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跟着Nature Communications学作图：纹理柱状图+添加显著性标签！
跟着「Nature Communications」学作图，今天主要通过复刻NC文章中的一张主图来巩固先前分享过的知识点，比如纹理柱状图、 添加显著性标签、拼图等，其中还会涉及数据处理的相关细节和具体过程。复现图片
主要复现红框部分，右侧的cd图与框中的图是同类型的，只不过需要构建更多数据相对麻烦，所以选择以左侧红框图进行学习和展示。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(agricolae) library(ggpattern) library(ggpubr)- 1
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读取数据集
cData1 <- read.csv("cData1.csv", header = T, row.names = 1) head(cData1) # Type Deep ctValue ftValue Stripe_Angle # 1 BT Top 55 73 135 # 2 BT Top 61 78 135 # 3 BT Top 69 80 135 # 4 BT Center 35 50 135 # 5 BT Center 42 41 135 # 6 BT Center 43 57 135- 1
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数据包括以下指标：2个分类变量、2个数值变量、和1个整数变量。
数据可视化
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；
- 进行组内的方差分析及多重比较；
- 进行组间的t检验；
计算均值和标准差
cData1_mean <- cData1 %>% gather(key = "var_type", value = "value", 3:4) %>% group_by(Type, Deep, var_type, Stripe_Angle) %>% summarise(mean = mean(value), sd = sd(value)) cData1_mean # A tibble: 12 × 6 # Groups: Type, Deep, var_type [12] # Type Deep var_type Stripe_Angle mean sd #- 1
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方差分析
# 方差分析 groups <- NULL vl <- unique((cData1 %>% gather(key = "var_type", value = "value", 3:4) %>% unite("unique_col", c(Type, var_type), sep = "-"))$unique_col) vl for(i in 1:length(vl)){ df <- cData1 %>% gather(key = "var_type", value = "value", 3:4) %>% unite("unique_col", c(Type, var_type), sep = "-") %>% filter(unique_col == vl[i]) aov <- aov(value ~ Deep, df) lsd <- LSD.test(aov, "Deep", p.adj = "bonferroni") %>% .$groups %>% mutate(Deep = rownames(.), unique_col = vl[i]) %>% dplyr::select(-value) %>% as.data.frame() groups <- rbind(groups, lsd) } groups <- groups %>% separate(unique_col, c("Type", "var_type")) groups # groups Deep Type var_type # Top a Top BT ctValue # Bottom b Bottom BT ctValue # Center b Center BT ctValue # Top1 a Top CK ctValue # Bottom1 a Bottom CK ctValue # Center1 a Center CK ctValue # Top2 a Top BT ftValue # Center2 b Center BT ftValue # Bottom2 b Bottom BT ftValue # Top3 a Top CK ftValue # Bottom3 a Bottom CK ftValue # Center3 a Center CK ftValue- 1
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使用aov函数和LSD.test函数实现方差分析及对应的多重比较，并提取显著性字母标签。
然后将多重比较的结果与原均值标准差的数据进行合并：
cData1_mean1 <- left_join(cData1_mean, groups, by = c("Deep", "Type", "var_type")) %>% arrange(var_type) %>% group_by(Type, var_type) %>% mutate(label_to_show = n_distinct(groups)) cData1_mean1 # A tibble: 12 × 8 # Groups: Type, var_type [4] # Type Deep var_type Stripe_Angle mean sd groups label_to_show #- 1
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- 需要注意的是：这里添加了label_to_show一列，目的是为了后续再进行字母标签添加时可以识别没有显著性的结果。
组间t-test
cData1_summary <- cData1 %>% gather(key = "var_type", value = "value", 3:4) %>% # unite("unique_col", c(Type, Deep), sep = "-") %>% unique_col group_by(Deep, var_type) %>% summarize( p_value = round(t.test(value ~ Type)$p.value, 2) ) %>% mutate( label = ifelse(p_value <= 0.001, "***", ifelse(p_value <= 0.01, "**", ifelse(p_value <= 0.05, "*", ifelse(p_value <= 0.1, "●", NA)))) ) cData1_summary # Deep var_type p_value label #- 1
