论文
题目：Death rates at specific life stages mold the sex gap in life expectancy
网址： https://www.pnas.org/doi/full/10.1073/pnas.2010588118
代码网址
https://github.com/CPop-SDU/sex-gap-e0-pnas
该文章发表于2021年，论文中图形对我们一部分同学仍具参考价值。作者提供的全套的代码和数据，可以直接使用。此外，作者的数据和代码写的非常的规整。但是，需要看懂和运行代码，还是需要有一定的基础。
论文主图
论文主图仅有两张，如下图所示。
代码
Figure 1
# function to localize paths
devtools::source_gist("32e9aa2a971c6d2682ea8d6af5eb5cde")
# prepare session
source(lp("0-prepare-session.R"))
# theme -------------------------------------------------------------------
load("../dat/palettes.rda" %>% lp)
theme_custom <- theme_minimal(base_family = font_rc) +
theme(
legend.position = "bottom",
strip.background = element_blank(),
strip.text = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_line(size = .25),
panel.ontop = T
)
作者将相关的代码编写在其他的R脚本中，使用时直接进行调用。
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# Fig 1 -- RELATIVE ----------------------------------
load("../dat/a6gap33cntrs.rda" %>% lp)
# relative
df6 %>%
filter(country %>% is_in(c("SWE", "USA", "JPN", "RUS"))) %>%
mutate(
name = name %>%
fct_recode(USA = "United States") %>%
fct_rev()
) %>%
ggplot() +
geom_col(
aes(year, ctb_rel %>% multiply_by(100), fill = age_group),
position = position_stack(reverse = TRUE),
color = NA,
width = 1
) +
facet_grid(name ~ ., scales = "free_y", space = "free") +
coord_cartesian(ylim = c(-10, 120), expand = FALSE)+
scale_x_continuous(breaks = seq(1800, 2000, 50))+
scale_y_continuous(breaks = seq(0, 100, 25), position = "right")+
scale_fill_manual(
values = pal_six,
guide = guide_legend(ncol = 1, reverse = TRUE)
) +
theme_minimal(base_family = font_rc, base_size = 20) +
theme(
legend.position = c(.6, .5),
strip.background = element_blank(),
strip.text = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_line(size = .1),
panel.spacing = unit(0, "lines"),
panel.ontop = T
)+
labs(x = NULL,
y = "Contribution, %",
fill = "Age group")+
# label countries
geom_text(data = . %>% select(name, row, column) %>% distinct(),
aes(label = name, color = name),
x = 2015, y = 120,
hjust = 1, vjust = 1, size = 9, fontface = 2,
family = font_rc)+
scale_color_manual(values = pal_four %>% rev,
guide = FALSE)
one_outer <- last_plot()
one_outer
# plot ratio
load("../dat/df4qx.rda" %>% lp)
df4qx %>%
pivot_wider(names_from = sex, values_from = qx) %>%
ggplot(aes(age, y = m/f, color = country))+
geom_hline(yintercept = 1, color = "gray25", size = .5)+
geom_smooth(se = F, size = 1, color = "#ffffff", span = .25)+
geom_smooth(se = F, size = .5, span = .25)+
scale_x_continuous(breaks = c(0, 15, 40, 60, 80))+
scale_y_continuous(
trans = "log",
breaks = c(.5, 1, 2, 3),
labels = c("", 1, 2, 3),
limits = c(.75, 3.5)
)+
scale_color_manual(NULL, values = pal_four)+
theme_minimal(base_family = font_rc, base_size = 16)+
theme(
legend.position = "none",
panel.grid.minor = element_blank()
)+
labs(
y = "Sex ratio, log scale",
x = "Age"
)+
annotate(
"text", x = 50, y = .9,
label = "Most recent year",
size = 8.5, color = "grey50", alpha = .5,
vjust = 1, family = font_rc, fontface = 2
