Question

我有四个长度不等的大向量。下面，我提供了一个类似于原始数据集的玩具数据集：

a <- c(1021.923, 3491.31, 102.3, 12019.11, 879.2, 583.1)
b <- c(21,32,523,123.1,123.4545,12345,95.434, 879.25, 1021.9,11,12,662)
c <- c(52,21,1021.9288,12019.12, 879.1)
d <- c(432.432,23466.3,45435,3456,123,6688,1021.95)

有没有一种方法可以将所有这些向量一一比较，匹配的允许阈值为±0.5？换句话说，我要报告所有四个向量之间的通用数字，同时允许0.5的漂移。

对于上面的玩具数据集，最终答案是：

    Match1
a 1021.923
b 1021.900
c 1021.929
d 1021.950

我知道这对于两个向量都是可行的，但是我怎么能对四个向量进行呢？

相关

Answer 1

这是一个数据表解决方案。

它可以扩展到n个向量，因此请尽可能多地添加它。当所有向量中有多个值都具有“ hits”时，它也表现良好。

样本数据

a <- c(1021.923, 3491.31, 102.3, 12019.11, 879.2, 583.1)
b <- c(21,32,523,123.1,123.4545,12345,95.434, 879.25, 1021.9,11,12,662)
c <- c(52,21,1021.9288,12019.12, 879.1)
d <- c(432.432,23466.3,45435,3456,123,6688,1021.95)

代码

library(data.table)

#create list with vectors
l <- list( a,b,c,d )
names(l) <- letters[1:4]
#create data.table to work with
DT <- rbindlist( lapply(l, function(x) {data.table( value = x)} ), idcol = "group")
#add margins to each value
DT[, `:=`( id = 1:.N, start = value - 0.5, end = value + 0.5 ) ]
#set keys for joining
setkey(DT, start, end)
#perform overlap-join
result <- foverlaps(DT,DT)

#cast, to check how the 'hits' each id has in each group (a,b,c,d)
answer <- dcast( result, 
             group + value ~ i.group, 
             fun.aggregate = function(x){ x * 1 }, 
             value.var = "i.value", 
             fill = NA )

#get your final answer
#set columns to look at (i.e. the names from the earlier created list)
cols = names(l)
#keep the rows without NA (use rowSums, because TRUE = 1, FALSE = 0 )
#so if rowSums == 0, then columns in the vactor 'cols' do not contain a 'NA'
answer[ rowSums( is.na( answer[ , ..cols ] ) ) == 0, ]

输出

#    group    value        a      b        c       d
# 1:     a 1021.923 1021.923 1021.9 1021.929 1021.95
# 2:     b 1021.900 1021.923 1021.9 1021.929 1021.95
# 3:     c 1021.929 1021.923 1021.9 1021.929 1021.95
# 4:     d 1021.950 1021.923 1021.9 1021.929 1021.95

比较四个具有公差区间的数值向量并报告常用值

1 个答案: