重新整理眼动追踪数据

【问题标题】：Re arrange eye tracking data重新整理眼动追踪数据
【发布时间】：2019-08-05 19:09:04
【问题描述】：

我有一个需要转换的眼动追踪数据文件。让我解释一下，我的数据格式如下：

Event; Info; Pupil size

Message; Start_trial_0;
Fixation; L; 1020
Fixation; L; 1200
Fixation; L; 980
Fixation; L; 990
Fixation; L; 1003
Message; Trial_0;
Message; ACC_1;
Message; RT_850;
Message; Stop_trial_0;
Message; Start_trial_1;
Fixation; L; 1023
Fixation; L; 1020
Fixation; L; 997
Fixation; L; 1123
Message; Trial_1;
Message; ACC_1;
Message; RT_920;
Message; Stop_trial_1;
Message; Strat_trial_2;
...

知道这一点，我每次试验的“Fixation”线数都不相同。

我希望我的数据是这样的：

Trial_0; ACC_0; RT_850; Fixation; L; 1020
Trial_0; ACC_0; RT_850; Fixation; L; 1200
Trial_0; ACC_0; RT_850; Fixation; L; 980
Trial_0; ACC_0; RT_850; Fixation; L; 990
Trial_0; ACC_0; RT_850; Fixation; L; 1003
Trial_1; ACC_1; RT_920; Fixation; L; 1023
Trial_1; ACC_1; RT_920; Fixation; L; 1020
Trial_1; ACC_1; RT_920; Fixation; L; 997
Trial_1; ACC_1; RT_920; Fixation; L; 1123
...

由于我不是一个实验过的 R 用户，我绝对不知道该怎么做（如果可能的话）。而且由于我的数据文件包含超过 1000000 行，因此无法手动完成...

提前感谢您的宝贵帮助！ 吊臂。

【问题讨论】：

  • 这是一个相当复杂的数据处理过程。每次试用只有一个 ACC_* 代码和一个 RT_* 代码，还是可以多个？
  • 是的，它非常复杂，而且超出了我在 R 中的能力……是的，我每次试验只有一个 ACC 和一个 RT 代码，因为它代表了这次试验的准确性和响应时间.我有一个 2000 Hz 的眼动仪，所以我有这么大的文件。

标签： r


【解决方案1】：

一般的方法是将您的行拆分为所有相同试验的存储桶，然后提取元数据与数据行，并将它们制成一个数据框（假设这是您最终想要的）。

library(stringr)
library(purrr)

# You may be reading this in with `readLines` or similar,
#   in which case you may not need to split on "\n" below

eye_text <- 
"Event; Info; Pupil size

Message; Start_trial_0;
Fixation; L; 1020
Fixation; L; 1200
Fixation; L; 980
Fixation; L; 990
Fixation; L; 1003
Message; Trial_0;
Message; ACC_1;
Message; RT_850;
Message; Stop_trial_0;
Message; Start_trial_1;
Fixation; L; 1023
Fixation; L; 1020
Fixation; L; 997
Fixation; L; 1123
Message; Trial_1;
Message; ACC_1;
Message; RT_920;
Message; Stop_trial_1;
Message; Start_trial_2;"  # Fixed typo?

# Depending how you read in the data, may already be a vector of lines
eye_lines <- str_split(eye_text, "\n")[[1]]

# Figure out where each trial starts
eye_starts <- cumsum(str_detect(eye_lines, "Start"))

拆分数据

str_detect(eye_lines, "Start") 为您提供TRUE/FALSE 的向量，指示每个试验的开始。 cumsum 将其强制为 1/0 并获取运行总数。这样，您最终会得到0 0 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 3，或示例文本中的四组（标题、试用 0、试用 1 和试用 2 的一行）。

eye_parser <- function(strings) {
  message_indices <- str_detect(strings, "Message;") & !str_detect(strings, "Start|Stop")

  messages <- 
    strings[message_indices] %>% 
    str_remove_all("Message; ") %>% 
    str_c(collapse = " ")

  if (length(messages) == 0) return(NULL)

  observations <- strings[!str_detect(strings, "Message")]

  str_c(messages, observations, sep = " ")
}

这里我们对字符串进行两次子集化：首先我们得到所有Message; 行（但不是Start*/Stop* 行），然后我们得到所有非Message; 行。

  1. 对于消息，我们去掉了“Message;”，它为您留下了元数据值（"Trial_0;", "ACC_1;", ... 等的向量）。然后你 str_c 将它们重新组合成一个元数据行："Trial_0; ACC_1; RT_850;"。

    • 此时如果消息都是空的（如标头和部分试用），我们只返回NULL。

  2. 对于观察结果，我们只是按原样处理。然后我们将str_c 消息和观察结果放在一起，在每条观察线前面重复messages。

要使用此功能，我们首先将您的所有行从上面分成组，然后将purrr::map 函数用于每组字符串。 unlist 将其从向量列表中提取为单个向量，然后str_split(..., "; ", simplify = T) 将其分解为具有列的字符矩阵。最后as.data.frame 将它变成了一个数据框。

split(eye_lines, eye_starts) %>% 
  map(eye_parser) %>% 
  unlist(use.names = F) %>% 
  str_split("; ", simplify = T) %>% 
  as.data.frame()

       V1    V2     V3       V4 V5   V6
1 Trial_0 ACC_1 RT_850 Fixation  L 1020
2 Trial_0 ACC_1 RT_850 Fixation  L 1200
3 Trial_0 ACC_1 RT_850 Fixation  L  980
4 Trial_0 ACC_1 RT_850 Fixation  L  990
5 Trial_0 ACC_1 RT_850 Fixation  L 1003
6 Trial_1 ACC_1 RT_920 Fixation  L 1023
7 Trial_1 ACC_1 RT_920 Fixation  L 1020
8 Trial_1 ACC_1 RT_920 Fixation  L  997
9 Trial_1 ACC_1 RT_920 Fixation  L 1123

注意事项：

如果您的元数据并不总是按“试用”、“ACC”、“RT”的顺序排列，您可能需要专门提取这些元数据。您可以使用我用于messages 的相同代码模式，但单独用于每个代码模式。然后，您可以确保它们存在且顺序正确。

