plutor

In ancient Roman mythology, Pluto was the ruler of the underworld and presides over the afterlife.
Pluto was frequently conflated with Plutus, 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 plutor helps you on the tour!

installation

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()

plots

Description values plot:

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.
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     1

A 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)

color

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 RColorBrewer package presets

brewer_colors("Blues", 5) %>% plot_colors()

select colors from ggsci package 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

theme

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_xxx to unit pt under 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

IO

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')