Question

我有一个数据集，其中每列都是一个变量，每一行都是观察（如时间序列数据。看起来像这样（我为格式道歉，但我不能显示数据）：

我想知道一个人或一群人是否在说一段时间。我熟悉n-gram，但它并不是我所需要的。任何帮助，将不胜感激。

这是I＆＃cd喜欢的输出：

对不起所有编辑不好的评论;仍然习惯了网站。

Answer 1

如果您想查看与每个人相关的每条评论的频率以及新列就绪，您可以使用以下代码执行此操作：

set.seed(123456)

### I use the same data as the previous example, thank you for providing this ! 
data <-data.frame(date = Sys.Date() - sample(100),
                Group = c("Cars","Trucks") %>% sample(100,replace=T),
                Reporting_person = c("A","B","C") %>% sample(100,replace=T),
                Comments = c("Awesome","Meh","NC") %>% sample(100,replace=T),
            Ready = as.character(c("Yes","No") %>% sample(100,replace=T))
            ) 

library(dplyr)

data %>% 
    group_by(Reporting_person,Ready) %>%
    count(Comments) %>%
    mutate(prop = prop.table(n))

如果您要问的是查看评论中是否发生了一段时间的变化，并查看该更改是否与事件相关（如Ready），您可以使用以下代码：

library(dplyr)

### Creating a column comments at time + plus
new = data %>% 
        arrange(Reporting_person,Group,date) %>%
        group_by(Group,Reporting_person) %>%
        mutate(comments_plusone=lag(Comments))

new = na.omit(new)

### Creating the change column   1 is a change , 0 no change

new$Change = as.numeric(new$Comments != new$comments_plusone)

### Get the correlation between Change and the events...

### Chi-test to test if correlation between the event and the change
### Not that using Pearson correlation is not pertinent here : 


tbl <- table(new$Ready,new$Change)

chi2 = chisq.test(tbl, correct=F)
c(chi2$statistic, chi2$p.value)
sqrt(chi2$statistic / sum(tbl))

你应该与这个例子没有任何意义的相关性。你可以清楚地看到你何时说明这张桌子。

plot(tbl)

使用cor函数并不适合使用两个二进制变量。

此主题中的帖子为...... Correlation between two binary

国家变更的变化频率

发表评论后，我正在添加此代码：

newR = data %>% 
        arrange(Reporting_person,Group,date) %>%
        group_by(Group,Reporting_person) %>%
        mutate(Ready_plusone=lag(Ready)) 


newR = na.omit(newR)

###------------------------Add the column to the new data frame
### Creating the REady change column   1 is a change , 0 no change
### Creating the change of state , I use this because you seem to have more than 2 levels.
new$State_change = paste(newR$Ready,newR$Ready_plusone,sep="_")

### Getting the frequency of Change by Change of State(Ready Yes-no..no-yes..)
result <- new %>% 
                group_by(Reporting_person,State_change) %>%
                count(Change) %>%
                mutate(Frequence = prop.table(n))%>%
                filter(Change==1)

 ### Tidyr is a great library for reshape data, you want the wide format of the previous long 
 ### dataframe... However doing this will generate a lot of NA so If I were you I would get 
 ### the result format instead of the following but this could be helpful for future need so here you go.

library(tidyr)

final = as.data.frame(spread(result, key = State_change, value = prop))[,c(1,4:7)]

希望这有帮助：）

Answer 2

这样的东西？

df <-data.frame(date = Sys.Date() - sample(10),
                Group = c("Cars","Trucks") %>% sample(10,replace=T),
                Reporting_person = c("A","B","C") %>% sample(10,replace=T),
                Comments = c("Awesome","Meh","NC") %>% sample(10,replace=T))   

#          date  Group Reporting_person Comments
# 1  2017-06-08 Trucks                B  Awesome
# 2  2017-06-05 Trucks                A  Awesome
# 3  2017-06-14   Cars                B      Meh
# 4  2017-06-06   Cars                B  Awesome
# 5  2017-06-11   Cars                A      Meh
# 6  2017-06-07   Cars                B       NC
# 7  2017-06-09   Cars                A       NC
# 8  2017-06-10   Cars                A       NC
# 9  2017-06-13 Trucks                C  Awesome
# 10 2017-06-12 Trucks                B       NC

aggregate(date ~ .,df,length)

#    Group Reporting_person Comments date
# 1 Trucks                A  Awesome    1
# 2   Cars                B  Awesome    1
# 3 Trucks                B  Awesome    1
# 4 Trucks                C  Awesome    1
# 5   Cars                A      Meh    1
# 6   Cars                B      Meh    1
# 7   Cars                A       NC    2
# 8   Cars                B       NC    1
# 9 Trucks                B       NC    1

随着时间的推移逐组查找重复的句子/单词/短语

2 个答案:

国家变更的变化频率