在医学研究中，数据可视化是展示研究成果的重要手段。一张清晰的图表往往比大段文字更能直观地传达信息。今天我们就来深入解析如何使用R语言绘制专业的医学图表，以光学相干断层扫描（OCT）对经皮冠状动脉介入治疗（PCI）决策的影响为例。
在循证医学时代，医生和研究人员需要处理大量临床数据，并通过可视化手段发现数据中的规律和趋势。良好的数据可视化能够：
1.提高研究结果的传达效率
2.帮助识别潜在的数据模式和异常值
3.增强学术报告和论文的说服力
4.促进临床决策的科学化
光学相干断层扫描（OCT）是一种高分辨率的冠状动脉成像技术，能够提供比传统血管造影更详细的血管内部信息。本研究通过分析552个病变，发现OCT提供的信息在88%的病变中改变了基于血管造影的治疗决策，其中术前OCT的影响占83%，术后OCT的影响占31%。
必要的R包：R语言的强大功能很大程度上来自于其丰富的扩展包。
ggplot2：基于图形语法的绘图系统，提供强大而灵活的绘图功能
dplyr和tidyr：数据整理和转换的利器
gridExtra：提供图形排列和布局功能
showtext：解决中文字体显示问题
scales：提供坐标轴标签格式调整功能
pacman包是一个方便的包管理工具，可以自动安装和加载所需的包。
实用技巧与注意事项
1.字体大小适配：根据输出介质调整字体大小，印刷品需要比屏幕显示更大的字体
2.颜色对比度：确保颜色有足够的对比度，考虑色盲人群的可读性
3.数据标签：避免标签重叠，适当调整位置和大小
4.图例设计：保持图例简洁明了，避免过度装饰
5.输出格式：矢量格式（PDF、SVG）适合印刷，位图格式（PNG、JPG）适合网络传播
# 设置工作目录和清理环境
rm(list = ls())
if (!is.null(dev.list())) dev.off()
setwd("C:/Users/hyy/Desktop/")
# 创建结果文件夹
if (!dir.exists("Results")) dir.create("Results")
# 加载必要的包
if (!require(pacman)) install.packages("pacman")
pacman::p_load(ggplot2, dplyr, tidyr, gridExtra, showtext, scales)
# 添加中文字体支持
font_add("SimSun", "simsun.ttc")
showtext_auto()
# 创建数据框
data <- data.frame(
Category = c("Lesion Type (A, B, C)", "Lesion Morphology", "Treat Peradenopathy",
"Vessel Prep", "Treatment Type", "Number of Stents", "Stent Length",
"Edge Detection", "Malapposition", "Underexpansion", "Geographic Miss", "Other"),
Pre_PCI = c(48, 22, 28, 2, 8, 11, 0, 0, 0, 0, 0, 0), # 前6个有数据，后面为0
Post_PCI = c(0, 0, 0, 0, 0, 0, 25, 4, 11, 2, 3, 0) # 后6个有数据，前面为0
)
# 将数据转换为长格式
data_long <- data %>%
pivot_longer(cols = c(Pre_PCI, Post_PCI),
names_to = "Procedure",
values_to = "Percentage")
# 创建颜色方案
colors <- c("#1f77b4", "#aec7e8", "#ff7f0e", "#ffbb78", "#2ca02c", "#98df8a",
"#d62728", "#ff9896", "#9467bd", "#c5b0d5", "#8c564b", "#c49c94")
# 创建主图表
p_main <- ggplot(data_long, aes(x = Procedure, y = Percentage, fill = Category)) +
geom_bar(stat = "identity", position = "stack") +
scale_fill_manual(values = colors) +
scale_y_continuous(labels = percent_format(scale = 1)) +
labs(title = "OCT-derived information changes angiographic-based decisions in 88% of lesions",
subtitle = "Impact of Pre PCI OCT: 83% | Impact of Post PCI OCT: 31% | N=552",
x = "", y = "Percentage") +
theme_minimal() +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 12, hjust = 0.5),
axis.text.x = element_text(size = 12, face = "bold"),
axis.text.y = element_text(size = 10),
axis.title.y = element_text(size = 12),
legend.position = "right",
legend.title = element_blank(),
legend.text = element_text(size = 9)
)
p_main
# 添加百分比标签
p_main <- p_main +
geom_text(data = subset(data_long, Percentage > 0),
aes(label = paste0(Percentage, "%")),
position = position_stack(vjust = 0.5),
size = 3.5, color = "white", fontface = "bold")
# 创建图例单独显示
legend_plot <- ggplot(data_long, aes(x = Procedure, y = Percentage, fill = Category)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = colors) +
theme_minimal() +
theme(legend.position = "bottom",
legend.box = "horizontal",
legend.text = element_text(size = 8)) +
