From 9835d067a1e45b769298f9be6c37fb34583cf912 Mon Sep 17 00:00:00 2001 From: DeCicco Date: Fri, 1 Feb 2019 14:35:47 -0600 Subject: [PATCH 01/26] Adding percent calcs to output. --- R/runPairs.R | 45 ++++++++++++++++++++++++++++++++------------- 1 file changed, 32 insertions(+), 13 deletions(-) diff --git a/R/runPairs.R b/R/runPairs.R index 3bd0a3b0..09a40c86 100644 --- a/R/runPairs.R +++ b/R/runPairs.R @@ -55,6 +55,9 @@ #' x11 \tab The results using the CQR for year 1, but using the QD specified by the user for year 1.\tab \cr #' x20 \tab The results using the CQR for year 2, but using the QD for the entire period. \cr #' x22 \tab The results for the CQR for year 2, but using the QD specified by the user for year 2. \cr +#' Total Percent Change \tab this is the Total Change divided by x11\cr +#' CQTC Percent \tab this is the CQTC divided by x11\cr +#' QTC Percent \tab this is the QTC divided by x11\cr #' } #' @examples #' eList <- Choptank_eList @@ -321,18 +324,34 @@ runPairs <- function(eList, year1, year2, windowSide, cDeltaTotal <- c22 - c11 cRSpart <- c20 - c10 cFDpart <- cDeltaTotal - cRSpart + fDeltaTotal <- f22 - f11 fRSpart <- f20 - f10 fFDpart <- fDeltaTotal - fRSpart - pairResults <- as.data.frame(matrix(ncol = 7, nrow = 2)) + + totChangePct_conc <- cDeltaTotal/c11 + totChangePct_flux <- fDeltaTotal/f11 + + CQTC_percent_conc <- cRSpart / c11 # CQTC Percent + QTC_percent_conc <- cFDpart / c11 # Q Trend Component Percent + + CQTC_percent_flux <- fRSpart / f11 + QTC_percent_flux <- fFDpart / f11 + + pairResults <- as.data.frame(matrix(ncol = 10, nrow = 2)) colnames(pairResults) <- c("TotalChange", "CQTC", "QTC", - "x10", "x11", "x20", "x22") + "x10", "x11", "x20", "x22", + "Total Percent Change", + "CQTC Percent", + "QTC Percent") rownames(pairResults) <- c("Conc", "Flux") pairResults[1, ] <- c(cDeltaTotal, cRSpart, cFDpart, c10, - c11, c20, c22) + c11, c20, c22, + totChangePct_conc, CQTC_percent_conc, QTC_percent_conc) # 0.00036525 is magic number to convert to million kg/year pairResults[2, ] <- 0.00036525 * c(fDeltaTotal, fRSpart, - fFDpart, f10, f11, f20, f22) + fFDpart, f10, f11, f20, f22, + totChangePct_flux, CQTC_percent_flux, QTC_percent_flux) yearPairInfo <- c(paStart, paLong, year1, year2) names(yearPairInfo) <- c("paStart","paLong","year1","year2") @@ -364,23 +383,23 @@ runPairs <- function(eList, year1, year2, windowSide, as.character(sample1EndDate), "\n") cat("\n Change estimates ", year2, " minus ", year1, "\n") totChange <- format(pairResults[1, 1], digits = 3) - totChangePct <- format(100 * ((c22 - c11)/c11), digits = 2) + totChangePct_conc_f <- format(100 *totChangePct_conc, digits = 2) cat("\n For concentration: total change is ", totChange, "mg/L") - cat("\n expressed as Percent Change is ", totChangePct, "%") - pctRS <- format(100 * (cRSpart/c11), digits = 2) - pctFD <- format(100 * (cFDpart/c11), digits = 2) + cat("\n expressed as Percent Change is ", totChangePct_conc_f, "%") + pctRS <- format(100 * CQTC_percent_conc, digits = 2) + pctFD <- format(100 * QTC_percent_conc, digits = 2) cat("\n\n Concentration v. Q Trend Component ", pctRS, "%\n Q Trend Component ", pctFD, "% \n\n") totChange <- format(pairResults[2, 1], digits = 3) - totChangePct <- format(100 * ((f22 - f11)/f11), digits = 2) + totChangePct_flux_f <- format(100 * (totChangePct_flux), digits = 2) cat("\n For flux: total change is ", totChange, "million kg/year") - cat("\n expressed as Percent Change is ", totChangePct, "%") - pctRS <- format(100 * (fRSpart/f11), digits = 2) - pctFD <- format(100 * (fFDpart/f11), digits = 2) + cat("\n expressed as Percent Change is ", totChangePct_flux_f, "%") + pctRS <- format(100 * CQTC_percent_flux, digits = 2) + pctFD <- format(100 * QTC_percent_flux, digits = 2) cat("\n\n Concentration v. Q Trend Component ", pctRS, "%\n Q Trend Component ", pctFD, "% \n\n") - print(pairResults, digits = 2) + print(pairResults[,1:7], digits = 2) return(pairResults) } \ No newline at end of file From eb1f0650d3f8d9ed22ebedbfc267c2759a91b8d2 Mon Sep 17 00:00:00 2001 From: DeCicco Date: Fri, 1 Feb 2019 14:42:03 -0600 Subject: [PATCH 02/26] Using new outputs --- man/runPairs.Rd | 3 +++ vignettes/Enhancements.Rmd | 2 +- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/man/runPairs.Rd b/man/runPairs.Rd index 52959f07..d63d0431 100644 --- a/man/runPairs.Rd +++ b/man/runPairs.Rd @@ -83,6 +83,9 @@ in the Daily data frame).\cr x11 \tab The results using the CQR for year 1, but using the QD specified by the user for year 1.\tab \cr x20 \tab The results using the CQR for year 2, but using the QD for the entire period. \cr x22 \tab The results for the CQR for year 2, but using the QD specified by the user for year 2. \cr +Total Percent Change \tab this is the Total Change divided by x11\cr +CQTC Percent \tab this is the CQTC divided by x11\cr +QTC Percent \tab this is the QTC divided by x11\cr } } \description{ diff --git a/vignettes/Enhancements.Rmd b/vignettes/Enhancements.Rmd index 654d323b..9f1aea21 100644 --- a/vignettes/Enhancements.Rmd +++ b/vignettes/Enhancements.Rmd @@ -173,7 +173,7 @@ pairResults <- runPairs(eList, Let's take a look at this output. The information about Sample1 and Sample2 is there so we can visually check on the size of our Sample data set to make sure that the default settings for minNumObs and minNumUncen are reasonable. In this case Sample1 and Sample2 are the same, but if we had a **wall** they would be different and we want to make sure that neither of them is too small (e.g. less than 100 samples). -Next we see output that is pretty self-explanatory. We express the change in concentration first in concentration units and then as a percentage change, in this case + `r format(100 * ((pairResults$x22[1] - pairResults$x11[1])/pairResults$x11[1]), digits = 2)` %. Then we see the line that tells us what part of that change was due to a change in the Concentration versus Discharge Trend Component (CQTC). In this case it is `r format(100 * (pairResults$x20[1] - pairResults$x10[1]/pairResults$x11[1]), digits = 2)` %. What this means is that if there had been no change in the probability distribution of discharge the concentration trend would have been `r format(100 * (pairResults$x20[1] - pairResults$x10[1]/pairResults$x11[1]), digits = 2)` %. Then we see the Discharge Trend Component (QTC) which is `r format(100 * ((pairResults$x22[1] - pairResults$x11[1] - (pairResults$x20[1] - pairResults$x10[1]) )/pairResults$x11[1]), digits = 2)` %. This would be the amount of the trend if the Concentration versus Discharge relationships hadn't changed at all over the period, but the probability distribution of discharge had changed. +Next we see output that is pretty self-explanatory. We express the change in concentration first in concentration units and then as a percentage change, in this case + `r format(100 * (pairResults$`Total Percent Change`[1]), digits = 2)` %. Then we see the line that tells us what part of that change was due to a change in the Concentration versus Discharge Trend Component (CQTC). In this case it is `r format(100 * (pairResults$`CQTC Percent`[1]), digits = 2)` %. What this means is that if there had been no change in the probability distribution of discharge the concentration trend would have been `r format(100 * (pairResults$`CQTC Percent`[1]), digits = 2)` %. Then we see the Discharge Trend Component (QTC) which is `r format(100 * (pairResults$`QTC Percent`), digits = 2)` %. This would be the amount of the trend if the Concentration versus Discharge relationships hadn't changed at all over the period, but the probability distribution of discharge had changed. Then we see the exact same kind of results for flux expressed in millions of kg / year. In this case it is a much larger percentage change than we see for concentration and that is because the changes in concentration are most pronounced at high discharge and there is a good deal of positive trend in the upper end of the distribution of discharge. From 00ed91c3225b8e323794d296f2907906ee23f2c1 Mon Sep 17 00:00:00 2001 From: DeCicco Date: Fri, 1 Feb 2019 15:01:15 -0600 Subject: [PATCH 03/26] Fixed some formatting --- vignettes/Enhancements.