与previous question相关是否有任何方法可以将名称元素列表转换为数据表，其中NA值实际按照它们在列表中出现的顺序显示在数据表中？
例如：列表
testlist <- list("Blue", "405", "Truck", "400", "Car", "White", "500", "Truck")
testnames <- c("Color", "HP", "Type", "HP", "Type", "Color", "HP", "Type")
names(testlist) <- testnames
$Color
[1] "Blue"
$HP
[1] "405"
$Type
[1] "Truck"
$HP
[1] "400"
$Type
[1] "Car"
$Color
[1] "White"
$HP
[1] "500"
$Type
[1] "Truck"
可以使用以下方法更改为数据表：
dcast(setDT(melt(testlist))[, N:=1:.N, L1], N~L1, value.var='value')
但输出是这样的：
N Color HP Type
1 1 Blue 405 Truck
2 2 White 400 Car
3 3 <NA> 500 Truck
我想要的时候：
N Color HP Type
1 1 Blue 405 Truck
2 2 <NA> 400 Car
3 3 White 500 Truck
有没有人建议如何解决这个问题？我很感激帮助。
答案 0 :(得分：9)
一种方法是预先分配一个具有正确行数和正确数量，名称和列类型的表，然后通过索引分配原始列表所覆盖的单元格。
cns <- c('Color','HP','Type');
lcis <- match(names(testlist),cns);
lris <- c(1L,cumsum(diff(lcis)<=0L)+1L);
df <- as.data.frame(testlist[match(1:length(cns),lcis)],stringsAsFactors=F)[0,];
df[max(lris),] <- NA;
df;
## Color HP Type
## 1 <NA> <NA> <NA>
## 2 <NA> <NA> <NA>
## 3 <NA> <NA> <NA>
for (ci in 1:length(cns)) { m <- lcis==ci; df[lris[m],ci] <- do.call(c,testlist[m]); };
df;
## Color HP Type
## 1 Blue 405 Truck
## 2 <NA> 400 Car
## 3 White 500 Truck
在我的解决方案中，我小心地分别处理每一列，这提供了潜在的好处，如果输出表中的不同列（对应于输入列表中不同的组件子集）具有不同的数据类型，那么这些数据类型将保留在决赛桌上。这就是我为索引分配选择for循环的原因。这当然不是你的确切输入列表所必需的，它只有字符类型，但我认为无论如何这都是一个有价值的目标。
cns输出表中的列名。lcis每个输入列表组件的列索引将在输出表中。这是通过简单地将输入列表组件的名称与cns匹配来计算的。lris每个输入列表组件在输出表中将具有的行索引。这个变量的计算有点令人感兴趣并且是解决方案的核心。由于输入列表中的列表示不完整（IOW输入列表中可能存在“缺少列”），但是您认为输入列表组件是按照它们在输出表中的行方式排序，我们可以'使用常规索引（例如将每三个组件作为一行），我们也不能使用任何单个列名作为每行的标记，因为任何行中都可能缺少任何列。根据我的想法，唯一正确的方法是确定何时在输入列表中的较高索引（或等索引）列之后立即出现较低索引（或等索引，实际）列，并将其作为换行符。因此，我们可以使用diff(lcis)<=0L来获取表示行中断的逻辑向量，使用cumsum()并添加1来获取行索引，我们还必须手动前置1来完成向量。ci输出表中的列索引。在for循环期间用于迭代每个输出列。m为ci循环中的每个for计算。表示哪个输入列表组件属于当前列ci的逻辑向量。用于索引lris（以提取要分配的行索引）和输入列表本身（以提取要分配的实际值）。我从dropbox抓取了您的真实数据并将其存储为testlist。以下是我的调查结果。
首先，我按照它们出现的顺序检查了唯一的组件名称，将它们视为cns：
## first reasonable assumption about cns
cns <- unique(names(testlist));
cns;
## [1] "Status" "Make" "Model"
## [4] "Kilometres" "Stock Number" "Engine"
## [7] "Number of Hours" "Front axle" "Rear axle"
## [10] "Suspension" "Wheelbase" "Transmission"
## [13] "Price" "Style/Trim" "Brakes"
## [16] "Mfg Exterior Colour" "Tires" "Engine (HP)"
## [19] "Exterior Colour"
我们可以从中计算出新的暂定lcis：
## examine lcis for ordering
lcis <- match(names(testlist),cns);
lcis;
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 1 2 3 4 5 6 7 8 9 10 11 12
## [26] 13 1 2 3 4 5 6 7 8 9 10 11 12 13 1 2 3 4 5 6 7 8 9 10 11
## [51] 12 13 1 2 3 4 5 6 7 8 9 10 11 12 13 1 2 3 4 5 6 7 8 9 10
## [76] 11 12 13 1 2 3 4 5 6 7 8 9 10 11 12 13 1 2 3 4 5 6 7 8 9
## [101] 10 11 12 13 1 2 3 4 5 6 7 8 9 10 11 12 13 1 2 3 4 5 6 7 8
## [126] 9 10 11 12 13 1 2 3 4 5 6 7 8 9 10 11 12 13 1 2 3 4 5 6 7
## [151] 8 9 10 11 12 13 1 2 3 4 14 13 1 2 3 4 5 6 7 8 9 10 11 12 13
## [176] 1 2 3 4 5 15 16 6 8 9 10 17 11 18 12 19 13 1 2 3 4 5 15 16 6
## [201] 8 9 10 17 11 18 12 19 13
仔细观察上面的矢量，我们可以看到它始于1:13的许多常规重复。事实上，只有在矢量结束时它才会变得不规则，我们看到14然后是13,16然后是6,10-11-12与17-18-19交错等等。
但是我们可以在这里做出的一个重要观察是，向量似乎由1和13描述的组构成。换句话说，对于所有似乎有规律性的范围（即使也有一些不规则性），它们似乎从1开始，以13结束。这一观察结果与您对车辆数据中间无序的评论一致。我们称之为1/13假设。
我们可以通过分割这个1/13边界来获得更清晰的群体视图：
## recognizing 1/13 consistency, split on it to see how each (possible) row looks under this assumption
split(lcis,cumsum(lcis==1L));
## $`1`
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13
##
## $`2`
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13
##
## $`3`
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13
##
## $`4`
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13
##
## $`5`
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13
##
## $`6`
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13
##
## $`7`
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13
##
## $`8`
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13
##
## $`9`
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13
##
## $`10`
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13
##
## $`11`
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13
##
## $`12`
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13
##
## $`13`
## [1] 1 2 3 4 14 13
##
## $`14`
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13
##
## $`15`
## [1] 1 2 3 4 5 15 16 6 8 9 10 17 11 18 12 19 13
##
## $`16`
## [1] 1 2 3 4 5 15 16 6 8 9 10 17 11 18 12 19 13
现在，如果您在上述群组中仔细查看非常，您可以发现可以重新排序cns，以便所有群组都按升序排序。它们不是连续的，但我为原始问题设计的解决方案不需要连续性;所有必要的是升序。
例如，我们需要在13之前订购第14列，我们需要在6,8,9等之前订购第15和第16列：
## recognizing the possibility of reordering to achieve perfect within-row ascending order, reorder cns to cns2
cns2 <- cns[c(1,2,3,4,14,5,15,16,6,7,8,9,10,17,11,18,12,19,13)];
cns2;
## [1] "Status" "Make" "Model"
## [4] "Kilometres" "Style/Trim" "Stock Number"
## [7] "Brakes" "Mfg Exterior Colour" "Engine"
## [10] "Number of Hours" "Front axle" "Rear axle"
## [13] "Suspension" "Tires" "Wheelbase"
## [16] "Engine (HP)" "Transmission" "Exterior Colour"
## [19] "Price"
现在我们可以重新计算lcis，我现在称之为lcis2，并展示新的群组订单：
## calculate lcis2 from cns2, and prove that we've successfully ordered each individual row under the 1/13 (now 1/19) break assumption
lcis2 <- match(names(testlist),cns2);
split(lcis2,cumsum(lcis2==1L));
## $`1`
## [1] 1 2 3 4 6 9 10 11 12 13 15 17 19
##
## $`2`
## [1] 1 2 3 4 6 9 10 11 12 13 15 17 19
##
## $`3`
## [1] 1 2 3 4 6 9 10 11 12 13 15 17 19
##
## $`4`
## [1] 1 2 3 4 6 9 10 11 12 13 15 17 19
##
## $`5`
## [1] 1 2 3 4 6 9 10 11 12 13 15 17 19
##
## $`6`
## [1] 1 2 3 4 6 9 10 11 12 13 15 17 19
##
## $`7`
## [1] 1 2 3 4 6 9 10 11 12 13 15 17 19
##
## $`8`
## [1] 1 2 3 4 6 9 10 11 12 13 15 17 19
##
## $`9`
## [1] 1 2 3 4 6 9 10 11 12 13 15 17 19
##
## $`10`
## [1] 1 2 3 4 6 9 10 11 12 13 15 17 19
##
## $`11`
## [1] 1 2 3 4 6 9 10 11 12 13 15 17 19
##
## $`12`
## [1] 1 2 3 4 6 9 10 11 12 13 15 17 19
##
## $`13`
## [1] 1 2 3 4 5 19
##
## $`14`
## [1] 1 2 3 4 6 9 10 11 12 13 15 17 19
##
## $`15`
## [1] 1 2 3 4 6 7 8 9 11 12 13 14 15 16 17 18 19
##
## $`16`
## [1] 1 2 3 4 6 7 8 9 11 12 13 14 15 16 17 18 19
最后，我们可以运行整个解决方案，现在小心使用带有2个后缀的变量名称：
