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R 计算数据框中3列的平均值

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R 计算数据框中3列的平均值,r,R,我有3个数据帧，它们只是复制品。所以我想把它们绑起来，计算每个分数的平均值 三个数据帧： 1号 第2号 第3号 我使用下面的函数来绑定它们。每个数据帧中的行数不同，在某些情况下，名称也不同，因此最终表中的行数应多于每个数据帧中的行数 mylist <- list(tbl_gel1,tbl_gel2,tbl_gel3) tbl_all <- Reduce(function(x, y) merge(x, y, all=T,by="Name",sort=F),

我有3个数据帧，它们只是复制品。所以我想把它们绑起来，计算每个分数的平均值

三个数据帧：

1号

第2号

第3号

我使用下面的函数来绑定它们。每个数据帧中的行数不同，在某些情况下，名称也不同，因此最终表中的行数应多于每个数据帧中的行数

mylist <- list(tbl_gel1,tbl_gel2,tbl_gel3)
tbl_all <- Reduce(function(x, y) merge(x, y, all=T,by="Name",sort=F), 
                  mylist, accumulate=F)

mylist我试过你的代码了。。。我没有得到零分。。NA或零以外的值…问题是您只有我的数据的
头。数据太大，无法执行
dput
。如果需要，我可以上传到某个地方。也许你知道我可以用来计算平均值的其他函数吗？你在处理NAs吗？如果没有，则尝试使用
na.rm=TRUE
…NAs出现问题。。。看起来我不小心改变了旧剧本，“分数”是什么？这不是一个用来描述矩阵子集的术语，所以你能澄清一下吗？我也不确定
cbind
是否在做您期望的事情
tbl_all[1]
是单个值。这怎么可能与一个列表绑定呢？

> dput(head(tbl_gel2))
structure(list(Name = c("yal003w", "yal005c", "yal012w", "yal016w", 
"yal035w", "yal038w"), `2_1` = c(NA_real_, NA_real_, NA_real_, 
NA_real_, NA_real_, NA_real_), `2_2` = c(1.0548947840373, NA, 
NA, NA, NA, NA), `2_3` = c(1.61794716486303, 0.346821796129205, 
NA, NA, NA, NA), `2_4` = c(1, NA, NA, 0.378254379051086, NA, 
NA), `2_5` = c(0.670710809411423, NA, NA, 1, NA, NA), `2_6` = c(0.313872585645673, 
NA, NA, NA, NA, NA), `2_7` = c(0.299293639466945, 0.13920907824675, 
NA, NA, NA, NA), `2_8` = c(0.311431376422469, 0.511742245543671, 
0.342807141055383, NA, NA, NA), `2_9` = c(0.243672215177189, 
1, 0.689138745271004, NA, NA, 0.0540861571772987), `2_10` = c(0.154732102234279, 
1.08973258347909, 1, NA, NA, NA), `2_11` = c(0.149365726324845, 
1.1210733533474, 1.0427649268992, NA, NA, 0.0955468461925663), 
    `2_12` = c(0.153741630869067, 2.96276072446013, 1, NA, NA, 
    NA), `2_13` = c(0.629371115599316, 0.952868912207058, 0.0771105403237483, 
    NA, NA, 0.0885212695236819), `2_14` = c(0.907644486740723, 
    1.43000783337778, NA, NA, NA, 0.138102409899801), `2_15` = c(1.09683345304359, 
    0.423641943213571, NA, NA, NA, 0.255699738225622), `2_16` = c(0.913095779338154, 
    0.510977400533081, NA, NA, 0.520556617688936, 0.284898552722227
    ), `2_17` = c(0.935941553863477, 0.388225948821767, NA, NA, 
    1.14984991998928, 1), `2_18` = c(2.21746156904543, 0.642743615867438, 
    NA, NA, NA, 2.22716071647178), `2_19` = c(0.500618035526774, 
    0.282924681750454, NA, NA, NA, 1), `2_20` = c(0.701627311828743, 
    0.254001731153973, NA, NA, 1, 1.15996914621286), `2_21` = c(1.97359874904275, 
    NA, NA, NA, 1.67526802494991, 1.38709456754353), `2_22` = c(2.09198896289293, 
    NA, NA, NA, NA, 0.921672834103247), `2_23` = c(1.18791465369551, 
    NA, NA, NA, NA, 0.576309066193914), `2_24` = c(0.473199477125101, 
    0.176144702328764, NA, NA, 1, 0.130236848112641)), .Names = c("Name", 
"2_1", "2_2", "2_3", "2_4", "2_5", "2_6", "2_7", "2_8", "2_9", 
"2_10", "2_11", "2_12", "2_13", "2_14", "2_15", "2_16", "2_17", 
"2_18", "2_19", "2_20", "2_21", "2_22", "2_23", "2_24"), row.names = c(NA, 
6L), class = "data.frame")



