Is there an R function for grouping the data by one variable (column)?

Question

I measured bacterial inhibating power on viruses. I have data matrix of n rows (individuals) and 4 columns (a,b,c,x). Depending on column x I would like to define them as good or bad inhibators. However, I am not sure how to put a treshold of column x, depending on other measured columns (a,b,c). Is there any R function that could separate/group my dataframe?

Answer 1

In dplyr logic there is group_by() , it works like this:

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.

Answer 2

data.table example below. In the data, we have 50 observations (a) across 5 groups (Group).

Data

dt = data.table(
  a = runif(1:50),
  Group = sample(LETTERS[1:5], 50, replace = T)
)

Example 1

Firstly, we can calculate the Group mean of a and label it 'Good' if it is above 0.5 and 'Bad' if below. Note that this summary does not include 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

Similarly to Fnguyen's answer, the following code will not summarise the data per group; it will merely show the Group Mean and Label next to each observation.

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

Lastly, we can of course apply a conditional argument to create a new column without having previously calculated a Grouped variable. The following example tests a combined condition on columns a and b.

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