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❝本周在会员交流群内看到如下一张图，其主要是条形图+方差分析与散点图+拟合曲线的结合；其主要点在得到正确的绘图数据。小编随机构建了两份数据来做一下绘图展示，整个过程仅参考。希望对各位观众老爷能有所帮助。「数据代码已经整合上传到2023VIP交流群」，加群的观众老爷可自行下载，有需要的朋友可关注文末介绍加入VIP交流群。
❞
绘制拟合曲线散点图
p2 <- gapminder %>% select(2,1,year,lifeExp,gdpPercap) %>%
filter(continent=="Asia") %>%
filter(country %in% c("India","Cambodia","Afghanistan","Bangladesh","Indonesia")) %>%
ggplot(aes(lifeExp,gdpPercap))+
geom_point(aes(color=country))+
stat_smooth(linetype=2,color="red",size=0.5)+
stat_poly_eq(use_label(c("eq","adj.R2","p"), sep = "*\"; \"*"),
size=3,label.x.npc = "left", label.y.npc = "top",rr.digits =3)+
scale_color_manual(values=wes_palette("Zissou1"))+
scale_y_continuous(position = "right")+
labs(x=NULL,y=NULL)+
theme_test()+
theme(legend.position = "non")
数据清洗
df <- read_tsv("data.xls") %>%
arrange(conc) %>%
unite("Type", Treatment:Type, sep="_", remove = TRUE, na.rm = FALSE) %>%
mutate(Plant = str_remove(Plant, "[0-9]+")) %>%
filter(conc %in% c(95,175,250,350)) %>%
split(.$conc)
aov_data <- data.frame()
定义函数
perform_anova <- function(data) {
data <- rename(data, Plant=Plant, Type=Type, conc=conc, uptake=uptake)
anova <- aov(uptake ~ Type, data = data)
Tukey <- TukeyHSD(anova)
cld <- multcompLetters4(anova, Tukey)
dt <- data %>%
group_by(Plant, Type, conc) %>%
summarise(value_mean = mean(uptake), sd = sd(uptake), .groups = 'drop') %>%
arrange(desc(value_mean))
cld <- as.data.frame.list(cld$`Type`)
dt$Tukey <- cld$Letters
dt
}
方差分析
aov_data <- bind_rows(lapply(df, perform_anova))
aov_data <- aov_data %>%
arrange(Plant) %>%
mutate(conc = fct_relevel(factor(conc), c("95", "175", "250", "350")))
绘制方差分析图
p1 <- aov_data %>%
ggplot(aes(conc,value_mean,fill=Type))+
geom_bar(stat = "identity", position = "dodge",width=0.5) +
geom_errorbar(aes(ymax = value_mean + sd, ymin = value_mean - sd),
position = position_dodge(0.5),width = 0.2,color = "Gray25")+
scale_fill_manual(values=wes_palette("Zissou1"))+
scale_y_continuous(expand = expansion(0))+
geom_text(aes(label=Tukey, y = value_mean + sd + 1.5), color = NA,
show.legend = FALSE,position = position_dodge(0.5))+
geom_text(aes(label=Tukey, y = value_mean + sd +0.8), size = 3, color = "black",
show.legend = FALSE,position = position_dodge(0.5))+
labs(x=NULL,y=NULL)+
theme(axis.ticks.x=element_blank(),
axis.text.y=element_text(color="black",size = 10,margin = margin(r =2)),
axis.text.x=element_text(color="black",size = 10),
panel.background = element_rect(fill = NA,color = NA),
panel.grid.minor= element_line(size=0.2,color="#e5e5e5"),
panel.grid.major = element_line(size=0.2,color="#e5e5e5"),
panel.border = element_rect(fill=NA,color="black",size=0.3,linetype="solid"),
legend.title = element_blank(),
legend.text = element_text(color="black",size=8),
legend.spacing.x=unit(0.1,'cm'),
legend.key.width=unit(0.4,'cm'),
legend.key.height=unit(0.4,'cm'),
legend.position = c(0.35,1),legend.justification=c(1,1),
legend.background=element_blank())
拼图
p1+p2+plot_layout(widths = c(1.5,1))
❝本节内容介绍到此结束，过程仅供参考；有需要学习时间可视化的朋友，欢迎到小编的「淘宝店铺」 「R语言数据分析指南」购买「2023年度会员文档」同步更新中「售价149元」，内容主要包括各种「高分论文的图表分析复现以及一些个性化图表的绘制」均包含数据+代码；按照往年数据小编年产出约在150+以上
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