########
#remotes::install_github("csdaw/ggprism")
#if "Error: Does not appear to be an R package (no DESCRIPTION)",
#run Sys.setenv("TAR" = "internal")
library(ggprism)
########################
library(ggplot2)
library(tibble)
library(rlang)
library(patchwork)
####################
p1 <- ggplot(ToothGrowth, aes(x = factor(dose), y = len)) +
stat_summary(aes(fill = factor(dose)), na.rm = TRUE,
geom = "col", fun = mean, colour = "black", size = 0.9) +
scale_y_continuous(limits = c(0, 30), expand = c(0, 0))
p2 <- p1 + theme_prism(base_size = 14)
p1 + p2
p3 <- p1 + theme_prism(palette = "mustard_field", base_size = 14)
p4 <- p1 + theme_prism(palette = "flames", base_size = 14)
p3 + p4
p <- ggplot(ToothGrowth, aes(x = factor(supp), y = len)) +
geom_boxplot(aes(colour = factor(supp), fill = factor(supp))) +
theme_prism(base_size = 14)
p1 <- p + scale_colour_prism(palette = "floral") +
scale_fill_prism(palette = "floral")
p2 <- p + scale_colour_prism(palette = "flames") +
scale_fill_prism(palette = "flames")
p1 + p2
#######################
p <- ggplot(ToothGrowth, aes(x = factor(dose), y = len)) +
geom_jitter(aes(shape = factor(dose)), width = 0.2, size = 2) +
scale_shape_prism() +
theme_prism() +
theme(legend.position = "none")
p1 <- p + scale_y_continuous(limits = c(0, 40), guide = "prism_minor")
p2 <- p + scale_x_discrete(guide = "prism_bracket") +
scale_y_continuous(limits = c(0, 40))
p3 <- p + scale_y_continuous(limits = c(0, 40), guide = "prism_offset")
p4 <- p + scale_y_continuous(limits = c(0, 40), guide = "prism_offset_minor")
(p1 + p2) / (p3 + p4)
df_p_val <- data.frame(
group1 = "OJ",
group2 = "VC",
p.adj = 0.0606,
y.position = 36
)
# make a plot
p1 <- ggplot(ToothGrowth, aes(x = factor(supp), y = len)) +
geom_boxplot(aes(fill = factor(supp))) +
scale_fill_prism(palette = "candy_bright") +
theme_prism() +
theme(legend.position = "none")
# add the p-value
p2 <- p1 + add_pvalue(df_p_val)
p1
p2
tg <- ToothGrowth
tg$dose <- as.factor(tg$dose)
base <- ggplot(tg, aes(x = dose, y = len)) +
geom_violin(aes(colour = dose, fill = dose), trim = FALSE) +
geom_boxplot(aes(fill = dose), width = 0.2, colour = "black") +
scale_y_continuous(limits = c(-5, 40))
p_vals <- tibble::tribble(
~group1, ~group2, ~p.adj, ~y.position,
"0.5", "1", 8.80e-14, 35,
"0.5", "2", 1.27e-7, 39
)
base
base +
scale_color_prism("floral") +
scale_fill_prism("floral") +
guides(y = "prism_offset_minor") +
theme_prism(base_size = 16) +
theme(legend.position = "none") +
add_pvalue(p_vals, label = "p = {p.adj}", tip.length = 0, label.size = 4)
library(ggnewscale)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(tidyr)
# construct the data.frame, log10 transform the agonist concentration
# convert the data.frame to long format, then remove any rows with NA
df <- data.frame(
agonist = c(1e-10, 1e-8, 3e-8, 1e-7, 3e-7, 1e-6, 3e-6, 1e-5, 3e-5, 1e-4, 3e-4),
ctr1 = c(0, 11, 125, 190, 258, 322, 354, 348, NA, 412, NA),
ctr2 = c(3, 33, 141, 218, 289, 353, 359, 298, NA, 378, NA),
ctr3 = c(2, 25, 160, 196, 345, 328, 369, 372, NA, 399, NA),
trt1 = c(3, NA, 11, 52, 80, 171, 289, 272, 359, 352, 389),
trt2 = c(5, NA, 25, 55, 77, 195, 230, 333, 306, 320, 338),
trt3 = c(4, NA, 28, 61, 44, 246, 243, 310, 297, 365, NA)
) %>%
mutate(log.agonist = log10(agonist)) %>%
pivot_longer(
c(-agonist, -log.agonist),
names_pattern = "(.{3})([0-9])",
names_to = c("treatment", "rep"),
