{r} month=global3 %>% group_by(Month,movement_type) %>% summarise(count=n()) month$Month<-as.character(month$Month) ####创建净流入表并合并 month1<-data.frame(Month=c("2019-01-01","2019-02-01","2019-03-01","2019-04-01","2019-05-01","2019-06-01","2019-07-01","2019-08-01","2019-09-01"), movement_type=c("净流入"), count=c("1","-4","5","2","4","11","5","14","8")) month1$count<-as.integer(month1$count) month<-rbind(month,month1) t4<-echartr(month, Month, count,movement_type, type='line') %>% setTitle('月变动情况')%>% setSymbols('emptycircle')%>% setTheme("macarons")%>% setToolbox(show=FALSE) t4将这个表背后的格子去掉r语言，然后改变样式

这段代码中的表格是通过使用 `echartr` 函数创建的，它是 `rCharts` 包中的一个函数，用于创建交互式的 JavaScript 图表。如果您想要去掉表格背后的网格线，可以在 `echartr` 函数中添加 `grid` 参数，并将其设置为 `FALSE`，如下所示：

t4 <- echartr(month, Month, count, movement_type, type='line', grid=FALSE) %>% 
  setTitle('月变动情况') %>% 
  setSymbols('emptycircle') %>% 
  setTheme("macarons") %>% 
  setToolbox(show=FALSE)

这将创建一个没有网格线的折线图。如果您想要改变图表的样式，可以使用 `setTheme` 函数来选择不同的主题。您可以在 `rCharts` 包的文档中找到更多关于如何自定义图表样式的信息。

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。

帮我修改以下代码，使它可以正确运行：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) %>% 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()

这段代码看起来是对一个数据集进行处理并使用 `ggplot2` 包绘制一条线图，但是缺少数据集名称。你可以将该代码与你的数据集结合使用，例如：

your_data %>% 
  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) %>% 
  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()

在这个代码中，`your_data` 是你的数据集，你需要将其替换为你真正使用的数据集的名称。此外，你还需要确保你已经正确加载了 `ggplot2` 包。