【讨论】：

  • 非常感谢布赖恩，这看起来是一个很好的解决方案，问题是我正在努力迈出第一步，str_split 和 str_detect 返回一个错误：“argument is not an atomic vector: coercing " 我的第一行代码是：S1_data &lt;- read.csv(file = "D:/Stim ST &amp; ET/data/S1_data_t.csv", header = TRUE, sep = ";")S1_data_filter &lt;- S1_data %&gt;% select(1,2,3,4,5,6,9)
  • @Jibs 我想说不要使用read.csv。您需要将这些行作为字符串读取，以便自己拆分它们。试试readLines（然后你可以跳过"\n"的拆分）。我对您如何从原始数据中选择多列 (1-6,9) 感到有些困惑，但原始数据似乎只有 3 个。
  • 哦，好的。我在想，将每一行作为向量读取可能是一个问题，但不知道如何解决这个问题。对于多列选择，它来自我也保持眼睛位置的旧版本。现在我只想要瞳孔大小。
  • 嗨，布赖恩，希望你还在。 readlines 比 read.csv 你是对的更好。我现在对最后一个拆分功能有疑问。它向我返回一条错误消息：警告消息：在 split.default(eye_lines, eye_starts) 中：数据长度不是拆分变量的倍数
  • 其实eye_lines &lt;- str_split(eye_text, "\n")[[1]]这行我也不知道怎么办。
【解决方案2】： 
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51L, 51L, 51L, 51L, 51L, 14L, 76L, 220L, 3L, 94L, 5L, 216L, 195L, 
152L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 15L, 77L, 219L, 3L, 118L, 5L, 218L, 197L, 153L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 18L, 79L, 219L, 3L, 122L, 5L, 216L, 198L, 154L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 34L, 60L, 220L, 3L, 119L, 7L, 216L, 199L, 155L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 42L, 67L, 220L, 
3L, 108L, 7L, 218L, 200L, 51L, 156L, 51L, 51L, 51L, 51L, 51L, 
43L, 68L, 219L, 3L, 96L, 7L, 216L, 201L, 157L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 19L, 80L, 219L, 
3L, 123L, 7L, 218L, 202L, 158L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 12L, 76L, 219L, 3L, 111L, 7L, 217L, 203L, 159L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 13L, 76L, 220L, 
3L, 113L, 7L, 217L, 204L, 160L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 16L, 78L, 220L, 2L, 104L, 7L, 216L, 205L, 161L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 17L, 79L, 219L, 
3L, 109L, 7L, 216L, 206L, 163L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 23L, 54L, 219L, 3L, 105L, 7L, 216L, 208L, 51L, 164L, 
51L, 51L, 51L, 51L, 51L, 24L, 54L, 220L, 3L, 92L, 7L, 217L, 209L, 
165L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 47L, 70L, 
220L, 3L, 87L, 7L, 217L, 210L, 166L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L), .Label = c("36=32mod4", "correct_0", 
"correct_1", "difficulty_Easy", "difficulty_Hard", "difficulty_Intermediate", 
"difficulty_Very", "id_1058", "id_10975", "id_11207", "id_1129", 
"id_12052", "id_12069", "id_12131", "id_12453", "id_13285", "id_13741", 
"id_13817", "id_14467", "id_14596", "id_14907", "id_15262", "id_1544", 
"id_1555", "id_15661", "id_15684", "id_15693", "id_1685", "id_2295", 
"id_2479", "id_2820", "id_313", "id_3645", "id_3985", "id_4333", 
"id_4541", "id_5249", "id_5426", "id_5684", "id_5756", "id_6016", 
"id_6326", "id_7019", "id_7064", "id_7885", "id_8660", "id_8728", 
"id_9028", "id_9263", "id_9419", "L", "modulo_26", "modulo_36", 
"modulo_40", "modulo_42", "modulo_46", "modulo_47", "modulo_50", 
"modulo_55", "modulo_57", "modulo_58", "modulo_59", "modulo_63", 
"modulo_64", "modulo_65", "modulo_66", "modulo_68", "modulo_71", 
"modulo_74", "modulo_77", "modulo_78", "modulo_79", "modulo_80", 
"modulo_85", "modulo_86", "modulo_89", "modulo_90", "modulo_93", 
"modulo_94", "modulo_96", "modulo_97", "modulo_98", "modulo_99", 
"RT_10590", "RT_1367", "RT_14182", "RT_15412", "RT_1550", "RT_17151", 
"RT_17302", "RT_1736", "RT_1891", "RT_2002", "RT_2227", "RT_2241", 
"RT_2432", "RT_2510", "RT_2624", "RT_2660", "RT_2840", "RT_2956", 
"RT_2984", "RT_3029", "RT_3154", "RT_3273", "RT_3283", "RT_3727", 
"RT_3900", "RT_4493", "RT_4544", "RT_4840", "RT_5095", "RT_5368", 
"RT_5583", "RT_5618", "RT_6009", "RT_6385", "RT_6423", "RT_6489", 
"RT_6689", "RT_7471", "RT_7669", "RT_7697", "RT_8156", "RT_8752", 
"RT_8784", "start_consigne", "start_trial_0", "start_trial_1", 
"start_trial_10", "start_trial_11", "start_trial_12", "start_trial_13", 
"start_trial_14", "start_trial_15", "start_trial_16", "start_trial_17", 
"start_trial_18", "start_trial_19", "start_trial_2", "start_trial_20", 
"start_trial_21", "start_trial_22", "start_trial_23", "start_trial_24", 
"start_trial_25", "start_trial_26", "start_trial_27", "start_trial_28", 
"start_trial_29", "start_trial_3", "start_trial_30", "start_trial_31", 
"start_trial_32", "start_trial_33", "start_trial_34", "start_trial_35", 
"start_trial_36", "start_trial_37", "start_trial_38", "start_trial_39", 
"start_trial_4", "start_trial_40", "start_trial_41", "start_trial_42", 
"start_trial_43", "start_trial_5", "start_trial_6", "start_trial_7", 
"start_trial_8", "start_trial_9", "stop_consigne", "stop_trial_0", 
"stop_trial_1", "stop_trial_10", "stop_trial_11", "stop_trial_12", 
"stop_trial_13", "stop_trial_14", "stop_trial_15", "stop_trial_16", 
"stop_trial_17", "stop_trial_18", "stop_trial_19", "stop_trial_2", 
"stop_trial_20", "stop_trial_21", "stop_trial_22", "stop_trial_23", 
"stop_trial_24", "stop_trial_25", "stop_trial_26", "stop_trial_27", 
"stop_trial_28", "stop_trial_29", "stop_trial_3", "stop_trial_30", 
"stop_trial_31", "stop_trial_32", "stop_trial_33", "stop_trial_34", 
"stop_trial_35", "stop_trial_36", "stop_trial_37", "stop_trial_38", 
"stop_trial_39", "stop_trial_4", "stop_trial_40", "stop_trial_41", 
"stop_trial_42", "stop_trial_5", "stop_trial_6", "stop_trial_7", 
"stop_trial_8", "stop_trial_9", "strat_1", "strat_2", "strat_4", 
"val_0", "val_1"), class = "factor"), PS = c(NA, 904L, 906L, 
838L, 805L, 789L, 797L, 876L, 924L, 928L, 964L, 957L, 935L, 861L, 
834L, 856L, 846L, 811L, 825L, 869L, 904L, 936L, 969L, 965L, 1016L, 
1018L, 1030L, 1015L, 999L, 987L, 1017L, 1064L, 1080L, 1061L, 
1075L, 1046L, 1005L, 1014L, 1023L, 1040L, 1051L, 1046L, 1010L, 
971L, 994L, 1071L, 1082L, 1120L, 1119L, 1044L, 1023L, 978L, 947L, 
925L, 900L, 940L, NA, 963L, NA, 995L, 1013L, 1046L, 1005L, 1013L, 
1043L, 1146L, 1205L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1306L, 
1334L, 1285L, 1297L, 1257L, 1206L, 1206L, 1256L, 1252L, 1189L, 
1254L, 1214L, 1203L, 1207L, 1263L, 1224L, 1235L, 1258L, 1210L, 
1186L, 1201L, 1271L, 1246L, 1274L, 1337L, 1325L, 1551L, 1733L, 
1812L, 1568L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1272L, 1218L, 
1227L, 1165L, 1145L, 1192L, 1199L, 1208L, 1248L, 1280L, 1224L, 
NA, NA, NA, NA, NA, NA, NA, NA, 1220L, NA, 1229L, 1250L, 1372L, 
1102L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1141L, 1163L, 1146L, 
1129L, 1190L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1182L, 1152L, 
1134L, 1179L, 1178L, 1267L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1272L, 1186L, 1164L, 1173L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1265L, 1191L, 1109L, 1150L, 1125L, 1090L, 1139L, 1205L, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 1277L, 1164L, 1122L, 1113L, 1115L, 
1121L, 1168L, NA, NA, NA, NA, NA, NA, NA, NA, 1235L, NA, 1207L, 
1164L, 1145L, 1177L, 1242L, 1224L, 1281L, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, 1234L, 1232L, 1204L, 1198L, 1108L, 1131L, 1220L, 
1228L, 1227L, 1231L, 1299L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1211L, 1266L, 1294L, 1292L, 1129L, 1182L, 1175L, 1211L, 1233L, 
1206L, 1185L, 1307L, 1209L, 1206L, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 1245L, 1264L, 1283L, 1246L, 1290L, 1344L, 1311L, 1267L, 
1201L, 1188L, 1164L, 1218L, 1188L, 1156L, 1144L, 1121L, 1145L, 
1176L, 1155L, 1103L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1173L, 
