我想从18个文本值的向量中生成153个对，其中没有一个对完全相同。有没有办法使用示例函数来做到这一点？
我将下面介绍的内容包括在内，现在已经很基本了，我只想知道如何在此基础上构建所需的功能
#vector of species included in the survey
rewildingspps<c("lynx","wolf","wildcat","bison","beaver","moose","boar","owl","goshawk","osprey","nightheron","pelican","spoonbill","stork","eagle","bustard","crane","capercaillie")
#sample- will pick 2 species randomly from the rewildingspps vector
sample(rewildingspps,2)
答案 0 :(得分：3)
combn函数有一个方便的FUN参数：
combn(18,2,FUN = function(x) rewildingspps[x])
或更妙的是，来自上面的评论：
combn(rewildingspps,2)
答案 1 :(得分：1)
matrix( unlist( combn( rewildingspps, 2 ) ), ncol = 2 )
[,1] [,2]
[1,] "lynx" "owl"
[2,] "wolf" "moose"
[3,] "lynx" "goshawk"
[4,] "wildcat" "moose"
[5,] "lynx" "osprey"
[6,] "bison" "moose"
[7,] "lynx" "nightheron"
[8,] "beaver" "moose"
[9,] "lynx" "pelican"
[10,] "moose" "moose"
[11,] "lynx" "spoonbill"
[12,] "boar" "moose"
[13,] "lynx" "stork"
[14,] "owl" "moose"
[15,] "lynx" "eagle"
[16,] "goshawk" "moose"
[17,] "lynx" "bustard"
[18,] "osprey" "moose"
[19,] "lynx" "crane"
[20,] "nightheron" "moose"
[21,] "lynx" "capercaillie"
[22,] "pelican" "boar"
[23,] "lynx" "owl"
[24,] "spoonbill" "boar"
[25,] "lynx" "goshawk"
[26,] "stork" "boar"
[27,] "lynx" "osprey"
[28,] "eagle" "boar"
[29,] "lynx" "nightheron"
[30,] "bustard" "boar"
[31,] "lynx" "pelican"
[32,] "crane" "boar"
[33,] "lynx" "spoonbill"
[34,] "capercaillie" "boar"
[35,] "wolf" "stork"
[36,] "wildcat" "boar"
[37,] "wolf" "eagle"
[38,] "bison" "boar"
[39,] "wolf" "bustard"
[40,] "beaver" "boar"
[41,] "wolf" "crane"
[42,] "moose" "boar"
[43,] "wolf" "capercaillie"
[44,] "boar" "owl"
[45,] "wolf" "goshawk"
[46,] "owl" "owl"
[47,] "wolf" "osprey"
[48,] "goshawk" "owl"
[49,] "wolf" "nightheron"
[50,] "osprey" "owl"
[51,] "wolf" "pelican"
[52,] "nightheron" "owl"
[53,] "wolf" "spoonbill"
[54,] "pelican" "owl"
[55,] "wolf" "stork"
[56,] "spoonbill" "owl"
[57,] "wolf" "eagle"
[58,] "stork" "owl"
[59,] "wolf" "bustard"
[60,] "eagle" "owl"
[61,] "wolf" "crane"
[62,] "bustard" "owl"
[63,] "wolf" "capercaillie"
[64,] "crane" "goshawk"
[65,] "wolf" "osprey"
[66,] "capercaillie" "goshawk"
[67,] "wildcat" "nightheron"
[68,] "bison" "goshawk"
[69,] "wildcat" "pelican"
[70,] "beaver" "goshawk"
[71,] "wildcat" "spoonbill"
[72,] "moose" "goshawk"
[73,] "wildcat" "stork"
[74,] "boar" "goshawk"
[75,] "wildcat" "eagle"
[76,] "owl" "goshawk"
[77,] "wildcat" "bustard"
[78,] "goshawk" "goshawk"
[79,] "wildcat" "crane"
[80,] "osprey" "goshawk"
[81,] "wildcat" "capercaillie"
[82,] "nightheron" "osprey"
[83,] "wildcat" "nightheron"
