Analysis of the income level of the people in US
Shreyas Jadhav
26 January 2018
Name: Shreyas Jadhav
Email: essjeyy0412@gmail.com
College:Dwarkadas J.Sanghvi College of Engineering
Initial Analysis
NOTE: The income levels are binned at below 50K and above 50K.
Variable: income_level
-50,000 -> income level below 50k
+50,000 -> income level above 50k
Reading the data set and visualizing the length and breadth of dataset.
setwd("C:/Users/Shreyas Jadhav/Downloads")
usincome <- read.csv(paste("project.csv",sep="."))
View(usincome)
attach(usincome)
dim(usincome)## [1] 10000 41usincome$income_level[usincome$income_level == -50000] <- 'Below 50k'
usincome$income_level[usincome$income_level == 50000] <- 'Above 50k'Descriptive statsistics
summary(usincome)## age class_of_worker industry_code
## Min. : 0.00 Not in universe :4881 Min. : 0.00
## 1st Qu.:15.00 Private :3731 1st Qu.: 0.00
## Median :33.00 Self-employed-not incorporated: 409 Median : 2.00
## Mean :34.46 Local government : 407 Mean :15.82
## 3rd Qu.:49.00 State government : 245 3rd Qu.:33.00
## Max. :90.00 Federal government : 153 Max. :51.00
## (Other) : 174
## occupation_code education wage_per_hour
## Min. : 0.00 High school graduate :2454 Min. : 0.00
## 1st Qu.: 0.00 Children :2338 1st Qu.: 0.00
## Median : 2.00 Some college but no degree:1418 Median : 0.00
## Mean :11.65 Bachelors degree(BA AB BS): 973 Mean : 61.24
## 3rd Qu.:26.00 7th and 8th grade : 456 3rd Qu.: 0.00
## Max. :46.00 10th grade : 384 Max. :9000.00
## (Other) :1977
## enrolled_in_edu_inst_lastwk marital_status
## College or university: 286 Divorced : 653
## High school : 358 Married-A F spouse present : 28
## Not in universe :9356 Married-civilian spouse present:4274
## Married-spouse absent : 80
## Never married :4307
## Separated : 148
## Widowed : 510
## major_industry_code
## Not in universe or children :4898
## Retail trade : 891
## Education : 451
## Manufacturing-durable goods : 451
## Manufacturing-nondurable goods : 385
## Finance insurance and real estate: 331
## (Other) :2593
## major_occupation_code
## Not in universe :4898
## Adm support including clerical: 784
## Professional specialty : 700
## Executive admin and managerial: 638
## Other service : 628
## Sales : 627
## (Other) :1725
## race hispanic_origin
## Amer Indian Aleut or Eskimo: 118 All other :8672
## Asian or Pacific Islander : 309 Mexican-American : 401
## Black :1027 Mexican (Mexicano) : 362
## Other : 183 Central or South American: 195
## White :8363 Puerto Rican : 144
## (Other) : 195
## NA's : 31
## sex member_of_labor_union reason_for_unemployment
## Female:5312 No : 857 Job leaver : 30
## Male :4688 Not in universe:8996 Job loser - on layoff: 42
## Yes : 147 New entrant : 17
## Not in universe :9728
## Other job loser : 97
## Re-entrant : 86
##
## full_parttime_employment_stat capital_gains
## Children or Armed Forces :6156 Min. : 0.0
## Full-time schedules :2097 1st Qu.: 0.0
## Not in labor force :1314 Median : 0.0
## PT for non-econ reasons usually FT: 184 Mean : 395.4
## Unemployed full-time : 119 3rd Qu.: 0.0
## PT for econ reasons usually PT : 72 Max. :99999.0
## (Other) : 58
## capital_losses dividend_from_Stocks
## Min. : 0.00 Min. : 0.0
## 1st Qu.: 0.00 1st Qu.: 0.0
## Median : 0.00 Median : 0.0
## Mean : 39.81 Mean : 164.5
## 3rd Qu.: 0.00 3rd Qu.: 0.0
## Max. :4608.00 Max. :99999.0
##
## tax_filer_status region_of_previous_residence
## Head of household : 388 Abroad : 23
## Joint both 65+ : 397 Midwest : 200
## Joint both under 65 :3478 Northeast : 147
## Joint one under 65 & one 65+: 173 Not in universe:9218
## Nonfiler :3676 South : 225
## Single :1888 West : 187
##
## state_of_previous_residence
## Not in universe:9218
## California : 68
## Utah : 62
## North Carolina : 46
## Minnesota : 36
## (Other) : 524
## NA's : 46
## d_household_family_stat
## Householder :2689
## Child <18 never marr not in subfamily :2511
## Spouse of householder :2107
## Nonfamily householder :1104
## Child 18+ never marr Not in a subfamily: 575
## Secondary individual : 317
## (Other) : 697
## d_household_summary migration_msa
## Householder :3795 Nonmover :4088
## Child under 18 never married :2518 MSA to MSA : 534
## Spouse of householder :2107 NonMSA to nonMSA: 143
## Child 18 or older : 708 Not in universe : 87
## Other relative of householder: 472 MSA to nonMSA : 39
## Nonrelative of householder : 393 (Other) : 66
## (Other) : 7 NA's :5043
## migration_reg migration_within_reg
## Nonmover :4088 Nonmover :4088
## Same county : 506 Same county : 506
## Different county same state: 133 Different county same state: 133
## Not in universe : 87 Not in universe : 87
## Different region : 61 Different state in South : 45
## (Other) : 82 (Other) : 98
## NA's :5043 NA's :5043
## live_1_year_ago migration_sunbelt
## No : 782 No : 519
## Not in universe under 1 year old:5130 Not in universe:4175
## Yes :4088 Yes : 263
## NA's :5043
##
##
##
## num_person_Worked_employer family_members_under_18
## Min. :0.000 Both parents present :1921
## 1st Qu.:0.000 Father only present : 82
## Median :1.000 Mother only present : 676
## Mean :2.027 Neither parent present: 83
## 3rd Qu.:4.000 Not in universe :7238
## Max. :6.000
##
## country_father country_mother country_self
## United-States:8011 United-States:8065 United-States:8873
## Mexico : 484 Mexico : 488 Mexico : 278
## Puerto-Rico : 122 Puerto-Rico : 117 Puerto-Rico : 62
## Italy : 103 Italy : 87 Germany : 51
## Germany : 77 Germany : 74 Cuba : 46
## (Other) : 904 (Other) : 886 (Other) : 537
## NA's : 299 NA's : 283 NA's : 153
## citizenship
## Foreign born- Not a citizen of U S : 675
## Foreign born- U S citizen by naturalization: 298
## Native- Born abroad of American Parent(s) : 88
## Native- Born in Puerto Rico or U S Outlying: 66
## Native- Born in the United States :8873
##
##
## business_or_self_employed fill_questionnaire_veteran_admin
## Min. :0.0000 No : 84
## 1st Qu.:0.0000 Not in universe:9895
## Median :0.0000 Yes : 21
## Mean :0.1724
## 3rd Qu.:0.0000
## Max. :2.0000
##
## veterans_benefits weeks_worked_in_year year income_level
## Min. :0.000 Min. : 0.00 Min. :94.0 Length:10000
## 1st Qu.:2.000 1st Qu.: 0.00 1st Qu.:94.0 Class :character
## Median :2.000 Median :12.00 Median :95.0 Mode :character
## Mean :1.522 Mean :23.78 Mean :94.5
## 3rd Qu.:2.000 3rd Qu.:52.00 3rd Qu.:95.0
## Max. :2.000 Max. :52.00 Max. :95.0
## library(psych)
describe(usincome)[,0:9]## Warning in describe(usincome): NAs introduced by coercion## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning
## Inf## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf## vars n mean sd median trimmed
## age 1 10000 34.46 22.07 33 33.27
## class_of_worker* 2 10000 4.50 1.13 4 4.44
## industry_code 3 10000 15.82 18.11 2 14.16
## occupation_code 4 10000 11.65 14.52 2 9.70
## education* 5 10000 11.09 4.11 11 11.45
## wage_per_hour 6 10000 61.25 299.35 0 0.00
## enrolled_in_edu_inst_lastwk* 7 10000 2.91 0.38 3 3.00
## marital_status* 8 10000 3.98 1.41 4 4.00
## major_industry_code* 9 10000 13.99 4.83 15 14.30
## major_occupation_code* 10 10000 7.30 3.16 7 7.25
## race* 11 10000 4.64 0.87 5 4.86
## hispanic_origin* 12 9969 1.66 1.85 1 1.07
## sex* 13 10000 1.47 0.50 1 1.46
## member_of_labor_union* 14 10000 1.93 0.31 2 2.00
## reason_for_unemployment* 15 10000 4.01 0.30 4 4.00
## full_parttime_employment_stat* 16 10000 1.70 1.20 1 1.45
## capital_gains 17 10000 395.41 4271.33 0 0.00
## capital_losses 18 10000 39.81 281.44 0 0.00
## dividend_from_Stocks 19 10000 164.53 1860.00 0 0.04
## tax_filer_status* 20 10000 4.20 1.39 5 4.27
## region_of_previous_residence* 21 10000 4.00 0.46 4 4.00
## state_of_previous_residence* 22 9954 35.15 5.04 36 36.00
## d_household_family_stat* 23 10000 13.90 9.15 14 13.75
## d_household_summary* 24 10000 4.98 2.07 5 5.02
## migration_msa* 25 4957 4.90 0.97 5 4.96
## migration_reg* 26 4957 6.05 1.03 6 6.03
## migration_within_reg* 27 4957 7.00 1.19 7 7.03
## live_1_year_ago* 28 10000 2.33 0.61 2 2.39
## migration_sunbelt* 29 4957 1.95 0.39 2 1.99
## num_person_Worked_employer 30 10000 2.03 2.39 1 1.78
## family_members_under_18* 31 10000 4.06 1.60 5 4.33
## country_father* 32 9701 36.67 8.55 40 39.09
## country_mother* 33 9717 36.79 8.34 40 39.15
## country_self* 34 9847 38.12 6.65 40 40.00
## citizenship* 35 10000 4.62 1.11 5 4.98
## business_or_self_employed 36 10000 0.17 0.55 0 0.00
## fill_questionnaire_veteran_admin* 37 10000 1.99 0.10 2 2.00
## veterans_benefits 38 10000 1.52 0.85 2 1.65
## weeks_worked_in_year 39 10000 23.78 24.39 12 23.22
## year 40 10000 94.50 0.50 95 94.51
## income_level* 41 10000 NaN NA NA NaN
## mad min max
## age 25.20 0 90
## class_of_worker* 1.48 1 9
## industry_code 2.97 0 51
## occupation_code 2.97 0 46
## education* 2.97 1 17
## wage_per_hour 0.00 0 9000
## enrolled_in_edu_inst_lastwk* 0.00 1 3
## marital_status* 1.48 1 7
## major_industry_code* 1.48 1 24
## major_occupation_code* 1.48 1 15
## race* 0.00 1 5
## hispanic_origin* 0.00 1 9
## sex* 0.00 1 2
## member_of_labor_union* 0.00 1 3
## reason_for_unemployment* 0.00 1 6
## full_parttime_employment_stat* 0.00 1 8
## capital_gains 0.00 0 99999
## capital_losses 0.00 0 4608
## dividend_from_Stocks 0.00 0 99999
## tax_filer_status* 1.48 1 6
## region_of_previous_residence* 0.00 1 6
## state_of_previous_residence* 0.00 1 50
## d_household_family_stat* 14.83 1 27
## d_household_summary* 2.97 1 8
## migration_msa* 0.00 1 9
## migration_reg* 0.00 1 8
## migration_within_reg* 0.00 1 9
## live_1_year_ago* 0.00 1 3
## migration_sunbelt* 0.00 1 3
## num_person_Worked_employer 1.48 0 6
## family_members_under_18* 0.00 1 5
## country_father* 0.00 1 42
## country_mother* 0.00 1 42
## country_self* 0.00 1 42
## citizenship* 0.00 1 5
## business_or_self_employed 0.00 0 2
## fill_questionnaire_veteran_admin* 0.00 1 3
## veterans_benefits 0.00 0 2
## weeks_worked_in_year 17.79 0 52
## year 0.00 94 95
## income_level* NA Inf -InfOne-way contingency tables for the categorical variables.
table(age)## age
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## 147 161 149 160 150 149 146 144 146 171 184 170 166 138 157 176 160 125
## 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
## 110 139 126 102 121 120 152 146 132 144 145 160 155 161 149 181 161 168
## 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53
## 169 191 170 154 153 176 159 155 143 153 158 146 111 105 114 108 117 105
## 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71
## 88 91 75 94 70 75 71 83 85 93 77 76 70 81 51 56 56 60
## 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89
## 50 70 66 48 47 41 59 40 42 28 35 27 27 21 19 16 12 9
## 90
## 33table(age!=0)##
## FALSE TRUE
## 147 9853prop.table(table(age!=0 & wage_per_hour!=0))*100##
## FALSE TRUE
## 93.85 6.15table(class_of_worker)## class_of_worker
## Federal government Local government
## 153 407
## Never worked Not in universe
## 17 4881
## Private Self-employed-incorporated
## 3731 148
## Self-employed-not incorporated State government
## 409 245
## Without pay
## 9table(class_of_worker!="Not in universe")##
## FALSE TRUE
## 4881 5119table(education)## education
## 10th grade
## 384
## 11th grade
## 318
## 12th grade no diploma
## 84
## 1st 2nd 3rd or 4th grade
## 96
## 5th or 6th grade
## 135
## 7th and 8th grade
## 456
## 9th grade
## 322
## Associates degree-academic program
## 218
## Associates degree-occup /vocational
## 261
## Bachelors degree(BA AB BS)
## 973
## Children
## 2338
## Doctorate degree(PhD EdD)
## 69
## High school graduate
## 2454
## Less than 1st grade
## 39
## Masters degree(MA MS MEng MEd MSW MBA)
## 347
## Prof school degree (MD DDS DVM LLB JD)
## 88
## Some college but no degree
## 1418prop.table(table(age!=0 & class_of_worker!="Not in universe"))*100##
## FALSE TRUE
## 48.81 51.19table(industry_code)## industry_code
## 0 1 2 3 4 5 6 7 8 9 11 12 13 14 15
## 4898 39 115 25 308 27 25 19 23 59 97 56 50 16 19
## 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
## 28 9 23 82 1 26 50 34 86 53 10 37 6 218 55
## 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
## 56 192 891 153 178 59 207 61 147 90 187 246 451 125 218
## 46 47 48 49 50 51
## 8 69 36 38 92 2prop.table(table(industry_code & wage_per_hour!=0))*100##
## FALSE TRUE
## 93.85 6.15prop.table(table(citizenship))*100## citizenship
## Foreign born- Not a citizen of U S
## 6.75
## Foreign born- U S citizen by naturalization
## 2.98
## Native- Born abroad of American Parent(s)
## 0.88
## Native- Born in Puerto Rico or U S Outlying
## 0.66
## Native- Born in the United States
## 88.73Two-way contingency tables for the categorical variables in your dataset.
age_and_education<-as.data.frame(table(age,education))
c1<-cut(as.numeric(as.character(age_and_education$age)),breaks=seq(0,90,by=10))
table(age_and_education$education,c1)## c1
## (0,10] (10,20] (20,30] (30,40]
## 10th grade 10 10 10 10
## 11th grade 10 10 10 10
## 12th grade no diploma 10 10 10 10
## 1st 2nd 3rd or 4th grade 10 10 10 10
## 5th or 6th grade 10 10 10 10
## 7th and 8th grade 10 10 10 10
## 9th grade 10 10 10 10
## Associates degree-academic program 10 10 10 10
## Associates degree-occup /vocational 10 10 10 10
## Bachelors degree(BA AB BS) 10 10 10 10
## Children 10 10 10 10
## Doctorate degree(PhD EdD) 10 10 10 10
## High school graduate 10 10 10 10
## Less than 1st grade 10 10 10 10
## Masters degree(MA MS MEng MEd MSW MBA) 10 10 10 10
## Prof school degree (MD DDS DVM LLB JD) 10 10 10 10
## Some college but no degree 10 10 10 10
## c1
## (40,50] (50,60] (60,70] (70,80]
## 10th grade 10 10 10 10
## 11th grade 10 10 10 10
## 12th grade no diploma 10 10 10 10
## 1st 2nd 3rd or 4th grade 10 10 10 10
## 5th or 6th grade 10 10 10 10
## 7th and 8th grade 10 10 10 10
## 9th grade 10 10 10 10
## Associates degree-academic program 10 10 10 10
## Associates degree-occup /vocational 10 10 10 10
## Bachelors degree(BA AB BS) 10 10 10 10
## Children 10 10 10 10
## Doctorate degree(PhD EdD) 10 10 10 10
## High school graduate 10 10 10 10
## Less than 1st grade 10 10 10 10
## Masters degree(MA MS MEng MEd MSW MBA) 10 10 10 10
## Prof school degree (MD DDS DVM LLB JD) 10 10 10 10
## Some college but no degree 10 10 10 10
## c1
## (80,90]
## 10th grade 10
## 11th grade 10
## 12th grade no diploma 10
## 1st 2nd 3rd or 4th grade 10
## 5th or 6th grade 10
## 7th and 8th grade 10
## 9th grade 10
## Associates degree-academic program 10
## Associates degree-occup /vocational 10
## Bachelors degree(BA AB BS) 10
## Children 10
## Doctorate degree(PhD EdD) 10
## High school graduate 10
## Less than 1st grade 10
## Masters degree(MA MS MEng MEd MSW MBA) 10
## Prof school degree (MD DDS DVM LLB JD) 10
## Some college but no degree 10mytable<-xtabs(~race+age,data=usincome)
mytable## age
## race 0 1 2 3 4 5 6 7 8 9 10
## Amer Indian Aleut or Eskimo 2 5 2 2 3 2 2 2 1 2 6
## Asian or Pacific Islander 5 5 5 7 1 6 7 6 10 6 5
## Black 23 23 19 18 18 23 15 22 15 20 28
## Other 3 8 3 3 7 7 6 6 7 4 3
## White 114 120 120 130 121 111 116 108 113 139 142
## age
## race 11 12 13 14 15 16 17 18 19 20 21
## Amer Indian Aleut or Eskimo 8 2 1 2 4 3 1 1 0 1 0
## Asian or Pacific Islander 3 7 7 2 6 3 2 4 5 3 6
## Black 23 19 16 16 36 21 16 15 17 19 13
## Other 1 6 3 2 3 5 4 3 1 1 0
## White 135 132 111 135 127 128 102 87 116 102 83
## age
## race 22 23 24 25 26 27 28 29 30 31 32
## Amer Indian Aleut or Eskimo 1 2 2 0 5 0 2 2 1 2 0
## Asian or Pacific Islander 7 5 6 4 2 5 7 7 4 7 5
## Black 10 11 16 16 7 16 18 27 13 13 19
## Other 6 6 2 5 4 5 2 7 2 5 3
## White 97 96 126 121 114 118 116 117 135 134 122
## age
## race 33 34 35 36 37 38 39 40 41 42 43
## Amer Indian Aleut or Eskimo 2 0 2 3 1 7 1 1 2 2 2
## Asian or Pacific Islander 7 5 8 6 6 3 5 7 6 4 4
## Black 21 18 8 9 13 18 18 12 13 18 12
## Other 1 1 2 1 1 3 3 3 2 1 4
## White 150 137 148 150 170 139 127 130 153 134 133
## age
## race 44 45 46 47 48 49 50 51 52 53 54
## Amer Indian Aleut or Eskimo 2 0 1 2 2 0 2 2 1 2 1
## Asian or Pacific Islander 5 8 8 2 4 1 6 2 3 4 3
## Black 15 14 11 14 4 10 12 9 10 8 7
## Other 3 3 1 4 0 2 1 2 2 0 0
## White 118 128 137 124 101 92 93 93 101 91 77
## age
## race 55 56 57 58 59 60 61 62 63 64 65
## Amer Indian Aleut or Eskimo 1 1 0 0 1 0 2 1 1 0 1
## Asian or Pacific Islander 5 3 1 1 1 2 2 0 2 1 1
## Black 8 13 6 8 6 3 8 8 7 4 8
## Other 1 0 0 0 1 1 0 0 2 0 0
## White 76 58 87 61 66 65 71 76 81 72 66
## age
## race 66 67 68 69 70 71 72 73 74 75 76
## Amer Indian Aleut or Eskimo 1 0 0 0 1 0 0 0 1 0 0
## Asian or Pacific Islander 1 1 0 1 1 0 1 0 2 0 0
## Black 2 5 3 6 3 4 4 4 6 4 4
## Other 0 2 0 0 0 0 1 0 1 0 0
## White 66 73 48 49 51 56 44 66 56 44 43
## age
## race 77 78 79 80 81 82 83 84 85 86 87
## Amer Indian Aleut or Eskimo 0 0 0 0 0 0 0 0 0 0 0
## Asian or Pacific Islander 1 1 1 0 0 1 1 0 0 0 1
## Black 1 5 1 2 4 3 2 5 1 0 0
## Other 0 0 0 0 1 0 0 0 0 0 0
## White 39 53 38 40 23 31 24 22 20 19 15
## age
## race 88 89 90
## Amer Indian Aleut or Eskimo 0 0 0
## Asian or Pacific Islander 0 0 0
## Black 0 0 4
## Other 0 0 0
## White 12 9 29mytable1<-xtabs(~class_of_worker+marital_status,data=usincome)
mytable1## marital_status
