# setwd("path/to/Norpred") # 已注释：请按本地环境设置工作目录 DASIR <- read.csv('1\\depression\\incidence\\inyouthASR_data.csv') DASPR <- read.csv('1\\depression\\prevalence\\PyouthASR_data.csv') DASDR <- read.csv('1\\depression\\DALYS\\inDYyouthASR_data.csv') library(ggplot2) library(dplyr) # 定义高级的绿色色调 color_female <- "#66CDAA" # MediumAquamarine color_male <- "#8FBC8F" # DarkSeaGreen # 分割数据：2022年之前、2021-2022和2022年之后的数据 DASIR_before_2022 <- DASIR %>% filter(year < 2022 & sex %in% c("Female", "Male")) DASIR_2021_2022 <- DASIR %>% filter(year %in% c(2021, 2022) & sex %in% c("Female", "Male")) DASIR_after_2022 <- DASIR %>% filter(year > 2022 & sex %in% c("Female", "Male")) # 绘制DASIR图表并保存到对象p1中 p1 <- ggplot() + geom_line(data = DASIR_before_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "solid") + geom_line(data = DASIR_2021_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "dashed") + geom_line(data = DASIR_after_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "dashed") + geom_point(data = DASIR_before_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 2) + geom_point(data = DASIR_2021_2022 %>% filter(year == 2021), aes(x = year, y = ASR, color = sex, group = sex), size = 2) + labs(title = "the ASIR of Depressive disorders", x = "Year", y = "ASIR (per 100,000)") + theme_minimal(base_family = "Arial") + theme(panel.background = element_rect(fill = "white", color = "black"), plot.background = element_rect(fill = "white", color = "black"), panel.border = element_rect(color = "black", fill = NA, size = 1)) + scale_color_manual(values = c("Female" = color_female, "Male" = color_male)) + scale_y_continuous(breaks = seq(0, max(DASIR$ASR, na.rm = TRUE), by = 200)) + # 设置纵坐标刻度 scale_x_continuous(breaks = seq(min(DASIR$year, na.rm = TRUE), max(DASIR$year, na.rm = TRUE), by = 5)) + # 设置横坐标刻度 geom_vline(xintercept = 2021, linetype = "dashed", color = "grey", size = 1) # 添加2021年的虚线 # 分割数据：2022年之前、2021-2022和2022年之后的数据 DASPR_before_2022 <- DASPR %>% filter(year < 2022 & sex %in% c("Female", "Male")) DASPR_2021_2022 <- DASPR %>% filter(year %in% c(2021, 2022) & sex %in% c("Female", "Male")) DASPR_after_2022 <- DASPR %>% filter(year > 2022 & sex %in% c("Female", "Male")) # 绘制DASPR图表并保存到对象p2中 p2 <- ggplot() + geom_line(data = DASPR_before_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "solid") + geom_line(data = DASPR_2021_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "dashed") + geom_line(data = DASPR_after_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "dashed") + geom_point(data = DASPR_before_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 2) + geom_point(data = DASPR_2021_2022 %>% filter(year == 2021), aes(x = year, y = ASR, color = sex, group = sex), size = 2) + labs(title = "the ASPR of Depressive disorders", x = "Year", y = "ASPR (per 100,000)") + theme_minimal(base_family = "Arial") + theme(panel.background = element_rect(fill = "white", color = "black"), plot.background = element_rect(fill = "white", color = "black"), panel.border = element_rect(color = "black", fill = NA, size = 1)) + scale_color_manual(values = c("Female" = color_female, "Male" = color_male)) + scale_y_continuous(breaks = seq(0, max(DASPR$ASR, na.rm = TRUE), by = 200)) + # 设置纵坐标刻度 scale_x_continuous(breaks = seq(min(DASPR$year, na.rm = TRUE), max(DASPR$year, na.rm = TRUE), by = 5)) + # 设置横坐标刻度 geom_vline(xintercept = 2021, linetype = "dashed", color = "grey", size = 1) # 添加2021年的虚线 # 分割数据：2022年之前、2021-2022和2022年之后的数据 