Homework 2

1

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
## ✔ tibble  3.1.8     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.1
## ✔ readr   2.1.2     ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library(skimr)

# Loading the NHANES data set
data(NHANES, package = "NHANES")

2

glimpse(NHANES)

## Rows: 10,000
## Columns: 76
## $ ID               <int> 51624, 51624, 51624, 51625, 51630, 51638, 51646, 5164…
## $ SurveyYr         <fct> 2009_10, 2009_10, 2009_10, 2009_10, 2009_10, 2009_10,…
## $ Gender           <fct> male, male, male, male, female, male, male, female, f…
## $ Age              <int> 34, 34, 34, 4, 49, 9, 8, 45, 45, 45, 66, 58, 54, 10, …
## $ AgeDecade        <fct>  30-39,  30-39,  30-39,  0-9,  40-49,  0-9,  0-9,  40…
## $ AgeMonths        <int> 409, 409, 409, 49, 596, 115, 101, 541, 541, 541, 795,…
## $ Race1            <fct> White, White, White, Other, White, White, White, Whit…
## $ Race3            <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ Education        <fct> High School, High School, High School, NA, Some Colle…
## $ MaritalStatus    <fct> Married, Married, Married, NA, LivePartner, NA, NA, M…
## $ HHIncome         <fct> 25000-34999, 25000-34999, 25000-34999, 20000-24999, 3…
## $ HHIncomeMid      <int> 30000, 30000, 30000, 22500, 40000, 87500, 60000, 8750…
## $ Poverty          <dbl> 1.36, 1.36, 1.36, 1.07, 1.91, 1.84, 2.33, 5.00, 5.00,…
## $ HomeRooms        <int> 6, 6, 6, 9, 5, 6, 7, 6, 6, 6, 5, 10, 6, 10, 10, 4, 3,…
## $ HomeOwn          <fct> Own, Own, Own, Own, Rent, Rent, Own, Own, Own, Own, O…
## $ Work             <fct> NotWorking, NotWorking, NotWorking, NA, NotWorking, N…
## $ Weight           <dbl> 87.4, 87.4, 87.4, 17.0, 86.7, 29.8, 35.2, 75.7, 75.7,…
## $ Length           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ HeadCirc         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ Height           <dbl> 164.7, 164.7, 164.7, 105.4, 168.4, 133.1, 130.6, 166.…
## $ BMI              <dbl> 32.22, 32.22, 32.22, 15.30, 30.57, 16.82, 20.64, 27.2…
## $ BMICatUnder20yrs <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ BMI_WHO          <fct> 30.0_plus, 30.0_plus, 30.0_plus, 12.0_18.5, 30.0_plus…
## $ Pulse            <int> 70, 70, 70, NA, 86, 82, 72, 62, 62, 62, 60, 62, 76, 8…
## $ BPSysAve         <int> 113, 113, 113, NA, 112, 86, 107, 118, 118, 118, 111, …
## $ BPDiaAve         <int> 85, 85, 85, NA, 75, 47, 37, 64, 64, 64, 63, 74, 85, 6…
## $ BPSys1           <int> 114, 114, 114, NA, 118, 84, 114, 106, 106, 106, 124, …
## $ BPDia1           <int> 88, 88, 88, NA, 82, 50, 46, 62, 62, 62, 64, 76, 86, 6…
## $ BPSys2           <int> 114, 114, 114, NA, 108, 84, 108, 118, 118, 118, 108, …
## $ BPDia2           <int> 88, 88, 88, NA, 74, 50, 36, 68, 68, 68, 62, 72, 88, 6…
## $ BPSys3           <int> 112, 112, 112, NA, 116, 88, 106, 118, 118, 118, 114, …
## $ BPDia3           <int> 82, 82, 82, NA, 76, 44, 38, 60, 60, 60, 64, 76, 82, 7…
## $ Testosterone     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ DirectChol       <dbl> 1.29, 1.29, 1.29, NA, 1.16, 1.34, 1.55, 2.12, 2.12, 2…
## $ TotChol          <dbl> 3.49, 3.49, 3.49, NA, 6.70, 4.86, 4.09, 5.82, 5.82, 5…
## $ UrineVol1        <int> 352, 352, 352, NA, 77, 123, 238, 106, 106, 106, 113, …
## $ UrineFlow1       <dbl> NA, NA, NA, NA, 0.094, 1.538, 1.322, 1.116, 1.116, 1.