我正在处理一个相当大的数据集（2000列11行）。其中一个子集是我在下面的内容
Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10
1 2509.60840 650.44520 1208.00000 1795.00000 2889.000000 2158.00000 1827.25070 1482.000000 1858.00000 2544.39450
2 58.03994 -23.13326 -15.49636 12.09075 86.300341 34.46581 13.94546 -4.533958 15.89606 60.50394
3 94.15235 48.95360 80.27238 120.64317 62.791467 309.90168 175.94835 175.427179 83.94264 173.50097
4 245.69264 55.18302 137.75501 200.89969 -26.608255 161.46379 237.20321 84.304826 178.47248 97.40814
5 48.63110 70.49905 111.77759 181.52456 -107.426908 278.38505 116.95109 151.338578 218.93919 57.89194
6 264.90391 71.18622 129.02855 116.35842 -49.956152 147.17100 147.19528 229.756718 98.92684 193.43460
7 208.02697 75.31583 70.26002 136.39284 4.383722 150.74978 156.59314 162.983479 87.34028 358.01097
8 168.77588 71.57783 156.26653 132.90093 46.189138 254.28341 131.49356 211.540117 69.87448 312.34010
9 212.42247 53.04903 165.36475 49.42609 -14.050163 75.35783 160.37957 150.106290 96.19735 170.49562
10 175.34079 56.51675 148.31084 150.04976 32.373020 120.79866 147.48743 126.595648 84.46121 142.00257
11 249.20497 60.53568 139.56612 145.09104 6.308908 127.05724 141.34019 134.092717 103.45419 294.44998
12 67.80644 46.36427 101.25284 128.60477 -33.661878 361.66227 81.30154 88.232663 138.95064 83.37984
如何从该数据集中创建瀑布线图？看起来像这样的https://andrewgelman.com/2012/08/26/graphs-showing-regression-uncertainty-the-code/
请注意，第一行{2509.60840 650.44520 1208.00000 1795.00000 2889.000000 2158.00000 1827.25070 1482.000000 1858.00000 2544.39450}中的值为我的x值，其余为与每个x值相对应的y值。例如。
{58.03994, 94.15235, 245.69264, 48.63110,264.90391.....67.80644}是我与y相关的x=2509.60840值，依此类推。
答案 0 :(得分：1)
从here获得vwReg代码
将数据框更改为可行的
library(tidyverse)
structure(list(Col1 = c(2509.6084, 58.03994, 94.15235, 245.69264,
48.6311, 264.90391, 208.02697, 168.77588, 212.42247, 175.34079,
249.20497, 67.80644), Col2 = c(650.4452, -23.13326, 48.9536,
55.18302, 70.49905, 71.18622, 75.31583, 71.57783, 53.04903, 56.51675,
60.53568, 46.36427), Col3 = c(1208, -15.49636, 80.27238, 137.75501,
111.77759, 129.02855, 70.26002, 156.26653, 165.36475, 148.31084,
139.56612, 101.25284), Col4 = c(1795, 12.09075, 120.64317, 200.89969,
181.52456, 116.35842, 136.39284, 132.90093, 49.42609, 150.04976,
145.09104, 128.60477), Col5 = c(2889, 86.300341, 62.791467, -26.608255,
-107.426908, -49.956152, 4.383722, 46.189138, -14.050163, 32.37302,
6.308908, -33.661878), Col6 = c(2158, 34.46581, 309.90168, 161.46379,
278.38505, 147.171, 150.74978, 254.28341, 75.35783, 120.79866,
127.05724, 361.66227), Col7 = c(1827.2507, 13.94546, 175.94835,
237.20321, 116.95109, 147.19528, 156.59314, 131.49356, 160.37957,
147.48743, 141.34019, 81.30154), Col8 = c(1482, -4.533958, 175.427179,
84.304826, 151.338578, 229.756718, 162.983479, 211.540117, 150.10629,
126.595648, 134.092717, 88.232663), Col9 = c(1858, 15.89606,
83.94264, 178.47248, 218.93919, 98.92684, 87.34028, 69.87448,
96.19735, 84.46121, 103.45419, 138.95064), Col10 = c(2544.3945,
60.50394, 173.50097, 97.40814, 57.89194, 193.4346, 358.01097,
312.3401, 170.49562, 142.00257, 294.44998, 83.37984)), class = "data.frame", row.names = c(NA,
-12L)) -> df
t(df) %>%
data.frame() %>%
gather(variable, y, -X1) -> new_df
创造你的身材
vwReg(y ~ X1, new_df)