我的linux VPS上运行了以下R脚本，经常返回错误，从而中断脚本。我不知道如何围绕错误进行编程，并且想知道是否有办法强制脚本继续运行尽管它们。错误通常作为“结果”表的超出界限错误发生。将代码直接粘贴到R＆lt;但是当超出范围引用“结果”表时，它将保持其先前设置的值0，因此代码按预期工作。有关如何从linux命令行示例自动运行的任何帮助：（Rscript / folder / file.R）将不胜感激。
library(RMySQL)
library(twitteR)
library(plyr)
library(stringr)
library(sentiment)
Date<-format(Sys.time(),"%Y-%m-%d %H:%M")
Time<-format(Sys.time(),"%H:%M")
tweets.con<-dbConnect(MySQL(),user="xxxxxxxxxxxx",password="xxxxxxxxxxxx",dbname="xxxxxxxxxx",host="xxxxxxxxxxxxxxxxxxxx.com")
Feel<-dbGetQuery(tweets.con,"select `tweet_text` from `tweets` where `created_at` BETWEEN timestamp(DATE_ADD(NOW(), INTERVAL 49 MINUTE)) AND timestamp(DATE_ADD(NOW(), INTERVAL 60 MINUTE))")
length(as.matrix(Feel))
n<-length(as.matrix(Feel))
Total_Count<-length(as.matrix(Feel))
results.con<-dbConnect(MySQL(),user="xxxxxxxxxxx",password="xxxxxxxxxxxxxxxxxx",dbname="xxxxxxxxxxxxxx",host="xxxxxxxxxxxxxxxxxx")
last.results.alt<-dbGetQuery(results.con,"select `Neg_Prop_Alt`,`Neu_Prop_Alt`,`Pos_Prop_Alt`,`neg5_Prop`,`neg4_Prop`,`neg3_Prop`,`neg2_Prop`,`neg1_Prop`,`zero_Prop`,`pos1_Prop`,`pos2_Prop`,`pos3_Prop`,`pos4_Prop`,`pos5_Prop` from `results_10m_alt` ORDER BY Date DESC LIMIT 1")
# function score.sentiment
score.sentiment = function(sentences, pos.words, neg.words, .progress='none')
{
# Parameters
# sentences: vector of text to score
# pos.words: vector of words of postive sentiment
# neg.words: vector of words of negative sentiment
# .progress: passed to laply() to control of progress bar
# create simple array of scores with laply
scores = laply(sentences,
function(sentence, pos.words, neg.words)
{
# remove retweet entities
sentence = gsub("(RT|via)((?:\\b\\W*@\\w+)+)", "", sentence)
# remove at people
sentence = gsub("@\\w+", "", sentence)
# remove punctuation
sentence = gsub("[[:punct:]]", "", sentence)
# remove numbers
sentence = gsub("[[:digit:]]", "", sentence)
# remove control characters
sentence = gsub("[[:cntrl:]]", "", sentence)
# remove html links
sentence = gsub("http\\w+", "", sentence)
# remove unnecessary spaces
sentence = gsub("[ \t]{2,}", "", sentence)
sentence = gsub("^\\s+|\\s+$", "", sentence)
# define error handling function when trying tolower
tryTolower = function(x)
{
# create missing value
y = NA
# tryCatch error
try_error = tryCatch(tolower(x), error=function(e) e)
# if not an error
if (!inherits(try_error, "error"))
y = tolower(x)
# result
return(y)
}
# use tryTolower with sapply
sentence = sapply(sentence, tryTolower)
# split sentence into words with str_split (stringr package)
word.list = str_split(sentence, "\\s+")
words = unlist(word.list)
# compare words to the dictionaries of positive & negative terms
pos.matches = match(words, pos.words)
neg.matches = match(words, neg.words)
# get the position of the matched term or NA
# we just want a TRUE/FALSE
pos.matches = !is.na(pos.matches)
neg.matches = !is.na(neg.matches)
# final score
score = sum(pos.matches) - sum(neg.matches)
return(score)
}, pos.words, neg.words, .progress=.progress )
# data frame with scores for each sentence
scores.df = data.frame(text=sentences, score=scores)
return(scores.df)
}
# import positive and negative words
pos = readLines("/home/jgraab/R/scripts/positive_words.txt")
neg = readLines("/home/jgraab/R/scripts/negative_words.txt")
