library(tidyverse)

# Define Data and Replicates tibble objects
Data <- tibble(
  Name = c("100", "100", "200", "250", "1E5", "1E5", "Negative", "Negative"),
  Pos = c("A3", "A4", "B3", "B4", "C3", "C4", "D3", "D4"), 
  Output = c("20.00", "20.10", "21.67", "23.24", "21.97", "22.03", "38.99", "38.99")
)

Replicates <- tibble(
  Replicates = c("A3, A4", "C3, C4", "D3, D4"),
  Mean.Cq = c(20.05, 22.00, 38.99),
  STD.Cq = c(0.05, 0.03, 0.00)
)

# Split `Replicates$Replicates` into two fields
Replicates$R1 <- gsub(x = Replicates$Replicates, pattern = "^(.*),.*", replacement = "\\1")
Replicates$R2 <- gsub(x = Replicates$Replicates, pattern = ".*,\\s(.*)", replacement = "\\1")

# Filter Data for each of the Replicates fields
df_R1 <- Data %>%
  filter(Pos == "A3" | Pos == "B4") %>%
  select(Name, Mean.Cq, STD.Cq)
df_R2 <- Data %>%
  filter(Pos %in% c("C3", "D4")) %>%
  select(Name, Mean.Cq, STD.Cq)

# Inner-join `df_R1` and `df_R2` with Replicates
df <- left_join(df_R1, Replicates, by = "Pos") %>%
  inner_join(df_R2, by = "Pos")

unique(df[, c("Name", "Mean.Cq", "STD.Cq")])
#> # A tibble: 5 x 3
#>   Name     Mean.Cq STD.Cq
#>   &lt;chr&gt;      &lt;dbl&gt;  &lt;dbl&gt;
#> 1 100         20.0   0.05
#> 2 200         NA    NA   
#> 3 250         NA    NA   
#> 4 1E5         22     0.03
#> 5 Negative    39.0   0