possibly passing all checks w/o notes

.travis.yml

@@ -9,7 +9,6 @@ before_install:
 
 install:
   - ./travis-tool.sh install_deps
-  - ./travis_tool.sh install_r Rcpp
 
 script: ./travis-tool.sh run_tests
 

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: SWMPr
 Type: Package
 Title: SWMPr package for estuarine monitoring data
-Version: 1.9.1
-Date: 2015-3-30
+Version: 1.9.2
+Date: 2015-3-31
 Author: Marcus Beck
 Maintainer: Marcus Beck <[email protected]>
 Description: This packages provides functions for retrieving, organizing, and

R/swmpr_analyze.R

@@ -190,27 +190,28 @@ aggregate_metab.swmpr <- function(swmpr_in, by = 'weeks', na.action = na.pass, a
   }
 
   # long-form
-  to_agg <- tidyr::gather(to_agg, Estimate, Value, -date)
+  to_agg <- reshape2::melt(to_agg, measure.vars = c('Pg', 'Rt', 'NEM'))
+  names(to_agg) <- c('date', 'Estimate', 'Value')
   to_agg$Estimate <- as.character(to_agg$Estimate)
 
   # aggregate
-  sum_fun <- function(x, alpha_in = alpha, out){
+  sum_fun <- function(x, alpha_in = alpha){
       x <- na.omit(x)
       means <- mean(x)
       margs <- suppressWarnings(
         qt(1 - alpha_in/2, length(x) - 1) * sd(x)/sqrt(length(x))
       )
       upper <- means + margs
       lower <- means - margs
-      return(get(out))
-    }
-  aggs <- dplyr::group_by(to_agg, date, Estimate)
-  aggs <- dplyr::summarize(aggs,
-    means = sum_fun(Value, out = 'means'),
-    lower = sum_fun(Value, out = 'lower'),
-    upper = sum_fun(Value, out = 'upper')
-  )
 
+      return(c(means, upper, lower))
+    }
+  aggs <- stats::aggregate(Value ~ date + Estimate, to_agg, 
+    FUN = function(x) sum_fun(x, alpha_in = alpha))
+  aggs_vals <- data.frame(aggs[, 'Value'])
+  names(aggs_vals) <- c('means', 'lower', 'upper')
+  aggs <- data.frame(aggs[, c('date', 'Estimate')], aggs_vals)
+
   # return output
   return(aggs)
 
