Breast Cancer Diagnosis Dataset Analysis

Question 1

Please submit a R file (“HW3_LastName_FirstName.R”) along with the PDF file. Use the space to answer questions, explain problem-solving steps, and/or write R commands.

Breast cancer is the most diagnosed cancer among American women. About 1 in 8 U.S. Women will develop invasive breast cancer over the course of her lifetime [1]. Wisconsin Diagnostic Breast Cancer (WDBC) dataset provides 286 samples of 9 variables related to breast cancer diagnosis [2-3]:

i. Age: age (in years at last birthday) of the patient at the time of diagnosis.

ii. Menopause: whether the patient is pre- or post- menopausal at time of diagnosis.

iii. Tumor.size: the greatest diameter (in mm) of the excised tumor.

iv. Inv.nodes: the number (range 0 - 39) of axillary lymph nodes that contain metastatic breast cancer visible on histological examination.

v. Node.caps: if the cancer does metastasize to a lymph node, although outside the original site of the tumor it may remain “contained” by the capsule of the lymph node. However, over time, and with more aggressive disease, the tumor may replace the lymph node and then penetrate the capsule, allowing it to invade the surrounding tissues.

vi. Delig.malig: degree of malignancy as the histological grade (range 1-3) of the tumor – tumors that are grade 1 predominantly consist of cells that, while neoplastic, retain many of their usual characteristics; grade 3 tumors predominately consist of cells that are highly abnormal.

vii. Breast: breast cancer may obviously occur in either breast.

viii. Breast.quad: four quadrants of breast, using the nipple as a central point;

ix. Irradiat: if received radiation therapy as treatment that uses high-energy x-rays to destroy cancer cells.

(1) Download “breast-cancer.csv” from Canvas. Spend some time understanding the dataset and perform necessary data preparation. Hint: refer to steps in HW2. Q1(1). [10 pts]

(2) Propose a question that you would like to answer with the dataset. [5 pts]

(3) Manipulate the dataset and prepare for the visualization. [10 pts]

(4) Create a visualization that helps you answer the question of interest. Elaborate briefly on the reason why you choose such graph. [15 pts]

(5) Tune the appearance, such as color, size. Make sure to include labels. [10 pts]

(6) Elaborate on the insights you learn. Add annotations for extra points. Please attach the graph. You can add a page if you need more space. [10+5 pts]

Note: This is an example only to illustrate the format for (2) and (6). It does NOT mean that it is a good visualization or argument. You should NOT create a visualization that is very similar this one.

Is there a relationship between menopause stage, tumor sizes and tumor type on different positions for women of mid-age (40-59)?

We can visually inspect an increased likelihood of detecting malignant tumors that are large sized on the left breast for participants who are post menopause between 50-59. There are fewer data points of participants who are post-menopause between age 40 – 49 compared to the other sectors. For those participants, the likelihood of diagnosing benign tumors is low. 

The Airbnb NYC data provides information such as host name, price, number of reviews. Download“AB_NYC_2019.csv” from Canvas and explore the dataset.

(1) Propose a question of interest. Create a heatmap and tune its appearance. [20 pts] Hint: make a meaningful subset based on the proposed question.

(2) Propose a different question of interest. Create a treemap and tune its appearance. [20 pts]