values_to = "response"
) %>%
filter(!is.na(response))
head(df)
## # A tibble: 6 x 5
## agonist log.agonist treatment rep response
## <dbl> <dbl> <chr> <chr> <dbl>
## 1 0.0000000001 -10 ctr 1 0
## 2 0.0000000001 -10 ctr 2 3
## 3 0.0000000001 -10 ctr 3 2
## 4 0.0000000001 -10 trt 1 3
## 5 0.0000000001 -10 trt 2 5
## 6 0.0000000001 -10 trt 3 4
# plot the log10(agonist concentration) vs the response
p <- ggplot(df, aes(x = log.agonist, y = response))
# define model (note x and ec50 are swapped around because ec50 is already -ve)
dose_resp <- y ~ min + ((max - min) / (1 + exp(hill_coefficient * (ec50 - x))))
# fit separate curves to the data from the two treatment types
p <- p + geom_smooth(
aes(colour = treatment),
method = "nls", formula = dose_resp, se = FALSE,
method.args = list(start = list(min = 1.67, max = 397, ec50 = -7, hill_coefficient = 1))
)
p
# apply a manual colour scale to the curves
p <- p + scale_colour_manual(
labels = c("No inhibitor", "Inhibitor"),
values = c("#00167B", "#9FA3FE")
)
p
# reset the colour scale, add the data points, then use a new colour scale
p <- p + ggnewscale::new_scale_colour() +
geom_point(aes(colour = treatment, shape = treatment), size = 3) +
scale_colour_prism(
palette = "winter_bright",
labels = c("No inhibitor",
"Inhibitor")
) +
scale_shape_prism(
labels = c("No inhibitor",
"Inhibitor")
)
p
# use the Winter Bright theme and make the size of all plot elements larger
p <- p + theme_prism(palette = "winter_bright", base_size = 16)
p
# adjust the axis limits, major tick positions, and axis guide
p <- p + scale_y_continuous(
limits = c(-100, 500),
breaks = seq(-100, 500, 100),
guide = "prism_offset"
)
p
# adjust the axis limits, major and minor tick positions, axis guide, and
# axis text (aka. label) appearance
p <- p + scale_x_continuous(
limits = c(-10, -3),
breaks = -10:-3,
guide = "prism_offset_minor",
minor_breaks = log10(rep(1:9, 7)*(10^rep(-10:-4, each = 9))),
labels = function(lab) {
do.call(
expression,
lapply(paste(lab), function(x) bquote(bold("10"^.(x))))
)
}
)
p
# remove the y axis title and legend title, change the x axis title and
# move the legend to the top left
p <- p + theme(
axis.title.y = element_blank(),
legend.title = element_blank(),
legend.position = c(0.05, 0.95),
legend.justification = c(0.05, 0.95)
) +
labs(x = "[Agonist], M")
p
dose_resp <- y ~ min + ((max - min) / (1 + exp(hill_coefficient * (ec50 - x))))
ggplot(df, aes(x = log.agonist, y = response)) +
geom_smooth(
aes(colour = treatment),
method = "nls", formula = dose_resp, se = FALSE,
method.args = list(start = list(min = 1.67, max = 397, ec50 = -7, hill_coefficient = 1))
) +
scale_colour_manual(labels = c("No inhibitor", "Inhibitor"),
values = c("#00167B", "#9FA3FE")) +
ggnewscale::new_scale_colour() +
geom_point(aes(colour = treatment, shape = treatment), size = 3) +
scale_colour_prism(palette = "winter_bright",
labels = c("No inhibitor", "Inhibitor")) +
scale_shape_prism(labels = c("No inhibitor", "Inhibitor")) +
theme_prism(palette = "winter_bright", base_size = 16) +
scale_y_continuous(limits = c(-100, 500),
breaks = seq(-100, 500, 100),
guide = "prism_offset") +
scale_x_continuous(
limits = c(-10, -3),
breaks = -10:-3,
guide = "prism_offset_minor",
minor_breaks = log10(rep(1:9, 7)*(10^rep(-10:-4, each = 9))),
labels = function(lab) {
do.call(
expression,
