multiple plots from data frames in a list after a conditional test

I have a tricky problem with applying a function to a list of data frames. Ultimately I want to plot individual time series charts for large data set of drug usage figures.

My dataset comprises 30 different antibiotics with a usage rate that has been collected monthly over a 5 year period. It has 3 columns and 1692 rows.

So far I have made a list of individual data frames for each antibiotic class. (The name of the list is drug and drug.class is a character vector of drug names from the original data frame)

drugList <- list()
n<-length(drug.class)
for (i in 1:n){
  drugList[[i]] <-AB[Drug==(drug.class[i]),]
}

For example, I have 30 开发者_开发知识库data frames in a list with the following columns:

[[29]]
           Drug  Usage      DateA
1353 Tobramycin  5.06 01-Jan-2006
1354 Tobramycin  4.21 01-Feb-2006
1355 Tobramycin  6.34 01-Mar-2006
.
.
. 
          Drug  Usage       DateA
678 Vancomycin  11.62 01-Jan-2006
679 Vancomycin  11.94 01-Feb-2006
680 Vancomycin  14.29 01-Mar-2006

Before each plot is made a logical test is performed to determine if the time series is autocorrelated. The data frmaes in the list are of verying lengths. I have written a function to perform the test as follows:

acTest <- function(){
    id<-ts(1:length(DateA))
    a1<-ts(Usage)
    a2<-lag(a1-1)
    tg<-ts.union(a1,id,a2)
    mg<-lm(a1~a2+bs(id,df=3), data=tg)
    a2Pval <- summary(mg)$coefficients[2, 4]
    if (a2Pval<=0.05) {
        TRUE
    } else {
        FALSE
    }
}

I have previously tested all my functions on individual data frames and they work as expected.

I am trying to work out how to apply the test to each data frame in the drug list. I believe if I can get help working this out I will be in a position to apply the time series functions in the same manner.

Thanks in advance for any assistance offered.

A few suggestions:

Change your acTest function so that it actually accepts a data.frame as a parameter. Otherwise you'll have lots of problems with the function looking for (and modifying) objects named DateA and Usage in the global environment.

acTest <- function(dat){
    id<-ts(1:length(dat$DateA))
    a1<-ts(dat$Usage)
    a2<-lag(a1-1)
    tg<-ts.union(a1,id,a2)
    mg<-lm(a1~a2+bs(id,df=3), data=tg)
    a2Pval <- summary(mg)$coefficients[2, 4]
    if (a2Pval<=0.05) {
        TRUE
    } else {
        FALSE
    }
}

Applying a function to each element of a list is a common task in R. It is (most often) done using lapply.

lapply(drugList,FUN=acTest)

Finally, you can do tasks like this without storing each data frame as a separate list element by using tools like ddply (among others) that split a data frame using one variable, apply a function to each piece and then reassemble them into a single data frame again. In your case, that would look something like:

ddply(AB,.(Drug),.fun = acTest)