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dplyr / tidyr - 用条件汇总数据

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Problem 我正在尝试使用dyplr＆tidyr来实现一个输出表（就像我认为的列联表），它将这些数据汇总到频率中（例如， Headers ，描述和主体的数量是负数，中性和正数） . 我尝试了许多不同的方法，我能找到的最接近的例子是Using Tidyr/Dplyr to summarise counts of groups of strings . 但这完全不合适 .

Example Data 数据看起来有点像......

df <- data.frame( "story_title"=c(0.0,0.0,0.0,-1.0,1.0),
                  "story_description"=c(-0.3,-0.3,-0.3,0.5,0.3),
                  "story_body"=c(-0.3,0.2,0.4,0.2,0))

Desired Output 输出有望看起来像这样，显示每个故事部分的摘要频率......

Negative  Neutral  Positive 
story_title              1         3        1         
story_description        3         0        2
story_body               1         1        3

（编辑了story_body的总数 - 谢谢Akrun）

Attempted Approach

如果我是对的，那么第一步就是使用 gather 来重塑数据......

df <- df %>% gather(type,score,starts_with("story"))

> df 
      type score
1        story_title   0.0
2        story_title   0.0
3        story_title   0.0
4        story_title  -1.0
5        story_title   1.0
6  story_description  -0.3
7  story_description  -0.3
8  story_description  -0.3
9  story_description   0.5
10 story_description   0.3
11        story_body  -0.3
12        story_body   0.2
13        story_body   0.4
14        story_body   0.2
15        story_body   0.0

从这里我认为这是group_by和summary的组合，我试过......

df %>% group_by(sentiment) %>%
          summarise(Negative = count("sentiment_title"<0),
                    Neutral  = count("sentiment_title"=0),
                    Positive  = count("sentiment_title">0)
                   )

显然这没有用 .

Can anyone help with a dplyr/tidyr solution (a base table answer would also be useful as an example)?

r dplyr tidyr

3 回答

  • 1

    尝试

    library(dplyr)
library(tidyr)
gather(df) %>% 
      group_by(key,value= sign(value))%>%
      tally()  %>% 
      mutate(ind= factor(value, levels=c(-1,0,1), 
                    labels=c('Negative', 'Neutral', 'Positive'))) %>% 
      select(-value) %>% 
      spread(ind, n, fill=0)
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  • 2

    尝试使用 cut 重新标记这三个类别 . 然后，这只是用 gather 解冻数据并用 dcast 重塑'wide'的问题 .

    library(tidyr)
library(reshape2)
df[] <- lapply(df, function(x) {cut(x, c(-Inf,-1e-4,0,Inf), c("Negative", "Neutral", "Positive"))})
dcast(gather(df), key~value)
#            key Negative Neutral Positive
#1       story_title        1       3        1
#2 story_description        3       0        2
#3        story_body        1       1        3
    回复于
  • 1

    你为什么不只使用原生R的xtabs？

    继您的代码后：

    >df <- df %>% gather(type,score,starts_with("story"))
>df$movement<-ifelse(df$score ==0 ,"Neutral",ifelse(df$score < 0 ,"Negative","Positive"))
>xtabs(~df$type+df$movement)

                      df$movement
  df$type             Negative Neutral Positive
  story_title              1       3        1
  story_description        3       0        2
  story_body               1       1        3
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