fixed annual_aggs again

Author: fawda123

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: WRTDStidal
 Type: Package
 Title: Weighted regression for water quality evaluation in tidal waters
-Version: 0.0.49.9000
-Date: 2016-10-12
+Version: 1.0.1.9000
+Date: 2016-11-01
 Author: Marcus W. Beck [aut, cre]
 Maintainer: Marcus W. Beck <[email protected]>
 Description: An adaptation for estuaries (tidal waters) of weighted regression

R/annual_agg.R

@@ -5,6 +5,7 @@
 #' @param dat_in input tidal or tidalmean object
 #' @param mo_strt numeric indicating month to start aggregation years, defaults to October for USGS water year from October to September
 #' @param min_mo numeric value from one to twelve indicating the minimum number of months with observations for averaging by years
+#' @param logspace logical indicating if aggregated data are to be shown in log-space or not
 #' @param ... arguments passed to or from other methods
 #' 
 #' @details WRTDS output is averaged by year for both predictions and flow-normalized predictions. Years are averaged only if one observation is contained in each of the minimum number of months specified by \code{min_mo} averaging, otherwise results are not returned for the given year.  Note that setting \code{min_mo} to values smaller than the default can produce inaccurate trends for years with very few results. 
@@ -30,15 +31,20 @@ annual_agg <- function(dat_in, ...) UseMethod('annual_agg')
 #' @export
 #'
 #' @method annual_agg default
-annual_agg.default <- function(dat_in, mo_strt = 10, min_mo = 9,  ...){
+annual_agg.default <- function(dat_in, mo_strt = 10, min_mo = 9, logspace = TRUE, ...){
 
   # put data in long format by response, fits, norms
   # split by response variable
   # create new year column based on mo_strt
   # average by new year column only if minimum number of months are present
-  dat_agg <- select(dat_in, matches('date|fit|norm|res')) %>% 
-    mutate(res = exp(res)) %>% # this has to be back-transformed before average 
-    tidyr::gather('var', 'val', -date) %>% 
+  dat_agg <- select(dat_in, matches('date|fit|norm|res')) 
+  
+  # this has to be back-transformed before average if values are not shown in logspace
+  if(!logspace)
+    dat_agg <- mutate(dat_agg, res = exp(res))
+ 
+  # take annual averages
+  dat_agg <- tidyr::gather(dat_agg, 'var', 'val', -date) %>% 
     split(.$var) %>% 
     lapply(., function(x){
 
@@ -61,10 +67,13 @@ annual_agg.default <- function(dat_in, mo_strt = 10, min_mo = 9,  ...){
     ungroup %>% 
     rename(date = yrchg) %>% 
     mutate(
-      date = as.Date(paste0(mo_strt, '-01-', date), format = '%m-%d-%Y'),
-      res = log(res)  # transform res back to log)
+      date = as.Date(paste0(mo_strt, '-01-', date), format = '%m-%d-%Y')
       )
 
+  # transform back to log if values not shown in logspace
+  if(!logspace)
+    dat_agg <- mutate(dat_agg, res = log(res))
+  
   return(dat_agg)
 
 }

R/fitplot.R

@@ -123,7 +123,7 @@ fitplot.tidal <- function(dat_in, tau = NULL, predicted = TRUE, annuals = TRUE,
   # annual aggregations if TRUE, otherwise monthly agg
   if(annuals){
 
-    to_plo <- annual_agg(to_plo, min_mo = min_mo, mo_strt = mo_strt)
+    to_plo <- annual_agg(to_plo, min_mo = min_mo, mo_strt = mo_strt, logspace = logspace)
 
   } 
 
@@ -248,7 +248,7 @@ fitplot.tidalmean <- function(dat_in, predicted = TRUE, annuals = TRUE, logspace
   # annual aggregations if TRUE, otherwise monthly agg
   if(annuals){
 
-    to_plo <- annual_agg(to_plo, min_mo = min_mo, mo_strt = mo_strt)
+    to_plo <- annual_agg(to_plo, min_mo = min_mo, mo_strt = mo_strt, logspace = logspace)
 
   } 
 

R/prdnrmplot.R

@@ -124,7 +124,7 @@ prdnrmplot.tidal <- function(dat_in, tau = NULL, annuals = TRUE, logspace = TRUE
   # annual aggregations if TRUE
   if(annuals){
 
-    to_plo <- annual_agg(to_plo, min_mo = min_mo, mo_strt = mo_strt)
+    to_plo <- annual_agg(to_plo, min_mo = min_mo, mo_strt = mo_strt, logspace = logspace)
 
   }
 
@@ -234,7 +234,7 @@ prdnrmplot.tidalmean <- function(dat_in, annuals = TRUE, logspace = TRUE, dt_rng
   # annual aggregations if TRUE
   if(annuals){
 
-    to_plo <- annual_agg(to_plo, min_mo = min_mo, mo_strt = mo_strt)
+    to_plo <- annual_agg(to_plo, min_mo = min_mo, mo_strt = mo_strt, logspace = logspace)
 
   }
 

R/wrtdstrnd.R

@@ -61,7 +61,7 @@ wrtdstrnd.default <- function(dat_in, mobrks, yrbrks, molabs, yrlabs, aves = FAL
   # aggregate/summarize separately for each category
 
   # annual aggs
-  yrdat <- annual_agg(dat_in, mo_strt = mo_strt, min_mo = min_mo) %>% 
+  yrdat <- annual_agg(dat_in, mo_strt = mo_strt, min_mo = min_mo, logspace = FALSE) %>% 
     select(date, norm) %>% 
     mutate(
       date = year(date), 

README.Rmd

@@ -248,7 +248,7 @@ Tabular summaries of model performance and trends over time can be obtained for
 ```{r}
 wrtdsperf(tidfit)
 # setup month, year categories for trend summaries
-mobrks <- c(-Inf, 3, 6, 9, Inf)
+mobrks <- list(c(1, 2, 3), c(4, 5, 6), c(7, 8, 9), c(10, 11, 12))
 yrbrks <- c(-Inf, 1985, 1994, 2003, Inf)
 molabs <- c('JFM', 'AMJ', 'JAS', 'OND')
 yrlabs <- c('1974-1985', '1986-1994', '1995-2003', '2004-2012')

README.md

@@ -390,7 +390,7 @@ wrtdsperf(tidfit)
 
 ```r
 # setup month, year categories for trend summaries
-mobrks <- c(-Inf, 3, 6, 9, Inf)
+mobrks <- list(c(1, 2, 3), c(4, 5, 6), c(7, 8, 9), c(10, 11, 12))
 yrbrks <- c(-Inf, 1985, 1994, 2003, Inf)
 molabs <- c('JFM', 'AMJ', 'JAS', 'OND')
 yrlabs <- c('1974-1985', '1986-1994', '1995-2003', '2004-2012')