Had to manually specify column types because after 1000 rows of NAs, read_csv defaults to col_logical.

Calculated total duration for each unique trip.

Catch and effort was not evenly distributed among years or decades.

Locations of salmon catch by month.

Center of salmon catch by decade.

Catch per hours of effort in each 1x1 lat long block - all gear types and years included

Areas where salmon were not caught in orange.

Monthly utilization distributions for all gear types, all years

Mean temperature by block when salmon were caught

## # A tibble: 8 x 5
##   month      mean     n   min   max
##   <fct>     <dbl> <dbl> <dbl> <dbl>
## 1 March       4.1    32   3.6   6.1
## 2 April       4.2   119   2     5.3
## 3 May         5.2  1088   1    10.5
## 4 June        8.4  3763   0.6  15.4
## 5 July       10.6  1371   1    16.5
## 6 August      5.2  3594   1.4  13.4
## 7 September   5    2412   0.5   9.7
## 8 October     6.3  1004   2.1   8.7

Gear types

## # A tibble: 10 x 4
## # Groups:   gear_type [5]
##    gear_type              nosalmon     n     sum
##    <chr>                  <chr>    <int>   <dbl>
##  1 drift net              absent     123   657. 
##  2 drift net              present   1951 17150. 
##  3 longline               absent       6    28.7
##  4 longline               present     33   489. 
##  5 salmon trap and leader absent      53  1081. 
##  6 salmon trap and leader present    214  3652. 
##  7 set net                absent     114   582. 
##  8 set net                present    840  2220. 
##  9 surface trawl          absent      57    64.9
## 10 surface trawl          present     24    28.4