循环在特定列上匹配模式（在数据框中）上跨行执行计算？

本文介绍了循环在特定列上匹配模式（在数据框中）上跨行执行计算？的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述 我有一个数据框与一些布尔值（1/0）如下（抱歉，我不知道如何把它做成一个聪明的表） Flag1.Sam Flag2.Sam Flag3.Sam Flag1.Ted Flag2.Ted Flag3.Ted probe1 0 1 0 1 0 0 probe2 0 0 0 0 0 0 probe3 1 0 0 0 0 0 probe4 0 0 0 0 0 0 probe5 1 1 0 1 0 0 我有64个样本（Sam / Ted ....等），它们在一个名为files的列表中; files 我想创建一个列，将每个样本的标志值相加来创建以下内容： Sam Ted probe1.flagsum 1 1 probe2.flagsum 0 0 probe3.flagsum 1 0 probe4.flagsum 0 0 probe5.flagsum 2 1 我对R相当陌生，试图学习需要知道的基础，但我已经尝试了以下内容： $ { $ FLAGS $ i #greping列过滤一个样本 filter1< ; - grep（names（filters），pattern = y）＃列出这些列的总和值 FLAGS $ y } } 上面的代码不起作用，前面。 任何人都可以帮我解决这个问题，或者指点我使用的命令/工具的正确方向。 谢谢。解决方案 这很容易在R reshape ，尽管使用重塑或 reshape2 packa ges可能更直观。 这是一个基于R的解决方案： ＃这里是你的数据目前的形式 dat = read.table（header = TRUE，text =Flag1.Sam Flag2.Sam Flag3.Sam Flag1.Ted Flag2.Ted Flag3.Ted probe1 0 1 0 1 0 0 probe2 0 0 0 0 0 0 probe3 1 0 0 0 0 0 probe4 0 0 0 0 0 0 probe5 1 1 0 1 0 0 ）＃生成一个ID行 dat $ id = row.names（dat）＃重新整形为长 r.dat = reshape（dat，direction =long ， timevar =probe， vary = 1：6，sep =。）＃计算行数 r.dat $ sum = rowSums（r。 dat [3：5]）＃重塑成宽格式，放弃你不感兴趣的 reshape（r.dat，direction =wide， idvar =id ，timevar =probe， drop = 3：5） ## id sum.Sam sum.Ted ## probe1.Sam probe1 1 1 ## probe2 .Sam probe2 0 0 ## probe3.Sam probe3 1 0 ## probe4.Sam probe4 0 0 ## probe5.Sam probe5 2 1 不止一种方法可以让猫变成皮肤 你也可以调用这样一个函数： myFun = function（data，varnames）{ temp = vector（list，length（varnames）） for（i in 1：length（varnames））{ temp [[i]] = colSums（t（dat [grep（varnames [i]，names（data））]）） names（temp）[[i]] = varnames [i] } data.frame（temp）} $ b $ p然后，利用你有名字的向量： files = c（Sam，Ted） myFun（dat，files） ## Sam ted ## probe1 1 1 ## probe2 0 0 ## probe3 1 0 ## probe4 0 0 ## probe5 2 1 享受！ I have a dataframe with some boolean values (1/0) as follows (sorry I couldn't work out how to make this into a smart table) Flag1.Sam Flag2.Sam Flag3.Sam Flag1.Ted Flag2.Ted Flag3.Tedprobe1 0 1 0 1 0 0probe2 0 0 0 0 0 0probe3 1 0 0 0 0 0probe4 0 0 0 0 0 0probe5 1 1 0 1 0 0I have 64 samples (Sam/Ted....etc) which are in a list called files i.e;files <- c("Sam", "Ted", "Ann", ....) And I would like to create a a column summing the flag values for each sample to create the following: Sam Ted probe1.flagsum 1 1probe2.flagsum 0 0 probe3.flagsum 1 0 probe4.flagsum 0 0probe5.flagsum 2 1I am fairly new to R, trying to learn on a need to know basis but I have tried the following: for(i in files) { FLAGS$i <- cbind(sapply(i, function(y) { #greping columns to filter for one sample filter1 <- grep(names(filters), pattern=y) #print out the summed values for those columns FLAGS$y <-rowSums(filters[,(filter1)]) }}The above code does not work and I am bit lost as how to move forward.Can anyone help me untangle this problem or point me in the right direction of the commands/tools to use.Thank you. 解决方案 This is easily doable in base R reshape, though using the reshape or reshape2 packages might be more intuitive.Here's a solution in base R:# Here's your data in its current formdat = read.table(header=TRUE, text="Flag1.Sam Flag2.Sam Flag3.Sam Flag1.Ted Flag2.Ted Flag3.Tedprobe1 0 1 0 1 0 0probe2 0 0 0 0 0 0probe3 1 0 0 0 0 0probe4 0 0 0 0 0 0probe5 1 1 0 1 0 0")# Generate an ID rowdat$id = row.names(dat)# Reshape wide to longr.dat = reshape(dat, direction="long", timevar="probe", varying=1:6, sep=".")# Calculate row sumsr.dat$sum = rowSums(r.dat[3:5])# Reshape back to wide format, dropping what you're not interested inreshape(r.dat, direction="wide", idvar="id", timevar="probe", drop=3:5)## id sum.Sam sum.Ted## probe1.Sam probe1 1 1## probe2.Sam probe2 0 0## probe3.Sam probe3 1 0## probe4.Sam probe4 0 0## probe5.Sam probe5 2 1More than one way to skin a catYou can also whip up a function like this one:myFun = function(data, varnames) { temp = vector("list", length(varnames)) for (i in 1:length(varnames)) { temp[[i]] = colSums(t(dat[grep(varnames[i], names(data))])) names(temp)[[i]] = varnames[i] } data.frame(temp)}Then, making use of the vector that you have of names:files = c("Sam", "Ted")myFun(dat, files)## Sam Ted## probe1 1 1## probe2 0 0## probe3 1 0## probe4 0 0## probe5 2 1Enjoy! 这篇关于循环在特定列上匹配模式（在数据框中）上跨行执行计算？的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！