Homework 5 - Data 101 with Prof. Alraee

sistema_de_rúbrica_automática_sencillo_y_robusto

Tres_Prompts_Optimizados_por_Asignatura

Plantilla_Maestra_Evalua_Lote_de_estudiantes

# ==================================================== # Figure 1: Technological Evolution Curve (2016-2030) # ==================================================== # 1. 检查并安装必要的包 required_packages <- c("ggplot2", "dplyr", "tidyr", "scales") for(pkg in required_packages) { if(!require(pkg, character.only = TRUE)) { install.packages(pkg) library(pkg, character.only = TRUE) } } # 2. 创建数据框 df <- data.frame( Year = 2016:2030, EI_Score = c(0.32, 0.36, 0.41, 0.45, 0.52, 0.58, 0.65, 0.71, 0.82, 0.94, 0.97, 0.99, 1.01, 1.02, 1.03), Accuracy = c(75, 77, 80, 82, 86, 88, 90, 92, 94, 95, 96, 97, 98, 98.5, 98.5), Type = c(rep("Actual", 10), rep("Predicted", 5)) ) # 3. 创建区域数据框用于背景色 regions <- data.frame( xmin = c(2016, 2018.5, 2020.5, 2022.5, 2024.5), xmax = c(2018.5, 2020.5, 2022.5, 2024.5, 2030.5), Era = c("Early CNNs", "Hybrid Models", "Transformer Era", "Personalization Era", "Clinical Translation Era") ) # 4. 创建标注数据框 annotations <- data.frame( Year = c(2020, 2021, 2023), EI = c(0.58, 0.65, 0.71), EI_label = c(0.65, 0.72, 0.78), Label = c("ViT (2020)", "MAE (2021)", "DINOv2 (2023)") ) # 5. 分割数据为实际值和预测值 df_actual <- df[1:10, ] df_pred <- df[10:15, ] # 6. 使用ggplot2创建图形 p <- ggplot() + # 添加背景区域 geom_rect(data = regions, aes(xmin = xmin, xmax = xmax, ymin = -Inf, ymax = Inf, fill = Era), alpha = 0.2, inherit.aes = FALSE) + # 添加EI曲线 - 实际值 geom_line(data = df_actual, aes(x = Year, y = EI_Score, color = "EI Score"), linetype = "solid", size = 1) + geom_point(data = df_actual, aes(x = Year, y = EI_Score, color = "EI Score"), shape = 16, size = 3) + # 添加EI曲线 - 预测值 geom_line(data = df_pred, aes(x = Year, y = EI_Score, color = "EI Score"), linetype = "dashed", size = 1) + geom_point(data = df_pred, aes(x = Year, y = EI_Score, color = "EI Score"), shape = 1, size = 3) + # 添加Accuracy曲线 - 实际值 geom_line(data = df_actual, aes(x = Year, y = Accuracy/83.33, color = "Accuracy"), linetype = "solid", size = 1) + geom_point(data = df_actual, aes(x = Year, y = Accuracy/83.33, color = "Accuracy"), shape = 15, size = 3) + # 添加Accuracy曲线 - 预测值 geom_line(data = df_pred, aes(x = Year, y = Accuracy/83.33, color = "Accuracy"), linetype = "dashed", size = 1) + geom_point(data = df_pred, aes(x = Year, y = Accuracy/83.33, color = "Accuracy"), shape = 0, size = 3) + # 添加垂直线 geom_vline(xintercept = 2025, linetype = "dashed", color = "black", alpha = 0.5) + # 添加标注 