如何编写循环以运行数据帧的t检验? [英] How to write a loop to run the t-test of a data frame?

问题描述

我遇到了对存储在数据帧中的某些数据进行t检验的问题.我知道如何一个接一个地做，但是根本没有效率.我可以问一下如何编写循环吗?

I met a problem of running a t-test for some data stored in a data frame. I know how to do it one by one but not efficient at all. May I ask how to write a loop to do it?

例如，我已经在testData中获得了数据:

For example, I have got the data in the testData:

testData <- dput(testData)
structure(list(Label = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), .Label = c("Bad", "Good"), class = "factor"), F1 = c(0.647789237, 
0.546087915, 0.461342005, 0.794212207, 0.569199511, 0.735685704, 
0.650942066, 0.457497016, 0.808619288, 0.673100668, 0.68781739, 
0.470094549, 0.958591821, 1, 0.46908343, 0.578755283, 0.289380462, 
0.685117658, 0.296011479, 0.208821225, 0.461487258, 0.176144907, 
0.325684001), F2 = c(0.634327378, 0.602685034, 0.70643658, 0.577336318, 
0.61069332, 0.676176013, 0.685433524, 0.601847779, 0.641738937, 
0.822097452, 0.549508092, 0.711380436, 0.605492874, 0.419354439, 
0.654424433, 0.782191133, 0.826394651, 0.63269692, 0.835389099, 
0.760279322, 0.711607982, 1, 0.858631893), F3 = c(0.881115444, 
0.850553659, 0.855405201, 0.732706141, 0.816063806, 0.841134018, 
0.899594853, 0.788591779, 0.767461265, 0.954481259, 0.840970764, 
0.897785959, 0.789288481, 0.604922471, 0.865024811, 0.947356946, 
0.96622214, 0.879623595, 0.953189022, 0.960153373, 0.868949632, 
1, 0.945716439), F4 = c(0.96939781, 0.758302, 0.652984943, 0.803719964, 
0.980135127, 0.945287339, 0.84045753, 0.926053105, 0.974856922, 
0.829936068, 0.89662815, 0.823594767, 1, 0.886954348, 0.825638185, 
0.798524271, 0.524755093, 0.844685467, 0.522120663, 0.388604114, 
0.725126521, 0.46430556, 0.604943457), F5 = c(0.908895247, 0.614799496, 
0.529111461, 0.726753028, 0.942601677, 0.86641298, 0.75771251, 
0.88237302, 1, 0.817706498, 0.834060845, 0.813550164, 0.927107922, 
0.827680764, 0.797814872, 0.768118872, 0.271122929, 0.790632558, 
0.391325631, 0.257446927, 0.687042673, 0.239520504, 0.521753545
), F6 = c(0.589651031, 0.170481902, 0.137755423, 0.24453692, 
0.505348067, 0.642589538, 0.308854104, 0.286913756, 0.60756673, 
0.531315171, 0.389958915, 0.236113471, 1, 0.687877983, 0.305962183, 
0.40469629, 0.08012222, 0.376774451, 0.098261016, 0.046544022, 
0.201513755, 0.02085411, 0.113698232), F7 = c(0.460358642, 0.629499543, 
0.598616653, 0.623674078, 0.526920757, 0.494086383, 0.504021253, 
0.635105287, 0.558992452, 0.397770725, 0.543528957, 0.538542617, 
0.646897446, 0.543646493, 0.47463817, 0.385081029, 0.555731206, 
0.43769237, 0.501754893, 0.586155312, 0.496028109, 1, 0.522921361
), F8 = c(0.523850222, 0.448936418, 0.339311791, 0.487421437, 
0.462073661, 0.493421514, 0.464091025, 0.496938844, 0.5817454, 
0.474404602, 0.720114482, 0.493098785, 1, 0.528538582, 0.478233718, 
0.2695123, 0.362377901, 0.462252858, 0.287725327, 0.335584366, 
0.397324649, 0.469082387, 0.403397835), F9 = c(0.481230473, 0.349419856, 
0.309729777, 0.410783763, 0.465172146, 0.520935471, 0.380916463, 
0.422238573, 0.572283353, 0.434705384, 0.512705279, 0.358892539, 
1, 0.606926979, 0.370574926, 0.319739889, 0.249984729, 0.381053882, 
0.245597953, 0.22883148, 0.314061676, 0.233511631, 0.269890359
), F10 = c(0.592403628, 0.249811036, 0.256613757, 0.305839002, 
0.497637944, 0.601946334, 0.401643991, 0.302626606, 0.623582766, 
0.706254724, 0.435846561, 0.324357521, 1, 0.740362812, 0.402588813, 
0.537414966, 0.216458806, 0.464852608, 0.251228269, 0.181500378, 
0.31840514, 0.068594104, 0.253873772), F11 = c(0.490032261, 0.366486136, 
0.336749996, 0.421899324, 0.479339762, 0.527364467, 0.398297911, 
0.432190187, 0.584030586, 0.453666402, 0.526861753, 0.388880674, 
1, 0.615835576, 0.39058525, 0.350811433, 0.290220147, 0.397424867, 
0.288095106, 0.274852912, 0.340129804, 0.271099396, 0.305499273
)), .Names = c("Label", "F1", "F2", "F3", "F4", "F5", "F6", "F7", 
"F8", "F9", "F10", "F11"), class = "data.frame", row.names = c(NA, 
-23L))

我需要针对具有两个独立组的每列进行t检验，即针对多个功能"F1"至"F11"的良好"与不良".我试图做类似的事情:

I need to run the t-test for each column with two independent groups, i.e., "Good" vs. "Bad" for several features "F1" to "F11". I tried to do something like:

GoodF1 <- subset(testData, Label == 'Good', select=c("F1"))
BadF1  <- subset(testData, Label == 'Bad', select=c("F1"))
t.test(GoodF1$F1,BadF1$F1)

然后将其余的"F2"更改为"F11"，但显然效率不高.如果您有更好的想法可以循环运行，我非常感谢.非常感谢.

And then do the rest of "F2" to "F11" but obviously not efficient. I really appreciate if you have better ideas to run it in a loop. Thanks very much.

推荐答案

这是一个简单的解决方案，不需要其他软件包:

Here's a simple solution, which doesn't require additional packages:

lapply(testData[-1], function(x) t.test(x ~ testData$Label))

此处testData[-1]引用testData的所有列，但第一列(包含标签).负索引用于排除数据.

Here testData[-1] refers to all columns of testData but the first one (which contains the labels). Negative indexing is used for excluding data.

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