Medical_code/HRS_P/HRS_preprocess.py

import pandas as pd
import math
import numpy as np

if __name__ == "__main__":
    # age
    # sex
    # marital status
    # education
    # smoking status
    # drinking status
    # physical activity level
    # body mass index (BMI)
    # glycated haemoglobin (HbA1c)
    # systolic blood pressure (SBP)
    # high-density lipoprotein cholesterol (HDL-C)
    # C-reactive protein

    # 获取1992数据
    with(open("/root/r_base/HRS/1992/data/health.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        BIRTH_YEAR_list = []
        SEX_list = []
        MARITAL_STATUS_list = []
        EDUCATION_list = []
        SMOKED_list = []
        DRINKED_list = []
        PHYSICAL_ACTIVITY_LEVEL_list = []
        BMI_list = []
        HEART_PROBL_list = []
        STROKE_list=[]
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            BIRTH_YEAR = line[249:254]
            # 1.Male 
            # 2.Female
            SEX = line[109:110]
            # 1.      Married [Inap in V228-V238]
            # 2.      Partner [Inap in V226-V227]
            # 3.      Separated [Inap in V226-V234]
            # 4.      Divorced [Inap in V226-V234]
            # 5.      Widowed [Inap in V226-V234]
            # 6.      Never married
            # 7.      Married with 2 family residences--both
            #         sampleable
            # 8.      Married with 2 family residences--one
            #         residence is not sampleable (institution
            #         or out of the country)
            # 9.      NA
            MARITAL_STATUS = line[302:303]
            # 1 Married or Partner; 5 other
            MARITAL_STATUS = "1" if MARITAL_STATUS=="1" or MARITAL_STATUS=="2" or MARITAL_STATUS=="7" or MARITAL_STATUS=="8" else "5" if not MARITAL_STATUS=="9" else np.nan
            # 0 For no formal education 
            # 1-11 .....Grades 
            # 12 .......High school 
            # 13-15 ....Some college
            # 16 .......College grad
            # 17 .......Post college (17+ years)
            # 97 .......Other
            EDUCATION = line[264:266]
            # 1.  Yes
            # 5.  No [Inap in V502-V505]
            SMOKED = line[519:520]
            # 1.  Yes
            # 5.  No [Inap in V507]
            DRINKED = line[527:528]
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 重度活动
            VIGOROUS_PHYSICAL = line[534:535]
            # 轻度活动
            LIGHT_PHYSICAL = line[533:534]
            PHYSICAL_ACTIVITY_LEVEL = 3 if VIGOROUS_PHYSICAL == "1" or VIGOROUS_PHYSICAL =="2" else 2 if LIGHT_PHYSICAL=="1" or LIGHT_PHYSICAL=="2" else 1
            # 体重
            WEIGH= float(line[536:539])*0.45359237
            # 身高
            HIGHT = float(line[542:543])*0.3048 + float(line[543:545])*0.0254
            # BMI
            BMI = WEIGH / math.pow(HIGHT,2)
            # A heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            HEART_PROBL = line[459:460]
            # STROKE 
            STROKE = line[473:474]
            HHID_list.append(HHID)
            PN_list.append(PN)
            BIRTH_YEAR_list.append(BIRTH_YEAR)
            SEX_list.append(SEX)
            MARITAL_STATUS_list.append(MARITAL_STATUS)
            EDUCATION_list.append(EDUCATION)
            SMOKED_list.append(SMOKED)
            DRINKED_list.append(DRINKED)
            PHYSICAL_ACTIVITY_LEVEL_list.append(PHYSICAL_ACTIVITY_LEVEL)
            BMI_list.append(BMI)
            HEART_PROBL_list.append(HEART_PROBL)
            STROKE_list.append(STROKE)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "BIRTH_YEAR":BIRTH_YEAR_list,
            "SEX":SEX_list,
            "MARITAL_STATUS":MARITAL_STATUS_list,
            "EDUCATION":EDUCATION_list,
            "SMOKED":SMOKED_list,
            "DRINKED":DRINKED_list,
            "PHYSICAL_ACTIVITY_LEVEL":PHYSICAL_ACTIVITY_LEVEL_list,
            "BMI":BMI_list,
            "HEART_PROBL":HEART_PROBL_list,
            "STROKE":STROKE_list
        }
        data["WAVE"] = 1992
        result = pd.DataFrame(data)
    # 获取1993数据
    with(open("/root/r_base/HRS/1993/data/BR21.DA", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        BIRTH_YEAR_list = []
        SEX_list = []
        MARITAL_STATUS_list = []
        EDUCATION_list = []
        SMOKED_list = []
        DRINKED_list = []
        PHYSICAL_ACTIVITY_LEVEL_list = []
        BMI_list = []
        HEART_PROBL_list = []
        STROKE_list=[]
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            BIRTH_YEAR = line[61:65]
            # 1.Male 
            # 2.Female
            SEX = line[16:17]
            # MARRIED, SPOUSE PRESENT...........  1  
            # MARRIED, SPOUSE ABSENT............  2  
            # LIVING WITH SOMEONE...............  3 GO TO A11b 
            # DIVORCED/SEPARATED................  4 GO TO A11g 
            # WIDOWED...........................  5 GO TO A11g 
            # NEVER MARRIED.....................  6 GO TO B1 
            MARITAL_STATUS = line[98:99]
            # 1 Married or Partner; 5 other
            MARITAL_STATUS = "1" if MARITAL_STATUS=="1" or MARITAL_STATUS=="2" or MARITAL_STATUS=="3" else "5"
            # 0 For no formal education 
            # 1-11 .....Grades 
            # 12 .......High school 
            # 13-15 ....Some college
            # 16 .......College grad
            # 17 .......Post college (17+ years)
            # 97 .......Other
            EDUCATION = line[74:76]
            #CURRENT SMOKER.....................  1 
            # FORMER SMOKER......................  2 GO TO B20 
            # NEVER SMOKED.......................  3 GO TO B20 
            # 1.  Yes
            # 5.  No [Inap in V502-V505]
            SMOKED = "1" if line[172:173] == "1" or line[172:173] == "2" else "5"
            # 1.  Yes
            # 5.  No [Inap in V507]
            DRINKED = line[176:177]
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 重度活动
            VIGOROUS_PHYSICAL = np.nan
            # 轻度活动
            LIGHT_PHYSICAL = np.nan
            PHYSICAL_ACTIVITY_LEVEL = np.nan
            # 体重
            WEIGH= float(line[179:182])*0.45359237
            # 身高
            HEIGHT = float(line[182:184])*0.0254
            # BMI
            BMI = WEIGH / math.pow(HEIGHT,2)
            # A heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            HEART_PROBL = line[139:140]
            # STROKE 
            STROKE = line[142:143]
            HHID_list.append(HHID)
            PN_list.append(PN)
            BIRTH_YEAR_list.append(BIRTH_YEAR)
            SEX_list.append(SEX)
            MARITAL_STATUS_list.append(MARITAL_STATUS)
            EDUCATION_list.append(EDUCATION)
            SMOKED_list.append(SMOKED)
            DRINKED_list.append(DRINKED)
            PHYSICAL_ACTIVITY_LEVEL_list.append(PHYSICAL_ACTIVITY_LEVEL)
            BMI_list.append(BMI)
            HEART_PROBL_list.append(HEART_PROBL)
            STROKE_list.append(STROKE)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "BIRTH_YEAR":BIRTH_YEAR_list,
            "SEX":SEX_list,
            "MARITAL_STATUS":MARITAL_STATUS_list,
            "EDUCATION":EDUCATION_list,
            "SMOKED":SMOKED_list,
            "DRINKED":DRINKED_list,
            "PHYSICAL_ACTIVITY_LEVEL":PHYSICAL_ACTIVITY_LEVEL_list,
            "BMI":BMI_list,
            "HEART_PROBL":HEART_PROBL_list,
            "STROKE":STROKE_list
        }
        data["WAVE"] = 1993
        result_1993 = pd.DataFrame(data)
        result = pd.concat([result, result_1993], axis=0)
    # 获取1994数据
    with(open("/root/r_base/HRS/1994/data/W2a.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        BIRTH_YEAR_list = []
        SEX_list = []
        MARITAL_STATUS_list = []
        EDUCATION_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            BIRTH_YEAR = line[26:30]
            # 1.Male 
            # 2.Female
            SEX = line[22:23]
            # 1.      Married [Inap in V228-V238]
            # 2.      Partner [Inap in V226-V227]
            # 3.      Separated [Inap in V226-V234]
            # 4.      Divorced [Inap in V226-V234]
            # 5.      Widowed [Inap in V226-V234]
            # 6.      Never married
            # 7.      Married (Not Institutionalized/not out of country)
            # 8.      Married (Institutionalized/out of country)
            # 9.      NA
            MARITAL_STATUS = line[55:57]
            # 1 Married or Partner; 5 other
            MARITAL_STATUS = "1" if MARITAL_STATUS=="1" or MARITAL_STATUS=="2" or MARITAL_STATUS=="7" or MARITAL_STATUS=="8" else "5" if not MARITAL_STATUS=="9" else np.nan
            # 0 For no formal education 
            # 1-11 .....Grades 
            # 12 .......High school 
            # 13-15 ....Some college
            # 16 .......College grad
            # 17 .......Post college (17+ years)
            # 97 .......Other
            # 98.     Don't Know; DK
            # 99.     Not Ascertained; NA
            EDUCATION = line[112:115]
            HHID_list.append(HHID)
            PN_list.append(PN)
            BIRTH_YEAR_list.append(BIRTH_YEAR)
            SEX_list.append(SEX)
            MARITAL_STATUS_list.append(MARITAL_STATUS)
            EDUCATION_list.append(EDUCATION)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "BIRTH_YEAR":BIRTH_YEAR_list,
            "SEX":SEX_list,
            "MARITAL_STATUS":MARITAL_STATUS_list,
            "EDUCATION":EDUCATION_list,
        }
        data["WAVE"] = 1994
        result_1994_one = pd.DataFrame(data)
    with(open("/root/r_base/HRS/1994/data/W2B.DA", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        SMOKED_list = []
        DRINKED_list = []
        PHYSICAL_ACTIVITY_LEVEL_list = []
        BMI_list = []
        HEART_PROBL_list = []
        STROKE_list=[]        
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            #1.      Yes
            #5.      No [GO TO B41]
            #8.      Don't Know; DK [GO TO B41]
            #9.      Refused; RF [GO TO B41]
            #0.      Inap.
            #        Proxy interview for deceased Wave-1 R
            SMOKED = line[356:358]
            #1.      Yes
            #5.      No [GO TO B41]
            #8.      Don't Know; DK [GO TO B41]
            #9.      Refused; RF [GO TO B41]
            #0.      Inap.
            #        Proxy interview for deceased Wave-1 R
            DRINKED = line[367:369]
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 活动单位
            # 02.     Week
            # 04.     Month
            # 06.     Year
            # 07.     Other (specify)
            # 11.     Day
            # 98.     Don't Know/Not Ascertained; DK/NA
            # 99.     Refused; RF
            # 00.     Inap.
            #         Proxy interview for deceased Wave-1 R
            #         [B42a: or B42=995-999]
            #         [B43a: or B43=995-999]
            # 重度活动
            VIGOROUS_PHYSICAL = line[378:382]
            VIGOROUS_UNIT = line[382:385]
            VIGOROUS_PHYSICAL_FLAG = np.nan
            if VIGOROUS_UNIT == "02" and float(VIGOROUS_PHYSICAL)>0 and not VIGOROUS_PHYSICAL=="994" and not float(VIGOROUS_PHYSICAL)>995:
                VIGOROUS_PHYSICAL_FLAG = True
            if VIGOROUS_UNIT == "04" and float(VIGOROUS_PHYSICAL)>3 and not VIGOROUS_PHYSICAL=="994" and not float(VIGOROUS_PHYSICAL)>995:
                VIGOROUS_PHYSICAL_FLAG = True
            if VIGOROUS_UNIT == "06" and float(VIGOROUS_PHYSICAL)>51 and not VIGOROUS_PHYSICAL=="994" and not float(VIGOROUS_PHYSICAL)>995:
                VIGOROUS_PHYSICAL_FLAG = True
            if VIGOROUS_PHYSICAL=="00":
                VIGOROUS_PHYSICAL_FLAG = False
            # 轻度活动
            LIGHT_PHYSICAL = line[371:375]
            LIGHT_UNIT = line[375:378]
            # 判断是否符合轻运动  1符合；0不符合
            LIGHT_PHYSICAL_FLAG = np.nan
            if LIGHT_UNIT == "02" and float(LIGHT_PHYSICAL)>0 and not LIGHT_PHYSICAL=="994" and not float(LIGHT_PHYSICAL)>995:
                LIGHT_PHYSICAL_FLAG = True
            if LIGHT_UNIT == "04" and float(LIGHT_PHYSICAL)>3 and not LIGHT_PHYSICAL=="994" and not float(LIGHT_PHYSICAL)>995:
                LIGHT_PHYSICAL_FLAG = True
            if LIGHT_UNIT == "06" and float(LIGHT_PHYSICAL)>51 and not LIGHT_PHYSICAL=="994" and not float(LIGHT_PHYSICAL)>995:
                LIGHT_PHYSICAL_FLAG = True
            if LIGHT_PHYSICAL=="00":
                LIGHT_PHYSICAL_FLAG = False
            PHYSICAL_ACTIVITY_LEVEL = 3 if VIGOROUS_PHYSICAL_FLAG == True else 2 if LIGHT_PHYSICAL_FLAG==True else 1 if LIGHT_PHYSICAL_FLAG==False or VIGOROUS_PHYSICAL_FLAG==False else np.nan
            # 体重
            WEIGH= float(line[385:389])*0.45359237 if not float(line[385:389])>500 else np.nan
            # 身高
            HIGHT = float(line[389:392])*0.3048 + float(line[392:395])*0.0254 if not float(line[389:392])>95 and not float(line[392:395])>95 else np.nan
            # BMI
            BMI = WEIGH / math.pow(HIGHT,2) if not np.isnan(WEIGH) and not np.isnan(HIGHT) else np.nan
            # A heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            HEART_PROBL = line[147:149] if not line[147:149]=="8" and not line[147:149]=="9" and not line[147:149]=="0" else np.nan
            # STROKE 
            STROKE = line[173:175] if not line[173:175]=="8" and not line[173:175]=="9" and not line[173:175]=="0" else np.nan
            HHID_list.append(HHID)
            PN_list.append(PN)
            SMOKED_list.append(SMOKED)
            DRINKED_list.append(DRINKED)
            PHYSICAL_ACTIVITY_LEVEL_list.append(PHYSICAL_ACTIVITY_LEVEL)
            BMI_list.append(BMI)
            HEART_PROBL_list.append(HEART_PROBL)
            STROKE_list.append(STROKE)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "SMOKED":SMOKED_list,
            "DRINKED":DRINKED_list,
            "PHYSICAL_ACTIVITY_LEVEL":PHYSICAL_ACTIVITY_LEVEL_list,
            "BMI":BMI_list,
            "HEART_PROBL":HEART_PROBL_list,
            "STROKE":STROKE_list
        }
        result_1994_two = pd.DataFrame(data)
        result_1994 = pd.merge(result_1994_one, result_1994_two, on=["HHID", "PN"], how="left")
        result = pd.concat([result, result_1994], axis=0)
    print(result.head())
    # 获取1995数据
    with(open("/root/r_base/HRS/1995/data/A95A_R.DA", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        BIRTH_YEAR_list = []
        MARITAL_STATUS_list = []
        EDUCATION_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            BIRTH_YEAR = line[30:34]