geom_segment(data = annotations, aes(x = Year, xend = Year, y = EI, yend = EI_label), arrow = arrow(length = unit(0.2, "cm")), color = "black") + geom_text(data = annotations, aes(x = Year, y = EI_label + 0.02, label = Label), size = 3, hjust = 0.5) + # 添加区域文本标签 geom_text(data = data.frame(x = c(2017.25, 2019.5, 2021.5, 2023.5, 2027.5), y = rep(1.15, 5), label = c("Early CNNs", "Hybrid Models", "Transformer Era", "Personalization", "Clinical Translation")), aes(x = x, y = y, label = label), size = 3.5, fontface = "italic") + # 设置坐标轴 scale_x_continuous(breaks = seq(2016, 2030, 2), limits = c(2016, 2030)) + scale_y_continuous( name = "Evolution Index (EI)", sec.axis = sec_axis(~.*83.33, name = "Accuracy (%)", breaks = seq(70, 100, 5)), limits = c(0, 1.2), breaks = seq(0, 1.2, 0.2) ) + # 设置颜色 scale_color_manual( name = "", values = c("EI Score" = "#2563EB", "Accuracy" = "#DC2626"), labels = c("EI Score (Actual)", "Accuracy (Actual)") ) + # 设置填充色 scale_fill_manual( name = "Technological Eras", values = c("Early CNNs" = "gray80", "Hybrid Models" = "lightblue", "Transformer Era" = "lightgreen", "Personalization Era" = "lightyellow", "Clinical Translation Era" = "orange") ) + # 添加主题设置 theme_minimal() + theme( plot.title = element_text(size = 14, face = "bold", hjust = 0.5, margin = margin(b = 20)), axis.title.x = element_text(size = 12, face = "bold"), axis.title.y.left = element_text(color = "#2563EB", size = 12, face = "bold"), axis.title.y.right = element_text(color = "#DC2626", size = 12, face = "bold"), axis.text.y.left = element_text(color = "#2563EB"), axis.text.y.right = element_text(color = "#DC2626"), legend.position = "bottom", legend.box = "vertical", legend.margin = margin(t = 10), panel.grid.minor = element_blank(), panel.grid.major = element_line(color = "gray90"), plot.margin = margin(1, 1, 1, 1, "cm") ) + # 添加标题 labs( title = "Figure 1: Technological Evolution of Pain Expression Recognition (2016-2030)", x = "Year", caption = "Predicted values (2025-2030) shown with dashed lines. Actual data (2016-2025) shown with solid lines. Predictions generated from exponential growth model (α=0.25, β=0.15, γ=0.12, δ=0.08, ω=2.1)." ) + # 添加图例说明 annotate("text", x = 2027, y = 0.2, label = "Solid: Actual\nDashed: Predicted", size = 3, hjust = 0, fontface = "italic") # 7. 显示图形 print(p) # 8. 保存为高分辨率PNG ggsave("figure1_evolution_curve.png", plot = p, width = 12, height = 6, dpi = 600, bg = "white") # 9. 保存为SVG矢量格式（用于出版物） ggsave("figure1_evolution_curve.svg", plot = p, width = 12, height = 6, bg = "white")