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我们将计算出来的p值，并用* 或者 ●进行了赋值。然后合并相关结果：
cData1_summary1 <- left_join(cData1_mean1, cData1_summary, by = c("Deep", "var_type")) cData1_summary1 # Type Deep var_type Stripe_Angle mean sd groups label_to_show p_value label #- 1
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- 需要注意的是：添加的label也是为了后续筛选掉没有显著性结果做准备。
图a可视化过程
ctValue <- ggplot( data = cData1_mean1 %>% filter(var_type == "ctValue") %>% mutate(Deep = factor(Deep, levels = c("Top", "Center", "Bottom"))), aes(x = Type, y = mean, fill = Deep, pattern = Type, width = 0.75) ) + geom_bar_pattern( position = position_dodge(preserve = "single"), stat = "identity", pattern_fill = "white", pattern_color = "white", pattern_angle = -50, pattern_spacing = 0.05, color = "grey", width = 0.75 ) + scale_pattern_manual( values = c(CK = "stripe", BT = "none") ) + geom_errorbar( data = cData1_mean %>% filter(var_type == "ctValue") %>% mutate(Deep = factor(Deep, levels = c("Top", "Center", "Bottom"))), aes(x = Type, y = mean, ymin = mean - sd, ymax = mean + sd, width = 0.2), position = position_dodge(0.75), )+ geom_point( data = cData1 %>% mutate(Deep = factor(Deep, levels = c("Top", "Center", "Bottom"))), aes(x = Type, y = ctValue, group = Deep), color = "black", fill = "#D2D2D2", shape = 21, position = position_dodge(0.75), size = 3 )+ geom_text( data = cData1_mean1 %>% filter(var_type == "ctValue", label_to_show > 1) %>% mutate(Deep = factor(Deep, levels = c("Top", "Center", "Bottom"))), aes(x = Type, y = mean + sd, label = groups), position = position_dodge(0.75), vjust = -0.5, size = 5 ) + geom_segment( data = cData1_summary1 %>% filter(p_value <= 0.1 & var_type == "ctValue"), aes(x = 0.75, xend = 0.75, y = 73, yend = 76) )+ geom_segment( data = cData1_summary1 %>% filter(p_value <= 0.1 & var_type == "ctValue"), aes(x = 0.75, xend = 1.75, y = 76, yend = 76) )+ geom_segment( data = cData1_summary1 %>% filter(p_value <= 0.1 & var_type == "ctValue"), aes(x = 1.75, xend = 1.75, y = 73, yend = 76) )+ geom_text( data = cData1_summary1 %>% filter(p_value <= 0.1 & var_type == "ctValue"), aes(x = 1.25, y = 76, label = paste0("p = ", p_value)), vjust = -0.5, size = 5 )+ geom_text( data = cData1_summary1 %>% filter(p_value <= 0.1 & var_type == "ctValue"), aes(x = 1.25, y = 78, label = label), vjust = -1, size = 5 )+ scale_fill_manual( values = c("#393939", "#A2A2A2", "#CCCCCC") ) + scale_y_continuous( expand = c(0, 0), limits = c(0, 100), breaks = seq(0, 100, 50) ) + theme_classic()+ theme( legend.position = "top", axis.ticks.length.y = unit(0.2, "cm"), axis.text.y = element_text(color = "black", size = 12), axis.title.y = element_text(color = "black", size = 12, face = "bold"), axis.title.x = element_blank(), axis.text.x = element_blank(), axis.line.x = element_blank(), axis.ticks.x = element_blank(), plot.margin = margin(t = 0, r = 0, b = 1, l = 0, "lines") )+ labs(y = "CTvalue", fill = "", pattern = "");ctValue- 1
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图b可视化过程
