame_20, mapping = aes(x = m_index,
y = prob_max_abs_cor_20),
colour = 'blue') +
geom_point()
plot1 + labs(x = " Values of m ",
y = " Maximum Absolute Correlation ",
title = "Dot plot of probability")
First, I would suggest neatening your ggplot code a little. This is equivalent to your posted code;
ggplot() +
geom_point(data = data_frame_50, aes(x = m_index, y = prob_max_abs_cor_50,
colour = 'red')) +
geom_point(data = data_frame_20, aes(x = m_index, y = prob_max_abs_cor_20,
colour = 'blue')) +
labs(x = " Values of m ", y = " Maximum Absolute Correlation ",
title = "Dot plot of probability")
You won't get a legend here, because you are plotting different datasets with only one category in each. You need to have a single dataset with a column grouping your data (i.e. 20 or 50). So using some example data, this is the equivalent of what you are plotting and ggplot won't provide a legend;
ggplot() +
geom_point(data = iris, aes(x = Sepal.Length, y = Petal.Width), colour = 'red') +
geom_point(data = iris, aes(x = Sepal.Length, y = Petal.Length), colour = 'blue')
If you want to colour by category, include a colour argument inside the aes call;
ggplot() +
geom_point(data = iris, aes(x = Sepal.Length, y = Petal.Width,
colour = factor(Species)))
Have a look at the iris dataset to get a sense of how you need to shape your data. It's hard to give precise advice, because you haven't provided an idea of what your data look like, but something like this might work;
df.20 <- data.frame("m" = 1:100, "Group" = 20, "Numbers" = prob_max_abs_cor_20)
df.50 <- data.frame("m" = 1:100, "Group" = 50, "Numbers" = prob_max_abs_cor_50)
df.All <- rbind(df.20, df.50)
The below code produces a scatter plot with regression lines for each group. Instead of the sloped regression lines is it possible to plot horizontal lines that represent the average of each group's y values? I tried modifying the formula parameter to "y ~ 0 *x" but can't think of anything else that's obvious to use.
Thanks
ggplot(data = iris, aes(y = Sepal.Length, x = Sepal.Width, colour = Species)) + geom_point() +
geom_smooth(method = 'lm', formula = y ~ x , se = F)
We can specify the formula as y ~ 1.
library(ggplot2)
ggplot(data = iris, aes(y = Sepal.Length, x = Sepal.Width, colour = Species)) +
geom_point() +
geom_smooth(method = "lm", formula = y ~ 1)