guides(fill = guide_legend(nrow = 4, byrow = TRUE))
# 提取图例
legend <- cowplot::get_legend(legend_plot)
# 创建最终图表布局
final_plot <- grid.arrange(
p_main + theme(legend.position = "none"),
legend,
nrow = 2,
heights = c(8, 2)
)
# 保存图表
ggsave("Results/Figure1_OCT_Impact.jpg", final_plot, width = 12, height = 10, dpi = 300)
ggsave("Results/Figure1_OCT_Impact.pdf", final_plot, width = 12, height = 10)
# 添加参考文献文本
ref_text <- "1. O'u.e.K. et al. / Am Coll Cardiol, 2020 Oct. 76; 17. Supplement S) B175-E175."
writeLines(ref_text, "Results/Figure1_Reference.txt")
# 显示图表
print(final_plot)
# 设置工作目录和清理环境
rm(list = ls())
if (!is.null(dev.list())) dev.off()
setwd("C:/Users/hyy/Desktop/")
# 创建结果文件夹
if (!dir.exists("Results")) dir.create("Results")
# 加载必要的包
if (!require(pacman)) install.packages("pacman")
pacman::p_load(ggplot2, dplyr, tidyr, gridExtra, showtext, scales, patchwork)
# 添加中文字体支持
font_add("SimSun", "simsun.ttc")
showtext_auto()
# 创建数据框
data <- data.frame(
Category = c("Lesion Type (A, B, C)", "Lesion Morphology", "Treat Peradenopathy",
"Vessel Prep", "Treatment Type", "Number of Stents", "Stent Length",
"Edge Detection", "Malapposition", "Underexpansion", "Geographic Miss", "Other"),
Pre_PCI = c(48, 22, 28, 2, 8, 11, 0, 0, 0, 0, 0, 0), # 前6个有数据，后面为0
Post_PCI = c(0, 0, 0, 0, 0, 0, 25, 4, 11, 2, 3, 0) # 后6个有数据，前面为0
)
# 将数据转换为长格式
data_long <- data %>%
pivot_longer(cols = c(Pre_PCI, Post_PCI),
names_to = "Procedure",
values_to = "Percentage")
# 创建颜色方案
pre_colors <- c("#1f77b4", "#aec7e8", "#ff7f0e", "#ffbb78", "#2ca02c", "#98df8a")
post_colors <- c("#d62728", "#ff9896", "#9467bd", "#c5b0d5", "#8c564b", "#c49c94")
# 创建Pre-PCI柱状图
pre_data <- data_long %>%
filter(Procedure == "Pre_PCI", Percentage > 0) %>%
mutate(Category = factor(Category, levels = Category))
pre_plot <- ggplot(pre_data, aes(x = Category, y = Percentage, fill = Category)) +
geom_bar(stat = "identity", width = 0.7) +
scale_fill_manual(values = pre_colors) +
labs(title = "Pre-PCI OCT Impact (83%)",
x = "", y = "Percentage") +
theme_minimal() +
theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
axis.text.y = element_text(size = 10),
axis.title.y = element_text(size = 12),
legend.position = "none"
) +
geom_text(aes(label = paste0(Percentage, "%")),
vjust = -0.5, size = 4, fontface = "bold") +
ylim(0, max(pre_data$Percentage) * 1.1)
pre_plot
# 创建Post-PCI柱状图
post_data <- data_long %>%
filter(Procedure == "Post_PCI", Percentage > 0) %>%
mutate(Category = factor(Category, levels = Category))
post_plot <- ggplot(post_data, aes(x = Category, y = Percentage, fill = Category)) +
geom_bar(stat = "identity", width = 0.7) +
scale_fill_manual(values = post_colors) +
labs(title = "Post-PCI OCT Impact (31%)",
x = "", y = "Percentage") +
theme_minimal() +
theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
axis.text.y = element_text(size = 10),
axis.title.y = element_text(size = 12),
legend.position = "none"
) +
geom_text(aes(label = paste0(Percentage, "%")),