> dput(head(tbl_gel3))
structure(list(Name = c("yal003w", "yal005c", "yal012w", "yal016w", 
"yal035w", "yal038w"), `3_1` = c(NA_real_, NA_real_, NA_real_, 
NA_real_, NA_real_, NA_real_), `3_2` = c(1, 1.4605309655311, 
NA, NA, NA, NA), `3_3` = c(1.74480713727388, 0.42825619952525, 
NA, NA, NA, NA), `3_4` = c(1, 0.431712121875013, NA, 0.395182020245312, 
NA, NA), `3_5` = c(2.26247329056518, 0.644462177666441, NA, 1, 
NA, NA), `3_6` = c(0.619783374266709, 0.472094874244026, NA, 
NA, NA, NA), `3_7` = c(0.45731912574756, 0.176354321796083, NA, 
NA, NA, NA), `3_8` = c(0.271829278733367, 0.517232771669986, 
0.153774052052871, NA, NA, NA), `3_9` = c(0.141017619508583, 
1.41279969394534, 0.651948154271122, NA, NA, NA), `3_10` = c(NA, 
1.64435171100405, 0.998807430240956, NA, NA, NA), `3_11` = c(0.110046035477971, 
1.33684444261939, 1.25595310581771, NA, NA, 0.0236163735479745
), `3_12` = c(NA, 0.982250906830292, 0.39283619985401, NA, NA, 
0.0688303458902568), `3_13` = c(0.136798076436642, 0.55729642483448, 
0.176525038283566, NA, NA, 0.0251189412372225), `3_14` = c(0.316623893146817, 
1, NA, NA, NA, 0.0727823461722849), `3_15` = c(NA, 0.607991038574375, 
NA, NA, NA, 0.133968257432001), `3_16` = c(0.362994392402489, 
0.547183167896534, NA, NA, NA, 0.0777347708647245), `3_17` = c(1, 
0.116561118715651, NA, NA, 0.710972173471528, 1), `3_18` = c(NA, 
3.63330458071475, NA, NA, NA, 3.24019081192985), `3_19` = c(NA, 
NA, NA, NA, NA, 2.46635222132474), `3_20` = c(0.452303676849426, 
0.0896715384025126, NA, NA, 1, 1), `3_21` = c(1.50169299468485, 
0.513442106966708, NA, NA, 1.45124841710635, 1.02529618467026
), `3_22` = c(0.565232592993276, 0.748536315065533, NA, NA, 2.9089322117881, 
0.782555457293307), `3_23` = c(1.62622280168665, 0.704926586534075, 
NA, NA, NA, 0.584486806995139), `3_24` = c(NA_real_, NA_real_, 
NA_real_, NA_real_, NA_real_, NA_real_)), .Names = c("Name", 
"3_1", "3_2", "3_3", "3_4", "3_5", "3_6", "3_7", "3_8", "3_9", 
"3_10", "3_11", "3_12", "3_13", "3_14", "3_15", "3_16", "3_17", 
"3_18", "3_19", "3_20", "3_21", "3_22", "3_23", "3_24"), row.names = c(NA, 
6L), class = "data.frame")



mylist <- list(tbl_gel1,tbl_gel2,tbl_gel3)
tbl_all <- Reduce(function(x, y) merge(x, y, all=T,by="Name",sort=F), 
                  mylist, accumulate=F)



## Calculating the mean
tbl_all1 <- tbl_all[-1]
ind <- c(1, 25, 49)
tbl_mean <- cbind(tbl_all[1], sapply(0:23, function(i) rowMeans(tbl_all1[ind+i])))