…
## $ UrineVol2        <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ UrineFlow2       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ Diabetes         <fct> No, No, No, No, No, No, No, No, No, No, No, No, No, N…
## $ DiabetesAge      <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ HealthGen        <fct> Good, Good, Good, NA, Good, NA, NA, Vgood, Vgood, Vgo…
## $ DaysPhysHlthBad  <int> 0, 0, 0, NA, 0, NA, NA, 0, 0, 0, 10, 0, 4, NA, NA, 0,…
## $ DaysMentHlthBad  <int> 15, 15, 15, NA, 10, NA, NA, 3, 3, 3, 0, 0, 0, NA, NA,…
## $ LittleInterest   <fct> Most, Most, Most, NA, Several, NA, NA, None, None, No…
## $ Depressed        <fct> Several, Several, Several, NA, Several, NA, NA, None,…
## $ nPregnancies     <int> NA, NA, NA, NA, 2, NA, NA, 1, 1, 1, NA, NA, NA, NA, N…
## $ nBabies          <int> NA, NA, NA, NA, 2, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ Age1stBaby       <int> NA, NA, NA, NA, 27, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ SleepHrsNight    <int> 4, 4, 4, NA, 8, NA, NA, 8, 8, 8, 7, 5, 4, NA, 5, 7, N…
## $ SleepTrouble     <fct> Yes, Yes, Yes, NA, Yes, NA, NA, No, No, No, No, No, Y…
## $ PhysActive       <fct> No, No, No, NA, No, NA, NA, Yes, Yes, Yes, Yes, Yes, …
## $ PhysActiveDays   <int> NA, NA, NA, NA, NA, NA, NA, 5, 5, 5, 7, 5, 1, NA, 2, …
## $ TVHrsDay         <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ CompHrsDay       <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ TVHrsDayChild    <int> NA, NA, NA, 4, NA, 5, 1, NA, NA, NA, NA, NA, NA, 4, N…
## $ CompHrsDayChild  <int> NA, NA, NA, 1, NA, 0, 6, NA, NA, NA, NA, NA, NA, 3, N…
## $ Alcohol12PlusYr  <fct> Yes, Yes, Yes, NA, Yes, NA, NA, Yes, Yes, Yes, Yes, Y…
## $ AlcoholDay       <int> NA, NA, NA, NA, 2, NA, NA, 3, 3, 3, 1, 2, 6, NA, NA, …
## $ AlcoholYear      <int> 0, 0, 0, NA, 20, NA, NA, 52, 52, 52, 100, 104, 364, N…
## $ SmokeNow         <fct> No, No, No, NA, Yes, NA, NA, NA, NA, NA, No, NA, NA, …
## $ Smoke100         <fct> Yes, Yes, Yes, NA, Yes, NA, NA, No, No, No, Yes, No, …
## $ Smoke100n        <fct> Smoker, Smoker, Smoker, NA, Smoker, NA, NA, Non-Smoke…
## $ SmokeAge         <int> 18, 18, 18, NA, 38, NA, NA, NA, NA, NA, 13, NA, NA, N…
## $ Marijuana        <fct> Yes, Yes, Yes, NA, Yes, NA, NA, Yes, Yes, Yes, NA, Ye…
## $ AgeFirstMarij    <int> 17, 17, 17, NA, 18, NA, NA, 13, 13, 13, NA, 19, 15, N…
## $ RegularMarij     <fct> No, No, No, NA, No, NA, NA, No, No, No, NA, Yes, Yes,…
## $ AgeRegMarij      <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 20, 15, N…
## $ HardDrugs        <fct> Yes, Yes, Yes, NA, Yes, NA, NA, No, No, No, No, Yes, …
## $ SexEver          <fct> Yes, Yes, Yes, NA, Yes, NA, NA, Yes, Yes, Yes, Yes, Y…
## $ SexAge           <int> 16, 16, 16, NA, 12, NA, NA, 13, 13, 13, 17, 22, 12, N…
## $ SexNumPartnLife  <int> 8, 8, 8, NA, 10, NA, NA, 20, 20, 20, 15, 7, 100, NA, …
## $ SexNumPartYear   <int> 1, 1, 1, NA, 1, NA, NA, 0, 0, 0, NA, 1, 1, NA, NA, 1,…
## $ SameSex          <fct> No, No, No, NA, Yes, NA, NA, Yes, Yes, Yes, No, No, N…
## $ SexOrientation   <fct> Heterosexual, Heterosexual, Heterosexual, NA, Heteros…
## $ PregnantNow      <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…

