跟着Nature正刊学作图 | 双轴柱状+折线散点图！

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📋文章目录

复现图片
设置工作路径和加载相关R包
读取数据集
数据可视化
计算均值和标准差
可视化过程

跟着「Nature」正刊学作图，今天复现Nature文章中的一张双轴图–左边为分组柱状图、右边为折线散点图。

复现图片

在这里插入图片描述
图中的a是我们今天准备复刻的，该图由柱状图和散点图组合的双轴图。

设置工作路径和加载相关R包

rm(list = ls()) # 清空当前环境变量
setwd("C:/Users/Zz/Desktop/公众号 SES") # 设置工作路径
# 加载R包
library(ggplot2)
library(tidyverse)

读取数据集

cData <- read_csv("cData.csv")
head(cData)
# Weeks Type               lfValue rgValue
# <dbl> <chr>                <dbl>   <dbl>
# 1    20 By week of testing    2500    1.3 
# 2    20 By week of testing    2550    1.5 
# 3    20 By week of testing    2450    1.45
# 4    21 By week of testing    2750    1.2 
# 5    21 By week of testing    2780    1.25
# 6    21 By week of testing    2680    1.18

数据可视化

# 物种组成堆叠面积图
library(ggplot2)
library(ggalluvial)
ggplot(data = top10,aes(x = Depth, y = Abundance, fill = reorder(Phylum, -Abundance),colour = reorder(Phylum, -Abundance),stratum = reorder(Phylum, -Abundance) ,alluvium = reorder(Phylum, -Abundance))) +geom_alluvium(aes(fill = reorder(Phylum, -Abundance)), alpha = 0.7, decreasing = FALSE) +geom_stratum(aes(fill = reorder(Phylum, Abundance)), width = 0.3, size = 0.1, color = "black") +scale_y_continuous(expand = c(0, 0)) +theme_bw() +facet_grid(. ~ Treat, scales = "fixed") +scale_fill_manual(values = c("#EB7369", "#CF8B0B", "#9D9F20", "#2BB077", "#2BB077","#1BB3B7", "#29A4DE", "#8989C1", "#B174AD","#DE66A1"), name =  "Phylum") +scale_color_manual(values = c("#EB7369", "#CF8B0B", "#9D9F20", "#2BB077", "#2BB077","#1BB3B7", "#29A4DE", "#8989C1", "#B174AD","#DE66A1")) +guides(color = "none")+theme(panel.grid=element_blank(),panel.spacing.x = unit(0, units = "cm"),strip.background = element_rect(color = "white", fill = "white", linetype = "solid", size = 1),strip.placement = "outside",axis.line.y.left = element_line(color = "black", size = 0.7),axis.line.x.bottom = element_line(color = "black", size = 0.7),strip.text.x = element_text(size = 14, face = "bold"),axis.text = element_text(face = "bold", size = 12, color = "black"),axis.title = element_text(face = "bold", size = 14, colour = "black"),legend.title = element_text(face = "bold", size = 12, color = "black"),legend.text = element_text(face = "bold", size = 12, color = "black"),axis.ticks.x = element_line(size = 1),axis.ticks.y = element_line(size = 1),)+labs(x = "Depth",y= "Relative Abundance of Phylum (%)")

数据包括以下指标：2个（左边和右边）数值变量、2个分类变量。

在可视化前，我们需要先思考图中构成的元素，由哪些组成。

计算每个分组或处理下的均值和标准差；

计算均值和标准差

cData_summary <- cData %>%group_by(Weeks, Type) %>%summarise(avg_lfValue = mean(lfValue),sd_lfValue = sd(lfValue),avg_rgValue = mean(rgValue),sd_rgValue = sd(rgValue),)
cData_summary
# Weeks Type               avg_lfValue sd_lfValue avg_rgValue sd_rgValue
# <dbl> <chr>                    <dbl>      <dbl>       <dbl>      <dbl>
# 1    20 By week of onset         2623.       25.2        1.98     0.0764
# 2    20 By week of testing       2500        50          1.42     0.104 
# 3    21 By week of onset         3543.       40.4        1.74     0.0361
# 4    21 By week of testing       2737.       51.3        1.21     0.0361
# 5    22 By week of onset         2770        26.5        1.28     0.0300
# 6    22 By week of testing       2160        60          1.10     0.0839
# 7    23 By week of onset         2143.       40.4        1.31     0.0208
# 8    23 By week of testing       1777.       75.1        1.02     0.0153
# 9    24 By week of onset         1823.       25.2        1.15     0.0300
# 10    24 By week of testing       1667.       61.1        1.07     0.0265
# 11    25 By week of onset         1690        36.1        1.23     0.0208
# 12    25 By week of testing       1610        36.1        1.2      0.0300
# 13    26 By week of onset         1607.       30.6        1.18     0.0252
# 14    26 By week of testing       1673.       30.6        1.16     0.0361

可视化过程

ggplot()+geom_bar(data = cData_summary %>% mutate(Type = factor(Type, levels = c("By week of testing","By week of onset"))),aes(x = Weeks, y = avg_lfValue, fill = Type), alpha = 0.5, stat = "identity", position = position_dodge(0.75), width = 0.75) +geom_errorbar(data = cData_summary %>% mutate(Type = factor(Type, levels = c("By week of testing","By week of onset"))),aes(x = Weeks, y = avg_lfValue, ymin = avg_lfValue - sd_lfValue, ymax = avg_lfValue + sd_lfValue,group = Type), color = "black",position = position_dodge(0.75), width = 0.2) +geom_line(data = cData_summary %>% mutate(Type = factor(Type, levels = c("By week of testing","By week of onset"))),aes(x = Weeks, avg_rgValue*1950, group = Type, color = Type),position = position_dodge(0.75), linewidth = 0.8) +geom_point(data = cData_summary %>% mutate(Type = factor(Type, levels = c("By week of testing","By week of onset"))),aes(x = Weeks, y = avg_rgValue*1950, color = Type), position = position_dodge(0.75), size = 2.5) + scale_x_continuous(breaks = seq(20, 26, 1)) +scale_y_continuous(name = c("Number of laboratory-confirmed\n sympotomatic cases"),sec.axis = sec_axis(~ ./1950, name = c("Test positivity rate (%)"),breaks = seq(0, 2, 1)),limits = c(0, 4000),breaks = seq(0, 4000, 500),expand = c(0, 0)) +scale_color_manual(values = c("#FE8F3C", "#1E899A")) +scale_fill_manual(values = c("#FE8F3C", "#1E899A")) +theme_bw() +theme(legend.position = c(0.9, 0.9),legend.background = element_blank(),panel.grid.major = element_blank(),panel.grid.minor = element_blank(),axis.text.x.bottom = element_text(color = "black", size = 12),axis.text.y.left = element_text(color = "black", size = 12),axis.text.y.right = element_text(color = "#44909A", size = 12),axis.title.y.right = element_text(color = "#44909A", size = 12, angle = 90),axis.line.y.right = element_line(color = "#44909A"),axis.ticks.y.right = element_line(color = "#44909A"),axis.title = element_text(color = "black", size = 12)) +labs(x = "Week",color = "",fill = "")