三天'；s"；巢；R中的函数不'；t识别变量并打印：“；警告消息：未知或未初始化列“；

我正在使用一个数据集，该数据集有一列名为“ccode”的国家代码：

当我创建另一个列以创建名为“country”的国家名称时，我使用了从CRAN表下载的countrycode包中的函数“countrycode”，结果如下：

votes_processed <- votes %>%
  filter(vote <= 3) %>%
  mutate(year = session + 1945,
         country = countrycode(ccode,"cown","country.name"))

由于无法为这些国家/地区代码分配国家/地区名称，因此我将它们从数据框中筛选出来：

> table(is.na(votes_processed$country))

 FALSE   TRUE 
350844   2703 
> votes_processed <- filter(votes_processed,!is.na(country))
> table(is.na(votes_processed$country))

 FALSE 
350844 

有人能给我解释一下这个“国家”专栏到底发生了什么，为什么R不认识它，我能做些什么

我是这个平台的初学者。我收到一条要求提供数据样本的评论，我将其粘贴在这里：

rcid<-c(5168,4317,3598,2314,1220,5024,3151,2042,2513,238,4171,3748,2595,
        5160,4476,308,3621,874,2025,3793,3595,1191,987,1207,2255,211,
        2585,2319,3590,189)
session<- c(66,56,46,36,26,64,42,34,38,4,54,48,38,66,58,6,46,18,34,
            48,46,26,22,26,36,4,38,36,46,4)
vote<- c(1,8,1,8,9,1,3,2,2,9,2,1,3,1,1,1,1,1,1,1,1,1,9,2,1,9,1,1,1,2)
ccode<-as.integer(c(816,816,816,816,816,816,260,260,260,260,2,42,2,20,
                    31,41,20,42,41,31,70,95,80,93,58,51,53,90,55,90))

sample_data_votes<-data.frame("rcid"=rcid,"session"=session, "vote"= vote,
                              "ccode"=ccode)

---

rcid
按年份\u国家/地区进行分组，因此您需要先取消分组，然后进行嵌套

library(tidyverse)
by_year_country %>% ungroup() %>% 
                     nest(-country) %>% head(n=2)

# A tibble: 2 x 2
  country   data            
 <chr>     <list>          
1 Guatemala <tibble [2 x 3]>
2 Haiti     <tibble [2 x 3]>

库（tidyverse）
按年份\u国家%>%ungroup（）%%>%
巢（-country）%>%头（n=2）
#一个tibble:2x2
国家数据
1危地马拉
2海地
按年份\u国家/地区进行分组，因此您需要先取消分组，然后进行嵌套

library(tidyverse)
by_year_country %>% ungroup() %>% 
                     nest(-country) %>% head(n=2)

# A tibble: 2 x 2
  country   data            
 <chr>     <list>          
1 Guatemala <tibble [2 x 3]>
2 Haiti     <tibble [2 x 3]>

库（tidyverse）
按年份\u国家%>%ungroup（）%%>%
巢（-country）%>%头（n=2）
#一个tibble:2x2
国家数据
1危地马拉
2海地
看起来您需要从调用
nest
时删除
-country
部分

library(dplyr)
library(tidyr)
library(countrycode)
rcid<-c(5168,4317,3598,2314,1220,5024,3151,2042,2513,238,4171,3748,2595,
        5160,4476,308,3621,874,2025,3793,3595,1191,987,1207,2255,211,
        2585,2319,3590,189)
session<- c(66,56,46,36,26,64,42,34,38,4,54,48,38,66,58,6,46,18,34,
            48,46,26,22,26,36,4,38,36,46,4)
vote<- c(1,8,1,8,9,1,3,2,2,9,2,1,3,1,1,1,1,1,1,1,1,1,9,2,1,9,1,1,1,2)
ccode<-as.integer(c(816,816,816,816,816,816,260,260,260,260,2,42,2,20,
                    31,41,20,42,41,31,70,95,80,93,58,51,53,90,55,90))

votes<-data.frame("rcid"=rcid,"session"=session, "vote"= vote,
                              "ccode"=ccode)
votes_processed <- votes %>%
  filter(vote <= 3) %>%
  mutate(year = session + 1945,
         country = countrycode(ccode,"cown","country.name")) %>% 
  filter(!is.na(country))

by_year_country <- votes_processed %>%
  group_by(year,country) %>%
  summarize(total = n(),
            percent_yes = mean(vote == 1))

nested <- by_year_country %>%
  nest()