There are 10000 rows and 76.
Age is an integer, BMI is a double, and Diabetes is a factor.

3

skim(NHANES)

Data summary
Name	NHANES
Number of rows	10000
Number of columns	76
_______________________
Column type frequency:
factor	31
numeric	45
________________________
Group variables	None

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
SurveyYr	0	1.00	FALSE	2	200: 5000, 201: 5000
Gender	0	1.00	FALSE	2	fem: 5020, mal: 4980
AgeDecade	333	0.97	FALSE	8	40: 1398, 0-: 1391, 10: 1374, 20: 1356
Race1	0	1.00	FALSE	5	Whi: 6372, Bla: 1197, Mex: 1015, Oth: 806
Race3	5000	0.50	FALSE	6	Whi: 3135, Bla: 589, Mex: 480, His: 350
Education	2779	0.72	FALSE	5	Som: 2267, Col: 2098, Hig: 1517, 9 -: 888
MaritalStatus	2769	0.72	FALSE	6	Mar: 3945, Nev: 1380, Div: 707, Liv: 560
HHIncome	811	0.92	FALSE	12	mor: 2220, 750: 1084, 250: 958, 350: 863
HomeOwn	63	0.99	FALSE	3	Own: 6425, Ren: 3287, Oth: 225
Work	2229	0.78	FALSE	3	Wor: 4613, Not: 2847, Loo: 311
BMICatUnder20yrs	8726	0.13	FALSE	4	Nor: 805, Obe: 221, Ove: 193, Und: 55
BMI_WHO	397	0.96	FALSE	4	18.: 2911, 30.: 2751, 25.: 2664, 12.: 1277
Diabetes	142	0.99	FALSE	2	No: 9098, Yes: 760
HealthGen	2461	0.75	FALSE	5	Goo: 2956, Vgo: 2508, Fai: 1010, Exc: 878
LittleInterest	3333	0.67	FALSE	3	Non: 5103, Sev: 1130, Mos: 434
Depressed	3327	0.67	FALSE	3	Non: 5246, Sev: 1009, Mos: 418
SleepTrouble	2228	0.78	FALSE	2	No: 5799, Yes: 1973
PhysActive	1674	0.83	FALSE	2	Yes: 4649, No: 3677
TVHrsDay	5141	0.49	FALSE	7	2_h: 1275, 1_h: 884, 3_h: 836, 0_t: 638
CompHrsDay	5137	0.49	FALSE	7	0_t: 1409, 0_h: 1073, 1_h: 1030, 2_h: 589
Alcohol12PlusYr	3420	0.66	FALSE	2	Yes: 5212, No: 1368
SmokeNow	6789	0.32	FALSE	2	No: 1745, Yes: 1466
Smoke100	2765	0.72	FALSE	2	No: 4024, Yes: 3211
Smoke100n	2765	0.72	FALSE	2	Non: 4024, Smo: 3211
Marijuana	5059	0.49	FALSE	2	Yes: 2892, No: 2049
RegularMarij	5059	0.49	FALSE	2	No: 3575, Yes: 1366
HardDrugs	4235	0.58	FALSE	2	No: 4700, Yes: 1065
SexEver	4233	0.58	FALSE	2	Yes: 5544, No: 223
SameSex	4232	0.58	FALSE	2	No: 5353, Yes: 415
SexOrientation	5158	0.48	FALSE	3	Het: 4638, Bis: 119, Hom: 85
PregnantNow	8304	0.17	FALSE	3	No: 1573, Yes: 72, Unk: 51

