another experiment: base ggplot2, draw x = mean(x_var) line
Evangeline Reynolds
2023-10-12
Traditional approach, pre-computation w/ vline
library(tidyverse)mean_wt <- data.frame(cyl = c(4, 6, 8),
wt = c(2.28, 3.11, 4.00))
ggplot(mtcars, aes(wt,mpg, colour = wt)) +
geom_point() +
geom_vline(aes(xintercept = wt, colour = wt), mean_wt) +
facet_wrap(~ cyl,
ncol = 1)
Auto compute experiments. Use base ggplot2 to give compute the xmean
Use geom_smooth, w/ ‘y’ orientation and formula y~1
ggplot(mtcars) +
aes(x = wt, y = mpg, color = factor(am)) +
geom_rug() +
geom_point() +
geom_smooth(orientation = "y", xseq = c(10,35),
method = lm, se = F,
formula = y ~ 1)
# can fullrange be used? Unsure of what's going on here, maybe x and y got switched
ggplot(mtcars) +
aes(x = wt, y = mpg, color = factor(am)) +
geom_rug() +
geom_point() +
geom_smooth(orientation = "y",
method = lm, se = T,
formula = y ~ 1,
fullrange = TRUE)
# can fullrange be used? Unsure of what's going on here, maybe x and y got switched
ggplot(mtcars) +
aes(x = mpg, y = wt, color = factor(am)) +
geom_rug() +
geom_point() +
geom_smooth(orientation = "x",
method = lm, se = FALSE,
formula = y ~ 1,
fullrange = TRUE)
# seems true...
last_plot() +
coord_flip()
univariate (no y) gets more hacky
ggplot(mtcars) +
aes(x = wt) +
geom_rug() +
geom_histogram() +
geom_smooth(
orientation = "y",
method = lm,
formula = y ~ 1,
se = F,
aes(y = new_var), # it a required aesthetic - you can kind of put any var so we just put x again...
data = . %>% mutate(new_var = row_number()), # so you can use the genaric new_var...
xseq = c(0, Inf) # determined looking histogram first
)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# not good cause of the row numbers 'randomly' covering different groups
last_plot() +
aes(color = factor(am))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
geom_x_mean <- function(...){
geom_smooth(
orientation = "y",
method = lm,
formula = y ~ 1,
se = F,
aes(y = new_var), # it a required aesthetic - you can kind of put any var so we just put x again...
data = . %>% mutate(new_var = row_number()),
xseq = c(0, Inf), # determined looking histogram first
...
)
}
ggplot(mtcars) +
aes(x = wt) +
geom_rug() +
geom_histogram() +
geom_x_mean(linetype = "dotted")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# not good cause of the row numbers 'randomly' covering different groups
last_plot() +
aes(color = factor(am))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Not very hopeful about fullrange at present but maybe after bug fix?Use stat_summary w/ geom = “point”, shape = 3 (cross hairs)
a terrible(ly fun?) hack
ggplot(mtcars) +
aes(x = wt) +
geom_rug() +
geom_histogram() +
stat_summary(fun = "mean", # calculate the mean
orientation = "y", # for x
geom = "point", # place a point there
aes(y = 0), # with the value 0 for y
shape = 3, # use the 'cross hairs' shape
size = 300, # make the cross hairs huge
show.legend = F # legend with will overwhelm plot because of size
) ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.last_plot() +
aes(color = factor(am)) +
aes(fill = factor(am)) +
facet_wrap(~factor(am),
ncol = 1) ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
use geom_segment with stage, after_stat?
library(tidyverse)
ggplot(mtcars) +
aes(x = wt) +
geom_rug() +
geom_histogram() +
geom_segment(aes(xend = wt, y = -Inf, yend = Inf))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
layer_data(last_plot())## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## x PANEL group colour linewidth linetype alpha
## 1 2.620 1 -1 black 0.5 1 NA
## 2 2.875 1 -1 black 0.5 1 NA
## 3 2.320 1 -1 black 0.5 1 NA
## 4 3.215 1 -1 black 0.5 1 NA
## 5 3.440 1 -1 black 0.5 1 NA
## 6 3.460 1 -1 black 0.5 1 NA
## 7 3.570 1 -1 black 0.5 1 NA
## 8 3.190 1 -1 black 0.5 1 NA
## 9 3.150 1 -1 black 0.5 1 NA
## 10 3.440 1 -1 black 0.5 1 NA
## 11 3.440 1 -1 black 0.5 1 NA
## 12 4.070 1 -1 black 0.5 1 NA
## 13 3.730 1 -1 black 0.5 1 NA
## 14 3.780 1 -1 black 0.5 1 NA
## 15 5.250 1 -1 black 0.5 1 NA
## 16 5.424 1 -1 black 0.5 1 NA
## 17 5.345 1 -1 black 0.5 1 NA
## 18 2.200 1 -1 black 0.5 1 NA
## 19 1.615 1 -1 black 0.5 1 NA
## 20 1.835 1 -1 black 0.5 1 NA
## 21 2.465 1 -1 black 0.5 1 NA
## 22 3.520 1 -1 black 0.5 1 NA
## 23 3.435 1 -1 black 0.5 1 NA
## 24 3.840 1 -1 black 0.5 1 NA
## 25 3.845 1 -1 black 0.5 1 NA
## 26 1.935 1 -1 black 0.5 1 NA
## 27 2.140 1 -1 black 0.5 1 NA
## 28 1.513 1 -1 black 0.5 1 NA
## 29 3.170 1 -1 black 0.5 1 NA
## 30 2.770 1 -1 black 0.5 1 NA
## 31 3.570 1 -1 black 0.5 1 NA
## 32 2.780 1 -1 black 0.5 1 NAggtrace::ggtrace_inspect_args(
x = last_plot(),
method = ggplot2:::Layer$compute_statistic
)$data %>% head()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## x PANEL group
## 1 2.620 1 -1
## 2 2.875 1 -1
## 3 2.320 1 -1
## 4 3.215 1 -1
## 5 3.440 1 -1
## 6 3.460 1 -1# computes for all data, after_stat alone errors
ggplot(mtcars) +
aes(x = wt) +
geom_rug() +
geom_histogram() +
geom_segment(aes(x = stage(start = wt, after_stat = mean(x)),
xend = stage(start = wt, after_stat = mean(x)),
y = -Inf, yend = Inf))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# does not work as expected
last_plot() +
aes(color = factor(am))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
other notes
# also, expect 2 rows of data, but get 32
layer_data(last_plot(), 3) %>%
head()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## colour x xend y yend PANEL group linewidth linetype alpha
## 1 #00BFC4 3.21725 3.21725 -Inf Inf 1 2 0.5 1 NA
## 2 #00BFC4 3.21725 3.21725 -Inf Inf 1 2 0.5 1 NA
## 3 #00BFC4 3.21725 3.21725 -Inf Inf 1 2 0.5 1 NA
## 4 #F8766D 3.21725 3.21725 -Inf Inf 1 1 0.5 1 NA
## 5 #F8766D 3.21725 3.21725 -Inf Inf 1 1 0.5 1 NA
## 6 #F8766D 3.21725 3.21725 -Inf Inf 1 1 0.5 1 NA# a knock against segment shown below..
# requires y, whereas vline will draw without, via range back computation
ggplot(mtcars) +
aes(x = wt) +
geom_rug() +
geom_segment(aes(xend = wt, y = -Inf, yend = Inf))