[84,] "pelican" "osprey"
[85,] "wildcat" "pelican"
[86,] "spoonbill" "osprey"
[87,] "wildcat" "spoonbill"
[88,] "stork" "osprey"
[89,] "wildcat" "stork"
[90,] "eagle" "osprey"
[91,] "wildcat" "eagle"
[92,] "bustard" "osprey"
[93,] "wildcat" "bustard"
[94,] "crane" "osprey"
[95,] "wildcat" "crane"
[96,] "capercaillie" "osprey"
[97,] "bison" "capercaillie"
[98,] "beaver" "nightheron"
[99,] "bison" "pelican"
[100,] "moose" "nightheron"
[101,] "bison" "spoonbill"
[102,] "boar" "nightheron"
[103,] "bison" "stork"
[104,] "owl" "nightheron"
[105,] "bison" "eagle"
[106,] "goshawk" "nightheron"
[107,] "bison" "bustard"
[108,] "osprey" "nightheron"
[109,] "bison" "crane"
[110,] "nightheron" "nightheron"
[111,] "bison" "capercaillie"
[112,] "pelican" "pelican"
[113,] "bison" "spoonbill"
[114,] "spoonbill" "pelican"
[115,] "bison" "stork"
[116,] "stork" "pelican"
[117,] "bison" "eagle"
[118,] "eagle" "pelican"
[119,] "bison" "bustard"
[120,] "bustard" "pelican"
[121,] "bison" "crane"
[122,] "crane" "pelican"
[123,] "bison" "capercaillie"
[124,] "capercaillie" "spoonbill"
[125,] "beaver" "stork"
[126,] "moose" "spoonbill"
[127,] "beaver" "eagle"
[128,] "boar" "spoonbill"
[129,] "beaver" "bustard"
[130,] "owl" "spoonbill"
[131,] "beaver" "crane"
[132,] "goshawk" "spoonbill"
[133,] "beaver" "capercaillie"
[134,] "osprey" "stork"
[135,] "beaver" "eagle"
[136,] "nightheron" "stork"
[137,] "beaver" "bustard"
[138,] "pelican" "stork"
[139,] "beaver" "crane"
[140,] "spoonbill" "stork"
[141,] "beaver" "capercaillie"
[142,] "stork" "eagle"
[143,] "beaver" "bustard"
[144,] "eagle" "eagle"
[145,] "beaver" "crane"
[146,] "bustard" "eagle"
[147,] "beaver" "capercaillie"
[148,] "crane" "bustard"
[149,] "beaver" "crane"
[150,] "capercaillie" "bustard"
[151,] "moose" "capercaillie"
[152,] "boar" "crane"
[153,] "moose" "capercaillie"
答案 2 :(得分：1)
除了@joran的答案外，由于标题为“随机选择对”，因此我们可以combn对索引进行随机化处理，以sample生成的对（来自combn的对是根据输入向量的顺序排序，因此不是随机的）：
pairs <- t(combn(rewildingspps, 2))
pairs[sample(1:nrow(pairs), nrow(pairs)),]
输出：
[,1] [,2]
[1,] "boar" "stork"
[2,] "wildcat" "stork"
[3,] "owl" "eagle"
[4,] "wolf" "boar"
[5,] "goshawk" "pelican"
[6,] "wildcat" "beaver"
[7,] "osprey" "nightheron"
[8,] "lynx" "spoonbill"
[9,] "lynx" "nightheron"
[10,] "lynx" "osprey"
[11,] "owl" "spoonbill"
[12,] "owl" "nightheron"
[13,] "moose" "bustard"
[14,] "goshawk" "capercaillie"
[15,] "wolf" "stork"
[16,] "pelican" "stork"
[17,] "nightheron" "spoonbill"
[18,] "osprey" "pelican"
[19,] "osprey" "crane"
[20,] "spoonbill" "bustard"
...