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ID	0	1.00	61944.64	5871.17	51624.00	56904.50	62159.50	67039.00	71915.00	▇▇▇▇▇
Age	0	1.00	36.74	22.40	0.00	17.00	36.00	54.00	80.00	▇▇▇▆▅
AgeMonths	5038	0.50	420.12	259.04	0.00	199.00	418.00	624.00	959.00	▇▇▇▆▃
HHIncomeMid	811	0.92	57206.17	33020.28	2500.00	30000.00	50000.00	87500.00	100000.00	▃▆▃▁▇
Poverty	726	0.93	2.80	1.68	0.00	1.24	2.70	4.71	5.00	▅▅▃▃▇
HomeRooms	69	0.99	6.25	2.28	1.00	5.00	6.00	8.00	13.00	▂▆▇▂▁
Weight	78	0.99	70.98	29.13	2.80	56.10	72.70	88.90	230.70	▂▇▂▁▁
Length	9457	0.05	85.02	13.71	47.10	75.70	87.00	96.10	112.20	▁▃▆▇▃
HeadCirc	9912	0.01	41.18	2.31	34.20	39.58	41.45	42.92	45.40	▁▂▇▇▅
Height	353	0.96	161.88	20.19	83.60	156.80	166.00	174.50	200.40	▁▁▁▇▂
BMI	366	0.96	26.66	7.38	12.88	21.58	25.98	30.89	81.25	▇▆▁▁▁
Pulse	1437	0.86	73.56	12.16	40.00	64.00	72.00	82.00	136.00	▂▇▃▁▁
BPSysAve	1449	0.86	118.15	17.25	76.00	106.00	116.00	127.00	226.00	▃▇▂▁▁
BPDiaAve	1449	0.86	67.48	14.35	0.00	61.00	69.00	76.00	116.00	▁▁▇▇▁
BPSys1	1763	0.82	119.09	17.50	72.00	106.00	116.00	128.00	232.00	▂▇▂▁▁
BPDia1	1763	0.82	68.28	13.78	0.00	62.00	70.00	76.00	118.00	▁▁▇▆▁
BPSys2	1647	0.84	118.48	17.49	76.00	106.00	116.00	128.00	226.00	▃▇▂▁▁
BPDia2	1647	0.84	67.66	14.42	0.00	60.00	68.00	76.00	118.00	▁▁▇▆▁
BPSys3	1635	0.84	117.93	17.18	76.00	106.00	116.00	126.00	226.00	▃▇▂▁▁
BPDia3	1635	0.84	67.30	14.96	0.00	60.00	68.00	76.00	116.00	▁▁▇▇▁
Testosterone	5874	0.41	197.90	226.50	0.25	17.70	43.82	362.41	1795.60	▇▂▁▁▁
DirectChol	1526	0.85	1.36	0.40	0.39	1.09	1.29	1.58	4.03	▅▇▂▁▁
TotChol	1526	0.85	4.88	1.08	1.53	4.11	4.78	5.53	13.65	▂▇▁▁▁
UrineVol1	987	0.90	118.52	90.34	0.00	50.00	94.00	164.00	510.00	▇▅▂▁▁
UrineFlow1	1603	0.84	0.98	0.95	0.00	0.40	0.70	1.22	17.17	▇▁▁▁▁
UrineVol2	8522	0.15	119.68	90.16	0.00	52.00	95.00	171.75	409.00	▇▆▃▂▁
UrineFlow2	8524	0.15	1.15	1.07	0.00	0.48	0.76	1.51	13.69	▇▁▁▁▁
DiabetesAge	9371	0.06	48.42	15.68	1.00	40.00	50.00	58.00	80.00	▁▂▆▇▂
DaysPhysHlthBad	2468	0.75	3.33	7.40	0.00	0.00	0.00	3.00	30.00	▇▁▁▁▁
DaysMentHlthBad	2466	0.75	4.13	7.83	0.00	0.00	0.00	4.00	30.00	▇▁▁▁▁
nPregnancies	7396	0.26	3.03	1.80	1.00	2.00	3.00	4.00	32.00	▇▁▁▁▁
nBabies	7584	0.24	2.46	1.32	0.00	2.00	2.00	3.00	12.00	▇▅▁▁▁
Age1stBaby	8116	0.19	22.65	4.77	14.00	19.00	22.00	26.00	39.00	▆▇▅▂▁
SleepHrsNight	2245	0.78	6.93	1.35	2.00	6.00	7.00	8.00	12.00	▁▅▇▁▁
PhysActiveDays	5337	0.47	3.74	1.84	1.00	2.00	3.00	5.00	7.00	▇▇▃▅▅
TVHrsDayChild	9347	0.07	1.94	1.43	0.00	1.00	2.00	3.00	6.00	▇▆▂▂▂
CompHrsDayChild	9347	0.07	2.20	2.52	0.00	0.00	1.00	6.00	6.00	▇▁▁▁▃
AlcoholDay	5086	0.49	2.91	3.18	1.00	1.00	2.00	3.00	82.00	▇▁▁▁▁
AlcoholYear	4078	0.59	75.10	103.03	0.00	3.00	24.00	104.00	364.00	▇▁▁▁▁
SmokeAge	6920	0.31	17.83	5.33	6.00	15.00	17.00	19.00	72.00	▇▂▁▁▁
AgeFirstMarij	7109	0.29	17.02	3.90	1.00	15.00	16.00	19.00	48.00	▁▇▂▁▁
AgeRegMarij	8634	0.14	17.69	4.81	5.00	15.00	17.00	19.00	52.00	▂▇▁▁▁
SexAge	4460	0.55	17.43	3.72	9.00	15.00	17.00	19.00	50.00	▇▅▁▁▁
SexNumPartnLife	4275	0.57	15.09	57.85	0.00	2.00	5.00	12.00	2000.00	▇▁▁▁▁
SexNumPartYear	5072	0.49	1.34	2.78	0.00	1.00	1.00	1.00	69.00	▇▁▁▁▁

