v2.0.7.9031

* refactor: Update Shiny app
    + typos
    + indents
    + replace some tables with figures

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: ContDataQC
 Title: Quality Control (QC) of Continous Monitoring Data
-Version: 2.0.7.9031
+Version: 2.0.7.9032
 Authors@R: c(
 	person("Erik W", "Leppo", email="[email protected]",role=c("aut","cre")),
 	person("Ann","Roseberry Lincoln", role="ctb"),

NEWS

@@ -3,12 +3,21 @@ NEWS-ContDataQC
 
 <!-- NEWS.md is generated from NEWS.Rmd. Please edit that file -->
 
-    #> Last Update: 2024-02-27 14:17:40.109261
+    #> Last Update: 2024-03-01 13:34:32.498829
 
 # Version History
 
 ## v2.0.7.9031
 
+2024-03-01
+
+- refactor: Update Shiny app
+  - typos
+  - indents
+  - replace some tables with figures
+
+## v2.0.7.9031
+
 2024-02-27
 
 - refactor: Update Shiny app text and tables, Issue \#156

NEWS.md

@@ -3,12 +3,21 @@ NEWS-ContDataQC
 
 <!-- NEWS.md is generated from NEWS.Rmd. Please edit that file -->
 
-    #> Last Update: 2024-02-27 14:17:40.109261
+    #> Last Update: 2024-03-01 13:34:32.498829
 
 # Version History
 
 ## v2.0.7.9031
 
+2024-03-01
+
+- refactor: Update Shiny app
+  - typos
+  - indents
+  - replace some tables with figures
+
+## v2.0.7.9031
+
 2024-02-27
 
 - refactor: Update Shiny app text and tables, Issue \#156

NEWS.rmd

@@ -21,6 +21,14 @@ cat(paste0("Last Update: ",Sys.time()))
 
 # Version History
 
+## v2.0.7.9031
+2024-03-01
+
+* refactor: Update Shiny app
+    + typos
+    + indents
+    + replace some tables with figures
+
 ## v2.0.7.9031
 2024-02-27
 

inst/shiny-examples/ContDataQC/global.R

@@ -22,7 +22,7 @@ library(shinyjs)
 # Sys.setenv(PATH = paste(Sys.getenv("PATH"), "C:\\Rtools\\bin", sep = ";"))
 
 # Version Number
-version <- "2.0.7.9031"
+version <- "2.0.7.9032"
 
 #Maximum individual file size that can be uploaded is 70 MB
 options(shiny.maxRequestSize = 70 * 1024^2)

inst/shiny-examples/ContDataQC/rmd/App_1b_TestData.rmd

@@ -44,4 +44,4 @@ You’ll see two sets of folders. Within each folder are example input and outpu
 
 * **miniDOT_concatenate** - When data are initially downloaded from miniDOT sensors, there are separate .txt files for each day (in this example, 325 individual files). Go to the **Automated Reformat – miniDOT** tab and run the **Concatenate** function to combine them all into one file. 
 
-*	**miniDOT_reformat** – run the combined file through the Reformat function. The function reformats the file so that it is ready to run through the QC report function. Next, go to the Main **Functions-Import Files**s tab, import the reformatted file, run it through the ‘QC raw data’ function, and check the flagged data (edit if needed).
+*	**miniDOT_reformat** – run the combined file through the Reformat function. The function reformats the file so that it is ready to run through the QC report function. Next, go to the Main **Functions-Import Files** tab, import the reformatted file, run it through the ‘QC raw data’ function, and check the flagged data (edit if needed).

inst/shiny-examples/ContDataQC/rmd/App_1c_FAQ.rmd

@@ -23,7 +23,7 @@ if(boo_DEBUG==TRUE){
 
 **Failure to run:** When the Shiny app fails to run, the screen will gray out and the message 'disconnected from the server' will appear. The problem typically stems from a formatting issue with the input file, in particular the Date/Time field. Check your input file, refresh the app and try again. If you still have problems, click the 'contact us' link at the bottom of this page and request assistance. Be prepared to share  a copy of your input file(s) and the error message.
 