)
one_a <- last_plot()
one_a
# Death risk Ratio, Sweden, years 1750, 1800, 1850, 1900, 1960, 2019
# plot qx
load("../dat/qxdiff.rda" %>% lp)
qxdiff %>%
filter(country == "SWE",
year %>% is_in(c(1800, 1900, 1960, 2019 ))) %>%
ggplot(aes(age, y = ratio, color = year %>% factor))+
geom_hline(yintercept = 1, color = "gray25", size = .5)+
geom_smooth(se = F, size = .75, span = .4)+
scale_x_continuous(breaks = c(0, 15, 40, 60, 80))+
scale_y_continuous(
trans = "log",
breaks = c(.5, 1, 2, 3),
labels = c("", 1, 2, 3),
limits = c(.75, 3.5)
)+
scale_color_viridis_d(end = .97)+
theme_minimal(base_family = font_rc, base_size = 16)+
theme(
legend.position = c(.85, .75),
legend.spacing.x = unit(.1, "line"),
legend.key.height = unit(1, "line"),
panel.grid.minor = element_blank()
)+
labs(
color = "Year",
y = "Sex ratio, log scale",
x = "Age"
)+
annotate(
"text", x = 50, y = .9,
label = "Sweden",
size = 8.5, color = "#009C9C",
vjust = 1, family = font_rc, fontface = 2
)
one_b <- last_plot()
one_b
# plot difference
df4qx %>%
pivot_wider(names_from = sex, values_from = qx) %>%
ggplot(aes(x = age, y = m - f, color = country, group = country)) +
geom_path(size = .5)+
scale_color_manual(NULL, values = pal_four)+
scale_x_continuous(breaks = c(0, 15, 40, 60, 80))+
scale_y_continuous(
trans = "log",
breaks = c(.0001, .001, .01, .05),
labels = c(.0001, .001, .01, .05) %>% paste %>% str_replace("0.", "."),
limits = c(9e-6, .1)
)+
theme_minimal(base_family = font_rc, base_size = 16)+
theme(legend.position = c(.77, .25),
legend.spacing.x = unit(.1, "line"),
legend.key.height = unit(1, "line"),
legend.text = element_text(size = 16),
panel.grid.minor = element_blank())+
labs(
y = "Sex gap, log scale",
x = "Age"
)
one_c <- last_plot()
one_c
# arrange and save
blank <- ggplot(tibble(x = 1, y = 1), aes(x, y))+
geom_rect(xmin = -Inf, xmax = Inf,
ymin = -Inf, ymax = Inf,
fill = "#ffffff",
color = NA)+
theme_void()
library(cowplot)
one <- ggdraw() +
draw_plot(one_outer) +
# white space for plots
draw_plot(blank, x = 0, y = .75, width = 0.7, height = 0.25)+
draw_plot(blank, x = 0, y = .55, width = 0.33, height = 0.42)+
draw_plot(blank, x = 0, y = .33, width = 0.33, height = 0.67)+
# inset plots
draw_plot(one_a, x = 0, y = .66, width = .33, height = .33)+
draw_plot(one_c, x = .34, y = .66, width = .33, height = .33)+
draw_plot(one_b, x = 0, y = 0.35, width = .33, height = .33)+
# annotate plot letters
draw_text(
LETTERS[c(1,3,2,4)],
x = c(.01, .35, .01, .01),
y = c(.99, .99, .66, .3),
hjust = 0, vjust = 1, size = 20,
family = font_rc, fontface = 2
)
ggsave(
filename = "out/main-one.png" %>% lp,
plot = one, width = 10, height = 10,
type = "cairo-png"
)
**这样一连串的的就绘制出图1。但是，有多少同学可以知道作者绘制每个图形的数据类型是什么样呢？
**
如果大家有时间时间和精力可以可以试一下，如果不行，那么在本文的中点赞或留言，我们一起分开绘制每个图形，一起学习！！！！
附图
appendix-1b.png
appendix-1c.png
appendix-3.png
appendix-8.png
ENDING！！
往期文章：
1. 最全WGCNA教程（替换数据即可出全部结果与图形）
2. 精美图形绘制教程
小杜的生信筆記，主要发表或收录生物信息学的教程，以及基于R的分析和可视化（包括数据分析，图形绘制等）；分享感兴趣的文献和学习资料!!
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