## now we can apply the preallocate/fill-in solution using cns2 and lcis2
## will use lris2 and df2 just to be consistent
lris2 <- c(1L,cumsum(diff(lcis2)<=0L)+1L);
df2 <- as.data.frame(testlist[match(1:length(cns2),lcis2)],stringsAsFactors=F)[0,];
df2[max(lris2),] <- NA;
df2;
## Status Make Model Kilometres Style.Trim Stock.Number Brakes Mfg.Exterior.Colour Engine Number.of.Hours Front.axle Rear.axle Suspension Tires Wheelbase Engine..HP. Transmission Exterior.Colour Price
## 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 4 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 8 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 9 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 10 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 11 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 12 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 13 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 14 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 15 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 16 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
for (ci in 1:length(cns2)) { m <- lcis2==ci; df2[lris2[m],ci] <- do.call(c,testlist[m]); };
df2;
## Status Make Model Kilometres Style.Trim Stock.Number Brakes Mfg.Exterior.Colour Engine Number.of.Hours Front.axle Rear.axle Suspension Tires Wheelbase Engine..HP. Transmission Exterior.Colour Price
## 1 New Peterbilt 367 Tri-Drive c/w 58'' Sleeper 3,360 km <NA> 12949 <NA> <NA> Cummins ISX15 (550 hp) 44 Dana Spicer D2000 (20,000lb) Dana T69-170 (wide track) t Peterbilt Air-Trak (66,000lb) <NA> 267'' <NA> RTLO18918B Fuller (18 speed) <NA> $217,770
## 2 New Kenworth T800 T/A Tractor 82,230 km <NA> 10720 <NA> <NA> Cummins ISX15 (550hp) 2,712 Dana Spicer D2000 (20,000 lb) Dana D46-170HPW (46,000 lb) ta Neway ADZ252 (52,000lb) Air <NA> 244'' <NA> Fuller 18 spd main AT1202 2 sp <NA> $199,500
## 3 New Kenworth T800 Tandem Tractor w/ 38'' Sleeper 98,521 km <NA> 10722 <NA> <NA> Cummins ISX15 (550hp) 2,790 Dana Spicer D2000 (20,000 lb) Dana D46-170HPW (46,000 lb) ta Neway ADZ252 (52,000lb) Air <NA> 244'' <NA> Fuller 18 spd main AT1202 2 sp <NA> $199,500
## 4 Used Kenworth W900 Tri-Drive Sleeper Truck Tractor 170,422 km <NA> 13227 <NA> <NA> Cummins ISX15 (600 hp) 4,925 Meritor FL941 (20,000 lb) Meritor RZ-166 (69,000 lb) Kenworth AG690 (69,000lb) Air <NA> 259'' <NA> 18 speed main & 4 speed au <NA> $197,750
## 5 New Peterbilt 367 T/A Wet-Kit Tractor c/w 