R 如何识别每个人'；最大数字跨度是多少？_R

R 如何识别每个人'；最大数字跨度是多少？

R 如何识别每个人'；最大数字跨度是多少？,r,R,我正试图编写一个脚本，用于在R中的标准数字跨度任务中识别个人的最大数字跨度，我似乎遇到了一些问题。编辑以包含下面的完整数据集我要做的是确定“corrAns”列中的最大位数，对应于“DSAns.corr”列中每个首字母和每个会话的最后一个“1”。例如，参与者#92的分数为7，因为他们正确记忆的最大位数为0,3,6,1,0,4,7（第3列，第9行）我已经编写了下面的函数来迭代不同的首字母和会话，但是我不确定如何编写实际语句来实际生成我要查找的数字跨度值。提前感谢您的指导 i = 1 #sessi

我正试图编写一个脚本，用于在R中的标准数字跨度任务中识别个人的最大数字跨度，我似乎遇到了一些问题。编辑以包含下面的完整数据集

我要做的是确定“corrAns”列中的最大位数，对应于“DSAns.corr”列中每个首字母和每个会话的最后一个“1”。例如，参与者#92的分数为7，因为他们正确记忆的最大位数为0,3,6,1,0,4,7（第3列，第9行）

我已经编写了下面的函数来迭代不同的首字母和会话，但是我不确定如何编写实际语句来实际生成我要查找的数字跨度值。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)
  }
}

完整数据集在此：

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>)

结构（会话=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, 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），首字母=c（“92”、“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”“西南”“西南”“西南”“西南”“西南”“西南”， “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”， “西南”、“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", "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”、“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", "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”，“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,

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)