我正试图编写一个脚本，用于识别R中标准数字跨度任务上的个人最大数字跨度，我似乎遇到了一些麻烦。编辑以包括下面的完整数据集
我要做的是确定列"corrAns"中的最大位数。这对应于最后一个"1"。in column " dsans . quot;对于每个首字母和每个会话。例如，参与者#92将得到7分，因为他们正确记住的数字的最大数量是0,3,6,1,0,4,7(第3列，第9行)。
我已经编写了以下函数，用于遍历不同的首字母和会话，但我不确定如何编写实际语句来实际生成我正在寻找的数字跨度值。提前感谢您的指导!
i = 1 #session ID
j = 1 #participant ID
for (i in 1:2) {
for (j in 1:length(unique(data$initials))) {
digitspan = sum(data$DSAns.corr) #this is the line I'm having trouble with
print(digitspan)
}
}
完整数据集在这里:
structure(list(session = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1), initials = c("92", "92", "92", "92", "92", "92", "92", "92",
"92", "92", "92", "92", "92", "92", "92", "92", "88", "88", "88",
"88", "88", "88", "88", "88", "88", "88", "88", "88", "88", "88",
"88", "88", "88", "88", "88", "88", "88", "88", "88", "88", "23",
"23", "23", "23", "23", "23", "23", "23", "23", "23", "23", "23",
"23", "23", "23", "23", "23", "23", "23", "47", "47", "47", "47",
"47", "47", "47", "47", "47", "47", "47", "47", "47", "47", "47",
"47", "47", "47", "47", "47", "47", "87", "87", "87", "87", "87",
"87", "87", "87", "87", "87", "87", "87", "87", "87", "87", "87",
"87", "87", "87", "87", "87", "SW", "SW", "SW", "SW", "SW", "SW",
"SW", "SW", "SW", "SW", "SW", "SW", "SW", "SW", "SW", "SW", "SW",
"SW", "SW", "SW", "SW", "SW", "SW", "SW", "SW", "SW", "SW", "SW",
"SW", "11", "11", "11", "11", "11", "11", "11", "11", "11", "11",
"11", "11", "11", "11", "11", "11", "11", "11", "11", "11", "11",
"11", "11", "11", "11", "11", "11", "88", "88", "88", "88", "88",
"88", "88", "88", "88", "88", "88", "88", "88", "88", "88", "88",
"88", "88", "88", "88", "88", "88", "88", "88", "88", "88", "88",
"88", "88", "88", "88", "67", "67", "67", "67", "67", "67", "67",
"67", "67", "67", "67", "67", "67", "67", "67", "67", "67", "67",
"67", "AS", "AS", "AS", "AS", "AS", "AS", "AS", "AS", "AS", "AS",
"AS", "AS", "90", "90", "90", "90", "90", "90", "90", "90", "90",
"90", "90", "90", "90", "90", "90", "90", "90", "90", "90", "90",
"90", "00", "00", "00", "00", "00", "00", "00", "00", "00", "00",
"00", "00", "00", "00", "00", "00", "00", "00", "14", "14", "14",
"14", "14", "14", "14", "14", "14", "14", "14", "14", "14", "14",
"14", "14", "14", "14", "14", "14", "14", "14", "14", "14", "14",
"14", "14", "14", "14", "14", "14", "14", "14", "14", "14", "14",
"14", "14", "14", "14", "14", "14", "14", "14", "14", "14", "14",
"14", "14", "89", "89", "89", "89", "89", "89", "89", "89", "89",
"89", "89", "89", "89", "89", "89", "89", "89", "23", "23", "23",
