library(magrittr) res_1 %>% mutate(group = case_when( log2FoldChange >= 2 & padj <= 0.05 ~"UP", log2FoldChange <= -2 & padj <= 0.05 ~"DOWN", )) -> res_2Error in mutate(., group = case_when(log2FoldChange >= 2 & padj <= 0.05 ~ : could not find function "mutate" >

根据错误信息，提示无法找到函数`mutate`。

这是因为`mutate`函数是`dplyr`包中的函数，而不是`magrittr`包中的函数。

请确保你已经安装了`dplyr`包。如果没有安装，可以运行以下命令进行安装：

install.packages("dplyr")

安装完成后，加载`dplyr`包：

library(dplyr)

然后你可以继续使用`mutate`函数来创建新的列：

res_1 %>%
  mutate(group = case_when(
    log2FoldChange >= 2 & padj <= 0.05 ~ "UP",
    log2FoldChange <= -2 & padj <= 0.05 ~ "DOWN"
  )) -> res_2

这样就可以根据条件创建名为`group`的新列，并将结果保存在`res_2`中。

希望这次能帮到你！如果还有其他问题，请随时提问。

相关问题

帮我修改以下代码，使它可以正确运行：library(tidyr)# 1. 数据处理gss_cat %>% filter(!is.na(age)) %>% mutate(age_group = case_when( age <= 45 ~ "youth", age >= 46 & age <= 69 ~ "middle_aged", age >= 70 ~ "old_people" )) %>% mutate(age_group = fct_relevel(as.factor(age_group), "youth", "middle_aged", "old_people")) %>% group_by(year, age_group, race) %>% summarise(n = n()) %>% # 2. 数据转换 pivot_wider(names_from = age_group, values_from = n) %>% mutate(youth_prop = youth / (youth + middle_aged + old_people)) %>% pivot_longer(cols = youth_prop, names_to = "age_group", values_to = "prop") %>% separate(age_group, into = c("age_group", "prop_type"), sep = "_") %>% pivot_wider(names_from = prop_type, values_from = prop) %>% arrange(year, race, age_group)library(ggplot2)# 1. 数据处理gss_cat %>% filter(!is.na(age)) %>% mutate(age_group = case_when( age <= 45 ~ "youth", age >= 46 & age <= 69 ~ "middle_aged", age >= 70 ~ "old_people" )) %>% mutate(age_group = fct_relevel(as.factor(age_group), "youth", "middle_aged", "old_people")) %>% group_by(year, age_group, race) %>% summarise(n = n()) %>% pivot_wider(names_from = age_group, values_from = n) %>% mutate(youth_prop = youth / (youth + middle_aged + old_people)) %>% pivot_longer(cols = youth_prop, names_to = "age_group", values_to = "prop") %>% separate(age_group, into = c("age_group", "prop_type"), sep = "_") %>% pivot_wider(names_from = prop_type, values_from = prop) %>% # 2. 绘图 ggplot(aes(x = year, y = youth_prop, group = race, color = race)) + geom_line() + labs(x = "年份", y = "青年人比例", title = "不同种族青年人比例随时间变化趋势") + theme_minimal()

以下是修改后的代码：

library(tidyr)
library(ggplot2)

gss_cat %>%  
  filter(!is.na(age)) %>%  
  mutate(age_group = case_when(    
    age <= 45 ~ "youth",    
    age >= 46 & age <= 69 ~ "middle_aged",    
    age >= 70 ~ "old_people"  
  )) %>%  
  mutate(age_group = fct_relevel(as.factor(age_group), "youth", "middle_aged", "old_people")) %>%  
  group_by(year, age_group, race) %>%  
  summarise(n = n()) %>%  
  pivot_wider(names_from = age_group, values_from = n) %>%  
  mutate(youth_prop = youth / (youth + middle_aged + old_people)) %>%  
  pivot_longer(cols = youth_prop, names_to = "prop_type", values_to = "prop") %>%  
  separate(prop_type, into = c("age_group", "prop_type"), sep = "_") %>%  
  pivot_wider(names_from = prop_type, values_from = prop) %>%  
  arrange(year, race, age_group) -> df

ggplot(df, aes(x = year, y = youth_prop, group = race, color = race)) +  
  geom_line() +  
  labs(x = "年份", y = "青年人比例", title = "不同种族青年人比例随时间变化趋势") +  
  theme_minimal()

主要修改如下：

1. 在 `pivot_longer()` 函数中，将 `names_to` 参数修改为 "prop_type"，values_to 参数修改为 "prop"，以保证数据格式正确。
2. 在 `separate()` 函数中，将 `names_to` 参数修改为 "prop_type"，以保留"age_group"和"prop_type"两个变量。
3. 将最后一行的 `pivot_wider()` 函数移动到 `summarise()` 函数之前，以保证数据格式正确。
4. 将整个代码块用括号括起来，并使用箭头符号将结果赋值给一个新的数据框，以保证代码的可读性和可维护性。

hire_text<-read.csv("C:/Users/ASUS/Desktop/text.csv") hire_text hire_text %>% mutate(id = 1:n()) -> hire_txt #给文档编号3 worker(type = "tag") -> wk #构造一个分词器，需要得到词性56 hire_txt %>% mutate(words = map(hire_text,tagging,jieba = wk)) %>% mutate(word_tag = map(words,enframe,name = "tag",value = "word")) %>% select(id,word_tag) -> hire_words #构造提取关键词的函数 extract_keywords = function(dt){ textrank_keywords(dt$word,relevant = str_detect(dt$tag,"^n"),ngram_max = 2) %>% keywords } hire_words %>% mutate(textrank.key = map(word_tag,extract_keywords)) %>% select(-word_tag) -> tr_keyword

根据你提供的代码，出现了一个错误。错误在于`map()`函数的第一个参数`word_tag`，它需要的是一个列表，但是`word_tag`是一个列名，所以会导致以下错误信息：

Error in `map()`: In argument: `textrank.key = map(word_tag, extract_keywords)`. Caused by error in `map()`: In index: 1. With name: text.

如果你想要使用`word_tag`这一列，应该使用`hire_words$word_tag`，而不是`word_tag`。请修改代码如下：

hire_words %>% 
  mutate(textrank.key = map(hire_words$word_tag, extract_keywords)) %>% 
  select(-word_tag) -> tr_keyword

这样修改后，`map()`函数的第一个参数就是一个列表了，可以正确地运行提取关键词的函数`extract_keywords()`。