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vignettes/Enhancements.Rmd b/vignettes/Enhancements.Rmd index 9f1aea21..2d0279f7 100644 --- a/vignettes/Enhancements.Rmd +++ b/vignettes/Enhancements.Rmd @@ -173,7 +173,7 @@ pairResults <- runPairs(eList, Let's take a look at this output. The information about Sample1 and Sample2 is there so we can visually check on the size of our Sample data set to make sure that the default settings for minNumObs and minNumUncen are reasonable. In this case Sample1 and Sample2 are the same, but if we had a **wall** they would be different and we want to make sure that neither of them is too small (e.g. less than 100 samples). -Next we see output that is pretty self-explanatory. We express the change in concentration first in concentration units and then as a percentage change, in this case + `r format(100 * (pairResults$`Total Percent Change`[1]), digits = 2)` %. Then we see the line that tells us what part of that change was due to a change in the Concentration versus Discharge Trend Component (CQTC). In this case it is `r format(100 * (pairResults$`CQTC Percent`[1]), digits = 2)` %. What this means is that if there had been no change in the probability distribution of discharge the concentration trend would have been `r format(100 * (pairResults$`CQTC Percent`[1]), digits = 2)` %. Then we see the Discharge Trend Component (QTC) which is `r format(100 * (pairResults$`QTC Percent`), digits = 2)` %. This would be the amount of the trend if the Concentration versus Discharge relationships hadn't changed at all over the period, but the probability distribution of discharge had changed. +Next we see output that is pretty self-explanatory. We express the change in concentration first in concentration units and then as a percentage change, in this case + `r format(100 * pairResults[1, "Total Percent Change"], digits = 2)` %. Then we see the line that tells us what part of that change was due to a change in the Concentration versus Discharge Trend Component (CQTC). In this case it is `r format(100 * pairResults[1, "CQTC Percent"], digits = 2)` %. What this means is that if there had been no change in the probability distribution of discharge the concentration trend would have been `r format(100 * pairResults[1,"CQTC Percent"], digits = 2)` %. Then we see the Discharge Trend Component (QTC) which is `r format(100 * pairResults[1,"QTC Percent"], digits = 2)` %. This would be the amount of the trend if the Concentration versus Discharge relationships hadn't changed at all over the period, but the probability distribution of discharge had changed. Then we see the exact same kind of results for flux expressed in millions of kg / year. In this case it is a much larger percentage change than we see for concentration and that is because the changes in concentration are most pronounced at high discharge and there is a good deal of positive trend in the upper end of the distribution of discharge. From 80dd5a508584f6b8a1893d7823be40d1ae5193c6 Mon Sep 17 00:00:00 2001 From: DeCicco Date: Fri, 1 Feb 2019 15:24:44 -0600 Subject: [PATCH 04/26] Out of date dist? --- .travis.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 83a54539..1ae27f5c 100644 --- a/.travis.yml +++ b/.travis.yml @@ -6,7 +6,6 @@ language: r cache: packages -dist: trusty matrix: include: From 2051c4a5b6351dca1484ef589b35a7f80ded6a09 Mon Sep 17 00:00:00 2001 From: DeCicco Date: Fri, 1 Feb 2019 15:41:44 -0600 Subject: [PATCH 05/26] Updating test --- tests/testthat/tests_flow_normalization.R | 19 +++++++++++++++---- 1 file changed, 15 insertions(+), 4 deletions(-) diff --git a/tests/testthat/tests_flow_normalization.R b/tests/testthat/tests_flow_normalization.R index 147f2e42..b9e4c33e 100644 --- a/tests/testthat/tests_flow_normalization.R +++ b/tests/testthat/tests_flow_normalization.R @@ -250,21 +250,29 @@ test_that("runPairs",{ expect_equal(attr(pairOut_1, "dateInfo"), attr(pairOut_1_orig, "dateInfo")) expect_equal(attr(pairOut_1, "yearPair"), attr(pairOut_1_orig, "yearPair")) - expect_true(all(names(pairOut_1) %in% c("TotalChange","CQTC","QTC","x10","x11","x20","x22" ))) + expect_true(all(names(pairOut_1) %in% c("TotalChange","CQTC","QTC","x10","x11","x20","x22", + "Total Percent Change", + "CQTC Percent", + "QTC Percent" ))) expect_equal(round(pairOut_1$TotalChange[1], digits = 4), 0.429) # Option 2: pairOut_2 <- runPairs(eList, year1, year2, windowSide = 7) - expect_true(all(names(pairOut_2) %in% c("TotalChange","CQTC","QTC","x10","x11","x20","x22" ))) + expect_true(all(names(pairOut_2) %in% c("TotalChange","CQTC","QTC","x10","x11","x20","x22", + "Total Percent Change", + "CQTC Percent", + "QTC Percent" ))) expect_equal(round(pairOut_2$TotalChange[1], digits = 4), 0.3968) # Option 3: pairOut_3 <- runPairs(eList, year1, year2, windowSide = 0, flowBreak = TRUE, Q1EndDate = "1990-09-30") - expect_true(all(names(pairOut_3) %in% c("TotalChange","CQTC","QTC","x10","x11","x20","x22" ))) + expect_true(all(names(pairOut_3) %in% c("TotalChange","CQTC","QTC","x10","x11","x20","x22","Total Percent Change", + "CQTC Percent", + "QTC Percent"))) expect_equal(round(pairOut_3$TotalChange[1], digits = 4), 0.4081) # Option 4: @@ -272,7 +280,10 @@ test_that("runPairs",{ windowSide = 7, flowBreak = TRUE, Q1EndDate = "1990-09-30") - expect_true(all(names(pairOut_4) %in% c("TotalChange","CQTC","QTC","x10","x11","x20","x22" ))) + expect_true(all(names(pairOut_4) %in% c("TotalChange","CQTC","QTC","x10","x11","x20","x22", + "Total Percent Change", + "CQTC Percent", + "QTC Percent"))) expect_equal(round(pairOut_4$TotalChange[1], digits = 4), 0.3995) }) From f19bcbfa9cd680636347e74ab256028933d382d2 Mon Sep 17 00:00:00 2001 From: DeCicco Date: Fri, 1 Feb 2019 15:59:39 -0600 Subject: [PATCH 06/26] Does this work to stop travis from taking so long? --- .travis.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.travis.yml b/.travis.yml index 1ae27f5c..daa87505 100644 --- a/.travis.yml +++ b/.travis.yml @@ -26,11 +26,11 @@ repos: env: global: - - R_BUILD_ARGS="--no-vignettes --no-manual" - - R_CHECK_ARGS="--no-vignettes --no-manual --as-cran" - NOT_CRAN="true" - _R_CHECK_FORCE_SUGGESTS_=false - + +r_build_args: "--no-build-vignettes --no-manual --no-resave-data"" +r_check_args: "--no-build-vignettes --no-manual --as-cran" warnings_are_errors: true r_github_packages: From 64a345d31c7ffac4ccbb42de3829b9f08fa5479b Mon Sep 17 00:00:00 2001 From: DeCicco Date: Fri, 1 Feb 2019 16:00:36 -0600 Subject: [PATCH 07/26] wayward quote --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index daa87505..3bae2293 100644 --- a/.travis.yml +++ b/.travis.yml @@ -29,7 +29,7 @@ env: - NOT_CRAN="true" - _R_CHECK_FORCE_SUGGESTS_=false -r_build_args: "--no-build-vignettes --no-manual --no-resave-data"" +r_build_args: "--no-build-vignettes --no-manual --no-resave-data" r_check_args: "--no-build-vignettes --no-manual --as-cran" warnings_are_errors: true From 8b9c3879904ae1bd0806442f3d1f3975592471e0 Mon Sep 17 00:00:00 2001 From: DeCicco Date: Mon, 4 Feb 2019 10:18:03 -0600 Subject: [PATCH 08/26] Speeding up vignette building --- vignettes/Green.Cl.RData | Bin 1306591 -> 0 bytes vignettes/eListOut.rds | Bin 0 -> 1631154 bytes vignettes/pairResults.rds | Bin 0 -> 643 bytes vignettes/pairResults2.rds | Bin 0 -> 645 bytes vignettes/rResid.Rmd | 8 +++++--- 5 files changed, 5 insertions(+), 3 deletions(-) delete mode 100644 vignettes/Green.Cl.RData create mode 100644 vignettes/eListOut.rds create mode 100644 vignettes/pairResults.rds create mode 100644 vignettes/pairResults2.rds diff --git a/vignettes/Green.Cl.RData b/vignettes/Green.Cl.RData deleted file mode 100644 index 3a14bceab15515fc18e70d9401e8d47aca9eccf1..