-**Speed:** If you have a slow internet connection, you can run ContDataQC as a Shiny app on your local computer and it will likely be faster. Running it locally requires that you have R software and the ContDataQC R package installed on your computer, which can be downloaded from GitHub <a class="menu__link" href="https://github.com/leppott/ContDataQC" target="_blank">GitHub<span class="usa-tag external-link__tag" title="Exit EPA Website">
+**Speed:** If you have a slow internet connection, you can run ContDataQC as a Shiny app on your local computer and it will likely be faster. Running it locally requires that you have R software and the ContDataQC R package installed on your computer, which can be downloaded from <a class="menu__link" href="https://github.com/leppott/ContDataQC" target="_blank">GitHub<span class="usa-tag external-link__tag" title="Exit EPA Website">
                     <span aria-hidden="true">Exit</span>
                     <span class="u-visually-hidden"> Exit EPA Website</span>
                   </span>
@@ -42,7 +42,7 @@ if(boo_DEBUG==TRUE){
 **Usability on phones:** Mobile use of this app is possible although the screen size of a phone may make it impractical.
 
 **Data summary and visualization:** The ContDataSumViz app has more summary and visualization options.
-Shiny app (beta version): <a class="menu__link" href="https://contdataqcsumviz_containerized_stg.app.cloud.gov/" target="_blank">https://contdataqcsumviz_containerized_stg.app.cloud.gov/<span class="usa-tag external-link__tag" title="Exit EPA Website">
+Shiny app (beta version): <a class="menu__link" href="https://dmap-contdataqcsumviz.app.cloud.gov/" target="_blank">https://dmap-contdataqcsumviz.app.cloud.gov/<span class="usa-tag external-link__tag" title="Exit EPA Website">
                     <span aria-hidden="true">Exit</span>
                     <span class="u-visually-hidden"> Exit EPA Website</span>
                   </span>

inst/shiny-examples/ContDataQC/rmd/App_1d_Tips.rmd

@@ -24,52 +24,77 @@ Below are tips and links to resources from the Regional Monitoring Network (RMN)
 ## Site visits
 
 It helps speed up the Quality Control (QC) process if you follow a checklist during site visits and document anything that might affect the quality of the data (e.g., sensor out of water or buried in sediment, beaver activity, low battery).
+
 * **Resource:** [Site visit checklist](SiteVisitChecklist.zip) [ZIP] 
 
 ## Sensor configuration
 
 Some people have had problems with air and water sensors being out of sync (
-e.g., one records at 11:00 and the other records at 11:07). If you are deploying air and water sensors at a site, make sure you configure them so that they are recording at the same time. This will make data processing faster and easier. 
+e.g., one records at 11:00 and the other records at 11:07). If you are deploying
+air and water sensors at a site, make sure you configure them so that they are
+recording at the same time. This will make data processing faster and easier. 
+
 * **Resource:** [Onset HOBO configuration tips](HOBO_ConfigLaunch_20170803.pdf) [PDF] 
 
 Another issue that sometimes occurs is data overlap (where more than one file has measurements covering part of the same time periods). Make sure you clear the sensor's memory when you download data and relaunch sensors to avoid overlapping data.
+
 * **Resource:** [Onset HOBO download and relaunch tips](HOBO_DataDownload_20170823.pdf) [PDF]
 
 ## QC workflow
 
-There are generally two scenarios: 1) users QC their data after each download and work with one file per site at a time; or 2) users have a backlog of data that cover multiple deployment periods that they aggregate into one file before performing QC. 
+There are generally two scenarios: 1) users QC their data after each download 
+and work with one file per site at a time; or 2) users have a backlog of data 
+that cover multiple deployment periods that they aggregate into one file before
+performing QC.
+
 * **Resources:** Suggested workflows for each scenario:
   + [Single file](Workflow_QC_report_20220824.pdf) [PDF]
   + [Multiple files](Workflow_DataPileup_20220824.pdf) [PDF]
 
 ## Data edits
 
-When reviewing the QC reports, it is important to be consistent in how you handle flagged data. Here is an example of an approach -
+When reviewing the QC reports, it is important to be consistent in how you 
+handle flagged data. Here is an example of an approach -
 
-*	If you are certain a data point is erroneous, delete the measurement and flag as ‘F’. If you are not sure, flag the data as ‘S’ and do not delete. Let the people using the data decide whether to remove questionable measurements from their analysis.
+*	If you are certain a data point is erroneous, delete the measurement and flag 
+as ‘F’. If you are not sure, flag the data as ‘S’ and do not delete. Let the 
+people using the data decide whether to remove questionable measurements from 
+their analysis.
+
+*	Document that you checked each point flagged as ‘F’ and ‘S’ by adding a note 
+or data qualifier to the Comment column.
 
