I would like to take a faceted histogram and add text on each plot indicating the total number of observations in that facet. So for carb = 1 the total count would be 7, carb = 2 the total count would be 10 etc.
p <- ggplot(mtcars, aes(x = mpg, stat = "count",fill=as.factor(carb))) + geom_histogram(bins = 8)
p <- p + facet_grid(as.factor(carb) ~ .)
p
I can do this with the table function but for more complex faceting how can I do it quickly?
You can try this. Maybe is not the most optimal because you have to define the x and y position for the label (this is done in Labels for x and in geom_text() for y with 3). But it can help you:
#Other
library(tidyverse)
#Create similar data for labels
Labels <- mtcars %>% group_by(carb) %>% summarise(N=paste0('Number is: ',n()))
#X position
Labels$mpg <- 25
#Plot
ggplot(mtcars, aes(x = mpg, stat = "count",fill=as.factor(carb))) + geom_histogram(bins = 8)+
geom_text(data = Labels,aes(x=mpg,y=3,label=N))+facet_grid(as.factor(carb) ~ .)
Related
I'm looking for help in order to merge two plots and respect their respective scale size.
Here is a reproductible example:
data1<- subset(mtcars, cyl = 4)
data1$mpg <- data1$mp*5.6
data2<- subset(mtcars, cyl = 8)
p1 <- ggplot(data1, aes(wt, mpg, colour = cyl)) + geom_point()
p2 <- ggplot(data2, aes(wt, mpg, colour = cyl)) + geom_point()
grid.arrange(p1, p2, ncol = 2)
But what I'm looking for is to merge the two plots and respect the scale size and get something like :
It would be nice to not use a package which need to define the ratio since it's difficult to known how much I should reduce the second plot compared to the first one... And event more difficult when we have more than 2 plots.
I think what you are trying to achieve is something like this:
library(tidyverse)
mtcars %>%
filter(cyl %in% c(4, 8)) %>%
mutate(mpg = ifelse(cyl == 4, mpg * 5.6, mpg)) %>%
ggplot(aes(x = wt, y = mpg, col = as.factor(cyl))) +
geom_point(show.legend = FALSE) +
facet_wrap(~ cyl)
NOTE: I see some bugs in your original code. For example, if you want to use subset() to subset your data, you have to change your code from:
data1 <- subset(mtcars, cyl = 4)
to:
data1 <- subset(mtcars, cyl == 4)
subset(mtcars, cyl = 4) does not do anything.
I have a figure with 16 regression lines and I need to be able to identify them. Using a color gradient or symbols or different line types do not really help.
My idea therefore is, to just (haha) annotate every line.
Therefore, I build a dataset (hpAnnotatedLines) with the different maximum x values. This is the position the text should start. However, I have no idea how to automatically extract the respective y values of the predicted regression lines at the maximum x-axis values, which is different for each line.
Please find a smaller data set using mtcars as an example
library(ggplot2)
library(dplyr)
library(ggrepel)
#just select the data I need
mtcars1 <- select(mtcars, disp,cyl,hp)
mtcars1$cyl <- as.factor(mtcars1$cyl)
#extract max values
mtcars2 <- mtcars1 %>%
group_by(cyl) %>%
summarise(Max.disp= max(disp))
#build dataset for the annotation layer
#note that hp was done by hand. Here I need help
hpAnnotatedLines <- data.frame(cyl=levels(mtcars2$cyl),
disp=mtcars2$Max.disp,
hp=c(90,100,210))
#example plot
ggplot(mtcars, aes(x=disp, y=hp, color = factor(cyl))) +
geom_point() +
geom_smooth(method=lm)+
coord_cartesian(xlim = c(min(mtcars$disp), max(mtcars$disp) + 50)) +
geom_text_repel(
data = hpAnnotatedLines,
aes(label = cyl),
size = 3,
nudge_x = 1)
Instead of extracting the fitted values you could add the labels via geom_text by switching the stat to smooth and setting the label aesthetic via after_stat such that only the last point of each regression line gets labelled:
library(ggplot2)
library(dplyr)
myfun <- function(x, color) {
data.frame(x = x, color = color) %>%
group_by(color) %>%
mutate(label = ifelse(x %in% max(x), as.character(color), "")) %>%
pull(label)
}
ggplot(mtcars, aes(x=disp, y=hp, color = factor(cyl))) +
geom_point() +
geom_smooth(method=lm) +
geom_text(aes(label = after_stat(myfun(x, color))),
stat = "smooth", method = "lm", hjust = 0, size = 3, nudge_x = 1, show.legend = FALSE) +
coord_cartesian(xlim = c(min(mtcars$disp), max(mtcars$disp) + 50))
It's a bit of a hack, but you can extract the data from the compiled plot object. For example first make the plot without the labels,
myplot <- ggplot(mtcars, aes(x=disp, y=hp, color = factor(cyl))) +
geom_point() +
geom_smooth(method=lm)+
coord_cartesian(xlim = c(min(mtcars$disp), max(mtcars$disp) + 50))
Then use ggplot_build to get the data from the second layer (The geom_smooth layer) and transform it back into the names used by your data. Here we find the largest x value per group, and then take that y value.