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1306591 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8786f236..04aee7b0 100644 --- a/vignettes/rResid.Rmd +++ b/vignettes/rResid.Rmd @@ -107,11 +107,13 @@ In order to use the **rObserved** and **rResid** in making graphs in EGRET the p 2. Run **modelEstimation** in the usual way: **eList <- modelEstimation(eList)** 3. Then augment the **Sample data** frame that is inside of the eList with the command: - **eList <- makeAugmentedSample(eList)** - +```{r augment1, eval=FALSE} +eList <- makeAugmentedSample(eList) +``` + 4. To produce any one of the following graphics, using the **rObserved** or **rResid** values simply add the argument **randomCensored = TRUE** to the call to the graphical function. Note that it doesn't matter if what is being plotted is an observed value or a residual, the argument is always **randomCensored = TRUE**. The original option of showing the vertical lines for censored values remains available as the default for any of these functions. The call can either say **randomCensored = FALSE** or just not include **randomCensored** in the argument list and the graphs will appear without these random values and without the open circle/closed circle symbology. The censored values or censored residuals will be shown as the vertical lines. The functions where this approach applies are these: **plotConcPred, plotConcQ, plotConcTime, plotConcTimeDaily, plotFluxPred, plotFluxQ, plotFluxTImeDaily, plotResidPred, plotResidQ, plotResidTime**. It is also available in the two multiple plot functions: **multiPlotOverview, and fluxBiasMulti** -For example. +For example: ```{r, fig.height = 9, fig.width = 8} multiPlotDataOverview(eList, qUnit = 4, randomCensored = TRUE) From 78af999163b02b906713dea98147c4b45e327296 Mon Sep 17 00:00:00 2001 From: DeCicco Date: Mon, 4 Feb 2019 10:32:35 -0600 Subject: [PATCH 09/26] Still speeding up the vignettes --- vignettes/Enhancements.Rmd | 129 ++++++++++++++++++++++++++++++++----- 1 file changed, 112 insertions(+), 17 deletions(-) diff --git a/vignettes/Enhancements.Rmd b/vignettes/Enhancements.Rmd index 2d0279f7..ed627007 100644 --- a/vignettes/Enhancements.Rmd +++ b/vignettes/Enhancements.Rmd @@ -160,7 +160,7 @@ We can use the **windowSide** argument with a value that is some integer greater In this first example we will specify that **year1** is 1985 and **year2** is 2010 and we will not have a wall, and we will use automatic flow window designation, with a **windowSide** value of 7. -```{r loadData} +```{r loadData, eval=FALSE} library(EGRET) eList <- Choptank_eList @@ -170,6 +170,37 @@ pairResults <- runPairs(eList, windowSide = 7) ``` +```{r loadDataReal, echo=FALSE} +eList <- Choptank_eList +pairResults <- readRDS("pairResults.rds") + +``` + +``` +## +## Choptank River +## Inorganic nitrogen (nitrate and nitrite) +## Water Year +## +## Change estimates 2010 minus 1985 +## +## For concentration: total change is 0.397 mg/L +## expressed as Percent Change is 39 % +## +## Concentration v. Q Trend Component 42 % +## Q Trend Component -3.1 % +## +## +## For flux: total change is 0.0543 million kg/year +## expressed as Percent Change is 51 % +## +## Concentration v. Q Trend Component 32 % +## Q Trend Component 19 % +## +## TotalChange CQTC QTC x10 x11 x20 x22 +## Conc 0.397 0.429 -0.032 1.01 1.03 1.44 1.43 +## Flux 0.054 0.034 0.020 0.12 0.11 0.15 0.16 +``` Let's take a look at this output. The information about Sample1 and Sample2 is there so we can visually check on the size of our Sample data set to make sure that the default settings for minNumObs and minNumUncen are reasonable. In this case Sample1 and Sample2 are the same, but if we had a **wall** they would be different and we want to make sure that neither of them is too small (e.g. less than 100 samples). @@ -234,7 +265,7 @@ summary(eList$Sample$Date) Let's say we want the analysis to only include the discharge data for water years 1980 - 2010. So we would set **QStartDate = "1979-10-01" and QEndDate = "2010-09-30".** Because the dam likely changed the discharge distribution we want to put in a **flowBreak** at the time of the dam construction so we need to specify: **flowBreak = TRUE and Q1EndDate = "1995-05-31"**. And, let's say that we are concerned that there may also be trends in the flow distribution in both the pre-dam period and the post-dam period. So let's set **windowSide = 7**. We also think that the existence of the dam would result in the change in the concentration versus discharge relationship (because the dam creates a large mixing volume and post-dam the concentrations are much less influenced by discharge than was the case before the dam was built). So we want **wall = TRUE and sample1EndDate = "1995-05-31"** Finally, we would like to restrict our analysis to the period April - August of each year and we are doing our pair comparison between the years 1985 and 2010. Thus: **paStart = 4, paLong = 5, year1 = 1985, and year2 = 2010.** All of the other arguments will be left to their default values. -```{r pairs2} +```{r pairs2, eval=FALSE} pairResults2 <- runPairs(eList, year1 = 1985, year2 = 2010, windowSide = 7, flowBreak = TRUE, Q1EndDate = "1995-05-31", wall = TRUE, @@ -242,7 +273,43 @@ pairResults2 <- runPairs(eList, year1 = 1985, year2 = 2010, QStartDate = "1979-10-01", QEndDate = "2010-09-30", paStart = 4, paLong = 5) +``` + +```{r loadPair2, echo=FALSE} +pairResults2 <- readRDS("pairResults2.rds") + +``` + +``` +## +## Choptank River +## Inorganic nitrogen (nitrate and nitrite) +## Season Consisting of Apr May Jun Jul Aug +## +## Sample data set was partitioned with a wall right after 1995-05-31 +## +## Change estimates 2010 minus 1985 +## +## For concentration: total change is 0.412 mg/L +## expressed as Percent Change is 45 % +## +## Concentration v. Q Trend Component 47 % +## Q Trend Component -2.4 % +## +## +## For flux: total change is 0.0456 million kg/year +## expressed as Percent Change is 51 % +## +## Concentration v. Q Trend Component 37 % +## Q Trend Component 14 % +## +## TotalChange CQTC QTC x10 x11 x20 x22 +## Conc 0.412 0.435 -0.022 0.915 0.923 1.35 1.34 +## Flux 0.046 0.033 0.012 0.092 0.089 0.13 0.13 + +``` +```{r pairResultsAttrs} attr(pairResults2, "yearPair") attr(pairResults2, "dateInfo") attr(pairResults2, "SampleBlocks") @@ -278,10 +345,13 @@ One other argument of note is **oldSurface**. It is a logical variable. The only You can also set the last argument **verbose** to FALSE if you want to avoid the printing of percentage progress indicators on the console. -Let's do an analysis with the same set up that we used in the first example. Then we will look at some of the standard outputs we might want to consider. +Let's do an analysis with the same set up that we used in the first example. Then we will look at some of the standard outputs we might want to consider. To comply with CRAN requirements on how long it takes to build a vignette, we’ll leave this example up to be run by the user. -```{r series1} +```{r series1, eval=FALSE} eListOut <- runSeries(eList, windowSide = 7, verbose = FALSE) +``` + +```{r series1run, eval=FALSE} tableResults(eListOut) plotConcHist(eListOut) plotFluxHist(eListOut) @@ -290,7 +360,7 @@ tableChange(eListOut, yearPoints = c(1985, 1995, 2010)) We might be uncomfortable with the slightly jagged nature of the flow-normalized flux line and may prefer to use a higher value of windowSide. Let's see what happens if we increase to **windowSide = 9** . -```{r series2} +```{r series2, eval=FALSE} eListOut <- runSeries(eList, windowSide = 9, verbose = FALSE) plotConcHist(eListOut) plotFluxHist(eListOut) @@ -309,8 +379,7 @@ In the case of **runSeries** we can do uncertainty analysis which provides us wi We will run an analysis here of a very long record that requires the use of several important features of **EGRET 