58'' Sleeper 3,367 km <NA> 12180 <NA> <NA> Cummins ISX15 (550hp) 38 Dana Spicer E14621 (14,600 lb Dana D46-170 (46,000lb) ta Peterbilt Air-Trak (46,000lb) <NA> 244'' <NA> RTLO18918B Fuller (18 speed) <NA> $193,300
## 6 New Peterbilt 367 T/A Wet-Kit Tractor c/w 58'' Sleeper 3,421 km <NA> 12179 <NA> <NA> Cummins ISX15 (550hp) 46 Dana Spicer E14621 (14,600 lb Dana D46-170 (46,000lb) ta Peterbilt Air-Trak (46,000lb) <NA> 244'' <NA> RTLO18918B Fuller (18 speed) <NA> $193,300
## 7 New Peterbilt 367 T/A Wet-Kit Tractor c/w 58'' Sleeper 2,157 km <NA> 12181 <NA> <NA> Cummins ISX15 (550hp) 64 Dana Spicer E14621 (14,600 lb Dana D46-170 (46,000lb) ta Peterbilt Air-Trak (46,000lb) <NA> 244'' <NA> RTLO18918B Fuller (18 speed) <NA> $189,880
## 8 New Peterbilt 367 T/A Wet-Kit Tractor c/w 58'' Sleeper 3,444 km <NA> 12954 <NA> <NA> Cummins ISX15 (550hp) 45 Dana Spicer E14621 (14,600 lb Dana D46-170 (46,000lb) ta Peterbilt Air-Trak (46,000lb) <NA> 244'' <NA> RTLO18918B Fuller (18 speed) <NA> $189,880
## 9 New Peterbilt 367 T/A Wet-Kit Tractor c/w 58'' Sleeper 3,427 km <NA> 12955 <NA> <NA> Cummins ISX15 (550hp) 43 Dana Spicer E14621 (14,600 lb Dana D46-170 (46,000lb) ta Peterbilt Air-Trak (46,000lb) <NA> 244'' <NA> RTLO18918B Fuller (18 speed) <NA> $189,880
## 10 New Peterbilt 367 T/A Wet-Kit Tractor c/w 58'' Sleeper 3,982 km <NA> 12182 <NA> <NA> Cummins ISX15 (550hp) 78 Dana Spicer E14621 (14,600 lb Dana D46-170 (46,000lb) ta Peterbilt Air-Trak (46,000lb) <NA> 244'' <NA> RTLO18918B Fuller (18 speed) <NA> $189,880
## 11 New Peterbilt 367 T/A Wet-Kit Tractor c/w 58'' Sleeper 23,293 km <NA> 12953 <NA> <NA> Cummins ISX15 (550hp) 394 Dana Spicer E14621 (14,600 lb Dana D46-170 (46,000lb) ta Peterbilt Air-Trak (46,000lb) <NA> 244'' <NA> RTLO18918B Fuller (18 speed) <NA> $189,880
## 12 New Peterbilt 367 T/A Wet-Kit Tractor c/w 58'' Sleeper 27,215 km <NA> 12509 <NA> <NA> Cummins ISX15 (550hp) 458 Dana Spicer E14621 (14,600 lb Dana D46-170 (46,000lb) ta Peterbilt Air-Trak (46,000lb) <NA> 244'' <NA> RTLO18918B Fuller (18 speed) <NA> $186,600
## 13 Used Volvo VNL64T 780-730 72,000 km VNL64T780-730 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> $185,000
## 14 New Peterbilt 367 T/A Wet Kit Tractor c/w 58'' Sleeper 60,657 km <NA> 10838 <NA> <NA> Cummins ISX15 (550hp) 1,822 Dana Spicer E14621 (14,600 lb Dana D46-170HP (46,000lb) tand Peterbilt Air-Trak (46,000lb) <NA> 244'' <NA> RTLO18918B Fuller (18 speed) <NA> $171,800
## 15 Used International ProStar +122 36,236 km <NA> 463555 Air White Cummins ISX <NA> Arvin Meritor 13200 lb Arvin Meritor 40000 lb Int'l IROS 11R22.5 228 in 450 Eaton Fuller D/O (18 spd) White $168,750
## 16 Used International ProStar +122 33,000 km <NA> 463543 Air White Cummins ISX <NA> Arvin Meritor 13200 lb Arvin Meritor 46000 lb Int'l IROS 11R/22.5 236 in 475 Eaton Fuller D/O (18 spd) White $165,900
现在，我意识到可能最好完全从“升序假设”（让我们称之为）转移到1/13假设，我们可以通过更改lris来做到这一点。计算。这将使我们无需根据我们从cns电话中收到的订单重新排序unique()。