            # MARRIED, SPOUSE PRESENT...........  1  
            # MARRIED, SPOUSE ABSENT............  2  
            # LIVING WITH SOMEONE...............  3 GO TO A11b 
            # DIVORCED/SEPARATED................  4 GO TO A11g 
            # WIDOWED...........................  5 GO TO A11g 
            # NEVER MARRIED.....................  6 GO TO B1 
            # 0. Exit proxy was taken before the interview with the surviving spouse.
            # 7. MARRIED, SPOUSE ABSENT (NOT INSTITUTION)
            MARITAL_STATUS = line[76:77]
            # 1 Married or Partner; 5 other
            MARITAL_STATUS = "1" if MARITAL_STATUS=="1" or MARITAL_STATUS=="2" or MARITAL_STATUS=="3" else np.nan if MARITAL_STATUS=="0" or MARITAL_STATUS=="7" else "5"
            # 0 For no formal education 
            # 1-11 .....Grades 
            # 12 .......High school 
            # 13-15 ....Some college
            # 16 .......College grad
            # 17 .......Post college     (17+ years)
            # 97 .......Other
            # 98. DK (don't know); NA (not ascertained)
            # 99. RF (refused)
            EDUCATION = np.nan if line[49:51] == "97" or line[49:51] == "98" or line[49:51] == "99" else line[49:51]
            HHID_list.append(HHID)
            PN_list.append(PN)
            BIRTH_YEAR_list.append(BIRTH_YEAR)
            MARITAL_STATUS_list.append(MARITAL_STATUS)
            EDUCATION_list.append(EDUCATION)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "BIRTH_YEAR":BIRTH_YEAR_list,
            "MARITAL_STATUS":MARITAL_STATUS_list,
            "EDUCATION":EDUCATION_list,
        }
        data["WAVE"] = 1995
        result_1995_one = pd.DataFrame(data)
    with(open("/root/r_base/HRS/1995/data/A95CS_R.DA", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        SEX_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1.Male 
            # 2.Female
            SEX = line[36:37] if not line[36:37]=="0" else np.nan
        HHID_list.append(HHID)
        PN_list.append(PN)
        SEX_list.append(SEX)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "SEX":SEX_list,
        }
        result_1995_two = pd.DataFrame(data)
        result_1995 = pd.merge(result_1995_one, result_1995_two, on=["HHID", "PN"], how="left")
    with(open("/root/r_base/HRS/1995/data/A95B_R.DA", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        SMOKED_list = []
        DRINKED_list = []
        PHYSICAL_ACTIVITY_LEVEL_list = []
        BMI_list = []
        HEART_PROBL_list = []
        STROKE_list=[]    
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. YES
            # 2. Cigars
            # 5. NO
            # 7. Other
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # 合并后
            # 1.  Yes
            # 5.  No [Inap in V502-V505]
            SMOKED = "1" if line[153:154] == "1" or line[153:154] == "2" else "5" if line[153:154] == "5" else np.nan
            # 1. YES
            # 3. [VOL] NEVER HAVE USED ALCOHOL
            # 5. NO
            # 7. Other
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # 合并后
            # 1.  Yes
            # 5.  No [Inap in V502-V505]
            DRINKED = "1" if line[157:158] == "1"  else "5" if line[157:158] == "5" or line[157:158] == "3" else np.nan
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 重度活动
            VIGOROUS_PHYSICAL = np.nan
            # 轻度活动
            LIGHT_PHYSICAL = np.nan
            PHYSICAL_ACTIVITY_LEVEL = np.nan
            # 体重
            WEIGH= float(line[164:167])*0.45359237 if not float(line[164:167])>400 else np.nan
            # 身高
            HIGHT = line[168:169]
            if not line[168:169] == " " and not line[168:169] == "8" and not line[168:169] == "9" and not pd.isna(WEIGH):
                HIGHT = float(line[168:169])*0.3048 + float(line[169:171])*0.0254
                # BMI
                BMI = WEIGH / math.pow(HEIGHT,2)
            else :
                BMI = np.nan
            # 1. YES
            # 3. [VOL] DISPUTES W1 RECORD
            # 5. NO
            # 7. Other
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)

            # A heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems
            #   1.      Yes
            #   3. [VOL] DISPUTES W1 RECORD
            #   5.      No [Inap in V418-V421]
            HEART_PROBL = np.nan if line[63:64] == "8" else line[63:64]
            # STROKE 
            #   1.      Yes
            #   3. [VOL] DISPUTES W1 RECORD
            #   5.      No [Inap in V418-V421]
            STROKE = np.nan if line[84:85] == "8" else "5" if line[84:85] == "2" or line[84:85] == "5" else line[84:85]
            HHID_list.append(HHID)
            PN_list.append(PN) 
            SMOKED_list.append(SMOKED)
            DRINKED_list.append(DRINKED)
            PHYSICAL_ACTIVITY_LEVEL_list.append(PHYSICAL_ACTIVITY_LEVEL)
            BMI_list.append(BMI)
            HEART_PROBL_list.append(HEART_PROBL)
            STROKE_list.append(STROKE)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "SMOKED":SMOKED_list,
            "DRINKED":DRINKED_list,
            "PHYSICAL_ACTIVITY_LEVEL":PHYSICAL_ACTIVITY_LEVEL_list,
            "BMI":BMI_list,
            "HEART_PROBL":HEART_PROBL_list,
            "STROKE":STROKE_list
        }
        result_1995_three = pd.DataFrame(data)
        result_1995 = pd.merge(result_1995, result_1995_three, on=["HHID", "PN"], how="left")
    result = pd.concat([result, result_1995], axis=0)
    # 获取1996数据
    with(open("/root/r_base/HRS/1996/data/H96A_R.DA", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        BIRTH_YEAR_list = []
        MARITAL_STATUS_list = []
        EDUCATION_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            BIRTH_YEAR = line[25:29]
            # MARRIED, SPOUSE PRESENT...........  1  
            # MARRIED, SPOUSE ABSENT............  2  
            # LIVING WITH SOMEONE...............  3 GO TO A11b 
            # DIVORCED/SEPARATED................  4 GO TO A11g 
            # WIDOWED...........................  5 GO TO A11g 
            # NEVER MARRIED.....................  6 GO TO B1 
            # 7. MARRIED, SPOUSE ABSENT (NOT INSTITUTION)
            MARITAL_STATUS = line[70:71]
            # 1 Married or Partner; 5 other
            MARITAL_STATUS = "1" if MARITAL_STATUS=="1" or MARITAL_STATUS=="2" or MARITAL_STATUS=="3" else "5" if MARITAL_STATUS=="4" or MARITAL_STATUS=="5" or MARITAL_STATUS=="6" or MARITAL_STATUS=="7" else np.nan
            # 0 For no formal education 
            # 1-11 .....Grades 
            # 12 .......High school 
            # 13-15 ....Some college
            # 16 .......College grad
            # 17 .......Post college     (17+ years)
            # 97 .......Other
            # 98. DK (don't know); NA (not ascertained)
            # 99. RF (refused)
            EDUCATION = np.nan if line[43:45] == "97" or line[43:45] == "98" or line[43:45] == "99" else line[43:45]
            HHID_list.append(HHID)
            PN_list.append(PN)
            BIRTH_YEAR_list.append(BIRTH_YEAR)
            MARITAL_STATUS_list.append(MARITAL_STATUS)
            EDUCATION_list.append(EDUCATION)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "BIRTH_YEAR":BIRTH_YEAR_list,
            "MARITAL_STATUS":MARITAL_STATUS_list,
            "EDUCATION":EDUCATION_list,
        }
        data["WAVE"] = 1996
        result_1996_one = pd.DataFrame(data)
    with(open("/root/r_base/HRS/1996/data/H96CS_R.DA", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        SEX_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1.Male 
            # 2.Female
            SEX = line[74:75]
        HHID_list.append(HHID)
        PN_list.append(PN)
        SEX_list.append(SEX)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "SEX":SEX_list,
        }
        result_1996_two = pd.DataFrame(data)
        result_1996 = pd.merge(result_1996_one, result_1996_two, on=["HHID", "PN"], how="left")
    with(open("/root/r_base/HRS/1996/data/H96B_R.DA", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        SMOKED_list = []
        DRINKED_list = []
        PHYSICAL_ACTIVITY_LEVEL_list = []
        BMI_list = []
        HEART_PROBL_list = []
        STROKE_list=[]    
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. YES
            # 2. Cigars
            # 3. PIPE (IF VOLUNTEERED)
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # 合并后
            # 1.  Yes
            # 5.  No [Inap in V502-V505]
            SMOKED = "1" if line[160:161] == "1" or line[160:161] == "2" or line[160:161] == "3" else "5" if line[160:161] == "5" else np.nan
            # 1. YES
            # 3. [VOL] NEVER HAVE USED ALCOHOL
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # 合并后
            # 1.  Yes
            # 5.  No [Inap in V502-V505]
            DRINKED = "1" if line[166:167] == "1"  else "5" if line[166:167] == "5" or line[166:167] == "3" else np.nan
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 重度活动
            VIGOROUS_PHYSICAL = np.nan
            # 轻度活动
            LIGHT_PHYSICAL = np.nan
            PHYSICAL_ACTIVITY_LEVEL = np.nan
            # 体重
            WEIGH= float(line[174:177])*0.45359237 if not line[174:177].strip() =="" and not float(line[174:177])>400 else np.nan
            # 身高
            HIGHT = line[178:180].strip()
            if not HIGHT == "" and not HIGHT == "98" and not HIGHT == "99" and not pd.isna(WEIGH):
                HIGHT = float(HIGHT)*0.3048 + float(line[180:182])*0.0254
                # BMI
                BMI = WEIGH / math.pow(HEIGHT,2)
            else :
                BMI = np.nan
            # 1. YES
            # 3. [VOL] DISPUTES W1 RECORD
            # 5. NO
            # 7. Other
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # A heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems
            #   1.      Yes
            #   3. [VOL] DISPUTES W1 RECORD
            #   5.      No [Inap in V418-V421]
            HEART_PROBL = np.nan if line[66:67] == "9" or  line[66:67] == "" else line[66:67]
            # STROKE 
            #   1.      Yes
            #   3. [VOL] DISPUTES W1 RECORD
            #   5.      No [Inap in V418-V421]
            STROKE = np.nan if line[87:88] == "8" else "5" if line[87:88] == "2" or line[87:88] == "5" else line[87:88]
            HHID_list.append(HHID)
            PN_list.append(PN)
            SMOKED_list.append(SMOKED)
            DRINKED_list.append(DRINKED)
            PHYSICAL_ACTIVITY_LEVEL_list.append(PHYSICAL_ACTIVITY_LEVEL)
            BMI_list.append(BMI)
            HEART_PROBL_list.append(HEART_PROBL)
            STROKE_list.append(STROKE)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "SMOKED":SMOKED_list,
            "DRINKED":DRINKED_list,
            "PHYSICAL_ACTIVITY_LEVEL":PHYSICAL_ACTIVITY_LEVEL_list,
            "BMI":BMI_list,
            "HEART_PROBL":HEART_PROBL_list,
            "STROKE":STROKE_list
        }
        result_1996_three = pd.DataFrame(data)
        result_1996 = pd.merge(result_1996, result_1996_three, on=["HHID", "PN"], how="left")
    result = pd.concat([result, result_1996], axis=0)    
    # 获取1998数据
    with(open("/root/r_base/HRS/1998/data/H98A_R.DA", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        BIRTH_YEAR_list = []
        MARITAL_STATUS_list = []
        EDUCATION_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            BIRTH_YEAR = line[31:35] if not line[31:35] == "9998" else np.nan
            # 0. DK (don't know); NA (not ascertained); RF (refused)
            # 1. MARRIED
            # 3. SEPARATED
            # 4. DIVORCED
            # 5. WIDOWED
            # 6. NEVER MARRIED
            # 7. OTHER (SPECIFY)
            MARITAL_STATUS = line[150:151]
            # 1 Married or Partner; 5 other
            MARITAL_STATUS = "1" if MARITAL_STATUS=="1" else "5" if MARITAL_STATUS=="3" or MARITAL_STATUS=="4" or MARITAL_STATUS=="5" or MARITAL_STATUS=="6" or MARITAL_STATUS=="7" else np.nan
            # 0 For no formal education 
            # 1-11 .....Grades 
            # 12 .......High school 
            # 13-15 ....Some college
            # 16 .......College grad
            # 17 .......Post college     (17+ years)
            # 97 .......Other
            # 98. DK (don't know); NA (not ascertained)
            # 99. RF (refused)
            EDUCATION = np.nan if line[61:62] == "97" or line[61:62] == "98" or line[61:62] == "99" else line[61:62]
            HHID_list.append(HHID)
            PN_list.append(PN)
            BIRTH_YEAR_list.append(BIRTH_YEAR)
            MARITAL_STATUS_list.append(MARITAL_STATUS)
            EDUCATION_list.append(EDUCATION)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "BIRTH_YEAR":BIRTH_YEAR_list,
            "MARITAL_STATUS":MARITAL_STATUS_list,
            "EDUCATION":EDUCATION_list,
        }
        data["WAVE"] = 1998
        result_1998_one = pd.DataFrame(data)
    with(open("/root/r_base/HRS/1998/data/H98CS_R.DA", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        SEX_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1.Male 
            # 2.Female
            SEX = line[70:71]