BMICatUnder20Years is the factor variable with the most missing values. HeadCirc is the numeric variable with the most missing values.

NHANES_sub <- select(NHANES, 1:5)
glimpse(NHANES_sub)

## Rows: 10,000
## Columns: 5
## $ ID        <int> 51624, 51624, 51624, 51625, 51630, 51638, 51646, 51647, 5164…
## $ SurveyYr  <fct> 2009_10, 2009_10, 2009_10, 2009_10, 2009_10, 2009_10, 2009_1…
## $ Gender    <fct> male, male, male, male, female, male, male, female, female, …
## $ Age       <int> 34, 34, 34, 4, 49, 9, 8, 45, 45, 45, 66, 58, 54, 10, 58, 50,…
## $ AgeDecade <fct>  30-39,  30-39,  30-39,  0-9,  40-49,  0-9,  0-9,  40-49,  4…

skim(NHANES_sub)

Data summary
Name	NHANES_sub
Number of rows	10000
Number of columns	5
_______________________
Column type frequency:
factor	3
numeric	2
________________________
Group variables	None

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
SurveyYr	0	1.00	FALSE	2	200: 5000, 201: 5000
Gender	0	1.00	FALSE	2	fem: 5020, mal: 4980
AgeDecade	333	0.97	FALSE	8	40: 1398, 0-: 1391, 10: 1374, 20: 1356

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ID	0	1	61944.64	5871.17	51624	56904.5	62159.5	67039	71915	▇▇▇▇▇
Age	0	1	36.74	22.40	0	17.0	36.0	54	80	▇▇▇▆▅

count(NHANES, SmokeNow, Diabetes)

## # A tibble: 8 × 3
##   SmokeNow Diabetes     n
##   <fct>    <fct>    <int>
## 1 No       No        1476
## 2 No       Yes        267
## 3 No       <NA>         2
## 4 Yes      No        1360
## 5 Yes      Yes        106
## 6 <NA>     No        6262
## 7 <NA>     Yes        387
## 8 <NA>     <NA>       140

There are 9858 people who are and are not diabetic.
142
7.2305593% people who smoke have diabetes and 15.3008596% people who don’t smoke that have diabetes.
The data set does not align with this because possibly because the sample isn’t representative of the trends we see in the population.

