How to subset data for a specific column with ddpl

I would like to know if there is a simple way to achieve what I describe below using ddply. My data frame describes an experiment with two conditions. Participants had to select between options A and B, and we recorded how long they took to decide, and whether their responses were accurate or not.

I use ddply to create averages by condition. The column nAccurate summarizes the number of accurate responses in each condition. I also want to know how much time they took to decide and express it in the column RT. However, I want to calculate average response times only when participants got the response right (i.e. Accuracy==1). Currently, the code below can only calculate average reaction times for all responses (accurate and inaccurate ones). Is there a simple way to modify it to get average response times computed only in accurate trials?

See sample code below and thanks!

library(plyr)

# Create sample data frame. 
Condition = c(rep(1,6), rep(2,6))                               #two conditions
Response  = c("A","A","A","A","B","A","B","B","B","B","A","A")  #whether option "A" or "B" was selected
Accuracy  = rep(c(1,1,0),4)                                     #whether the response was accurate or not
RT        = c(110,133,121,122,145,166,178,433,300,340,250,674)  #response times
df        = data.frame(Condition,Response, Accuracy,RT)

head(df)

  Condition Response Accuracy  RT
1         1        A        1 110
2         1        A        1 133
3         1        A        0 121
4         1        A        1 122
5         1        B        1 145
6         1        A        0 166

# Calculate averages.  
avg <- ddply(df, .(Condition), summarise, 
                 N          = length(Response),
                 nAccurate  = sum(Accuracy),
                 RT         = mean(RT))

# The problem: response times are calculated over all trials. I would like
# to calculate mean response times *for accurate responses only*.

avg
  Condition N nAccurate       RT
          1 6         4 132.8333
          2 6         4 362.5000

回答1:

With plyr, you can do it as follows:

ddply(df,
      .(Condition), summarise, 
      N          = length(Response),
      nAccurate  = sum(Accuracy),
      RT         = mean(RT[Accuracy==1]))

this gives:

   Condition N nAccurate     RT
1:         1 6         4 127.50
2:         2 6         4 300.25

If you use data.table, then this is an alternative way:

library(data.table)
setDT(df)[, .(N = .N,
              nAccurate = sum(Accuracy),
              RT = mean(RT[Accuracy==1])),
          by = Condition]

回答2:

Using dplyr package:

library(dplyr)
df %>%
  group_by(Condition) %>%
  summarise(N = n(),
            nAccurate = sum(Accuracy),
            RT = mean(RT[Accuracy == 1]))