你熟悉SQL吗？ 这样您就可以避免其他信息对不同键具有不同索引的问题。 您可以为 JMP 表构建查询以将其转换为以下格式：
然后创建汇总表和拆分表并将它们连接在一起
概括：
分裂：
拆分更新为摘要：
Names Default To Here(1);
dt = Current Data Table();
dt_query = Query(
Table(dt, "INFO"),
"\[
select t1.assay, t1."Useful info 2" as "Control Lot number",
t1."Useful info 4" as "Control result", t1."Value for other info", t2."Value for other info"
from INFO t1
join INFO t2 on "Result for " || t1."Other info" = t2."Other info" and t1.assay = t2.assay
]\"
);
dt_summary = dt_query << Summary(
Group(:Assay, :Control Lot number, :Control result),
Freq("None"),
Weight("None"),
Link to original data table(0)
);
dt_split = dt_query << Split(
Split By(:Value for other info),
Split(:Value for other info 2),
Group(:Assay),
Remaining Columns(Drop All),
Sort by Column Property
);
dt_summary << Update(With(dt_split), Match Columns(:Assay = :Assay));
//Close(dt_summary, No save);
编辑：
我必须将原始数据列其他信息“有用信息 X”值重新编码为“有用信息 X”（小写）才能使查询工作。 也可以在SQL查询中完成
我会分几个部分来做
- 通过摘要可以相当容易地获得测定、质控批号和质控结果
- 随着其他信息索引的变化，休息会变得更加困难（如果它们始终相同，这应该很可能是相当简单的重新编码和拆分）
- 根据分组（仅检测？检测+对照批号？），我将循环数据表并在使用关联数组时添加值
代码非常混乱（部分原因是无法使用 For Each 和我很懒）：
Names Default To Here(1);
dt = New Table("Untitled",
Add Rows(22),
New Column("Assay", Numeric, "Continuous", Format("Best", 12), Set Values([1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3])),
New Column("Useful info 1",
Character(18),
"Nominal",
Set Values(
{"Control Lot number", "Control Lot number", "Control Lot number", "Control Lot number", "Control Lot number", "Control Lot number",
"Control Lot number", "Control Lot number", "Control Lot number", "Control Lot number", "Control Lot number", "Control Lot number",
"Control Lot number", "Control Lot number", "Control Lot number", "Control Lot number", "Control Lot number", "Control Lot number",
"Control Lot number", "Control Lot number", "Control Lot number", "Control Lot number"}
)
),
New Column("Useful info 2",
Character(16),
"Nominal",
Set Values(
{"abcdefg", "abcdefg", "abcdefg", "abcdefg", "abcdefg", "abcdefg", "abcdefg", "abcdefg", "abcdefg", "abcdefg", "abcdefg", "abcdefg",
"abcdefg", "abcdefg", "abcdefg", "abcdefg", "abcdefg", "abcdefg", "abcdefg", "abcdefg", "abcdefg", "abcdefg"}
)
),
New Column("Useful info 3",
Character(16),
"Nominal",
Set Values(
{"Control result", "Control result", "Control result", "Control result", "Control result", "Control result", "Control result",
"Control result", "Control result", "Control result", "Control result", "Control result", "Control result", "Control result",
"Control result", "Control result", "Control result", "Control result", "Control result", "Control result", "Control result",
"Control result"}
)
),
New Column("Useful info 4",
Numeric,
"Continuous",
Format("Best", 12),
Set Values([10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10])
),
New Column("Other info",
Character(24),
"Nominal",
Set Values(
{"Useful info 5", "Useful info 6", "Result for useful info 5", "Result for useful info 6", "Useful info 5", "Useful info 6",
"Useful info 7", "Useful info 8", "Result for useful info 5", "Result for useful info 6", "Result for useful info 7",
"Result for useful info 8", "Useful info 5", "Useful info 6", "Useful info 7", "Useful info 8", "Useful info 9",
"Result for useful info 5", "Result for useful info 6", "Result for useful info 7", "Result for useful info 8",
"Result for useful info 9"}
)
),
New Column("Value for other info",
Character(16),
"Nominal",
Set Values(
{"Method ID", "Method Version", "Method x", "1", "Method ID", "Method Version", "Sample result", "Equipment Number", "Method x", "1",
"12", "123456", "Method ID", "Method Version", "Sample result", "Equipment Type", "Equipment Number", "Method x", "1", "12", "Reader",
"123456"}
)
)
);
dt_result = dt << Summary(
Group(:Assay, :Useful info 2, :Useful info 4),
Freq("None"),
Weight("None"),
Link to original data table(0)
);
a_m = dt[0, "Assay"];
aa = Associative Array(a_m);
// will use slower for loop due to usage of JMP14 instead of For Each
aa_keys = aa << get keys;
For(i = 1, i <= N Items(aa_keys), i++,
// initialize associative array to aa
aa[aa_keys[i]] = Associative Array();
//Get rows for current assay
cur_rows = Loc(a_m, aa_keys[i]);
//
aa_temp = Associative Array(dt[cur_rows, "Other info"], dt[cur_rows, "Value for other info"]);
aa_temp_keys = aa_temp << get keys;
For(k = i, k <= N Items(aa_temp_keys), k++,
// if starts with Useful info -> this will be new key to aa[aa_keys]
If(Starts With(aa_temp_keys[k], "Useful info"),
aa[aa_keys[i]][aa_temp[aa_temp_keys[k]]] = aa_temp["Result for " || Lowercase(aa_temp_keys[k])]; // get value
);
);
);
//then loop over the AA to set values to dt_result. create new columns as you go
aa_keys = aa << get keys;
For(i = 1, i <= N Items(aa_keys), i++,
aa_new_cols = aa[aa_keys[i]];
cur_cols = Associative Array(dt_result << get column names("String"));
aa_new_cols << Remove(cur_cols);
new_cols = aa_new_cols << get keys;
For(k = 1, k <= N Items(new_cols), k++,
dt_result << New Column(new_cols[k], Character, Nominal);
);
dt_result[i, aa[aa_keys[i]] << get keys] = aa[aa_keys[i]] << get values;
);