库（dplyr）
图书馆（tidyr）
图书馆（国家代码）
rcid看起来您需要从调用
nest

library(dplyr)
library(tidyr)
library(countrycode)
rcid<-c(5168,4317,3598,2314,1220,5024,3151,2042,2513,238,4171,3748,2595,
        5160,4476,308,3621,874,2025,3793,3595,1191,987,1207,2255,211,
        2585,2319,3590,189)
session<- c(66,56,46,36,26,64,42,34,38,4,54,48,38,66,58,6,46,18,34,
            48,46,26,22,26,36,4,38,36,46,4)
vote<- c(1,8,1,8,9,1,3,2,2,9,2,1,3,1,1,1,1,1,1,1,1,1,9,2,1,9,1,1,1,2)
ccode<-as.integer(c(816,816,816,816,816,816,260,260,260,260,2,42,2,20,
                    31,41,20,42,41,31,70,95,80,93,58,51,53,90,55,90))

votes<-data.frame("rcid"=rcid,"session"=session, "vote"= vote,
                              "ccode"=ccode)
votes_processed <- votes %>%
  filter(vote <= 3) %>%
  mutate(year = session + 1945,
         country = countrycode(ccode,"cown","country.name")) %>% 
  filter(!is.na(country))

by_year_country <- votes_processed %>%
  group_by(year,country) %>%
  summarize(total = n(),
            percent_yes = mean(vote == 1))

nested <- by_year_country %>%
  nest()

库（dplyr）
图书馆（tidyr）
图书馆（国家代码）
RCIDW在寻求帮助时，您应该提供一个简单的示例输入和所需输出，用于测试和验证可能的解决方案。数据的图片不便于实际运行和测试代码。还要确保指定此
countrycode
函数的来源。@MrFlick非常感谢您的评论。我编辑这个问题是为了添加示例数据，并解释说我从CRAN获得了countrycode包。在寻求帮助时，您应该包括一个简单的示例输入和所需的输出，用于测试和验证可能的解决方案。数据的图片不便于实际运行和测试代码。还要确保指定此
countrycode
函数的来源。@MrFlick非常感谢您的评论。我编辑了这个问题来添加示例数据，并解释说我从CRAN获得了countrycode包。非常感谢！我想按国家分类筑巢，所以先把它们分开帮助我做到了。非常感谢！我想按国家分类筑巢，因此，先把它们分门别类有助于我做到这一点。
library(tidyverse)
by_year_country %>% ungroup() %>% 
                     nest(-country) %>% head(n=2)

# A tibble: 2 x 2
  country   data            
 <chr>     <list>          
1 Guatemala <tibble [2 x 3]>
2 Haiti     <tibble [2 x 3]>

library(dplyr)
library(tidyr)
library(countrycode)
rcid<-c(5168,4317,3598,2314,1220,5024,3151,2042,2513,238,4171,3748,2595,
        5160,4476,308,3621,874,2025,3793,3595,1191,987,1207,2255,211,
        2585,2319,3590,189)
session<- c(66,56,46,36,26,64,42,34,38,4,54,48,38,66,58,6,46,18,34,
            48,46,26,22,26,36,4,38,36,46,4)
vote<- c(1,8,1,8,9,1,3,2,2,9,2,1,3,1,1,1,1,1,1,1,1,1,9,2,1,9,1,1,1,2)
ccode<-as.integer(c(816,816,816,816,816,816,260,260,260,260,2,42,2,20,
                    31,41,20,42,41,31,70,95,80,93,58,51,53,90,55,90))

votes<-data.frame("rcid"=rcid,"session"=session, "vote"= vote,
                              "ccode"=ccode)
votes_processed <- votes %>%
  filter(vote <= 3) %>%
  mutate(year = session + 1945,
         country = countrycode(ccode,"cown","country.name")) %>% 
  filter(!is.na(country))

by_year_country <- votes_processed %>%
  group_by(year,country) %>%
  summarize(total = n(),
            percent_yes = mean(vote == 1))

nested <- by_year_country %>%
  nest()