计算摘要统计信息，然后将所有结果合并到单个data.frame中

时间：2012-08-08 07:44:48

标签： r merge reshape

我正在尝试学习简化代码并将多个data.frames（＆gt; 2）同时合并到一个数据集中。首先，我想为四个PCA列中的每一列计算“网站”mean，sd和n（每个网站的“个人数”）{{1 }，Morph_PC1，...）。其次，将结果合并为单个data.frame。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一个不需要合并的单个数据集，这很好，但我也想知道如何从包{{1}创建Morph_PC2命令工作。

示例数据：

merge_all

代码：

reshape

编辑： 现在我有一个很好的结果，可以快速将摘要统计信息分成三个文件，但我仍然遇到问题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") （虽然我不确定我是否应该使用## 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")) - 无论我得到相同的错误）结果。这是我的尝试：

merge_all

错误输出：

merge_recurse

3 个答案:

答案 0 :(得分：9)

住在基地R，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“合并”。

首先是错误。示例中失败的行失败，原因是：

  1. 结构不正确;要合并的data.frame应位于list。
  2. 它引用了您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。

    3. 其次，这里有一些其他的尝试。修复列名：

    # 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，试试merge_all。请注意，您需要提供list data.frame个。 似乎 merge_all总是只给出两列---但也许我做错了。

    # 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

    转到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

    您也可以在基础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))

答案 1 :(得分：7)

我建议你专注于学习一些plyr善良。

使用[{1}}功能，您可以真正简化代码。以下是使用一行代码计算数据中所有列的ddply的方法：

mean

同样，标准差：

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

我经常使用这种类型的分析。有了这个策略，我几乎不必同时合并多个数据帧。

PS。包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 已过时了 - 您应该使用reshape代替，reshape2功能不再包含merge_all()

答案 2 :(得分：0)

使用具有信息性变量名称的plyr的一些解决方案。 

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
相关问题
最新问题