4

slice_tail() is the analogous function for printing the last n rows of the data set.

slice_min(NHANES, BMI, n=5)

## # A tibble: 6 × 76
##      ID Surve…¹ Gender   Age AgeDe…² AgeMo…³ Race1 Race3 Educa…⁴ Marit…⁵ HHInc…⁶
##   <int> <fct>   <fct>  <int> <fct>     <int> <fct> <fct> <fct>   <fct>   <fct>  
## 1 60527 2009_10 female     3 " 0-9"       42 Other <NA>  <NA>    <NA>    more 9…
## 2 52534 2009_10 male       9 " 0-9"      109 Mexi… <NA>  <NA>    <NA>    25000-…
## 3 64250 2011_12 female     3 " 0-9"       NA Mexi… Mexi… <NA>    <NA>    35000-…
## 4 64751 2011_12 male       9 " 0-9"       NA White White <NA>    <NA>    <NA>   
## 5 64751 2011_12 male       9 " 0-9"       NA White White <NA>    <NA>    <NA>   
## 6 64751 2011_12 male       9 " 0-9"       NA White White <NA>    <NA>    <NA>   
## # … with 65 more variables: HHIncomeMid <int>, Poverty <dbl>, HomeRooms <int>,
## #   HomeOwn <fct>, Work <fct>, Weight <dbl>, Length <dbl>, HeadCirc <dbl>,
## #   Height <dbl>, BMI <dbl>, BMICatUnder20yrs <fct>, BMI_WHO <fct>,
## #   Pulse <int>, BPSysAve <int>, BPDiaAve <int>, BPSys1 <int>, BPDia1 <int>,
## #   BPSys2 <int>, BPDia2 <int>, BPSys3 <int>, BPDia3 <int>, Testosterone <dbl>,
## #   DirectChol <dbl>, TotChol <dbl>, UrineVol1 <int>, UrineFlow1 <dbl>,
## #   UrineVol2 <int>, UrineFlow2 <dbl>, Diabetes <fct>, DiabetesAge <int>, …

slice_max(NHANES, BMI, n=5)

## # A tibble: 5 × 76
##      ID Surve…¹ Gender   Age AgeDe…² AgeMo…³ Race1 Race3 Educa…⁴ Marit…⁵ HHInc…⁶
##   <int> <fct>   <fct>  <int> <fct>     <int> <fct> <fct> <fct>   <fct>   <fct>  
## 1 52315 2009_10 female    52 " 50-5…     629 Black <NA>  High S… Married "45000…
## 2 52315 2009_10 female    52 " 50-5…     629 Black <NA>  High S… Married "45000…
## 3 70466 2011_12 female    25 " 20-2…      NA White White High S… LivePa… " 0-49…
## 4 69207 2011_12 female    30 " 30-3…      NA Black Black High S… NeverM… "20000…
## 5 61693 2009_10 female    33 " 30-3…     406 Hisp… <NA>  Some C… NeverM… "15000…
## # … with 65 more variables: HHIncomeMid <int>, Poverty <dbl>, HomeRooms <int>,
## #   HomeOwn <fct>, Work <fct>, Weight <dbl>, Length <dbl>, HeadCirc <dbl>,
## #   Height <dbl>, BMI <dbl>, BMICatUnder20yrs <fct>, BMI_WHO <fct>,
## #   Pulse <int>, BPSysAve <int>, BPDiaAve <int>, BPSys1 <int>, BPDia1 <int>,
## #   BPSys2 <int>, BPDia2 <int>, BPSys3 <int>, BPDia3 <int>, Testosterone <dbl>,
## #   DirectChol <dbl>, TotChol <dbl>, UrineVol1 <int>, UrineFlow1 <dbl>,
## #   UrineVol2 <int>, UrineFlow2 <dbl>, Diabetes <fct>, DiabetesAge <int>, …

The majority of low BMI individuals are male, white, home owners, working, weigh less, were shorter, had lower BMIs, pulse rates, blood pressures, testosterone, and higher cholesterol. Overall, I think that these differences allow us to conclude that people with lower BMIs are generally healthier, in addition to being shorter. What ends up being more interesting to note is that these individuals are also generally white and home owners which could imply that wealth and socioeconomic status have some sort of relationship to BMI and health.

Homework 2

Phuc Dang

9/25/2022

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