pobj <- ggplot_build(myplot)
hpAnnotatedLines <- pobj$data[[2]] %>% group_by(group) %>%
top_n(1, x) %>%
transmute(disp=x, hp=y, cyl=levels(mtcars$cyl)[group])
Then add an additional layer to your plot
myplot +
geom_text_repel(
data = hpAnnotatedLines,
aes(label = cyl),
size = 3,
nudge_x = 1)
If your data is not that huge, you can extract the predictions out using augment() from broom and take that with the largest value:
library(broom)
library(dplyr)
library(ggplot2)
hpAnn = mtcars %>% group_by(cyl) %>%
do(augment(lm(hp ~ disp,data=.))) %>%
top_n(1,disp) %>%
select(cyl,disp,.fitted) %>%
rename(hp = .fitted)
# A tibble: 3 x 3
# Groups: cyl [3]
cyl disp hp
<dbl> <dbl> <dbl>
1 4 147. 96.7
2 6 258 99.9
3 8 472 220.
Then plot:
ggplot(mtcars, aes(x=disp, y=hp, color = factor(cyl))) +
geom_point() +
geom_smooth(method=lm)+
coord_cartesian(xlim = c(min(mtcars$disp), max(mtcars$disp) + 50))+
geom_text_repel(
data = hpAnn,
aes(label = cyl),
size = 3,
nudge_x = 1)
I am trying to create a custom histogram with a rug plot showing the original values on the X axis.
I am going to use the mtcars dataset to illustrate. Its not be best dataset for this question...but hopefully the reader will understand what I am trying to achieve...
Below shows the basic histogram, without any rug plot attempt.
I want to create the histogram using geom_bar as this allows for more flexibility with custom bins.
I also want a small gap between the histgram bars (i.e width = 0.95) .... which adds to this
problem's complexity.
library(dplyr)
library(ggplot2)
# create custom bins
vct_seq <- c(seq(from = 10, to = 25, by = 5), 34)
mtcars$bin <- cut(mtcars$mpg, breaks = vct_seq)
# create data.frame for the ggplot graph..using bins above
df_mtcars_count <- mtcars %>% group_by(bin) %>% summarise(count = n())
# indicative labels
vct_labels <- c("bin 1", "bin 2", "bin 3", "bin 4")
# attempt 1 - basic plot -- no rug plot
p <- ggplot(data = df_mtcars_count, aes(x = bin, y = count))
p <- p + geom_bar(stat = "identity", width = 0.95)
p <- p + geom_text(aes(label = count), vjust = -0.5)
p <- p + scale_x_discrete("x title to go here", labels = df_mtcars_count$bin, breaks = df_mtcars_count$bin)
p
Next, try and add a basic rug plot on the X axis. This obviously doesn't work as the geom_bar and geom_rug have completely different scales.
# attempt 2 with no scaling.... doesn't work as x scale for ordinal (bins) and
# x scale for continuous (mpg) do not match
p <- ggplot(data = df_mtcars_count, aes(x = bin, y = count))
p <- p + geom_bar(stat = "identity", width = 0.95)
p <- p + geom_text(aes(label = count), vjust = -0.5)
p <- p + scale_x_discrete("x title to go here", labels = df_mtcars_count$bin, breaks = df_mtcars_count$bin)
p <- p + geom_rug(data = mtcars, aes(x = mpg), inherit.aes = F, alpha = 0.3)
p
Now, try and rescale the mpg column to match with the ordinal scale....
First define a linear mapping function...
fn_linear_map <- function(vct_existing_val, vct_new_range) {
# example....converts 1:20 into the range 1 to 10 like this:
# fn_linear_map(1:20, c(1, 10))
fn_r_diff <- function(x) x %>% range() %>% diff()
flt_ratio <- fn_r_diff(vct_new_range) / fn_r_diff(vct_existing_val)
vct_old_min_offset <- vct_existing_val - min(vct_existing_val)
vct_new_range_val <- (vct_old_min_offset * flt_ratio) + min(vct_new_range)
return(vct_new_range_val)
}
Now apply the function...we try and map mpg to the range 1 to 4 (which is an attempt to match
the ordinal scale)
mtcars$mpg_remap <- fn_linear_map(mtcars$mpg, c(1, 4))
Try the plot again.... getting closer ... but not really accurate...