vjust = -0.5, size = 4, fontface = "bold") +
ylim(0, max(post_data$Percentage) * 1.1)
post_plot
# 创建组合数据用于折线图
combined_data <- data.frame(
Procedure = rep(c("Pre-PCI", "Post-PCI"), each = 6),
Category = c(
"Lesion Type (A, B, C)", "Lesion Morphology", "Treat Peradenopathy",
"Vessel Prep", "Treatment Type", "Number of Stents",
"Stent Length", "Edge Detection", "Malapposition",
"Underexpansion", "Geographic Miss", "Other"
),
Percentage = c(48, 22, 28, 2, 8, 11, 25, 4, 11, 2, 3, 0)
)
# 创建折线图
line_plot <- ggplot(combined_data, aes(x = Category, y = Percentage, group = Procedure, color = Procedure)) +
geom_line(size = 1.5) +
geom_point(size = 3) +
scale_color_manual(values = c("Pre-PCI" = "#1f77b4", "Post-PCI" = "#d62728")) +
labs(title = "OCT Impact Comparison: Pre-PCI vs Post-PCI",
x = "Category", y = "Percentage") +
theme_minimal() +
theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
axis.text.y = element_text(size = 10),
axis.title.y = element_text(size = 12),
legend.position = "bottom"
)
line_plot
# 使用patchwork包组合图表
combined_plot <- (pre_plot + post_plot) / line_plot +
plot_annotation(
title = "OCT-derived information changes angiographic-based decisions in 88% of lesions",
subtitle = "N=552 | Omni-arrive OCT impact through progression of workflow",
theme = theme(plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 12, hjust = 0.5))
)
# 保存图表
ggsave("Results/Figure1_OCT_Impact_Separate.jpg", combined_plot, width = 14, height = 12, dpi = 300)
ggsave("Results/Figure1_OCT_Impact_Separate.pdf", combined_plot, width = 14, height = 12)
# 添加参考文献文本
ref_text <- "1. O'u.e.K. et al. / Am Coll Cardiol, 2020 Oct. 76; 17. Supplement S) B175-E175."
writeLines(ref_text, "Results/Figure1_Reference.txt")
# 显示图表
print(combined_plot)
# 设置工作目录和清理环境
rm(list = ls())
if (!is.null(dev.list())) dev.off()
setwd("C:/Users/hyy/Desktop/")
# 创建结果文件夹
if (!dir.exists("Results")) dir.create("Results")
# 加载必要的包
if (!require(pacman)) install.packages("pacman")
pacman::p_load(ggplot2, dplyr, tidyr, gridExtra, showtext, scales, patchwork, grid)
# 添加中文字体支持
font_add("SimSun", "simsun.ttc")
showtext_auto()
# 创建数据框
data <- data.frame(
Category = c("Lesion Type (A, B, C)", "Lesion Morphology", "Treat Peradenopathy",
"Vessel Prep", "Treatment Type", "Number of Stents", "Stent Length",
"Edge Detection", "Malapposition", "Underexpansion", "Geographic Miss", "Other"),
Pre_PCI = c(48, 22, 28, 2, 8, 11, 0, 0, 0, 0, 0, 0), # 前6个有数据，后面为0
Post_PCI = c(0, 0, 0, 0, 0, 0, 25, 4, 11, 2, 3, 0) # 后6个有数据，前面为0
)
# 计算累计百分比
data$Pre_PCI_Cumulative <- cumsum(data$Pre_PCI)
data$Post_PCI_Cumulative <- cumsum(data$Post_PCI)
# 将数据转换为长格式
data_long <- data %>%
pivot_longer(cols = c(Pre_PCI, Post_PCI),
names_to = "Procedure",
values_to = "Percentage")
# 创建累计百分比的长格式数据
cumulative_long <- data %>%
pivot_longer(cols = c(Pre_PCI_Cumulative, Post_PCI_Cumulative),
names_to = "Procedure_Cumulative",
values_to = "Cumulative_Percentage") %>%
mutate(Procedure = ifelse(Procedure_Cumulative == "Pre_PCI_Cumulative", "Pre_PCI", "Post_PCI"))