## class_of_worker Divorced Married-A F spouse present
## Federal government 22 2
## Local government 42 2
## Never worked 0 0
## Not in universe 145 11
## Private 371 10
## Self-employed-incorporated 8 0
## Self-employed-not incorporated 33 2
## State government 32 1
## Without pay 0 0
## marital_status
## class_of_worker Married-civilian spouse present
## Federal government 93
## Local government 279
## Never worked 2
## Not in universe 1237
## Private 2085
## Self-employed-incorporated 117
## Self-employed-not incorporated 311
## State government 145
## Without pay 5
## marital_status
## class_of_worker Married-spouse absent Never married
## Federal government 3 25
## Local government 3 61
## Never worked 0 15
## Not in universe 19 3017
## Private 51 1061
## Self-employed-incorporated 0 21
## Self-employed-not incorporated 3 42
## State government 1 61
## Without pay 0 4
## marital_status
## class_of_worker Separated Widowed
## Federal government 2 6
## Local government 7 13
## Never worked 0 0
## Not in universe 41 411
## Private 92 61
## Self-employed-incorporated 1 1
## Self-employed-not incorporated 4 14
## State government 1 4
## Without pay 0 0mytable2<-xtabs(~major_industry_code+income_level,data=usincome)
mytable2## income_level
## major_industry_code Above 50k Below 50k
## Agriculture 11 143
## Armed Forces 0 2
## Business and repair services 27 241
## Communications 10 45
## Construction 24 284
## Education 53 398
## Entertainment 5 85
## Finance insurance and real estate 54 277
## Forestry and fisheries 0 8
## Hospital services 26 161
## Manufacturing-durable goods 79 372
## Manufacturing-nondurable goods 44 341
## Medical except hospital 22 224
## Mining 4 21
## Not in universe or children 36 4862
## Other professional services 48 170
## Personal services except private HH 4 143
## Private household services 0 59
## Public administration 31 204
## Retail trade 38 853
## Social services 2 123
## Transportation 29 189
## Utilities and sanitary services 20 36
## Wholesale trade 20 172prop.table(table(major_industry_code,sex))*100## sex
## major_industry_code Female Male
## Agriculture 0.39 1.15
## Armed Forces 0.00 0.02
## Business and repair services 1.01 1.67
## Communications 0.24 0.31
## Construction 0.34 2.74
## Education 3.16 1.35
## Entertainment 0.46 0.44
## Finance insurance and real estate 2.01 1.30
## Forestry and fisheries 0.00 0.08
## Hospital services 1.43 0.44
## Manufacturing-durable goods 1.25 3.26
## Manufacturing-nondurable goods 1.71 2.14
## Medical except hospital 2.03 0.43
## Mining 0.02 0.23
## Not in universe or children 28.18 20.80
## Other professional services 1.19 0.99
## Personal services except private HH 0.98 0.49
## Private household services 0.53 0.06
## Public administration 1.06 1.29
## Retail trade 4.61 4.30
## Social services 1.05 0.20
## Transportation 0.70 1.48
## Utilities and sanitary services 0.08 0.48
## Wholesale trade 0.69 1.23prop.table(table(major_industry_code,migration_msa))*100## migration_msa
## major_industry_code Abroad to MSA Abroad to nonMSA
## Agriculture 0.00000000 0.00000000
## Armed Forces 0.00000000 0.00000000
## Business and repair services 0.00000000 0.00000000
## Communications 0.00000000 0.00000000
## Construction 0.04034698 0.00000000
## Education 0.00000000 0.00000000
## Entertainment 0.00000000 0.00000000
## Finance insurance and real estate 0.00000000 0.00000000
## Forestry and fisheries 0.00000000 0.00000000
## Hospital services 0.00000000 0.00000000
## Manufacturing-durable goods 0.04034698 0.00000000
## Manufacturing-nondurable goods 0.02017349 0.00000000
## Medical except hospital 0.00000000 0.00000000
## Mining 0.00000000 0.00000000
## Not in universe or children 0.20173492 0.04034698
## Other professional services 0.02017349 0.00000000
## Personal services except private HH 0.00000000 0.00000000
## Private household services 0.02017349 0.00000000
## Public administration 0.00000000 0.00000000
## Retail trade 0.00000000 0.04034698
## Social services 0.00000000 0.00000000
## Transportation 0.02017349 0.00000000
## Utilities and sanitary services 0.00000000 0.00000000
## Wholesale trade 0.02017349 0.00000000
## migration_msa
## major_industry_code MSA to MSA MSA to nonMSA
## Agriculture 0.04034698 0.00000000
## Armed Forces 0.02017349 0.00000000
## Business and repair services 0.30260238 0.12104095
## Communications 0.08069397 0.00000000
## Construction 0.48416381 0.04034698
## Education 0.58503127 0.00000000
## Entertainment 0.12104095 0.00000000
## Finance insurance and real estate 0.36312286 0.00000000
## Forestry and fisheries 0.04034698 0.00000000
## Hospital services 0.16138794 0.00000000
## Manufacturing-durable goods 0.50433730 0.06052048
## Manufacturing-nondurable goods 0.42364333 0.04034698
## Medical except hospital 0.30260238 0.02017349
## Mining 0.02017349 0.00000000
## Not in universe or children 4.47851523 0.40346984
## Other professional services 0.22190841 0.00000000
## Personal services except private HH 0.24208190 0.02017349
## Private household services 0.06052048 0.02017349
## Public administration 0.12104095 0.00000000
## Retail trade 1.45249143 0.06052048
## Social services 0.18156143 0.00000000
## Transportation 0.20173492 0.00000000
## Utilities and sanitary services 0.06052048 0.00000000
## Wholesale trade 0.30260238 0.00000000
## migration_msa
## major_industry_code Nonmover NonMSA to MSA
## Agriculture 1.41214444 0.02017349
## Armed Forces 0.02017349 0.00000000
## Business and repair services 2.03752270 0.02017349
## Communications 0.38329635 0.00000000
## Construction 2.25943111 0.02017349
## Education 3.79261650 0.02017349
## Entertainment 0.72624571 0.00000000
## Finance insurance and real estate 2.80411539 0.00000000
## Forestry and fisheries 0.02017349 0.00000000
## Hospital services 1.63405285 0.00000000
## Manufacturing-durable goods 4.11539237 0.00000000
## Manufacturing-nondurable goods 2.96550333 0.00000000
## Medical except hospital 1.93665524 0.02017349
## Mining 0.22190841 0.00000000
## Not in universe or children 40.22594311 0.22190841
## Other professional services 1.95682873 0.02017349
## Personal services except private HH 1.00867460 0.00000000
## Private household services 0.62537825 0.02017349
## Public administration 2.13839016 0.02017349
## Retail trade 7.20193666 0.06052048
## Social services 1.08936857 0.00000000
## Transportation 1.85596127 0.00000000
## Utilities and sanitary services 0.40346984 0.00000000
## Wholesale trade 1.63405285 0.02017349
## migration_msa
## major_industry_code NonMSA to nonMSA Not identifiable
## Agriculture 0.10086746 0.00000000
## Armed Forces 0.00000000 0.00000000
## Business and repair services 0.04034698 0.00000000
## Communications 0.00000000 0.00000000
## Construction 0.06052048 0.06052048
## Education 0.20173492 0.00000000
## Entertainment 0.02017349 0.00000000
## Finance insurance and real estate 0.14121444 0.00000000
## Forestry and fisheries 0.02017349 0.00000000
## Hospital services 0.06052048 0.02017349
## Manufacturing-durable goods 0.12104095 0.04034698
## Manufacturing-nondurable goods 0.22190841 0.00000000
## Medical except hospital 0.08069397 0.02017349
## Mining 0.00000000 0.02017349
## Not in universe or children 1.25075651 0.06052048
## Other professional services 0.02017349 0.00000000
## Personal services except private HH 0.02017349 0.06052048
## Private household services 0.02017349 0.00000000
## Public administration 0.06052048 0.00000000
## Retail trade 0.40346984 0.12104095
## Social services 0.00000000 0.00000000
## Transportation 0.04034698 0.00000000
## Utilities and sanitary services 0.00000000 0.00000000
## Wholesale trade 0.00000000 0.00000000
## migration_msa
## major_industry_code Not in universe
## Agriculture 0.00000000
## Armed Forces 0.00000000
## Business and repair services 0.00000000
## Communications 0.00000000
## Construction 0.00000000
## Education 0.00000000
## Entertainment 0.00000000
## Finance insurance and real estate 0.00000000
## Forestry and fisheries 0.00000000
## Hospital services 0.00000000
## Manufacturing-durable goods 0.00000000
## Manufacturing-nondurable goods 0.00000000
## Medical except hospital 0.00000000
## Mining 0.00000000
## Not in universe or children 1.75509381
## Other professional services 0.00000000
## Personal services except private