下面我演示这个，回过头来看非常有用的未填充的变量名，稍后会看到：
## change lris calculation to depend directly on 1/13 assumption; don't bother reordering
cns <- unique(names(testlist));
lcis <- match(names(testlist),cns);
lris <- c(1L,cumsum(lcis[-1]==1L)+1L);
df <- as.data.frame(testlist[match(1:length(cns),lcis)],stringsAsFactors=F)[0,];
df[max(lris),] <- NA;
for (ci in 1:length(cns)) { m <- lcis==ci; df[lris[m],ci] <- do.call(c,testlist[m]); };
df;
## Status Make Model Kilometres Stock.Number Engine Number.of.Hours Front.axle Rear.axle Suspension Wheelbase Transmission Price Style.Trim Brakes Mfg.Exterior.Colour Tires Engine..HP. Exterior.Colour
## 1 New Peterbilt 367 Tri-Drive c/w 58'' Sleeper 3,360 km 12949 Cummins ISX15 (550 hp) 44 Dana Spicer D2000 (20,000lb) Dana T69-170 (wide track) t Peterbilt Air-Trak (66,000lb) 267'' RTLO18918B Fuller (18 speed) $217,770 <NA> <NA> <NA> <NA> <NA> <NA>
## 2 New Kenworth T800 T/A Tractor 82,230 km 10720 Cummins ISX15 (550hp) 2,712 Dana Spicer D2000 (20,000 lb) Dana D46-170HPW (46,000 lb) ta Neway ADZ252 (52,000lb) Air 244'' Fuller 18 spd main AT1202 2 sp $199,500 <NA> <NA> <NA> <NA> <NA> <NA>
## 3 New Kenworth T800 Tandem Tractor w/ 38'' Sleeper 98,521 km 10722 Cummins ISX15 (550hp) 2,790 Dana Spicer D2000 (20,000 lb) Dana D46-170HPW (46,000 lb) ta Neway ADZ252 (52,000lb) Air 244'' Fuller 18 spd main AT1202 2 sp $199,500 <NA> <NA> <NA> <NA> <NA> <NA>
## 4 Used Kenworth W900 Tri-Drive Sleeper Truck Tractor 170,422 km 13227 Cummins ISX15 (600 hp) 4,925 Meritor FL941 (20,000 lb) Meritor RZ-166 (69,000 lb) Kenworth AG690 (69,000lb) Air 259'' 18 speed main & 4 speed au $197,750 <NA> <NA> <NA> <NA> <NA> <NA>
## 5 New Peterbilt 367 T/A Wet-Kit Tractor c/w 58'' Sleeper 3,367 km 12180 Cummins ISX15 (550hp) 38 Dana Spicer E14621 (14,600 lb Dana D46-170 (46,000lb) ta Peterbilt Air-Trak (46,000lb) 244'' RTLO18918B Fuller (18 speed) $193,300 <NA> <NA> <NA> <NA> <NA> <NA>
## 6 New Peterbilt 367 T/A Wet-Kit Tractor c/w 58'' Sleeper 3,421 km 12179 Cummins ISX15 (550hp) 46 Dana Spicer E14621 (14,600 lb Dana D46-170 (46,000lb) ta Peterbilt Air-Trak (46,000lb) 244'' RTLO18918B Fuller (18 speed) $193,300 <NA> <NA> <NA> <NA> <NA> <NA>
## 7 New Peterbilt 367 T/A Wet-Kit Tractor c/w 58'' Sleeper 2,157 km 12181 Cummins ISX15 (550hp) 64 Dana Spicer E14621 (14,600 lb Dana D46-170 (46,000lb) ta Peterbilt Air-Trak (46,000lb) 244'' RTLO18918B Fuller (18 speed) $189,880 <NA> <NA> <NA> <NA> <NA> <NA>
## 8 New Peterbilt 367 T/A Wet-Kit Tractor c/w 58'' Sleeper 3,444 km 12954 Cummins ISX15 (550hp) 45 Dana Spicer E14621 (14,600 lb Dana D46-170 (46,000lb) ta Peterbilt Air-Trak (46,000lb) 244'' RTLO18918B Fuller (18 speed) $189,880 <NA> <NA> <NA> <NA> <NA> <NA>
## 9 New Peterbilt 367 T/A Wet-Kit Tractor c/w 