DASDR_before_2022 <- DASDR %>% filter(year < 2022 & sex %in% c("Female", "Male")) DASDR_2021_2022 <- DASDR %>% filter(year %in% c(2021, 2022) & sex %in% c("Female", "Male")) DASDR_after_2022 <- DASDR %>% filter(year > 2022 & sex %in% c("Female", "Male")) # 绘制DASDR图表并保存到对象p3中 p3 <- ggplot() + geom_line(data = DASDR_before_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "solid") + geom_line(data = DASDR_2021_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "dashed") + geom_line(data = DASDR_after_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "dashed") + geom_point(data = DASDR_before_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 2) + geom_point(data = DASDR_2021_2022 %>% filter(year == 2021), aes(x = year, y = ASR, color = sex, group = sex), size = 2) + labs(title = "the ASDR of Depressive disorders", x = "Year", y = "ASDR (per 100,000)") + theme_minimal(base_family = "Arial") + theme(panel.background = element_rect(fill = "white", color = "black"), plot.background = element_rect(fill = "white", color = "black"), panel.border = element_rect(color = "black", fill = NA, size = 1)) + scale_color_manual(values = c("Female" = color_female, "Male" = color_male)) + scale_y_continuous(breaks = seq(0, max(DASDR$ASR, na.rm = TRUE), by = 200)) + # 设置纵坐标刻度 scale_x_continuous(breaks = seq(min(DASDR$year, na.rm = TRUE), max(DASDR$year, na.rm = TRUE), by = 5)) + # 设置横坐标刻度 geom_vline(xintercept = 2021, linetype = "dashed", color = "grey", size = 1) # 添加2021年的虚线 # 将三个图放在一行三列中 # 加载所需的库 library(gridExtra) # 将三个图放在一行三列中 p <- grid.arrange(p1, p2, p3, ncol = 3) # 保存图像 ggsave("1/Depressive-disorders.png", plot = p, width = 20, height = 5, dpi = 300) # setwd("path/to/Norpred") # 已注释：请按本地环境设置工作目录 DASIR <- read.csv('1\\Dysthymia\\incidence\\inDYyouthASR_data.csv') DASPR <- read.csv('1\\Dysthymia\\prevalence\\PDYyouthASR_data.csv') DASDR <- read.csv('1\\Dysthymia\\DALYS\\DALYDYyouthASR_data.csv') library(ggplot2) library(dplyr) # 定义高级的绿色色调 color_female <- "#66CDAA" # MediumAquamarine color_male <- "#8FBC8F" # DarkSeaGreen # 分割数据：2022年之前、2021-2022和2022年之后的数据 DASIR_before_2022 <- DASIR %>% filter(year < 2022 & sex %in% c("Female", "Male")) DASIR_2021_2022 <- DASIR %>% filter(year %in% c(2021, 2022) & sex %in% c("Female", "Male")) DASIR_after_2022 <- DASIR %>% filter(year > 2022 & sex %in% c("Female", "Male")) # 绘制DASIR图表并保存到对象p1中 p1 <- ggplot() + geom_line(data = DASIR_before_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "solid") + geom_line(data = DASIR_2021_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "dashed") + geom_line(data = DASIR_after_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "dashed") + geom_point(data = DASIR_before_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 2) + geom_point(data = DASIR_2021_2022 %>% filter(year == 2021), aes(x = year, y = ASR, color = sex, group = sex), size = 2) + labs(title = "the ASIR of Dysthymia", x = "Year", y = "ASIR (per 100,000)") + theme_minimal(base_family = "Arial") + theme(panel.background = element_rect(fill = "white", color = "black"), plot.background = element_rect(fill = "white", color = "black"), panel.border = element_rect(color = "black", fill = NA, size = 1)) + scale_color_manual(values = c("Female" = color_female, "Male" = color_male)) + scale_y_continuous(breaks = seq(0, max(DASIR$ASR, na.rm = TRUE), by = 200)) + # 设置纵坐标刻度 scale_x_continuous(breaks = seq(min(DASIR$year, na.rm = TRUE), max(DASIR$year, na.rm = TRUE), by = 5)) + # 设置横坐标刻度 geom_vline(xintercept = 2021, linetype = "dashed", color = "grey", size = 1) # 添加2021年的虚线 # 分割数据：2022年之前、2021-2022和2022年之后的数据 DASPR_before_2022 <- DASPR %>% filter(year < 2022 & sex %in% c("Female", "Male")) DASPR_2021_2022 <- DASPR %>% filter(year %in% c(2021, 2022) & sex %in% c("Female", "Male")) DASPR_after_2022 <- DASPR %>% filter(year > 2022 & sex %in% c("Female", "Male")) # 绘制DASPR图表并保存到对象p2中 p2 <- ggplot() + geom_line(data = DASPR_before_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "solid") + geom_line(data = DASPR_2021_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "dashed") + geom_line(data = DASPR_after_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "dashed") + geom_point(data = DASPR_before_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 2) + geom_point(data = DASPR_2021_2022 %>% filter(year == 2021), aes(x = year, y = ASR, color = sex, group = sex), size = 2) + labs(title = "the ASPR of Dysthymia", x = "Year", y = "ASPR (per 100,000)") + theme_minimal(base_family = "Arial") + theme(panel.background = element_rect(fill = "white", color = "black"), plot.background = element_rect(fill = "white", color = "black"), panel.border = element_rect(color = "black", fill = NA, size = 1)) + scale_color_manual(values = c("Female" = color_female, "Male" = color_male)) + scale_y_continuous(breaks = seq(0, max(DASPR$ASR, na.rm = TRUE), by = 200)) + # 设置纵坐标刻度 scale_x_continuous(breaks = seq(min(DASPR$year, na.rm = TRUE), max(DASPR$year, na.rm = TRUE), by = 5)) + # 设置横坐标刻度 geom_vline(xintercept = 2021, linetype = "dashed", color = "grey", size = 1) # 添加2021年的虚线 # 分割数据：2022年之前、2021-2022和2022年之后的数据 DASDR_before_2022 <- DASDR %>% filter(year < 2022 & sex %in% c("Female", "Male")) DASDR_2021_2022 <- DASDR %>% filter(year %in% c(2021, 2022) & sex %in% c("Female", "Male")) DASDR_after_2022 <- DASDR %>% filter(year > 2022 & sex %in% c("Female", "Male")) # 绘制DASDR图表并保存到对象p3中 p3 <- ggplot() + geom_line(data = DASDR_before_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "solid") + geom_line(data = DASDR_2021_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "dashed") + geom_line(data = DASDR_after_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "dashed") + geom_point(data = DASDR_before_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 2) + geom_point(data = DASDR_2021_2022 %>% filter(year == 2021), aes(x = year, y = ASR, color = sex, group = sex), size = 2) + labs(title = "the ASDR of Dysthymia", x = "Year", y = "ASDR (per 100,000)") + theme_minimal(base_family = "Arial") + theme(panel.background = element_rect(fill = "white", color = "black"), plot.background = element_rect(fill = "white", color = "black"), panel.border = element_rect(color = "black", fill = NA, size = 1)) + scale_color_manual(values = c("Female" = color_female, "Male" = color_male)) + scale_y_continuous(breaks = seq(0, max(DASDR$ASR, na.rm = TRUE), by = 200)) + # 设置纵坐标刻度 scale_x_continuous(breaks = seq(min(DASDR$year, na.rm = TRUE), max(DASDR$year, na.rm = TRUE), by = 5)) + # 设置横坐标刻度 geom_vline(xintercept = 2021, linetype = "dashed", color = "grey", size = 1) # 添加2021年的虚线 # 将三个图放在一行三列中 # 加载所需的库 library(gridExtra) # 将三个图放在一行三列中 p <- grid.arrange(p1, p2, p3, ncol = 3) # 保存图像 ggsave("1/Dysthymia.png", plot = p, width = 20, height = 5, dpi = 300) # setwd("path/to/Norpred") # 已注释：请按本地环境设置工作目录 DASIR <- read.csv('1\\Major-depressive-disorder\\incidence\\inMAyouthASR_data.csv') DASPR <- read.csv('1\\Major-depressive-disorder\\prevalence\\PMAyouthASR_data.csv') DASDR <- read.csv('1\\Major-depressive-disorder\\DALYS\\DALYMAyouthASR_data.csv') library(ggplot2) library(dplyr) # 定义高级的绿色色调 color_female <- "#66CDAA" # MediumAquamarine color_male <- "#8FBC8F" # DarkSeaGreen # 分割数据：2022年之前、2021-2022和2022年之后的数据 DASIR_before_2022 <- DASIR %>% filter(year < 2022 & sex %in% c("Female", "Male")) DASIR_2021_2022 <- DASIR %>% filter(year %in% c(2021, 2022) & sex %in% c("Female", "Male")) DASIR_after_2022 <- DASIR %>% filter(year > 2022 & sex %in% c("Female", "Male")) # 绘制DASIR图表并保存到对象p1中 p1 <- ggplot() + geom_line(data = DASIR_before_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "solid") + geom_line(data = DASIR_2021_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "dashed") + geom_line(data = DASIR_after_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "dashed") + geom_point(data = DASIR_before_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 2) + geom_point(data = DASIR_2021_2022 %>% filter(year == 2021), aes(x = year, y = ASR, color = sex, group = sex), size = 2) + labs(title = "the ASIR of Major depressive disorder", x = "Year", y = "ASIR (per 100,000)") + theme_minimal(base_family = "Arial") + theme(panel.background = element_rect(fill = "white", color = "black"), plot.background = element_rect(fill = "white", color = "black"), panel.border = element_rect(color = "black", fill = NA, size = 1)) + scale_color_manual(values = c("Female" = color_female, "Male" = color_male)) + scale_y_continuous(breaks = seq(0, max(DASIR$ASR, na.rm = TRUE), by = 200)) + # 设置纵坐标刻度 scale_x_continuous(breaks = seq(min(DASIR$year, na.rm = TRUE), max(DASIR$year, na.rm = TRUE), by = 5)) + # 设置横坐标刻度 geom_vline(xintercept = 2021, linetype = "dashed", color = "grey", size = 1) # 添加2021年的虚线 # 分割数据：2022年之前、2021-2022和2022年之后的数据 DASPR_before_2022 <- DASPR %>% filter(year < 2022 & sex %in% c("Female", "Male")) DASPR_2021_2022 <- DASPR %>% filter(year %in% c(2021, 2022) & sex %in% c("Female", "Male")) DASPR_after_2022 <- DASPR %>% filter(year > 2022 & sex %in% c("Female", "Male")) # 绘制DASPR图表并保存到对象p2中 p2 <- ggplot() + geom_line(data = DASPR_before_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "solid") + geom_line(data = DASPR_2021_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "dashed") + geom_line(data = DASPR_after_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "dashed") + geom_point(data = DASPR_before_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 2) + geom_point(data = DASPR_2021_2022 %>% filter(year == 2021), aes(x = year, y = ASR, color = sex, group = sex), size = 2) + labs(title = "the ASPR of Major depressive disorder", x = "Year", y = "ASPR (per 100,000)") + theme_minimal(base_family = "Arial") + theme(panel.background = element_rect(fill = "white", color = "black"), plot.background = element_rect(fill = "white", color = "black"), panel.border = element_rect(color = "black", fill = NA, size = 1)) + scale_color_manual(values = c("Female" = color_female, "Male" = color_male)) + scale_y_continuous(breaks = seq(0, max(DASPR$ASR, na.rm = TRUE), by = 200)) + # 设置纵坐标刻度 scale_x_continuous(breaks = seq(min(DASPR$year, na.rm = TRUE), max(DASPR$year, na.rm = TRUE), by = 5)) + # 设置横坐标刻度 geom_vline(xintercept = 2021, linetype = "dashed", color = "grey", size = 1) # 添加2021年的虚线 # 分割数据：2022年之前、2021-2022和2022年之后的数据 DASDR_before_2022 <- DASDR %>% filter(year < 2022 & sex %in% c("Female", "Male")) DASDR_2021_2022 <- DASDR %>% filter(year %in% c(2021, 2022) & sex %in% c("Female", "Male")) DASDR_after_2022 <- DASDR %>% filter(year > 2022 & sex %in% c("Female", "Male")) # 绘制DASDR图表并保存到对象p3中 p3 <- ggplot() + geom_line(data = DASDR_before_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "solid") + geom_line(data = DASDR_2021_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "dashed") + geom_line(data = DASDR_after_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "dashed") + geom_point(data = DASDR_before_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 2) + geom_point(data = DASDR_2021_2022 %>% filter(year == 2021), aes(x = year, y = ASR, color = sex, group = sex), size = 2) + labs(title = "the ASDR of Major depressive disorder", x = "Year", y = "ASDR (per 100,000)") + theme_minimal(base_family = "Arial") + theme(panel.background = element_rect(fill = "white", color = "black"), plot.background = element_rect(fill = "white", color = "black"), panel.border = element_rect(color = "black", fill = NA, size = 1)) + scale_color_manual(values = c("Female" = color_female, "Male" = color_male)) + scale_y_continuous(breaks = seq(0, max(DASDR$ASR, na.rm = TRUE), by = 200)) + # 设置纵坐标刻度 scale_x_continuous(breaks = seq(min(DASDR$year, na.rm = TRUE), max(DASDR$year, na.rm = TRUE), by = 5)) + # 设置横坐标刻度 geom_vline(xintercept = 2021, linetype = "dashed", color = "grey", size = 1) # 添加2021年的虚线 # 将三个图放在一行三列中 # 加载所需的库 library(gridExtra) # 将三个图放在一行三列中 p <- grid.arrange(p1, p2, p3, ncol = 3) # 保存图像 ggsave("1/Major-depressive-disorder.png", plot = p, width = 20, height = 5, dpi = 300) #############绘制柱状图和折线图################## # setwd("path/to/Norpred") # 已注释：请按本地环境设置工作目录 data <- read.csv('1\\ASR.csv') DASIR <- subset(data, measure == 'Incidence' & cause == 'Depressive disorders') data1 <- read.csv('1\\depression\\incidence\\inyouthNum_data.csv') library(ggplot2) library(dplyr) # 定义高级的绿色色调 color_female <- "#66CDAA" # MediumAquamarine color_male <- "#8FBC8F" # DarkSeaGreen color_both <- "#32CD32" # LimeGreen # 过滤数据，保留1990-2021年，并且性别为Female, Male或Both的数据 DASIR_1990_2021 <- DASIR %>% filter(year >= 1990 & year <= 2021 & sex %in% c("Female", "Male", "Both")) data1_1990_2021 <- data1 %>% filter(year >= 1990 & year <= 2021 & sex %in% c("Female", "Male", "Both")) # 确保两个数据集的y轴范围适合显示 max_asr <- max(DASIR_1990_2021$ASR, na.rm = TRUE) max_num <- max(data1_1990_2021$number, na.rm = TRUE) # 绘制DASIR图表并保存到对象p1中 p1 <- ggplot() + geom_bar(data = data1_1990_2021, aes(x = year, y = number, fill = sex), stat = "identity", position = "dodge", alpha = 0.6) + geom_line(data = DASIR_1990_2021, aes(x = year, y = ASR * (max_num / max_asr), color = sex, group = sex), size = 1.2, linetype = "solid") + geom_point(data = DASIR_1990_2021, aes(x = year, y = ASR * (max_num / max_asr), color = sex, group = sex), size = 2) + labs(title = "Depressive disorders", x = "Year", y = "Number of cases", fill = "Sex", color = "Sex") + theme_minimal(base_family = "Arial") + theme(panel.background = element_rect(fill = "white", color = "black"), plot.background = element_rect(fill = "white", color = "black"), panel.border = element_rect(color = "black", fill = NA, size = 1)) + scale_color_manual(values = c("Female" = color_female, "Male" = color_male, "Both" = color_both)) + scale_fill_manual(values = c("Female" = color_female, "Male" = color_male, "Both" = color_both)) + scale_y_continuous(name = "Number of cases", sec.axis = sec_axis(~ . * (max_asr / max_num), name = "ASIR (per 100,000)")) + # 设置次坐标轴 scale_x_continuous(breaks = seq(1990, 2021, by = 5)) # 设置横坐标刻度为每年一个标记 # 显示图表 print(p1) ggsave("1/抑郁症ASIR.png", plot = p1, width = 8, height = 5, dpi = 300) ##############################################绘制柱状图################################### # 读取数据 innum_data <- read.csv('1\\depression\\incidence\\inyouthNum_data.csv') # setwd("path/to/Norpred") # 已注释：请按本地环境设置工作目录 DASIR <- read.csv('1\\depression\\incidence\\inyouthASR_data.csv') DASPR <- read.csv('1\\depression\\prevalence\\PyouthASR_data.csv') DASDR <- read.csv('1\\depression\\DALYS\\inDYyouthASR_data.csv') # 加载必要的库 library(ggplot2) # 绘制柱状图 p2 <- ggplot(innum_data, aes(x = year, y = number, fill = sex)) + geom_bar(stat = "identity", position = "dodge") + labs(title = "Number of Cases by Year and Sex", x = "Year", y = "Number of Cases", fill = "Sex") + theme_minimal(base_family = "Arial") + theme(panel.background = element_rect(fill = "white", color = "black"), plot.background = element_rect(fill = "white", color = "black"), panel.border = element_rect(color = "black", fill = NA, size = 1)) + scale_fill_manual(values = c("Female" = "#66CDAA", "Male" = "#8FBC8F")) # 显示图表 print(p2) ggsave("1/1.png", plot = p2, width = 8, height = 5, dpi = 300) # 保存图形到文件 ggsave("1\\20-44_Prevalence_青年抑郁症.png", plot = p, width = 10, height = 6, dpi = 300) ##########绘制置信区间############### library(ggplot2) library(dplyr) # 定义高级的绿色色调 color_female <- "#66CDAA" # MediumAquamarine # 加载数据 ASR <- read.csv("1\\PyouthASR区间_data.csv") # 确保 ASR 列为数值类型 ASR$ASR <- as.numeric(ASR$ASR) ASR$lower <- as.numeric(ASR$lower) ASR$upper <- as.numeric(ASR$upper) # 分割数据：2022年之前、2021-2022和2022年之后的数据 ASR_before_2022 <- ASR %>% filter(year < 2022 & sex == "Female") ASR_2021_2022 <- ASR %>% filter(year %in% c(2021, 2022) & sex == "Female") ASR_after_2022 <- ASR %>% filter(year > 2022 & sex == "Female") # 绘制ASR图表并保存到对象p中 p <- ggplot() + geom_line(data = ASR_before_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "solid") + geom_line(data = ASR_2021_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "dashed") + geom_line(data = ASR_after_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 1.2, linetype = "dashed") + geom_point(data = ASR_before_2022, aes(x = year, y = ASR, color = sex, group = sex), size = 2) + geom_point(data = ASR_2021_2022 %>% filter(year == 2021), aes(x = year, y = ASR, color = sex, group = sex), size = 2) + geom_ribbon(data = ASR_after_2022, aes(x = year, ymin = lower, ymax = upper, fill = sex), alpha = 0.2) + # 添加预测区间阴影 labs(title = "the ASR of Depressive disorders (Female)", x = "Year", y = "ASR (per 100,000)") + theme_minimal(base_family = "Arial") + theme(panel.background = element_rect(fill = "white", color = "black"), plot.background = element_rect(fill = "white", color = "black"), panel.border = element_rect(color = "black", fill = NA, size = 1)) + scale_color_manual(values = c("Female" = color_female)) + scale_fill_manual(values = c("Female" = color_female)) + scale_y_continuous(breaks = seq(0, max(ASR$ASR, na.rm = TRUE), by = 200)) + # 设置纵坐标刻度 scale_x_continuous(breaks = seq(min(ASR$year, na.rm = TRUE), max(ASR$year, na.rm = TRUE), by = 5)) + # 设置横坐标刻度 geom_vline(xintercept = 2021, linetype = "dashed", color = "grey", size = 1) # 添加2021年的虚线 # 保存图形到文件 ggsave("1/20-44_Prevalence_女青年抑郁症.png", plot = p, width = 10, height = 6, dpi = 300)