HH 0.00000000
## Private household services 0.00000000
## Public administration 0.00000000
## Retail trade 0.00000000
## Social services 0.00000000
## Transportation 0.00000000
## Utilities and sanitary services 0.00000000
## Wholesale trade 0.00000000prop.table(table(major_industry_code,migration_reg))*100## migration_reg
## major_industry_code Abroad
## Agriculture 0.00000000
## Armed Forces 0.00000000
## Business and repair services 0.00000000
## Communications 0.00000000
## Construction 0.04034698
## Education 0.00000000
## Entertainment 0.00000000
## Finance insurance and real estate 0.00000000
## Forestry and fisheries 0.00000000
## Hospital services 0.00000000
## Manufacturing-durable goods 0.04034698
## Manufacturing-nondurable goods 0.02017349
## Medical except hospital 0.00000000
## Mining 0.00000000
## Not in universe or children 0.24208190
## Other professional services 0.02017349
## Personal services except private HH 0.00000000
## Private household services 0.02017349
## Public administration 0.00000000
## Retail trade 0.04034698
## Social services 0.00000000
## Transportation 0.02017349
## Utilities and sanitary services 0.00000000
## Wholesale trade 0.02017349
## migration_reg
## major_industry_code Different county same state
## Agriculture 0.02017349
## Armed Forces 0.00000000
## Business and repair services 0.14121444
## Communications 0.04034698
## Construction 0.18156143
## Education 0.18156143
## Entertainment 0.06052048
## Finance insurance and real estate 0.08069397
## Forestry and fisheries 0.04034698
## Hospital services 0.02017349
## Manufacturing-durable goods 0.18156143
## Manufacturing-nondurable goods 0.06052048
## Medical except hospital 0.10086746
## Mining 0.00000000
## Not in universe or children 0.90780714
## Other professional services 0.02017349
## Personal services except private HH 0.06052048
## Private household services 0.02017349
## Public administration 0.02017349
## Retail trade 0.44381682
## Social services 0.02017349
## Transportation 0.06052048
## Utilities and sanitary services 0.02017349
## Wholesale trade 0.00000000
## migration_reg
## major_industry_code Different division same region
## Agriculture 0.02017349
## Armed Forces 0.00000000
## Business and repair services 0.02017349
## Communications 0.00000000
## Construction 0.00000000
## Education 0.04034698
## Entertainment 0.00000000
## Finance insurance and real estate 0.02017349
## Forestry and fisheries 0.00000000
## Hospital services 0.00000000
## Manufacturing-durable goods 0.00000000
## Manufacturing-nondurable goods 0.04034698
## Medical except hospital 0.02017349
## Mining 0.00000000
## Not in universe or children 0.26225540
## Other professional services 0.02017349
## Personal services except private HH 0.00000000
## Private household services 0.00000000
## Public administration 0.00000000
## Retail trade 0.00000000
## Social services 0.00000000
## Transportation 0.00000000
## Utilities and sanitary services 0.00000000
## Wholesale trade 0.00000000
## migration_reg
## major_industry_code Different region
## Agriculture 0.00000000
## Armed Forces 0.02017349
## Business and repair services 0.04034698
## Communications 0.00000000
## Construction 0.04034698
## Education 0.04034698
## Entertainment 0.00000000
## Finance insurance and real estate 0.02017349
## Forestry and fisheries 0.00000000
## Hospital services 0.00000000
## Manufacturing-durable goods 0.06052048
## Manufacturing-nondurable goods 0.04034698
## Medical except hospital 0.00000000
## Mining 0.00000000
## Not in universe or children 0.58503127
## Other professional services 0.04034698
## Personal services except private HH 0.04034698
## Private household services 0.00000000
## Public administration 0.04034698
## Retail trade 0.20173492
## Social services 0.00000000
## Transportation 0.02017349
## Utilities and sanitary services 0.02017349
## Wholesale trade 0.02017349
## migration_reg
## major_industry_code Different state same division
## Agriculture 0.00000000
## Armed Forces 0.00000000
## Business and repair services 0.04034698
## Communications 0.00000000
## Construction 0.04034698
## Education 0.06052048
## Entertainment 0.00000000
## Finance insurance and real estate 0.02017349
## Forestry and fisheries 0.00000000
## Hospital services 0.00000000
## Manufacturing-durable goods 0.04034698
## Manufacturing-nondurable goods 0.00000000
## Medical except hospital 0.02017349
## Mining 0.02017349
## Not in universe or children 0.34294936
## Other professional services 0.02017349
## Personal services except private HH 0.00000000
## Private household services 0.02017349
## Public administration 0.00000000
## Retail trade 0.10086746
## Social services 0.00000000
## Transportation 0.00000000
## Utilities and sanitary services 0.00000000
## Wholesale trade 0.02017349
## migration_reg
## major_industry_code Nonmover Not in universe
## Agriculture 1.41214444 0.00000000
## Armed Forces 0.02017349 0.00000000
## Business and repair services 2.03752270 0.00000000
## Communications 0.38329635 0.00000000
## Construction 2.25943111 0.00000000
## Education 3.79261650 0.00000000
## Entertainment 0.72624571 0.00000000
## Finance insurance and real estate 2.80411539 0.00000000
## Forestry and fisheries 0.02017349 0.00000000
## Hospital services 1.63405285 0.00000000
## Manufacturing-durable goods 4.11539237 0.00000000
## Manufacturing-nondurable goods 2.96550333 0.00000000
## Medical except hospital 1.93665524 0.00000000
## Mining 0.22190841 0.00000000
## Not in universe or children 40.22594311 1.75509381
## Other professional services 1.95682873 0.00000000
## Personal services except private HH 1.00867460 0.00000000
## Private household services 0.62537825 0.00000000
## Public administration 2.13839016 0.00000000
## Retail trade 7.20193666 0.00000000
## Social services 1.08936857 0.00000000
## Transportation 1.85596127 0.00000000
## Utilities and sanitary services 0.40346984 0.00000000
## Wholesale trade 1.63405285 0.00000000
## migration_reg
## major_industry_code Same county
## Agriculture 0.12104095
## Armed Forces 0.00000000
## Business and repair services 0.24208190
## Communications 0.04034698
## Construction 0.40346984
## Education 0.48416381
## Entertainment 0.08069397
## Finance insurance and real estate 0.36312286
## Forestry and fisheries 0.02017349
## Hospital services 0.22190841
## Manufacturing-durable goods 0.44381682
## Manufacturing-nondurable goods 0.54468428
## Medical except hospital 0.30260238
## Mining 0.02017349
## Not in universe or children 4.31712729
## Other professional services 0.16138794
## Personal services except private HH 0.24208190
## Private household services 0.08069397
## Public administration 0.14121444
## Retail trade 1.35162397
## Social services 0.16138794
## Transportation 0.16138794
## Utilities and sanitary services 0.02017349
## Wholesale trade 0.28242889prop.table(table(major_industry_code,weeks_worked_in_year))*100## weeks_worked_in_year
## major_industry_code 0 1 2 3 4 5
## Agriculture 0.07 0.00 0.00 0.00 0.01 0.00
## Armed Forces 0.00 0.00 0.00 0.00 0.00 0.00
## Business and repair services 0.09 0.04 0.02 0.00 0.01 0.00
## Communications 0.00 0.00 0.00 0.00 0.00 0.00
## Construction 0.11 0.00 0.00 0.01 0.01 0.00
## Education 0.07 0.00 0.01 0.00 0.03 0.00
## Entertainment 0.04 0.02 0.00 0.00 0.00 0.00
## Finance insurance and real estate 0.05 0.00 0.01 0.01 0.00 0.01
## Forestry and fisheries 0.00 0.00 0.00 0.00 0.00 0.00
## Hospital services 0.03 0.00 0.01 0.00 0.00 0.00
## Manufacturing-durable goods 0.12 0.01 0.01 0.00 0.01 0.00
## Manufacturing-nondurable goods 0.11 0.01 0.00 0.00 0.00 0.00
## Medical except hospital 0.09 0.00 0.00 0.00 0.02 0.01
## Mining 0.00 0.00 0.00 0.00 0.00 0.00
## Not in universe or children 44.96 0.22 0.13 0.10 0.26 0.08
## Other professional services 0.05 0.01 0.00 0.01 0.02 0.00
## Personal services except private HH 0.09 0.00 0.00 0.00 0.01 0.00
## Private household services 0.04 0.01 0.00 0.01 0.01 0.00
## Public administration 0.03 0.00 0.03 0.00 0.00 0.00
## Retail trade 0.45 0.00 0.02 0.03 0.09 0.01
## Social services 0.07 0.01 0.01 0.00 0.00 0.00
## Transportation 0.05 0.00 0.00 0.00 0.01 0.01
## Utilities and sanitary services 0.00 0.00 0.00 0.00 0.00 0.00
## Wholesale trade 0.04 0.00 0.01 0.00 0.01 0.00
## weeks_worked_in_year
## major_industry_code 6 7 8 9 10 11
## Agriculture 0.02 0.02 0.03 0.00 0.02 0.00
## Armed Forces 0.00 0.00 0.00 0.00 0.00 0.00
## Business and repair services 0.00 0.00 0.01 0.00 0.02 0.00
## Communications 0.00 0.00 0.00 0.00 0.00 0.00
## Construction 0.00 0.01 0.03 0.01 0.01 0.00
## Education 0.00 0.00 0.02 0.01 0.02 0.01
## Entertainment 0.01 0.02 0.02 0.00 0.00 0.01
## Finance insurance and real estate 0.00 0.00 0.00 0.01 0.01 0.00
## Forestry and fisheries 0.01 0.00 0.00 0.00 0.00 0.00
## Hospital services 0.00 0.00 0.02 0.00 0.00 0.00
## Manufacturing-durable goods 0.02 0.00 0.02 0.02 0.01 0.00
## Manufacturing-nondurable goods 0.01 0.00 0.06 0.00 0.00 0.01
## Medical except hospital 0.00 0.00 0.02 0.00 0.01 0.00
## Mining 0.00 0.00 0.00 0.00 0.00 0.00
## Not in universe or children 0.11 0.03 0.21 0.08 0.14 0.03
## Other professional services 0.00 0.00 0.00 0.00 0.02 0.00
## Personal services except private HH 0.00 0.00 0.02 0.00 0.00 0.00
## Private household services 0.00 0.00 0.00 0.01 0.02 0.00
## Public administration 0.02 0.00 0.01 0.00 0.00 0.00
## Retail trade 0.04 0.00 0.08 0.03 