@@ -580,7 +581,9 @@ decomp_cj.swmpr <- function(swmpr_in, param, vals_out = FALSE, ...){
   # otherwise, ggplot
   to_plo <- out
   to_plo <- reshape2::melt(to_plo, id.var = 'Time')
-  plo <- ggplot(to_plo, aes(x = Time, y = value, group = variable)) +
+  plo <- ggplot(to_plo, 
+      aes_string(x = 'Time', y = 'value', group = 'variable')
+    ) +
     geom_line() +
     facet_wrap(~variable, ncol = 1, scales = 'free_y') + 
     theme_bw()
@@ -863,7 +866,7 @@ plot_summary.swmpr <- function(swmpr_in, param, years = NULL, ...){
   to_plo <- dat_plo
   to_plo$month <- factor(to_plo$month, levels = rev(mo_levs), labels = rev(mo_labs))
   p3 <- ggplot(to_plo, aes_string(x = param)) + 
-    geom_histogram(aes(y = ..density..), colour = 'lightblue', binwidth = diff(range(to_plo[, param], na.rm = T))/30) + 
+    geom_histogram(aes_string(y = '..density..'), colour = 'lightblue', binwidth = diff(range(to_plo[, param], na.rm = T))/30) + 
     facet_grid(month ~ .) + 
     xlab(ylab) +
     theme_bw(base_family = 'Times') + 
@@ -882,10 +885,10 @@ plot_summary.swmpr <- function(swmpr_in, param, years = NULL, ...){
   to_plo$month <- factor(to_plo$month, labels = mo_labs, levels = mo_levs)
   names(to_plo)[names(to_plo) %in% param] <- 'V1'
   midpt <- mean(to_plo$V1, na.rm = T)
-  p4 <- ggplot(subset(to_plo, !is.na(V1)), 
-      aes(x = year, y = month, fill = V1)) +
+  p4 <- ggplot(subset(to_plo, !is.na(to_plo$V1)), 
+      aes_string(x = 'year', y = 'month', fill = 'V1')) +
     geom_tile() +
-    geom_tile(data = subset(to_plo, is.na(V1)), 
+    geom_tile(data = subset(to_plo, is.na(to_plo$V1)), 
       aes(x = year, y = month), fill = NA
       )  +
     scale_fill_gradient2(name = ylab,
@@ -903,10 +906,10 @@ plot_summary.swmpr <- function(swmpr_in, param, years = NULL, ...){
   names(to_plo)[names(to_plo) %in% param] <- 'trend'
   to_plo$anom <- with(to_plo, V1 - trend)
   rngs <- max(abs(range(to_plo$anom, na.rm = T)))
-  p5 <- ggplot(subset(to_plo, !is.na(anom)), 
-      aes(x = year, y = month, fill = anom)) +
+  p5 <- ggplot(subset(to_plo, !is.na(to_plo$anom)), 
+      aes_string(x = 'year', y = 'month', fill = 'anom')) +
     geom_tile() +
-    geom_tile(data = subset(to_plo, is.na(anom)), 
+    geom_tile(data = subset(to_plo, is.na(to_plo$anom)), 
       aes(x = year, y = month), fill = NA
       ) +
     scale_fill_gradient2(name = ylab,
@@ -922,7 +925,8 @@ plot_summary.swmpr <- function(swmpr_in, param, years = NULL, ...){
   # annual anomalies
   yr_avg <- mean(yr_agg[, param], na.rm = T)
   yr_agg$anom <- yr_agg[, param] - yr_avg
-  p6 <- ggplot(yr_agg, aes(x = year, y = anom, group = 1, fill = anom)) +
+  p6 <- ggplot(yr_agg, 
+      aes_string(x = 'year', y = 'anom', group = '1', fill = 'anom')) +
     geom_bar(stat = 'identity') +
     scale_fill_gradient2(name = ylab,
       low = 'lightblue', mid = 'lightgreen', high = 'tomato', midpoint = 0
@@ -1083,9 +1087,14 @@ ecometab.swmpr <- function(swmpr_in, depth_val = NULL, metab_units = 'mmol', tra
   months <- as.character(format(dat$datetimestamp, '%m'))
   hours <- as.character(format(dat$datetimestamp, '%H'))
   clim_means <-dplyr:: mutate(dat, months = months, hours = hours) 
-  clim_means <- dplyr::select(clim_means, months, hours, atemp, wspd, bp)
-  clim_means <- dplyr::group_by(clim_means, months, hours)
-  clim_means <- dplyr::summarise_each(clim_means, dplyr::funs(mean(., na.rm = T)))
+  clim_means <- clim_means[, c('months', 'hours', 'atemp', 'wspd', 'bp')]
+  clim_means <- reshape2::melt(clim_means, 
+    measure.vars = c('atemp', 'wspd', 'bp')
+    )
+  clim_means <- dplyr::group_by(clim_means, 'months', 'hours', 'variable')
+  clim_means <- aggregate(value ~ months + hours + variable, clim_means, 
+    FUN = mean, na.rm = T)
+  clim_means <- tidyr::spread(clim_means, 'variable', 'value')
 
   # merge with original data
   to_join <- data.frame(datetimestamp = dat$datetimestamp, months, 
@@ -1118,10 +1127,10 @@ ecometab.swmpr <- function(swmpr_in, depth_val = NULL, metab_units = 'mmol', tra
   dosat <- with(dat, do_mgl/(oxySol(temp * (1000 + sigt)/1000, sal)))
 