        HHID_list.append(HHID)
        PN_list.append(PN)    
        SEX_list.append(SEX)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "SEX":SEX_list,
        }
        result_1998_two = pd.DataFrame(data)
        result_1998 = pd.merge(result_1998_one, result_1998_two, on=["HHID", "PN"], how="left")
    with(open("/root/r_base/HRS/1998/data/H98B_R.DA", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        SMOKED_list = []
        DRINKED_list = []
        PHYSICAL_ACTIVITY_LEVEL_list = []
        BMI_list = []
        HEART_PROBL_list = []
        STROKE_list=[]    
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. YES
            # 5. NO
            SMOKED = line[157:158] if line[157:158] == "1" or line[157:158] == "5" else np.nan
            # 1. YES
            # 3. [VOL] NEVER HAVE USED ALCOHOL
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # 合并后
            # 1.  Yes
            # 5.  No [Inap in V502-V505]
            DRINKED = "1" if line[184:185] == "1"  else "5" if line[184:185] == "5" or line[184:185] == "3" else np.nan
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 重度活动
            VIGOROUS_PHYSICAL = np.nan
            # 轻度活动
            LIGHT_PHYSICAL = np.nan
            PHYSICAL_ACTIVITY_LEVEL = np.nan
            # 体重
            WEIGH= float(line[196:199])*0.45359237 if not float(line[196:199])>400 else np.nan
            # 身高
            HEIGHT = line[200:202].strip()
            if not HEIGHT == "" and not HEIGHT == "98" and not HEIGHT == "99" and not pd.isna(WEIGH):
                if not line[202:204] == "98":
                    HEIGHT = float(HEIGHT)*0.3048 + float(line[202:204])*0.0254
                    # BMI
                    BMI = WEIGH / math.pow(HEIGHT,2)
                else:
                    HEIGHT = float(line[200:202])*0.3048
                    # BMI
                    BMI = WEIGH / math.pow(HEIGHT,2)
            else :
                BMI = np.nan
            # 1. YES
            # 3. [VOL] DISPUTES PREVIOUS WAVE RECORD
            # 5. NO
            # 6. PRELOAD ERROR: Condition reported at prior wave but said no to
            #    new event
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # A heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems
            #   1.      Yes
            #   3. [VOL] DISPUTES W1 RECORD
            #   5.      No [Inap in V418-V421]
            HEART_PROBL = np.nan if line[66:67] == "8" else line[66:67]
            # 1. YES
            # 2. [VOL] POSSIBLE STROKE OR TIA (TRANSIENT ISCHEMIC ATTACK)
            # 3. [VOL] DISPUTES PREVIOUS WAVE RECORD
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # STROKE 
            #   1.      Yes
            #   3. [VOL] DISPUTES W1 RECORD
            #   5.      No [Inap in V418-V421]
            STROKE = np.nan if line[87:88] == "8" else "5" if line[87:88] == "2" or line[87:88] == "5" else line[87:88]
            HHID_list.append(HHID)
            PN_list.append(PN)
            SMOKED_list.append(SMOKED)
            DRINKED_list.append(DRINKED)
            PHYSICAL_ACTIVITY_LEVEL_list.append(PHYSICAL_ACTIVITY_LEVEL)
            BMI_list.append(BMI)
            HEART_PROBL_list.append(HEART_PROBL)
            STROKE_list.append(STROKE)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "SMOKED":SMOKED_list,
            "DRINKED":DRINKED_list,
            "PHYSICAL_ACTIVITY_LEVEL":PHYSICAL_ACTIVITY_LEVEL_list,
            "BMI":BMI_list,
            "HEART_PROBL":HEART_PROBL_list,
            "STROKE":STROKE_list
        }
        result_1998_three = pd.DataFrame(data)
        result_1998 = pd.merge(result_1998, result_1998_three, on=["HHID", "PN"], how="left")
    result = pd.concat([result, result_1998], axis=0)       
    # 获取2000数据
    with(open("/root/r_base/HRS/2000/data/H00A_R.DA", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        BIRTH_YEAR_list = []
        MARITAL_STATUS_list = []
        EDUCATION_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            BIRTH_YEAR = line[31:35]
            # 0. DK (don't know); NA (not ascertained); RF (refused)
            # 1. MARRIED
            # 3. SEPARATED
            # 4. DIVORCED
            # 5. WIDOWED
            # 6. NEVER MARRIED
            # 7. OTHER (SPECIFY)
            MARITAL_STATUS = line[152:153]
            # 1 Married or Partner; 5 other
            MARITAL_STATUS = "1" if MARITAL_STATUS=="1" else "5" if MARITAL_STATUS=="3" or MARITAL_STATUS=="4" or MARITAL_STATUS=="5" or MARITAL_STATUS=="6" or MARITAL_STATUS=="7" else np.nan
            # 0 For no formal education 
            # 1-11 .....Grades 
            # 12 .......High school 
            # 13-15 ....Some college
            # 16 .......College grad
            # 17 .......Post college     (17+ years)
            # 97 .......Other
            # 98. DK (don't know); NA (not ascertained)
            # 99. RF (refused)
            EDUCATION = np.nan if line[63:65] == "97" or line[63:65] == "98" or line[63:65] == "99" else line[63:65]
            HHID_list.append(HHID)
            PN_list.append(PN)
            BIRTH_YEAR_list.append(BIRTH_YEAR)
            MARITAL_STATUS_list.append(MARITAL_STATUS)
            EDUCATION_list.append(EDUCATION)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "BIRTH_YEAR":BIRTH_YEAR_list,
            "MARITAL_STATUS":MARITAL_STATUS_list,
            "EDUCATION":EDUCATION_list,
        }
        data["WAVE"] = 2000
        result_2000_one = pd.DataFrame(data)
    with(open("/root/r_base/HRS/2000/data/H00CS_R.DA", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        SEX_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1.Male 
            # 2.Female
            SEX = line[79:80]
        HHID_list.append(HHID)
        PN_list.append(PN)
        SEX_list.append(SEX)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "SEX":SEX_list,
        }
        result_2000_two = pd.DataFrame(data)
        result_2000 = pd.merge(result_2000_one, result_2000_two, on=["HHID", "PN"], how="left")
    with(open("/root/r_base/HRS/2000/data/H00B_R.DA", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        SMOKED_list = []
        DRINKED_list = []
        PHYSICAL_ACTIVITY_LEVEL_list = []
        BMI_list = []
        HEART_PROBL_list = []
        STROKE_list=[]    
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. YES
            # 5. NO
            SMOKED = line[154:155] if line[154:155] == "1" or line[154:155] == "5" else np.nan
            # 1. YES
            # 3. [VOL] NEVER HAVE USED ALCOHOL
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # 合并后
            # 1.  Yes
            # 5.  No [Inap in V502-V505]
            DRINKED = "1" if line[181:182] == "1"  else "5" if line[181:182] == "5" or line[181:182] == "3" else np.nan
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 重度活动
            VIGOROUS_PHYSICAL = np.nan
            # 轻度活动
            LIGHT_PHYSICAL = np.nan
            PHYSICAL_ACTIVITY_LEVEL = np.nan
            # 体重
            WEIGH= float(line[193:196])*0.45359237 if not float(line[193:196])>400 else np.nan
            # 身高
            HEIGHT = line[197:198].strip()
            if not HEIGHT == "" and not HEIGHT == "9" and not pd.isna(WEIGH):
                HEIGHT = float(HEIGHT)*0.3048 + float(line[198:200])*0.0254
                # BMI
                BMI = WEIGH / math.pow(HEIGHT,2)
            else :
                BMI = np.nan
            # 1. YES
            # 3. DISPUTES PREVIOUS WAVE RECORD, BUT NOW HAS CONDITION
            # 4. DISPUTES PREVIOUS WAVE RECORD, DOES NOT HAVE CONDITION
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # A heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            HEART_PROBL = np.nan if line[65:66] == "8" or line[65:66] == "9" else "1" if line[65:66] == "1" or line[65:66] == "3" else "5"
            # 1. YES
            # 2. [VOL] POSSIBLE STROKE OR TIA (TRANSIENT ISCHEMIC ATTACK)
            # 3. DISPUTES PREVIOUS WAVE RECORD, BUT NOW HAS CONDITION
            # 4. DISPUTES PREVIOUS WAVE RECORD, DOES NOT HAVE CONDITION
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # STROKE 
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            STROKE = np.nan if line[86:87] == "8" or line[86:87] == "9" else "5" if line[86:87] == "2" or line[86:87] == "4" or line[86:87] == "5" else "1"
            HHID_list.append(HHID)
            PN_list.append(PN)
            SMOKED_list.append(SMOKED)
            DRINKED_list.append(DRINKED)
            PHYSICAL_ACTIVITY_LEVEL_list.append(PHYSICAL_ACTIVITY_LEVEL)
            BMI_list.append(BMI)
            HEART_PROBL_list.append(HEART_PROBL)
            STROKE_list.append(STROKE)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "SMOKED":SMOKED_list,
            "DRINKED":DRINKED_list,
            "PHYSICAL_ACTIVITY_LEVEL":PHYSICAL_ACTIVITY_LEVEL_list,
            "BMI":BMI_list,
            "HEART_PROBL":HEART_PROBL_list,
            "STROKE":STROKE_list
        }
        result_2000_three = pd.DataFrame(data)
        result_2000 = pd.merge(result_2000, result_2000_three, on=["HHID", "PN"], how="left")
    result = pd.concat([result, result_2000], axis=0)      
    # 获取2002数据
    with(open("/root/r_base/HRS/2002/data/H02PR_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        BIRTH_YEAR_list = []
        SEX_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            BIRTH_YEAR = line[36:40]
            # 1.Male 
            # 2.Female
            SEX = line[19:20]
            HHID_list.append(HHID)
            PN_list.append(PN)
            BIRTH_YEAR_list.append(BIRTH_YEAR)
            SEX_list.append(SEX)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "BIRTH_YEAR":BIRTH_YEAR_list,
            "SEX":SEX_list,
        }
        data["WAVE"] = 2002
        result_2002_one = pd.DataFrame(data)
    with(open("/root/r_base/HRS/2002/data/H02C_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        SMOKED_list = []
        DRINKED_list = []
        BMI_list = []
        HEART_PROBL_list = []
        STROKE_list=[]    
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. YES
            # 5. NO
            SMOKED = line[155:156] if line[155:156] == "1" or line[155:156] == "5" else np.nan
            # 1. YES
            # 3. [VOL] NEVER HAVE USED ALCOHOL
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # 合并后
            # 1.  Yes
            # 5.  No [Inap in V502-V505]
            DRINKED = "1" if line[179:180] == "1"  else "5" if line[179:180] == "5" or line[179:180] == "3" else np.nan
            # 体重
            WEIGH= float(line[190:193])*0.45359237 if not float(line[190:193])>400 else np.nan
            # 身高
            HEIGHT = line[194:195].strip()
            if not HEIGHT == "" and not HEIGHT == "8" and not pd.isna(WEIGH):
                if not line[195:197] == "98" and not line[195:197]=="":
                    HEIGHT = float(HEIGHT)*0.3048 + float(line[195:197])*0.0254
                    # BMI
                    BMI = WEIGH / math.pow(HEIGHT,2)
                else:
                    HEIGHT = float(line[194:195])*0.3048
                    # BMI
                    BMI = WEIGH / math.pow(HEIGHT,2)
            else :
                BMI = np.nan
            # 1. YES
            # 3. DISPUTES PREVIOUS WAVE RECORD, BUT NOW HAS CONDITION
            # 4. DISPUTES PREVIOUS WAVE RECORD, DOES NOT HAVE CONDITION
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # A heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            HEART_PROBL = np.nan if line[59:60] == "8" or line[59:60] == "9" else "1" if line[59:60] == "1" or line[59:60] == "3" else "5"
            # 1. YES
            # 2. [VOL] POSSIBLE STROKE OR TIA (TRANSIENT ISCHEMIC ATTACK)
            # 3. DISPUTES PREVIOUS WAVE RECORD, BUT NOW HAS CONDITION
            # 4. DISPUTES PREVIOUS WAVE RECORD, DOES NOT HAVE CONDITION
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # STROKE 
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            STROKE = np.nan if line[80:81] == "8" or line[80:81] == "9" else "5" if line[80:81] == "2" or line[80:81] == "4" or line[80:81] == "5" else "1"
            HHID_list.append(HHID)
            PN_list.append(PN)
            SMOKED_list.append(SMOKED)
            DRINKED_list.append(DRINKED)
            BMI_list.append(BMI)
            HEART_PROBL_list.append(HEART_PROBL)
            STROKE_list.append(STROKE)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "SMOKED":SMOKED_list,
            "DRINKED":DRINKED_list,
            "BMI":BMI_list,
            "HEART_PROBL":HEART_PROBL_list,
            "STROKE":STROKE_list
        }
        result_2002_two = pd.DataFrame(data)
        result_2002 = pd.merge(result_2002_one, result_2002_two, on=["HHID", "PN"], how="left")
    with(open("/root/r_base/HRS/2002/data/H02V_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        PHYSICAL_ACTIVITY_LEVEL_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. MORE THAN ONCE A WEEK
            # 2. ONCE A WEEK
            # 3. ONCE TO THREE TIMES A MONTH
            # 4. HARDLY EVER OR NEVER
            # 8. DK (Don't Know)
            # 9. RF (Refused)