-*	Document that you checked each point flagged as ‘F’ and ‘S’ by adding a note or data qualifier to the Comment column.
 * **Resource:** [List of example data qualifiers](DataQualifiers_20220210.xlsx) [XLSX]
 
 *	Leave missing data cells as is (vs. deleting)
 
 
 ## Accuracy checks
 
-Accuracy checks are comparisons of discrete or in situ measurements taken in the lab and/or in the field with sensor measurements from the closest date/time. The difference between the sensor and discrete measurements should be within the accuracy quoted by the manufacturer (e.g., ±0.2°C if you are using the Onset HOBO proV2 sensor).
+Accuracy checks are comparisons of discrete or in situ measurements taken in the
+lab and/or in the field with sensor measurements from the closest date/time. The
+difference between the sensor and discrete measurements should be within the 
+accuracy quoted by the manufacturer (e.g., ±0.2°C if you are using the Onset 
+HOBO proV2 sensor).
+
 * **Resource:** [Example accuracy check worksheet](EXAMPLE_AccuracyCheckWkst.xlsx) [XLSX] 
 
 ## Visual checks of time series plots
 
-Visual checks of time series plots are an important part of the QC process. Some issues, such as dewatering, sediment burial, ice cover and beaver activity, show fairly common patterns, and we've been compiling examples of these patterns to help people recognize potential QC issues with their data.
+Visual checks of time series plots are an important part of the QC process. Some issues, such as dewatering, sediment burial, ice cover and beaver activity, show fairly common patterns, and we've been compiling examples of these patterns to 
+help people recognize potential QC issues with their data.
+
 * **Resource:** [Visual checks](PlotQC_WatchList_20220824.pdf) [PDF]
 
 ## Checking sensor data against other data sources
 
-Some partners have been downloading data from nearby weather stations and USGS gages, as well as modeled air temperature and precipitation data from sources such as Daymet (<a class="menu__link" href="https://daymet.ornl.gov/getdata" target="_blank">https://daymet.ornl.gov/getdata<span class="usa-tag external-link__tag" title="Exit EPA Website">
+Some partners have been downloading data from nearby weather stations and USGS 
+gages, as well as modeled air temperature and precipitation data from sources 
+such as Daymet (<a class="menu__link" href="https://daymet.ornl.gov/getdata" target="_blank">https://daymet.ornl.gov/getdata<span class="usa-tag external-link__tag" title="Exit EPA Website">
                     <span aria-hidden="true">Exit</span>
                     <span class="u-visually-hidden"> Exit EPA Website</span>
                   </span>
                 </a>), and comparing those data to sensor measurements as part of their QC process.
+
 * **Resource:** [Daymet or USGS gage check](Daymet_Wx_Gage.zip) [ZIP]
 

inst/shiny-examples/ContDataQC/rmd/App_1f_RelatedApps.rmd

@@ -27,7 +27,7 @@ Below are links to some additional Shiny apps and/or R code that may also be use
 
 ### ContDataSumViz, for summarizing and visualizing QC’d continuous sensor data
 
-Shiny app (beta version): <a class="menu__link" href="https://contdataqcsumviz_containerized_stg.app.cloud.gov/" target="_blank">https://contdataqcsumviz_containerized_stg.app.cloud.gov/<span class="usa-tag external-link__tag" title="Exit EPA Website">
+Shiny app (beta version): <a class="menu__link" href="https://dmap-contdataqcsumviz.app.cloud.gov/" target="_blank">https://dmap-contdataqcsumviz.app.cloud.gov/<span class="usa-tag external-link__tag" title="Exit EPA Website">
                     <span aria-hidden="true">Exit</span>
                     <span class="u-visually-hidden"> Exit EPA Website</span>
                   </span>
@@ -65,7 +65,7 @@ Shiny app: <a class="menu__link" href="https://nalms.shinyapps.io/LakeMonitoR/"
                   </span>
                 </a>
 
-GitHub: <a class="menu__link" https://github.com/leppott/LakeMonitoR/" target="_blank">https://github.com/leppott/LakeMonitoR/<span class="usa-tag external-link__tag" title="Exit EPA Website">
+GitHub: <a class="menu__link" href="https://github.com/leppott/LakeMonitoR/" target="_blank">https://github.com/leppott/LakeMonitoR/<span class="usa-tag external-link__tag" title="Exit EPA Website">
                     <span aria-hidden="true">Exit</span>
                     <span class="u-visually-hidden"> Exit EPA Website</span>
                   </span>

inst/shiny-examples/ContDataQC/rmd/App_2a1_HOBO.rmd

@@ -85,49 +85,12 @@ Running the HOBOware reformat function on a HOBO U20 water level logger file.
 