   #station depth, defaults to mean depth value, floored at 1 in case not on bottom
-  #uses 'depth.val' if provided
+  #uses 'depth_val' if provided
   if(is.null(depth_val))
     H <- rep(0.5 + mean(pmax(1, dat$depth), na.rm = T), nrow(dat))
-  else H <- rep(depth.val, nrow(dat))
+  else H <- rep(depth_val, nrow(dat))
 
   #use metab_day to add columns indicating light/day, date, and hours of sunlight
   dat <- metab_day(dat, stat)
@@ -1269,13 +1278,14 @@ plot_metab.swmpr <- function(swmpr_in, by = 'months', alpha = 0.05, width = 10,
   to_plo <- aggregate_metab(swmpr_in, by = by, alpha = alpha)
 
   ## base plot
-  p <- ggplot(to_plo, aes(x = date, y = means, group = Estimate)) +
+  p <- ggplot(to_plo, aes_string(x = 'date', y = 'means', group = 'Estimate')) +
     geom_line()
 
   # add bars if not days and alpha not null
   if(by != 'days' & !is.null(alpha))
     p <- p +
-      geom_errorbar(aes(ymin = lower, ymax = upper, group = Estimate), 
+      geom_errorbar(
+        aes_string(ymin = 'lower', ymax = 'upper', group = 'Estimate'), 
       width = width) 
 
   # return blank
@@ -1288,16 +1298,16 @@ plot_metab.swmpr <- function(swmpr_in, by = 'months', alpha = 0.05, width = 10,
     ylabs <- expression(paste('g ', O [2], ' ', m^-2, d^-1))
 
   p <- p + 
-    geom_line(aes(colour = Estimate)) +
-    geom_point(aes(colour = Estimate)) +
+    geom_line(aes_string(colour = 'Estimate')) +
+    geom_point(aes_string(colour = 'Estimate')) +
     theme_bw() +
     theme(axis.title.x = element_blank()) +
     scale_y_continuous(ylabs)
 
   if(by != 'days' & !is.null(alpha))
     p <- p + 
-      geom_errorbar(aes(ymin = lower, ymax = upper,
-        colour = Estimate, group = Estimate), width = width)
+      geom_errorbar(aes_string(ymin = 'lower', ymax = 'upper',
+        colour = 'Estimate', group = 'Estimate'), width = width)
 
   return(p)
 

R/swmpr_misc.R

@@ -297,7 +297,8 @@ param_names <- function(param_type = c('nut', 'wq', 'met')){
 map_reserve <- function(nerr_site_id, zoom = 11, text_sz = 6, text_col = 'black', map_type = 'terrain'){
 
   # subset stat_locs by reserve
-  stats <- stat_locs[grepl(paste0('^', nerr_site_id), stat_locs$station_code), ]
+  dat_locs <- get('stat_locs')
+  stats <- dat_locs[grepl(paste0('^', nerr_site_id), dat_locs$station_code), ]
 
   # base map
   mapImageData <- ggmap::get_map(
@@ -312,8 +313,8 @@ map_reserve <- function(nerr_site_id, zoom = 11, text_sz = 6, text_col = 'black'
   p <- ggmap::ggmap(mapImageData,
     extent = "panel"
       ) + 
-    geom_text(data = stats, aes(x = longitude, y = latitude, 
-      label= station_code), size = text_sz, colour = text_col
+    geom_text(data = stats, aes_string(x = 'longitude', y = 'latitude', 
+      label= 'station_code'), size = text_sz, colour = text_col
       ) +
     ylab('Latitude') +
     xlab('Longitude')
@@ -503,7 +504,9 @@ map_reserve <- function(nerr_site_id, zoom = 11, text_sz = 6, text_col = 'black'
 #' 
 metab_day <- function(dat_in, stat_in){
 