"23", "23", "23", "23", "23", "23", "23", "23", "23", "23", "23",
"23", "23", "23", "23", "23", "23", "92", "92", "92", "92", "92",
"92", "92", "92", "92", "92", "92", "92", "92", "92", "14", "14",
"14", "14", "14", "14", "14", "14", "14", "14", "14", "14", "14",
"14", "14", "14", "14", "14", "14", "87", "87", "87", "87", "87",
"87", "87", "87", "87", "87", "87", "87", "87", "87", "87", "87",
"87", "87", "87", "87", "87", "47", "47", "47", "47", "47", "47",
"47", "47", "47", "47", "47", "47", "47", "47", "47", "47", "47",
"47", "90", "90", "90", "90", "90", "90", "90", "90", "90", "90",
"90", "90", "90", "90", "90", "90", "67", "67", "67", "67", "67",
"67", "67", "67", "67", "67", "67", "67", "67", "67", "67", "67",
"67", "67", "67", "67", "67", "67"), corrAns = c("3,2,3", "1,3,1",
"2,7,2,6", "7,2,9,0", "8,6,4,5,0", "3,6,7,4,6", "0,2,3,4,6,5",
"2,6,8,0,1,5", "0,3,6,1,0,4,7", "2,9,7,0,6,2,0", "6,9,0,6,7,3,5,3",
"5,8,4", "0,3,2", "0,7,1,3", "9,8,5,3", "8,7,0,9,6", "3,2,3",
"1,3,1", "2,7,2,6", "7,2,9,0", "8,6,4,5,0", "3,6,7,4,6", "0,2,3,4,6,5",
"2,6,8,0,1,5", "0,3,6,1,0,4,7", "5,8,4", "0,3,2", "0,7,1,3",
"9,8,5,3", "8,7,0,9,6", "5,9,7,9,1", "5,7,8,4,7,6", "2,7,9,0,6,7",
"5,7,5,0,8,1,6", "9,1,2,1,4,7,9", "5,1,6,9,6,2,6,1", "3,4,8,1,3,2,7,1",
"8,5,6,0,8,4,6,0,4", "7,9,6,3,8,1,9,2,9", "2,3,2,7,1,0,2,7,4,5",
"3,2,3", "1,3,1", "2,7,2,6", "7,2,9,0", "8,6,4,5,0", "3,6,7,4,6",
"0,2,3,4,6,5", "2,6,8,0,1,5", "0,3,6,1,0,4,7", "2,9,7,0,6,2,0",
"5,8,4", "0,3,2", "0,7,1,3", "9,8,5,3", "8,7,0,9,6", "5,9,7,9,1",
"5,7,8,4,7,6", "2,7,9,0,6,7", "5,7,5,0,8,1,6", "3,2,3", "1,3,1",
"2,7,2,6", "7,2,9,0", "8,6,4,5,0", "3,6,7,4,6", "0,2,3,4,6,5",
"2,6,8,0,1,5", "0,3,6,1,0,4,7", "2,9,7,0,6,2,0", "5,8,4", "0,3,2",
"0,7,1,3", "9,8,5,3", "8,7,0,9,6", "5,9,7,9,1", "5,7,8,4,7,6",
"2,7,9,0,6,7", "5,7,5,0,8,1,6", "9,1,2,1,4,7,9", "5,1,6,9,6,2,6,1",
"3,2,3", "1,3,1", "2,7,2,6", "7,2,9,0", "8,6,4,5,0", "3,6,7,4,6",
"0,2,3,4,6,5", "2,6,8,0,1,5", "0,3,6,1,0,4,7", "2,9,7,0,6,2,0",
"6,9,0,6,7,3,5,3", "7,2,9,0,7,0,1,3", "7,4,3,4,2,5,3,8,5", "3,2,5,9,3,4,5,7,1",
"5,8,4", "0,3,2", "0,7,1,3", "9,8,5,3", "8,7,0,9,6", "5,9,7,9,1",
"5,7,8,4,7,6", "3,2,3", "1,3,1", "2,7,2,6", "7,2,9,0", "8,6,4,5,0",
"3,6,7,4,6", "0,2,3,4,6,5", "2,6,8,0,1,5", "0,3,6,1,0,4,7", "2,9,7,0,6,2,0",
"6,9,0,6,7,3,5,3", "7,2,9,0,7,0,1,3", "7,4,3,4,2,5,3,8,5", "3,2,5,9,3,4,5,7,1",
"5,1,0,2,4,1,2,9,8,2", "9,5,3,7,5,6,4,1,6,7", "1,8,4,8,8,7,6,0,3,9,5",
"5,8,4", "0,3,2", "0,7,1,3", "9,8,5,3", "8,7,0,9,6", "5,9,7,9,1",
"5,7,8,4,7,6", "2,7,9,0,6,7", "5,7,5,0,8,1,6", "9,1,2,1,4,7,9",
