我正在尝试学习简化我的代码并将多个data.frames
(>2)同时合并到单个数据集中。首先,我想计算四个 PCA 列(Morph_PC1
、Morph_PC2
、...)中每个列的"站点"mean
、sd
和n
(每个站点的"个人"数量)。其次,将结果合并到单个数据帧中。下面是我尝试此任务的示例数据和代码。
我意识到可能有一种方法可以生成不需要合并的单个数据集,这会很棒,但我也想知道如何使包中的merge_all
命令reshape
工作。
示例数据:
WW_Data <- structure(list(Individual_ID = c("WW_00A_05", "WW_00A_03", "WW_00A_02",
"WW_00A_01", "WW_00A_04", "WW_00A_06", "WW_00A_08", "WW_00A_09",
"WW_00A_07", "WW_00A_10", "WW_09AB_14", "WW_09AB_09", "WW_09AB_13",
"WW_10AD_01", "WW_10AD_09", "WW_10AD_04", "WW_10AD_02", "WW_10AD_03",
"WW_10AD_07", "WW_10AD_08"), Site_Name = c("Alnön", "Alnön",
"Alnön", "Alnön", "Alnön", "Alnön", "Alnön", "Alnön", "Alnön",
"Alnön", "Anjan", "Anjan", "Anjan", "Anjan", "Anjan", "Anjan",
"Anjan", "Anjan", "Anjan", "Anjan"), Morph_PC1 = c(-2.08424433316496,
-1.85413711191957, -1.67227075271696, -1.0486265729884, -0.809415702756541,
-2.81781338129716, -2.08471369525797, -0.183840575363918, -0.753930407169699,
0.0719252507535882, 1.02353521593315, 1.34441686821234, 0.755249445355964,
-0.564426004755035, 0.720689649641627, -0.243471506156601, -0.245437522679261,
-0.69936850894502, 0.9160796809062, 2.2881261039382), Morph_PC2 = c(1.28499189140338,
-0.349487815669147, 0.0148183164519594, -1.55929148726881, -0.681590397005219,
1.21595114750227, 0.116028310510466, 0.187613229042593, -0.923592436104444,
-1.50956083294446, 1.44864057855388, 1.46254159976068, 1.20375736157205,
0.174071006609975, -0.722049893415186, 1.03516327411773, 0.808851776990861,
-0.928263134752596, -0.175511637463994, -0.389421342417043),
Morph_PC3 = c(-0.445087364125436, -0.704903876393893, 0.161983939922481,
1.14604411022773, 0.701508422965674, -0.78133408496171, -0.306619974141955,
1.05643337302175, 0.163868647932456, -0.673344807228353,
-0.337986608605208, -1.01911125040091, 0.258004835638601,
-0.648040419259003, -0.196770002944659, 0.614010430132367,
0.755886614924319, -0.0631407344114064, -1.28178468134549,
0.226362214551239), Morph_PC4 = c(0.0476276463048772, 0.342957387676778,
-0.117383887482525, 0.289881853573214, 0.649579005842321,
0.600433718752986, 0.295294947111845, -0.293754065807853,
-0.43805381119461, 0.520363554131325, -0.393329204345947,
-1.05629143416274, -0.370922397397109, 0.115121369773473,
0.91445926597504, 0.280048079793911, -0.802245210297552,
0.00368405602889952, -0.251898295768711, -0.607995193037228
)), .Names = c("Individual_ID", "Site_Name", "Morph_PC1",
"Morph_PC2", "Morph_PC3", "Morph_PC4"), row.names = c(36L, 37L,
38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 137L, 138L, 139L, 140L,
141L, 142L, 143L, 144L, 145L, 146L), class = "data.frame")
代码:
## Calculate statistics for each site ##
WW_PC1_Mean <- subset(melt(tapply(WW_Data$Morph_PC1,list(WW_Data$Site_Name),mean)), value != FALSE)
WW_PC1_SD <- subset(melt(tapply(WW_Data$Morph_PC1,list(WW_Data$Site_Name),sd)), value != FALSE)
WW_PC2_Mean <- subset(melt(tapply(WW_Data$Morph_PC2,list(WW_Data$Site_Name),mean)), value != FALSE)
WW_Site_SD <- subset(melt(tapply(WW_Data$Morph_PC2,list(WW_Data$Site_Name),sd)), value != FALSE)