3.0** and **EGRETci 2.0**. The record is from USGS streamgage 09234500, Green River near Greendale, UT and the constituent is chloride (parameter code 00940). The streamgage is located less than a kilometer downstream of Flaming Gorge Dam, which is about 150 meters tall and stores up to 4.7 km^3 which is equivalent to a little more than twice the long-term annual discharge of the river. So it is a very large dam in comparison to the discharge of the river. The closure of the dam was around 1962-1963 and we will, somewhat arbitrarily place it at 1963-03-31. The discharge record we will work with runs from 1950-10-01 to 2018-04-01. The chloride record consists of 601 observations running from 1956-10-04 through 2018-02-01. However, there is a large gap in the chloride record from 2000-08-03 to 2012-08-09 and there are only 51 observations after this gap. ```{r, echo = FALSE} -load("Green.Cl.RData") -eList_Green <- eList +eListOut <- readRDS("eListOut.rds") eList <- Choptank_eList ``` @@ -325,7 +394,7 @@ INFO$shortName <- "Green River near Greendale, UT" eList_Green <- mergeReport(INFO, Daily, Sample) ``` -```{r runSeries, echo=TRUE, eval=TRUE} +```{r runSeries, eval=FALSE} eListOut <- runSeries(eList_Green, windowSide = 12, flowBreak = TRUE, Q1EndDate = "1963-03-31", wall = TRUE, sample1EndDate = "1963-03-01", @@ -333,7 +402,7 @@ eListOut <- runSeries(eList_Green, windowSide = 12, ``` -```{r} +```{r greenOutRun} plotConcHist(eListOut) plotFluxHist(eListOut) tableResults(eListOut) @@ -379,19 +448,17 @@ What we see here is a gradual decline in chloride concentration and once we get Now, we might want to consider how the results would have been different if we had run this same data set with no wall. What we would anticipate is that the transition at the time of the dam closure would be smeared out rather than changing abruptly. Let's run through such a case: -```{r runWall, eval = TRUE} +```{r runWall, eval = FALSE} eListOutNoWall <- runSeries(eList_Green, windowSide = 12, flowBreak = TRUE, Q1EndDate = "1963-03-31", wall = FALSE, verbose = FALSE) -``` - -```{r, fig.height = 6, fig.width = 11} plotContours(eListOutNoWall, 1961, 1966, 10, 100, contourLevels = seq(0,55,5), flowDuration = FALSE) + ``` -The difference is that without the wall the transition in the contour plot is very gradual and doesn't reflect the abrupt nature of the change. +The difference is that without the wall the transition in the contour plot is very gradual and doesn't reflect the abrupt nature of the change. To comply with CRAN requirements on how long it takes to build a vignette, we'll leave this example up to be run by the user. # Group analysis @@ -419,7 +486,7 @@ All of these arguments should look familiar from the **runPairs** or **runSeries As an example, suppose there were a sudden change in a point source of nitrate in the watershed (using the same Choptank watershed example data), and those went into effect at the end of water year 2005 (2004-09-30), but we only wanted to evaluate their impact on the months of March and April. And, furthermore we would like to make a clean separation with the early data to fit the early-period model and the later data to fit the late-period model. We can do this by placing a **wall** at **2004-09-30**. Finally, we want to use GFN, with a windowSide = 7. Here is what we would get. -```{r echo=TRUE, eval=TRUE} +```{r echo=TRUE, eval=FALSE} groupResults <- runGroups(eList, group1firstYear = 1995, group1lastYear = 2004, group2firstYear = 2005, group2lastYear = 2010, @@ -428,6 +495,35 @@ groupResults <- runGroups(eList, paStart = 4, paLong = 2, verbose = FALSE) ``` +``` +## +## Choptank River +## Inorganic nitrogen (nitrate and nitrite) +## Season Consisting of Apr May +## +## Sample data set was partitioned with a wall right after 2004-10-30 +## +## Change estimates for +## average of 2005 through 2010 minus average of 1995 through 2004 +## +## For concentration: total change is 0.153 mg/L +## expressed as Percent Change is 14 % +## +## Concentration v. Q Trend Component 13 % +## Q Trend Component 0.95 % +## +## +## For flux: total change is 0.00931 million kg/year +## expressed as Percent Change is 5.2 % +## +## Concentration v. Q Trend Component 9.1 % +## Q Trend Component -3.9 % +## +## TotalChange CQTC QTC x10 x11 x20 x22 +## Conc 0.1529 0.142 0.0106 1.12 1.12 1.26 1.27 +## Flux 0.0093 0.016 -0.0069 0.18 0.18 0.19 0.19 +``` + The statement of the results in the output are fairly self-explanatory, and very similar to the outputs from runPairs. For completeness, the explanation of these outputs is given here. The last part of the output is the matrix consisting of 2 rows and 7 columns. This is actually the object called groupResults which is what runGroups returns (the only difference being that what is printed has a more limited number of significant figures). @@ -444,7 +540,6 @@ x20: these are the results using the CQR for group 2, but using the QD for the e x22: these are the results for the CQR for group 2, but using the QD specified by the user for group 2. - TotalChange: this is the difference between the results for group2 - group1. They are equal to x22 - x11. This is what is shown above in the output as "total change." CQTC: this number is the difference between the two groups, but only the part that is due to the change in the CQR. It is x20 - x10. In the results reported above as, "Concentration v. Q Trend Component" it is computed as x20 - x10. @@ -453,7 +548,7 @@ QTC: this number is the difference between the two groups, but only the part tha The analysis of uncertainty about these results is done with the **runGroupsBoot** function in the **EGRETci** package. All of the information that sets up the problem is passed to that function via the **groupResults** object. All of the argument values that were used by **runGroups** are in the attributes of **groupResults**. To see them just requires using the following commands. -```{r} +```{r groupResultsAttr, eval=FALSE} attr(groupResults, "groupInfo") attr(groupResults, "dateInfo") attr(groupResults, "SampleBlocks") From 3f79afef8e9da9128ecc96e7d608d5d413366bb7 Mon Sep 17 00:00:00 2001 From: DeCicco Date: Mon, 4 Feb 2019 10:49:23 -0600 Subject: [PATCH 10/26] Let's just lose the appveyor. --- appveyor.yml | 40 ---------------------------------------- 1 file changed, 40 deletions(-) delete mode 100644 appveyor.yml diff --git a/appveyor.yml b/appveyor.yml deleted file mode 100644 index 0ad5519e..00000000 --- a/appveyor.yml +++ /dev/null @@ -1,40 +0,0 @@ -init: - ps: | - $ErrorActionPreference = "Stop" - Invoke-WebRequest http://raw.github.com/krlmlr/r-appveyor/master/scripts/appveyor-tool.ps1 -OutFile "..\appveyor-tool.ps1" - Import-Module '..