Feel_txt = sapply(Feel, function(x) gettext(x))
scores.df = score.sentiment(Feel_txt, pos, neg, .progress='text')
results<-table(scores.df[,2])+.0001
#Set Table Defaults
Neg_Count_Alt<-0
Neg_Prop_Alt<-0
Neg_Change_Alt<-0
Neu_Count_Alt<-0
Neu_Prop_Alt<-0
Neu_Change_Alt<-0
Pos_Count_Alt<-0
Pos_Prop_Alt<-0
Pos_Change_Alt<-0
neg5_Count<-0
neg5_Prop<-0
neg5_Change<-0
neg4_Count<-0
neg4_Prop<-0
neg4_Change<-0
neg3_Count<-0
neg3_Prop<-0
neg3_Change<-0
neg2_Count<-0
neg2_Prop<-0
neg2_Change<-0
neg1_Count<-0
neg1_Prop<-0
neg1_Change<-0
zero_Count<-0
zero_Prop<-0
zero_Change<-0
pos1_Count<-0
pos1_Prop<-0
pos1_Change<-0
pos2_Count<-0
pos2_Prop<-0
pos2_Change<-0
pos3_Count<-0
pos3_Prop<-0
pos3_Change<-0
pos4_Count<-0
pos4_Prop<-0
pos4_Change<-0
pos5_Count<-0
pos5_Prop<-0
pos5_Change<-0
#Get Table Results
neg5_Count<-results[["-5"]]
neg5_Prop<-neg5_Count/Total_Count
neg5_Change<-(neg5_Prop-as.numeric(last.results.alt[[4]]))/as.numeric(last.results.alt[[4]])*100
neg4_Count<-results[["-4"]]
neg4_Prop<-neg4_Count/Total_Count
neg4_Change<-(neg4_Prop-as.numeric(last.results.alt[[5]]))/as.numeric(last.results.alt[[5]])*100
neg3_Count<-results[["-3"]]
neg3_Prop<-neg3_Count/Total_Count
neg3_Change<-(neg3_Prop-as.numeric(last.results.alt[[6]]))/as.numeric(last.results.alt[[6]])*100
neg2_Count<-results[["-2"]]
neg2_Prop<-neg2_Count/Total_Count
neg2_Change<-(neg2_Prop-as.numeric(last.results.alt[[7]]))/as.numeric(last.results.alt[[7]])*100
neg1_Count<-results[["-1"]]
neg1_Prop<-neg1_Count/Total_Count
neg1_Change<-(neg1_Prop-as.numeric(last.results.alt[[8]]))/as.numeric(last.results.alt[[8]])*100
zero_Count<-results[["0"]]
zero_Prop<-zero_Count/Total_Count
zero_Change<-(zero_Prop-as.numeric(last.results.alt[[9]]))/as.numeric(last.results.alt[[9]])*100
pos1_Count<-results[["1"]]
pos1_Prop<-pos1_Count/Total_Count
pos1_Change<-(pos1_Prop-as.numeric(last.results.alt[[10]]))/as.numeric(last.results.alt[[10]])*100
pos2_Count<-results[["2"]]
pos2_Prop<-pos2_Count/Total_Count
pos2_Change<-(pos2_Prop-as.numeric(last.results.alt[[11]]))/as.numeric(last.results.alt[[11]])*100
pos3_Count<-results[["3"]]
pos3_Prop<-pos3_Count/Total_Count
pos3_Change<-(pos3_Prop-as.numeric(last.results.alt[[12]]))/as.numeric(last.results.alt[[12]])*100
pos4_Count<-results[["4"]]
pos4_Prop<-pos4_Count/Total_Count
pos4_Change<-(pos4_Prop-as.numeric(last.results.alt[[13]]))/as.numeric(last.results.alt[[13]])*100
pos5_Count<-results[["5"]]
Pos5_Prop<-pos5_Count/Total_Count
Pos5_Change<-(pos5_Prop-as.numeric(last.results.alt[[14]]))/as.numeric(last.results.alt[[14]])*100
#Get Negative, Neutral, and Positive Totals
Neg_Count_Alt<-neg5_Count+neg4_Count+neg3_Count+neg2_Count+neg1_Count
Neg_Prop_Alt<-Neg_Count_Alt/Total_Count
Neg_Change_Alt<-(Neg_Prop_Alt-as.numeric(last.results.alt[[1]]))/as.numeric(last.results.alt[[1]])*100
Neu_Count_Alt<-zero_Count
Neu_Prop_Alt<-Neu_Count_Alt/Total_Count
Neu_Change_Alt<-(Neu_Prop_Alt-as.numeric(last.results.alt[[2]]))/as.numeric(last.results.alt[[2]])*100
Pos_Count_Alt<-pos1_Count+pos2_Count+pos3_Count+pos4_Count+pos5_Count
Pos_Prop_Alt<-Pos_Count_Alt/Total_Count
Pos_Change_Alt<-(Pos_Prop_Alt-as.numeric(last.results.alt[[3]]))/as.numeric(last.results.alt[[3]])*100