"5,1,6,9,6,2,6,1", "3,4,8,1,3,2,7,1", "3,2,3", "1,3,1", "2,7,2,6",
"7,2,9,0", "8,6,4,5,0", "3,6,7,4,6", "0,2,3,4,6,5", "2,6,8,0,1,5",
"0,3,6,1,0,4,7", "2,9,7,0,6,2,0", "6,9,0,6,7,3,5,3", "7,2,9,0,7,0,1,3",
"7,4,3,4,2,5,3,8,5", "3,2,5,9,3,4,5,7,1", "5,1,0,2,4,1,2,9,8,2",
"5,8,4", "0,3,2", "0,7,1,3", "9,8,5,3", "8,7,0,9,6", "5,9,7,9,1",
"5,7,8,4,7,6", "2,7,9,0,6,7", "5,7,5,0,8,1,6", "9,1,2,1,4,7,9",
"5,1,6,9,6,2,6,1", "3,4,8,1,3,2,7,1", "3,2,3", "1,3,1", "2,7,2,6",
"7,2,9,0", "8,6,4,5,0", "3,6,7,4,6", "0,2,3,4,6,5", "2,6,8,0,1,5",
"0,3,6,1,0,4,7", "2,9,7,0,6,2,0", "6,9,0,6,7,3,5,3", "7,2,9,0,7,0,1,3",
"7,4,3,4,2,5,3,8,5", "3,2,5,9,3,4,5,7,1", "5,1,0,2,4,1,2,9,8,2",
"5,8,4", "0,3,2", "0,7,1,3", "9,8,5,3", "8,7,0,9,6", "5,9,7,9,1",
"5,7,8,4,7,6", "2,7,9,0,6,7", "5,7,5,0,8,1,6", "9,1,2,1,4,7,9",
"5,1,6,9,6,2,6,1", "3,4,8,1,3,2,7,1", "8,5,6,0,8,4,6,0,4", "7,9,6,3,8,1,9,2,9",
"2,3,2,7,1,0,2,7,4,5", "6,0,7,8,4,3,4,0,5,8", "3,2,3", "1,3,1",
"2,7,2,6", "7,2,9,0", "8,6,4,5,0", "3,6,7,4,6", "0,2,3,4,6,5",
"2,6,8,0,1,5", "0,3,6,1,0,4,7", "2,9,7,0,6,2,0", "6,9,0,6,7,3,5,3",
"7,2,9,0,7,0,1,3", "5,8,4", "0,3,2", "0,7,1,3", "9,8,5,3", "8,7,0,9,6",
"5,9,7,9,1", "5,7,8,4,7,6", "3,2,3", "1,3,1", "5,8,4", "0,3,2",
"0,7,1,3", "9,8,5,3", "8,7,0,9,6", "5,9,7,9,1", "5,7,8,4,7,6",
"2,7,9,0,6,7", "5,7,5,0,8,1,6", "9,1,2,1,4,7,9", "3,2,3", "1,3,1",
"2,7,2,6", "7,2,9,0", "8,6,4,5,0", "3,6,7,4,6", "0,2,3,4,6,5",
"2,6,8,0,1,5", "0,3,6,1,0,4,7", "2,9,7,0,6,2,0", "6,9,0,6,7,3,5,3",
"7,2,9,0,7,0,1,3", "7,4,3,4,2,5,3,8,5", "3,2,5,9,3,4,5,7,1",
"5,1,0,2,4,1,2,9,8,2", "5,8,4", "0,3,2", "0,7,1,3", "9,8,5,3",
"8,7,0,9,6", "5,9,7,9,1", "3,2,3", "1,3,1", "2,7,2,6", "7,2,9,0",
"8,6,4,5,0", "3,6,7,4,6", "0,2,3,4,6,5", "2,6,8,0,1,5", "0,3,6,1,0,4,7",
"2,9,7,0,6,2,0", "5,8,4", "0,3,2", "0,7,1,3", "9,8,5,3", "8,7,0,9,6",
"5,9,7,9,1", "5,7,8,4,7,6", "2,7,9,0,6,7", "3,2,3", "1,3,1",
"2,7,2,6", "7,2,9,0", "8,6,4,5,0", "3,6,7,4,6", "0,2,3,4,6,5",
"2,6,8,0,1,5", "0,3,6,1,0,4,7", "2,9,7,0,6,2,0", "5,8,4", "0,3,2",
"0,7,1,3", "9,8,5,3", "8,7,0,9,6", "5,9,7,9,1", "5,7,8,4,7,6",
"2,7,9,0,6,7", "3,2,3", "1,3,1", "2,7,2,6", "7,2,9,0", "8,6,4,5,0",
"3,6,7,4,6", "0,2,3,4,6,5", "2,6,8,0,1,5", "0,3,6,1,0,4,7", "2,9,7,0,6,2,0",
"6,9,0,6,7,3,5,3", "7,2,9,0,7,0,1,3", "7,4,3,4,2,5,3,8,5", "3,2,5,9,3,4,5,7,1",
"5,1,0,2,4,1,2,9,8,2", "9,5,3,7,5,6,4,1,6,7", "1,8,4,8,8,7,6,0,3,9,5",
"8,3,3,6,0,9,8,2,7,6,0", "5,8,4", "0,3,2", "0,7,1,3", "9,8,5,3",
"8,7,0,9,6", "5,9,7,9,1", "5,7,8,4,7,6", "2,7,9,0,6,7", "5,7,5,0,8,1,6",