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 重度活动
            VIGOROUS_PHYSICAL = "3" if line[151:152] == "1" or line[151:152] == "2" else "1" if line[151:152] == "3" or line[151:152] == "4" else np.nan
            #中度活动
            MODERATE_PHYSICAL = "2" if line[152:153] == "1" or line[152:153] == "2" else "1" if line[152:153] == "3" or line[152:153] == "4" else np.nan
            # 轻度活动
            LIGHT_PHYSICAL = np.nan
            PHYSICAL_ACTIVITY_LEVEL = "3" if VIGOROUS_PHYSICAL=="3" else "2" if MODERATE_PHYSICAL=="2" else "1" if VIGOROUS_PHYSICAL=="1" or MODERATE_PHYSICAL=="1" else np.nan
            HHID_list.append(HHID)
            PN_list.append(PN)
            PHYSICAL_ACTIVITY_LEVEL_list.append(PHYSICAL_ACTIVITY_LEVEL)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "PHYSICAL_ACTIVITY_LEVEL":PHYSICAL_ACTIVITY_LEVEL_list
        }
        result_2002_three = pd.DataFrame(data)
        result_2002 = pd.merge(result_2002, result_2002_three, on=["HHID", "PN"], how="left")
    with(open("/root/r_base/HRS/2002/data/H02B_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        MARITAL_STATUS_list = []
        EDUCATION_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. Married
            # 3. Separated
            # 4. Divorced
            # 5. Widowed
            # 6. Never Married
            # 7. Other (Specify)
            # 8. DK (Don't Know)
            # 9. RF (Refused)
            MARITAL_STATUS = line[131:132]
            # 1 Married or Partner; 5 other
            MARITAL_STATUS = "1" if MARITAL_STATUS=="1" else "5" if MARITAL_STATUS=="3" or MARITAL_STATUS=="4" or MARITAL_STATUS=="5" or MARITAL_STATUS=="6" or MARITAL_STATUS=="7" else np.nan
            # 0 For no formal education 
            # 1-11 .....Grades 
            # 12 .......High school 
            # 13-15 ....Some college
            # 16 .......College grad
            # 17 .......Post college     (17+ years)
            # 97 .......Other
            # 98. DK (don't know); NA (not ascertained)
            # 99. RF (refused)
            EDUCATION = np.nan if line[42:44] == "97" or line[42:44] == "98" or line[42:44] == "99" else line[42:44]
            HHID_list.append(HHID)
            PN_list.append(PN)
            MARITAL_STATUS_list.append(MARITAL_STATUS)
            EDUCATION_list.append(EDUCATION)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "MARITAL_STATUS":MARITAL_STATUS_list,
            "EDUCATION":EDUCATION_list
        }
        result_2002_four = pd.DataFrame(data)
        result_2002 = pd.merge(result_2002, result_2002_four, on=["HHID", "PN"], how="left")
    result = pd.concat([result, result_2002], axis=0)          
    # 获取2004数据
    with(open("/root/r_base/HRS/2004/data/H04PR_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        BIRTH_YEAR_list = []
        SEX_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            BIRTH_YEAR = line[25:29]
            # 1.Male 
            # 2.Female
            SEX = line[20:21]
            HHID_list.append(HHID)
            PN_list.append(PN)
            BIRTH_YEAR_list.append(BIRTH_YEAR)
            SEX_list.append(SEX)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "BIRTH_YEAR":BIRTH_YEAR_list,
            "SEX":SEX_list,
        }
        data["WAVE"] = 2004
        result_2004_one = pd.DataFrame(data)
    with(open("/root/r_base/HRS/2004/data/H04C_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        SMOKED_list = []
        DRINKED_list = []
        BMI_list = []
        HEART_PROBL_list = []
        PHYSICAL_ACTIVITY_LEVEL_list = []
        STROKE_list=[]    
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. YES
            # 5. NO
            SMOKED = line[167:168] if line[167:168] == "1" or line[167:168] == "5" else np.nan
            # 1. YES
            # 3. [VOL] NEVER HAVE USED ALCOHOL
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # 合并后
            # 1.  Yes
            # 5.  No [Inap in V502-V505]
            DRINKED = "1" if line[192:193] == "1"  else "5" if line[192:193] == "5" or line[192:193] == "3" else np.nan
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 体重
            WEIGH= float(line[203:206])*0.45359237 if not float(line[203:206])>400 else np.nan
            # 身高
            HEIGHT = line[222:223].strip()
            if not HEIGHT == "" and not HEIGHT == "8" and not pd.isna(WEIGH):
                if not line[223:225] == "98" and not line[223:225]=="":
                    HEIGHT = float(HEIGHT)*0.3048 + float(line[223:225])*0.0254
                    # BMI
                    BMI = WEIGH / math.pow(HEIGHT,2)
                else:
                    HEIGHT = float(line[222:223])*0.3048
                    # BMI
                    BMI = WEIGH / math.pow(HEIGHT,2)
            else :
                BMI = np.nan
            # 1. YES
            # 3. DISPUTES PREVIOUS WAVE RECORD, BUT NOW HAS CONDITION
            # 4. DISPUTES PREVIOUS WAVE RECORD, DOES NOT HAVE CONDITION
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # A heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            HEART_PROBL = np.nan if line[69:70] == "8" or line[69:70] == "9" else "1" if line[69:70] == "1" or line[69:70] == "3" else "5"
            # 1. YES
            # 2. [VOL] POSSIBLE STROKE OR TIA (TRANSIENT ISCHEMIC ATTACK)
            # 3. DISPUTES PREVIOUS WAVE RECORD, BUT NOW HAS CONDITION
            # 4. DISPUTES PREVIOUS WAVE RECORD, DOES NOT HAVE CONDITION
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # STROKE 
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            STROKE = np.nan if line[90:91] == "8" or line[90:91] == "9" else "5" if line[90:91] == "2" or line[90:91] == "4" or line[90:91] == "5" else "1"
            # 1. MORE THAN ONCE A WEEK
            # 2. ONCE A WEEK
            # 3. ONCE TO THREE TIMES A MONTH
            # 4. HARDLY EVER OR NEVER
            # 7. (VOL) EVERY DAY
            # 8. DK (Don't Know)
            # 9. RF (Refused)
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 重度活动
            VIGOROUS_PHYSICAL = "3" if line[164:165] == "1" or line[164:165] == "2" or line[164:165] == "7" else "1" if line[164:165] == "3" or line[164:165] == "4" else np.nan
            #中度活动
            MODERATE_PHYSICAL = "2" if line[165:166] == "1" or line[165:166] == "2" or line[165:166] == "7" else "1" if line[165:166] == "3" or line[165:166] == "4" else np.nan
            # 轻度活动
            LIGHT_PHYSICAL = np.nan
            PHYSICAL_ACTIVITY_LEVEL = "3" if VIGOROUS_PHYSICAL=="3" else "2" if MODERATE_PHYSICAL=="2" else "1" if VIGOROUS_PHYSICAL=="1" or MODERATE_PHYSICAL=="1" else np.nan
            HHID_list.append(HHID)
            PN_list.append(PN)
            PHYSICAL_ACTIVITY_LEVEL_list.append(PHYSICAL_ACTIVITY_LEVEL)            
            SMOKED_list.append(SMOKED)
            DRINKED_list.append(DRINKED)
            BMI_list.append(BMI)
            HEART_PROBL_list.append(HEART_PROBL)
            STROKE_list.append(STROKE)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "SMOKED":SMOKED_list,
            "DRINKED":DRINKED_list,
            "BMI":BMI_list,
            "HEART_PROBL":HEART_PROBL_list,
            "STROKE":STROKE_list,
            "PHYSICAL_ACTIVITY_LEVEL":PHYSICAL_ACTIVITY_LEVEL_list
        }
        result_2004_two = pd.DataFrame(data)
        result_2004 = pd.merge(result_2004_one, result_2004_two, on=["HHID", "PN"], how="left")
    with(open("/root/r_base/HRS/2004/data/H04B_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        MARITAL_STATUS_list = []
        EDUCATION_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. MARRIED (VOL)
            # 2. ANULLED (VOL)
            # 3. SEPARATED
            # 4. DIVORCED
            # 5. WIDOWED
            # 6. NEVER MARRIED
            # 7. OTHER (SPECIFY)
            MARITAL_STATUS = line[161:162]
            # 1 Married or Partner; 5 other
            MARITAL_STATUS = "1" if MARITAL_STATUS=="1" else "5" if MARITAL_STATUS=="2" or MARITAL_STATUS=="3" or MARITAL_STATUS=="4" or MARITAL_STATUS=="5" or MARITAL_STATUS=="6" or MARITAL_STATUS=="7" else np.nan
            # 0 For no formal education 
            # 1-11 .....Grades 
            # 12 .......High school 
            # 13-15 ....Some college
            # 16 .......College grad
            # 17 .......Post college     (17+ years)
            # 97 .......Other
            # 98. DK (don't know); NA (not ascertained)
            # 99. RF (refused)
            EDUCATION = np.nan if line[41:43] == "97" or line[41:43] == "98" or line[41:43] == "99" else line[41:43]
            HHID_list.append(HHID)
            PN_list.append(PN)
            MARITAL_STATUS_list.append(MARITAL_STATUS)
            EDUCATION_list.append(EDUCATION)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "MARITAL_STATUS":MARITAL_STATUS_list,
            "EDUCATION":EDUCATION_list
        }
        result_2004_four = pd.DataFrame(data)
        result_2004 = pd.merge(result_2004, result_2004_four, on=["HHID", "PN"], how="left")
    result = pd.concat([result, result_2004], axis=0)    
    # 获取2006数据
    with(open("/root/r_base/HRS/2006/data/H06PR_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        BIRTH_YEAR_list = []
        SEX_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            BIRTH_YEAR = line[25:29]
            # 1.Male 
            # 2.Female
            SEX = line[20:21]
            HHID_list.append(HHID)
            PN_list.append(PN)
            BIRTH_YEAR_list.append(BIRTH_YEAR)
            SEX_list.append(SEX)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "BIRTH_YEAR":BIRTH_YEAR_list,
            "SEX":SEX_list,
        }
        data["WAVE"] = 2006
        result_2006_one = pd.DataFrame(data)
    with(open("/root/r_base/HRS/2006/data/H06C_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        SMOKED_list = []
        DRINKED_list = []
        BMI_list = []
        HEART_PROBL_list = []
        PHYSICAL_ACTIVITY_LEVEL_list = []
        STROKE_list=[]    
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. YES
            # 5. NO
            SMOKED = line[181:182]
            SMOKED = SMOKED if SMOKED == "1" or SMOKED == "5" else np.nan
            # 1. YES
            # 3. [VOL] NEVER HAVE USED ALCOHOL
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # 合并后
            # 1.  Yes
            # 5.  No [Inap in V502-V505]
            DRINKED = line[207:208]
            DRINKED = "1" if DRINKED == "1"  else "5" if DRINKED == "5" or DRINKED == "3" else np.nan
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 体重
            WEIGH = line[218:221].strip()
            WEIGH= float(WEIGH)*0.45359237 if not WEIGH=="" and not float(WEIGH)>400 else np.nan
            # 身高
            HEIGHT_FEET = line[252:253]
            HEIGHT_INCHES = line[253:255]
            if not HEIGHT_FEET == "" and not HEIGHT_FEET == "8" and not HEIGHT_FEET == "9" and not pd.isna(WEIGH):
                if not HEIGHT_INCHES == "98" and not HEIGHT_INCHES=="":
                    HEIGHT = float(HEIGHT_FEET)*0.3048 + float(HEIGHT_INCHES)*0.0254
                    # BMI
                    BMI = WEIGH / math.pow(HEIGHT,2)
                else:
                    HEIGHT = float(HEIGHT_FEET)*0.3048
                    # BMI
                    BMI = WEIGH / math.pow(HEIGHT,2)
            else :
                BMI = np.nan
            # 1. YES
            # 3. DISPUTES PREVIOUS WAVE RECORD, BUT NOW HAS CONDITION
            # 4. DISPUTES PREVIOUS WAVE RECORD, DOES NOT HAVE CONDITION
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # A heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            HEART_PROBL = line[80:81]
            HEART_PROBL = np.nan if HEART_PROBL == "8" or HEART_PROBL == "9" or HEART_PROBL == "" else "1" if HEART_PROBL == "1" or HEART_PROBL == "3" else "5"
            # 1. YES
            # 2. [VOL] POSSIBLE STROKE OR TIA (TRANSIENT ISCHEMIC ATTACK)
            # 3. DISPUTES PREVIOUS WAVE RECORD, BUT NOW HAS CONDITION
            # 4. DISPUTES PREVIOUS WAVE RECORD, DOES NOT HAVE CONDITION
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # STROKE 
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            STROKE = line[101:102]
            STROKE = np.nan if STROKE == "8" or STROKE == "9" or STROKE == "" else "5" if STROKE == "2" or STROKE == "4" or STROKE == "5" else "1"
            # 1. MORE THAN ONCE A WEEK
            # 2. ONCE A WEEK
            # 3. ONCE TO THREE TIMES A MONTH
            # 4. HARDLY EVER OR NEVER
            # 7. (VOL) EVERY DAY
            # 8. DK (Don't Know)
            # 9. RF (Refused)
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 重度活动
            VIGOROUS_PHYSICAL = line[178:179]
            VIGOROUS_PHYSICAL = "3" if VIGOROUS_PHYSICAL == "1" or VIGOROUS_PHYSICAL == "2" or VIGOROUS_PHYSICAL == "7" else "1" if VIGOROUS_PHYSICAL == "3" or VIGOROUS_PHYSICAL == "4" else np.nan
            #中度活动
            MODERATE_PHYSICAL = line[179:180]
            MODERATE_PHYSICAL = "2" if MODERATE_PHYSICAL == "1" or MODERATE_PHYSICAL == "2" or MODERATE_PHYSICAL == "7" else "1" if MODERATE_PHYSICAL == "3" or MODERATE_PHYSICAL == "4" else np.nan
