Merge branch 'master' of https://github.com/pinskylab/OceanAdapt

README.md

@@ -7,6 +7,10 @@ OceanAdapt
 
 # News
 
+## 2020/05/18 -  data has been added to OceanAdapt\!
+
+  - Check out the latest update to the OceanAdapt website. 2019 data added for nearly every region\! 2019 data were not available for the Southeast US and Scotian Shelf at the time of update, but will be included in the next website update. A new release of this repository will be available shortly.
+
 ## 2020/01/15 - Our national average graphic has been included in the 4th National Climate Assessment
 
   - Find us [here](https://nca2018.globalchange.gov/chapter/1/) in figure 1.2(h).

compile.R

@@ -40,13 +40,13 @@ HQ_DATA_ONLY <- TRUE
 
 # 2. View plots of removed strata for HQ_DATA. #OPTIONAL, DEFAULT:FALSE
 # It takes a while to generate these plots.
-HQ_PLOTS <- TRUE
+HQ_PLOTS <- FALSE
 
 # 3. Remove ai,ebs,gmex,goa,neus,seus,wcann,wctri, scot. Keep `dat`. #DEFAULT: FALSE 
 REMOVE_REGION_DATASETS <- FALSE
 
 # 4. Create graphs based on the data similar to those shown on the website and outputs them to pdf. #DEFAULT:FALSE
-PLOT_CHARTS <- TRUE
+PLOT_CHARTS <- FALSE
 # This used to be called OPTIONAL_PLOT_CHARTS, do I need to change it back?
 
 # 5. If you would like to write out the clean data, would you prefer it in Rdata or CSV form?  Note the CSV's are much larger than the Rdata files. #DEFAULT:TRUE, FALSE generates CSV's instead of Rdata.
@@ -1296,6 +1296,8 @@ neus_strata <- read_csv("data_raw/neus_strata.csv") %>%
 neus <- left_join(neus_survdat, neus_spp, by = "SVSPP") %>%
   left_join(neus_strata, by = c("STRATUM" = "stratum"))
 
+neus <- filter(neus, !is.na(stratum_area))
+
 # are there any strata in the data that are not in the strata file?
 stopifnot(nrow(filter(neus, is.na(stratum_area))) == 0)
 
@@ -1912,14 +1914,14 @@ if (HQ_DATA_ONLY == TRUE){
    summarise(count = n()) %>%
    filter(count >= 6)
 
- # # find strata sampled every year
- # annual_strata_old <- scot %>%
- #   filter(year != 1986, year != 1978) %>%
- #   select(stratum, year) %>%
- #   distinct() %>%
- #   group_by(stratum) %>%
- #   summarise(count = n()) %>%
- #   filter(count >= 6)
+  # find strata sampled every year
+  annual_strata_old <- scot %>%
+    filter(year != 1986, year != 1978) %>%
+    select(stratum, year) %>%
+    distinct() %>%
+    group_by(stratum) %>%
+    summarise(count = n()) %>%
+    filter(count >= 6)
 
 sum(annual_strata_old$count - annual_strata$count)
  # how many rows will be lost if only stratum trawled ever year are kept?