I want to thank everyone for the help. I ended up having to use a loop to
assign values from the table to NinYear. However, as I have played with the
full datasets I have noticed that R is MUCH faster if I use vectors in the
loop rather than columns of a dataframe. In the specific case of 43,000
lines of data, assigning values from the table to the 43,000 elements of a
vector took 6 seconds whereas assigning values from the table to 43,000
elements of a dataframe took 21 minutes. Why is there such a huge
difference?
Tom
Tom La Bone wrote:
>
> Assume that I have the dataframe "data1", which is listed at the end of
> this message. I want count the number of lines that each person has for
> each year. For example, the person with ID=213 has 15 entries (NinYear)
> for 1953. The following bit of code calculates NinYear:
>
> for (i in 1:length(data1$ID)) {
> data1$NinYear[i] <- length(data1[data1$Year==data1$Year[i] &
> data1$ID==data1$ID[i],1]) }
>
> This seems to work but is horribly slow (some files I am working with have
> over 500,000 lines). Can anyone suggest a faster way of doing this,
> perhaps a way that does not use a for loop? Thanks.
>
> Tom
>
> ID Year NinYear
> 209 1971 0
> 209 1971 0
> 213 1951 0
> 213 1951 0
> 213 1953 0
> 213 1953 0
> 213 1953 0
> 213 1953 0
> 213 1953 0
> 213 1953 0
> 213 1953 0
> 213 1953 0
> 213 1953 0
> 213 1953 0
> 213 1953 0
> 213 1953 0
> 213 1953 0
> 213 1953 0
> 213 1953 0
> 213 1954 0
> 213 1954 0
> 213 1954 0
> 213 1954 0
> 213 1954 0
> 213 1954 0
> 213 1954 0
> 213 1954 0
> 213 1954 0
> 213 1954 0
> 213 1954 0
> 213 1955 0
> 213 1955 0
> 234 1953 0
> 234 1953 0
> 234 1953 0
> 234 1953 0
> 234 1953 0
> 234 1958 0
> 234 1958 0
> 234 1965 0
> 234 1965 0
> 234 1965 0
> 249 1952 0
> 249 1952 0
>
>
>
>
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