\appveyor-tool.ps1' - -install: - ps: Bootstrap - -environment: - NOT_CRAN: "true" - -build_script: - - travis-tool.sh install_github USGS-R/dataRetrieval - - travis-tool.sh install_deps - -test_script: - - travis-tool.sh run_tests - -on_failure: - - travis-tool.sh dump_logs - -artifacts: - - path: '*.Rcheck\**\*.log' - name: Logs - - - path: '*.Rcheck\**\*.out' - name: Logs - - - path: '*.Rcheck\**\*.fail' - name: Logs - - - path: '*.Rcheck\**\*.Rout' - name: Logs - - - path: '\*_*.tar.gz' - name: Bits - - - path: '\*_*.zip' - name: Bits From 56d8df5e29ec195db375faf6fa177f0c60926f60 Mon Sep 17 00:00:00 2001 From: DeCicco Date: Mon, 4 Feb 2019 10:51:08 -0600 Subject: [PATCH 11/26] Trying no quotes in travis --- .travis.yml | 4 ++-- README.Rmd | 1 - README.md | 2 -- 3 files changed, 2 insertions(+), 5 deletions(-) diff --git a/.travis.yml b/.travis.yml index 3bae2293..aec4cbb6 100644 --- a/.travis.yml +++ b/.travis.yml @@ -29,8 +29,8 @@ env: - NOT_CRAN="true" - _R_CHECK_FORCE_SUGGESTS_=false -r_build_args: "--no-build-vignettes --no-manual --no-resave-data" -r_check_args: "--no-build-vignettes --no-manual --as-cran" +r_build_args: --no-build-vignettes --no-manual --no-resave-data +r_check_args: --no-build-vignettes --no-manual --as-cran warnings_are_errors: true r_github_packages: diff --git a/README.Rmd b/README.Rmd index e325d573..f9e42177 100644 --- a/README.Rmd +++ b/README.Rmd @@ -20,7 +20,6 @@ knitr::opts_chunk$set( # EGRET [![travis](https://travis-ci.org/USGS-R/EGRET.svg?branch=master)](https://travis-ci.org/USGS-R/EGRET) -[![Build status](https://ci.appveyor.com/api/projects/status/i2hr35abwxx85vgs?svg=true)](https://ci.appveyor.com/project/ldecicco-USGS/EGRET) [![Coverage Status](https://coveralls.io/repos/github/USGS-R/EGRET/badge.svg?branch=master)](https://coveralls.io/github/USGS-R/EGRET?branch=master) [![status](https://img.shields.io/badge/USGS-Research-blue.svg)](https://owi.usgs.gov/R/packages.html#research) [![CRAN version](http://www.r-pkg.org/badges/version/EGRET)](https://cran.r-project.org/package=EGRET) diff --git a/README.md b/README.md index 74a2dd5b..77b5f355 100644 --- a/README.md +++ b/README.md @@ -2,8 +2,6 @@ EGRET ===== [![travis](https://travis-ci.org/USGS-R/EGRET.svg?branch=master)](https://travis-ci.org/USGS-R/EGRET) -[![Build -status](https://ci.appveyor.com/api/projects/status/i2hr35abwxx85vgs?svg=true)](https://ci.appveyor.com/project/ldecicco-USGS/EGRET) [![Coverage Status](https://coveralls.io/repos/github/USGS-R/EGRET/badge.svg?branch=master)](https://coveralls.io/github/USGS-R/EGRET?branch=master) [![status](https://img.shields.io/badge/USGS-Research-blue.svg)](https://owi.usgs.gov/R/packages.html#research) From 62893e72824602615c2641705741cb170227f745 Mon Sep 17 00:00:00 2001 From: DeCicco Date: Mon, 4 Feb 2019 11:06:44 -0600 Subject: [PATCH 12/26] Turn into prettier table --- vignettes/Enhancements.Rmd | 25 ++++++++++++------------- 1 file changed, 12 insertions(+), 13 deletions(-) diff --git a/vignettes/Enhancements.Rmd b/vignettes/Enhancements.Rmd index ed627007..f805dd3e 100644 --- a/vignettes/Enhancements.Rmd +++ b/vignettes/Enhancements.Rmd @@ -223,6 +223,9 @@ Everything on the first row is about concentration (in mg/L). Everything on the |TotalChange|This is the difference between the results from 2010-1985. They are equal to x22 - x11. This is what is shown above in the output as "total change".| |CQTC|This number is the difference between between the two years, but only the part that is due to the change in the CQR. It is x20 - x10. In the results reported above as, "Concentration v. Q Trend Component" it is computed as x20 - x10.| |QTC|This number is the difference between the two years, but only the part that is due to the change in the QD. It is the Total Change - CQTC. Or it can be stated as x22 - x11 - x20 + x10. It is known as the "Q Trend Component."| +|Total Percent Change|This is the Total Change divided by x11| +|CQTC Percent|This is the CQTC divided by x11| +|QTC Percent|This is the QTC divided by x11| One final note about this output is that we can also express the flux information as yields, by dividing by the drainage area. So we can do a matrix multiplication and end up with a new object that still has concentrations in the first row but has yields in the second row. For example if we want to do yield in kg/km^2/year. We could do this. First we see **pairResults** and second we see **pairResultsYield**. @@ -532,19 +535,15 @@ Everything on the first row is about concentration (in mg/L). Everything on the Here's what the columns represent, but we will take them out of order here: -x10: these are the results using the concentration versus discharge relationship (CQR) for group 1, but using the discharge distribution (QD) for the entire period of record (starting with **QStartDate** and ending with **QEndDate**, or if they aren't specified, it is all the discharge data in the **Daily** data frame). - -x11: these are the results using the CQR for group 1, but using the QD specified by the user for group 1. - -x20: these are the results using the CQR for group 2, but using the QD for the entire period. - -x22: these are the results for the CQR for group 2, but using the QD specified by the user for group 2. - -TotalChange: this is the difference between the results for group2 - group1. They are equal to x22 - x11. This is what is shown above in the output as "total change." - -CQTC: this number is the difference between the two groups, but only the part that is due to the change in the CQR. It is x20 - x10. In the results reported above as, "Concentration v. Q Trend Component" it is computed as x20 - x10. - -QTC: this number is the difference between the two groups, but only the part that is due to the change in the QD. It is the Total Change - CQTC. Or it can be stated as x22 - x11 - x20 + x10. In the results it is reported as, "Q Trend Component". +| Name | Description | +|-----------|---------------------------------------------------| +|x10| these are the results using the concentration versus discharge relationship (CQR) for group 1, but using the discharge distribution (QD) for the entire period of record (starting with **QStartDate** and ending with **QEndDate**, or if they aren't specified, it is all the discharge data in the **Daily** data frame).| +|x11| These are the results using the CQR for group 1, but using the QD specified by the user for group 1.| +|x20| These are the results using the CQR for group 2, but using the QD for the entire period.| +|x22| These are the results for the CQR for group 2, but using the QD specified by the user for group 2.| +|TotalChange| This is the difference between the results for group2 - group1. They are equal to x22 - x11. This is what is shown above in the output as "total change."| +|CQTC| |his number is the difference between the two groups, but only the part that is due to the change in the CQR. It is x20 - x10. In the results reported above as, "Concentration v. Q Trend Component" it is computed as x20 - x10.| +|QTC| This number is the difference between the two groups, but only the part that is due to the change in the QD. It is the Total Change - CQTC. Or it can be stated as x22 - x11 - x20 + x10. In the results it is reported as, "Q Trend Component".| The analysis of uncertainty about these results is done with the **runGroupsBoot** function in the **EGRETci** package. All of the information that sets up the problem is passed to that function via the **groupResults** object. All of the argument values that were used by **runGroups** are in the attributes of **groupResults**. To see them just requires using the following commands. From 3249ffef9b7475d15d35faf8616dd0bddb84aca6 Mon Sep 17 00:00:00 2001 From: DeCicco Date: Mon, 4 Feb 2019 15:13:35 -0600 Subject: [PATCH 13/26] updating vignettes --- inst/doc/Enhancements.R | 118 +++--- inst/doc/Enhancements.Rmd | 156 ++++++-- inst/doc/Enhancements.html | 723 +++++++++++-------------------------- inst/doc/parallel.html | 356 ++++-------------- inst/doc/rResid.R | 3 + inst/doc/rResid.Rmd | 8 +- inst/doc/rResid.html | 324 +++-------------- 7 files changed, 534 insertions(+), 1154 deletions(-) diff --git a/inst/doc/Enhancements.R b/inst/doc/Enhancements.R index 97bc4236..210d6862 100644 --- a/inst/doc/Enhancements.R +++ b/inst/doc/Enhancements.R @@ -51,14 +51,19 @@ for(i in 1994:2010) { abline(v = decimalDate("1981-08-06"), col = "blue", lwd = 2) abline(v = decimalDate("2016-01-14"), col = "blue", lwd = 2) -## ----loadData------------------------------------------------------------ -library(EGRET) +## ----loadData, eval=FALSE------------------------------------------------ +# library(EGRET) +# +# eList <- Choptank_eList +# +# pairResults <- runPairs(eList, +# year1 = 1985, year2 = 2010, +# windowSide = 7) +## ----loadDataReal, echo=FALSE-------------------------------------------- eList <- Choptank_eList +pairResults <- readRDS("pairResults.rds") -pairResults <- runPairs(eList, - year1 = 1985, year2 = 2010, - windowSide = 7) ## ----tableOut------------------------------------------------------------ knitr::kable(pairResults, digits = 4) @@ -78,15 +83,20 @@ attr(pairResults, "Other") summary(eList$Daily$Date) summary(eList$Sample$Date) -## ----pairs2-------------------------------------------------------------- -pairResults2 <- runPairs(eList, year1 = 1985, year2 = 2010, - windowSide = 7, flowBreak = TRUE, - Q1EndDate = "1995-05-31", wall = TRUE, - sample1EndDate = "1995-05-31", - QStartDate = "1979-10-01", - QEndDate = "2010-09-30", - paStart = 4, paLong = 5) +## ----pairs2, eval=FALSE-------------------------------------------------- +# pairResults2 <- runPairs(eList, year1 = 1985, year2 = 2010, +# windowSide = 7, flowBreak = TRUE, +# Q1EndDate = "1995-05-31", wall = TRUE, +# sample1EndDate = "1995-05-31", +# QStartDate = "1979-10-01", +# QEndDate = "2010-09-30", +# paStart = 4, paLong = 5) + +## ----loadPair2, echo=FALSE----------------------------------------------- +pairResults2 <- readRDS("pairResults2.rds") + +## ----pairResultsAttrs---------------------------------------------------- attr(pairResults2, "yearPair") attr(pairResults2, "dateInfo") attr(pairResults2, "SampleBlocks") @@ -106,22 +116,23 @@ attr(pairResults2, "Other") # windowS = 0.5, edgeAdjust = TRUE, # verbose = TRUE) -## ----series1------------------------------------------------------------- -eListOut <- runSeries(eList, windowSide = 7, verbose = FALSE) -tableResults(eListOut) -plotConcHist(eListOut) -plotFluxHist(eListOut) -tableChange(eListOut, yearPoints = c(1985, 1995, 2010)) +## ----series1, eval=FALSE------------------------------------------------- +# eListOut <- runSeries(eList, windowSide = 7, verbose = FALSE) -## ----series2------------------------------------------------------------- -eListOut <- runSeries(eList, windowSide = 9, verbose = FALSE) -plotConcHist(eListOut) -plotFluxHist(eListOut) -tableChange(eListOut, yearPoints = c(1985, 1995, 2010)) +## ----series1run, eval=FALSE---------------------------------------------- +# tableResults(eListOut) +# plotConcHist(eListOut) +# plotFluxHist(eListOut) +# tableChange(eListOut, yearPoints = c(1985, 1995, 2010)) + +## ----series2, eval=FALSE------------------------------------------------- +# eListOut <- runSeries(eList, windowSide = 9, verbose = FALSE) +# plotConcHist(eListOut) +# plotFluxHist(eListOut) +# tableChange(eListOut, yearPoints = c(1985, 1995, 2010)) ## ---- echo = FALSE------------------------------------------------------- -load("Green.Cl.RData") -eList_Green <- eList +eListOut <- readRDS("eListOut.rds") eList <- Choptank_eList ## ----loadDataPretendGreen, echo=TRUE, eval=FALSE------------------------- @@ -134,13 +145,13 @@ eList <- Choptank_eList # INFO$shortName <- "Green River near Greendale, UT" # eList_Green <- mergeReport(INFO, Daily, Sample) -## ----runSeries, echo=TRUE, eval=TRUE------------------------------------- -eListOut <- runSeries(eList_Green, windowSide = 12, - flowBreak = TRUE, Q1EndDate = "1963-03-31", - wall = TRUE, sample1EndDate = "1963-03-01", - verbose = FALSE) +## ----runSeries, eval=FALSE----------------------------------------------- +# eListOut <- runSeries(eList_Green, windowSide = 12, +# flowBreak = TRUE, Q1EndDate = "1963-03-31", +# wall = TRUE, sample1EndDate = "1963-03-01", +# verbose = FALSE) -## ------------------------------------------------------------------------ +## ----greenOutRun--------------------------------------------------------- plotConcHist(eListOut) plotFluxHist(eListOut) tableResults(eListOut) @@ -165,15 +176,14 @@ plotContours(eListOut, 1961, 1966, 10, 100, plotContours(eListOut, 1964, 1984, 10, 100, contourLevels = seq(0,55,5), flowDuration = FALSE) -## ----runWall, eval = TRUE------------------------------------------------ -eListOutNoWall <- runSeries(eList_Green, windowSide = 12, - flowBreak = TRUE, Q1EndDate = "1963-03-31", - wall = FALSE, verbose = FALSE) - - -## ---- fig.height = 6, fig.width = 11------------------------------------- -plotContours(eListOutNoWall, 1961, 1966, 10, 100, - contourLevels = seq(0,55,5), flowDuration = FALSE) +## ----runWall, eval = FALSE----------------------------------------------- +# eListOutNoWall <- runSeries(eList_Green, windowSide = 12, +# flowBreak = TRUE, Q1EndDate = "1963-03-31", +# wall = FALSE, verbose = FALSE) +# +# plotContours(eListOutNoWall, 1961, 1966, 10, 100, +# contourLevels = seq(0,55,5), flowDuration = FALSE) +# ## ----eval=FALSE---------------------------------------------------------- # groupResults <- runGroups(eList, windowSide, @@ -190,17 +200,17 @@ plotContours(eListOutNoWall, 1961, 1966, 10, 100, # windowY = 7, windowQ = 2, windowS = 0.5, # edgeAdjust = TRUE, verbose = TRUE) -## ----echo=TRUE, eval=TRUE------------------------------------------------ -groupResults <- runGroups(eList, - group1firstYear = 1995, group1lastYear = 2004, - group2firstYear = 2005, group2lastYear = 2010, - windowSide = 7, wall = TRUE, - sample1EndDate = "2004-10-30", - paStart = 4, paLong = 2, verbose = FALSE) - -## ------------------------------------------------------------------------ -attr(groupResults, "groupInfo") -attr(groupResults, "dateInfo") -attr(groupResults, "SampleBlocks") -attr(groupResults, "Other") +## ----echo=TRUE, eval=FALSE----------------------------------------------- +# groupResults <- runGroups(eList, +# group1firstYear = 1995, group1lastYear = 2004, +# group2firstYear = 2005, group2lastYear = 2010, +# windowSide = 7, wall = TRUE, +# sample1EndDate = "2004-10-30", +# paStart = 4, paLong = 2, verbose = FALSE) + +## ----groupResultsAttr, eval=FALSE---------------------------------------- +# attr(groupResults, "groupInfo") +# attr(groupResults, "dateInfo") +# attr(groupResults, "SampleBlocks") +# attr(groupResults, "Other") diff --git a/inst/doc/Enhancements.Rmd b/inst/doc/Enhancements.Rmd index 654d323b..f805dd3e 100644 --- a/inst/doc/Enhancements.Rmd +++ b/inst/doc/Enhancements.Rmd @@ -160,7 +160,7 @@ We can use the **windowSide** argument with a value that is some integer greater In this first example we will specify that **year1** is 1985 and **year2** is 2010 and we will not have a wall, and we will use automatic flow window designation, with a **windowSide** value of 7. -```{r loadData} +```{r loadData, eval=FALSE} library(EGRET) eList <- Choptank_eList @@ -170,10 +170,41 @@ pairResults <- runPairs(eList, windowSide = 7) ``` +```{r loadDataReal, echo=FALSE} +eList <- Choptank_eList +pairResults <- readRDS("pairResults.rds") + +``` + +``` +## +## Choptank River +## Inorganic nitrogen (nitrate and nitrite) +## Water Year +## +## Change estimates 2010 minus 1985 +## +## For concentration: total change is 0.397 mg/L +## expressed as Percent Change is 39 % +## +## Concentration v. Q Trend Component 42 % +## Q Trend Component -3.1 % +## +## +## For flux: total change is 0.0543 million kg/year +## expressed as Percent Change is 51 % +## +## Concentration v. Q Trend Component 32 % +## Q Trend Component 19 % +## +## TotalChange CQTC QTC x10 x11 x20 x22 +## Conc 0.397 0.429 -0.032 1.01 1.03 1.44 1.43 +## Flux 0.054 0.034 0.020 0.12 0.11 0.15 0.16 +``` Let's take a look at this output. The information about Sample1 and Sample2 is there so we can visually check on the size of our Sample data set to make sure that the default settings for minNumObs and minNumUncen are reasonable. In this case Sample1 and Sample2 are the same, but if we had a **wall** they would be different and we want to make sure that neither of them is too small (e.g. less than 100 samples). -Next we see output that is pretty self-explanatory. We express the change in concentration first in concentration units and then as a percentage change, in this case + `r format(100 * ((pairResults$x22[1] - pairResults$x11[1])/pairResults$x11[1]), digits = 2)` %. Then we see the line that tells us what part of that change was due to a change in the Concentration versus Discharge Trend Component (CQTC). In this case it is `r format(100 * (pairResults$x20[1] - pairResults$x10[1]/pairResults$x11[1]), digits = 2)` %. What this means is that if there had been no change in the probability distribution of discharge the concentration trend would have been `r format(100 * (pairResults$x20[1] - pairResults$x10[1]/pairResults$x11[1]), digits = 2)` %. Then we see the Discharge Trend Component (QTC) which is `r format(100 * ((pairResults$x22[1] - pairResults$x11[1] - (pairResults$x20[1] - pairResults$x10[1]) )/pairResults$x11[1]), digits = 2)` %. This would be the amount of the trend if the Concentration versus Discharge relationships hadn't changed at all over the period, but the probability distribution of discharge had changed. +Next we see output that is pretty self-explanatory. We express the change in concentration first in concentration units and then as a percentage change, in this case + `r format(100 * pairResults[1, "Total Percent Change"], digits = 2)` %. Then we see the line that tells us what part of that change was due to a change in the Concentration versus Discharge Trend Component (CQTC). In this case it is `r format(100 * pairResults[1, "CQTC Percent"], digits = 2)` %. What this means is that if there had been no change in the probability distribution of discharge the concentration trend would have been `r format(100 * pairResults[1,"CQTC Percent"], digits = 2)` %. Then we see the Discharge Trend Component (QTC) which is `r format(100 * pairResults[1,"QTC Percent"], digits = 2)` %. This would be the amount of the trend if the Concentration versus Discharge relationships hadn't changed at all over the period, but the probability distribution of discharge had changed. Then we see the exact same kind of results for flux expressed in millions of kg / year. In this case it is a much larger percentage change than we see for concentration and that is because the changes in concentration are most pronounced at high discharge and there is a good deal of positive trend in the upper end of the distribution of discharge. @@ -192,6 +223,9 @@ Everything on the first row is about concentration (in mg/L). Everything on the |TotalChange|This is the difference between the results from 2010-1985. They are equal to x22 - x11. This is what is shown above in the output as "total change".| |CQTC|This number is the difference between between the two years, but only the part that is due to the change in the CQR. It is x20 - x10. In the results reported above as, "Concentration v. Q Trend Component" it is computed as x20 - x10.| |QTC|This number is the difference between the two years, but only the part that is due to the change in the QD. It is the Total Change - CQTC. Or it can be stated as x22 - x11 - x20 + x10. It is known as the "Q Trend Component."| +|Total Percent Change|This is the Total Change divided by x11| +|CQTC Percent|This is the CQTC divided by x11| +|QTC Percent|This is the QTC divided by x11| One final note about this output is that we can also express the flux information as yields, by dividing by the drainage area. So we can do a matrix multiplication and end up with a new object that still has concentrations in the first row but has yields in the second row. For example if we want to do yield in kg/km^2/year. We could do this. First we see **pairResults** and second we see **pairResultsYield**. @@ -234,7 +268,7 @@ summary(eList$Sample$Date) Let's say we want the analysis to only include the discharge data for water years 1980 - 2010. So we would set **QStartDate = "1979-10-01" and QEndDate = "2010-09-30".** Because the dam likely changed the discharge distribution we want to put in a **flowBreak** at the time of the dam construction so we need to specify: **flowBreak = TRUE and Q1EndDate = "1995-05-31"**. And, let's say that we are concerned that there may also be trends in the flow distribution in both the pre-dam period and the post-dam period. So let's set **windowSide = 7**. We also think that the existence of the dam would result in the change in the concentration versus discharge relationship (because the dam creates a large mixing volume and post-dam the concentrations are much less influenced by discharge than was the case before the dam was built). So we want **wall = TRUE and sample1EndDate = "1995-05-31"** Finally, we would like to restrict our analysis to the period April - August of each year and we are doing our pair comparison between the years 1985 and 2010. Thus: **paStart = 4, paLong = 5, year1 = 1985, and year2 = 2010.** All of the other arguments will be left to their default values. -```{r pairs2} +```{r pairs2, eval=FALSE} pairResults2 <- runPairs(eList, year1 = 1985, year2 = 2010, windowSide = 7, flowBreak = TRUE, Q1EndDate = "1995-05-31", wall = TRUE, @@ -242,7 +276,43 @@ pairResults2 <- runPairs(eList, year1 = 1985, year2 = 2010, QStartDate = "1979-10-01", QEndDate = "2010-09-30", paStart = 4, paLong = 5) +``` +```{r loadPair2, echo=FALSE} +pairResults2 <- readRDS("pairResults2.rds") + +``` + +``` +## +## Choptank River +## Inorganic nitrogen (nitrate and nitrite) +## Season Consisting of Apr May Jun Jul Aug +## +## Sample data set was partitioned with a wall right after 1995-05-31 +## +## Change estimates 2010 minus 1985 +## +## For concentration: total change is 0.412 mg/L +## expressed as Percent Change is 45 % +## +## Concentration v. Q Trend Component 47 % +## Q Trend Component -2.4 % +## +## +## For flux: total change is 0.0456 million kg/year +## expressed as Percent Change is 51 % +## +## Concentration v. Q Trend Component 37 % +## Q Trend Component 14 % +## +## TotalChange CQTC QTC x10 x11 x20 x22 +## Conc 0.412 0.435 -0.022 0.915 0.923 1.35 1.34 +## Flux 0.046 0.033 0.012 0.092 0.089 0.13 0.13 + +``` + +```{r pairResultsAttrs} attr(pairResults2, "yearPair") attr(pairResults2, "dateInfo") attr(pairResults2, "SampleBlocks") @@ -278,10 +348,13 @@ One other argument of note is **oldSurface**. It is a logical variable. The only You can also set the last argument **verbose** to FALSE if you want to avoid the printing of percentage progress indicators on the console. -Let's do an analysis with the same set up that we used in the first example. Then we will look at some of the standard outputs we might want to consider. +Let's do an analysis with the same set up that we used in the first example. Then we will look at some of the standard outputs we might want to consider. To comply with CRAN requirements on how long it takes to build a vignette, we’ll leave this example up to be run by the user. -```{r series1} +```{r series1, eval=FALSE} eListOut <- runSeries(eList, windowSide = 7, verbose = FALSE) +``` + +```{r series1run, eval=FALSE} tableResults(eListOut) plotConcHist(eListOut) plotFluxHist(eListOut) @@ -290,7 +363,7 @@ tableChange(eListOut, yearPoints = c(1985, 1995, 2010)) We might be uncomfortable with the slightly jagged nature of the flow-normalized flux line and may prefer to use a higher value of windowSide. Let's see what happens if we increase to **windowSide = 9** . -```{r series2} +```{r series2, eval=FALSE} eListOut <- runSeries(eList, windowSide = 9, verbose = FALSE) plotConcHist(eListOut) plotFluxHist(eListOut) @@ -309,8 +382,7 @@ In the case of **runSeries** we can do uncertainty analysis which provides us wi We will run an analysis here of a very long record that requires the use of several important features of **EGRET 3.0** and **EGRETci 2.0**. The record is from USGS streamgage 09234500, Green River near Greendale, UT and the constituent is chloride (parameter code 00940). The streamgage is located less than a kilometer downstream of Flaming Gorge Dam, which is about 150 meters tall and stores up to 4.7 km^3 which is equivalent to a little more than twice the long-term annual discharge of the river. So it is a very large dam in comparison to the discharge of the river. The closure of the dam was around 1962-1963 and we will, somewhat arbitrarily place it at 1963-03-31. The