论文

https://www.pnas.org/content/118/20/e2010588118
Death rates at specific life stages mold the sex gap in life expectancy

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论文本地存储 e2010588118.full.pdf

很有意思的一篇论文，研究的内容是为什么女生比男生活的时间长（Why do women live longer than men?）哈哈哈。但是整篇论文我还没有看明白，所以先不给大家介绍结论了。

这篇论文的数据和代码是公开的，链接是 https://github.com/CPop-SDU/sex-gap-e0-pnas，我们按照他提供的代码和数据试着复原一下论文里的图。今天的推文重复的内容是论文中的Figure1A

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分组折线图

用到的数据集是链接里的dat文件夹下的 df4qx.rda文件，

首选是导入数据

load("data/df4qx.rda")
head(df4qx)

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这个是一个长格式数据，把它转变成宽格式

#install.packages("tidyverse")
library(tidyverse)
df4qx %>% 
  pivot_wider(names_from = sex,values_from = qx) %>% 
  head()

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这一步是为了方便计算不同年龄男女死亡率的比例

ggplot2作图

df4qx %>% 
  pivot_wider(names_from = sex,values_from = qx) -> dftemp

最基本的图

library(ggplot2)
dftemp %>% 
  ggplot(aes(age,y=m/f,color=country))+
  geom_smooth(se=F,size=1,color="#ffffff",span=0.25)+
  geom_smooth(se = F, size = .5, span = .25)+
  theme_minimal(base_size = 16)

image.png

这里原始代码还设置字体了，我这里就跳过了，因为我的电脑没有这个字体

接下来做细节调整

添加一条水平辅助线

dftemp %>% 
  ggplot(aes(age,y=m/f,color=country))+
  geom_smooth(se=F,size=1,color="#ffffff",span=0.25)+
  geom_smooth(se = F, size = .5, span = .25)+
  theme_minimal(base_size = 16,base_family = "serif")+
  geom_hline(yintercept = 1, color = "gray25",  size = .5)

image.png

更改x轴刻度范围

dftemp %>% 
  ggplot(aes(age,y=m/f,color=country))+
  geom_smooth(se=F,size=1,color="#ffffff",span=0.25)+
  geom_smooth(se = F, size = .5, span = .25)+
  theme_minimal(base_size = 16,base_family = "serif")+
  geom_hline(yintercept = 1, color = "gray25",  size = .5)+
  scale_x_continuous(breaks = c(0, 15, 40, 60, 80))

image.png

对y轴进行log2转换

dftemp %>% 
  ggplot(aes(age,y=m/f,color=country))+
  geom_smooth(se=F,size=1,color="#ffffff",span=0.25)+
  geom_smooth(se = F, size = .5, span = .25)+
  theme_minimal(base_size = 16,base_family = "serif")+
  geom_hline(yintercept = 1, color = "gray25",  size = .5)+
  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))

image.png

这一步为啥要做转化呢 有些没看明白

自定义配色

pal_safe_five <- c(
  "#eec21f", # default R 4.0 yellow
  "#009C9C", # light shade of teal: no red, equal green and blue
  "#df356b", # default R 4.0 red
  "#08479A", # blues9[8] "#08519C" made a bit darker
  "#003737" # very dark shade of teal
)

pal_safe_five_ordered <- pal_safe_five[c(5,2,1,3,4)]
pal_four <- pal_safe_five_ordered[c(2,5,3,4)]

dftemp %>% 
  ggplot(aes(age,y=m/f,color=country))+
  geom_smooth(se=F,size=1,color="#ffffff",span=0.25)+
  geom_smooth(se = F, size = .5, span = .25)+
  theme_minimal(base_size = 16,base_family = "serif")+
  geom_hline(yintercept = 1, color = "gray25",  size = .5)+
  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)

image.png

添加文本注释

dftemp %>% 
  ggplot(aes(age,y=m/f,color=country))+
  geom_smooth(se=F,size=1,color="#ffffff",span=0.25)+
  geom_smooth(se = F, size = .5, span = .25)+
  theme_minimal(base_size = 16,base_family = "serif")+
  geom_hline(yintercept = 1, color = "gray25",  size = .5)+
  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)+
  annotate(
    "text", x = 50, y = .9, 
    label = "Most recent year",
    size = 8.5, color = "grey50", alpha = .5,
    vjust = 1, family = "serif", fontface = 2
  )

image.png

去掉图例并更改坐标轴标题

dftemp %>% 
  ggplot(aes(age,y=m/f,color=country))+
  geom_smooth(se=F,size=1,color="#ffffff",span=0.25)+
  geom_smooth(se = F, size = .5, span = .25)+
  theme_minimal(base_size = 16,base_family = "serif")+
  geom_hline(yintercept = 1, color = "gray25",  size = .5)+
  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)+
  annotate(
    "text", x = 50, y = .9, 
    label = "Most recent year",
    size = 8.5, color = "grey50", alpha = .5,
    vjust = 1, family = "serif", fontface = 2
  )+
  theme(
    legend.position = "none",
    panel.grid.minor = element_blank()
  )+
  labs(
    y = "Sex ratio, log scale",
    x = "Age"
  )

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