# 创建颜色方案
pre_colors <- c("#1f77b4", "#aec7e8", "#ff7f0e", "#ffbb78", "#2ca02c", "#98df8a")
post_colors <- c("#d62728", "#ff9896", "#9467bd", "#c5b0d5", "#8c564b", "#c49c94")
# 创建Pre-PCI柱状图和折线图
pre_data <- data_long %>%
filter(Procedure == "Pre_PCI", Percentage > 0) %>%
mutate(Category = factor(Category, levels = Category))
pre_cumulative <- cumulative_long %>%
filter(Procedure == "Pre_PCI", Cumulative_Percentage > 0)
pre_plot <- ggplot(pre_data, aes(x = Category, y = Percentage)) +
geom_bar(stat = "identity", width = 0.7, fill = pre_colors[1:nrow(pre_data)]) +
geom_line(data = pre_cumulative, aes(x = Category, y = Cumulative_Percentage, group = 1),
color = "black", size = 1.5) +
geom_point(data = pre_cumulative, aes(x = Category, y = Cumulative_Percentage),
color = "black", size = 3) +
labs(title = "Pre-PCI OCT Impact (83%)",
x = "", y = "Percentage") +
theme_minimal() +
theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
axis.text.y = element_text(size = 10),
axis.title.y = element_text(size = 12),
legend.position = "none"
) +
geom_text(aes(label = paste0(Percentage, "%")),
vjust = -0.5, size = 4, fontface = "bold") +
geom_text(data = pre_cumulative, aes(x = Category, y = Cumulative_Percentage,
label = paste0(Cumulative_Percentage, "%")),
vjust = -1, size = 4, fontface = "bold", color = "black") +
ylim(0, max(pre_cumulative$Cumulative_Percentage) * 1.2)
pre_plot
# 创建Post-PCI柱状图和折线图
post_data <- data_long %>%
filter(Procedure == "Post_PCI", Percentage > 0) %>%
mutate(Category = factor(Category, levels = Category))
post_cumulative <- cumulative_long %>%
filter(Procedure == "Post_PCI", Cumulative_Percentage > 0)
post_plot <- ggplot(post_data, aes(x = Category, y = Percentage)) +
geom_bar(stat = "identity", width = 0.7, fill = post_colors[1:nrow(post_data)]) +
geom_line(data = post_cumulative, aes(x = Category, y = Cumulative_Percentage, group = 1),
color = "black", size = 1.5) +
geom_point(data = post_cumulative, aes(x = Category, y = Cumulative_Percentage),
color = "black", size = 3) +
labs(title = "Post-PCI OCT Impact (31%)",
x = "", y = "Percentage") +
theme_minimal() +
theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
axis.text.y = element_text(size = 10),
axis.title.y = element_text(size = 12),
legend.position = "none"
) +
geom_text(aes(label = paste0(Percentage, "%")),
vjust = -0.5, size = 4, fontface = "bold") +
geom_text(data = post_cumulative, aes(x = Category, y = Cumulative_Percentage,
label = paste0(Cumulative_Percentage, "%")),
vjust = -1, size = 4, fontface = "bold", color = "black") +
ylim(0, max(post_cumulative$Cumulative_Percentage) * 1.2)
post_plot
# 创建组合数据用于总折线图
combined_data <- data.frame(
Procedure = rep(c("Pre-PCI", "Post-PCI"), each = 6),
Category = c(
"Lesion Type (A, B, C)", "Lesion Morphology", "Treat Peradenopathy",
"Vessel Prep", "Treatment Type", "Number of Stents",
"Stent Length", "Edge Detection", "Malapposition",
"Underexpansion", "Geographic Miss", "Other"
