Here is an example of the data:
Test.Statistic P FDR_P Bonferroni_P Control_mean NH4._mean
1 8.203199 0.01654619 0.7405529 1 0.00 0.000000
2 7.622793 0.02211727 0.7405529 1 0.00 1.095238
3 7.501205 0.02350357 0.7405529 1 2.10 1.761905
4 6.510000 0.03858082 0.7405529 1 0.85 0.000000
5 6.149339 0.04620490 0.7405529 1 0.65 5.095238
6 6.052381 0.04850005 0.7405529 1 0.00 1.428571
NO3._mean
1 0.4285714
2 1.1904762
3 1.1428571
4 0.0000000
5 3.4285714
6 0.0000000
I want to apply the formula (trt_mean/control_mean)-1 to each treatment column (NH4 and NO3). I incorporated some comments but am still having trouble calling column 1 (control_mean) in dt.
dt <- as.data.frame.table(kw_res)
cols <- grep("_mean", colnames(dt))
rel_abund_function <- function(z) {
return((z / z[, 1])-1)
}
dt[, lapply(cols, rel_abund_function)]
Any suggestions?
答案 0 :(得分：0)
这样的事情，也许是：
> head(dt)
ctrl1 ctrl2 ctrl3 ctrl4 ctrl5 treatment1_mean treatment2_mean treatment3_mean treatment4_mean treatment5_mean rawval
1: 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
2: 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
3: 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
4: 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
5: 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
6: 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
数据
_mean代码
在此示例中获取具有特定名称格式（cols <- grep("_mean", colnames(dt))
my_mean_func <- function(z){
return((z-mean(z))/100)
}
dt[, lapply(.SD, my_mean_func), .SDcols = cols]
）的列，并应用自定义函数：
> head(dt[, lapply(.SD, my_mean_func), .SDcols = cols])
treatment1_mean treatment2_mean treatment3_mean treatment4_mean treatment5_mean
1: -0.0059725 -0.0138875 -0.004375 0.0059375 0.003125
2: -0.0034225 -0.0082875 -0.004375 0.0059375 0.003125
3: -0.0089725 0.0076125 0.005625 0.0059375 0.003125
4: -0.0000225 0.0159125 0.005625 -0.0040625 -0.006875
5: 0.0022275 -0.0082875 -0.004375 -0.0040625 -0.006875
6: 0.0024275 0.0237125 0.005625 -0.0040625 -0.006875
<强>输出
build.gradle