58'' Sleeper 3,427 km 12955 Cummins ISX15 (550hp) 43 Dana Spicer E14621 (14,600 lb Dana D46-170 (46,000lb) ta Peterbilt Air-Trak (46,000lb) 244'' RTLO18918B Fuller (18 speed) $189,880 <NA> <NA> <NA> <NA> <NA> <NA>
## 10 New Peterbilt 367 T/A Wet-Kit Tractor c/w 58'' Sleeper 3,982 km 12182 Cummins ISX15 (550hp) 78 Dana Spicer E14621 (14,600 lb Dana D46-170 (46,000lb) ta Peterbilt Air-Trak (46,000lb) 244'' RTLO18918B Fuller (18 speed) $189,880 <NA> <NA> <NA> <NA> <NA> <NA>
## 11 New Peterbilt 367 T/A Wet-Kit Tractor c/w 58'' Sleeper 23,293 km 12953 Cummins ISX15 (550hp) 394 Dana Spicer E14621 (14,600 lb Dana D46-170 (46,000lb) ta Peterbilt Air-Trak (46,000lb) 244'' RTLO18918B Fuller (18 speed) $189,880 <NA> <NA> <NA> <NA> <NA> <NA>
## 12 New Peterbilt 367 T/A Wet-Kit Tractor c/w 58'' Sleeper 27,215 km 12509 Cummins ISX15 (550hp) 458 Dana Spicer E14621 (14,600 lb Dana D46-170 (46,000lb) ta Peterbilt Air-Trak (46,000lb) 244'' RTLO18918B Fuller (18 speed) $186,600 <NA> <NA> <NA> <NA> <NA> <NA>
## 13 Used Volvo VNL64T 780-730 72,000 km <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> $185,000 VNL64T780-730 <NA> <NA> <NA> <NA> <NA>
## 14 New Peterbilt 367 T/A Wet Kit Tractor c/w 58'' Sleeper 60,657 km 10838 Cummins ISX15 (550hp) 1,822 Dana Spicer E14621 (14,600 lb Dana D46-170HP (46,000lb) tand Peterbilt Air-Trak (46,000lb) 244'' RTLO18918B Fuller (18 speed) $171,800 <NA> <NA> <NA> <NA> <NA> <NA>
## 15 Used International ProStar +122 36,236 km 463555 Cummins ISX <NA> Arvin Meritor 13200 lb Arvin Meritor 40000 lb Int'l IROS 228 in Eaton Fuller D/O (18 spd) $168,750 <NA> Air White 11R22.5 450 White
## 16 Used International ProStar +122 33,000 km 463543 Cummins ISX <NA> Arvin Meritor 13200 lb Arvin Meritor 46000 lb Int'l IROS 236 in Eaton Fuller D/O (18 spd) $165,900 <NA> Air White 11R/22.5 475 White
如您所见，df的列顺序与df2不同，但我们可以证明数据与以下内容相同：
## prove df2 and df are identical, ignoring the column order difference
identical(df,df2[names(df)]);
## [1] TRUE
答案 1 :(得分：5)
我能提出的最佳解决方案
library(data.table)
listnames <- names(testlist)
# "Color" "HP" "Type" "HP" "Type" "Color" "HP" "Type"
unames <- unique(listnames)
# "Color" "HP" "Type"
a <- setNames(1:length(unames), unames)
# Color HP Type
# 1 2 3
d <- unname(a[listnames])
# [1] 1 2 3 2 3 1 2 3
splitted_list <- split(testlist, cumsum(shift(d, fill=0)>d))
# results in testlist splitted by increasing sequences in d
# (1,2,3), (2,3), (1, 2, 3)
# You can impose a different splitting condition here, for instance,
# if each entry begins with 1, then cumsum(d==1) is adequate
# and the last step is pretty much self explanatory
rbindlist(lapply(splitted_list, data.frame), fill=TRUE)
# Color HP Type
# 1: Blue 405 Truck
# 2: NA 400 Car
# 3: White 500 Truck
希望它解决您的问题。
从分裂条件为cumsum(d==1)的Dropbox应用于您的测试数据时，结果为
structure(list(Status = structure(c(1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L), .Label = c("New", "Used"