ftValue <- ggplot( data = cData1_mean1 %>% filter(var_type == "ftValue") %>% mutate(Deep = factor(Deep, levels = c("Top", "Center", "Bottom"))), aes(x = Type, y = mean, fill = Deep, pattern = Type, width = 0.75) ) + geom_bar_pattern( position = position_dodge(preserve = "single"), stat = "identity", pattern_fill = "white", pattern_color = "white", pattern_angle = -50, pattern_spacing = 0.05, color = "grey", width = 0.75 ) + scale_pattern_manual( values = c(CK = "stripe", BT = "none") ) + geom_errorbar( data = cData1_mean %>% filter(var_type == "ftValue") %>% mutate(Deep = factor(Deep, levels = c("Top", "Center", "Bottom"))), aes(x = Type, y = mean, ymin = mean - sd, ymax = mean + sd, width = 0.2), position = position_dodge(0.75), )+ geom_point( data = cData1 %>% mutate(Deep = factor(Deep, levels = c("Top", "Center", "Bottom"))), aes(x = Type, y = ftValue, group = Deep), color = "black", fill = "#D2D2D2", shape = 21, position = position_dodge(0.75), size = 3 )+ geom_text( data = cData1_mean1 %>% filter(var_type == "ftValue", label_to_show > 1) %>% mutate(Deep = factor(Deep, levels = c("Top", "Center", "Bottom"))), aes(x = Type, y = mean + sd, label = groups), position = position_dodge(0.75), vjust = -0.5, size = 5 ) + geom_segment( data = cData1_summary1 %>% filter(p_value <= 0.1 & var_type == "ftValue"), aes(x = 0.75, xend = 0.75, y = 85, yend = 88) )+ geom_segment( data = cData1_summary1 %>% filter(p_value <= 0.1 & var_type == "ftValue"), aes(x = 0.75, xend = 1.75, y = 88, yend = 88) )+ geom_segment( data = cData1_summary1 %>% filter(p_value <= 0.1 & var_type == "ftValue"), aes(x = 1.75, xend = 1.75, y = 85, yend = 88) )+ geom_text( data = cData1_summary1 %>% filter(p_value <= 0.1 & var_type == "ftValue"), aes(x = 1.25, y = 88, label = paste0("p = ", p_value)), vjust = -0.5, size = 5 )+ geom_text( data = cData1_summary1 %>% filter(p_value <= 0.1 & var_type == "ftValue"), aes(x = 1.25, y = 90, label = label), vjust = -1, size = 5 )+ scale_fill_manual( values = c("#393939", "#A2A2A2", "#CCCCCC") ) + scale_y_continuous( expand = c(0, 0), limits = c(0, 100), breaks = seq(0, 100, 50) ) + theme_classic()+ theme( legend.position = "top", axis.ticks.length.y = unit(0.2, "cm"), axis.text.y = element_text(color = "black", size = 12), axis.title.y = element_text(color = "black", size = 12, face = "bold"), axis.title.x = element_blank(), axis.text.x = element_blank(), axis.line.x = element_blank(), axis.ticks.x = element_blank() )+ labs(y = "FTvalue", fill = "", pattern = "");ftValue- 1
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合并图ab
ggarrange(ctValue, ftValue, nrow = 2, ncol = 1, labels = c ("A", "B"), align = "hv", common.legend = T)- 1
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使用ggpubr包中的ggarrange函数完成拼图。这个图展示了基于不同深度（Top、Center、Bottom）和类型（CK、BT）的ctValue。以下是一个简短的解读：
柱状图：使用geom_bar_pattern函数创建柱状图。柱子的高度代表每种类型和深度的平均ctValue。柱子的颜色是根据深度填充的，而模式则是基于类型填充的。
误差条：使用geom_errorbar函数添加误差条，表示平均值上下的标准差。
点：使用geom_point函数绘制ctValue的单个数据点。注释：
geom_text函数向图表添加文本注释。似乎有某些群组和p值的注释。
使用geom_segment函数绘制的线条表示显著性的比较。美学和主题：
scale_fill_manual函数用于手动设置柱子的颜色。
使用theme_classic和theme函数定制图表的外观。
使用labs函数将图的y轴标记为"CTvalue"。
要可视化数据，您需要相应的数据框（cData1_mean1、cData1_mean、cData1和cData1_summary1），并确保加载了所需的库（ggplot2以及geom_bar_pattern等所需的其他库）。复现效果还是比较完美的。中间可视化代码细节比较多，大家可以自行学习，可以留言提问答疑。
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- 原文地址：https://blog.csdn.net/qq_42830713/article/details/134171497