1223L, 1218L, 1170L, 1120L, 1084L, 1096L, 1092L, 985L, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 1043L, 1092L, 1090L, 1126L, 1099L, 
1125L, 1175L, 1099L, 1102L, 1188L, 1215L, 1225L, 1197L, 1268L, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 1292L, 1338L, 1322L, 1284L, 
1296L, 1273L, 1251L, 1216L, 1205L, 1200L, 1165L, 1097L, 1132L, 
1209L, 1243L, 1295L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1288L, 
1286L, 1243L, 1245L, 1215L, 1213L, 1215L, 1283L, 1280L, 1275L, 
1334L, 1301L, 1205L, 1215L, 1267L, 1245L, 1203L, 1071L, 1113L, 
1160L, 1211L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1243L, 1249L, 
1268L, 1266L, 1299L, 1363L, 1215L, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 938L, 831L, 929L, 999L, 1033L, 1090L, 1092L, 1094L, 1139L, 
1144L, 1225L, 1203L, 1199L, 1261L, 1221L, 1230L, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, 1291L, 1308L, 1270L, 1250L, 1276L, 1226L, 
1197L, 1201L, 1213L, 1195L, 1202L, 1201L, 1194L, 1192L, 1190L, 
1206L, 1244L, 1203L, 1228L, 1239L, 1218L, 1218L, 1217L, 1218L, 
1202L, 1224L, 1177L, 1134L, 1134L, 1152L, 1159L, 1162L, 1168L, 
1107L, 1175L, 1200L, 1173L, 1203L, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 1266L, 1278L, 1227L, 1188L, 1184L, 1178L, 1167L, 1194L, 
1131L, 1166L, 1203L, 1211L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1223L, 1226L, 1218L, 1208L, 1142L, 1105L, 1122L, 1156L, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 1118L, 1133L, 1150L, 1115L, 1070L, 
1078L, 1145L, 1156L, 1175L, 1172L, 1129L, 1134L, 1089L, 1144L, 
1171L, 1179L, 1195L, 1194L, 1231L, 1275L, 1250L, 1273L, 1268L, 
1221L, 1245L, 1211L, 1195L, 1197L, 1194L, 1140L, 1168L, 1220L, 
1197L, 1191L, 1240L, 1288L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1339L, 1327L, 1324L, 1320L, 1242L, 1231L, 1253L, 1255L, 1268L, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 1297L, 1303L, 1282L, 1252L, 
1200L, 1202L, 1191L, 1177L, 1220L, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 1221L, 1224L, 1203L, 1162L, 1175L, 1187L, 1184L, 1165L, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 1200L, 1225L, 1200L, 1205L, 
1219L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1269L, 1209L, 1161L, 
1171L, 1165L, 1140L, 1120L, 1127L, 1076L, 1081L, 1081L, 1114L, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 1181L, 1186L, 1189L, 1200L, 
1179L, 1186L, 1171L, 1134L, 1012L, 1004L, 1134L, 1090L, 1146L, 
1222L, 1309L, 1334L, 1354L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1240L, 1121L, 1101L, 1104L, 1142L, 1157L, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, 1197L, 1264L, 1217L, 1181L, 1173L, 1160L, 1147L, 
1174L, 1188L, 1183L, 1162L, 1188L, 1273L, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, 1303L, 1335L, 1346L, 1284L, 1227L, 1245L, 1295L, 
1291L, 1284L, 1125L, 1176L, 1214L, 1206L, 1216L, 1232L, 1234L, 
1268L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1326L, 1284L, 1265L, 
1237L, 1206L, 1212L, 1197L, 1181L, 1216L, 1222L, 1205L, 1148L, 
1163L, 1154L, 1138L, 1146L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1250L, 1229L, 1209L, 1199L, 1165L, 1191L, 1145L, 1130L, 1116L, 
NA, NA, NA, NA, NA, NA, NA, NA, 1101L, NA, 1113L, 1126L, 1138L, 
1160L, 1128L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1155L, 1132L, 
1122L, 1146L, 1145L, 1146L, 1171L, 1103L, 1170L, 1136L, 1177L, 
1108L, 1106L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1175L, 1192L, 
1129L, 1163L, 1187L, 1177L, 1162L, 1184L, 1129L, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, 1221L, 1113L, 1089L, 1099L, 1022L, 995L, 
947L, 1012L, 1065L, 1114L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1163L, 1094L, 1098L, 1139L, 1130L, 1117L, 1087L, 1084L, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 1137L, 1145L, 1130L, 1105L, 1123L, 
1112L, 1048L, 1055L, 1078L, 1147L, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 1207L, 1164L, 1169L, 1188L, 1189L, 1140L, 1099L, 1178L, 
NA, NA, NA, NA, NA, NA, NA, NA, 1208L, NA, 1258L, 1207L, 1158L, 
1140L, 1099L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1123L, 1083L, 
1043L, 1066L, 1082L, 1049L, 1040L, 1090L, 1112L, 1069L, 1079L, 
1061L, 1029L, 1032L, 1046L, 1170L, 1197L, 956L, 941L, 1076L, 
1136L, 1208L, 1213L, 1207L, 1186L, 1225L, 1222L, 1232L, 1169L, 
1102L, 1144L, 1178L, 1218L, 1211L, 1229L, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, 1255L, 1260L, 1236L, 1271L, 1312L, 1346L, 1272L, 
1171L, 1192L, 1235L, 1296L), Modulo = structure(c(1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 13L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 19L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 44L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 9L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 14L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 26L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 43L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 8L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 27L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 39L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 42L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
18L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 29L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 7L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 10L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 23L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 24L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 15L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 17L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 40L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 41L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 11L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
22L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 28L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 30L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 16L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 33L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 34L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
37L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 12L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 20L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 21L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 38L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 31L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 32L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 35L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 36L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 6L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 25L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L), .Label = c(" ", "26=12mod6", "36=16mod4", "36=32mod4", 
"40=33mod8", "40=36mod2", "42=12mod6", "46=34mod6", "47=44mod2", 
"50=20mod4", "55=20mod4", "57=15mod6", "58=52mod4", "59=57mod2", 
"63=33mod4", "64=24mod8", "65=35mod6", "65=51mod6", "66=61mod6", 
"68=26mod6", "71=21mod6", "71=31mod4", "74=53mod8", "77=53mod4", 
"77=69mod2", "78=75mod4", "79=67mod6", "80=40mod4", "85=65mod4", 
"86=50mod8", "89=32mod2", "89=35mod2", "89=49mod4", "90=50mod6", 
"93=13mod8", "94=43mod4", "94=61mod4", "96=54mod4", "96=82mod6", 
"97=75mod2", "98=76mod2", "99=85mod8", "99=91mod8", "99=93mod6"
), class = "factor")), class = "data.frame", row.names = c(NA, 
-997L))