 **Before**
 
-```{r}
-library(readxl)
-library(knitr)
-library(kableExtra)
-# state directories
-table.dir  <- "tables"
-table.file <- "HOBOreformat_BEFORE_20240226.xlsx"
-table.sheet  <- "Sheet1"
-
-table <- read_excel(file.path(table.dir, table.file), sheet = table.sheet
-                     , na = c("NA", ""), trim_ws = TRUE, skip = 0
-                     , col_names = TRUE)  
-# Munge
-## remove extra colnames
-colnames(table)[2:6] <- ""
-
-options(knitr.kable.NA = '')
-# kable(table1)
-table %>% 
-  kbl() %>% 
-  kable_styling(full_width = F, position = "left")
-
-cat("\n\n")
+```{r, fig.alt="HOBO U-20 file before running the reformat function"}
+knitr::include_graphics("RMD_Images/HOBOreformat_BEFORE.jpg")
 ```
 
 **After**
 
-```{r}
-library(readxl)
-library(knitr)
-library(kableExtra)
-# state directories
-table.dir  <- "tables"
-table.file <- "HOBOreformat_AFTER_20240226.xlsx"
-table.sheet  <- "Sheet1"
-
-table <- read_excel(file.path(table.dir, table.file), sheet = table.sheet
-                     , na = c("NA", ""), trim_ws = TRUE, skip = 0
-                     , col_names = TRUE)  
-
-options(knitr.kable.NA = '')
-# kable(table1)
-table %>% 
-  kbl() %>% 
-  kable_styling(full_width = F, position = "left")
+```{r, fig.alt="HOBO U-20 file after running the reformat function."}
+knitr::include_graphics("RMD_Images/HOBOreformat_AFTER.jpg")
 ```

inst/shiny-examples/ContDataQC/rmd/App_2a2_miniDOT1.Rmd

@@ -33,24 +33,8 @@ When data are initially downloaded from miniDOT sensors, there are separate
 
 Below is an example of what the output file looks like after running the **Concatenate** function.
 
-```{r}
-library(readxl)
-library(knitr)
-library(kableExtra)
-# state directories
-table.dir  <- "tables"
-table.file <- "miniDOT_After_Concatenate_20240226.xlsx"
-table.sheet  <- "Sheet1"
-
-table <- read_excel(file.path(table.dir, table.file), sheet = table.sheet
-                     , na = c("NA", ""), trim_ws = TRUE, skip = 0
-                     , col_names = TRUE)  
-
-options(knitr.kable.NA = '')
-# kable(table1)
-table %>% 
-  kbl() %>% 
-  kable_styling(full_width = F, position = "left")
+```{r, fig.alt="miniDOT DO file after running the Concatenate function."}
+knitr::include_graphics("RMD_Images/miniDOT_AFTER_Concatenate.jpg")
 ```
 
 ## Additional Notes

inst/shiny-examples/ContDataQC/rmd/App_2a3_miniDOT2.Rmd

@@ -52,48 +52,15 @@ Below is an example of what files look like before and after running the **Refor
 
 **Before**
 
-```{r}
-library(readxl)
-library(knitr)
-library(kableExtra)
-# state directories
-table.dir  <- "tables"
-table.file <- "miniDOTreformat_ BEFORE_20240226.xlsx"
-table.sheet  <- "Sheet1"
-
-table <- read_excel(file.path(table.dir, table.file), sheet = table.sheet
-                     , na = c("NA", ""), trim_ws = TRUE, skip = 0
-                     , col_names = TRUE)  
-
-options(knitr.kable.NA = '')
-# kable(table1)
-table %>% 
-  kbl() %>% 
-  kable_styling(full_width = F, position = "left")
-
-cat("\n\n")
+
+```{r, fig.alt="miniDOT DO file before running the reformat function."}
+knitr::include_graphics("RMD_Images/miniDOT_Before_Reformat.jpg")
 ```
 