),
Percentage = c(48, 22, 28, 2, 8, 11, 25, 4, 11, 2, 3, 0)
)
# 创建总折线图
line_plot <- ggplot(combined_data, aes(x = Category, y = Percentage, group = Procedure, color = Procedure)) +
geom_line(size = 1.5) +
geom_point(size = 3) +
scale_color_manual(values = c("Pre-PCI" = "#1f77b4", "Post-PCI" = "#d62728")) +
labs(title = "OCT Impact Comparison: Pre-PCI vs Post-PCI",
x = "Category", y = "Percentage") +
theme_minimal() +
theme(
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
axis.text.y = element_text(size = 10),
axis.title.y = element_text(size = 12),
legend.position = "bottom"
)
line_plot
# 使用grid.arrange而不是patchwork来组合图表
combined_grob <- arrangeGrob(
arrangeGrob(pre_plot, post_plot, nrow = 1),
line_plot,
nrow = 2,
top = textGrob("OCT-derived information changes angiographic-based decisions in 88% of lesions",
gp = gpar(fontsize = 16, fontface = "bold")),
bottom = textGrob("N=552 | Omni-arrive OCT impact through progression of workflow",
gp = gpar(fontsize = 12))
)
# 保存图表
ggsave("Results/Figure1_OCT_Impact_With_Cumulative.jpg", combined_grob, width = 16, height = 12, dpi = 300)
ggsave("Results/Figure1_OCT_Impact_With_Cumulative.pdf", combined_grob, width = 16, height = 12)
# 添加参考文献文本
ref_text <- "1. O'u.e.K. et al. / Am Coll Cardiol, 2020 Oct. 76; 17. Supplement S) B175-E175."
writeLines(ref_text, "Results/Figure1_Reference.txt")
# 显示图表
grid.draw(combined_grob)
# 设置工作目录和清理环境
rm(list = ls())
if (!is.null(dev.list())) dev.off()
setwd("C:/Users/hyy/Desktop/")
# 创建结果文件夹
if (!dir.exists("Results")) dir.create("Results")
# 加载必要的包
if (!require(pacman)) install.packages("pacman")
pacman::p_load(ggplot2, dplyr, tidyr, showtext, scales, patchwork)
# 添加中文字体支持
font_add("SimSun", "simsun.ttc")
showtext_auto()
# 创建数据框
data <- data.frame(
Category = c("Lesion Type (A, B, C)", "Lesion Morphology", "Treat Peradenopathy",
"Vessel Prep", "Treatment Type", "Number of Stents", "Stent Length",
"Edge Detection", "Malapposition", "Underexpansion", "Geographic Miss", "Other"),
Pre_PCI = c(48, 22, 28, 2, 8, 11, 0, 0, 0, 0, 0, 0), # 前6个有数据，后面为0
Post_PCI = c(0, 0, 0, 0, 0, 0, 25, 4, 11, 2, 3, 0) # 后6个有数据，前面为0
)
# 计算累计百分比
data$Pre_PCI_Cumulative <- cumsum(data$Pre_PCI)
data$Post_PCI_Cumulative <- cumsum(data$Post_PCI)
# 将数据转换为长格式
data_long <- data %>%
pivot_longer(cols = c(Pre_PCI, Post_PCI),
names_to = "Procedure",
values_to = "Count")
# 创建颜色方案
pre_colors <- "#1f77b4" # Pre-PCI颜色
post_colors <- "#d62728" # Post-PCI颜色
# 创建合并的图表
combined_plot <- ggplot() +
# Pre-PCI柱状图
geom_bar(data = filter(data_long, Procedure == "Pre_PCI", Count > 0),
aes(x = Category, y = Count, fill = "Pre-PCI"),
stat = "identity", width = 0.7, alpha = 0.7) +
# Pre-PCI累计线
geom_line(data = filter(data, Pre_PCI > 0),
aes(x = Category, y = Pre_PCI_Cumulative, group = 1, color = "Pre-PCI Cumulative"),
size = 1.5) +
geom_point(data = filter(data, Pre_PCI > 0),
aes(x = Category, y = Pre_PCI_Cumulative, color = "Pre-PCI Cumulative"),
size = 3) +