【讨论】：

    【解决方案3】：

    @Brian 这里是我的数据集示例：

    1     MSG          start_consigne   NA          
2     EFI                       L  904          
3     EFI                       L  906          
4     EFI                       L  838          
5     EFI                       L  805          
6     EFI                       L  789          
7     EFI                       L  797          
8     EFI                       L  876          
9     EFI                       L  924          
10    EFI                       L  928          
11    EFI                       L  964          
12    EFI                       L  957          
13    EFI                       L  935          
14    EFI                       L  861          
15    EFI                       L  834          
16    EFI                       L  856          
17    EFI                       L  846          
18    EFI                       L  811          
19    EFI                       L  825          
20    EFI                       L  869          
21    EFI                       L  904          
22    EFI                       L  936          
23    EFI                       L  969          
24    EFI                       L  965          
25    EFI                       L 1016          
26    EFI                       L 1018          
27    EFI                       L 1030          
28    EFI                       L 1015          
29    EFI                       L  999          
30    EFI                       L  987          
31    EFI                       L 1017          
32    EFI                       L 1064          
33    EFI                       L 1080          
34    EFI                       L 1061          
35    EFI                       L 1075          
36    EFI                       L 1046          
37    EFI                       L 1005          
38    EFI                       L 1014          
39    EFI                       L 1023          
40    EFI                       L 1040          
41    EFI                       L 1051          
42    EFI                       L 1046          
43    EFI                       L 1010          
44    EFI                       L  971          
45    EFI                       L  994          
46    EFI                       L 1071          
47    EFI                       L 1082          
48    EFI                       L 1120          
49    EFI                       L 1119          
50    EFI                       L 1044          
51    EFI                       L 1023          
52    EFI                       L  978          
53    EFI                       L  947          
54    EFI                       L  925          
55    EFI                       L  900          
56    EFI                       L  940          
57    MSG           stop_consigne   NA          
58    EFI                       L  963          
59    MSG           start_trial_0   NA          
60    EFI                       L  995          
61    EFI                       L 1013          
62    EFI                       L 1046          
63    EFI                       L 1005          
64    EFI                       L 1013          
65    EFI                       L 1043          
66    EFI                       L 1146          
67    EFI                       L 1205          
68    MSG                 id_1129   NA          
69    MSG               36=32mod4   NA 36=32mod4
70    MSG                   val_1   NA          
71    MSG               correct_1   NA          
72    MSG                 RT_2241   NA          
73    MSG         difficulty_Very   NA          
74    MSG                 strat_4   NA          
75    MSG            stop_trial_0   NA          
76    MSG           start_trial_1   NA          
77    EFI                       L 1306          
78    EFI                       L 1334          
79    EFI                       L 1285          
80    EFI                       L 1297          
81    EFI                       L 1257          
82    EFI                       L 1206          
83    EFI                       L 1206          
84    EFI                       L 1256          
85    EFI                       L 1252          
86    EFI                       L 1189          
87    EFI                       L 1254          
88    EFI                       L 1214          
89    EFI                       L 1203          
90    EFI                       L 1207          
91    EFI                       L 1263          
92    EFI                       L 1224          
93    EFI                       L 1235          
94    EFI                       L 1258          
95    EFI                       L 1210          
96    EFI                       L 1186          
97    EFI                       L 1201          
98    EFI                       L 1271          
99    EFI                       L 1246          
100   EFI                       L 1274          
101   EFI                       L 1337          
102   EFI                       L 1325          
103   EFI                       L 1551          
104   EFI                       L 1733          
105   EFI                       L 1812          
106   EFI                       L 1568          
107   MSG                 id_4333   NA          
108   MSG               modulo_58   NA 58=52mod4
109   MSG                   val_0   NA          
110   MSG               correct_1   NA          
111   MSG                RT_14182   NA          
112   MSG         difficulty_Very   NA          
113   MSG                 strat_4   NA          
114   MSG            stop_trial_1   NA          
115   MSG           start_trial_2   NA          
116   EFI                       L 1272          
117   EFI                       L 1218          
118   EFI                       L 1227          
119   EFI                       L 1165          
120   EFI                       L 1145          
121   EFI                       L 1192          
122   EFI                       L 1199          
123   EFI                       L 1208          
124   EFI                       L 1248          
125   EFI                       L 1280          
126   EFI                       L 1224          
127   MSG                 id_6016   NA          
128   MSG               modulo_66   NA 66=61mod6
129   MSG                   val_0   NA          
130   MSG               correct_1   NA          
131   MSG                 RT_3727   NA          
132   MSG         difficulty_Very   NA          
133   MSG                 strat_1   NA          
134   MSG            stop_trial_2   NA          
135   EFI                       L 1220          
136   MSG           start_trial_3   NA          
137   EFI                       L 1229          
138   EFI                       L 1250          
139   EFI                       L 1372          
140   EFI                       L 1102          
141   MSG                id_15693   NA          
142   MSG               modulo_99   NA 99=93mod6
143   MSG                   val_1   NA          
144   MSG               correct_1   NA          
145   MSG                 RT_2624   NA          
146   MSG         difficulty_Very   NA          
147   MSG                 strat_1   NA          
148   MSG            stop_trial_3   NA          
149   MSG           start_trial_4   NA````

