student_scores <- tibble::tribble( ~student_id, ~math_score, ~english_score, ~science_score, ~sports_score, 1, 75, 80, 85, 90, 2, 90, 85, 70, 75, 3, 80, 70, 75, 85, 4, 85, 90, 80, 70, 5, 70, 75, 90, 80 ) 提取上述数据的主成分，用recipes包实现。

好的，以下是使用`recipes`包提取主成分的代码：

library(recipes)

# 创建数据框
student_scores <- tibble::tribble(
  ~student_id, ~math_score, ~english_score, ~science_score, ~sports_score,
  1, 75, 80, 85, 90,
  2, 90, 85, 70, 75,
  3, 80, 70, 75, 85,
  4, 85, 90, 80, 70,
  5, 70, 75, 90, 80
)

# 创建recipe对象
student_recipe <- recipe(~., data = student_scores) %>%
  step_center(all_predictors()) %>%
  step_scale(all_predictors()) %>%
  step_pca(all_predictors(), num_comp = 2)

# 应用recipe对象
student_pca <- student_recipe %>% prep() %>% bake(new_data = NULL)

# 打印结果
student_pca

其中，`step_center`和`step_scale`用于对所有预测变量进行中心化和标准化，`step_pca`用于提取两个主成分。最后，`prep()`和`bake()`函数用于应用和执行recipe对象。

相关问题

帮我修改以下代码，使他们可以正确运行：scores <- dplyr::data_frame( person = rep(c("A", "B", "C"), each = 2), time = rep(c("pre", "post"), 3), class1 = round(rnorm(6, mean = 80, sd = 4), 0), class2 = round(jitter(class1, 15), 0) ) scores_1 <- scores %>% pivot_longer(cols = c("class1", "class2"), names_to = "class", values_to = "score") scores_2 <- scores %>% spread(key = time, value = class1:class2) scores_3 <- scores_1 %>% group_by(person, class) %>% mutate(diff = score[time == "post"] - score[time == "pre"]) %>% ungroup() scores_3 %>% ggplot(aes(x = class, y = score, color = ifelse(diff >= 0, "green", "red"), shape = person)) + geom_point(size = 3) + labs(x = "Class", y = "Score", color = "Score change", shape = "Person") + theme_classic()

您好，这段代码的问题在于 `data_frame()` 函数已经被弃用，应该使用 `tibble()` 函数来创建数据框。同时，`spread()` 函数也已经被弃用，应该使用 `pivot_wider()` 函数来进行数据透视。下面是修改后的代码：

library(tidyverse)

scores <- tibble(
  person = rep(c("A", "B", "C"), each = 2),
  time = rep(c("pre", "post"), 3),
  class1 = round(rnorm(6, mean = 80, sd = 4), 0),
  class2 = round(jitter(class1, 15), 0)
)

scores_1 <- scores %>% 
  pivot_longer(cols = c("class1", "class2"), names_to = "class", values_to = "score")

scores_2 <- scores %>% 
  pivot_wider(names_from = time, values_from = c(class1, class2), names_sep = "_")

scores_3 <- scores_1 %>% 
  group_by(person, class) %>% 
  mutate(diff = score[time == "post"] - score[time == "pre"]) %>% 
  ungroup()

scores_3 %>% 
  ggplot(aes(x = class, y = score, color = ifelse(diff >= 0, "green", "red"), shape = person)) +
  geom_point(size = 3) +
  labs(x = "Class", y = "Score", color = "Score change", shape = "Person") +
  theme_classic()

这样，代码就可以正确运行了。

void load_student_scores_sum_and_average(student_scores stu[], int n); void load_score_list(student_scores stu[], int n); void score_loading(student_scores stu[])//学生成绩一旦录入完毕，立刻执行总分、平均分、排名的计算 { load_student_scores_sum_and_average(stu, stu_num); load_score_list(stu, stu_num); } //学生成绩标题栏打印 void score_title() { cout << "ID" << "\t\t" << "Name" << "\t"; for (int i = 0; i < course_num; i++) cout << "Course" << i + 1 << "\t"; cout << "Sum" << "\t" << "Average" << "\t" << "Rank" << endl; } //学生成绩输出（以成绩条的方式输出） void score_bar(student_scores stu[], int a)//a为学生序号 { cout << stu[a].stu_id << "\t" << stu[a].stu_name << "\t"; for (int i = 0; i < course_num; i++) cout << stu[a].stu_score[i] << "\t"; cout << stu[a].stu_sum << "\t" << setiosflags(ios::fixed) << setprecision(2) << stu[a].stu_average << "\t"; cout << stu[a].stu_rank << endl; } #pragma endregion解释这段大妈

这段代码是关于学生成绩的处理和输出的。其中，函数load_student_scores_sum_and_average用于计算每个学生的总分和平均分，函数load_score_list用于将学生按照总分从高到低排序，并计算每个学生的排名。而函数score_loading则是将这两个函数结合起来，实现在学生成绩录入完毕后立即计算总分、平均分和排名。函数score_title用于打印学生成绩的表头，而函数score_bar则是将每个学生的成绩以一条成绩条的方式输出。最后，#pragma endregion是一个注释，用于标记代码块的结束位置。