[英]Is there an R function for grouping the data by one variable (column)?
I measured bacterial inhibating power on viruses.我测量了细菌对病毒的抑制能力。 I have data matrix of n rows (individuals) and 4 columns (a,b,c,x).我有 n 行(个人)和 4 列(a,b,c,x)的数据矩阵。 Depending on column x I would like to define them as good or bad inhibators.根据 x 列,我想将它们定义为好的或坏的抑制剂。 However, I am not sure how to put a treshold of column x, depending on other measured columns (a,b,c).但是,我不确定如何设置 x 列的阈值,具体取决于其他测量的列(a、b、c)。 Is there any R function that could separate/group my dataframe?是否有任何 R function 可以分离/组合我的 dataframe?
In dplyr logic there is group_by()
, it works like this:在 dplyr 逻辑中有group_by()
,它的工作原理如下:
library(dplyr)
df %>%
group_by(A) %>% # df is now grouped by column A
summarise(Mean = mean(C)) # calculates the mean of C for each group of A, summarise will delete any other columns not summarised and show only distinct rows
df %>%
group_by(A) %>%
mutate(Mean = mean(C)) # This will add the grouped mean to each row without changing the data frame
If you summarise then you are done but after group_by and mutate you have to ungroup
your data frame at some point.如果你总结一下,那么你就完成了,但是在 group_by 和 mutate 之后,你必须在某个时候ungroup
对数据框的分组。
data.table example below. data.table 示例如下。 In the data, we have 50 observations (a) across 5 groups (Group).在数据中,我们在 5 个组 (Group) 中有 50 个观察值 (a)。
Data数据
dt = data.table(
a = runif(1:50),
Group = sample(LETTERS[1:5], 50, replace = T)
)
Example 1示例 1
Firstly, we can calculate the Group mean of a and label it 'Good' if it is above 0.5 and 'Bad' if below.首先,我们可以计算 a 和 label 的组均值,如果高于 0.5,则为“好”,如果低于 0.5,则为“差”。 Note that this summary does not include a.请注意,此摘要不包括 a。
dt1 = dt[, .(Mean = mean(a)), keyby = Group][, Label := ifelse(Mean > 0.5, 'Good', 'Bad')]
> dt1
Group Mean Label
1: A 0.2982229 Bad
2: B 0.4102181 Bad
3: C 0.6201973 Good
4: D 0.4841881 Bad
5: E 0.4443718 Bad
Example 2示例 2
Similarly to Fnguyen's answer, the following code will not summarise the data per group;与 Fnguyen 的回答类似,以下代码不会汇总每组的数据; it will merely show the Group Mean and Label next to each observation.它只会在每个观察值旁边显示组均值和 Label。
dt2 = dt[, Mean := mean(a), by = Group][, Label := ifelse(Mean > 0.5, 'Good', 'Bad')]
> head(dt2)
a Group Mean Label
1: 0.4253110 E 0.4443718 Bad
2: 0.4217955 A 0.2982229 Bad
3: 0.7389260 E 0.4443718 Bad
4: 0.2499628 E 0.4443718 Bad
5: 0.3807705 C 0.6201973 Good
6: 0.2841950 E 0.4443718 Bad
Example 3示例 3
Lastly, we can of course apply a conditional argument to create a new column without having previously calculated a Grouped variable.最后,我们当然可以应用条件参数来创建新列,而无需事先计算分组变量。 The following example tests a combined condition on columns a and b.以下示例测试列 a 和 b 上的组合条件。
dt3 = data.table(a = runif(100), b = runif(100))
dt3[, abGrThan0.5 := ifelse((a > 0.5 & b > 0.5), TRUE, FALSE)]
> head(dt3)
a b abGrThan0.5
1: 0.5132690 0.02104807 FALSE
2: 0.8466798 0.96845916 TRUE
3: 0.5776331 0.79215074 TRUE
4: 0.9740055 0.59381244 TRUE
5: 0.4311248 0.07473373 FALSE
6: 0.2547600 0.09513784 FALSE
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