Fix ggplot2 deprecation warnings

modules/data.atmosphere/R/debias_met_regression.R

@@ -1034,8 +1034,8 @@ debias.met.regression <- function(train.data, source.data, n.ens, vars.debias=NU
       print(
         ggplot2::ggplot(data=dat.pred[dat.pred$Year>=mean(dat.pred$Year)-1 & dat.pred$Year<=mean(dat.pred$Year)+1,]) +
           ggplot2::geom_ribbon(ggplot2::aes(x=.data$Date, ymin=.data$lwr, ymax=.data$upr, fill="corrected"), alpha=0.5) +
-          ggplot2::geom_line(ggplot2::aes(x=.data$Date, y=mean, color="corrected"), size=0.5) +
-          ggplot2::geom_line(ggplot2::aes(x=.data$Date, y=.data$obs, color="original"), size=0.5) +
+          ggplot2::geom_line(ggplot2::aes(x=.data$Date, y=mean, color="corrected"), linewidth=0.5) +
+          ggplot2::geom_line(ggplot2::aes(x=.data$Date, y=.data$obs, color="original"), linewidth=0.5) +
           ggplot2::scale_color_manual(values=c("corrected" = "red", "original"="black")) +
           ggplot2::scale_fill_manual(values=c("corrected" = "red", "original"="black")) +
           ggplot2::guides(fill=F) +
@@ -1058,7 +1058,7 @@ debias.met.regression <- function(train.data, source.data, n.ens, vars.debias=NU
       grDevices::png(file.path(path.diagnostics, paste(ens.name, v, "day2.png", sep="_")), height=6, width=6, units="in", res=220)
       print(
         ggplot2::ggplot(data=stack.sims[stack.sims$Year>=mean(stack.sims$Year)-2 & stack.sims$Year<=mean(stack.sims$Year)+2,]) +
-          ggplot2::geom_line(ggplot2::aes(x=.data$Date, y=values, color=ind), size=0.2, alpha=0.8) +
+          ggplot2::geom_line(ggplot2::aes(x=.data$Date, y=values, color=ind), linewidth=0.2, alpha=0.8) +
           ggplot2::ggtitle(paste0(v, " - example ensemble members (daily slice)")) +
           ggplot2::theme_bw()
       )
@@ -1074,8 +1074,8 @@ debias.met.regression <- function(train.data, source.data, n.ens, vars.debias=NU
       print(
         ggplot2::ggplot(data=dat.yr[,]) +
           ggplot2::geom_ribbon(ggplot2::aes(x=.data$Year, ymin=.data$lwr, ymax=.data$upr, fill="corrected"), alpha=0.5) +
-          ggplot2::geom_line(ggplot2::aes(x=.data$Year, y=mean, color="corrected"), size=0.5) +
-          ggplot2::geom_line(ggplot2::aes(x=.data$Year, y=.data$obs, color="original"), size=0.5) +
+          ggplot2::geom_line(ggplot2::aes(x=.data$Year, y=mean, color="corrected"), linewidth=0.5) +
+          ggplot2::geom_line(ggplot2::aes(x=.data$Year, y=.data$obs, color="original"), linewidth=0.5) +
           ggplot2::scale_color_manual(values=c("corrected" = "red", "original"="black")) +
           ggplot2::scale_fill_manual(values=c("corrected" = "red", "original"="black")) +
           ggplot2::guides(fill=F) +

modules/uncertainty/R/plots.R

@@ -93,7 +93,7 @@ plot_sensitivity <- function(sa.sample, sa.spline, trait, y.range = c(0, 50), me
   post.x <- seq(from = min(sa.sample), to = max(sa.sample), length.out = LENGTH_OUT)
 