lapply(paste(lab), function(x) bquote(bold("10"^.(x))))
)
}
) +
theme(axis.title.y = element_blank(),
legend.title = element_blank(),
legend.position = c(0.05, 0.95),
legend.justification = c(0.05, 0.95)) +
labs(x = "[Agonist], M")
#install.packages("ggbeeswarm")
library(ggbeeswarm)
library(tidyverse)
## -- Attaching packages ------------------------------------------------------------------- tidyverse 1.3.0 --
## √ readr 1.4.0 √ stringr 1.4.0
## √ purrr 0.3.4 √ forcats 0.5.0
## -- Conflicts ---------------------------------------------------------------------- tidyverse_conflicts() --
## x purrr::%@%() masks rlang::%@%()
## x purrr::as_function() masks rlang::as_function()
## x dplyr::filter() masks stats::filter()
## x purrr::flatten() masks rlang::flatten()
## x purrr::flatten_chr() masks rlang::flatten_chr()
## x purrr::flatten_dbl() masks rlang::flatten_dbl()
## x purrr::flatten_int() masks rlang::flatten_int()
## x purrr::flatten_lgl() masks rlang::flatten_lgl()
## x purrr::flatten_raw() masks rlang::flatten_raw()
## x purrr::invoke() masks rlang::invoke()
## x dplyr::lag() masks stats::lag()
## x purrr::list_along() masks rlang::list_along()
## x purrr::modify() masks rlang::modify()
## x purrr::prepend() masks rlang::prepend()
## x purrr::splice() masks rlang::splice()
library(rstatix)
##
## Attaching package: 'rstatix'
## The following object is masked from 'package:stats':
##
## filter
wings_pvals <- wings %>%
group_by(sex, measure) %>%
rstatix::t_test(
percent.change ~ genotype,
p.adjust.method = "BH",
var.equal = TRUE,
ref.group = "Tps1MIC/+"
) %>%
rstatix::add_x_position(x = "measure", dodge = 0.9) %>% # dodge must match points
mutate(label = c("***", "*", "P = 0.26", "***", "***", "P = 0.65"))
ggplot(wings, aes(x = measure, y = percent.change)) +
ggbeeswarm::geom_beeswarm(aes(fill = genotype), dodge.width = 0.9, shape = 21) +
facet_wrap(~ sex, scales = "free",
labeller = labeller(sex = c(male = "\u2642", female = "\u2640"))) +
geom_hline(yintercept = 0, linetype = 2, size = 0.3) +
stat_summary(geom = "crossbar", aes(fill = genotype),fun = mean,
position = position_dodge(0.9), colour = "red", size = 0.4, width = 0.7,
show.legend = FALSE) +
add_pvalue(wings_pvals, y = 10, xmin = "xmin", xmax = "xmax",
tip.length = 0, fontface = "italic",
lineend = "round", bracket.size = 0.5) +
theme_prism(base_fontface = "plain", base_line_size = 0.7,
base_family = "sans") +
scale_x_discrete(guide = guide_prism_bracket(width = 0.15),
labels = scales::wrap_format(5)) +
scale_y_continuous(limits = c(-20, 12), expand = c(0, 0),
breaks = seq(-20, 10, 5), guide = "prism_offset") +
labs(y = "% change") +
scale_fill_manual(values = c("#026FEE", "#87FFFF"),
labels = c(expression("Tps"*1^italic("MIC")~"/ +"),
expression("Tps"*1^italic("MIC")))) +
theme(legend.position = "bottom",
axis.title.x = element_blank(),
strip.text = element_text(size = 14),
legend.spacing.x = unit(0, "pt"),
legend.text = element_text(margin = margin(r = 20))) +
geom_text(data = data.frame(sex = factor("female", levels = c("male", "female")),
measure = "Cell Number",
percent.change = -18.5,
lab = "(n = 10)"),
aes(label = lab)) +
guides(fill = guide_legend(override.aes = list(size = 3)))
#########################
#ref
#https://mp.weixin.qq.com/s/lZWih1JHTwTaNSIO4S2WSg
#https://csdaw.github.io/ggprism/articles/ex2-wings.html
#https://csdaw.github.io/ggprism/