0.01 0.00
## Social services 0.00 0.00 0.00 0.00 0.01 0.00
## Transportation 0.01 0.00 0.00 0.00 0.00 0.00
## Utilities and sanitary services 0.00 0.00 0.00 0.00 0.00 0.00
## Wholesale trade 0.00 0.00 0.02 0.00 0.00 0.00
## weeks_worked_in_year
## major_industry_code 12 13 14 15 16 17
## Agriculture 0.03 0.01 0.00 0.01 0.00 0.01
## Armed Forces 0.00 0.00 0.00 0.00 0.00 0.00
## Business and repair services 0.08 0.01 0.01 0.01 0.05 0.01
## Communications 0.00 0.00 0.00 0.00 0.00 0.00
## Construction 0.01 0.01 0.02 0.00 0.02 0.00
## Education 0.04 0.00 0.00 0.04 0.06 0.00
## Entertainment 0.00 0.01 0.00 0.00 0.01 0.01
## Finance insurance and real estate 0.02 0.00 0.00 0.02 0.02 0.01
## Forestry and fisheries 0.00 0.00 0.00 0.00 0.01 0.00
## Hospital services 0.00 0.01 0.00 0.00 0.00 0.01
## Manufacturing-durable goods 0.05 0.00 0.01 0.00 0.01 0.00
## Manufacturing-nondurable goods 0.01 0.02 0.00 0.01 0.03 0.00
## Medical except hospital 0.00 0.01 0.01 0.00 0.02 0.01
## Mining 0.00 0.00 0.00 0.00 0.00 0.00
## Not in universe or children 0.37 0.09 0.05 0.03 0.13 0.02
## Other professional services 0.01 0.00 0.00 0.00 0.02 0.00
## Personal services except private HH 0.02 0.01 0.00 0.00 0.02 0.00
## Private household services 0.00 0.00 0.00 0.00 0.02 0.00
## Public administration 0.00 0.01 0.00 0.00 0.00 0.01
## Retail trade 0.22 0.04 0.02 0.02 0.04 0.03
## Social services 0.02 0.01 0.00 0.00 0.04 0.00
## Transportation 0.00 0.00 0.00 0.00 0.00 0.01
## Utilities and sanitary services 0.01 0.01 0.00 0.01 0.00 0.00
## Wholesale trade 0.03 0.00 0.00 0.00 0.02 0.01
## weeks_worked_in_year
## major_industry_code 18 19 20 21 22 23
## Agriculture 0.01 0.00 0.01 0.00 0.01 0.00
## Armed Forces 0.00 0.00 0.00 0.00 0.00 0.00
## Business and repair services 0.01 0.00 0.02 0.00 0.00 0.00
## Communications 0.00 0.00 0.01 0.00 0.00 0.01
## Construction 0.01 0.00 0.03 0.01 0.01 0.00
## Education 0.01 0.00 0.05 0.00 0.02 0.01
## Entertainment 0.01 0.00 0.01 0.00 0.00 0.00
## Finance insurance and real estate 0.00 0.01 0.02 0.00 0.01 0.00
## Forestry and fisheries 0.01 0.00 0.00 0.00 0.00 0.00
## Hospital services 0.00 0.00 0.00 0.00 0.00 0.00
## Manufacturing-durable goods 0.00 0.01 0.01 0.01 0.01 0.00
## Manufacturing-nondurable goods 0.00 0.00 0.01 0.01 0.01 0.00
## Medical except hospital 0.00 0.00 0.06 0.00 0.02 0.00
## Mining 0.00 0.00 0.00 0.00 0.00 0.00
## Not in universe or children 0.04 0.00 0.16 0.03 0.06 0.01
## Other professional services 0.00 0.00 0.01 0.00 0.00 0.00
## Personal services except private HH 0.00 0.00 0.02 0.01 0.02 0.00
## Private household services 0.00 0.00 0.00 0.00 0.00 0.00
## Public administration 0.01 0.00 0.01 0.00 0.00 0.00
## Retail trade 0.04 0.01 0.24 0.03 0.02 0.01
## Social services 0.00 0.00 0.03 0.00 0.00 0.00
## Transportation 0.01 0.00 0.01 0.00 0.01 0.00
## Utilities and sanitary services 0.00 0.00 0.00 0.00 0.00 0.00
## Wholesale trade 0.00 0.00 0.00 0.01 0.00 0.00
## weeks_worked_in_year
## major_industry_code 24 25 26 27 28 29
## Agriculture 0.00 0.00 0.02 0.00 0.02 0.00
## Armed Forces 0.00 0.00 0.00 0.00 0.00 0.00
## Business and repair services 0.02 0.00 0.05 0.00 0.04 0.00
## Communications 0.02 0.00 0.01 0.00 0.00 0.00
## Construction 0.02 0.01 0.15 0.00 0.02 0.00
## Education 0.00 0.06 0.03 0.02 0.03 0.01
## Entertainment 0.00 0.00 0.03 0.00 0.00 0.00
## Finance insurance and real estate 0.02 0.00 0.03 0.00 0.01 0.01
## Forestry and fisheries 0.00 0.00 0.00 0.00 0.00 0.00
## Hospital services 0.02 0.00 0.03 0.00 0.00 0.00
## Manufacturing-durable goods 0.04 0.00 0.09 0.00 0.03 0.00
## Manufacturing-nondurable goods 0.03 0.01 0.10 0.00 0.02 0.00
## Medical except hospital 0.00 0.01 0.01 0.00 0.01 0.00
## Mining 0.00 0.00 0.01 0.00 0.00 0.00
## Not in universe or children 0.05 0.03 0.29 0.01 0.04 0.01
## Other professional services 0.00 0.01 0.01 0.00 0.00 0.00
## Personal services except private HH 0.01 0.02 0.03 0.00 0.01 0.00
## Private household services 0.00 0.00 0.00 0.00 0.01 0.00
## Public administration 0.00 0.01 0.03 0.00 0.00 0.00
## Retail trade 0.09 0.03 0.27 0.01 0.07 0.01
## Social services 0.00 0.00 0.03 0.00 0.03 0.00
## Transportation 0.00 0.00 0.03 0.00 0.00 0.00
## Utilities and sanitary services 0.01 0.00 0.00 0.00 0.00 0.00
## Wholesale trade 0.02 0.00 0.01 0.00 0.02 0.00
## weeks_worked_in_year
## major_industry_code 30 32 33 34 35 36
## Agriculture 0.02 0.02 0.00 0.01 0.01 0.00
## Armed Forces 0.00 0.00 0.00 0.00 0.00 0.00
## Business and repair services 0.08 0.01 0.01 0.00 0.01 0.05
## Communications 0.00 0.01 0.00 0.00 0.00 0.00
## Construction 0.07 0.04 0.00 0.00 0.02 0.04
## Education 0.09 0.02 0.00 0.01 0.02 0.17
## Entertainment 0.01 0.01 0.00 0.00 0.01 0.03
## Finance insurance and real estate 0.01 0.01 0.00 0.00 0.01 0.03
## Forestry and fisheries 0.01 0.00 0.00 0.00 0.00 0.00
## Hospital services 0.00 0.00 0.00 0.01 0.00 0.01
## Manufacturing-durable goods 0.01 0.03 0.00 0.00 0.06 0.09
## Manufacturing-nondurable goods 0.01 0.00 0.00 0.01 0.00 0.04
## Medical except hospital 0.03 0.02 0.00 0.00 0.01 0.05
## Mining 0.00 0.00 0.00 0.00 0.00 0.00
## Not in universe or children 0.07 0.06 0.00 0.04 0.03 0.08
## Other professional services 0.01 0.01 0.01 0.00 0.01 0.01
## Personal services except private HH 0.01 0.02 0.00 0.00 0.00 0.01
## Private household services 0.03 0.04 0.00 0.00 0.01 0.01
## Public administration 0.06 0.00 0.00 0.00 0.01 0.02
## Retail trade 0.16 0.08 0.00 0.02 0.06 0.11
## Social services 0.00 0.00 0.00 0.00 0.01 0.04
## Transportation 0.00 0.01 0.00 0.01 0.01 0.01
## Utilities and sanitary services 0.00 0.00 0.00 0.00 0.00 0.00
## Wholesale trade 0.04 0.00 0.00 0.00 0.02 0.01
## weeks_worked_in_year
## major_industry_code 37 38 39 40 41 42
## Agriculture 0.00 0.02 0.00 0.02 0.00 0.00
## Armed Forces 0.00 0.00 0.00 0.01 0.00 0.00
## Business and repair services 0.00 0.02 0.01 0.08 0.01 0.00
## Communications 0.00 0.00 0.00 0.00 0.00 0.00
## Construction 0.00 0.01 0.01 0.10 0.00 0.04
## Education 0.01 0.09 0.03 0.28 0.01 0.09
## Entertainment 0.00 0.00 0.00 0.04 0.00 0.02
## Finance insurance and real estate 0.00 0.00 0.03 0.08 0.00 0.00
## Forestry and fisheries 0.00 0.00 0.00 0.00 0.00 0.00
## Hospital services 0.01 0.00 0.01 0.03 0.00 0.00
## Manufacturing-durable goods 0.02 0.00 0.02 0.09 0.00 0.02
## Manufacturing-nondurable goods 0.01 0.03 0.00 0.05 0.00 0.04
## Medical except hospital 0.00 0.01 0.01 0.07 0.00 0.01
## Mining 0.00 0.00 0.00 0.01 0.00 0.00
## Not in universe or children 0.01 0.02 0.02 0.11 0.00 0.00
## Other professional services 0.00 0.01 0.00 0.08 0.00 0.00
## Personal services except private HH 0.00 0.02 0.00 0.00 0.00 0.00
## Private household services 0.00 0.00 0.00 0.06 0.00 0.00
## Public administration 0.00 0.01 0.00 0.02 0.00 0.00
## Retail trade 0.01 0.05 0.06 0.34 0.00 0.05
## Social services 0.03 0.00 0.00 0.03 0.00 0.00
## Transportation 0.00 0.00 0.00 0.04 0.00 0.03
## Utilities and sanitary services 0.01 0.00 0.00 0.01 0.00 0.00
## Wholesale trade 0.00 0.00 0.02 0.02 0.00 0.00
## weeks_worked_in_year
## major_industry_code 43 44 45 46 47 48
## Agriculture 0.02 0.05 0.00 0.01 0.00 0.02
## Armed Forces 0.00 0.00 0.00 0.00 0.00 0.00
## Business and repair services 0.02 0.01 0.03 0.00 0.00 0.04
## Communications 0.00 0.00 0.01 0.00 0.00 0.01
## Construction 0.01 0.05 0.02 0.04 0.00 0.08
## Education 0.02 0.07 0.05 0.06 0.01 0.11
## Entertainment 0.00 0.00 0.02 0.01 0.00 0.03
## Finance insurance and real estate 0.00 0.00 0.03 0.00 0.00 0.03
## Forestry and fisheries 0.00 0.00 0.00 0.00 0.00 0.00
## Hospital services 0.01 0.02 0.01 0.01 0.01 0.01
## Manufacturing-durable goods 0.04 0.03 0.07 0.04 0.02 0.04
## Manufacturing-nondurable goods 0.02 0.06 0.05 0.01 0.02 0.03
## Medical except hospital 0.00 0.02 0.02 0.01 0.00 0.04
## Mining 0.00 0.00 0.00 0.00 0.00 0.00
## Not in universe or children 0.01 0.00 0.02 0.02 0.00 0.02
## Other professional services 0.00 0.02 0.01 0.00 0.02 0.01
## Personal services except private HH 0.02 0.01 0.03 0.02 0.00 0.03
## Private household services 0.00 0.01 0.01 0.00 0.00 0.00
## Public administration 0.00 0.01 0.00 0.02 0.01 0.01
## Retail trade 0.01 0.05 0.04 0.05 0.02 0.13
## Social services 0.01 0.00 0.01 0.02 0.02 0.01
## Transportation 0.01 0.00 0.01 0.03 0.00 0.06
## Utilities and sanitary services 0.00 0.00 0.00 0.00 0.00 0.01
## Wholesale trade 0.00 0.01 0.01 0.02 0.00 0.04
## weeks_worked_in_year
## major_industry_code 49 50 51 52
## Agriculture 0.00 0.02 0.03 0.99
## Armed Forces 0.00 0.00 0.00 0.01
## Business and repair services 0.02 0.06 0.02 1.70
## Communications 0.00 0.00 0.00 0.47
## Construction 0.03 0.15 0.01 1.85
## Education 0.00 0.07 0.00 2.75
## Entertainment 0.00 0.00 0.01 0.51
## Finance insurance and real estate 0.02 0.05 0.02 2.71
## Forestry and fisheries 0.00 0.00 0.00 0.04
## Hospital services 0.01 0.02 0.01 1.56
## Manufacturing-durable goods 0.04 0.09 0.10 3.21
## Manufacturing-nondurable goods 0.03 0.09 0.05 2.84
## Medical except hospital 0.02 0.06 0.02 1.75
## Mining 0.00 0.00 0.01 0.22
## Not in universe or children 0.00 0.01 0.00 0.69
## Other professional services 0.02 0.05 0.02 1.72
## Personal services except private HH 0.01 0.07 0.02 0.91
## Private household services 0.00 0.00 0.00 0.29
## Public administration 0.00 0.00 0.00 2.01
## Retail trade 0.05 0.18 0.11 5.37
## Social services 0.00 0.07 0.02 0.72
## Transportation 0.02 0.09 0.02 1.68
## Utilities and sanitary