data <- data.frame( Group = rep(c("A", "B"), each = 50), Value = c(rnorm(50, 0, 1), rnorm(50, 2, 1)) )
时间: 2024-05-27 11:07:52 浏览: 11
这段代码创建了一个名为 data 的数据框,其中包含两个列:Group 和 Value。Group 列有两个水平,分别为 A 和 B,每个水平有 50 行。Value 列包含了 100 个随机数,其中前 50 个随机数来自于均值为 0,标准差为 1 的正态分布,后 50 个随机数来自于均值为 2,标准差为 1 的正态分布。这个数据框可以用来进行分组统计分析。
相关问题
此代码是否有问题,如果有,请修改:# 生成数据data <- data.frame( group = rep(c("A", "B", "C"), each = 5), value = rnorm(15))# 进行方差分析anova_result <- aov(value ~ group, data = data)# 检查方差分析结果summary(anova_result)# 提取组内方差within_var <- summary(anova_result)$"within"[1]# 提取组间方差between_var <- summary(anova_result)$"between"[1]
这段代码没有问题,它可以正确地生成数据并进行方差分析,然后提取组内方差和组间方差。
值得注意的是,在生成数据时,使用了`rnorm()`函数来生成随机正态分布的数据,其中有一个分类因子`group`和一个数值因子`value`。在进行方差分析时,使用了`aov()`函数来对`value`和`group`进行方差分析。在检查方差分析结果时,使用了`summary()`函数,从中提取了组内方差和组间方差。
综上所述,这段代码没有问题。
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。
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