    【讨论】：

    • 这（和您的其他答案）看起来与您在原始问题中发布的示例非常不同。我是否正确假设这是您致电 read.csv(somefile, sep = ";") 后打印到您的控制台的内容？
    • 是的，没错，这就是 read.csv 与我的数据文件一起返回的内容。与sep = ";"
    【解决方案4】： 
    structure(list(Event = structure(c(2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("EFI", 
"MSG"), class = "factor"), Info = structure(c(127L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 172L, 51L, 128L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
11L, 1L, 220L, 3L, 95L, 7L, 218L, 173L, 129L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 35L, 61L, 219L, 3L, 86L, 7L, 218L, 174L, 140L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 41L, 66L, 219L, 
3L, 107L, 7L, 216L, 185L, 51L, 151L, 51L, 51L, 51L, 51L, 27L, 
83L, 220L, 3L, 98L, 7L, 216L, 196L, 162L, 51L, 51L, 51L, 51L, 
51L, 30L, 57L, 219L, 3L, 88L, 7L, 217L, 207L, 167L, 51L, 51L, 
51L, 51L, 51L, 51L, 36L, 62L, 220L, 3L, 93L, 7L, 217L, 211L, 
168L, 51L, 51L, 51L, 51L, 48L, 71L, 219L, 3L, 85L, 7L, 216L, 
212L, 169L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 26L, 83L, 
220L, 3L, 102L, 7L, 216L, 213L, 170L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 29L, 56L, 220L, 3L, 101L, 4L, 218L, 214L, 51L, 171L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 49L, 72L, 220L, 2L, 103L, 
4L, 216L, 215L, 130L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 20L, 80L, 219L, 3L, 116L, 4L, 218L, 175L, 131L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 25L, 83L, 219L, 3L, 125L, 4L, 216L, 176L, 132L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 32L, 52L, 219L, 3L, 126L, 4L, 218L, 
177L, 133L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 8L, 
53L, 220L, 3L, 97L, 4L, 218L, 178L, 134L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 40L, 65L, 219L, 
3L, 117L, 4L, 216L, 179L, 135L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 9L, 74L, 220L, 
3L, 121L, 4L, 216L, 180L, 136L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 28L, 55L, 220L, 3L, 84L, 6L, 218L, 181L, 137L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 31L, 58L, 219L, 3L, 112L, 6L, 218L, 
182L, 138L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 45L, 69L, 219L, 3L, 120L, 6L, 216L, 
183L, 139L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 46L, 70L, 220L, 2L, 90L, 6L, 216L, 184L, 141L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 37L, 63L, 
219L, 3L, 114L, 6L, 216L, 186L, 142L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 39L, 65L, 220L, 3L, 100L, 6L, 216L, 187L, 143L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 21L, 81L, 220L, 
2L, 89L, 6L, 217L, 188L, 144L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 22L, 82L, 220L, 3L, 106L, 6L, 217L, 189L, 145L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 33L, 59L, 219L, 
3L, 110L, 5L, 216L, 190L, 146L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 44L, 68L, 220L, 3L, 99L, 5L, 216L, 191L, 147L, 51L, 
51L, 51L, 51L, 51L, 50L, 73L, 220L, 3L, 91L, 5L, 218L, 192L, 
148L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 10L, 75L, 219L, 2L, 115L, 5L, 218L, 193L, 149L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 38L, 64L, 220L, 3L, 124L, 5L, 218L, 194L, 150L, 51L, 
51L, 51L, 51L, 51L, 51L, 14L, 76L, 220L, 3L, 94L, 5L, 216L, 195L, 
152L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 15L, 77L, 219L, 3L, 118L, 5L, 218L, 197L, 153L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 18L, 79L, 219L, 3L, 122L, 5L, 216L, 198L, 154L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 34L, 60L, 220L, 3L, 119L, 7L, 216L, 199L, 155L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 42L, 67L, 220L, 
3L, 108L, 7L, 218L, 200L, 51L, 156L, 51L, 51L, 51L, 51L, 51L, 
43L, 68L, 219L, 3L, 96L, 7L, 216L, 201L, 157L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 19L, 80L, 219L, 
3L, 123L, 7L, 218L, 202L, 158L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 12L, 76L, 219L, 3L, 111L, 7L, 217L, 203L, 159L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 13L, 76L, 220L, 
3L, 113L, 7L, 217L, 204L, 160L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 16L, 78L, 220L, 2L, 104L, 7L, 216L, 205L, 161L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 17L, 79L, 219L, 
3L, 109L, 7L, 216L, 206L, 163L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 23L, 54L, 219L, 3L, 105L, 7L, 216L, 208L, 51L, 164L, 
51L, 51L, 51L, 51L, 51L, 24L, 54L, 220L, 3L, 92L, 7L, 217L, 209L, 
165L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 51L, 47L, 70L, 
220L, 3L, 87L, 7L, 217L, 210L, 166L, 51L, 51L, 51L, 51L, 51L, 
51L, 51L, 51L, 51L, 51L, 51L), .Label = c("36=32mod4", "correct_0", 
"correct_1", "difficulty_Easy", "difficulty_Hard", "difficulty_Intermediate", 
"difficulty_Very", "id_1058", "id_10975", "id_11207", "id_1129", 
"id_12052", "id_12069", "id_12131", "id_12453", "id_13285", "id_13741", 
"id_13817", "id_14467", "id_14596", "id_14907", "id_15262", "id_1544", 
"id_1555", "id_15661", "id_15684", "id_15693", "id_1685", "id_2295", 
"id_2479", "id_2820", "id_313", "id_3645", "id_3985", "id_4333", 
"id_4541", "id_5249", "id_5426", "id_5684", "id_5756", "id_6016", 
"id_6326", "id_7019", "id_7064", "id_7885", "id_8660", "id_8728", 
"id_9028", "id_9263", "id_9419", "L", "modulo_26", "modulo_36", 
"modulo_40", "modulo_42", "modulo_46", "modulo_47", "modulo_50", 
"modulo_55", "modulo_57", "modulo_58", "modulo_59", "modulo_63", 
"modulo_64", "modulo_65", "modulo_66", "modulo_68", "modulo_71", 
"modulo_74", "modulo_77", "modulo_78", "modulo_79", "modulo_80", 
"modulo_85", "modulo_86", "modulo_89", "modulo_90", "modulo_93", 
"modulo_94", "modulo_96", "modulo_97", "modulo_98", "modulo_99", 
"RT_10590", "RT_1367", "RT_14182", "RT_15412", "RT_1550", "RT_17151", 
"RT_17302", "RT_1736", "RT_1891", "RT_2002", "RT_2227", "RT_2241", 
"RT_2432", "RT_2510", "RT_2624", "RT_2660", "RT_2840", "RT_2956", 
"RT_2984", "RT_3029", "RT_3154", "RT_3273", "RT_3283", "RT_3727", 
"RT_3900", "RT_4493", "RT_4544", "RT_4840", "RT_5095", "RT_5368", 
"RT_5583", "RT_5618", "RT_6009", "RT_6385", "RT_6423", "RT_6489", 
"RT_6689", "RT_7471", "RT_7669", "RT_7697", "RT_8156", "RT_8752", 
"RT_8784", "start_consigne", "start_trial_0", "start_trial_1", 
"start_trial_10", "start_trial_11", "start_trial_12", "start_trial_13", 
"start_trial_14", "start_trial_15", "start_trial_16", "start_trial_17", 
"start_trial_18", "start_trial_19", "start_trial_2", "start_trial_20", 
"start_trial_21", "start_trial_22", "start_trial_23", "start_trial_24", 
"start_trial_25", "start_trial_26", "start_trial_27", "start_trial_28", 
"start_trial_29", "start_trial_3", "start_trial_30", "start_trial_31", 
"start_trial_32", "start_trial_33", "start_trial_34", "start_trial_35", 
"start_trial_36", "start_trial_37", "start_trial_38", "start_trial_39", 
"start_trial_4", "start_trial_40", "start_trial_41", "start_trial_42", 
"start_trial_43", "start_trial_5", "start_trial_6", "start_trial_7", 
"start_trial_8", "start_trial_9", "stop_consigne", "stop_trial_0", 
"stop_trial_1", "stop_trial_10", "stop_trial_11", "stop_trial_12", 
"stop_trial_13", "stop_trial_14", "stop_trial_15", "stop_trial_16", 
"stop_trial_17", "stop_trial_18", "stop_trial_19", "stop_trial_2", 
"stop_trial_20", "stop_trial_21", "stop_trial_22", "stop_trial_23", 
"stop_trial_24", "stop_trial_25", "stop_trial_26", "stop_trial_27", 
"stop_trial_28", "stop_trial_29", "stop_trial_3", "stop_trial_30", 
"stop_trial_31", "stop_trial_32", "stop_trial_33", "stop_trial_34", 
"stop_trial_35", "stop_trial_36", "stop_trial_37", "stop_trial_38", 
"stop_trial_39", "stop_trial_4", "stop_trial_40", "stop_trial_41", 
"stop_trial_42", "stop_trial_5", "stop_trial_6", "stop_trial_7", 
"stop_trial_8", "stop_trial_9", "strat_1", "strat_2", "strat_4", 
"val_0", "val_1"), class = "factor"), PS = c(NA, 904L, 906L, 
838L, 805L, 789L, 797L, 876L, 924L, 928L, 964L, 957L, 935L, 861L, 
834L, 856L, 846L, 811L, 825L, 869L, 904L, 936L, 969L, 965L, 1016L, 