# attempt 3: getting closer but doesn't really match the ordinal scale
p <- ggplot(data = df_mtcars_count, aes(x = bin, y = count))
p <- p + geom_bar(stat = "identity", width = 0.95)
p <- p + geom_text(aes(label = count), vjust = -0.5)
p <- p + scale_x_discrete("x title to go here", labels = df_mtcars_count$bin, breaks = df_mtcars_count$bin)
p <- p + geom_rug(data = mtcars, aes(x = mpg_remap), inherit.aes = F, alpha = 0.3)
p
The graph above is getting close to what I want....but rug plot does not line up
with the actual data ... example the max observation (33.9) should be displayed
almost aligning with the right hand side of the bar.. see below:
mtcars %>% filter(bin == "(25,34]") %>% arrange(mpg) %>% dplyr::select(mpg, mpg_remap)
Your scale makes no sense to me, as you are showing a bin that is twice as wide using the same bar width. Doing that in combination with a rug strikes me as confusing as best and misleading at worst. I suggest you plot the bars with their correct widths, after which the rug is trivial.
I think the best solution is to just use geom_histogram:
ggplot(mtcars, aes(mpg)) +
geom_histogram(breaks = vct_seq, col = 'grey80') +
geom_rug(aes(mpg, y = NULL))
If you really want the gaps between the bars you'll have to do more work:
library(tidyr)
d <- mtcars %>%
count(bin) %>%
separate(bin, c('min', 'max'), sep = ',', remove = FALSE) %>%
mutate_at(vars('min', 'max'), readr::parse_number) %>%
mutate(
middle = min + (max - min) / 2,
width = 0.9 * (max - min)
)
ggplot(d, aes(middle, n)) +
geom_col(width = d$width) +
geom_rug(aes(mpg, y = NULL), mtcars)
I'm hoping to recreate the gridExtra output below with ggplot's facet_grid, but I'm unsure of what variable ggplot identifies with the layers in the plot. In this example, there are two geoms...
require(tidyverse)
a <- ggplot(mpg)
b <- geom_point(aes(displ, cyl, color = drv))
c <- geom_smooth(aes(displ, cyl, color = drv))
d <- a + b + c
# output below
gridExtra::grid.arrange(
a + b,
a + c,
ncol = 2
)
# Equivalent with gg's facet_grid
# needs a categorical var to iter over...
d$layers
#d + facet_grid(. ~ d$layers??)
The gridExtra output that I'm hoping to recreate is:
A hacky way of doing this is to take the existing data frame and create two, three, as many copies of the data frame you need with a value linked to it to be used for the facet and filtering later on. Union (or rbind) the data frames together into one data frame. Then set up the ggplot and geoms and filter each geom for the desired attribute. Also for the facet use the existing attribute to split the plots.
This can be seen below:
df1 <- data.frame(
graph = "point_plot",
mpg
)
df2 <- data.frame(
graph = "spline_plot",
mpg
)
df <- rbind(df1, df2)
ggplot(df, mapping = aes(x = displ, y = hwy, color = class)) +
geom_point(data = filter(df, graph == "point_plot")) +
geom_smooth(data = filter(df, graph == "spline_plot"), se=FALSE) +
facet_grid(. ~ graph)
If you really want to show different plots on different facets, one hacky way would be to make separate copies of the data and subset those...
mpg2 <- mpg %>% mutate(facet = 1) %>%
bind_rows(mpg %>% mutate(facet = 2))
ggplot(mpg2, aes(displ, cyl, color = drv)) +
geom_point(data = subset(mpg2, facet == 1)) +
geom_smooth(data = subset(mpg2, facet == 2)) +
facet_wrap(~facet)
I want to make a heatmap that creates a group of clarity & color combinations as the X axis and cut as the Y axis. The heatmap would color based upon the counts of clarity+color and its intersection with the cut.
library(ggplot2)
library(dplyr)
## rename diamonds df
# 1. Generate a count for the frequency of cut+clarity
# 2. Make a heatmap of this using the following bins
# 3. Red <= 100 Frequency
Yellow = between (100 and 500)
Green > 500
# place counts inside the cell:
df = diamonds %>%
select( cut, clarity) %>%
group_by(cut,clarity)%>%
mutate(count = n())
myplot = ggplot(df, aes(x = clarity, y=cut)) +
geom_bin2d( bins = c(100,500,50000), col='orange') #
geom_text( aes(label = count),col='red')
myplot
Try this:
df$col <- cut(df$count,breaks = c(-Inf,100,500,Inf),right = TRUE)
df$color<-df$col
levels(df$color) <- c("<=100","100<#<=500",">500")
ggplot(data = df, aes(x = clarity, y = cut)) +
geom_tile(aes(fill = df$color), colour = "white") +
scale_fill_brewer("Count",palette = "Set1")+
geom_text(aes(label = count),col='yellow',cex=3)