# Post-PCI柱状图
geom_bar(data = filter(data_long, Procedure == "Post_PCI", Count > 0),
aes(x = Category, y = Count, fill = "Post-PCI"),
stat = "identity", width = 0.7, alpha = 0.7) +
# 添加标志线分隔Pre和Post
geom_vline(xintercept = 6.5, linetype = "dashed", color = "black", size = 1) +
# 添加百分比标签
geom_text(data = filter(data_long, Procedure == "Pre_PCI", Count > 0),
aes(x = Category, y = Count, label = paste0(Count, "%")),
vjust = -0.5, size = 4, fontface = "bold") +
geom_text(data = filter(data_long, Procedure == "Post_PCI", Count > 0),
aes(x = Category, y = Count, label = paste0(Count, "%")),
vjust = -0.5, size = 4, fontface = "bold") +
# 添加累计百分比标签
geom_text(data = filter(data, Pre_PCI > 0),
aes(x = Category, y = Pre_PCI_Cumulative, label = paste0(Pre_PCI_Cumulative, "%")),
vjust = -1, size = 4, fontface = "bold", color = "#1f77b4") +
geom_text(data = filter(data, Post_PCI > 0),
aes(x = Category, y = Post_PCI_Cumulative, label = paste0(Post_PCI_Cumulative, "%")),
vjust = -1, size = 4, fontface = "bold", color = "#d62728") +
# 设置颜色
scale_fill_manual(name = "Procedure",
values = c("Pre-PCI" = pre_colors, "Post-PCI" = post_colors)) +
scale_color_manual(name = "Cumulative",
values = c("Pre-PCI Cumulative" = "#1f77b4",
"Post-PCI Cumulative" = "#d62728")) +
# 标题和标签
labs(title = "OCT-derived information changes angiographic-based decisions in 88% of lesions",
subtitle = "N=552 | Omni-arrive OCT impact through progression of workflow",
x = "", y = "Percentage") +
theme_minimal() +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = 12, hjust = 0.5),
axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
axis.text.y = element_text(size = 10),
axis.title.y = element_text(size = 12),
legend.position = "bottom",
legend.box = "horizontal"
) +
# 调整Y轴范围以容纳所有标签
ylim(0, max(c(data$Pre_PCI_Cumulative, data$Post_PCI_Cumulative)) * 1.2) +
# 添加Pre-PCI和Post-PCI的标题注释
annotate("text", x = 3.5, y = max(c(data$Pre_PCI_Cumulative, data$Post_PCI_Cumulative)) * 1.1,
label = "Impact of Pre PCI OCT: 83%", size = 5, fontface = "bold", color = pre_colors) +
annotate("text", x = 9.5, y = max(c(data$Pre_PCI_Cumulative, data$Post_PCI_Cumulative)) * 1.1,
label = "Impact of Post PCI OCT: 31%", size = 5, fontface = "bold", color = post_colors)
combined_plot
# 保存图表
ggsave("Results/Figure1_OCT_Impact_Combined.jpg", combined_plot, width = 16, height = 10, dpi = 300)
ggsave("Results/Figure1_OCT_Impact_Combined.pdf", combined_plot, width = 16, height = 10)
# 添加参考文献文本
ref_text <- "1. O'u.e.K. et al. / Am Coll Cardiol, 2020 Oct. 76; 17. Supplement S) B175-E175."
writeLines(ref_text, "Results/Figure1_Reference.txt")
# 显示图表
print(combined_plot)
通过本文的详细解析，我们不仅学习了如何绘制专业的医学图表，还了解了数据可视化在医学研究中的重要性。R语言提供了强大而灵活的可视化工具，能够帮助医学研究人员更好地展示和传达他们的研究成果。
无论你是医学研究者还是数据分析师，掌握这些可视化技能都将大大提高你的工作效率和研究影响力。希望本文能够为你提供有用的指导和灵感，在你的研究工作中发挥实际作用。
医学统计数据分析分享交流SPSS、R语言、Python、ArcGis、Geoda、GraphPad、数据分析图表制作等心得。承接数据分析，论文返修，医学统计，机器学习，生存分析，空间分析，问卷分析业务。若有投稿和数据分析代做需求，可以直接联系我，谢谢！
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