Hi,
Welcome to the RStudio community!
Before I give my solution: Next time you post it's better to share data in a format that can be copy-pasted into R as I now needed to spend a long time converting your output. You can take a look at the reprex guide on how to best share a reproductible example. A reprex consists of the minimal code and data needed to recreate the issue/question you're having. You can find instructions how to build and share one here:
Ok so now for your question. Here is my approach:
library(tidyverse)
ddCT_Gen = data.frame(
id = c(1L,2L,3L,4L,5L,6L,7L,8L,9L,
10L,11L,12L,13L,14L,15L,16L),
Gen = c("Gen1","Gen1","Gen1","Gen1","Gen1","Gen1","Gen1","Gen1","Gen2",
"Gen2","Gen2","Gen2","Gen2","Gen2","Gen2","Gen2"),
Treatment = c("Gen1_without","Gen1_without","Gen1_without","Gen1_without",
"Gen1_with","Gen1_with","Gen1_with","Gen1_with","Gen2_without",
"Gen2_without","Gen2_without","Gen2_without","Gen2_with",
"Gen2_with","Gen2_with","Gen2_with"),
Ko = c(1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L,1L),
M1 = c(1.8,1.4,1.3,1.3,1,1.1,1.3,1,1.5,2,1.9,1.5,1,0.8,1,1.1),
M2 = c(0.8,1,0.8,2,0.8,0.8,1.8,1.2,0.6,2.6,1.4,2.3,1,0.7,0.9,0.9),
M3 = c(1.1,2.2,0.8,1.1,1,1,0.9,1,1.1,2.2,1.2,1.4,0.7,0.8,0.9,1),
M4 = c(0.9,1.2,0.8,1.4,0.7,0.8,1.5,0.8,0.6,2.3,1,2.4,0.7,0.7,1,1),
M5 = c(0.9,1.5,1,1.2,0.4,0.8,1.5,0.9,0.7,2.9,1,2.3,0.4,0.7,0.9,1),
M6 = c(0.9,1.5,0.8,1.3,0.8,0.8,0.6,0.5,0.7,1.8,0.9,1.7,0.8,0.6,0.8,0.8),
M7 = c(0.8,1.3,0.8,1.2,0.9,0.6,0.8,1.2,0.7,2.8,1.3,1.6,1,0.6,0.9,0.8),
M8 = c(1.1,1.5,1.1,0.5,0.9,0.6,0.7,0.6,0.9,2.3,1.3,1.9,1.1,0.6,0.6,0.8)
)
newFormat = ddCT_Gen %>%
pivot_longer(M1:M8, names_to = "Sample", values_to = "Value") %>%
group_by(Sample, Gen, Treatment) %>%
mutate(Outlier = Value < quantile(Value, 0.25) - IQR(Value)*1.5 |
Value > quantile(Value, 0.75) + IQR(Value)*1.5) %>%
ungroup()
newFormat
#> # A tibble: 128 × 7
#> # Groups: Sample, Gen, Treatment [32]
#> id Gen Treatment Ko Sample Value Outlier
#> <int> <chr> <chr> <int> <chr> <dbl> <lgl>
#> 1 1 Gen1 Gen1_without 1 M1 1.8 TRUE
#> 2 1 Gen1 Gen1_without 1 M2 0.8 FALSE
#> 3 1 Gen1 Gen1_without 1 M3 1.1 FALSE
#> 4 1 Gen1 Gen1_without 1 M4 0.9 FALSE
#> 5 1 Gen1 Gen1_without 1 M5 0.9 FALSE
#> 6 1 Gen1 Gen1_without 1 M6 0.9 FALSE
#> 7 1 Gen1 Gen1_without 1 M7 0.8 FALSE
#> 8 1 Gen1 Gen1_without 1 M8 1.1 FALSE
#> 9 2 Gen1 Gen1_without 1 M1 1.4 FALSE
#> 10 2 Gen1 Gen1_without 1 M2 1 FALSE
#> # … with 118 more rows
#> # ℹ Use `print(n = ...)` to see more rows
Created on 2022-08-08 by the reprex package (v2.0.1)
EXPLANATION
I converted the data first from a wide into a long format to make the filtering easier using the pivot_longer function from tidyr. This created a new column "Sample" with for each sample its value.
In the long format, we can now group the data by "Sample", "Gen" and "Treatment" and create a new column "Outlier" that has a TRUE or FALSE depending on the requested logic.
You can now filter the data as you see fit, and should you want it back in its original wide format you can do that like so:
oldFormat = newFormat %>%
filter(!Outlier) %>% select(-Outlier) %>%
arrange(Sample) %>% #Just to make sure the column order is M1 - M8
pivot_wider(names_from = "Sample", values_from = "Value") %>%
arrange(Gen, Treatment)
oldFormat
#> # A tibble: 16 × 12
#> id Gen Treatment Ko M1 M2 M3 M4 M5 M6 M7 M8
#> <int> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 5 Gen1 Gen1_with 1 1 0.8 1 0.7 0.4 0.8 0.9 0.9
#> 2 6 Gen1 Gen1_with 1 1.1 0.8 1 0.8 0.8 0.8 0.6 0.6
#> 3 7 Gen1 Gen1_with 1 1.3 1.8 NA NA 1.5 0.6 0.8 0.7
#> 4 8 Gen1 Gen1_with 1 1 1.2 1 0.8 0.9 0.5 1.2 0.6
#> 5 2 Gen1 Gen1_witho… 1 1.4 1 NA 1.2 1.5 1.5 1.3 1.5
#> 6 3 Gen1 Gen1_witho… 1 1.3 0.8 0.8 0.8 1 0.8 0.8 1.1
#> 7 4 Gen1 Gen1_witho… 1 1.3 NA 1.1 1.4 1.2 1.3 1.2 NA
#> 8 1 Gen1 Gen1_witho… 1 NA 0.8 1.1 0.9 0.9 0.9 0.8 1.1
#> 9 13 Gen2 Gen2_with 1 1 1 0.7 0.7 0.4 0.8 1 1.1
#> 10 15 Gen2 Gen2_with 1 1 0.9 0.9 1 0.9 0.8 0.9 0.6
#> 11 16 Gen2 Gen2_with 1 1.1 0.9 1 1 1 0.8 0.8 0.8
#> 12 14 Gen2 Gen2_with 1 NA NA 0.8 0.7 0.7 NA 0.6 0.6
#> 13 9 Gen2 Gen2_witho… 1 1.5 0.6 1.1 0.6 0.7 0.7 0.7 0.9
#> 14 10 Gen2 Gen2_witho… 1 2 2.6 2.2 2.3 2.9 1.8 2.8 2.3
#> 15 11 Gen2 Gen2_witho… 1 1.9 1.4 1.2 1 1 0.9 1.3 1.3
#> 16 12 Gen2 Gen2_witho… 1 1.5 2.3 1.4 2.4 2.3 1.7 1.6 1.9
Created on 2022-08-08 by the reprex package (v2.0.1)
Hope this helps,
PJ