            # 轻度活动
            LIGHT_PHYSICAL = np.nan
            PHYSICAL_ACTIVITY_LEVEL = "3" if VIGOROUS_PHYSICAL=="3" else "2" if MODERATE_PHYSICAL=="2" else "1" if VIGOROUS_PHYSICAL=="1" or MODERATE_PHYSICAL=="1" else np.nan
            HHID_list.append(HHID)
            PN_list.append(PN)
            PHYSICAL_ACTIVITY_LEVEL_list.append(PHYSICAL_ACTIVITY_LEVEL)            
            SMOKED_list.append(SMOKED)
            DRINKED_list.append(DRINKED)
            BMI_list.append(BMI)
            HEART_PROBL_list.append(HEART_PROBL)
            STROKE_list.append(STROKE)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "SMOKED":SMOKED_list,
            "DRINKED":DRINKED_list,
            "BMI":BMI_list,
            "HEART_PROBL":HEART_PROBL_list,
            "STROKE":STROKE_list,
            "PHYSICAL_ACTIVITY_LEVEL":PHYSICAL_ACTIVITY_LEVEL_list
        }
        result_2006_two = pd.DataFrame(data)
        result_2006 = pd.merge(result_2006_one, result_2006_two, on=["HHID", "PN"], how="left")
    with(open("/root/r_base/HRS/2006/data/H06B_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        MARITAL_STATUS_list = []
        EDUCATION_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. MARRIED (VOL)
            # 2. ANULLED (VOL)
            # 3. SEPARATED
            # 4. DIVORCED
            # 5. WIDOWED
            # 6. NEVER MARRIED
            # 7. OTHER (SPECIFY)
            # 8.  DK (Don't Know); NA (Not Ascertained)
            # 9.  RF (Refused)
            MARITAL_STATUS = line[177:178]
            # 1 Married or Partner; 5 other
            MARITAL_STATUS = "1" if MARITAL_STATUS=="1" else "5" if MARITAL_STATUS=="2" or MARITAL_STATUS=="3" or MARITAL_STATUS=="4" or MARITAL_STATUS=="5" or MARITAL_STATUS=="6" or MARITAL_STATUS=="7" else np.nan
            # 0 For no formal education 
            # 1-11 .....Grades 
            # 12 .......High school 
            # 13-15 ....Some college
            # 16 .......College grad
            # 17 .......Post college     (17+ years)
            # 97 .......Other
            # 98. DK (don't know); NA (not ascertained)
            # 99. RF (refused)
            EDUCATION = line[47:49]
            EDUCATION = np.nan if EDUCATION == "97" or EDUCATION == "98" or EDUCATION == "99" else EDUCATION
            HHID_list.append(HHID)
            PN_list.append(PN)
            MARITAL_STATUS_list.append(MARITAL_STATUS)
            EDUCATION_list.append(EDUCATION)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "MARITAL_STATUS":MARITAL_STATUS_list,
            "EDUCATION":EDUCATION_list
        }
        result_2006_four = pd.DataFrame(data)
        result_2006 = pd.merge(result_2006, result_2006_four, on=["HHID", "PN"], how="left")
    result = pd.concat([result, result_2006], axis=0) 
    # 获取2008数据
    with(open("/root/r_base/HRS/2008/data/H08PR_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        BIRTH_YEAR_list = []
        SEX_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            BIRTH_YEAR = line[25:29]
            # 1.Male 
            # 2.Female
            SEX = line[20:21]
            HHID_list.append(HHID)
            PN_list.append(PN)
            BIRTH_YEAR_list.append(BIRTH_YEAR)
            SEX_list.append(SEX)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "BIRTH_YEAR":BIRTH_YEAR_list,
            "SEX":SEX_list,
        }
        data["WAVE"] = 2008
        result_2008_one = pd.DataFrame(data)
    with(open("/root/r_base/HRS/2008/data/H08C_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        SMOKED_list = []
        DRINKED_list = []
        BMI_list = []
        HEART_PROBL_list = []
        PHYSICAL_ACTIVITY_LEVEL_list = []
        STROKE_list=[]    
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. YES
            # 5. NO
            SMOKED = line[258:259]
            SMOKED = SMOKED if SMOKED == "1" or SMOKED == "5" else np.nan
            # 1. YES
            # 3. [VOL] NEVER HAVE USED ALCOHOL
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # 合并后
            # 1.  Yes
            # 5.  No [Inap in V502-V505]
            DRINKED = line[284:285]
            DRINKED = "1" if DRINKED == "1"  else "5" if DRINKED == "5" or DRINKED == "3" else np.nan
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 体重
            WEIGH = line[295:298]
            WEIGH= float(WEIGH)*0.45359237 if not float(WEIGH)>400 else np.nan
            # 身高
            HEIGHT_FEET = line[329:330].strip()
            HEIGHT_INCHES = line[330:338].strip()
            if not HEIGHT_FEET == "" and not HEIGHT_FEET == "8" and not HEIGHT_FEET == "9" and not pd.isna(WEIGH):
                if not HEIGHT_INCHES == "99998" and not HEIGHT_INCHES=="":
                    HEIGHT = float(HEIGHT_FEET)*0.3048 + float(HEIGHT_INCHES)*0.0254
                    # BMI
                    BMI = WEIGH / math.pow(HEIGHT,2)
                else:
                    HEIGHT = float(HEIGHT_FEET)*0.3048
                    # BMI
                    BMI = WEIGH / math.pow(HEIGHT,2)
            else :
                BMI = np.nan
            # 1. YES
            # 3. DISPUTES PREVIOUS WAVE RECORD, BUT NOW HAS CONDITION
            # 4. DISPUTES PREVIOUS WAVE RECORD, DOES NOT HAVE CONDITION
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # A heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            HEART_PROBL = line[128:129]
            HEART_PROBL = np.nan if HEART_PROBL == "8" or HEART_PROBL == "9" or HEART_PROBL == "" else "1" if HEART_PROBL == "1" or HEART_PROBL == "3" else "5"
            # 1. YES
            # 2. [VOL] POSSIBLE STROKE OR TIA (TRANSIENT ISCHEMIC ATTACK)
            # 3. DISPUTES PREVIOUS WAVE RECORD, BUT NOW HAS CONDITION
            # 4. DISPUTES PREVIOUS WAVE RECORD, DOES NOT HAVE CONDITION
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # STROKE 
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            STROKE = line[149:150]
            STROKE = np.nan if STROKE == "8" or STROKE == "9" or STROKE == "" else "5" if STROKE == "2" or STROKE == "4" or STROKE == "5" else "1"
            # 1. MORE THAN ONCE A WEEK
            # 2. ONCE A WEEK
            # 3. ONCE TO THREE TIMES A MONTH
            # 4. HARDLY EVER OR NEVER
            # 7. (VOL) EVERY DAY
            # 8. DK (Don't Know)
            # 9. RF (Refused)
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 重度活动
            VIGOROUS_PHYSICAL = line[255:256]
            VIGOROUS_PHYSICAL = "3" if VIGOROUS_PHYSICAL == "1" or VIGOROUS_PHYSICAL == "2" or VIGOROUS_PHYSICAL == "7" else "1" if VIGOROUS_PHYSICAL == "3" or VIGOROUS_PHYSICAL == "4" else np.nan
            #中度活动
            MODERATE_PHYSICAL = line[256:257]
            MODERATE_PHYSICAL = "2" if MODERATE_PHYSICAL == "1" or MODERATE_PHYSICAL == "2" or MODERATE_PHYSICAL == "7" else "1" if MODERATE_PHYSICAL == "3" or MODERATE_PHYSICAL == "4" else np.nan
            # 轻度活动
            LIGHT_PHYSICAL = np.nan
            PHYSICAL_ACTIVITY_LEVEL = "3" if VIGOROUS_PHYSICAL=="3" else "2" if MODERATE_PHYSICAL=="2" else "1" if VIGOROUS_PHYSICAL=="1" or MODERATE_PHYSICAL=="1" else np.nan
            HHID_list.append(HHID)
            PN_list.append(PN)
            PHYSICAL_ACTIVITY_LEVEL_list.append(PHYSICAL_ACTIVITY_LEVEL)            
            SMOKED_list.append(SMOKED)
            DRINKED_list.append(DRINKED)
            BMI_list.append(BMI)
            HEART_PROBL_list.append(HEART_PROBL)
            STROKE_list.append(STROKE)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "SMOKED":SMOKED_list,
            "DRINKED":DRINKED_list,
            "BMI":BMI_list,
            "HEART_PROBL":HEART_PROBL_list,
            "STROKE":STROKE_list,
            "PHYSICAL_ACTIVITY_LEVEL":PHYSICAL_ACTIVITY_LEVEL_list
        }
        result_2008_two = pd.DataFrame(data)
        result_2008 = pd.merge(result_2008_one, result_2008_two, on=["HHID", "PN"], how="left")
    with(open("/root/r_base/HRS/2008/data/H08B_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        MARITAL_STATUS_list = []
        EDUCATION_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. MARRIED (VOL)
            # 2. ANULLED (VOL)
            # 3. SEPARATED
            # 4. DIVORCED
            # 5. WIDOWED
            # 6. NEVER MARRIED
            # 7. OTHER (SPECIFY)
            # 8.  DK (Don't Know); NA (Not Ascertained)
            # 9.  RF (Refused)
            MARITAL_STATUS = line[311:312]
            # 1 Married or Partner; 5 other
            MARITAL_STATUS = "1" if MARITAL_STATUS=="1" else "5" if MARITAL_STATUS=="2" or MARITAL_STATUS=="3" or MARITAL_STATUS=="4" or MARITAL_STATUS=="5" or MARITAL_STATUS=="6" or MARITAL_STATUS=="7" else np.nan
            # 0 For no formal education 
            # 1-11 .....Grades 
            # 12 .......High school 
            # 13-15 ....Some college
            # 16 .......College grad
            # 17 .......Post college     (17+ years)
            # 97 .......Other
            # 98. DK (don't know); NA (not ascertained)
            # 99. RF (refused)
            EDUCATION = line[48:50]
            EDUCATION = np.nan if EDUCATION == "97" or EDUCATION == "98" or EDUCATION == "99" or EDUCATION == "" else EDUCATION
            HHID_list.append(HHID)
            PN_list.append(PN)
            MARITAL_STATUS_list.append(MARITAL_STATUS)
            EDUCATION_list.append(EDUCATION)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "MARITAL_STATUS":MARITAL_STATUS_list,
            "EDUCATION":EDUCATION_list
        }
        result_2008_four = pd.DataFrame(data)
        result_2008 = pd.merge(result_2008, result_2008_four, on=["HHID", "PN"], how="left")
    result = pd.concat([result, result_2008], axis=0) 
    # 获取2010数据
    with(open("/root/r_base/HRS/2010/data/H10PR_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        BIRTH_YEAR_list = []
        SEX_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            BIRTH_YEAR = line[25:29]
            # 1.Male 
            # 2.Female
            SEX = line[20:21]
            HHID_list.append(HHID)
            PN_list.append(PN)
            BIRTH_YEAR_list.append(BIRTH_YEAR)
            SEX_list.append(SEX)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "BIRTH_YEAR":BIRTH_YEAR_list,
            "SEX":SEX_list,
        }
        data["WAVE"] = 2010
        result_2010_one = pd.DataFrame(data)
    with(open("/root/r_base/HRS/2010/data/H10C_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        SMOKED_list = []
        DRINKED_list = []
        BMI_list = []
        HEART_PROBL_list = []
        PHYSICAL_ACTIVITY_LEVEL_list = []
        STROKE_list=[]    
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. YES
            # 5. NO
            SMOKED = line[250:251]
            SMOKED = SMOKED if SMOKED == "1" or SMOKED == "5" else np.nan
            # 1. YES
            # 3. [VOL] NEVER HAVE USED ALCOHOL
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # 合并后
            # 1.  Yes
            # 5.  No [Inap in V502-V505]
            DRINKED = line[276:277]
            DRINKED = "1" if DRINKED == "1"  else "5" if DRINKED == "5" or DRINKED == "3" else np.nan
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 体重
            WEIGH = line[287:290].strip()
            WEIGH= float(WEIGH)*0.45359237 if not WEIGH=="" and not float(WEIGH)>400 else np.nan
            # 身高
            HEIGHT_FEET = line[304:305].strip()
            HEIGHT_INCHES = line[305:313]
            if not HEIGHT_FEET == "" and not HEIGHT_FEET == "8" and not HEIGHT_FEET == "9" and not pd.isna(WEIGH):
                if not HEIGHT_INCHES == "99998" and not HEIGHT_INCHES=="":
                    HEIGHT = float(HEIGHT_FEET)*0.3048 + float(HEIGHT_INCHES)*0.0254
                    # BMI
                    BMI = WEIGH / math.pow(HEIGHT,2)
                else:
                    HEIGHT = float(HEIGHT_FEET)*0.3048
                    # BMI
                    BMI = WEIGH / math.pow(HEIGHT,2)
            else :
                BMI = np.nan
            # 1. YES
            # 3. DISPUTES PREVIOUS WAVE RECORD, BUT NOW HAS CONDITION
            # 4. DISPUTES PREVIOUS WAVE RECORD, DOES NOT HAVE CONDITION
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # A heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            HEART_PROBL = line[76:77]
            HEART_PROBL = np.nan if HEART_PROBL == "8" or HEART_PROBL == "9" or HEART_PROBL == "" else "1" if HEART_PROBL == "1" or HEART_PROBL == "3" else "5"
            # 1. YES
            # 2. [VOL] POSSIBLE STROKE OR TIA (TRANSIENT ISCHEMIC ATTACK)
            # 3. DISPUTES PREVIOUS WAVE RECORD, BUT NOW HAS CONDITION
            # 4. DISPUTES PREVIOUS WAVE RECORD, DOES NOT HAVE CONDITION
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # STROKE 
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            STROKE = line[139:140]
            STROKE = np.nan if STROKE == "8" or STROKE == "9" or STROKE == "" else "5" if STROKE == "2" or STROKE == "4" or STROKE == "5" else "1"
            # 1. MORE THAN ONCE A WEEK
            # 2. ONCE A WEEK
            # 3. ONCE TO THREE TIMES A MONTH
