使用{'1': 240,'2': 60} ，您可以获得以下信息：

plyr

您会得到：

Condition = c("Crop failure","Water shortage","Lang degradation","Lack of HH Labor","Lack of income from agriculture","Lack of manure / fertilizer","Others")
Type = c(1:7)
df = data.frame(Condition,Type)

vector = c(1,1,2,3,5,4,6,7)
t = plyr::count(vector)
colnames(t) = c("Type","Freq")

df =merge(df,t)

• 如果您想使用base R，以下解决方案可能会对您有所帮助。

假设您输入的是

response <- c(1,7)

然后

status <- c("Crop failure","Others")
df <- as.data.frame(table(factor(response,labels = status),dnn = list("Status")))

可以给您类似的输出

> df
                           Status Freq
1                    Crop failure    8
2                  Water shortage    7
3                Lang degradation    2
4                Lack of HH Labor    4
5 Lack of income from agriculture    2
6     Lack of manure / fertilizer    2
7                          Others    1

• 如果您想要详细的表格： 假设您输入的是：

r <- list(1,c(1,2),3),5),4),6),7))

type = seq(1,7)
dt <- as.data.frame(t(sapply(r,function(v) sapply(type,function(k) sum(k==v)))))
colnames(M) <- paste0("type",type)

给出

> dt
  type1 type2 type3 type4 type5 type6 type7
1     1     0     0     0     0     0     0
2     1     1     0     0     0     0     0
3     1     1     1     0     0     0     0
4     1     1     1     0     1     0     0
5     1     1     0     1     0     0     0
6     1     1     0     1     1     1     0
7     1     1     0     1     0     0     0
8     0     0     0     0     0     1     0
9     1     1     0     1     0     0     1

此外，每个类型条目的总和可以通过colSums计算：

> colSums(dt)
type1 type2 type3 type4 type5 type6 type7 
    8     7     2     4     2     2     1

或者您可以使用match()，即

dt <- as.data.frame(t(sapply(r,function(v) !is.na(match(type,v)))))
> dt
  type1 type2 type3 type4 type5 type6 type7
1  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
2  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
3  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
4  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE
5  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
6  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE
7  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
8 FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
9  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE

这是我解决问题的机会。我使用tidyverse，因为它为我加载了stringer和tidyr

library(tidyverse)

id <- data.frame(Code = 1:7,#Make a coding data frame so you can label the results
                 Cause = c("Crop failure","Land degradation","Others"),stringsAsFactors = FALSE))

data <- Book1 %>% 
  separate(X1,into = paste0("X",1:7),sep = ";") %>% #split the data by the ;,This induces NA that are removed later
  gather(key = "drop",value = "Code") %>% #put it into 1 column to exploit R's vectorization
  mutate(Code = as.integer(Code)) %>% #Make the code an integer for the join later
  filter(!is.na(Code)) %>% #remove those previous NAs
  group_by(Code) %>% 
  count() %>% # Counts 
  left_join(.,id) #labels

colnames(data) <- c("Code","Count","Cause")

它将在单独的行上发出警告，但是，这只是让您知道它正在用NA填充多余的单元格，我们稍后将其删除。您唯一需要更改的就是DataFrame和X1，具体取决于您为对象命名的名称。

这是我的搜索结果

 Code Count Cause                          
  <int> <int> <chr>                          
1     1     8 Crop failure                   
2     2     7 Water shortage                 
3     3     2 Land degradation               
4     4     4 Lack of HH Labor               
5     5     2 Lack of income from agriculture
6     6     2 Lack of manure / fertilizer    
7     7     1 Others  

希望有帮助！