), class = "factor"), Make = structure(c(1L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 4L, 4L), .Label = c("Peterbilt",
"Kenworth", "Volvo", "International"), class = "factor"), Model = structure(c(1L,
2L, 3L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 7L, 8L, 8L), .Label = c("367 Tri-Drive c/w 58'' Sleeper",
"T800 T/A Tractor", "T800 Tandem Tractor w/ 38'' Sleeper", "W900 Tri-Drive Sleeper Truck Tractor",
"367 T/A Wet-Kit Tractor c/w 58'' Sleeper", "VNL64T 780-730",
"367 T/A Wet Kit Tractor c/w 58'' Sleeper", "ProStar +122"
), class = "factor"), Kilometres = structure(1:16, .Label = c("3,360 km",
"82,230 km", "98,521 km", "170,422 km", "3,367 km", "3,421 km",
"2,157 km", "3,444 km", "3,427 km", "3,982 km", "23,293 km",
"27,215 km", "72,000 km", "60,657 km", "36,236 km", "33,000 km"
), class = "factor"), Stock.Number = structure(c(1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, NA, 13L, 14L, 15L), .Label = c("12949",
"10720", "10722", "13227", "12180", "12179", "12181", "12954",
"12955", "12182", "12953", "12509", "10838", "463555", "463543"
), class = "factor"), Engine = structure(c(1L, 2L, 2L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, NA, 4L, 5L, 5L), .Label = c("Cummins ISX15 (550 hp)",
"Cummins ISX15 (550hp)", "Cummins ISX15 (600 hp)", "Cummins ISX15 (550hp)",
"Cummins ISX"), class = "factor"), Number.of.Hours = structure(c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, NA, 13L, NA, NA
), .Label = c("44", "2,712", "2,790", "4,925", "38", "46", "64",
"45", "43", "78", "394", "458", "1,822"), class = "factor"),
Front.axle = structure(c(1L, 2L, 2L, 3L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, NA, 4L, 5L, 5L), .Label = c("Dana Spicer D2000 (20,000lb)",
"Dana Spicer D2000 (20,000 lb)", "Meritor FL941 (20,000 lb)",
"Dana Spicer E14621 (14,600 lb", "Arvin Meritor 13200 lb"
), class = "factor"), Rear.axle = structure(c(1L, 2L, 2L,
3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, NA, 5L, 6L, 7L), .Label = c("Dana T69-170 (wide track) t",
"Dana D46-170HPW (46,000 lb) ta", "Meritor RZ-166 (69,000 lb)",
"Dana D46-170 (46,000lb) ta", "Dana D46-170HP (46,000lb) tand",
"Arvin Meritor 40000 lb", "Arvin Meritor 46000 lb"), class = "factor"),
Suspension = structure(c(1L, 2L, 2L, 3L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, NA, 4L, 5L, 5L), .Label = c("Peterbilt Air-Trak (66,000lb)",
"Neway ADZ252 (52,000lb) Air", "Kenworth AG690 (69,000lb) Air",
"Peterbilt Air-Trak (46,000lb)", "Int'l IROS"), class = "factor"),
Wheelbase = structure(c(1L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, NA, 2L, 4L, 5L), .Label = c("267''", "244''",
"259''", "228 in", "236 in"), class = "factor"), Transmission = structure(c(1L,
2L, 2L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, 1L, 4L, 4L