            # 4. HARDLY EVER OR NEVER
            # 7. (VOL) EVERY DAY
            # 8. DK (Don't Know)
            # 9. RF (Refused)
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 重度活动
            VIGOROUS_PHYSICAL = line[247:248]
            VIGOROUS_PHYSICAL = "3" if VIGOROUS_PHYSICAL == "1" or VIGOROUS_PHYSICAL == "2" or VIGOROUS_PHYSICAL == "7" else "1" if VIGOROUS_PHYSICAL == "3" or VIGOROUS_PHYSICAL == "4" else np.nan
            #中度活动
            MODERATE_PHYSICAL = line[248:249]
            MODERATE_PHYSICAL = "2" if MODERATE_PHYSICAL == "1" or MODERATE_PHYSICAL == "2" or MODERATE_PHYSICAL == "7" else "1" if MODERATE_PHYSICAL == "3" or MODERATE_PHYSICAL == "4" else np.nan
            # 轻度活动
            LIGHT_PHYSICAL = np.nan
            PHYSICAL_ACTIVITY_LEVEL = "3" if VIGOROUS_PHYSICAL=="3" else "2" if MODERATE_PHYSICAL=="2" else "1" if VIGOROUS_PHYSICAL=="1" or MODERATE_PHYSICAL=="1" else np.nan
            HHID_list.append(HHID)
            PN_list.append(PN)
            PHYSICAL_ACTIVITY_LEVEL_list.append(PHYSICAL_ACTIVITY_LEVEL)            
            SMOKED_list.append(SMOKED)
            DRINKED_list.append(DRINKED)
            BMI_list.append(BMI)
            HEART_PROBL_list.append(HEART_PROBL)
            STROKE_list.append(STROKE)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "SMOKED":SMOKED_list,
            "DRINKED":DRINKED_list,
            "BMI":BMI_list,
            "HEART_PROBL":HEART_PROBL_list,
            "STROKE":STROKE_list,
            "PHYSICAL_ACTIVITY_LEVEL":PHYSICAL_ACTIVITY_LEVEL_list
        }
        result_2010_two = pd.DataFrame(data)
        result_2010 = pd.merge(result_2010_one, result_2010_two, on=["HHID", "PN"], how="left")
    with(open("/root/r_base/HRS/2010/data/H10B_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        MARITAL_STATUS_list = []
        EDUCATION_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. MARRIED (VOL)
            # 2. ANULLED (VOL)
            # 3. SEPARATED
            # 4. DIVORCED
            # 5. WIDOWED
            # 6. NEVER MARRIED
            # 7. OTHER (SPECIFY)
            # 8.  DK (Don't Know); NA (Not Ascertained)
            # 9.  RF (Refused)
            MARITAL_STATUS = line[305:306]
            # 1 Married or Partner; 5 other
            MARITAL_STATUS = "1" if MARITAL_STATUS=="1" else "5" if MARITAL_STATUS=="2" or MARITAL_STATUS=="3" or MARITAL_STATUS=="4" or MARITAL_STATUS=="5" or MARITAL_STATUS=="6" or MARITAL_STATUS=="7" else np.nan
            # 0 For no formal education 
            # 1-11 .....Grades 
            # 12 .......High school 
            # 13-15 ....Some college
            # 16 .......College grad
            # 17 .......Post college     (17+ years)
            # 97 .......Other
            # 98. DK (don't know); NA (not ascertained)
            # 99. RF (refused)
            EDUCATION = line[48:50]
            EDUCATION = np.nan if EDUCATION == "97" or EDUCATION == "98" or EDUCATION == "99" or EDUCATION == "" else EDUCATION
            HHID_list.append(HHID)
            PN_list.append(PN)
            MARITAL_STATUS_list.append(MARITAL_STATUS)
            EDUCATION_list.append(EDUCATION)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "MARITAL_STATUS":MARITAL_STATUS_list,
            "EDUCATION":EDUCATION_list
        }
        result_2010_four = pd.DataFrame(data)
        result_2010 = pd.merge(result_2010, result_2010_four, on=["HHID", "PN"], how="left")
    result = pd.concat([result, result_2010], axis=0) 
    # 获取2012数据
    with(open("/root/r_base/HRS/2012/data/H12PR_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        BIRTH_YEAR_list = []
        SEX_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            BIRTH_YEAR = line[25:29] if not line[25:29] == "" else np.nan
            # 1.Male 
            # 2.Female
            SEX = line[20:21] if not line[20:21] == "" else np.nan
            HHID_list.append(HHID)
            PN_list.append(PN)
            BIRTH_YEAR_list.append(BIRTH_YEAR)
            SEX_list.append(SEX)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "BIRTH_YEAR":BIRTH_YEAR_list,
            "SEX":SEX_list,
        }
        data["WAVE"] = 2012
        result_2012_one = pd.DataFrame(data)
    with(open("/root/r_base/HRS/2012/data/H12C_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        SMOKED_list = []
        DRINKED_list = []
        BMI_list = []
        HEART_PROBL_list = []
        PHYSICAL_ACTIVITY_LEVEL_list = []
        STROKE_list=[]    
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. YES
            # 5. NO
            SMOKED = line[249:250]
            SMOKED = SMOKED if SMOKED == "1" or SMOKED == "5" else np.nan
            # 1. YES
            # 3. [VOL] NEVER HAVE USED ALCOHOL
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # 合并后
            # 1.  Yes
            # 5.  No [Inap in V502-V505]
            DRINKED = line[276:277]
            DRINKED = "1" if DRINKED == "1"  else "5" if DRINKED == "5" or DRINKED == "3" else np.nan
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 体重
            WEIGH = line[287:290].strip()
            WEIGH= float(WEIGH)*0.45359237 if not WEIGH=="" and not float(WEIGH)>400 else np.nan
            # 身高
            HEIGHT_FEET = line[302:303].strip()
            HEIGHT_INCHES = line[303:308]
            if not HEIGHT_FEET == "" and not HEIGHT_FEET == "8" and not HEIGHT_FEET == "9" and not pd.isna(WEIGH):
                if not HEIGHT_INCHES == "98" and not HEIGHT_INCHES=="":
                    HEIGHT = float(HEIGHT_FEET)*0.3048 + float(HEIGHT_INCHES)*0.0254
                    # BMI
                    BMI = WEIGH / math.pow(HEIGHT,2)
                else:
                    HEIGHT = float(HEIGHT_FEET)*0.3048
                    # BMI
                    BMI = WEIGH / math.pow(HEIGHT,2)
            else :
                BMI = np.nan
            # 1. YES
            # 3. DISPUTES PREVIOUS WAVE RECORD, BUT NOW HAS CONDITION
            # 4. DISPUTES PREVIOUS WAVE RECORD, DOES NOT HAVE CONDITION
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # A heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            HEART_PROBL = line[82:83]
            HEART_PROBL = np.nan if HEART_PROBL == "8" or HEART_PROBL == "9" or HEART_PROBL == "" else "1" if HEART_PROBL == "1" or HEART_PROBL == "3" else "5"
            # 1. YES
            # 2. [VOL] POSSIBLE STROKE OR TIA (TRANSIENT ISCHEMIC ATTACK)
            # 3. DISPUTES PREVIOUS WAVE RECORD, BUT NOW HAS CONDITION
            # 4. DISPUTES PREVIOUS WAVE RECORD, DOES NOT HAVE CONDITION
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # STROKE 
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            STROKE = line[146:147]
            STROKE = np.nan if STROKE == "8" or STROKE == "9" or STROKE == "" else "5" if STROKE == "2" or STROKE == "4" or STROKE == "5" else "1"
            # 1. MORE THAN ONCE A WEEK
            # 2. ONCE A WEEK
            # 3. ONCE TO THREE TIMES A MONTH
            # 4. HARDLY EVER OR NEVER
            # 7. (VOL) EVERY DAY
            # 8. DK (Don't Know)
            # 9. RF (Refused)
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 重度活动
            VIGOROUS_PHYSICAL = line[246:247]
            VIGOROUS_PHYSICAL = "3" if VIGOROUS_PHYSICAL == "1" or VIGOROUS_PHYSICAL == "2" or VIGOROUS_PHYSICAL == "7" else "1" if VIGOROUS_PHYSICAL == "3" or VIGOROUS_PHYSICAL == "4" else np.nan
            #中度活动
            MODERATE_PHYSICAL = line[247:248]
            MODERATE_PHYSICAL = "2" if MODERATE_PHYSICAL == "1" or MODERATE_PHYSICAL == "2" or MODERATE_PHYSICAL == "7" else "1" if MODERATE_PHYSICAL == "3" or MODERATE_PHYSICAL == "4" else np.nan
            # 轻度活动
            LIGHT_PHYSICAL = np.nan
            PHYSICAL_ACTIVITY_LEVEL = "3" if VIGOROUS_PHYSICAL=="3" else "2" if MODERATE_PHYSICAL=="2" else "1" if VIGOROUS_PHYSICAL=="1" or MODERATE_PHYSICAL=="1" else np.nan
            HHID_list.append(HHID)
            PN_list.append(PN)
            PHYSICAL_ACTIVITY_LEVEL_list.append(PHYSICAL_ACTIVITY_LEVEL)            
            SMOKED_list.append(SMOKED)
            DRINKED_list.append(DRINKED)
            BMI_list.append(BMI)
            HEART_PROBL_list.append(HEART_PROBL)
            STROKE_list.append(STROKE)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "SMOKED":SMOKED_list,
            "DRINKED":DRINKED_list,
            "BMI":BMI_list,
            "HEART_PROBL":HEART_PROBL_list,
            "STROKE":STROKE_list,
            "PHYSICAL_ACTIVITY_LEVEL":PHYSICAL_ACTIVITY_LEVEL_list
        }
        result_2012_two = pd.DataFrame(data)
        result_2012 = pd.merge(result_2012_one, result_2012_two, on=["HHID", "PN"], how="left")
    with(open("/root/r_base/HRS/2012/data/H12B_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        MARITAL_STATUS_list = []
        EDUCATION_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. MARRIED (VOL)
            # 2. ANULLED (VOL)
            # 3. SEPARATED
            # 4. DIVORCED
            # 5. WIDOWED
            # 6. NEVER MARRIED
            # 7. OTHER (SPECIFY)
            # 8.  DK (Don't Know); NA (Not Ascertained)
            # 9.  RF (Refused)
            MARITAL_STATUS = line[294:295]
            # 1 Married or Partner; 5 other
            MARITAL_STATUS = "1" if MARITAL_STATUS=="1" else "5" if MARITAL_STATUS=="2" or MARITAL_STATUS=="3" or MARITAL_STATUS=="4" or MARITAL_STATUS=="5" or MARITAL_STATUS=="6" or MARITAL_STATUS=="7" else np.nan
            # 0 For no formal education 
            # 1-11 .....Grades 
            # 12 .......High school 
            # 13-15 ....Some college
            # 16 .......College grad
            # 17 .......Post college     (17+ years)
            # 97 .......Other
            # 98. DK (don't know); NA (not ascertained)
            # 99. RF (refused)
            EDUCATION = line[48:50]
            EDUCATION = np.nan if EDUCATION == "97" or EDUCATION == "98" or EDUCATION == "99" or EDUCATION == "" else EDUCATION
            HHID_list.append(HHID)
            PN_list.append(PN)
            MARITAL_STATUS_list.append(MARITAL_STATUS)
            EDUCATION_list.append(EDUCATION)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "MARITAL_STATUS":MARITAL_STATUS_list,
            "EDUCATION":EDUCATION_list
        }
        result_2012_four = pd.DataFrame(data)
        result_2012 = pd.merge(result_2012, result_2012_four, on=["HHID", "PN"], how="left")
    result = pd.concat([result, result_2012], axis=0) 
    # 获取2014数据
    with(open("/root/r_base/HRS/2014/data/H14PR_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        BIRTH_YEAR_list = []
        SEX_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            BIRTH_YEAR = line[25:29] if not line[25:29] == "" else np.nan
            # 1.Male 
            # 2.Female
            SEX = line[20:21] if not line[20:21] == "" else np.nan
            HHID_list.append(HHID)
            PN_list.append(PN)
            BIRTH_YEAR_list.append(BIRTH_YEAR)
            SEX_list.append(SEX)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "BIRTH_YEAR":BIRTH_YEAR_list,
            "SEX":SEX_list,
        }
        data["WAVE"] = 2014
        result_2014_one = pd.DataFrame(data)
    with(open("/root/r_base/HRS/2014/data/H14C_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        SMOKED_list = []
        DRINKED_list = []
        BMI_list = []
        HEART_PROBL_list = []
        PHYSICAL_ACTIVITY_LEVEL_list = []
        STROKE_list=[]    
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. YES
            # 5. NO
            SMOKED = line[214:215]
            SMOKED = SMOKED if SMOKED == "1" or SMOKED == "5" else np.nan
            # 1. YES
            # 3. [VOL] NEVER HAVE USED ALCOHOL
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # 合并后
            # 1.  Yes
            # 5.  No [Inap in V502-V505]
            DRINKED = line[239:240]
            DRINKED = "1" if DRINKED == "1"  else "5" if DRINKED == "5" or DRINKED == "3" else np.nan
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 体重
            WEIGH = line[250:253].strip()
            WEIGH= float(WEIGH)*0.45359237 if not WEIGH=="" and not float(WEIGH)>400 else np.nan
            # 身高
            HEIGHT_FEET = line[259:260].strip()
            HEIGHT_INCHES = line[260:265]
            if not HEIGHT_FEET == "" and not HEIGHT_FEET == "8" and not HEIGHT_FEET == "9" and not pd.isna(WEIGH):
                if not HEIGHT_INCHES == "98" and not HEIGHT_INCHES=="":
                    HEIGHT = float(HEIGHT_FEET)*0.3048 + float(HEIGHT_INCHES)*0.0254
                    # BMI
                    BMI = WEIGH / math.pow(HEIGHT,2)
                else:
                    HEIGHT = float(HEIGHT_FEET)*0.3048
                    # BMI
                    BMI = WEIGH / math.pow(HEIGHT,2)
            else :
                BMI = np.nan
            # 1. YES
            # 3. DISPUTES PREVIOUS WAVE RECORD, BUT NOW HAS CONDITION
            # 4. DISPUTES PREVIOUS WAVE RECORD, DOES NOT HAVE CONDITION
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # A heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            HEART_PROBL = line[66:67]
