Data Workflows II - Tidyverse

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Impetus

This homework focuses on how we can use tidyverse routines to answer the exact same set of questions we addressed in the previous homework. The operative verbs include:

• Select
  
• Filter
  
• Mutating
  
• Arranging
• Grouping
  
• Summarizing

These can be combined in various ways to gain inferences from the raw data.

Using built-in routines, this amounts to using a lot of logical indices and making intermediate data.frame objects. However, using tidyverse foundations, it becomes a much easier process.

The Data

For these questions, we will be using the data set from the Rice Rivers Center and is loaded in as raw data from the code below.

library( readr )
url <- "https://docs.google.com/spreadsheets/d/1Mk1YGH9LqjF7drJE-td1G_JkdADOU0eMlrP01WFBT8s/pub?gid=0&single=true&output=csv"
rice <- read_csv( url )
summary( rice )

   DateTime            RecordID          PAR           WindSpeed_mph   
 Length:8199        Min.   :43816   Min.   :   0.000   Min.   : 0.000  
 Class :character   1st Qu.:45866   1st Qu.:   0.000   1st Qu.: 2.467  
 Mode  :character   Median :47915   Median :   0.046   Median : 4.090  
                    Mean   :47915   Mean   : 241.984   Mean   : 5.446  
                    3rd Qu.:49964   3rd Qu.: 337.900   3rd Qu.: 7.292  
                    Max.   :52014   Max.   :1957.000   Max.   :30.650  
                                                                       
    WindDir          AirTempF       RelHumidity        BP_HG      
 Min.   :  0.00   Min.   : 3.749   Min.   :15.37   Min.   :29.11  
 1st Qu.: 37.31   1st Qu.:31.545   1st Qu.:42.25   1st Qu.:29.87  
 Median :137.30   Median :37.440   Median :56.40   Median :30.01  
 Mean   :146.20   Mean   :38.795   Mean   :58.37   Mean   :30.02  
 3rd Qu.:249.95   3rd Qu.:46.410   3rd Qu.:76.59   3rd Qu.:30.21  
 Max.   :360.00   Max.   :74.870   Max.   :93.00   Max.   :30.58  
                                                                  
    Rain_in            H2O_TempC       SpCond_mScm      Salinity_ppt   
 Min.   :0.0000000   Min.   :-0.140   Min.   :0.0110   Min.   :0.0000  
 1st Qu.:0.0000000   1st Qu.: 3.930   1st Qu.:0.1430   1st Qu.:0.0700  
 Median :0.0000000   Median : 5.450   Median :0.1650   Median :0.0800  
 Mean   :0.0008412   Mean   : 5.529   Mean   :0.1611   Mean   :0.0759  
 3rd Qu.:0.0000000   3rd Qu.: 7.410   3rd Qu.:0.1760   3rd Qu.:0.0800  
 Max.   :0.3470000   Max.   :13.300   Max.   :0.2110   Max.   :0.1000  
                     NA's   :1        NA's   :1        NA's   :1       
       PH           PH_mv        Turbidity_ntu       Chla_ugl    
 Min.   :6.43   Min.   :-113.8   Min.   :  6.20   Min.   :  1.3  
 1st Qu.:7.50   1st Qu.: -47.8   1st Qu.: 15.50   1st Qu.:  3.7  
 Median :7.58   Median : -43.8   Median : 21.80   Median :  6.7  
 Mean   :7.60   Mean   : -44.5   Mean   : 24.54   Mean   :137.3  
 3rd Qu.:7.69   3rd Qu.: -38.9   3rd Qu.: 30.30   3rd Qu.:302.6  
 Max.   :9.00   Max.   :  28.5   Max.   :187.70   Max.   :330.1  
 NA's   :1      NA's   :1        NA's   :1        NA's   :1      
   BGAPC_CML        BGAPC_rfu         ODO_sat         ODO_mgl     
 Min.   :   188   Min.   :  0.10   Min.   : 87.5   Min.   :10.34  
 1st Qu.:   971   1st Qu.:  0.50   1st Qu.: 99.2   1st Qu.:12.34  
 Median :  1369   Median :  0.70   Median :101.8   Median :12.88  
 Mean   :153571   Mean   : 72.91   Mean   :102.0   Mean   :12.88  
 3rd Qu.:345211   3rd Qu.:163.60   3rd Qu.:104.1   3rd Qu.:13.34  
 Max.   :345471   Max.   :163.70   Max.   :120.8   Max.   :14.99  
 NA's   :1        NA's   :1        NA's   :1       NA's   :1      
    Depth_ft        Depth_m      SurfaceWaterElev_m_levelNad83m
 Min.   :12.15   Min.   :3.705   Min.   :-32.53                
 1st Qu.:14.60   1st Qu.:4.451   1st Qu.:-31.78                
 Median :15.37   Median :4.684   Median :-31.55                
 Mean   :15.34   Mean   :4.677   Mean   :-31.55                
 3rd Qu.:16.12   3rd Qu.:4.913   3rd Qu.:-31.32                
 Max.   :17.89   Max.   :5.454   Max.   :-30.78                
                                                               

The Questions

Just like before, we will be answering the same set of questions as before. However, you should be using tidyverse approaches (pipes and such) to find the answers. Just like before, you should provide your answers as text (e.g., using complete sentences, etc.) and include visual output in tabular or graphical form to support your assertions. The key point here is that you need to develop an evidence-based narrative to address these questions.

1. On average, is there more rain on Mondays, at daytime, or at night?

2. What is the overall trend in salinity and pH? Does this pattern hold when considering each month individually?

3. Turbidity is a measurement of the opaqueness of water. In the rice data, we have a measure of Chlorophyll A in the water. For estimates where there is more than 200 \(µg*l^{-1}\), describe the relationship between these two variables.

4. Show the pattern of tides during the work week that includes Valentines Day in 2004.

5. Summarize estimates of Wind direction for February. Pay close attention to what this variable is actually measuring and how you want to display its underlying patterns.