1018L, 1030L, 1015L, 999L, 987L, 1017L, 1064L, 1080L, 1061L, 
1075L, 1046L, 1005L, 1014L, 1023L, 1040L, 1051L, 1046L, 1010L, 
971L, 994L, 1071L, 1082L, 1120L, 1119L, 1044L, 1023L, 978L, 947L, 
925L, 900L, 940L, NA, 963L, NA, 995L, 1013L, 1046L, 1005L, 1013L, 
1043L, 1146L, 1205L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1306L, 
1334L, 1285L, 1297L, 1257L, 1206L, 1206L, 1256L, 1252L, 1189L, 
1254L, 1214L, 1203L, 1207L, 1263L, 1224L, 1235L, 1258L, 1210L, 
1186L, 1201L, 1271L, 1246L, 1274L, 1337L, 1325L, 1551L, 1733L, 
1812L, 1568L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1272L, 1218L, 
1227L, 1165L, 1145L, 1192L, 1199L, 1208L, 1248L, 1280L, 1224L, 
NA, NA, NA, NA, NA, NA, NA, NA, 1220L, NA, 1229L, 1250L, 1372L, 
1102L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1141L, 1163L, 1146L, 
1129L, 1190L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1182L, 1152L, 
1134L, 1179L, 1178L, 1267L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1272L, 1186L, 1164L, 1173L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1265L, 1191L, 1109L, 1150L, 1125L, 1090L, 1139L, 1205L, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 1277L, 1164L, 1122L, 1113L, 1115L, 
1121L, 1168L, NA, NA, NA, NA, NA, NA, NA, NA, 1235L, NA, 1207L, 
1164L, 1145L, 1177L, 1242L, 1224L, 1281L, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, 1234L, 1232L, 1204L, 1198L, 1108L, 1131L, 1220L, 
1228L, 1227L, 1231L, 1299L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1211L, 1266L, 1294L, 1292L, 1129L, 1182L, 1175L, 1211L, 1233L, 
1206L, 1185L, 1307L, 1209L, 1206L, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 1245L, 1264L, 1283L, 1246L, 1290L, 1344L, 1311L, 1267L, 
1201L, 1188L, 1164L, 1218L, 1188L, 1156L, 1144L, 1121L, 1145L, 
1176L, 1155L, 1103L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1173L, 
1223L, 1218L, 1170L, 1120L, 1084L, 1096L, 1092L, 985L, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 1043L, 1092L, 1090L, 1126L, 1099L, 
1125L, 1175L, 1099L, 1102L, 1188L, 1215L, 1225L, 1197L, 1268L, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 1292L, 1338L, 1322L, 1284L, 
1296L, 1273L, 1251L, 1216L, 1205L, 1200L, 1165L, 1097L, 1132L, 
1209L, 1243L, 1295L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1288L, 
1286L, 1243L, 1245L, 1215L, 1213L, 1215L, 1283L, 1280L, 1275L, 
1334L, 1301L, 1205L, 1215L, 1267L, 1245L, 1203L, 1071L, 1113L, 
1160L, 1211L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1243L, 1249L, 
1268L, 1266L, 1299L, 1363L, 1215L, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 938L, 831L, 929L, 999L, 1033L, 1090L, 1092L, 1094L, 1139L, 
1144L, 1225L, 1203L, 1199L, 1261L, 1221L, 1230L, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, 1291L, 1308L, 1270L, 1250L, 1276L, 1226L, 
1197L, 1201L, 1213L, 1195L, 1202L, 1201L, 1194L, 1192L, 1190L, 
1206L, 1244L, 1203L, 1228L, 1239L, 1218L, 1218L, 1217L, 1218L, 
1202L, 1224L, 1177L, 1134L, 1134L, 1152L, 1159L, 1162L, 1168L, 
1107L, 1175L, 1200L, 1173L, 1203L, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 1266L, 1278L, 1227L, 1188L, 1184L, 1178L, 1167L, 1194L, 
1131L, 1166L, 1203L, 1211L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1223L, 1226L, 1218L, 1208L, 1142L, 1105L, 1122L, 1156L, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 1118L, 1133L, 1150L, 1115L, 1070L, 
1078L, 1145L, 1156L, 1175L, 1172L, 1129L, 1134L, 1089L, 1144L, 
1171L, 1179L, 1195L, 1194L, 1231L, 1275L, 1250L, 1273L, 1268L, 
1221L, 1245L, 1211L, 1195L, 1197L, 1194L, 1140L, 1168L, 1220L, 
1197L, 1191L, 1240L, 1288L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1339L, 1327L, 1324L, 1320L, 1242L, 1231L, 1253L, 1255L, 1268L, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 1297L, 1303L, 1282L, 1252L, 
1200L, 1202L, 1191L, 1177L, 1220L, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 1221L, 1224L, 1203L, 1162L, 1175L, 1187L, 1184L, 1165L, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 1200L, 1225L, 1200L, 1205L, 
1219L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1269L, 1209L, 1161L, 
1171L, 1165L, 1140L, 1120L, 1127L, 1076L, 1081L, 1081L, 1114L, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 1181L, 1186L, 1189L, 1200L, 
1179L, 1186L, 1171L, 1134L, 1012L, 1004L, 1134L, 1090L, 1146L, 
1222L, 1309L, 1334L, 1354L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1240L, 1121L, 1101L, 1104L, 1142L, 1157L, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, 1197L, 1264L, 1217L, 1181L, 1173L, 1160L, 1147L, 
1174L, 1188L, 1183L, 1162L, 1188L, 1273L, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, 1303L, 1335L, 1346L, 1284L, 1227L, 1245L, 1295L, 
1291L, 1284L, 1125L, 1176L, 1214L, 1206L, 1216L, 1232L, 1234L, 
1268L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1326L, 1284L, 1265L, 
1237L, 1206L, 1212L, 1197L, 1181L, 1216L, 1222L, 1205L, 1148L, 
1163L, 1154L, 1138L, 1146L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1250L, 1229L, 1209L, 1199L, 1165L, 1191L, 1145L, 1130L, 1116L, 
NA, NA, NA, NA, NA, NA, NA, NA, 1101L, NA, 1113L, 1126L, 1138L, 
1160L, 1128L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1155L, 1132L, 
1122L, 1146L, 1145L, 1146L, 1171L, 1103L, 1170L, 1136L, 1177L, 
1108L, 1106L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1175L, 1192L, 
1129L, 1163L, 1187L, 1177L, 1162L, 1184L, 1129L, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, 1221L, 1113L, 1089L, 1099L, 1022L, 995L, 
947L, 1012L, 1065L, 1114L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
1163L, 1094L, 1098L, 1139L, 1130L, 1117L, 1087L, 1084L, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 1137L, 1145L, 1130L, 1105L, 1123L, 
1112L, 1048L, 1055L, 1078L, 1147L, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, 1207L, 1164L, 1169L, 1188L, 1189L, 1140L, 1099L, 1178L, 
NA, NA, NA, NA, NA, NA, NA, NA, 1208L, NA, 1258L, 1207L, 1158L, 
1140L, 1099L, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1123L, 1083L, 
1043L, 1066L, 1082L, 1049L, 1040L, 1090L, 1112L, 1069L, 1079L, 
1061L, 1029L, 1032L, 1046L, 1170L, 1197L, 956L, 941L, 1076L, 
1136L, 1208L, 1213L, 1207L, 1186L, 1225L, 1222L, 1232L, 1169L, 
1102L, 1144L, 1178L, 1218L, 1211L, 1229L, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, 1255L, 1260L, 1236L, 1271L, 1312L, 1346L, 1272L, 
1171L, 1192L, 1235L, 1296L), Modulo = structure(c(1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 13L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 19L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 44L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 9L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 14L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 26L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 43L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 8L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 27L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 39L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 42L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
18L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 29L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 7L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 10L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 23L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 24L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 15L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 17L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 40L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 41L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 11L, 
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1L, 1L), .Label = c(" ", "26=12mod6", "36=16mod4", "36=32mod4", 
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"97=75mod2", "98=76mod2", "99=85mod8", "99=91mod8", "99=93mod6"
), class = "factor")), class = "data.frame", row.names = c(NA, 
-997L))````

