I want to extract the 'untreated control' value for each variable and weed_type. In excel is easy to lookup the filter the value in the row and return the "untreated_contol" result for that weed_type. I would then like to calculate the % control (i.e. (untreated - predicted.value) / untreated * 100). Any suggestions to simplify this process, as I have a large number of "weed_types"?

df <- data.frame(Weed_type = c("weed1", "weed1", "weed1", "weed2", "weed2", "weed2", "weed3", "weed3", "weed3"),
                 Treatment = c("untreated control", "Treatment1", "Treatment2", "untreated control", "Treatment1", "Treatment2", "untreated control", "Treatment1", "Treatment2"),
                 predicted.value = c(23.3, 0.4, 0,  .9, .15, .01, 87, 12,2)
)
df

new to r, used a lengthy work around as follows:

extracted the untreated values for each weed type

weed1_c <- df %>% filter(Weed_type == 'weed1' & Treatment == "untreated control")  %>% pull(predicted.value)
weed2_c <- df %>% filter(Weed_type == 'weed2' & Treatment == "untreated control")  %>% pull(predicted.value)
weed3_c <- df %>% filter(Weed_type == 'weed3' & Treatment == "untreated control")  %>% pull(predicted.value)

added these values back into the dataframe

df <- df %>% 
      mutate(untreated = case_when(
        Weed_type=="weed1" ~   weed1_c,
        Weed_type=="weed2" ~   weed2_c,
        Weed_type=="weed3" ~   weed3_c,
      ))

calculate a percentage control

df$percentage_control <- (df$untreated  - df$predicted.value) / df$untreated *100
df  

would appreciate an easier way to get to this result!

Here are two possible options to achieve your desired result without the need to store the untreated value for each weed type in a separate variable:

library(dplyr, warn.conflicts = FALSE)

df |> 
  mutate(
    untreated = predicted.value[Treatment == "untreated control"],
    .by = Weed_type
  ) |> 
  mutate(
    percentage_control = (untreated  - predicted.value) / untreated * 100
  )
#>   Weed_type         Treatment predicted.value untreated percentage_control
#> 1     weed1 untreated control           23.30      23.3            0.00000
#> 2     weed1        Treatment1            0.40      23.3           98.28326
#> 3     weed1        Treatment2            0.00      23.3          100.00000
#> 4     weed2 untreated control            0.90       0.9            0.00000
#> 5     weed2        Treatment1            0.15       0.9           83.33333
#> 6     weed2        Treatment2            0.01       0.9           98.88889
#> 7     weed3 untreated control           87.00      87.0            0.00000
#> 8     weed3        Treatment1           12.00      87.0           86.20690
#> 9     weed3        Treatment2            2.00      87.0           97.70115


df |> 
  filter(
    Treatment == "untreated control"
  ) |> 
  select(Weed_type, untreated = predicted.value) |> 
  right_join(x = df, by = "Weed_type") |> 
  mutate(
    percentage_control = (untreated  - predicted.value) / untreated * 100
  )
#>   Weed_type         Treatment predicted.value untreated percentage_control
#> 1     weed1 untreated control           23.30      23.3            0.00000
#> 2     weed1        Treatment1            0.40      23.3           98.28326
#> 3     weed1        Treatment2            0.00      23.3          100.00000
#> 4     weed2 untreated control            0.90       0.9            0.00000
#> 5     weed2        Treatment1            0.15       0.9           83.33333
#> 6     weed2        Treatment2            0.01       0.9           98.88889
#> 7     weed3 untreated control           87.00      87.0            0.00000
#> 8     weed3        Treatment1           12.00      87.0           86.20690
#> 9     weed3        Treatment2            2.00      87.0           97.70115