            HEART_PROBL = np.nan if HEART_PROBL == "8" or HEART_PROBL == "9" or HEART_PROBL == "" else "1" if HEART_PROBL == "1" or HEART_PROBL == "3" else "5"
            # 1. YES
            # 2. [VOL] POSSIBLE STROKE OR TIA (TRANSIENT ISCHEMIC ATTACK)
            # 3. DISPUTES PREVIOUS WAVE RECORD, BUT NOW HAS CONDITION
            # 4. DISPUTES PREVIOUS WAVE RECORD, DOES NOT HAVE CONDITION
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # STROKE 
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            STROKE = line[122:123]
            STROKE = np.nan if STROKE == "8" or STROKE == "9" or STROKE == "" else "5" if STROKE == "2" or STROKE == "4" or STROKE == "5" else "1"
            # 1. MORE THAN ONCE A WEEK
            # 2. ONCE A WEEK
            # 3. ONCE TO THREE TIMES A MONTH
            # 4. HARDLY EVER OR NEVER
            # 7. (VOL) EVERY DAY
            # 8. DK (Don't Know)
            # 9. RF (Refused)
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 重度活动
            VIGOROUS_PHYSICAL = line[211:212]
            VIGOROUS_PHYSICAL = "3" if VIGOROUS_PHYSICAL == "1" or VIGOROUS_PHYSICAL == "2" or VIGOROUS_PHYSICAL == "7" else "1" if VIGOROUS_PHYSICAL == "3" or VIGOROUS_PHYSICAL == "4" else np.nan
            #中度活动
            MODERATE_PHYSICAL = line[212:213]
            MODERATE_PHYSICAL = "2" if MODERATE_PHYSICAL == "1" or MODERATE_PHYSICAL == "2" or MODERATE_PHYSICAL == "7" else "1" if MODERATE_PHYSICAL == "3" or MODERATE_PHYSICAL == "4" else np.nan
            # 轻度活动
            LIGHT_PHYSICAL = np.nan
            PHYSICAL_ACTIVITY_LEVEL = "3" if VIGOROUS_PHYSICAL=="3" else "2" if MODERATE_PHYSICAL=="2" else "1" if VIGOROUS_PHYSICAL=="1" or MODERATE_PHYSICAL=="1" else np.nan
            HHID_list.append(HHID)
            PN_list.append(PN)
            PHYSICAL_ACTIVITY_LEVEL_list.append(PHYSICAL_ACTIVITY_LEVEL)            
            SMOKED_list.append(SMOKED)
            DRINKED_list.append(DRINKED)
            BMI_list.append(BMI)
            HEART_PROBL_list.append(HEART_PROBL)
            STROKE_list.append(STROKE)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "SMOKED":SMOKED_list,
            "DRINKED":DRINKED_list,
            "BMI":BMI_list,
            "HEART_PROBL":HEART_PROBL_list,
            "STROKE":STROKE_list,
            "PHYSICAL_ACTIVITY_LEVEL":PHYSICAL_ACTIVITY_LEVEL_list
        }
        result_2014_two = pd.DataFrame(data)
        result_2014 = pd.merge(result_2014_one, result_2014_two, on=["HHID", "PN"], how="left")
    with(open("/root/r_base/HRS/2014/data/H14B_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        MARITAL_STATUS_list = []
        EDUCATION_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. MARRIED (VOL)
            # 2. ANULLED (VOL)
            # 3. SEPARATED
            # 4. DIVORCED
            # 5. WIDOWED
            # 6. NEVER MARRIED
            # 7. OTHER (SPECIFY)
            # 8.  DK (Don't Know); NA (Not Ascertained)
            # 9.  RF (Refused)
            MARITAL_STATUS = line[274:275]
            # 1 Married or Partner; 5 other
            MARITAL_STATUS = "1" if MARITAL_STATUS=="1" else "5" if MARITAL_STATUS=="2" or MARITAL_STATUS=="3" or MARITAL_STATUS=="4" or MARITAL_STATUS=="5" or MARITAL_STATUS=="6" or MARITAL_STATUS=="7" else np.nan
            # 0 For no formal education 
            # 1-11 .....Grades 
            # 12 .......High school 
            # 13-15 ....Some college
            # 16 .......College grad
            # 17 .......Post college     (17+ years)
            # 97 .......Other
            # 98. DK (don't know); NA (not ascertained)
            # 99. RF (refused)
            EDUCATION = line[39:41]
            EDUCATION = np.nan if EDUCATION == "97" or EDUCATION == "98" or EDUCATION == "99" or EDUCATION == "" else EDUCATION
            HHID_list.append(HHID)
            PN_list.append(PN)
            MARITAL_STATUS_list.append(MARITAL_STATUS)
            EDUCATION_list.append(EDUCATION)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "MARITAL_STATUS":MARITAL_STATUS_list,
            "EDUCATION":EDUCATION_list
        }
        result_2014_four = pd.DataFrame(data)
        result_2014 = pd.merge(result_2014, result_2014_four, on=["HHID", "PN"], how="left")
    result = pd.concat([result, result_2014], axis=0) 
    # 获取2016数据
    with(open("/root/r_base/HRS/2016/data/H16PR_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        BIRTH_YEAR_list = []
        SEX_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            BIRTH_YEAR = line[25:29] if not line[25:29] == "" else np.nan
            # 1.Male 
            # 2.Female
            SEX = line[20:21] if not line[20:21] == "" else np.nan
            HHID_list.append(HHID)
            PN_list.append(PN)
            BIRTH_YEAR_list.append(BIRTH_YEAR)
            SEX_list.append(SEX)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "BIRTH_YEAR":BIRTH_YEAR_list,
            "SEX":SEX_list,
        }
        data["WAVE"] = 2016
        result_2016_one = pd.DataFrame(data)
    with(open("/root/r_base/HRS/2016/data/H16C_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        SMOKED_list = []
        DRINKED_list = []
        BMI_list = []
        HEART_PROBL_list = []
        PHYSICAL_ACTIVITY_LEVEL_list = []
        STROKE_list=[]    
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. YES
            # 5. NO
            SMOKED = line[237:238]
            SMOKED = SMOKED if SMOKED == "1" or SMOKED == "5" else np.nan
            # 1. YES
            # 3. [VOL] NEVER HAVE USED ALCOHOL
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # 合并后
            # 1.  Yes
            # 5.  No [Inap in V502-V505]
            DRINKED = line[262:263]
            DRINKED = "1" if DRINKED == "1"  else "5" if DRINKED == "5" or DRINKED == "3" else np.nan
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 体重
            WEIGH = line[273:276].strip()
            WEIGH= float(WEIGH)*0.45359237 if not WEIGH=="" and not float(WEIGH)>400 else np.nan
            # 身高
            HEIGHT_FEET = line[282:283].strip()
            HEIGHT_INCHES = line[283:288]
            if not HEIGHT_FEET == "" and not HEIGHT_FEET == "8" and not HEIGHT_FEET == "9" and not pd.isna(WEIGH):
                if not HEIGHT_INCHES == "98" and not HEIGHT_INCHES=="":
                    HEIGHT = float(HEIGHT_FEET)*0.3048 + float(HEIGHT_INCHES)*0.0254
                    # BMI
                    BMI = WEIGH / math.pow(HEIGHT,2)
                else:
                    HEIGHT = float(HEIGHT_FEET)*0.3048
                    # BMI
                    BMI = WEIGH / math.pow(HEIGHT,2)
            else :
                BMI = np.nan
            # 1. YES
            # 3. DISPUTES PREVIOUS WAVE RECORD, BUT NOW HAS CONDITION
            # 4. DISPUTES PREVIOUS WAVE RECORD, DOES NOT HAVE CONDITION
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # A heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            HEART_PROBL = line[67:68]
            HEART_PROBL = np.nan if HEART_PROBL == "8" or HEART_PROBL == "9" or HEART_PROBL == "" else "1" if HEART_PROBL == "1" or HEART_PROBL == "3" else "5"
            # 1. YES
            # 2. [VOL] POSSIBLE STROKE OR TIA (TRANSIENT ISCHEMIC ATTACK)
            # 3. DISPUTES PREVIOUS WAVE RECORD, BUT NOW HAS CONDITION
            # 4. DISPUTES PREVIOUS WAVE RECORD, DOES NOT HAVE CONDITION
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # STROKE 
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            STROKE = line[123:124]
            STROKE = np.nan if STROKE == "8" or STROKE == "9" or STROKE == "" else "5" if STROKE == "2" or STROKE == "4" or STROKE == "5" else "1"
            # 1. MORE THAN ONCE A WEEK
            # 2. ONCE A WEEK
            # 3. ONCE TO THREE TIMES A MONTH
            # 4. HARDLY EVER OR NEVER
            # 7. (VOL) EVERY DAY
            # 8. DK (Don't Know)
            # 9. RF (Refused)
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 重度活动
            VIGOROUS_PHYSICAL = line[234:235]
            VIGOROUS_PHYSICAL = "3" if VIGOROUS_PHYSICAL == "1" or VIGOROUS_PHYSICAL == "2" or VIGOROUS_PHYSICAL == "7" else "1" if VIGOROUS_PHYSICAL == "3" or VIGOROUS_PHYSICAL == "4" else np.nan
            #中度活动
            MODERATE_PHYSICAL = line[235:236]
            MODERATE_PHYSICAL = "2" if MODERATE_PHYSICAL == "1" or MODERATE_PHYSICAL == "2" or MODERATE_PHYSICAL == "7" else "1" if MODERATE_PHYSICAL == "3" or MODERATE_PHYSICAL == "4" else np.nan
            # 轻度活动
            LIGHT_PHYSICAL = np.nan
            PHYSICAL_ACTIVITY_LEVEL = "3" if VIGOROUS_PHYSICAL=="3" else "2" if MODERATE_PHYSICAL=="2" else "1" if VIGOROUS_PHYSICAL=="1" or MODERATE_PHYSICAL=="1" else np.nan
            HHID_list.append(HHID)
            PN_list.append(PN)
            PHYSICAL_ACTIVITY_LEVEL_list.append(PHYSICAL_ACTIVITY_LEVEL)            
            SMOKED_list.append(SMOKED)
            DRINKED_list.append(DRINKED)
            BMI_list.append(BMI)
            HEART_PROBL_list.append(HEART_PROBL)
            STROKE_list.append(STROKE)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "SMOKED":SMOKED_list,
            "DRINKED":DRINKED_list,
            "BMI":BMI_list,
            "HEART_PROBL":HEART_PROBL_list,
            "STROKE":STROKE_list,
            "PHYSICAL_ACTIVITY_LEVEL":PHYSICAL_ACTIVITY_LEVEL_list
        }
        result_2016_two = pd.DataFrame(data)
        result_2016 = pd.merge(result_2016_one, result_2016_two, on=["HHID", "PN"], how="left")
    with(open("/root/r_base/HRS/2016/data/H16B_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        MARITAL_STATUS_list = []
        EDUCATION_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. MARRIED (VOL)
            # 2. ANULLED (VOL)
            # 3. SEPARATED
            # 4. DIVORCED
            # 5. WIDOWED
            # 6. NEVER MARRIED
            # 7. OTHER (SPECIFY)
            # 8.  DK (Don't Know); NA (Not Ascertained)
            # 9.  RF (Refused)
            MARITAL_STATUS = line[275:276]
            # 1 Married or Partner; 5 other
            MARITAL_STATUS = "1" if MARITAL_STATUS=="1" else "5" if MARITAL_STATUS=="2" or MARITAL_STATUS=="3" or MARITAL_STATUS=="4" or MARITAL_STATUS=="5" or MARITAL_STATUS=="6" or MARITAL_STATUS=="7" else np.nan
            # 0 For no formal education 
            # 1-11 .....Grades 
            # 12 .......High school 
            # 13-15 ....Some college
            # 16 .......College grad
            # 17 .......Post college     (17+ years)
            # 97 .......Other
            # 98. DK (don't know); NA (not ascertained)
            # 99. RF (refused)
            EDUCATION = line[39:41]
            EDUCATION = np.nan if EDUCATION == "97" or EDUCATION == "98" or EDUCATION == "99" or EDUCATION == "" else EDUCATION
            HHID_list.append(HHID)
            PN_list.append(PN)
            MARITAL_STATUS_list.append(MARITAL_STATUS)
            EDUCATION_list.append(EDUCATION)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "MARITAL_STATUS":MARITAL_STATUS_list,
            "EDUCATION":EDUCATION_list
        }
        result_2016_four = pd.DataFrame(data)
        result_2016 = pd.merge(result_2016, result_2016_four, on=["HHID", "PN"], how="left")
    result = pd.concat([result, result_2016], axis=0) 
    # 获取2018数据
    with(open("/root/r_base/HRS/2018/data/H18PR_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        BIRTH_YEAR_list = []
        SEX_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            BIRTH_YEAR = line[25:29] if not line[25:29] == "" else np.nan
            # 1.Male 
            # 2.Female
            SEX = line[20:21] if not line[20:21] == "" else np.nan
            HHID_list.append(HHID)
            PN_list.append(PN)
            BIRTH_YEAR_list.append(BIRTH_YEAR)
            SEX_list.append(SEX)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "BIRTH_YEAR":BIRTH_YEAR_list,
            "SEX":SEX_list,
        }
        data["WAVE"] = 2018
        result_2018_one = pd.DataFrame(data)
    with(open("/root/r_base/HRS/2018/data/H18C_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        SMOKED_list = []
        DRINKED_list = []
        BMI_list = []
        HEART_PROBL_list = []
        PHYSICAL_ACTIVITY_LEVEL_list = []
        STROKE_list=[]    
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. YES
            # 5. NO
            SMOKED = line[381:382]
            SMOKED = SMOKED if SMOKED == "1" or SMOKED == "5" else np.nan
            # 1. YES
            # 3. [VOL] NEVER HAVE USED ALCOHOL
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # 合并后
            # 1.  Yes
            # 5.  No [Inap in V502-V505]
            DRINKED = line[404:406]
            DRINKED = "1" if DRINKED == "1"  else "5" if DRINKED == "5" or DRINKED == "3" else np.nan
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 体重
            WEIGH = line[418:421].strip()
            WEIGH= float(WEIGH)*0.45359237 if not WEIGH=="" and not float(WEIGH)>400 and not float(WEIGH)<0 else np.nan
            # 身高
            HEIGHT_FEET = line[428:430].strip()
