Palmer Penguins
Palmer Penguins
AP Research CT [Biology]
JulianJacklin
Computational Thinking (CT) modules bridge the gap between students' emerging proficiency in subject-area content, and the their ability to use computers to address complex problems in any field. Computational thinking consists of sets of skill we may use to approach complex problems in a concise, systematic, and reproducible way. Many of these skills are found in computer science: breaking-down a problem into digestible parts, recognizing patterns, filtering out unnecessary information or prioritizing essential data, and designing a set of steps or rules to approach a solution are all skills one cultivates when working with computers; hence, computational thinking. However, this type of problem-solving can be applied to meet challenges of almost any kind.
In this module, you will be discovering biological differences between species of antarctic penguins and their implications as discussed in this module's primary document: Ecological Sexual Dimorphism and Environmental Variability within a Community of Antarctic Penguins (Genus Pygoscelis) (Gorman et al., 2014). Biological research is a broad group of studies aimed at better understanding life and living things. Some questions in biology—say, about all antarctic penguins—are too complex or infeasible for a single human or group of humans to figure out on their own. This is where computers—but more crucially, computational thinking—play a role. Throughout the course of this module, you will pose a research question, collect data, visualize data, and communicate your findings using empirical evidence from the study.
This Computational Thinking module is intended for integration into the AP Research course.
In this module, you will be discovering biological differences between species of antarctic penguins and their implications as discussed in this module's primary document: Ecological Sexual Dimorphism and Environmental Variability within a Community of Antarctic Penguins (Genus Pygoscelis) (Gorman et al., 2014). Biological research is a broad group of studies aimed at better understanding life and living things. Some questions in biology—say, about all antarctic penguins—are too complex or infeasible for a single human or group of humans to figure out on their own. This is where computers—but more crucially, computational thinking—play a role. Throughout the course of this module, you will pose a research question, collect data, visualize data, and communicate your findings using empirical evidence from the study.
This Computational Thinking module is intended for integration into the AP Research course.
Research Question
Research Question
Background
Background
Gorman et al. (2014) asked one specific question: do male and female penguins differ physically, and does that depend on species or environment? But the dataset itself, including bill measurements, flipper length, body mass, species, sex, island, and year, can support many other questions the authors never asked.
Consider this!
Consider this!
To generate your own question, think about:
Swapping the outcome - Instead of body measurements, ask about species differences.
Swapping the grouping variable - Compare by island instead of sex.
Change the relationship - Test whether one variable predicts another (e.g., flipper length → body mass) instead of comparing groups.
And before finalizing, check your question:
- Can it be answered with variables that actually exist in the data?
- Is it specific (names exact variables/comparison)?
- Is there enough data in each group (as in groups of penguins) to compare meaningfully?
- Is this genuine research or just a quick, online search?
Swapping the outcome - Instead of body measurements, ask about species differences.
Swapping the grouping variable - Compare by island instead of sex.
Change the relationship - Test whether one variable predicts another (e.g., flipper length → body mass) instead of comparing groups.
And before finalizing, check your question:
- Can it be answered with variables that actually exist in the data?
- Is it specific (names exact variables/comparison)?
- Is there enough data in each group (as in groups of penguins) to compare meaningfully?
- Is this genuine research or just a quick, online search?
My Question:
My Question:
Data Collection
Data Collection
Methodology
Methodology
How do the researchers collect data? What are two important considerations for collecting said data? These can be of your own making, or directly from the study.
Import
Import
To work with our data, we need to first import it into our environment—meaning, we just need to have it on our screen, to be able to work with it. Fortunately, this dataset already exists within the Wolfram Data Repository, so we need not do much to grab it for ourselves. Run the following line of code to view the dataset.
In[]:=
ResourceData["Sample Tabular Data: Palmer Penguins"]
Out[]=
Tabular
How many penguins were observed in this study as shown by the data? How many variables were used, and what were they? You should use the Dimension and ResourceData functions to help answer these questions
Hint! You can begin typing any function in Wolfram Language input, and info will appear that may help you understand how to use it.
Hint! You can begin typing any function in Wolfram Language input, and info will appear that may help you understand how to use it.
Data Visualization
Data Visualization
In[]:=
data:=ResourceData["Sample Tabular Data: Palmer Penguins"]
Variables
Variables
Before visualizing anything, lets first identify what variables we'd like to use to answer our research question, and take note of how they are written in the dataset (BIll Length is not the same as bill_length—why do you think that is?).
grouped=GroupBy[data,Key["INSERT GROUPING VARIABLE"]];
Graphing
Graphing
Graphs and plots allow us to communicate trends in data clearly, typically catering towards wider audiences (why might this be?). In WL, there are several ways to plot data. Using the dataset and your chosen variables, your task is to determine which of the plots below communicates trends in your data the best (i.e. which graphs work, and which give you an error).
In[]:=
ListPlot[Normal/@(#[All,{"INSERT X VARIABLE","INSERT Y VARIABLE"}]&/@grouped),PlotLegendsKeys[grouped],PlotLabel"Scatterplot",FrameLabel{"INSERT VARIABLE X NAME","INSERT VARIABLE Y NAME"},FrameTrue]
counts=Normal[#[All,"INSERT VARIABLE X"]]&/@grouped;Histogram[Values[counts],ChartLegendsKeys[counts],PlotLabel"Histogram",FrameLabel{"INSERT VARIABLE NAME","Count"},FrameTrue,ChartStyleOpacity[0.5]]
lengths=Normal[#[All,"INSERT VARIABLE Y"]]&/@grouped;BoxWhiskerChart[lengths,ChartLabelsKeys[lengths],PlotLabel"Boxplot",FrameTrue]
Trends
Trends
What trends or patterns seem to appear in your data? What might these say about your research question? Do your findings make sense to you intuitively?
Results
Results
Now that we've got some visualizations for our data, it is time to use statistics to make empirical conclusions in regards to our research question.
Linear Regression: Can this predict that?
Linear Regression: Can this predict that?
If you are trying to understand if one variable (like bill length; my predictor) can predict another (like bill depth; my outcome), use the code and corresponding output below.
In[]:=
predictorOutcome=data[All,{"INSERT PREDICTOR","INSERT OUTCOME"}];model=LinearModelFit[predictorOutcome,x,x]["ParameterTable"]
Analysis of Variance (ANOVA): Is there a difference between these groups of things?
Analysis of Variance (ANOVA): Is there a difference between these groups of things?
If you are trying to understand if 3 or more groups (species) of associations (penguins) are statistically different by some quantity (bill length, bill depth, etc), use the code and corresponding output below.
In[]:=
<<ANOVA`group=Normal[data[All,"INSERT GROUP"]];quantity=QuantityMagnitude[Normal[data[All,"INSERT QUANTITY"]]];onewayList=Transpose[{group,quantity}];ANOVA[onewayList]
Conclusion
Conclusion
Discussion
Discussion
Given everything you have learned in this module, were you able to answer your research question? If you were able to, detail what empirical evidence you've collected that supports this claim(s).
If you were not able to adequately answer your research question, ask yourself: could I have answered my question using only the data provided? Was there something meaningful you learned about the topic regardless?
If you were not able to adequately answer your research question, ask yourself: could I have answered my question using only the data provided? Was there something meaningful you learned about the topic regardless?
Next Steps
Next Steps
How would you expand on the work of these researchers? Is there a different sample you would consider? A different research focus? Would you change how the research was conducted. Discuss this below.