我有两个data.frames,其中一个包含一式三份完成的多个实验的特定顺序(DF1设计表); 另一个包含这些实验的结果(一式三份,DF2结果表).第一个数据帧具有随机的实验顺序,结果表具有不同的顺序.
DF1的前六列包含实验的因素,例如温度,试剂的当量等......结果表DF2也具有相同的六列以及包含实验结果的其他列,例如产量,各种试剂的转换等......
表格的不同之处在于行数.结果表比设计表少三行.
我如何计算这两个表,以便将结果附加到设计中,以便设计表中的实验参数与实验表中的相应结果相匹配.
DF1
T1 A1 B1
T2 A1 B1
T1 A2 B1
T2 A2 B1
T1 A1 B2
T2 A1 B2
T1 A2 B2
T2 A2 B2
但一式三份.
DF2
T1 A2 B2 1
T1 A2 B1 3
T2 A2 B1 3
T1 A1 B1 1
T2 A1 B2 2
T2 A2 B2 2
T2 A1 B1 2
再次一式三份,注意到少了一行.请注意,结果列比显示的列多.
关于所有这些工作的重点:我正在研究是否可以将包RcmdrPlugin.DoE应用于某些实际数据.
至于我试过的......好吧,我想过使用sapply,cbind和ifelse与逻辑函数
sapply(
DF3 <- ifelse( DF1[,1] == DF2[,1] | DF1[,2] == DF2[,2] | DF2[,3] == DF2[,3],
cbind(DF1, DF2[,3]), NA)
)
我在这段代码中遇到了NA的问题.但在我到达NA之前,我发现我有一个参数'FUN'缺少错误.
我想我要么偏离标准,要么非常接近答案,但两者中的哪一个.有人能指出我正确的方向吗？
编辑...我拥有的七行数据的样本,我将标题更改为A,B,C和D,这两个数据都是data.frames共有的.
run.no run.no.std.rp Block.ccd A B C D
C0.17 1 C0.17 0 400 147.5 5 2.675
C0.7 2 C0.7 0 450 120.0 2 4.000
C0.6 3 C0.6 0 350 175.0 2 4.000
C0.3 4 C0.3 0 450 120.0 8 4.000
C0.4 5 C0.4 0 350 120.0 8 4.000
C0.16 6 C0.16 0 350 120.0 2 1.350
C0.15 7 C0.15 0 450 120.0 2 1.350
其他data.frame包含标题A,B,C和D以及具有良率,转换和其他结果的列.我需要第一个data.frame完全如所示,yield等标记在最后.
的data.table包(其允许对于x [Y]的语法),该作业非常容易.假设df1并且df2是您的data.frames:
require(data.table)
dt1 <- data.table(df1, key=c("V1","V2","V3"))
dt2 <- data.table(df2, key=c("V1","V2","V3"))
dt2[dt1]
# V1 V2 V3 V4
# 1: T1 A1 B1 1
# 2: T1 A1 B2 NA
# 3: T1 A2 B1 3
# 4: T1 A2 B2 1
# 5: T2 A1 B1 2
# 6: T2 A1 B2 2
# 7: T2 A2 B1 3
# 8: T2 A2 B2 2
给你想要的结果.
编辑:我已经使用了您编辑的数据,它似乎工作.
df1 <- structure(list(V1 = structure(c(1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L),
.Label = c("T1", "T2"), class = "factor"),
V2 = structure(c(1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L),
.Label = c("A1", "A2"), class = "factor"),
V3 = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L),
.Label = c("B1", "B2"), class = "factor")),
.Names = c("V1", "V2", "V3"),
class = "data.frame", row.names = c(NA, -8L))
df2 <- structure(list(V1 = structure(c(1L, 1L, 2L, 1L, 2L, 2L, 2L),
.Label = c("T1", "T2"), class = "factor"),
V2 = structure(c(2L, 2L, 2L, 1L, 1L, 2L, 1L),
.Label = c("A1", "A2"), class = "factor"),
V3 = structure(c(2L, 1L, 1L, 1L, 2L, 2L, 1L),
.Label = c("B1", "B2"), class = "factor"),
run.no = 1:7,
run.no.std.rp = structure(c(3L, 7L, 6L, 4L, 5L, 2L, 1L),
.Label = c("C0.15", "C0.16", "C0.17", "C0.3", "C0.4", "C0.6", "C0.7"),
class = "factor"),
Block.ccd = c(0L, 0L, 0L, 0L, 0L, 0L, 0L),
A = c(400L, 450L, 350L, 450L, 350L, 350L, 450L),
B = c(147.5, 120, 175, 120, 120, 120, 120),
C = c(5L, 2L, 2L, 8L, 8L, 2L, 2L),
D = c(2.675, 4, 4, 4, 4, 1.35, 1.35)),
.Names = c("V1", "V2", "V3", "run.no", "run.no.std.rp",
"Block.ccd", "A", "B", "C", "D"),
row.names = c("C0.17", "C0.7", "C0.6", "C0.3", "C0.4",
"C0.16", "C0.15"), class = "data.frame")
require(data.table)
dt1 <- data.table(df1, key=c("V1", "V2", "V3"))
dt2 <- data.table(df2, key=c("V1", "V2", "V3"))
dt2[dt1]
# V1 V2 V3 run.no run.no.std.rp Block.ccd A B C D
# 1: T1 A1 B1 4 C0.3 0 450 120.0 8 4.000
# 2: T1 A1 B2 NA NA NA NA NA NA NA
# 3: T1 A2 B1 2 C0.7 0 450 120.0 2 4.000
# 4: T1 A2 B2 1 C0.17 0 400 147.5 5 2.675
# 5: T2 A1 B1 7 C0.15 0 450 120.0 2 1.350
# 6: T2 A1 B2 5 C0.4 0 350 120.0 8 4.000
# 7: T2 A2 B1 3 C0.6 0 350 175.0 2 4.000
# 8: T2 A2 B2 6 C0.16 0 350 120.0 2 1.350