), .Label = c("RTLO18918B Fuller (18 speed)", "Fuller 18 spd main AT1202 2 sp",
"18 speed main & 4 speed au", "Eaton Fuller D/O (18 spd)"
), class = "factor"), Price = structure(c(1L, 2L, 2L, 3L,
4L, 4L, 5L, 5L, 5L, 5L, 5L, 6L, 7L, 8L, 9L, 10L), .Label = c("$217,770",
"$199,500", "$197,750", "$193,300", "$189,880", "$186,600",
"$185,000", "$171,800", "$168,750", "$165,900"), class = "factor"),
Style.Trim = structure(c(NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 1L, NA, NA, NA), .Label = "VNL64T780-730", class = "factor"),
Brakes = structure(c(NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 1L, 1L), .Label = "Air", class = "factor"),
Mfg.Exterior.Colour = structure(c(NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, 1L, 1L), .Label = "White", class = "factor"),
Tires = structure(c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 1L, 2L), .Label = c("11R22.5", "11R/22.5"
), class = "factor"), Engine..HP. = structure(c(NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1L, 2L), .Label = c("450",
"475"), class = "factor"), Exterior.Colour = structure(c(NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1L, 1L
), .Label = "White", class = "factor")), .Names = c("Status",
"Make", "Model", "Kilometres", "Stock.Number", "Engine", "Number.of.Hours",
"Front.axle", "Rear.axle", "Suspension", "Wheelbase", "Transmission",
"Price", "Style.Trim", "Brakes", "Mfg.Exterior.Colour", "Tires",
"Engine..HP.", "Exterior.Colour"), row.names = c(NA, -16L), class = "data.frame")
答案 2 :(得分：3)
可能不是最好的解决方案，因为它使用了while循环。但是，使用tidyr或您喜欢的其他重塑包。
testlist <- c("Blue", "405", "Truck", "400", "Car", "White", "500", "Truck")
testnames <- c("Color", "HP", "Type", "HP", "Type", "Color", "HP", "Type")
df <- data.frame(names = testnames, attributes = testlist, stringsAsFactors = FALSE)
# need to count number of vehicles inside data frame
# initialise while loop counters
df_index = 1
vehicle_index = vector(mode = "integer", length = nrow(df))
vehicle_count = 1
# now loop through the data frame to find attributes
# which belong to vehicle 1, 2, 3, etc...
while(df_index <= nrow(df)){
if (sum(c("Color", "HP", "Type") == df$names[df_index:(df_index+2)]) == 3) {
vehicle_index[df_index:(df_index+2)] <- vehicle_count
df_index = df_index + 3
vehicle_count = vehicle_count + 1
} else if (sum(c("Color", "HP", "Type") %in% df$names[df_index:(df_index+1)]) == 2) {
vehicle_index[df_index:(df_index+1)] <- vehicle_count
df_index = df_index + 2
vehicle_count = vehicle_count + 1
} else {
vehicle_index[df_index:(df_index)] <- vehicle_count
df_index = df_index + 1
vehicle_count = vehicle_count + 1
}
}
# finally, label the vehicle attributes with the vehicle number,
# and spread the data.
df_final <- data.frame(df, vehicle_index = vehicle_index)
tidyr::spread(df_final, key = "names", value = "attributes")