services 0.00 0.01 0.00 0.48
## Wholesale trade 0.01 0.05 0.01 1.46prop.table(table(major_industry_code,income_level))*100## income_level
## major_industry_code -50000 50000
## Agriculture 1.43 0.11
## Armed Forces 0.02 0.00
## Business and repair services 2.41 0.27
## Communications 0.45 0.10
## Construction 2.84 0.24
## Education 3.98 0.53
## Entertainment 0.85 0.05
## Finance insurance and real estate 2.77 0.54
## Forestry and fisheries 0.08 0.00
## Hospital services 1.61 0.26
## Manufacturing-durable goods 3.72 0.79
## Manufacturing-nondurable goods 3.41 0.44
## Medical except hospital 2.24 0.22
## Mining 0.21 0.04
## Not in universe or children 48.62 0.36
## Other professional services 1.70 0.48
## Personal services except private HH 1.43 0.04
## Private household services 0.59 0.00
## Public administration 2.04 0.31
## Retail trade 8.53 0.38
## Social services 1.23 0.02
## Transportation 1.89 0.29
## Utilities and sanitary services 0.36 0.20
## Wholesale trade 1.72 0.20prop.table(table(major_industry_code,full_parttime_employment_stat))*100## full_parttime_employment_stat
## major_industry_code Children or Armed Forces
## Agriculture 0.78
## Armed Forces 0.02
## Business and repair services 1.25
## Communications 0.23
## Construction 1.47
## Education 2.28
## Entertainment 0.43
## Finance insurance and real estate 1.64
## Forestry and fisheries 0.04
## Hospital services 0.93
## Manufacturing-durable goods 2.42
## Manufacturing-nondurable goods 1.82
## Medical except hospital 1.18
## Mining 0.13
## Not in universe or children 36.10
## Other professional services 1.11
## Personal services except private HH 0.67
## Private household services 0.38
## Public administration 1.16
## Retail trade 4.63
## Social services 0.63
## Transportation 1.05
## Utilities and sanitary services 0.23
## Wholesale trade 0.98
## full_parttime_employment_stat
## major_industry_code Full-time schedules
## Agriculture 0.54
## Armed Forces 0.00
## Business and repair services 1.12
## Communications 0.29
## Construction 1.18
## Education 1.80
## Entertainment 0.38
## Finance insurance and real estate 1.40
## Forestry and fisheries 0.01
## Hospital services 0.77
## Manufacturing-durable goods 1.83
## Manufacturing-nondurable goods 1.70
## Medical except hospital 1.08
## Mining 0.09
## Not in universe or children 0.00
## Other professional services 0.97
## Personal services except private HH 0.66
## Private household services 0.16
## Public administration 0.96
## Retail trade 3.53
## Social services 0.52
## Transportation 0.88
## Utilities and sanitary services 0.29
## Wholesale trade 0.81
## full_parttime_employment_stat
## major_industry_code Not in labor force
## Agriculture 0.01
## Armed Forces 0.00
## Business and repair services 0.03
## Communications 0.00
## Construction 0.04
## Education 0.01
## Entertainment 0.02
## Finance insurance and real estate 0.03
## Forestry and fisheries 0.02
## Hospital services 0.01
## Manufacturing-durable goods 0.00
## Manufacturing-nondurable goods 0.01
## Medical except hospital 0.02
## Mining 0.00
## Not in universe or children 12.79
## Other professional services 0.01
## Personal services except private HH 0.00
## Private household services 0.02
## Public administration 0.01
## Retail trade 0.07
## Social services 0.00
## Transportation 0.01
## Utilities and sanitary services 0.01
## Wholesale trade 0.02
## full_parttime_employment_stat
## major_industry_code PT for econ reasons usually FT
## Agriculture 0.03
## Armed Forces 0.00
## Business and repair services 0.03
## Communications 0.00
## Construction 0.00
## Education 0.02
## Entertainment 0.00
## Finance insurance and real estate 0.01
## Forestry and fisheries 0.00
## Hospital services 0.00
## Manufacturing-durable goods 0.02
## Manufacturing-nondurable goods 0.00
## Medical except hospital 0.00
## Mining 0.00
## Not in universe or children 0.00
## Other professional services 0.00
## Personal services except private HH 0.02
## Private household services 0.00
## Public administration 0.00
## Retail trade 0.06
## Social services 0.01
## Transportation 0.02
## Utilities and sanitary services 0.00
## Wholesale trade 0.01
## full_parttime_employment_stat
## major_industry_code PT for econ reasons usually PT
## Agriculture 0.08
## Armed Forces 0.00
## Business and repair services 0.07
## Communications 0.00
## Construction 0.11
## Education 0.04
## Entertainment 0.01
## Finance insurance and real estate 0.02
## Forestry and fisheries 0.01
## Hospital services 0.03
## Manufacturing-durable goods 0.03
## Manufacturing-nondurable goods 0.05
## Medical except hospital 0.02
## Mining 0.02
## Not in universe or children 0.00
## Other professional services 0.00
## Personal services except private HH 0.04
## Private household services 0.01
## Public administration 0.06
## Retail trade 0.05
## Social services 0.00
## Transportation 0.06
## Utilities and sanitary services 0.00
## Wholesale trade 0.01
## full_parttime_employment_stat
## major_industry_code PT for non-econ reasons usually FT
## Agriculture 0.04
## Armed Forces 0.00
## Business and repair services 0.09
## Communications 0.03
## Construction 0.16
## Education 0.25
## Entertainment 0.02
## Finance insurance and real estate 0.14
## Forestry and fisheries 0.00
## Hospital services 0.11
## Manufacturing-durable goods 0.08
## Manufacturing-nondurable goods 0.13
## Medical except hospital 0.07
## Mining 0.00
## Not in universe or children 0.00
## Other professional services 0.03
## Personal services except private HH 0.02
## Private household services 0.01
## Public administration 0.14
## Retail trade 0.28
## Social services 0.06
## Transportation 0.11
## Utilities and sanitary services 0.02
## Wholesale trade 0.05
## full_parttime_employment_stat
## major_industry_code Unemployed full-time
## Agriculture 0.06
## Armed Forces 0.00
## Business and repair services 0.08
## Communications 0.00
## Construction 0.11
## Education 0.06
## Entertainment 0.02
## Finance insurance and real estate 0.05
## Forestry and fisheries 0.00
## Hospital services 0.02
## Manufacturing-durable goods 0.13
## Manufacturing-nondurable goods 0.13
## Medical except hospital 0.06
## Mining 0.01
## Not in universe or children 0.05
## Other professional services 0.06
## Personal services except private HH 0.04
## Private household services 0.00
## Public administration 0.01
## Retail trade 0.20
## Social services 0.02
## Transportation 0.04
## Utilities and sanitary services 0.01
## Wholesale trade 0.03
## full_parttime_employment_stat
## major_industry_code Unemployed part- time
## Agriculture 0.00
## Armed Forces 0.00
## Business and repair services 0.01
## Communications 0.00
## Construction 0.01
## Education 0.05
## Entertainment 0.02
## Finance insurance and real estate 0.02
## Forestry and fisheries 0.00
## Hospital services 0.00
## Manufacturing-durable goods 0.00
## Manufacturing-nondurable goods 0.01
## Medical except hospital 0.03
## Mining 0.00
## Not in universe or children 0.04
## Other professional services 0.00
## Personal services except private HH 0.02
## Private household services 0.01
## Public administration 0.01
## Retail trade 0.09
## Social services 0.01
## Transportation 0.01
## Utilities and sanitary services 0.00
## Wholesale trade 0.01prop.table(table(major_industry_code,citizenship))*100## citizenship
## major_industry_code Foreign born- Not a citizen of U S
## Agriculture 0.16
## Armed Forces 0.00
## Business and repair services 0.24
## Communications 0.02
## Construction 0.27
## Education 0.21
## Entertainment 0.09
## Finance insurance and real estate 0.14
## Forestry and fisheries 0.01
## Hospital services 0.13
## Manufacturing-durable goods 0.41
## Manufacturing-nondurable goods 0.42
## Medical except hospital 0.19
## Mining 0.00
## Not in universe or children 2.79
## Other professional services 0.05
## Personal services except private HH 0.24
## Private household services 0.12
## Public administration 0.05
## Retail trade 0.77
## Social services 0.07
## Transportation 0.22
## Utilities and sanitary services 0.03
## Wholesale trade 0.12
## citizenship
## major_industry_code Foreign born- U S citizen by naturalization
## Agriculture 0.03
## Armed Forces 0.00
## Business and repair services 0.05
## Communications 0.03
## Construction 0.10
## Education 0.11
## Entertainment 0.02
## Finance insurance and real estate 0.19
## Forestry and fisheries 0.00
## Hospital services 0.14
## Manufacturing-durable goods 0.26
## Manufacturing-nondurable goods 0.18
## Medical except hospital 0.10
## Mining 0.00
## Not in universe or children 1.05
## Other professional services 0.08
## Personal services except private HH 0.05
## Private household services 0.06
## Public administration 0.07
## Retail trade 0.25
## Social services 0.04
## Transportation 0.09
## Utilities and sanitary services 0.02
## Wholesale trade 0.06
## citizenship
## major_industry_code Native- Born abroad of American Parent(s)
## Agriculture 0.01
## Armed Forces 0.00
## Business and repair services 0.04
## Communications 0.01
## Construction 0.04
## Education 0.04
## Entertainment 0.01
## Finance insurance and real estate 0.06
## Forestry and fisheries 0.00
## Hospital services 0.02
## Manufacturing-durable goods 0.01
## Manufacturing-nondurable goods 0.06
## Medical except hospital 0.01
## Mining 0.00
## Not in universe or children 0.35
## Other professional services 0.02
## Personal services