 **After**
 
-```{r}
-library(readxl)
-library(knitr)
-library(kableExtra)
-# state directories
-table.dir  <- "tables"
-table.file <- "miniDOTreformat_ AFTER_20240226.xlsx"
-table.sheet  <- "Sheet1"
-
-table <- read_excel(file.path(table.dir, table.file), sheet = table.sheet
-                     , na = c("NA", ""), trim_ws = TRUE, skip = 0
-                     , col_names = TRUE)  
-
-options(knitr.kable.NA = '')
-# kable(table1)
-table %>% 
-  kbl() %>% 
-  kable_styling(full_width = F, position = "left")
+```{r, fig.alt="miniDOT DO file after running the reformat function."}
+knitr::include_graphics("RMD_Images/miniDOT_After_Reformat.jpg")
 ```
 
 ## Additional Notes

inst/shiny-examples/ContDataQC/rmd/App_3c3_QCThresh_Eval.rmd

@@ -22,6 +22,9 @@ if(boo_DEBUG==TRUE){
 If you have one or more years of continuous data for a site, we encourage you to evaluate the performance of the QC test thresholds for each parameter at that site and customize the configuration file if needed. Make sure you consider what units you are using, since units have a large effect on thresholds.
 
 **Resources for evaluating thresholds:**
+
 * [Pivot tables and charts](EvaluateThresholds.zip) [ZIP] for evaluating the Unrealistic values ('Gross range') and Spike tests in Excel. 
+
 * [R code statistics](TimMartin_R_ThresholdEval.zip) [ZIP] that can help inform thresholds for all four QC tests.
-* [Threshold evaluation worksheet](ThresholdsCheckWorksheet_20220826.xlsx) [XLSX] that lists the default thresholds for each parameter and has columns where you enter customized thresholds for one or multiple sites.
+
+* [Threshold evaluation worksheet](ThresholdsCheckWorksheet_20220826.xlsx) [XLSX] that lists the default thresholds for each parameter and has columns where you enter customized thresholds for one or multiple sites.

inst/shiny-examples/ContDataQC/www/RMD_HTML/App_1b_TestData.html

@@ -55,7 +55,7 @@ <h2>miniDOT DO Sensors</h2>
 <li><p><strong>miniDOT_reformat</strong> – run the combined file through
 the Reformat function. The function reformats the file so that it is
 ready to run through the QC report function. Next, go to the Main
-<strong>Functions-Import Files</strong>s tab, import the reformatted
+<strong>Functions-Import Files</strong> tab, import the reformatted
 file, run it through the ‘QC raw data’ function, and check the flagged
 data (edit if needed).</p></li>
 </ul>

inst/shiny-examples/ContDataQC/www/RMD_HTML/App_1c_FAQ.html

@@ -18,7 +18,7 @@ <h1>Basic Information</h1>
 can run ContDataQC as a Shiny app on your local computer and it will
 likely be faster. Running it locally requires that you have R software
 and the ContDataQC R package installed on your computer, which can be
-downloaded from GitHub
+downloaded from
 <a class="menu__link" href="https://github.com/leppott/ContDataQC" target="_blank">GitHub<span class="usa-tag external-link__tag" title="Exit EPA Website"> <span aria-hidden="true">Exit</span> <span class="u-visually-hidden"> Exit EPA
 Website</span> </span> </a>.</p>
 <p><strong>Internet browsers:</strong> This Shiny app has been tested
@@ -49,7 +49,7 @@ <h1>Basic Information</h1>
 <p><strong>Data summary and visualization:</strong> The ContDataSumViz
 app has more summary and visualization options. Shiny app (beta
 version):
-<a class="menu__link" href="https://contdataqcsumviz_containerized_stg.app.cloud.gov/" target="_blank"><a href="https://contdataqcsumviz_containerized_stg.app.cloud.gov/" class="uri">https://contdataqcsumviz_containerized_stg.app.cloud.gov/</a><span class="usa-tag external-link__tag" title="Exit EPA Website"> <span aria-hidden="true">Exit</span> <span class="u-visually-hidden"> Exit EPA
+<a class="menu__link" href="https://dmap-contdataqcsumviz.app.cloud.gov/" target="_blank"><a href="https://dmap-contdataqcsumviz.app.cloud.gov/" class="uri">https://dmap-contdataqcsumviz.app.cloud.gov/</a><span class="usa-tag external-link__tag" title="Exit EPA Website"> <span aria-hidden="true">Exit</span> <span class="u-visually-hidden"> Exit EPA
 Website</span> </span> </a></p>
 <p>R code:
 <a class="menu__link" href="https://github.com/USEPA/dmap-ContDataQCSumViz" target="_blank"><a href="https://github.com/USEPA/dmap-ContDataQCSumViz" class="uri">https://github.com/USEPA/dmap-ContDataQCSumViz</a><span class="usa-tag external-link__tag" title="Exit EPA Website"> <span aria-hidden="true">Exit</span> <span class="u-visually-hidden"> Exit EPA