"9,1,2,1,4,7,9", "5,1,6,9,6,2,6,1", "3,4,8,1,3,2,7,1", "8,5,6,0,8,4,6,0,4",
"3,2,3", "1,3,1", "2,7,2,6", "7,2,9,0", "8,6,4,5,0", "3,6,7,4,6",
"0,2,3,4,6,5", "2,6,8,0,1,5", "0,3,6,1,0,4,7", "5,8,4", "0,3,2",
"0,7,1,3", "9,8,5,3", "8,7,0,9,6", "5,9,7,9,1", "5,7,8,4,7,6",
"2,7,9,0,6,7", "3,2,3", "1,3,1", "2,7,2,6", "7,2,9,0", "8,6,4,5,0",
"3,6,7,4,6", "0,2,3,4,6,5", "2,6,8,0,1,5", "0,3,6,1,0,4,7", "2,9,7,0,6,2,0",
"6,9,0,6,7,3,5,3", "7,2,9,0,7,0,1,3", "5,8,4", "0,3,2", "0,7,1,3",
"9,8,5,3", "8,7,0,9,6", "5,9,7,9,1", "5,7,8,4,7,6", "2,7,9,0,6,7",
"3,2,3", "1,3,1", "2,7,2,6", "7,2,9,0", "8,6,4,5,0", "3,6,7,4,6",
"0,2,3,4,6,5", "2,6,8,0,1,5", "5,8,4", "0,3,2", "0,7,1,3", "9,8,5,3",
"8,7,0,9,6", "5,9,7,9,1", "3,2,3", "1,3,1", "2,7,2,6", "7,2,9,0",
"8,6,4,5,0", "3,6,7,4,6", "0,2,3,4,6,5", "2,6,8,0,1,5", "0,3,6,1,0,4,7",
"5,8,4", "0,3,2", "0,7,1,3", "9,8,5,3", "8,7,0,9,6", "5,9,7,9,1",
"5,7,8,4,7,6", "2,7,9,0,6,7", "5,7,5,0,8,1,6", "9,1,2,1,4,7,9",
"3,2,3", "1,3,1", "2,7,2,6", "7,2,9,0", "8,6,4,5,0", "3,6,7,4,6",
"0,2,3,4,6,5", "2,6,8,0,1,5", "0,3,6,1,0,4,7", "2,9,7,0,6,2,0",
"6,9,0,6,7,3,5,3", "7,2,9,0,7,0,1,3", "5,8,4", "0,3,2", "0,7,1,3",
"9,8,5,3", "8,7,0,9,6", "5,9,7,9,1", "5,7,8,4,7,6", "2,7,9,0,6,7",
"5,7,5,0,8,1,6", "3,2,3", "1,3,1", "2,7,2,6", "7,2,9,0", "8,6,4,5,0",
"3,6,7,4,6", "0,2,3,4,6,5", "2,6,8,0,1,5", "0,3,6,1,0,4,7", "2,9,7,0,6,2,0",
"6,9,0,6,7,3,5,3", "7,2,9,0,7,0,1,3", "7,4,3,4,2,5,3,8,5", "5,8,4",
"0,3,2", "0,7,1,3", "9,8,5,3", "8,7,0,9,6", "3,2,3", "1,3,1",
"2,7,2,6", "7,2,9,0", "8,6,4,5,0", "3,6,7,4,6", "0,2,3,4,6,5",
"2,6,8,0,1,5", "0,3,6,1,0,4,7", "2,9,7,0,6,2,0", "5,8,4", "0,3,2",
"0,7,1,3", "9,8,5,3", "8,7,0,9,6", "5,9,7,9,1", "3,2,3", "1,3,1",
"2,7,2,6", "7,2,9,0", "8,6,4,5,0", "3,6,7,4,6", "0,2,3,4,6,5",
"2,6,8,0,1,5", "0,3,6,1,0,4,7", "2,9,7,0,6,2,0", "6,9,0,6,7,3,5,3",
"7,2,9,0,7,0,1,3", "5,8,4", "0,3,2", "0,7,1,3", "9,8,5,3", "8,7,0,9,6",
"5,9,7,9,1", "5,7,8,4,7,6", "2,7,9,0,6,7", "5,7,5,0,8,1,6", "9,1,2,1,4,7,9"
), DSAns.corr = c(0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, NA, NA, NA,
NA, NA, 1, 1, 1, 1, 1, 1, 1, 0, 0, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 1, 1, 1, 1,
1, 0, 1, 1, 1, 0, 1, 0, 0, NA, NA, NA, NA, NA, NA, NA, 1, 1,
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1,
0, 1, 1, 0, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, 1, 1, 1, 1,
1, 1, 0, 1, 1, 0, 0, NA, NA, NA, NA, NA, NA, NA, 0, 0, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1,
1, 1, 1, 0, 0, NA, NA, NA, NA, NA, NA, 1, 0, 1, 1, 1, 0, 1, 1,