except private HH 0.01
## Private household services 0.00
## Public administration 0.02
## Retail trade 0.12
## Social services 0.00
## Transportation 0.03
## Utilities and sanitary services 0.00
## Wholesale trade 0.02
## citizenship
## major_industry_code Native- Born in Puerto Rico or U S Outlying
## Agriculture 0.00
## Armed Forces 0.00
## Business and repair services 0.02
## Communications 0.00
## Construction 0.02
## Education 0.03
## Entertainment 0.00
## Finance insurance and real estate 0.03
## Forestry and fisheries 0.00
## Hospital services 0.02
## Manufacturing-durable goods 0.04
## Manufacturing-nondurable goods 0.01
## Medical except hospital 0.01
## Mining 0.00
## Not in universe or children 0.36
## Other professional services 0.00
## Personal services except private HH 0.00
## Private household services 0.00
## Public administration 0.00
## Retail trade 0.08
## Social services 0.01
## Transportation 0.01
## Utilities and sanitary services 0.00
## Wholesale trade 0.02
## citizenship
## major_industry_code Native- Born in the United States
## Agriculture 1.34
## Armed Forces 0.02
## Business and repair services 2.33
## Communications 0.49
## Construction 2.65
## Education 4.12
## Entertainment 0.78
## Finance insurance and real estate 2.89
## Forestry and fisheries 0.07
## Hospital services 1.56
## Manufacturing-durable goods 3.79
## Manufacturing-nondurable goods 3.18
## Medical except hospital 2.15
## Mining 0.25
## Not in universe or children 44.43
## Other professional services 2.03
## Personal services except private HH 1.17
## Private household services 0.41
## Public administration 2.21
## Retail trade 7.69
## Social services 1.13
## Transportation 1.83
## Utilities and sanitary services 0.51
## Wholesale trade 1.70prop.table(table(country_self,income_level))*100## income_level
## country_self -50000 50000
## Cambodia 0.06093226 0.00000000
## Canada 0.17264141 0.04062151
## China 0.28435056 0.01015538
## Columbia 0.20310755 0.00000000
## Cuba 0.44683660 0.02031075
## Dominican-Republic 0.38590434 0.01015538
## Ecuador 0.13201990 0.00000000
## El-Salvador 0.33512745 0.00000000
## England 0.24372905 0.06093226
## France 0.05077689 0.00000000
## Germany 0.44683660 0.07108764
## Greece 0.10155377 0.01015538
## Guatemala 0.12186453 0.00000000
## Haiti 0.15233066 0.01015538
## Holand-Netherlands 0.01015538 0.01015538
## Honduras 0.11170915 0.00000000
## Hong Kong 0.05077689 0.00000000
## Hungary 0.04062151 0.02031075
## India 0.16248604 0.01015538
## Iran 0.04062151 0.02031075
## Ireland 0.09139840 0.01015538
## Italy 0.24372905 0.01015538
## Jamaica 0.17264141 0.00000000
## Japan 0.08124302 0.03046613
## Laos 0.04062151 0.00000000
## Mexico 2.74195186 0.08124302
## Nicaragua 0.16248604 0.01015538
## Outlying-U S (Guam USVI etc) 0.04062151 0.00000000
## Panama 0.03046613 0.00000000
## Peru 0.11170915 0.00000000
## Philippines 0.38590434 0.05077689
## Poland 0.30466132 0.01015538
## Portugal 0.07108764 0.01015538
## Puerto-Rico 0.60932264 0.02031075
## Scotland 0.05077689 0.00000000
## South Korea 0.16248604 0.01015538
## Taiwan 0.07108764 0.04062151
## Thailand 0.05077689 0.02031075
## Trinadad&Tobago 0.05077689 0.00000000
## United-States 84.86848786 5.24017467
## Vietnam 0.21326292 0.03046613
## Yugoslavia 0.02031075 0.00000000prop.table(table(citizenship,income_level))*100## income_level
## citizenship -50000 50000
## Foreign born- Not a citizen of U S 6.39 0.36
## Foreign born- U S citizen by naturalization 2.70 0.28
## Native- Born abroad of American Parent(s) 0.83 0.05
## Native- Born in Puerto Rico or U S Outlying 0.64 0.02
## Native- Born in the United States 83.57 5.16prop.table(table(major_industry_code,sex))*100## sex
## major_industry_code Female Male
## Agriculture 0.39 1.15
## Armed Forces 0.00 0.02
## Business and repair services 1.01 1.67
## Communications 0.24 0.31
## Construction 0.34 2.74
## Education 3.16 1.35
## Entertainment 0.46 0.44
## Finance insurance and real estate 2.01 1.30
## Forestry and fisheries 0.00 0.08
## Hospital services 1.43 0.44
## Manufacturing-durable goods 1.25 3.26
## Manufacturing-nondurable goods 1.71 2.14
## Medical except hospital 2.03 0.43
## Mining 0.02 0.23
## Not in universe or children 28.18 20.80
## Other professional services 1.19 0.99
## Personal services except private HH 0.98 0.49
## Private household services 0.53 0.06
## Public administration 1.06 1.29
## Retail trade 4.61 4.30
## Social services 1.05 0.20
## Transportation 0.70 1.48
## Utilities and sanitary services 0.08 0.48
## Wholesale trade 0.69 1.23prop.table(table(marital_status,income_level))*100## income_level
## marital_status -50000 50000
## Divorced 6.02 0.51
## Married-A F spouse present 0.28 0.00
## Married-civilian spouse present 38.11 4.63
## Married-spouse absent 0.78 0.02
## Never married 42.60 0.47
## Separated 1.42 0.06
## Widowed 4.92 0.18prop.table(table(member_of_labor_union,income_level))*100## income_level
## member_of_labor_union -50000 50000
## No 7.77 0.80
## Not in universe 85.05 4.91
## Yes 1.31 0.16prop.table(table(race,sex))*100## sex
## race Female Male
## Amer Indian Aleut or Eskimo 0.66 0.52
## Asian or Pacific Islander 1.63 1.46
## Black 5.80 4.47
## Other 0.96 0.87
## White 44.07 39.56prop.table(table(race,country_self))*100## country_self
## race Cambodia Canada China
## Amer Indian Aleut or Eskimo 0.00000000 0.00000000 0.00000000
## Asian or Pacific Islander 0.05077689 0.00000000 0.29450594
## Black 0.00000000 0.01015538 0.00000000
## Other 0.00000000 0.00000000 0.00000000
## White 0.01015538 0.20310755 0.00000000
## country_self
## race Columbia Cuba Dominican-Republic
## Amer Indian Aleut or Eskimo 0.00000000 0.00000000 0.00000000
## Asian or Pacific Islander 0.00000000 0.00000000 0.00000000
## Black 0.00000000 0.00000000 0.02031075
## Other 0.00000000 0.02031075 0.09139840
## White 0.20310755 0.44683660 0.28435056
## country_self
## race Ecuador El-Salvador England
## Amer Indian Aleut or Eskimo 0.00000000 0.00000000 0.00000000
## Asian or Pacific Islander 0.00000000 0.00000000 0.00000000
## Black 0.00000000 0.01015538 0.00000000
## Other 0.05077689 0.02031075 0.00000000
## White 0.08124302 0.30466132 0.30466132
## country_self
## race France Germany Greece
## Amer Indian Aleut or Eskimo 0.00000000 0.00000000 0.00000000
## Asian or Pacific Islander 0.00000000 0.01015538 0.00000000
## Black 0.00000000 0.02031075 0.00000000
## Other 0.00000000 0.01015538 0.00000000
## White 0.05077689 0.47730273 0.11170915
## country_self
## race Guatemala Haiti Holand-Netherlands
## Amer Indian Aleut or Eskimo 0.00000000 0.00000000 0.00000000
## Asian or Pacific Islander 0.00000000 0.00000000 0.00000000
## Black 0.00000000 0.16248604 0.00000000
## Other 0.02031075 0.00000000 0.00000000
## White 0.10155377 0.00000000 0.02031075
## country_self
## race Honduras Hong Kong Hungary
## Amer Indian Aleut or Eskimo 0.00000000 0.00000000 0.00000000
## Asian or Pacific Islander 0.00000000 0.04062151 0.00000000
## Black 0.00000000 0.00000000 0.00000000
## Other 0.03046613 0.00000000 0.00000000
## White 0.08124302 0.01015538 0.06093226
## country_self
## race India Iran Ireland
## Amer Indian Aleut or Eskimo 0.00000000 0.00000000 0.00000000
## Asian or Pacific Islander 0.14217528 0.01015538 0.00000000
## Black 0.00000000 0.00000000 0.00000000
## Other 0.01015538 0.01015538 0.00000000
## White 0.02031075 0.04062151 0.10155377
## country_self
## race Italy Jamaica Japan
## Amer Indian Aleut or Eskimo 0.00000000 0.00000000 0.00000000
## Asian or Pacific Islander 0.00000000 0.01015538 0.08124302
## Black 0.01015538 0.14217528 0.00000000
## Other 0.00000000 0.01015538 0.00000000
## White 0.24372905 0.01015538 0.03046613
## country_self
## race Laos Mexico Nicaragua
## Amer Indian Aleut or Eskimo 0.00000000 0.00000000 0.00000000
## Asian or Pacific Islander 0.04062151 0.00000000 0.00000000
## Black 0.00000000 0.00000000 0.01015538
## Other 0.00000000 0.43668122 0.00000000
## White 0.00000000 2.38651366 0.16248604
## country_self
## race Outlying-U S (Guam USVI etc) Panama
## Amer Indian Aleut or Eskimo 0.00000000 0.00000000
## Asian or Pacific Islander 0.04062151 0.00000000
## Black 0.00000000 0.00000000
## Other 0.00000000 0.00000000
## White 0.00000000 0.03046613
## country_self
## race Peru Philippines Poland
## Amer Indian Aleut or Eskimo 0.00000000 0.00000000 0.00000000
## Asian or Pacific Islander 0.00000000 0.39605971 0.00000000
## Black 0.00000000 0.00000000 0.00000000
## Other 0.02031075 0.00000000 0.00000000
## White 0.09139840 0.04062151 0.31481670
## country_self
## race Portugal Puerto-Rico Scotland
## Amer Indian Aleut or Eskimo 0.00000000 0.00000000 0.00000000
## Asian or Pacific Islander 0.00000000 0.01015538 0.00000000
## Black 0.00000000 0.00000000 0.00000000
## Other 0.00000000 0.06093226 0.00000000
## White 0.08124302 0.55854575 0.05077689
## country_self
## race South Korea Taiwan Thailand
## Amer Indian Aleut or Eskimo 0.00000000 0.00000000 0.00000000
## Asian or Pacific Islander 0.17264141 0.11170915 0.07108764
## Black 0.00000000 0.00000000 0.00000000
## Other 0.00000000 0.00000000 0.00000000
## White 0.00000000 0.00000000 0.00000000
## country_self
## race Trinadad&Tobago United-States Vietnam
## Amer Indian Aleut or Eskimo 0.00000000 1.19833452 0.00000000
## Asian or Pacific Islander 0.00000000 1.19833452 0.24372905
## Black 0.03046613 9.79993907 0.00000000
## Other 0.01015538 0.98507160 0.00000000
