根据匹配标准将数据帧中的值从一列移动到另一列

Question

我正在接收来自JSON对象的输出，但是JSON根据输入返回三个字段，有时候是两个somtimes之一。其结果是我有一个数据帧，其看起来像这样：

 mixed  score  type 
1   1 0.0183232 positive 
2 neutral  <NA>  <NA> 
3 -0.566558 negative  <NA> 
4 0.473484 positive  <NA> 
5 0.856743 positive  <NA> 
6 -0.422655 negative  <NA> 

混合可以采用1个或0 分数的值可以取-1和+1 型之间的正或负的值可以采用的值或者正面，负面或中性

我不知道我怎么能重新排列这些值在data.frame让他们在正确的列即

 mixed  score  type 
1   1 0.018323 positive 
2  <NA> <NA>  neutral 
3  <NA> -0.566558 negative  
4  <NA> 0.473484 positive  
5  <NA> 0.856743 positive  
6  <NA> -0.422655 negative 

Answer 1

不是一个完美的解决方案在所有，但最好我可以想出来。

### Seeds initial Dataframe 


mixed = c("1", "neutral", "0.473484", "-0.566558", "0.856743", "-0.422655", "-0.692675") 
score = c("0.0183232", "0", "positive", "negative", "positive", "negative", "negative") 
type = c("positive", "0", "0", "0", "0", "0", "0") 

df = data.frame(mixed, score, type) 


# Create a new DF (3 cols by nrow ize) for output 
df <- as.data.frame(matrix(0, ncol = 3, nrow = i)) 
setnames(df, old=c("V1","V2", "V3"), new=c("mixed", "score", "type")) 
df 

# Create a 2nd new DF (3 cols by nrow ize) for output 
df.2 <- as.data.frame(matrix(0, ncol = 3, nrow = i)) 
setnames(df.2, old=c("V1","V2", "V3"), new=c("mixed", "score", "type")) 
df.2 




#Check each column cell by cell if it does copy it do the shadow dataframe 
# Set all <NA> values to Null 
df[is.na(df)] <- 0 



# Set interation length to column length 
l <- 51 

# Checked the mixed column for '1' and then copy it to the new frame 
for(l in 1:l) 

    if (df$mixed[l] == '1') 
    { 
    df.2$mixed[l] <-df$mixed[l] 
    } 

# Checked the mixed column for a value which is less than 1 and then copy it to the score column in the new frame 

for(l in 1:l) 

    if (df$mixed[l] < '1') 
    { 
    df.2$score[l] <-df$mixed[l] 
    } 

# Checked the mixed column for positive/negative/neutral and then copy it to the type column in the new frame 

for(l in 1:l) 

    if (df$mixed[l] == "positive" | df$mixed[l] == "negative" | df$mixed[l] == "neutral") 
    { 
    df.2$type[l] <-df$mixed[l] 
    } 


# Checked the score column for '1' and then copy it to mixed column in the new frame 
for(l in 1:l) 

    if (df$score[l] == '1') 
    { 
    df.2$mixed[l] <-df$score[l] 
    } 

# Checked the score column for a value which is less than 1 and then copy it to the score column in the new frame 

for(l in 1:l) 

    if (df$score[l] < '1') 
    { 
    df.2$score[l] <-df$score[l] 
    } 

# Checked the score column for positive/negative/neutral and then copy it to the type column in the new frame 

for(l in 1:l) 

    if (df$score[l] == "positive" | df$score[l] == "negative" | df$score[l] == "neutral") 
    { 
    df.2$type[l] <-df$score[l] 
    } 



# Checked the type column for '1' and then copy it to mixed column in the new frame **This one works*** 
for(l in 1:l) 

    if (df$type[l] == '1') 
    { 
    df.2$mixed[l] <-df$type[l] 
    } 

# Checked the type column for a value which is less than 1 and then copy it to the score column in the new frame ** this one is erasing data in the new frame** 

for(l in 1:l) 

    if (df$type[l] < '1') 
    { 
    df.2$score[l] <-df$type[l] 
    } 

# Checked the type column for positive/negative/neutral and then copy it to the type column in the new frame **This one works*** 

for(l in 1:l) 

    if (df$type[l] == "positive" | df$type[l] == "negative" | df$type[l] == "neutral") 
    { 
    df.2$type[l] <-df$type[l] 
    }