0, 0, NA, NA, NA, NA, NA, NA, NA, NA, 0, 1, 1, 1, 1, 1, 1, 1,
0, 0, NA, NA, NA, NA, NA, NA, NA, NA, 0, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 0, 1, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 1, 1, 1, 0, 1, 1, 1, 0, 0, NA, NA, NA, NA,
NA, NA, NA, NA, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, NA, NA, NA,
NA, NA, NA, NA, NA, 1, 1, 1, 1, 1, 1, 0, 0, NA, NA, NA, NA, NA,
NA, 0, 1, 1, 1, 1, 1, 1, 0, 0, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, NA, NA,
NA, NA, NA, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, NA, NA, NA, NA, NA,
NA, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA), DSAns_2.corr = c(NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 1, 1, 1, 0, 0, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 1, 1, 1, 1, 0, 1, 1, 0, 0, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0,
0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1,
1, 1, 1, 1, 0, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, 1,
1, 1, 1, 0, 1, 0, 1, 1, 0, 0, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
0, 1, 1, 0, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
1, 1, 0, 1, 1, 0, 0, NA, NA, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, 0,
1, 1, 0, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, 1, 1,
1, 0, 1, 0, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, 1,
1, 1, 0, 1, 0, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1,
0, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, 1, 1, 1, 1, 1, 0,
0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, 1, 1, 1,
1, 1, 0, 0, NA, NA, NA, NA, NA, NA, NA, NA, 1, 1, 1, 1, 0, 0,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, 1, 1, 1, 0, 1, 1, 1, 0,
0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, 1, 1, 1,
1, 1, 1, 0, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 1, 1, 1, 0, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1,
1, 0, 1, 0, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
1, 1, 1, 0, 1, 1, 0, 1, 0, 0)), row.names = c(NA, -454L), class = c("data.table",
"data.frame"), .internal.selfref = <pointer: 0x000001a95b0e1ef0>)
这里没有可重复的数据，但我认为这是可行的:
library(dplyr)
data %>%
# select last row of each user session with DNAns.corr == 1
group_by(session, initials) %>%
filter(DSAns.corr == 1) %>%
slice_tail(n = 1) %>% # EDIT: corrected from (1) to (n=1)
ungroup() %>%
# Count commas in corrAns plus 1 = # of comma-sep values
# (I'm assuming if zero correct it wouldn't make it through filter above)
mutate(digitspan = stringr::str_count(corrAns, ',') + 1)