PS 4: Data exploration

Learning objectives

• Practice using R Markdown to implement reproducible data analysis in R
  • Use chunk options to adjust appearance in the knitted document
• Practice basic steps in loading, checking, and preparing a dataset for analysis
• Hone understanding of ggplot to create more polished exploratory graphs
• Explore patterns in the Pasa and Bionutrient Institute data

Background

This week, we will continue our work to describe the data from Pasa and add the data from the Bionutrient Institute. We will learn some more advanced options in R Markdown and ggplot for data visualization and reproducible analysis. You will now work with a combined dataset that includes the Pasa data you explored previously, as well as the full Bionutrient Institute dataset.

Resources

• Data dictionary (for combined dataset)
• Chunk options
  • In addition to the options listed, you can also use results = "hide" to show the code but not the output in the final document
• Graphing resources
  • Customize axes
  • Facetting in ggplot
  • Box plots
  • Violin plots
  • Jitter plot on top of boxplot or violin plot
  • Scatter plots

Part 1: Create an R Markdown file for your problem set

• Make sure you are working in the R Project for this week in Posit Cloud (called Lab_04_Data_exploration)
  • This is important because if I have a question about what you did or how your code is working, I need to be able to find/access your code on Posit Cloud
• Create a new R Markdown document using the green plus sign in upper left
  • Title your document PS 4: Data exploration.
• Navigate to File -> Save As
  • Save the file as follows: 04_ps_Crop_Nutrient.Rmd. (replacing Crop and Nutrient with the name of your crop and nutrient)
• Adjust the R Markdown header
  • Write your student ID number (not your name) as the author of the script
• Use subheadings to organize your document into the sections shown below
  • Use ## for main headings
  • Use ### for subheadings
• Use code chunks to organize your code within each section (for those sections needing code)
  • Make sure that every one of your code chunks is named in the chunk header
  • Both the code and output should be included in your final problem set (this is the default)
  • Follow directions below to use R Markdown chunk options to make your output more readable

Part 2: Set expectations

Take a moment to record your expectations before you begin. Your notes should include the following (3-5 sentences):

• How do you expect the Pasa data to compare to the Bionutrient Institute data?
• How do you expect your nutrient density to vary across…
  • One management practice (of your choice)
  • One measure of soil status (of your choice)
  • Crop variety
• Do you think management or variety will have a larger influence on nutrient density? Why?

Part 3: Data Preparation

• Data loading + checking (code)
  • Load the tidyverse library using library()
  • Load the necessary dataset (combined_clean.csv) using read.csv()
  • Check the structure of the data using str()
  • Generate an initial summary of the data using summary()
  • Adjust chunk options so that the code shows, but the output is hidden in your knitted file
• Data transformation (code)
  • Filter the dataset using filter() to include only your crop and store it as a new dataframe
• Check your data and make sure it loaded correctly

Part 4: Exploratory graphs

A: Distribution of nutrient density

Create a well-formatted graph that shows the distribution of your assigned nutrient variable in the two datasets (Pasa and Bionutrient Institute).

Your graph should have the following characteristics:

• Graph type that is appropriate to show the distribution of a single numerical variable
• Compares Pasa to Bionutrient Institute data, on the same scale. This can be accomplished with:
  • Faceting to show Pasa vs. Bionutrient Institute in different panels (using facet_grid())
  • Adjusting other ggplot options for the particular graph (i.e. by adjusting aesthetics with aes())
  • Pasa vs. Bionutrient Institute is reflected in the variable group
• Uses geom_jitter() to show the data behind the distribution

B: Relationship of nutrient density to management

Create a well-formatted graph that compares the distribution of your assigned nutrient variable across levels of ONE management factor of your choice.

Your graph should have the following characteristics:

• Graph type that is appropriate to compare distribution of a numerical variable across groups
• Compares Pasa to Bionutrient Institute data, on the same scale. This can be accomplished in different ways:
  • Faceting to show Pasa vs. Bionutrient Institute in different panels (using facet_grid())
  • Adjusting other ggplot options for the particular graph (i.e. by adjusting aesthetics inside aes())
  • Pasa vs. Bionutrient Institute is reflected in the variable group
• Uses geom_jitter() to show the data behind the distribution

C: Relationship of nutrient density to soil status

Create a well-formatted graph that compares the distribution of your assigned nutrient variable across levels of ONE metric of soil status, of your choice. Measures of soil status include soil organic matter, soil respiration, and soil nutrient concentration. See the data dictionary for variable names and explanations.

Your graph should have the following characteristics:

• Graph type that is appropriate to visualize the relationship between two numerical variables
• Shows Pasa vs. Bionutrient Institute in different colors, on the same graph (using color =)
  • Example we looked at previously in class

D: Relationship of nutrient density to crop variety

Create a well-formatted graph that compares the distribution of your assigned nutrient variable across crop varieties in the dataset (represented by variable variety).

Your graph should have the following characteristics:

• Graph type that is appropriate to compare distribution of a numerical variable across groups
• Compares Pasa to Bionutrient Institute data, on the same scale. This can be accomplished in different ways:
  • Faceting to show Pasa vs. Bionutrient Institute in different panels (using facet_grid())
  • Adjusting other ggplot options for the particular graph (i.e. by adjusting aesthetics inside aes())
  • Pasa vs. Bionutrient Institute is reflected in the variable group
• Uses geom_jitter() to show the data behind the distribution

Part 5: Compare expectations to data

Revisit the expectations you recorded at the beginning. Examine your graphs and consider them in light of your expectations (3-5 sentences).

• Were your expectations met?
• What have you learned about the data?
• What do you recommend that we do next in light of what you found?

Submit your problem set!

Knit your R Markdown file using the Knit button at the top of the code editor. This is a good check on whether your analysis is reproducible!

To access your file, navigate to the Files tab in the lower right window. Find the .html file for your problem set and click the box next to it. Navigate to More –> Export to download the file. It will likely go to your downloads folder.

Examine the file closely to make sure that it knitted correctly and contains all parts of your problem set. If you need to make revisions, you can simply revise your code and then knit it again. Submit the .html file in the appropriate Moodle dropbox.

Session information

sessionInfo()

R version 4.3.2 (2023-10-31)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Monterey 12.4

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.7.1

loaded via a namespace (and not attached):
 [1] vctrs_0.6.5       httr_1.4.7        cli_3.6.2         knitr_1.45       
 [5] rlang_1.1.3       xfun_0.41         stringi_1.8.3     processx_3.8.3   
 [9] promises_1.2.1    jsonlite_1.8.8    glue_1.7.0        rprojroot_2.0.4  
[13] git2r_0.33.0      htmltools_0.5.7   httpuv_1.6.13     ps_1.7.5         
[17] sass_0.4.8        fansi_1.0.6       rmarkdown_2.25    jquerylib_0.1.4  
[21] tibble_3.2.1      evaluate_0.23     fastmap_1.1.1     yaml_2.3.8       
[25] lifecycle_1.0.4   whisker_0.4.1     stringr_1.5.1     compiler_4.3.2   
[29] fs_1.6.3          pkgconfig_2.0.3   Rcpp_1.0.12       rstudioapi_0.15.0
[33] later_1.3.2       digest_0.6.34     R6_2.5.1          utf8_1.2.4       
[37] pillar_1.9.0      callr_3.7.3       magrittr_2.0.3    bslib_0.6.1      
[41] tools_4.3.2       cachem_1.0.8      getPass_0.2-4