-
In ancient Roman mythology,
Plutowas the ruler of the underworld and presides over the afterlife. -
Plutowas frequently conflated withPlutus, the god of wealth, because mineral wealth was found underground. -
When plotting with R, you try once, twice, practice again and again, and finally you get a pretty figure you want.
-
It’s a
plot tour, a tour about repetition and reward. -
Hope
plutorhelps you on the tour!
You can install the development version of plutor like so:
devtools::install_github("william-swl/plutor")And load the package:
library(plutor)It is recommended to perform initialization, which adjusts the default
plotting parameters in an interactive environment (such as jupyter
notebook) and sets the default theme to theme_pl().
pl_init()Description values plot:
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The describe geom is used to create description values plot, including center symbol and error symbol.
-
The center symbol can be mean, median or other custom functions.
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The error symbol can be sd, quantile or other custom functions.
mini_diamond %>% ggplot(aes(x = cut, y = price)) +
geom_point() +
geom_describe()
center_func <- median
low_func <- function(x, na.rm) {
quantile(x, 0.25, na.rm = na.rm)
}
high_func <- function(x, na.rm) {
quantile(x, 0.75, na.rm = na.rm)
}
mini_diamond %>% ggplot(aes(x = cut, y = price)) +
geom_point() +
geom_describe(center_func = center_func, low_func = low_func, high_func = high_func)
Add p value and fold change on a plot
p <- ggplot(data = mini_diamond, mapping = aes(x = cut, y = price)) +
geom_point() +
geom_compare(cp_label = c("psymbol", "right_deno_fc"), lab_pos = 25000, step_increase = 0.3) +
ylim(0, 40000)
p
ggplot(data = mini_diamond, mapping = aes(x = cut, y = price)) +
geom_point() +
geom_compare(cp_ref = "Good", cp_inline = TRUE, lab_pos = 22000, brackets_widen = 0.1) +
geom_compare(cp_ref = "Ideal", cp_inline = TRUE, lab_pos = 25000, brackets_widen = 0.1) +
ylim(0, 40000)
extract the result of geom_compare from a ggplot object
head(extract_compare(p))
#> PANEL x xend n1 n2 p plim psymbol y1 y2 fc
#> 1 1 1 2 35 31 0.041 0.05 * 4995.057 3730.387 1.339018
#> 2 1 2 3 31 34 0.93 1.01 NS 3730.387 3036.588 1.228480
#> 3 1 1 3 35 34 0.018 0.05 * 4995.057 3036.588 1.644957
#> right_deno_fc left_deno_fc label cp_step y yend group
#> 1 1.3x 0.75x *\n1.3x 0 25000.0 25000.0 1
#> 2 1.2x 0.81x NS\n1.2x 1 30269.2 30269.2 1
#> 3 1.6x 0.61x *\n1.6x 2 35538.4 35538.4 1A new Stat class to add mean labels on a plot
mini_diamond %>% ggplot(aes(x = cut, y = price)) +
geom_point() +
geom_text(aes(label = price), stat = "meanPL")
A new Stat class to add count labels on a plot
mini_diamond %>% ggplot(aes(x = cut, y = price)) +
geom_point() +
geom_text(aes(label = price), stat = "countPL")
A new Stat class to add custom function labels on a plot
lab_func <- function(x) {
str_glue("mean = {round(mean(x))}\nn = {length(x)}")
}
mini_diamond %>% ggplot(aes(y = cut, x = price)) +
geom_point() +
geom_text(aes(label = price),
stat = "funcPL",
lab_func = lab_func, lab_pos = 25000
) +
xlim(0, 30000)
A new Position function to create float x/y position
mini_diamond %>% ggplot(aes(x = clarity, y = price)) +
geom_point() +
geom_text(aes(label = price),
stat = "countPL",
lab_pos = 20000, position = position_floatyPL()
)