            HEIGHT_INCHES = line[430:435].strip()
            if not HEIGHT_FEET == "" and not HEIGHT_FEET == "8" and not HEIGHT_FEET == "9" and not pd.isna(WEIGH):
                if not HEIGHT_INCHES == "98" and not HEIGHT_INCHES == "99" and not HEIGHT_INCHES=="":
                    HEIGHT = float(HEIGHT_FEET)*0.3048 + float(HEIGHT_INCHES)*0.0254
                    # BMI
                    BMI = WEIGH / math.pow(HEIGHT,2)
                else:
                    HEIGHT = float(HEIGHT_FEET)*0.3048
                    # BMI
                    BMI = WEIGH / math.pow(HEIGHT,2)
            else :
                BMI = np.nan
            # -8.  Web non-response
            #  1.  YES
            #  4.  [NEVER HAD HEART PROBLEM]
            #  5.  NO
            #  6.  [HAD HEART PROBLEM BEFORE, BUT DO NOT HAVE IT NOW AND AM NOT
            #      TAKING MEDICATION FOR IT]
            #  8.  DK (Don't Know); NA (Not Ascertained)
            #  9.  RF (Refused)
            # A heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            HEART_PROBL = line[86:88]
            HEART_PROBL = np.nan if HEART_PROBL == "8" or HEART_PROBL == "9" or HEART_PROBL == "" or HEART_PROBL == "-8" else "1" if HEART_PROBL == "1" else "5"
            # -8.  Web non-response
            #  1.  YES
            #  2.  [[VOL] POSSIBLE STROKE OR TIA (TRANSIENT ISCHEMIC
            #      ATTACK)/POSSIBLE STROKE OR TIA (TRANSIENT ISCHEMIC ATTACK)]
            #  4.  [NEVER HAD A STROKE]
            #  5.  NO
            #  8.  DK (Don't Know); NA (Not Ascertained)
            #  9.  RF (Refused)
            # STROKE 
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            STROKE = line[162:164]
            STROKE = np.nan if STROKE == "8" or STROKE == "9" or STROKE == "" or STROKE == "-8" else "5" if STROKE == "2" or STROKE == "4" or STROKE == "5" else "1"
            # 1. MORE THAN ONCE A WEEK
            # 2. ONCE A WEEK
            # 3. ONCE TO THREE TIMES A MONTH
            # 4. HARDLY EVER OR NEVER
            # 7. (VOL) EVERY DAY
            # 8. DK (Don't Know)
            # 9. RF (Refused)
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 重度活动
            VIGOROUS_PHYSICAL = line[367:369]
            VIGOROUS_PHYSICAL = "3" if VIGOROUS_PHYSICAL == "1" or VIGOROUS_PHYSICAL == "2" or VIGOROUS_PHYSICAL == "7" else "1" if VIGOROUS_PHYSICAL == "3" or VIGOROUS_PHYSICAL == "4" else np.nan
            #中度活动
            MODERATE_PHYSICAL = line[369:371]
            MODERATE_PHYSICAL = "2" if MODERATE_PHYSICAL == "1" or MODERATE_PHYSICAL == "2" or MODERATE_PHYSICAL == "7" else "1" if MODERATE_PHYSICAL == "3" or MODERATE_PHYSICAL == "4" else np.nan
            # 轻度活动
            LIGHT_PHYSICAL = np.nan
            PHYSICAL_ACTIVITY_LEVEL = "3" if VIGOROUS_PHYSICAL=="3" else "2" if MODERATE_PHYSICAL=="2" else "1" if VIGOROUS_PHYSICAL=="1" or MODERATE_PHYSICAL=="1" else np.nan
            HHID_list.append(HHID)
            PN_list.append(PN)
            PHYSICAL_ACTIVITY_LEVEL_list.append(PHYSICAL_ACTIVITY_LEVEL)            
            SMOKED_list.append(SMOKED)
            DRINKED_list.append(DRINKED)
            BMI_list.append(BMI)
            HEART_PROBL_list.append(HEART_PROBL)
            STROKE_list.append(STROKE)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "SMOKED":SMOKED_list,
            "DRINKED":DRINKED_list,
            "BMI":BMI_list,
            "HEART_PROBL":HEART_PROBL_list,
            "STROKE":STROKE_list,
            "PHYSICAL_ACTIVITY_LEVEL":PHYSICAL_ACTIVITY_LEVEL_list
        }
        result_2018_two = pd.DataFrame(data)
        result_2018 = pd.merge(result_2018_one, result_2018_two, on=["HHID", "PN"], how="left")
    with(open("/root/r_base/HRS/2018/data/H18B_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        MARITAL_STATUS_list = []
        EDUCATION_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. MARRIED (VOL)
            # 2. ANULLED (VOL)
            # 3. SEPARATED
            # 4. DIVORCED
            # 5. WIDOWED
            # 6. NEVER MARRIED
            # 7. OTHER (SPECIFY)
            # 8.  DK (Don't Know); NA (Not Ascertained)
            # 9.  RF (Refused)
            MARITAL_STATUS = line[287:288]
            # 1 Married or Partner; 5 other
            MARITAL_STATUS = "1" if MARITAL_STATUS=="1" else "5" if MARITAL_STATUS=="2" or MARITAL_STATUS=="3" or MARITAL_STATUS=="4" or MARITAL_STATUS=="5" or MARITAL_STATUS=="6" or MARITAL_STATUS=="7" else np.nan
            # 0 For no formal education 
            # 1-11 .....Grades 
            # 12 .......High school 
            # 13-15 ....Some college
            # 16 .......College grad
            # 17 .......Post college     (17+ years)
            # 97 .......Other
            # 98. DK (don't know); NA (not ascertained)
            # 99. RF (refused)
            EDUCATION = line[41:43]
            EDUCATION = np.nan if EDUCATION == "97" or EDUCATION == "98" or EDUCATION == "99" or EDUCATION == "" else EDUCATION
            HHID_list.append(HHID)
            PN_list.append(PN)
            MARITAL_STATUS_list.append(MARITAL_STATUS)
            EDUCATION_list.append(EDUCATION)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "MARITAL_STATUS":MARITAL_STATUS_list,
            "EDUCATION":EDUCATION_list
        }
        result_2018_four = pd.DataFrame(data)
        result_2018 = pd.merge(result_2018, result_2018_four, on=["HHID", "PN"], how="left")
    result = pd.concat([result, result_2018], axis=0) 
    # 获取2020数据
    with(open("/root/r_base/HRS/2020/data/H20PR_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        BIRTH_YEAR_list = []
        SEX_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            BIRTH_YEAR = line[36:40] if not line[36:40] == "" else np.nan
            # 1.Male 
            # 2.Female
            SEX = line[33:34] if not line[33:34] == "" else np.nan
            HHID_list.append(HHID)
            PN_list.append(PN)
            BIRTH_YEAR_list.append(BIRTH_YEAR)
            SEX_list.append(SEX)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "BIRTH_YEAR":BIRTH_YEAR_list,
            "SEX":SEX_list,
        }
        data["WAVE"] = 2020
        result_2020_one = pd.DataFrame(data)
    with(open("/root/r_base/HRS/2020/data/H20C_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        SMOKED_list = []
        DRINKED_list = []
        BMI_list = []
        HEART_PROBL_list = []
        PHYSICAL_ACTIVITY_LEVEL_list = []
        STROKE_list=[]    
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. YES
            # 5. NO
            SMOKED = line[339:340]
            SMOKED = SMOKED if SMOKED == "1" or SMOKED == "5" else np.nan
            # 1. YES
            # 3. [VOL] NEVER HAVE USED ALCOHOL
            # 5. NO
            # 8. DK (don't know); NA (not ascertained)
            # 9. RF (refused)
            # 合并后
            # 1.  Yes
            # 5.  No [Inap in V502-V505]
            DRINKED = line[363:365]
            DRINKED = "1" if DRINKED == "1"  else "5" if DRINKED == "5" or DRINKED == "3" else np.nan
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 体重
            WEIGH = line[380:383].strip()
            WEIGH= float(WEIGH)*0.45359237 if not WEIGH=="" and not float(WEIGH)>400 and not float(WEIGH)<0 else np.nan
            # 身高
            HEIGHT_FEET = line[389:390].strip()
            HEIGHT_INCHES = line[390:395].strip()
            if not HEIGHT_FEET == "" and not HEIGHT_FEET == "8" and not HEIGHT_FEET == "9" and not pd.isna(WEIGH):
                if not HEIGHT_INCHES == "98" and not HEIGHT_INCHES=="":
                    HEIGHT = float(HEIGHT_FEET)*0.3048 + float(HEIGHT_INCHES)*0.0254
                    # BMI
                    BMI = WEIGH / math.pow(HEIGHT,2)
                else:
                    HEIGHT = float(HEIGHT_FEET)*0.3048
                    # BMI
                    BMI = WEIGH / math.pow(HEIGHT,2)
            else :
                BMI = np.nan
            # -8.  Web non-response
            #  1.  YES
            #  4.  [NEVER HAD HEART PROBLEM]
            #  5.  NO
            #  6.  [HAD HEART PROBLEM BEFORE, BUT DO NOT HAVE IT NOW AND AM NOT
            #      TAKING MEDICATION FOR IT]
            #  8.  DK (Don't Know); NA (Not Ascertained)
            #  9.  RF (Refused)
            # A heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            HEART_PROBL = line[73:75]
            HEART_PROBL = np.nan if HEART_PROBL == "8" or HEART_PROBL == "9" or HEART_PROBL == "" or HEART_PROBL == "-8" else "1" if HEART_PROBL == "1" else "5"
            # -8.  Web non-response
            #  1.  YES
            #  2.  [[VOL] POSSIBLE STROKE OR TIA (TRANSIENT ISCHEMIC
            #      ATTACK)/POSSIBLE STROKE OR TIA (TRANSIENT ISCHEMIC ATTACK)]
            #  4.  [NEVER HAD A STROKE]
            #  5.  NO
            #  8.  DK (Don't Know); NA (Not Ascertained)
            #  9.  RF (Refused)
            # STROKE 
            #   1.      Yes
            #   5.      No [Inap in V418-V421]
            STROKE = line[138:140]
            STROKE = np.nan if STROKE == "8" or STROKE == "9" or STROKE == "" or STROKE == "-8" else "5" if STROKE == "2" or STROKE == "4" or STROKE == "5" else "1"
            # 1. MORE THAN ONCE A WEEK
            # 2. ONCE A WEEK
            # 3. ONCE TO THREE TIMES A MONTH
            # 4. HARDLY EVER OR NEVER
            # 7. (VOL) EVERY DAY
            # 8. DK (Don't Know)
            # 9. RF (Refused)
            # 3. vigorous (vigorous activity more than once a week)
            # 2. moderate (moderate activity more than once a week)
            # 1. inactive (the rest)
            # 重度活动
            VIGOROUS_PHYSICAL = line[325:327]
            VIGOROUS_PHYSICAL = "3" if VIGOROUS_PHYSICAL == "1" or VIGOROUS_PHYSICAL == "2" or VIGOROUS_PHYSICAL == "7" else "1" if VIGOROUS_PHYSICAL == "3" or VIGOROUS_PHYSICAL == "4" else np.nan
            #中度活动
            MODERATE_PHYSICAL = line[327:329]
            MODERATE_PHYSICAL = "2" if MODERATE_PHYSICAL == "1" or MODERATE_PHYSICAL == "2" or MODERATE_PHYSICAL == "7" else "1" if MODERATE_PHYSICAL == "3" or MODERATE_PHYSICAL == "4" else np.nan
            # 轻度活动
            LIGHT_PHYSICAL = np.nan
            PHYSICAL_ACTIVITY_LEVEL = "3" if VIGOROUS_PHYSICAL=="3" else "2" if MODERATE_PHYSICAL=="2" else "1" if VIGOROUS_PHYSICAL=="1" or MODERATE_PHYSICAL=="1" else np.nan
            HHID_list.append(HHID)
            PN_list.append(PN)
            PHYSICAL_ACTIVITY_LEVEL_list.append(PHYSICAL_ACTIVITY_LEVEL)            
            SMOKED_list.append(SMOKED)
            DRINKED_list.append(DRINKED)
            BMI_list.append(BMI)
            HEART_PROBL_list.append(HEART_PROBL)
            STROKE_list.append(STROKE)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "SMOKED":SMOKED_list,
            "DRINKED":DRINKED_list,
            "BMI":BMI_list,
            "HEART_PROBL":HEART_PROBL_list,
            "STROKE":STROKE_list,
            "PHYSICAL_ACTIVITY_LEVEL":PHYSICAL_ACTIVITY_LEVEL_list
        }
        result_2020_two = pd.DataFrame(data)
        result_2020 = pd.merge(result_2020_one, result_2020_two, on=["HHID", "PN"], how="left")
    with(open("/root/r_base/HRS/2020/data/H20B_R.da", "r", encoding="utf-8") )as file:
        HHID_list = []
        PN_list = []
        MARITAL_STATUS_list = []
        EDUCATION_list = []
        # 逐行读取文件
        for line in file:
            HHID = line[0:6]
            PN = line[6:9]
            # 1. MARRIED (VOL)
            # 2. ANULLED (VOL)
            # 3. SEPARATED
            # 4. DIVORCED
            # 5. WIDOWED
            # 6. NEVER MARRIED
            # 7. OTHER (SPECIFY)
            # 8.  DK (Don't Know); NA (Not Ascertained)
            # 9.  RF (Refused)
            MARITAL_STATUS = line[304:305]
            # 1 Married or Partner; 5 other
            MARITAL_STATUS = "1" if MARITAL_STATUS=="1" else "5" if MARITAL_STATUS=="2" or MARITAL_STATUS=="3" or MARITAL_STATUS=="4" or MARITAL_STATUS=="5" or MARITAL_STATUS=="6" or MARITAL_STATUS=="7" else np.nan
            # 0 For no formal education 
            # 1-11 .....Grades 
            # 12 .......High school 
            # 13-15 ....Some college
            # 16 .......College grad
            # 17 .......Post college     (17+ years)
            # 97 .......Other
            # 98. DK (don't know); NA (not ascertained)
            # 99. RF (refused)
            EDUCATION = line[40:42]
            EDUCATION = np.nan if EDUCATION == "97" or EDUCATION == "98" or EDUCATION == "99" or EDUCATION == "" else EDUCATION
            HHID_list.append(HHID)
            PN_list.append(PN)
            MARITAL_STATUS_list.append(MARITAL_STATUS)
            EDUCATION_list.append(EDUCATION)
        data = {
            "HHID":HHID_list,
            "PN":PN_list,
            "MARITAL_STATUS":MARITAL_STATUS_list,
            "EDUCATION":EDUCATION_list
        }
        result_2020_four = pd.DataFrame(data)
        result_2020 = pd.merge(result_2020, result_2020_four, on=["HHID", "PN"], how="left")
    result = pd.concat([result, result_2020], axis=0) 
    result.to_csv("/root/r_base/HRS/result_all.csv", index=False)