Ages and lengths for a hypothetical sample in Westerheim and Ricker (1979).

## Format

A data frame of 2369 observations on the following 3 variables:

ID

Unique fish identifiers

len

Length of an individual fish

age

Age of an individual fish

## Source

Simulated from Table 2A in Westerheim, S.J. and W.E. Ricker. 1979. Bias in using age-length key to estimate age-frequency distributions. Journal of the Fisheries Research Board of Canada. 35:184-189. CSV file

## Details

Age-length data in 5-cm increments taken exactly from Table 2A of the source which was a sample from a hypothetical population in which year-class strength varied in the ratio 2:1 and the rate of increase in length decreased with age. Actual lengths in each 5-cm interval were simulated with a uniform distribution. The aged fish in this file were randomly selected and an assessed age was assigned according to the information in Table 2A.

## Topic(s)

• Age-Length Key

## Examples

str(WR79)
#> 'data.frame':	2369 obs. of  3 variables:
#>  $ID : int 1 2 3 4 5 6 7 8 9 10 ... #>$ len: int  37 37 39 37 37 35 42 42 42 44 ...
#>  $age: int NA NA NA NA 4 4 NA NA NA NA ... head(WR79) #> ID len age #> 1 1 37 NA #> 2 2 37 NA #> 3 3 39 NA #> 4 4 37 NA #> 5 5 37 4 #> 6 6 35 4 ## Extract the aged sample WR79.aged <- subset(WR79,!is.na(age)) str(WR79.aged) #> 'data.frame': 203 obs. of 3 variables: #>$ ID : int  5 6 21 32 40 57 59 70 94 117 ...
#>  $len: int 37 35 42 43 40 41 44 46 45 47 ... #>$ age: int  4 4 4 4 4 4 4 4 4 4 ...

## Extract the length sample
WR79.length <- subset(WR79,is.na(age))
str(WR79.length)
#> 'data.frame':	2166 obs. of  3 variables:
#>  $ID : int 1 2 3 4 7 8 9 10 11 12 ... #>$ len: int  37 37 39 37 42 42 42 44 44 43 ...
#>  \$ age: int  NA NA NA NA NA NA NA NA NA NA ...