Mean<-(-5*neg5_Count-4*neg4_Count-3*neg3_Count-2*neg2_Count-neg1_Count+pos1_Count+2*pos2_Count+3*pos3_Count+4*pos4_Count+5*pos5_Count)/Total_Count
Feel_alt.df<-data.frame(Date,Time,Total_Count,Mean,Neg_Count_Alt,Neg_Prop_Alt,Neg_Change_Alt,Neu_Count_Alt,Neu_Prop_Alt,Neu_Change_Alt,Pos_Count_Alt,Pos_Prop_Alt,Pos_Change_Alt,
neg5_Count,neg5_Prop,neg5_Change,neg4_Count,neg4_Prop,neg4_Change,neg3_Count,neg3_Prop,neg3_Change,neg2_Count,neg2_Prop,neg2_Change,neg1_Count,neg1_Prop,neg1_Change,
zero_Count,zero_Prop,zero_Change,pos1_Count,pos1_Prop,pos1_Change,pos2_Count,pos2_Prop,pos2_Change,pos3_Count,pos3_Prop,pos3_Change,pos4_Count,pos4_Prop,pos4_Change,pos5_Count,pos5_Prop,pos5_Change)
dbWriteTable(results.con,name="results_10m_alt",Feel_alt.df,append=T,overwrite=F,row.names=F)
答案 0 :(得分：1)
使用try或tryCatch（前者更简单，通常只需要你）。您之前已经在使用tryCatch，因此也可以使用它来处理有问题的查询。
答案 1 :(得分：1)
这样的代码有很多错误是正常的！
trycatch之后。你的程序应该是这样的：
data <- load.tweets() ## read inputs
scores <- score.sentiment(data,...)) ## clean data/extract info
ratios <- compute.ratios(scores,data,...)) ## analysis
save.results(ratios,data,...)) ## save results
例如，在这里我尝试分解2个部分：
这里我使用mapply，因为你为所有系数重复相同的陈述。
compute.ratios <- function(){
mapply(function(x,y){
pos5_Count <-results[[x]]
Pos5_Prop <- pos5_Count/Total_Count
val <- as.numeric(last.results.alt[[y]]) ## you should check that val !=0
Pos5_Change <- (pos5_Prop-val)/val*100
},names(results),seq_along(last.results.alt))
}
load.tweets <- function(){
tweets.con <- dbConnect(MySQL(),user="xxxxxxxxxxxx",password="xxxxxxxxxxxx",
dbname="xxxxxxxxxx",host="xxxxxxxxxxxxxxxxxxxx.com")
Feel <- dbGetQuery(tweets.con,"SELECT `tweet_text`
FROM `tweets`
WHERE `created_at`
BETWEEN timestamp(DATE_ADD(NOW(), INTERVAL 49 MINUTE))
AND timestamp(DATE_ADD(NOW(), INTERVAL 60 MINUTE))")
n <- length(as.matrix(Feel))
Total_Count<- n
results.con<-dbConnect(MySQL(),user="xxxxxxxxxxx",password="xxxxxxxxxxxxxxxxxx",
dbname="xxxxxxxxxxxxxx",host="xxxxxxxxxxxxxxxxxx")
last.results.alt <- dbGetQuery(results.con,"SELECT `Neg_Prop_Alt`,`Neu_Prop_Alt`,
`Pos_Prop_Alt`,`neg5_Prop`,`neg4_Prop`,
`neg3_Prop`,`neg2_Prop`,`neg1_Prop`,
`zero_Prop`,`pos1_Prop`,`pos2_Prop`,`pos3_Prop`,
`pos4_Prop`,`pos5_Prop`
FROM `results_10m_alt`
ORDER BY Date DESC LIMIT 1")
list(Feel=Feel,last.results.alt =last.results.alt )
}
答案 2 :(得分：0)
您可以使用evaluate包。 knitr使用evaluate包处理文字文档。
我们可以使用knit来使用evaulate
一个简单的例子是脚本test.r
x <- -1:5
a <- 'a'
x <- x + a
print(x)
我们可以使用`kintr
library(knitr)
knit(text = paste('```{r}',source('test.r'),'```',collapse = '\n'))
## Error in x + a : non-numeric argument to binary operator
# note the error has occured, but evalulation proceeded on
# and x is defined
x
## [1] -1 0 1 2 3 4 5
以初步方式使用evaluate
# cleaning up to show that x will be redefined
rm(x)
evaluate(input = paste(readLines('test.r')))
## [[1]]
## $src
## [1] "x <- -1:5\n"
##
## attr(,"class")
## [1] "source"
##
## [[2]]
## $src
## [1] "a <- 'a'\n"
##
## attr(,"class")
## [1] "source"
##
## [[3]]
## $src
## [1] "x <- x + a\n"
##
## attr(,"class")
## [1] "source"
##
## [[4]]
## <simpleError in x + a: non-numeric argument to binary operator>
##
## [[5]]
## $src
## [1] "print(x)"
##
## attr(,"class")
## [1] "source"
##
## [[6]]
## [1] "[1] -1 0 1 2 3 4 5\n"
# and x is defined!
x
## [1] -1 0 1 2 3 4 5