    【讨论】：

      【解决方案5】：

      Brian 为您的问题提供了完美的解决方案。我的方法略有不同，但结果相似。为了完整和/或多样性，我将发布它。

      我的思路是这样的：

      您首先读取文件并将其传递到数据框df

      library(dplyr) # load the libraries we are going to be using first
library(tidyr)
library(zoo)

df <- read.csv('~/Desktop/test', sep = ';', header = T) # I named your .txt file test here and put it on my Desktop
>df
      Event           Info Pupil.size
1   Message  Start_trial_0         NA
2  Fixation              L       1020
3  Fixation              L       1200
4  Fixation              L        980
5  Fixation              L        990
6  Fixation              L       1003
7   Message        Trial_0         NA
8   Message          ACC_0         NA
9   Message         RT_850         NA
10  Message   Stop_trial_0         NA
11  Message  Start_trial_1         NA
12 Fixation              L       1023
13 Fixation              L       1020
14 Fixation              L        997
15 Fixation              L       1123
16  Message        Trial_1         NA
17  Message          ACC_1         NA
18  Message         RT_920         NA
19  Message   Stop_trial_1         NA
20  Message  Strat_trial_2         NA

      然后我们创建一个名为 trial 的新列，其中对于 Info 上包含试用信息的每一行（在本例中为 Start 和 Stop），我们传递对应的试用版，否则填写NA，如：

      选项1（原始文件数据）：

      df <- df %>% mutate(trial=ifelse(Event=='Message'&grepl('trial', df$Info), gsub('.*_(trial_\\d)$', '\\1', df$Info), NA))
      Event           Info Pupil.size   trial
1   Message  Start_trial_0         NA trial_0
2  Fixation              L       1020    <NA>
3  Fixation              L       1200    <NA>
4  Fixation              L        980    <NA>
5  Fixation              L        990    <NA>
6  Fixation              L       1003    <NA>
7   Message        Trial_0         NA    <NA>
8   Message          ACC_0         NA    <NA>
9   Message         RT_850         NA    <NA>
10  Message   Stop_trial_0         NA trial_0
11  Message  Start_trial_1         NA trial_1
12 Fixation              L       1023    <NA>
13 Fixation              L       1020    <NA>
14 Fixation              L        997    <NA>
15 Fixation              L       1123    <NA>
16  Message        Trial_1         NA    <NA>
17  Message          ACC_1         NA    <NA>
18  Message         RT_920         NA    <NA>
19  Message   Stop_trial_1         NA trial_1
20  Message  Strat_trial_2         NA trial_2