   saplot <- saplot + ## plot spline function
-    ggplot2::geom_line(ggplot2::aes(x=.data$x, y=.data$y), data = data.frame(x = post.x, y = sa.spline(post.x)), size = linesize) + 
+    ggplot2::geom_line(ggplot2::aes(x=.data$x, y=.data$y), data = data.frame(x = post.x, y = sa.spline(post.x)), linewidth = linesize) + 
     ## plot points used to evaluate spline
     ggplot2::geom_point(ggplot2::aes(x=.data$x, y=.data$y), data = data.frame(x = sa.sample, y = sa.spline(sa.sample)), 
                size = dotsize) + # indicate median with larger point
@@ -115,7 +115,7 @@ plot_sensitivity <- function(sa.sample, sa.spline, trait, y.range = c(0, 50), me
     prior.x <- seq(from = min(prior.sa.sample), to = max(prior.sa.sample), length.out = LENGTH_OUT)
     saplot <- saplot + ## plot spline
       ggplot2::geom_line(ggplot2::aes(x = .data$x, y= .data$y), data = data.frame(x = prior.x, y = prior.sa.spline(prior.x)), 
-                         size = linesize, color = "grey") + ## plot points used to evaluate spline
+                         linewidth = linesize, color = "grey") + ## plot points used to evaluate spline
       ggplot2::geom_point(ggplot2::aes(x= .data$x, y= .data$y), data = data.frame(x = prior.sa.sample, y = prior.sa.spline(prior.sa.sample)), 
                           size = dotsize, color = "grey") + ## indicate location of medians
       ggplot2::geom_point(ggplot2::aes(x = .data$x, y= .data$y), data = data.frame(x = prior.sa.sample[median.i], y = prior.sa.spline(prior.sa.sample[median.i])), 

shiny/ForecastingDashboard/animated_WCr_graphs.R

@@ -78,7 +78,7 @@ qle_lower = -50
 
 p <-ggplot(nee.data, aes(group = start_date, ids = start_date, frame = start_date)) + #, label = NEE - Predicted
     geom_ribbon(aes(x = Time, ymin=Lower, ymax=Upper, fill="95% Confidence Interval"), alpha = 0.4)  + 
-    geom_line(aes(x = Time, y = NEE, color = "Observed Data"), size = 1) + 
+    geom_line(aes(x = Time, y = NEE, color = "Observed Data"), linewidth = 1) + 
     geom_line(aes(x = Time, y = Predicted, color = "Predicted Mean")) + 
     ggtitle(paste0("Net Ecosystem Exchange for ", frame_start, " to ", frame_end, ", Willow Creek, Wisconsin")) +
     scale_color_manual(name = "Legend", labels = c("Predicted Mean", "Observed Data"), values=c("Predicted Mean" = "skyblue1", "Observed Data" = "firebrick4")) +
@@ -90,7 +90,7 @@ p <-ggplot(nee.data, aes(group = start_date, ids = start_date, frame = start_dat
 
 q <- ggplot(le.data, aes(group = start_date, ids = start_date, frame = start_date)) + #, label= LE - Predicted
   geom_ribbon(aes(x = Time, ymin=Lower, ymax=Upper, fill="95% Confidence Interval"), alpha = 0.4)  + 
-  geom_line(aes(x = Time, y = LE, color = "Observed Data"), size = 1) + 
+  geom_line(aes(x = Time, y = LE, color = "Observed Data"), linewidth = 1) + 
   geom_line(aes(x = Time, y = Predicted, color = "Predicted Mean")) + 
   ggtitle(paste0("Latent Energy for ", frame_start, " to ", frame_end, ", Willow Creek, Wisconsin")) +
   scale_color_manual(name = "Legend", labels = c("Predicted Mean", "Observed Data"), values=c("Predicted Mean" = "skyblue1", "Observed Data" = "firebrick4")) +

shiny/ViewMet/server.R

@@ -187,7 +187,7 @@ server <- function(input, output, session) {
     p_overlay <- ggplot(rv$plot.data) + geom_line(aes(x=date, y=var, color=met)) + 
       ylab(input$var) + ggtitle(input$var)
 
-    p_facet <-   ggplot(rv$plot.data) + geom_line(aes(x=date, y=var, color=met), size=1) + 
+    p_facet <-   ggplot(rv$plot.data) + geom_line(aes(x=date, y=var, color=met), linewidth=1) + 
       ylab(input$var) + ggtitle(input$var) + 
       facet_grid(met ~ .)