discharge record we will work with runs from 1950-10-01 to 2018-04-01. The chloride record consists of 601 observations running from 1956-10-04 through 2018-02-01. However, there is a large gap in the chloride record from 2000-08-03 to 2012-08-09 and there are only 51 observations after this gap. ```{r, echo = FALSE} -load("Green.Cl.RData") -eList_Green <- eList +eListOut <- readRDS("eListOut.rds") eList <- Choptank_eList ``` @@ -325,7 +397,7 @@ INFO$shortName <- "Green River near Greendale, UT" eList_Green <- mergeReport(INFO, Daily, Sample) ``` -```{r runSeries, echo=TRUE, eval=TRUE} +```{r runSeries, eval=FALSE} eListOut <- runSeries(eList_Green, windowSide = 12, flowBreak = TRUE, Q1EndDate = "1963-03-31", wall = TRUE, sample1EndDate = "1963-03-01", @@ -333,7 +405,7 @@ eListOut <- runSeries(eList_Green, windowSide = 12, ``` -```{r} +```{r greenOutRun} plotConcHist(eListOut) plotFluxHist(eListOut) tableResults(eListOut) @@ -379,19 +451,17 @@ What we see here is a gradual decline in chloride concentration and once we get Now, we might want to consider how the results would have been different if we had run this same data set with no wall. What we would anticipate is that the transition at the time of the dam closure would be smeared out rather than changing abruptly. Let's run through such a case: -```{r runWall, eval = TRUE} +```{r runWall, eval = FALSE} eListOutNoWall <- runSeries(eList_Green, windowSide = 12, flowBreak = TRUE, Q1EndDate = "1963-03-31", wall = FALSE, verbose = FALSE) -``` - -```{r, fig.height = 6, fig.width = 11} plotContours(eListOutNoWall, 1961, 1966, 10, 100, contourLevels = seq(0,55,5), flowDuration = FALSE) + ``` -The difference is that without the wall the transition in the contour plot is very gradual and doesn't reflect the abrupt nature of the change. +The difference is that without the wall the transition in the contour plot is very gradual and doesn't reflect the abrupt nature of the change. To comply with CRAN requirements on how long it takes to build a vignette, we'll leave this example up to be run by the user. # Group analysis @@ -419,7 +489,7 @@ All of these arguments should look familiar from the **runPairs** or **runSeries As an example, suppose there were a sudden change in a point source of nitrate in the watershed (using the same Choptank watershed example data), and those went into effect at the end of water year 2005 (2004-09-30), but we only wanted to evaluate their impact on the months of March and April. And, furthermore we would like to make a clean separation with the early data to fit the early-period model and the later data to fit the late-period model. We can do this by placing a **wall** at **2004-09-30**. Finally, we want to use GFN, with a windowSide = 7. Here is what we would get. -```{r echo=TRUE, eval=TRUE} +```{r echo=TRUE, eval=FALSE} groupResults <- runGroups(eList, group1firstYear = 1995, group1lastYear = 2004, group2firstYear = 2005, group2lastYear = 2010, @@ -428,6 +498,35 @@ groupResults <- runGroups(eList, paStart = 4, paLong = 2, verbose = FALSE) ``` +``` +## +## Choptank River +## Inorganic nitrogen (nitrate and nitrite) +## Season Consisting of Apr May +## +## Sample data set was partitioned with a wall right after 2004-10-30 +## +## Change estimates for +## average of 2005 through 2010 minus average of 1995 through 2004 +## +## For concentration: total change is 0.153 mg/L +## expressed as Percent Change is 14 % +## +## Concentration v. Q Trend Component 13 % +## Q Trend Component 0.95 % +## +## +## For flux: total change is 0.00931 million kg/year +## expressed as Percent Change is 5.2 % +## +## Concentration v. Q Trend Component 9.1 % +## Q Trend Component -3.9 % +## +## TotalChange CQTC QTC x10 x11 x20 x22 +## Conc 0.1529 0.142 0.0106 1.12 1.12 1.26 1.27 +## Flux 0.0093 0.016 -0.0069 0.18 0.18 0.19 0.19 +``` + The statement of the results in the output are fairly self-explanatory, and very similar to the outputs from runPairs. For completeness, the explanation of these outputs is given here. The last part of the output is the matrix consisting of 2 rows and 7 columns. This is actually the object called groupResults which is what runGroups returns (the only difference being that what is printed has a more limited number of significant figures). @@ -436,24 +535,19 @@ Everything on the first row is about concentration (in mg/L). Everything on the Here's what the columns represent, but we will take them out of order here: -x10: these are the results using the concentration versus discharge relationship (CQR) for group 1, but using the discharge distribution (QD) for the entire period of record (starting with **QStartDate** and ending with **QEndDate**, or if they aren't specified, it is all the discharge data in the **Daily** data frame). - -x11: these are the results using the CQR for group 1, but using the QD specified by the user for group 1. - -x20: these are the results using the CQR for group 2, but using the QD for the entire period. - -x22: these are the results for the CQR for group 2, but using the QD specified by the user for group 2. - - -TotalChange: this is the difference between the results for group2 - group1. They are equal to x22 - x11. This is what is shown above in the output as "total change." - -CQTC: this number is the difference between the two groups, but only the part that is due to the change in the CQR. It is x20 - x10. In the results reported above as, "Concentration v. Q Trend Component" it is computed as x20 - x10. - -QTC: this number is the difference between the two groups, but only the part that is due to the change in the QD. It is the Total Change - CQTC. Or it can be stated as x22 - x11 - x20 + x10. In the results it is reported as, "Q Trend Component". +| Name | Description | +|-----------|---------------------------------------------------| +|x10| these are the results using the concentration versus discharge relationship (CQR) for group 1, but using the discharge distribution (QD) for the entire period of record (starting with **QStartDate** and ending with **QEndDate**, or if they aren't specified, it is all the discharge data in the **Daily** data frame).| +|x11| These are the results using the CQR for group 1, but using the QD specified by the user for group 1.| +|x20| These are the results using the CQR for group 2, but using the QD for the entire period.| +|x22| These are the results for the CQR for group 2, but using the QD specified by the user for group 2.| +|TotalChange| This is the difference between the results for group2 - group1. They are equal to x22 - x11. This is what is shown above in the output as "total change."| +|CQTC| |his number is the difference between the two groups, but only the part that is due to the change in the CQR. It is x20 - x10. In the results reported above as, "Concentration v. Q Trend Component" it is computed as x20 - x10.| +|QTC| This number is the difference between the two groups, but only the part that is due to the change in the QD. It is the Total Change - CQTC. Or it can be stated as x22 - x11 - x20 + x10. In the results it is reported as, "Q Trend Component".| The analysis of uncertainty about these results is done with the **runGroupsBoot** function in the **EGRETci** package. All of the information that sets up the problem is passed to that function via the **groupResults** object. All of the argument values that were used by **runGroups** are in the attributes of **groupResults**. To see them just requires using the following commands. -```{r} +```{r groupResultsAttr, eval=FALSE} attr(groupResults, "groupInfo") attr(groupResults, "dateInfo") attr(groupResults, "SampleBlocks") diff --git a/inst/doc/Enhancements.html b/inst/doc/Enhancements.html index ec39d2b4..8af11be5 100644 --- a/inst/doc/Enhancements.html +++ b/inst/doc/Enhancements.html @@ -20,67 +20,41 @@

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