## White 0.01015538 76.92698284 0.00000000
## country_self
## race Yugoslavia
## Amer Indian Aleut or Eskimo 0.00000000
## Asian or Pacific Islander 0.00000000
## Black 0.00000000
## Other 0.00000000
## White 0.02031075prop.table(table(race,citizenship))*100## citizenship
## race Foreign born- Not a citizen of U S
## Amer Indian Aleut or Eskimo 0.00
## Asian or Pacific Islander 1.20
## Black 0.39
## Other 0.66
## White 4.50
## citizenship
## race Foreign born- U S citizen by naturalization
## Amer Indian Aleut or Eskimo 0.00
## Asian or Pacific Islander 0.58
## Black 0.16
## Other 0.11
## White 2.13
## citizenship
## race Native- Born abroad of American Parent(s)
## Amer Indian Aleut or Eskimo 0.00
## Asian or Pacific Islander 0.08
## Black 0.07
## Other 0.03
## White 0.70
## citizenship
## race Native- Born in Puerto Rico or U S Outlying
## Amer Indian Aleut or Eskimo 0.00
## Asian or Pacific Islander 0.05
## Black 0.00
## Other 0.06
## White 0.55
## citizenship
## race Native- Born in the United States
## Amer Indian Aleut or Eskimo 1.18
## Asian or Pacific Islander 1.18
## Black 9.65
## Other 0.97
## White 75.75prop.table(table(race,citizenship))*100## citizenship
## race Foreign born- Not a citizen of U S
## Amer Indian Aleut or Eskimo 0.00
## Asian or Pacific Islander 1.20
## Black 0.39
## Other 0.66
## White 4.50
## citizenship
## race Foreign born- U S citizen by naturalization
## Amer Indian Aleut or Eskimo 0.00
## Asian or Pacific Islander 0.58
## Black 0.16
## Other 0.11
## White 2.13
## citizenship
## race Native- Born abroad of American Parent(s)
## Amer Indian Aleut or Eskimo 0.00
## Asian or Pacific Islander 0.08
## Black 0.07
## Other 0.03
## White 0.70
## citizenship
## race Native- Born in Puerto Rico or U S Outlying
## Amer Indian Aleut or Eskimo 0.00
## Asian or Pacific Islander 0.05
## Black 0.00
## Other 0.06
## White 0.55
## citizenship
## race Native- Born in the United States
## Amer Indian Aleut or Eskimo 1.18
## Asian or Pacific Islander 1.18
## Black 9.65
## Other 0.97
## White 75.75prop.table(table(race,income_level))*100## income_level
## race -50000 50000
## Amer Indian Aleut or Eskimo 1.13 0.05
## Asian or Pacific Islander 2.86 0.23
## Black 10.02 0.25
## Other 1.78 0.05
## White 78.34 5.29prop.table(table(race,income_level))*100## income_level
## race -50000 50000
## Amer Indian Aleut or Eskimo 1.13 0.05
## Asian or Pacific Islander 2.86 0.23
## Black 10.02 0.25
## Other 1.78 0.05
## White 78.34 5.29prop.table(table(hispanic_origin,income_level))*100## income_level
## hispanic_origin -50000 50000
## All other 81.41237837 5.57728960
## Central or South American 1.93600160 0.02006219
## Chicano 0.12037316 0.00000000
## Cuban 0.59183469 0.02006219
## Do not know 0.16049754 0.00000000
## Mexican-American 3.90209650 0.12037316
## Mexican (Mexicano) 3.58110141 0.05015548
## Other Spanish 1.00310964 0.06018658
## Puerto Rican 1.41438459 0.03009329prop.table(table(race=="White"))*100##
## FALSE TRUE
## 16.37 83.63prop.table(table(sex,income_level))*100## income_level
## sex -50000 50000
## Female 52.00 1.12
## Male 42.13 4.75prop.table(table(sex,tax_filer_status))*100## tax_filer_status
## sex Head of household Joint both 65+ Joint both under 65
## Female 3.10 1.94 17.85
## Male 0.78 2.03 16.93
## tax_filer_status
## sex Joint one under 65 & one 65+ Nonfiler Single
## Female 0.85 20.05 9.33
## Male 0.88 16.71 9.55prop.table(table(member_of_labor_union,citizenship))*100## citizenship
## member_of_labor_union Foreign born- Not a citizen of U S
## No 0.57
## Not in universe 6.11
## Yes 0.07
## citizenship
## member_of_labor_union Foreign born- U S citizen by naturalization
## No 0.28
## Not in universe 2.60
## Yes 0.10
## citizenship
## member_of_labor_union Native- Born abroad of American Parent(s)
## No 0.05
## Not in universe 0.82
## Yes 0.01
## citizenship
## member_of_labor_union Native- Born in Puerto Rico or U S Outlying
## No 0.02
## Not in universe 0.63
## Yes 0.01
## citizenship
## member_of_labor_union Native- Born in the United States
## No 7.65
## Not in universe 79.80
## Yes 1.28Boxplots of the different variables
boxplot(age~class_of_worker,data=usincome,main="Boxplot Representation of Age Vs Class of Worker",xlab="Class of Worker",ylab="Age",las=2)
boxplot(age~income_level,data=usincome,main="Boxplot Representation of Age Vs Income_Level",xlab="Income Level",ylab="Age")
boxplot(industry_code~income_level,data=usincome,main="Boxplot Representation of Industry Code Vs Income_Level",xlab="Income_Level",ylab="Industry")
boxplot(wage_per_hour~income_level,data=usincome,main="Boxplot Representation of Wage per Hour Vs Income_Level",xlab="Income_Level",ylab="Wage per Hour")
Histogram for suitable data fields.
library(lattice)
histogram(~age,data=usincome,main="Distribution of Age",xlab="Age Variation",col="pink")
library(lattice)
histogram(~class_of_worker,data=usincome,main="Distribution of Class of Worker", xlab="Class of Worker",col="green",las=2)
histogram(~occupation_code,main="Occupation Code Frequency Distribution",xlab="Occupation Code",col="gold2")
histogram(~industry_code, main="Industry Code Frequency Distribution",xlab="Occupation Code",col="black")
Different plots for data fields.
library(car)##
## Attaching package: 'car'## The following object is masked from 'package:psych':
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## logitscatterplot(age,income_level,main="Variation in Salary with age",xlab="Age",ylab="Salary (US Dollars)")
library(lattice)
marriage_and_wage<-as.data.frame(table(marital_status,wage_per_hour))
xyplot(marriage_and_wage$marital_status~marriage_and_wage$Freq,type=c("p","g","smooth"),xlab="Wage/Hour",ylab="Frequency of Earning",xlim=c(0,200))
plot(industry_code~wage_per_hour,main="Industry code Vs Wage / Hour")
abline(0,1)
Occ<-as.data.frame(table(education,occupation_code))
library(UsingR)## Loading required package: MASS## Loading required package: HistData## Loading required package: Hmisc## Loading required package: survival## Loading required package: Formula## Loading required package: ggplot2##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
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## %+%, alpha##
## Attaching package: 'Hmisc'## The following object is masked from 'package:psych':
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## describe## The following objects are masked from 'package:base':
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## format.pval, units##
## Attaching package: 'UsingR'## The following object is masked from 'package:survival':
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## cancer## The following objects are masked from 'package:psych':
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## galton, headtaillibrary(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:Hmisc':
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## src, summarize## The following object is masked from 'package:MASS':
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## select## The following object is masked from 'package:car':
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## recode## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, uniong<-ggplot(filter(Occ,Freq>0),aes(x=occupation_code,y=education))
g=g+scale_size(range = c(2,20),guide="none")
g<-g+geom_point(colour="gold2",aes(size=Freq+20,show_guide=FALSE))## Warning: Ignoring unknown aesthetics: show_guideg
Correlation Matrix using corrgram
x<-usincome[,c(1,3,4,6,17,18,19,39,41)]library(corrgram)
cols4<-colorRampPalette(c("peachpuff","lightpink","royalblue3","navyblue"))
corrgram(x, order=FALSE, col.regions=cols4, lower.panel=panel.shade, upper.panel=panel.pie, text.panel=panel.txt, main="Corrgram of variables")
Scatter plot Matrix
library(car)
scatterplotMatrix(formula=~wage_per_hour+class_of_worker+race,cex=0.6,data=usincome,diagonal="density")
Different Tests
cor.test(income_level,age)##
## Pearson's product-moment correlation
##
## data: income_level and age
## t = 13.223, df = 9998, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1117873 0.1503139
## sample estimates:
## cor
## 0.1311001cor.test(income_level,wage_per_hour)##
## Pearson's product-moment correlation
##
## data: income_level and wage_per_hour
## t = 2.5488, df = 9998, p-value = 0.01082
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.005885531 0.045060225
## sample estimates:
## cor
## 0.02548266cor.test(income_level,capital_gains)##
## Pearson's product-moment correlation
##
## data: income_level and capital_gains
## t = 23.912, df = 9998, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2139579 0.2510383
## sample estimates:
## cor
## 0.2325826cor.test(income_level,capital_losses)##
## Pearson's product-moment correlation
##
## data: income_level and capital_losses
## t = 14.154, df = 9998, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1208917 0.1593220
## sample estimates:
## cor
## 0.1401596chisq.test(mytable)## Warning in chisq.test(mytable): Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: mytable
## X-squared = 431.81, df = 360, p-value = 0.005527chisq.test(mytable1)## Warning in chisq.test(mytable1): Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: mytable1
## X-squared = 2079.7, df = 48, p-value < 2.2e-16chisq.test(mytable2)## Warning in chisq.test(mytable2): Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: mytable2
## X-squared = 776.6, df = 23, p-value < 2.2e-16