Your attempt was just overly complicated. All you needed was
threshold <- c( .2 , .4 , .5 )[ df$track ]
df$value <- pmax(threshold, df$value)
df # desired outcome
Cheers
On Thu, Feb 9, 2012 at 3:56 PM, Idris Raja <idris.r...@gmail.com> wrote:
> # I have a dataframe in the following form:
>
> track <- c(rep('A', 3), rep('B', 4), rep('C', 4))
> value <- c(0.15, 0.25, 0.35, 0.05, 0.99, 0.32, 0.13, 0.80, 0.75, 0.60, 0.44)
> df <- data.frame(track=factor(track), value=value)
>
> #> print(df)
> #track value
> #1 A 0.15
> #2 A 0.25
> #3 A 0.35
> #4 B 0.05
> #5 B 0.99
> #6 B 0.32
> #7 B 0.13
> #8 C 0.80
> #9 C 0.75
> #10 C 0.60
> #11 C 0.44
>
>
> # If any of the values are below a threshold value, I want to replace it
> with the
> # threshold value. The twist is that there is a different threshold value
> for
> # every track.
>
> # I tried something like this, but it's not working
>
> threshold <- list()
> threshold['A'] <- 0.2
> threshold['B'] <- 0.4
> threshold['C'] <- 0.5
>
>
> for (track in levels(df$track)){
> df[df$track==track,]$outcome <- pmax(df[df$track==track,]$outcome,
> threshold[track])
> }
> # Warning messages:
> # 1: In is.na(mmm) : is.na() applied to non-(list or vector) of type 'NULL'
> # 2: In is.na(mmm) : is.na() applied to non-(list or vector) of type 'NULL'
> # 3: In is.na(mmm) : is.na() applied to non-(list or vector) of type 'NULL'
>
>
> #******************
> # Desired Results:
>
> #> print(df)
> #track value
> #1 A 0.20 # value changed
> #2 A 0.25
> #3 A 0.35
> #4 B 0.40 # value changed
> #5 B 0.99
> #6 B 0.40 # value changed
> #7 B 0.40 # value changed
> #8 C 0.80
> #9 C 0.75
> #10 C 0.60
> #11 C 0.50 # value changed
>
>
> # Any ideas? Thanks for reading.
>
> [[alternative HTML version deleted]]
>
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