Data Regression Tools - Week 1

Describing the data

The sample of 6504 students in 7^th to 12^th grade was taken from 80 high schools and 52 of their feeder schools from across the United States. The ethnic composition was predominantly White (N=4038, 62.3%), Black (N=1483, 22.9%), Asian(N=212, 3.3%), Native American/Indian (N=68, 1.0%), Other(N=362, 5.6%), and Multiple(N=322, 5.0%). The gender composition was Female (N=3356, 51.6%), Male(N=3147, 48.4%) and one respondent was unknown.

The school-based clustered sampling of the 132 school was primarily conducted by Quality Education Data Inc. through the use of in-school questionnaires, in-home interviews, school administrator questionnaires, in-home interviews, and parent questionnaires.

Grade retention was drawn from the question “Were you ever retained or held back a grade?” Students who responded “Yes” were then asked which grade(s) they had been retained. The data was combined into one variable and binned into four categories, “Never held back”, “Elementary School”, “Secondary” and “Multiple”

Violence was drawn from three different questions with the stem “How often the last 12 months have you…”

• ·         Gotten into a fight?
• ·         Pulled a knife or gun on someone?
• ·         Shot or stabbed someone?

Respondents who indicated any level of involvement in any of the three categories were listed as positive for violence while all who had not indicated any involvement were coded as negative for violence.

The age variable was calculated by subtracting the birth year from the year the survey was completed. Since the age variable itself is not used in the calculations, it's exact calculation isn't pertinent. Three respondents had a negative age calculation using this method and that data was disregarded.

Code to extract the above information (except pie chart)

1. import pandas as pd  
2. import numpy as np  
3. import matplotlib.pyplot as plt  
4.   
5.   
6. filename="addhealth_pds.csv"  
7. data = pd.read_csv(filename, low_memory=False)  
8.   
9.   
10. data['H1GI6A']=data['H1GI6A'].replace(6, np.nan)  
11. data['H1GI6A']=data['H1GI6A'].replace(8, np.nan)  
12. data['H1GI6B']=data['H1GI6B'].replace(6, np.nan)  
13. data['H1GI6C']=data['H1GI6C'].replace(6, np.nan)  
14. data['H1GI6D']=data['H1GI6D'].replace(6, np.nan)  
15. data['H1GI6E']=data['H1GI6E'].replace(6, np.nan)  
16.   
17. data['H1GI6B']=data['H1GI6B'].replace(8, np.nan)  
18. data['H1GI6C']=data['H1GI6C'].replace(8, np.nan)  
19. data['H1GI6D']=data['H1GI6D'].replace(8, np.nan)  
20. data['H1GI6E']=data['H1GI6E'].replace(8, np.nan)  
21. data['BIO_SEX']=data['BIO_SEX'].replace(6, np.nan)  
22.   
23. data['NUMETHNIC']=(data['H1GI6A']+ data['H1GI6B'] + data['H1GI6C'] + data['H1GI6D']  
24.  + data['H1GI6E'])  
25.   
26. data["AGE"]=data['IYEAR'] - data['H1GI1Y']  
27. #data["AGE"]=data["AGE"].replace(-1, np.nan)  
28.   
29. age=data['AGE'].value_counts()  
30. print(age)  
31. agedesc=data['AGE'].describe()  
32. print('\nStatistical Description of Age')  
33. print(agedesc)  
34.   
35.   
36. genderlabel={1:"Male", 2:'Female', 6:'unknown'}  
37. data['BIO_SEX']=data['BIO_SEX'].map(genderlabel).astype('category')  
38. gender = data['BIO_SEX'].value_counts()  
39.   
40. genderprc =data['BIO_SEX'].value_counts(normalize=True)  
41. print("\nCount of Gender")  
42. print(gender)  
43. print("\nPercent of each Gender")  
44. print(genderprc)  
45.   
46. def ethnic (row):  
47.     if row["NUMETHNIC"] > 1:  
48.         return 6  
49.     if row['H1GI6A'] == 1:  
50.         return 1  
51.     if row['H1GI6B'] == 1:  
52.         return 2  
53.     if row['H1GI6C'] == 1:  
54.         return 3  
55.     if row['H1GI6D'] == 1:  
56.         return 4  
57.     if row['H1GI6E'] == 1:  
58.         return 5  
59.     else:  
60.         return np.nan  
61.   
62. ethniclabels = {1:'White', 2:"Black", 3:'Native American', 4:"Asian", 5:'Other',  
63.                 6:'Multiple'}  
64. data['ETHNICITY'] = data.apply(lambda row: ethnic (row), axis=1)  
65.   
66. data['ETHNICITY']=data['ETHNICITY'].map(ethniclabels)  
67. subwhite = data[(data['ETHNICITY'] == 'White')]    
68. whiteage = subwhite['AGE'].tolist()   
69.   
70. subblack = data[(data['ETHNICITY'] == 'Black')]    
71. blackage = subblack['AGE'].tolist()   
72.   
73. subnative = data[(data['ETHNICITY'] == 'Native American')]    
74. nativeage = subnative['AGE'].tolist()  
75.   
76. subasian = data[(data['ETHNICITY'] == 'Asian')]    
77. asianage = subasian['AGE'].tolist()  
78.   
79. subother = data[(data['ETHNICITY'] == 'Other')]    
80. otherage = subother['AGE'].tolist()  
81.   
82. submulti = data[(data['ETHNICITY'] == 'Multiple')]    
83. multiage = submulti['AGE'].tolist()  
84.    
85. ethnicities=[whiteage, blackage, nativeage, asianage, otherage, multiage]   
86.   
87. ethnicity = data['ETHNICITY'].value_counts()  
88. ethnicprc =data['ETHNICITY'].value_counts(normalize=True)  
89. print("\nCounts for Ethnic Groups")  
90. print(ethnicity)  
91. print("\nPercentage of each Ethnic Group")  
92. print(ethnicprc)  
93.   
94. #clean=data[['AGE', 'ETHNICITY', 'BIO_SEX']].dropna()   
95. plt.boxplot(ethnicities, labels = ('White', 'Black', 'Nat. Am.',  
96.                                    'Asian', 'Other', 'Multiple'))  
97. plt.xlabel("Ethnicity")  
98. plt.ylabel("Age")  
99. plt.title("Age Range of ADDHEALTH Respondents")  
100. plt.show()  

The output (boxplot is shown above)

Ages (Count)

17    1169

 16    1162

 18    1149

 15    1081

 14     903

 13     578

 19     389

 20      43

 21      18

 12       9

-1        3

Name: AGE, dtype: int64

Statistical Description of Age

count    6504.000000

mean       16.028905

std         1.810004

min        -1.000000

25%        15.000000

50%        16.000000

75%        17.000000

max        21.000000

Name: AGE, dtype: float64

Count of Gender

Female    3356

Male      3147

Name: BIO_SEX, dtype: int64

Percent of each Gender

Female    0.51607

Male      0.48393

Name: BIO_SEX, dtype: float64

Counts for Ethnic Groups

White              4038

Black              1483

Other               362

Multiple            322

Asian               212

Native American      68

Name: ETHNICITY, dtype: int64