我正在使用R.处理大量的药物和反应数据集,目前,我的数据结构是一个非常高的数据框架,其中列出了报告ID号、药物名称和报告的反应。正如你所知道的,在药物和药物与反应之间有一对多的关系。
请记住,这个数据集比我在这里可以复制的数据要大得多,我想知道如何找到--什么药物对导致什么反应,以及在什么频率下发生。
最重要的是,我对如何处理这样的问题感兴趣。数据结构是否正确?我应该读些什么概念或图书馆?
下面是一些真实数据的链接:result.csv
ID DRUG REACTION
1 1827 ASPIRIN CHEST PAIN
2 1827 CLARINEX CHEST PAIN
3 1827 ASPIRIN COUGH
4 1827 CLARINEX COUGH
5 1827 ASPIRIN HAEMOGLOBIN DECREASED
6 1827 CLARINEX HAEMOGLOBIN DECREASED
7 1827 ASPIRIN NEUTROPHIL COUNT INCREASED
8 1827 CLARINEX NEUTROPHIL COUNT INCREASED
9 1827 ASPIRIN PHARYNGOLARYNGEAL PAIN
10 1827 CLARINEX PHARYNGOLARYNGEAL PAIN
...在我小小的大脑里,最终的结果是这样的.
Drug1 Drug2 Reaction Frequency
1 tylenol alcohol hepatic failure 298
2 advil aleve bleeding 201
3 aspirin advil renal failure 199
4 docusate senna diarrhea 146
5 senna sudafed palpitations 121
6 xanax alcohol sedation 111
7 clarinex benadryl dry mouth 96
...
569 ASPIRIN CLARINEX CHEST PAIN 2Drug1和Drug2是整个数据集中频率最高的药物对。“药物对”被定义为两种药物具有相同报告ID的任意组合,上面的示例输出将被解释为:“第1行有298个唯一的报告ID,其反应是肝衰竭。”
发布于 2014-08-10 16:53:08
好的,我试着回答-我希望我的问题是正确的。代码的目的是给出一些想法,而不是优雅的/最终的。
请注意:我有意使用循环而不是可能的向量化/应用函数,以便更容易理解(熟悉应用函数的人也会理解for循环;-)。
请注意2:因为我只有一小块数据,所以我无法测试整个数据集的代码!
编辑:基于上述示例的列-可能与csv数据不同。
要点是:
unique、[等。utils::combn希望这能帮上忙!
require(utils)
df <- read.table(header=TRUE,
text="LINE ID DRUG REACTION
1 1827 ASPIRIN CHEST_PAIN
2 1827 CLARINEX CHEST_PAIN
3 1827 ASPIRIN COUGH
4 1827 CLARINEX COUGH
5 1827 ASPIRIN HAEMOGLOBIN_DECREASED
6 1827 CLARINEX HAEMOGLOBIN_DECREASED
7 1827 ASPIRIN NEUTROPHIL_COUNT_INCREASED
8 1827 CLARINEX NEUTROPHIL_COUNT_INCREASED
9 1827 ASPIRIN PHARYNGOLARYNGEAL_PAIN
10 1827 CLARINEX PHARYNGOLARYNGEAL_PAIN")
# temporary object to collect if a combination is present
Results <- data.frame(Drug1=NA, Drug2=NA, Reaction=NA, Reaction.occurs=NA)
n=1 # start first line in Results object
# walk through each ID ...
for (ID in unique(df$ID)) {
# ... and each possible pair of drugs within a (report) ID ...
drug.pairs <- utils::combn(x=unique(df[df$ID == ID, "DRUG"]), m=2) # the columns
for (ii in 1:ncol(drug.pairs)) {
# ... and each reaction ...
for (reaction in unique(df$REACTION)) {
Results[n, "Drug1"] <- drug.pairs[1,ii]
Results[n, "Drug2"] <- drug.pairs[2,ii]
Results[n, "Reaction"] <- reaction
Results[n, "Reaction.occurs"] <- drug.pairs[1,ii] %in% df[df$REACTION == reaction & df$ID == ID, "DRUG"] &
drug.pairs[2,ii] %in% df[df$REACTION == reaction & df$ID == ID, "DRUG"]
n <- n+1
}
}
}
head(Results)
# then find the unique Drug1 - Drug2 -Reaction combinations, and count the TRUE values:
(Results[!duplicated(Results[,1:3]), ][,1:3])
(unique(Results[, 1:3]))
# Results2 contains only the unique combinations
Results2 <- Results[!duplicated(Results[,1:3]), ][,1:3]
# calculatethe frequencies
for (i in 1:nrow(Results2)) {
Results2[i, "Frequency"] <- sum(Results[Results$Drug1 == Results2[i, "Drug1"] &
Results$Drug2 == Results2[i, "Drug2"] &
Results$Reaction == Results2[i, "Reaction"], ]$Reaction.occurs)
}
Results2
# --- end ----给予:
Drug1 Drug2 Reaction Frequency
1 ASPIRIN CLARINEX CHEST_PAIN 1
2 ASPIRIN CLARINEX COUGH 1
3 ASPIRIN CLARINEX HAEMOGLOBIN_DECREASED 1
4 ASPIRIN CLARINEX NEUTROPHIL_COUNT_INCREASED 1
5 ASPIRIN CLARINEX PHARYNGOLARYNGEAL_PAIN 1https://stackoverflow.com/questions/25229372
复制相似问题