如何从最大长度的R中的向量中提取所有字符串值？
如果我们有一个字符串向量，则向量中的所有值都不可能具有相同的大小，因此我们可能正在寻找较小的值或较大的值。因此，如果我们想从R中具有最大长度的向量中提取所有字符串值，即使存在重复项，也可以使用max和nchar函数来完成，如以下示例所示。
示例
x1<-sample(c("india","russia","egypt","canada"),100,replace=TRUE)x1
输出结果[1] "egypt" "canada" "india" "russia" "canada" "russia" "canada" "russia"[9] "egypt" "russia" "egypt" "canada" "india" "canada" "russia" "egypt"
[17] "canada" "egypt" "canada" "canada" "canada" "egypt" "russia" "egypt"
[25] "russia" "canada" "india" "canada" "india" "russia" "india" "india"
[33] "russia" "canada" "canada" "egypt" "india" "russia" "egypt" "russia"
[41] "canada" "egypt" "canada" "canada" "canada" "india" "canada" "canada"
[49] "canada" "egypt" "egypt" "egypt" "india" "russia" "canada" "egypt"
[57] "india" "russia" "egypt" "canada" "india" "egypt" "canada" "egypt"
[65] "russia" "canada" "russia" "canada" "egypt" "india" "canada" "india"
[73] "india" "canada" "india" "russia" "canada" "canada" "canada" "egypt"
[81] "egypt" "canada" "russia" "canada" "egypt" "canada" "india" "egypt"
[89] "canada" "egypt" "india" "canada" "india" "russia" "india" "egypt"
[97] "india" "india" "india" "russia"
x1 [nchar(x1)== max（nchar(x1)）]
[1] "canada" "russia" "canada" "russia" "canada" "russia" "russia" "canada"[9] "canada" "russia" "canada" "canada" "canada" "canada" "russia" "russia"
[17] "canada" "canada" "russia" "russia" "canada" "canada" "russia" "russia"
[25] "canada" "canada" "canada" "canada" "canada" "canada" "canada" "russia"
[33] "canada" "russia" "canada" "canada" "russia" "canada" "russia" "canada"
[41] "canada" "canada" "russia" "canada" "canada" "canada" "canada" "russia"
[49] "canada" "canada" "canada" "canada" "russia" "russia"
示例
x2<-sample(c("ABC","BCD","AC","BCD","ABD","AD","BC","CA","A","B","C"),150,replace=TRUE)x2
输出结果[1] "AC" "B" "ABD" "AC" "BC" "C" "AD" "B" "BCD" "A" "BC" "AC"[13] "BCD" "BCD" "CA" "ABC" "CA" "A" "BCD" "CA" "AC" "BCD" "BC" "BCD"
[25] "BCD" "AD" "CA" "CA" "BC" "ABD" "BCD" "A" "AD" "AC" "BCD" "ABC"
[37] "BCD" "BC" "B" "AD" "AC" "BCD" "BCD" "BC" "B" "CA" "AD" "AD"
[49] "AC" "C" "B" "C" "ABC" "A" "CA" "A" "AD" "BCD" "C" "BC"
[61] "BCD" "C" "BCD" "ABD" "BCD" "AD" "AC" "BCD" "AC" "A" "BCD" "ABD"
[73] "BCD" "B" "AC" "AD" "B" "BCD" "A" "AC" "AD" "ABD" "AD" "BC"
[85] "ABC" "AD" "BCD" "BCD" "AC" "C" "BCD" "AC" "AC" "BCD" "ABC" "BCD"
[97] "A" "CA" "C" "BC" "CA" "C" "C" "AD" "A" "AD" "AC" "B"
[109] "BCD" "CA" "ABD" "AC" "CA" "ABC" "C" "C" "B" "AD" "CA" "ABC"
[121] "AD" "ABC" "A" "BCD" "A" "C" "BCD" "CA" "C" "AC" "AD" "ABD"
[133] "BCD" "C" "C" "BCD" "CA" "B" "BCD" "ABD" "B" "BCD" "AC" "B"
[145] "A" "A" "BC" "CA" "B" "AD"
x2 [nchar(x2)== max（nchar(x2)）]
[1] "ABD" "BCD" "BCD" "BCD" "ABC" "BCD" "BCD" "BCD" "BCD" "ABD" "BCD" "BCD"[13] "ABC" "BCD" "BCD" "BCD" "ABC" "BCD" "BCD" "BCD" "ABD" "BCD" "BCD" "BCD"
[25] "ABD" "BCD" "BCD" "ABD" "ABC" "BCD" "BCD" "BCD" "BCD" "ABC" "BCD" "BCD"
[37] "ABD" "ABC" "ABC" "ABC" "BCD" "BCD" "ABD" "BCD" "BCD" "BCD" "ABD" "BCD"
示例