      选项 2（新输入文件 - 请记住，这会保留您可能想要删除的中间试验数据）：

      df <- df %>% mutate(trial=ifelse(Event=='MSG'&grepl('trial', df$Info), gsub('.*_(trial_\\d)$', '\\1', df$Info), 
                                 ifelse(Event=='MSG'&grepl('consigne', df$Info), gsub('.*_(consigne)$', '\\1', df$Info),
                                        NA)))

      我正在填写NA，因为在下一步中，我们想用最早的非NA 值替换NA（因此在开始-停止之间的每一行上分配正确的试验）。这可以使用 na.locf 包中的 zoo 来完成。

      df$trial <- na.locf(df$trial)
> df
      Event           Info Pupil.size   trial
1   Message  Start_trial_0         NA trial_0
2  Fixation              L       1020 trial_0
3  Fixation              L       1200 trial_0
4  Fixation              L        980 trial_0
5  Fixation              L        990 trial_0
6  Fixation              L       1003 trial_0
7   Message        Trial_0         NA trial_0
8   Message          ACC_0         NA trial_0
9   Message         RT_850         NA trial_0
10  Message   Stop_trial_0         NA trial_0
11  Message  Start_trial_1         NA trial_1
12 Fixation              L       1023 trial_1
13 Fixation              L       1020 trial_1
14 Fixation              L        997 trial_1
15 Fixation              L       1123 trial_1
16  Message        Trial_1         NA trial_1
17  Message          ACC_1         NA trial_1
18  Message         RT_920         NA trial_1
19  Message   Stop_trial_1         NA trial_1
20  Message  Strat_trial_2         NA trial_2

      我们现在可以去掉 Info 列中带有 Trial “元数据”的行。

      df <- df %>% filter(!grepl('[T,t]rial', df$Info))

      接下来，我们需要每次试验的最终“元数据”信息，即 ACC 和 RT 信息。这些信息都在 Info 列中，所以我们必须以某种方式将它们拉出来。为此，我们首先创建两个名为 ACC 和 RT 的新列。

      df <- df %>% mutate(ACC=ifelse(grepl('ACC', df$Info), as.character(df$Info), NA),
              RT=ifelse(grepl('RT', df$Info), as.character(df$Info), NA))

> df
      Event    Info Pupil.size   trial    ACC      RT
1  Fixation       L       1020 trial_0   <NA>    <NA>
2  Fixation       L       1200 trial_0   <NA>    <NA>
3  Fixation       L        980 trial_0   <NA>    <NA>
4  Fixation       L        990 trial_0   <NA>    <NA>
5  Fixation       L       1003 trial_0   <NA>    <NA>
6   Message   ACC_0         NA trial_0  ACC_0    <NA>
7   Message  RT_850         NA trial_0   <NA>  RT_850
8  Fixation       L       1023 trial_1   <NA>    <NA>
9  Fixation       L       1020 trial_1   <NA>    <NA>
10 Fixation       L        997 trial_1   <NA>    <NA>
11 Fixation       L       1123 trial_1   <NA>    <NA>
12  Message   ACC_1         NA trial_1  ACC_1    <NA>
13  Message  RT_920         NA trial_1   <NA>  RT_920

      我们还需要确定每个试验对应的 ACC 和 RT 属性。为此，我们通过 dplyr 创建了两个新的小型数据帧，它们为我们提供了所有 ACC 和 RT 信息。

      infoACC <- df %>% group_by(trial, Info) %>% summarize() %>% filter(grepl('ACC', Info))

> infoACC
# A tibble: 2 x 2
# Groups:   trial [2]
  trial   Info    
  <chr>   <fct>   
1 trial_0 " ACC_0"
2 trial_1 " ACC_1"

infoRT <- df %>% group_by(trial, Info) %>% summarize() %>% filter(grepl('RT', Info))

> infoRT
# A tibble: 2 x 2
# Groups:   trial [2]
  trial   Info     
  <chr>   <fct>    
1 trial_0 " RT_850"
2 trial_1 " RT_920"

      然后只需加入我们的 df 和两个新数据帧以获取 ACC 和 RT 信息，删除额外的列和剩余的行（消息行）

      df <- left_join(left_join(df, infoACC, by='trial'), infoRT, by='trial') %>% select(-ACC, -RT) %>% filter(!Event=='Message')

      并通过修复列名来结束这一点。

      colnames(df) <- c('Event', 'Info', 'Pupil.size', 'Trial', 'ACC', 'RT')
> df
     Event Info Pupil.size   Trial    ACC      RT
1 Fixation    L       1020 trial_0  ACC_0  RT_850
2 Fixation    L       1200 trial_0  ACC_0  RT_850
3 Fixation    L        980 trial_0  ACC_0  RT_850
4 Fixation    L        990 trial_0  ACC_0  RT_850
5 Fixation    L       1003 trial_0  ACC_0  RT_850
6 Fixation    L       1023 trial_1  ACC_1  RT_920
7 Fixation    L       1020 trial_1  ACC_1  RT_920
8 Fixation    L        997 trial_1  ACC_1  RT_920
9 Fixation    L       1123 trial_1  ACC_1  RT_920

      您现在可以将其保存为新的 .csv 或将其保存为数据框，以便在 R 中进行进一步操作。

      我承认它的解决方案有点复杂，但我想提供我的思考过程，希望向您展示在 R 中解决问题的方法有很多，并且您可以逐步解决问题。

      希望对你有帮助

      【讨论】：

      • 嗨史蒂夫，非常感谢您的回复。这也是一个很好的解决方案。它开始很好，但是当用 na.locf 替换 NA 时，它给我一个错误；替换有939行，数据有997。我想我找到了问题。在我的数据中，我有一些试验间瞳孔大小测量。因此，每次试验之间的 NA 可能是问题所在。我不知道如何解决这个问题。
      • 我明白你的意思。这些行是否有任何显着特征？喜欢消息;中期审判？或者我们可以用来先修复这些行然后运行 ​​na.locf 的东西？
      • 不幸的是，试用版和试用版之间没有区别。它们只是看起来一样...您认为可以排除它们吗？就像他们在停止之后和开始之前一样？
      • 是的，我们可以做到。您可以添加包含该部分的 dput() 输出吗？
      • dput() 是为了让您能够阅读或了解它的数据框吗？它是给我一个文件还是我可以给你的东西？我会这样做的！
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