1- # Homework
1+ # BMI 565/665 Bioinformatics Programming and Scripting
22
33Submit source code and write-up (including program output) through Sakai.
44
5+ 56## Background
67
78A bunch of your friends really like wine, specifically Portuguese wine. One
@@ -52,14 +53,12 @@ You like to deal with comma-separated files (CSVs). Unfortunately, you find out
5253that the data comes in a "semi-colon" separated file.
5354
5455Use ` sed ` to convert these "semi-colon" separated files into a comma-separated
55- files.
56- 57- Save these converted data into the directory ` data ` .
56+ files, and save these converted data into the directory ` data ` .
5857
5958
6059** Subset Data**
6160
62- For your analysis you only want a couple physicochemical variables to check.
61+ For your analysis, you only want a couple physicochemical variables to check.
6362There are a total of 12 variables, but you're only interested in:
6463
6564- Citric acid
@@ -79,28 +78,28 @@ for good wine.
7978| ` white_wine_poor.csv ` | <= 5 | Poor quality white wine |
8079| ` white_wine_good.csv ` | > 5 | Good quality white wine |
8180
81+ ** Hint** : ` awk ` can be used to quickly subset the data and create the 4 files.
82+ 8283Put there four files into the ` data ` directory.
8384
8485
8586** Compare Low and High Quality**
8687
87- Let's use Python to help us figure out what makes wine good or not.
88- 89- Create a Python function to read in data from a given path and calculate the
90- average value of a given variable name.
88+ Now use Python code to determine what makes wine good or not. Create a Python
89+ function to read in data from a given path and calculate the average value of a
90+ given variable name.
9191
9292``` python
9393# Example
9494avg_chloride_results = calculate_avg_value(data, " chlorides"
9595```
9696
97- You want to be lazy and automate as much as possible. So let's create a Python
98- function that takes in an array of the file names and returns a dictionary.
97+ You want to automate this as much as possible. So create a Python function
98+ that takes in a list of the file names and returns a dictionary.
9999
100- The dictionary will have four keys equal to just the file names they come from
101- e.g. the key of ` white_wine_good.csv ` will be ` white_wine_good ` . The values of
102- each key will be another dictionary with each key being the average value of
103- one of the four variables we're interested in:
100+ The dictionary will have four keys equal to the file names (e.g. the key of ` white_wine_good.csv ` will be ` white_wine_good ` ). The values of
101+ each filename key will be another dictionary with keys being the averages of
102+ each variable:
104103
105104- Citric acid
106105- Chlorides
@@ -116,37 +115,16 @@ avg_values = find_average_wines(wine_paths)
116115
117116** Save Results**
118117
119- 120- Write a Python function to save your dictionary of results to four separate
121- files. Save your dictionaries as JavaScript Object Notation (JSON) files.
122- 123- Use the built-in ` json ` Python package. Here's a hint on using it.
124- 125- ``` python
126- # Example on using the json package
127- import json
128- 129- your_dictionary = {" some_date" " date"
130- f = open (' destFile.txt' ' w+'
131- f.write(json.dumps(your_dictionary))
132- f.close()
133- ```
134- 135- Save your four results into a directory ` results ` .
118+ Use the ` cPickle ` Python module to save the resulting dictionary to a file in a
119+ directory called ` results ` (Note: you'll have to create this directory
120+ beforehand).
136121
137122
138- ** Challenge **
123+ ** Wrap-Up Workflow **
139124
140- You want to automate everything as much as possible, so you want to create a
141- Makefile to make everything. There are two Make rule: ` all ` and ` clean ` .
142- 143- ``` shell
144- # Run the entire analysis
145- make all
146- 147- # Remove all downloaded and created files from data/, download/, results/
148- make clean
149- ```
125+ Now, to automate the entire workflow, create bash scripts that will
126+ automatically download and subset the data, then run the analysis (calculating
127+ average values) and save the results.
150128
151129
152130## Homework File Structure
@@ -156,8 +134,8 @@ analysis.
156134
157135```
158136.
159- |-- analyze_wine.py
160- |-- analysis.sh
137+ |-- LastName_hw2.sh
138+ |-- LastName_analyze_wine.py
161139|-- data/
162140|-- results/
163141`-- download/
@@ -169,3 +147,5 @@ analysis.
169147- A single bash script to automate your analysis
170148- A Python script to calculate the average citric acid, chlorides, pH, and
171149 alcohol values of good and poor quality red and white wine.
150+ - A brief write-up describing the workflow that was implemented and results
151+ produced (` LastName_hw2.doc ` )
0 commit comments