-  stat_meta <- stat_locs[grep(gsub('wq$', '', stat_in), stat_locs$station_code),]
+  # station locations
+  dat_locs <- get('stat_locs')
+  stat_meta <- dat_locs[grep(gsub('wq$', '', stat_in), dat_locs$station_code),]
 
   # all times are standard - no DST!
   gmt_tab <- data.frame(

R/swmpr_organize.R

@@ -211,7 +211,7 @@ qaqcchk.swmpr <- function(swmpr_in){
   # format output as data.frame
   out <- melt(out)
   names(out) <- c('flag', 'count', 'variable')
-  out <- tidyr::spread(out, variable, count)
+  out <- tidyr::spread(out, 'variable', 'count')
   out[is.na(out)] <- 0
 
   # return output

README.Rmd

@@ -22,7 +22,7 @@ SWMPr is an R package that contains functions for retrieving, organizing, and an
 
 To cite this package:
 
-*Beck MW. 2015. SWMPr: An R package for the National Estuarine Research Reserve System.  Version 1.9.1. https://github.com/fawda123/SWMPr*
+*Beck MW. 2015. SWMPr: An R package for the National Estuarine Research Reserve System.  Version 1.9.2. https://github.com/fawda123/SWMPr*
 
 #Installing the package
 

README.html

@@ -90,7 +90,7 @@ <h1>Overview</h1>
 <p>SWMPr is an R package that contains functions for retrieving, organizing, and analyzing estuary monitoring data from the System Wide Monitoring Program (<a href="http://nerrs.noaa.gov/RCDefault.aspx?ID=18">SWMP</a>). SWMP was implemented by the National Estuarine Research Reserve System (<a href="http://nerrs.noaa.gov/">NERRS</a>) in 1995 to provide continuous monitoring data at over 300 stations in 28 estuaries across the United States. SWMP data are maintained online by the Centralized Data Management Office (CDMO). This R package provides several functions to retrieve, organize, and analyze SWMP data from the CDMO. All data obtained from the CDMO should be <a href="http://cdmo.baruch.sc.edu/data/citation.cfm">cited</a> using the format:</p>
 <p><em>National Estuarine Research Reserve System (NERRS). 2012. System-wide Monitoring Program. Data accessed from the NOAA NERRS Centralized Data Management Office website: <a href="http://cdmo.baruch.sc.edu/" class="uri">http://cdmo.baruch.sc.edu/</a>; accessed 12 October 2012.</em></p>
 <p>To cite this package:</p>
-<p><em>Beck MW. 2015. SWMPr: An R package for the National Estuarine Research Reserve System. Version 1.9.1. <a href="https://github.com/fawda123/SWMPr" class="uri">https://github.com/fawda123/SWMPr</a></em></p>
+<p><em>Beck MW. 2015. SWMPr: An R package for the National Estuarine Research Reserve System. Version 1.9.2. <a href="https://github.com/fawda123/SWMPr" class="uri">https://github.com/fawda123/SWMPr</a></em></p>
 </div>
 <div id="installing-the-package" class="section level1">
 <h1>Installing the package</h1>

README.md

@@ -13,7 +13,7 @@ SWMPr is an R package that contains functions for retrieving, organizing, and an
 
 To cite this package:
 
-*Beck MW. 2015. SWMPr: An R package for the National Estuarine Research Reserve System.  Version 1.9.1. https://github.com/fawda123/SWMPr*
+*Beck MW. 2015. SWMPr: An R package for the National Estuarine Research Reserve System.  Version 1.9.2. https://github.com/fawda123/SWMPr*
 
 #Installing the package
 

appveyor.yml

@@ -14,7 +14,7 @@ install:
 
 build_script:
   - travis-tool.sh install_deps
-  - travis-tool.sh install_r Rcpp
+  - travis-tool.sh install_r Rcpp # this is important
 
 test_script:
   - travis-tool.sh run_tests