# ==================================================== # Figure 1: Technological Evolution Curve (2016-2030) # ==================================================== # 1. 检查并安装必要的包 required_packages <- c("ggplot2", "dplyr", "tidyr", "scales") for(pkg in required_packages) { if(!require(pkg, character.only = TRUE)) { install.packages(pkg) library(pkg, character.only = TRUE) } } # 2. 创建数据框 df <- data.frame( Year = 2016:2030, EI_Score = c(0.32, 0.36, 0.41, 0.45, 0.52, 0.58, 0.65, 0.71, 0.82, 0.94, 0.97, 0.99, 1.01, 1.02, 1.03), Accuracy = c(75, 77, 80, 82, 86, 88, 90, 92, 94, 95, 96, 97, 98, 98.5, 98.5), Type = c(rep("Actual", 10), rep("Predicted", 5)) ) # 3. 创建区域数据框用于背景色 regions <- data.frame( xmin = c(2016, 2018.5, 2020.5, 2022.5, 2024.5), xmax = c(2018.5, 2020.5, 2022.5, 2024.5, 2030.5), Era = c("Early CNNs", "Hybrid Models", "Transformer Era", "Personalization Era", "Clinical Translation Era") ) # 4. 创建标注数据框 annotations <- data.frame( Year = c(2020, 2021, 2023), EI = c(0.58, 0.65, 0.71), EI_label = c(0.65, 0.72, 0.78), Label = c("ViT (2020)", "MAE (2021)", "DINOv2 (2023)") ) # 5. 分割数据为实际值和预测值 df_actual <- df[1:10, ] df_pred <- df[10:15, ] # 6. 使用ggplot2创建图形 p <- ggplot() + # 添加背景区域 geom_rect(data = regions, aes(xmin = xmin, xmax = xmax, ymin = -Inf, ymax = Inf, fill = Era), alpha = 0.2, inherit.aes = FALSE) + # 添加EI曲线 - 实际值 geom_line(data = df_actual, aes(x = Year, y = EI_Score, color = "EI Score"), linetype = "solid", size = 1) + geom_point(data = df_actual, aes(x = Year, y = EI_Score, color = "EI Score"), shape = 16, size = 3) + # 添加EI曲线 - 预测值 geom_line(data = df_pred, aes(x = Year, y = EI_Score, color = "EI Score"), linetype = "dashed", size = 1) + geom_point(data = df_pred, aes(x = Year, y = EI_Score, color = "EI Score"), shape = 1, size = 3) + # 添加Accuracy曲线 - 实际值 geom_line(data = df_actual, aes(x = Year, y = Accuracy/83.33, color = "Accuracy"), linetype = "solid", size = 1) + geom_point(data = df_actual, aes(x = Year, y = Accuracy/83.33, color = "Accuracy"), shape = 15, size = 3) + # 添加Accuracy曲线 - 预测值 geom_line(data = df_pred, aes(x = Year, y = Accuracy/83.33, color = "Accuracy"), linetype = "dashed", size = 1) + geom_point(data = df_pred, aes(x = Year, y = Accuracy/83.33, color = "Accuracy"), shape = 0, size = 3) + # 添加垂直线 geom_vline(xintercept = 2025, linetype = "dashed", color = "black", alpha = 0.5) + # 添加标注 geom_segment(data = annotations, aes(x = Year, xend = Year, y = EI, yend = EI_label), arrow = arrow(length = unit(0.2, "cm")), color = "black") + geom_text(data = annotations, aes(x = Year, y = EI_label + 0.02, label = Label), size = 3, hjust = 0.5) + # 添加区域文本标签 geom_text(data = data.frame(x = c(2017.25, 2019.5, 2021.5, 2023.5, 2027.5), y = rep(1.15, 5), label = c("Early CNNs", "Hybrid Models", "Transformer Era", "Personalization", "Clinical Translation")), aes(x = x, y = y, label = label), size = 3.5, fontface = "italic") + # 设置坐标轴 scale_x_continuous(breaks = seq(2016, 2030, 2), limits = c(2016, 2030)) + scale_y_continuous( name = "Evolution Index (EI)", sec.axis = sec_axis(~.*83.33, name = "Accuracy (%)", breaks = seq(70, 100, 5)), limits = c(0, 1.2), breaks = seq(0, 1.2, 0.2) ) + # 设置颜色 scale_color_manual( name = "", values = c("EI Score" = "#2563EB", "Accuracy" = "#DC2626"), labels = c("EI Score (Actual)", "Accuracy (Actual)") ) + # 设置填充色 scale_fill_manual( name = "Technological Eras", values = c("Early CNNs" = "gray80", "Hybrid Models" = "lightblue", "Transformer Era" = "lightgreen", "Personalization Era" = "lightyellow", "Clinical Translation Era" = "orange") ) + # 添加主题设置 theme_minimal() + theme( plot.title = element_text(size = 14, face = "bold", hjust = 0.5, margin = margin(b = 20)), axis.title.x = element_text(size = 12, face = "bold"), axis.title.y.left = element_text(color = "#2563EB", size = 12, face = "bold"), axis.title.y.right = element_text(color = "#DC2626", size = 12, face = "bold"), axis.text.y.left = element_text(color = "#2563EB"), axis.text.y.right = element_text(color = "#DC2626"), legend.position = "bottom", legend.box = "vertical", legend.margin = margin(t = 10), panel.grid.minor = element_blank(), panel.grid.major = element_line(color = "gray90"), plot.margin = margin(1, 1, 1, 1, "cm") ) + # 添加标题 labs( title = "Figure 1: Technological Evolution of Pain Expression Recognition (2016-2030)", x = "Year", caption = "Predicted values (2025-2030) shown with dashed lines. Actual data (2016-2025) shown with solid lines. Predictions generated from exponential growth model (α=0.25, β=0.15, γ=0.12, δ=0.08, ω=2.1)." ) + # 添加图例说明 annotate("text", x = 2027, y = 0.2, label = "Solid: Actual\nDashed: Predicted", size = 3, hjust = 0, fontface = "italic") # 7. 显示图形 print(p) # 8. 保存为高分辨率PNG ggsave("figure1_evolution_curve.png", plot = p, width = 12, height = 6, dpi = 600, bg = "white") # 9. 保存为SVG矢量格式（用于出版物） ggsave("figure1_evolution_curve.svg", plot = p, width = 12, height = 6, bg = "white")

Prompt_universal_de_evaluación_Hoja_Manuscrita