A variant of scale_y_log10() to show axis minor breaks and better axis
labels
mini_diamond %>% ggplot(aes(x = cut, y = price)) +
geom_point() +
geom_text(stat = "meanPL", lab_pos = 30000) +
scale_y_log10_pl(show_minor_breaks = TRUE, limits = c(100, 100000))
A variant of scale_y_continuous() to show axis minor breaks
mini_diamond %>% ggplot(aes(x = cut, y = price)) +
geom_point() +
geom_text(stat = "meanPL", lab_pos = 25000) +
scale_y_continuous_pl(limits = c(0, 40000), minor_break_step = 2500)
- generate gradient colors
gradient_colors(c("blue", "red"), 10)
#> [1] "#0000FF" "#1C00E2" "#3800C6" "#5500AA" "#71008D" "#8D0071" "#AA0055"
#> [8] "#C60038" "#E2001C" "#FF0000"- show colors
plot_colors(gradient_colors(c("blue", "red"), 10))
- select colors from
RColorBrewerpackage presets
brewer_colors("Blues", 5) %>% plot_colors()
- select colors from
ggscipackage presets
sci_colors("npg", 5) %>% plot_colors()
- assign colors by a column in a tibble, for the convenience to use
scale_color_identity()
assign_colors(mini_diamond, cut, colors = sci_colors("nejm", 8))
#> # A tibble: 100 × 8
#> id carat cut clarity price x y assigned_colors
#> <chr> <dbl> <chr> <chr> <int> <dbl> <dbl> <chr>
#> 1 id-1 1.02 Fair SI1 3027 6.25 6.18 #BC3C29FF
#> 2 id-2 1.51 Good VS2 11746 7.27 7.18 #0072B5FF
#> 3 id-3 0.52 Ideal VVS1 2029 5.15 5.18 #E18727FF
#> 4 id-4 1.54 Ideal SI2 9452 7.43 7.45 #E18727FF
#> 5 id-5 0.72 Ideal VS1 2498 5.73 5.77 #E18727FF
#> 6 id-6 2.02 Fair SI2 14080 8.33 8.37 #BC3C29FF
#> 7 id-7 0.27 Good VVS1 752 4.1 4.07 #0072B5FF
#> 8 id-8 0.51 Good SI2 1029 5.05 5.08 #0072B5FF
#> 9 id-9 1.01 Ideal SI1 5590 6.43 6.4 #E18727FF
#> 10 id-10 0.7 Fair VVS1 1691 5.56 5.41 #BC3C29FF
#> # … with 90 more rows- colors of nucleotides and amino acids
# bioletter_colors- a custom flexible theme
ggplot(mini_diamond, aes(x = x, y = y, color = clarity)) +
geom_point(size = 2) +
facet_grid(. ~ cut) +
labs(title = "title", tag = "tag", caption = "caption") +
theme_pl()
- a blank theme
ggplot(mini_diamond, aes(x = x, y = y, color = clarity)) +
geom_point(size = 2) +
facet_grid(. ~ cut) +
labs(title = "title", tag = "tag", caption = "caption") +
theme_pl0()
- a fixed mapping from size in
geom_xxxto unitptunder 300 dpi
# for text and points
# geom_point(..., size = ppt(5))
# geom_text(..., size = tpt(5))
# for lines
# geom_line(..., linewidth = lpt(1))- set repr size and resolution
pl_size(w = 4, h = 3, res = 300)- units transformation
# inches <-> centimeters
inch2cm(1)
#> [1] 2.54
#> attr(,"unit")
#> [1] 1
in2cm(1)
#> [1] 2.54
#> attr(,"unit")
#> [1] 1
cm2inch(1)
#> [1] 0.3937008
#> attr(,"unit")
#> [1] 2
cm2in(1)
#> [1] 0.3937008
#> attr(,"unit")
#> [1] 2
# inches <-> millimeters
inch2mm(1)
#> [1] 25.4
#> attr(,"unit")
#> [1] 7
in2mm(1)
#> [1] 25.4
#> attr(,"unit")
#> [1] 7
mm2inch(1)
#> [1] 0.03937008
#> attr(,"unit")
#> [1] 2
mm2in(1)
#> [1] 0.03937008
#> attr(,"unit")
#> [1] 2
# points <-> centimeters
pt2cm(1)
#> [1] 0.03514598
#> attr(,"unit")
#> [1] 1
cm2pt(1)
#> [1] 28.45276
#> attr(,"unit")
#> [1] 8
# points <-> millimeters
pt2mm(1)
#> [1] 0.3514598
#> attr(,"unit")
#> [1] 7
mm2pt(1)
#> [1] 2.845276
#> attr(,"unit")
#> [1] 8- save a plot
# pl_save(p, 'plot.pdf', width=14, height=10)- save a plot into an blank A4 canvas, or a custom canvas
# pl_save(p, 'plot.pdf', width=14, height=10, canvas='A4', units='cm')
# pl_save(p, 'plot.pdf', width=14, height=10, canvas=c(20, 25), units='cm')