x3<-sample(c("1","101","01","121","1234","02","21","41","2142","0214","3214","0124","1024","03","04","2"),150,replace=TRUE)x3
输出结果[1] "3214" "0214" "2142" "02" "0214" "1" "0124" "1234" "1024" "03"[11] "0214" "41" "04" "1234" "02" "1" "2142" "41" "21" "01"
[21] "04" "121" "101" "21" "0124" "41" "3214" "1024" "0124" "3214"
[31] "03" "121" "0124" "1" "41" "0214" "0214" "01" "01" "02"
[41] "1234" "1234" "1" "21" "0214" "1" "121" "1234" "3214" "0214"
[51] "3214" "121" "1234" "1234" "03" "101" "04" "121" "02" "1234"
[61] "1" "21" "03" "0214" "101" "1" "41" "01" "0124" "2"
[71] "0124" "2142" "1024" "121" "1" "1234" "03" "02" "0214" "03"
[81] "04" "1024" "2142" "41" "2142" "3214" "2" "0124" "2" "21"
[91] "01" "21" "21" "02" "0124" "3214" "02" "3214" "03" "04"
[101] "121" "03" "21" "121" "0214" "2142" "1234" "2142" "41" "02"
[111] "2" "41" "2142" "1024" "0214" "41" "1234" "2142" "03" "2142"
[121] "2142" "0124" "0214" "121" "0124" "01" "3214" "21" "0124" "121"
[131] "0124" "01" "02" "2" "1234" "3214" "02" "03" "2142" "1234"
[141] "0124" "1024" "04" "121" "02" "2142" "2" "41" "1024" "0124"
x3 [nchar(x3)== max（nchar(x3)）]
[1] "3214" "0214" "2142" "0214" "0124" "1234" "1024" "0214" "1234" "2142"[11] "0124" "3214" "1024" "0124" "3214" "0124" "0214" "0214" "1234" "1234"
[21] "0214" "1234" "3214" "0214" "3214" "1234" "1234" "1234" "0214" "0124"
[31] "0124" "2142" "1024" "1234" "0214" "1024" "2142" "2142" "3214" "0124"
[41] "0124" "3214" "3214" "0214" "2142" "1234" "2142" "2142" "1024" "0214"
[51] "1234" "2142" "2142" "2142" "0124" "0214" "0124" "3214" "0124" "0124"
[61] "1234" "3214" "2142" "1234" "0124" "1024" "2142" "1024" "0124"
示例
x4<-sample(c("abc","ac","ab","ca","cab","cbc","c","a"),150,replace=TRUE)x4
输出结果[1] "ab" "ab" "a" "abc" "a" "abc" "ab" "ca" "abc" "cbc" "ac" "ab"[13] "ab" "a" "ca" "abc" "ac" "abc" "abc" "cab" "abc" "cbc" "c" "cab"
[25] "ac" "abc" "cbc" "abc" "ca" "abc" "a" "ca" "ab" "ab" "ab" "a"
[37] "cab" "ab" "ca" "ca" "ca" "ab" "cbc" "c" "a" "a" "cab" "ac"
[49] "c" "cbc" "abc" "c" "c" "cbc" "a" "ac" "cab" "abc" "ac" "ab"
[61] "c" "c" "abc" "cbc" "ca" "c" "ac" "ca" "abc" "cbc" "cab" "c"
[73] "ca" "abc" "a" "ac" "c" "c" "ca" "cab" "ca" "a" "abc" "cbc"
[85] "abc" "ab" "c" "ab" "c" "ab" "cab" "ab" "cab" "c" "cab" "abc"
[97] "a" "a" "ab" "ac" "abc" "cbc" "a" "a" "cbc" "c" "abc" "abc"
[109] "ca" "ac" "cab" "cbc" "cbc" "abc" "cbc" "abc" "ac" "ca" "c" "ab"
[121] "abc" "a" "c" "abc" "cbc" "a" "c" "cab" "ab" "abc" "cab" "cbc"
[133] "ac" "a" "ca" "ab" "abc" "a" "abc" "c" "ca" "cab" "abc" "cbc"
[145] "cbc" "c" "ca" "cbc" "abc" "a"
x4 [nchar(x4)== max（nchar(x4)）]
[1] "abc" "abc" "abc" "cbc" "abc" "abc" "abc" "cab" "abc" "cbc" "cab" "abc"[13] "cbc" "abc" "abc" "cab" "cbc" "cab" "cbc" "abc" "cbc" "cab" "abc" "abc"
[25] "cbc" "abc" "cbc" "cab" "abc" "cab" "abc" "cbc" "abc" "cab" "cab" "cab"
[37] "abc" "abc" "cbc" "cbc" "abc" "abc" "cab" "cbc" "cbc" "abc" "cbc" "abc"
[49] "abc" "abc" "cbc" "cab" "abc" "cab" "cbc" "abc" "abc" "cab" "abc" "cbc"
[61] "cbc" "cbc" "abc"
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