## merge the all the datasets with one command - THIS FAILS!
WW_Stats <- merge_all(WW_Site_PC1_Mean, WW_Site_PC1_SD, WW_Site_PC2_Mean, by = c("indices"))
编辑:现在我有一个很好的结果,可以将摘要统计信息快速放入三个文件中,但我仍然在尝试merge_all
(尽管我不确定我是否应该使用 merge_recurse
- 无论我得到相同的错误)结果时仍然有问题。这是我的尝试:
## Calculate statistics for each site ##
WW_Site_PC_Mean <- ddply(WW_Data, .(Site_Name), numcolwise(mean))
colnames(WW_Site_PC_Mean) <- c("Site_Name", "PC1_Mean", "PC2_Mean", "PC3_Mean", "PC4_Mean")
WW_Site_PC_SD <- ddply(WW_Data, .(Site_Name), numcolwise(sd))
colnames(WW_Site_PC_Mean) <- c("Site_Name", "PC1_SD", "PC2_SD", "PC3_SD", "PC4_SD")
WW_Site_PC_N <- count(WW_Data$Site_Name)
colnames(WW_Site_PC_N) <- c("Site_Name", "PCA_N")
## merge the all the datasets with one command - THIS FAILS!
WW_Stats <- merge_recurse(WW_Site_PC_Mean, WW_Site_PC_SD, WW_Site_PC_N, by = "Site_Name")
错误输出:
Error in fix.by(by.x, x) :
'by' must specify column(s) as numbers, names or logical
留在基本 R 中,您可以使用 aggregate
:
WW_Data_mean = aggregate(list(mean = WW_Data[, -c(1, 2)]),
list(Site_Name = WW_Data$Site_Name), mean)
WW_Data_sd = aggregate(list(mean = WW_Data[, -c(1, 2)]),
list(Site_Name = WW_Data$Site_Name), sd)
更新(问题的第二部分)
你的代码有几个错误,也许你需要更多地"玩"合并。
首先,错误。示例中失败的行失败,因为:
- 它的结构不正确;要合并的
data.frame
应该在list
中。 - 它引用了示例中不存在的对象!您正在尝试合并名为
WW_Site_Name_PC1_Mean
的对象,但该对象的名称为WW_PC1_Mean
。
其次,这里有一些其他的事情可以尝试。修正列名称:
# Fix your column names
# There's probably an easier way to do this, but...
names(WW_PC1_Mean)[2] = "WW_PC1_Mean"
names(WW_PC1_SD)[2] = "WW_PC1_SD"
names(WW_PC2_Mean)[2] = "WW_PC2_Mean"
names(WW_Site_SD)[2] = "WW_Site_SD"
现在,请尝试merge_all
。请注意,您需要提供 data.frame
s 的list
。似乎merge_all
总是只给出两列---但也许我做错了什么。
# Not what you want
merge_all(list(WW_PC1_Mean, WW_PC1_SD,
WW_PC2_Mean, WW_Site_SD), by="indices")
indices WW_PC1_Mean
1 Alnön -1.3237067
2 Anjan 0.5295393
继续merge_recurse
.这有效:
# This is what you want
merge_recurse(list(WW_PC1_Mean, WW_PC1_SD,
WW_PC2_Mean, WW_Site_SD), by="indices")
indices WW_PC1_Mean WW_PC1_SD WW_PC2_Mean WW_Site_SD
1 Alnön -1.3237067 0.9252417 -0.220412 0.9912227
2 Anjan 0.5295393 0.9511800 0.391778 0.9112450
您还可以在基本 R 中使用Reduce
。
# Base R also has a solution
Reduce(function(x, y) merge(x, y, all=TRUE),
list(WW_PC1_Mean, WW_PC1_SD, WW_PC2_Mean, WW_Site_SD))
我建议你把精力集中在学习一些plyr
好的方面。
使用函数ddply
您可以真正简化代码。下面介绍如何使用一行代码计算数据中所有列的mean
:
library(plyr)
ddply(WW_Data, .(Site_Name), numcolwise(mean))
Site_Name Morph_PC1 Morph_PC2 Morph_PC3 Morph_PC4
1 Alnön -1.3237067 -0.220412 0.03185484 0.1896946
2 Anjan 0.5295393 0.391778 -0.16925696 -0.2169369
同样,标准差:
ddply(WW_Data, .(Site_Name), numcolwise(sd))
Site_Name Morph_PC1 Morph_PC2 Morph_PC3 Morph_PC4
1 Alnön 0.9252417 0.9912227 0.7316201 0.3766064
2 Anjan 0.9511800 0.9112450 0.6698389 0.5717482
我经常使用这种类型的分析。使用此策略,我几乎不必同时合并多个数据帧。
附言。软件包reshape
是旧的 - 您应该改用reshape2
,它不再包含merge_all()
函数
一些使用带有信息变量名称的 plyr 的解决方案。
ms <- function(x) cbind("mean"=mean(x),"sd"=sd(x))
do.call(rbind,dlply(WW_Data, .(Site_Name), function(dat) numcolwise(ms)(dat)))
Morph_PC1.mean Morph_PC1.sd Morph_PC2.mean Morph_PC2.sd Morph_PC3.mean Morph_PC3.sd Morph_PC4.mean Morph_PC4.sd
Alnön -1.3237067 0.9252417 -0.2204120 0.9912227 0.03185484 0.73162007 0.1896946 0.3766064
Anjan 0.5295393 0.9511800 0.3917780 0.9112450 -0.16925696 0.66983885 -0.2169369 0.5717482