Data Dive
Julia French
2023-09-04
R Markdown
Dataset - National Health Interview Adult Survey
The dataset is data from the 2021 National Health Interview Adult Survey. The survey contained questions related to household and family composition, demographics about the survey taker, satisfaction with life, health insurance, medication, immunization, preventive screenings, and multiple health problems such as hypertension, cardiovascular conditions, cancer, vision, hearing, mobility, and more.
This survey is important in following the health of American’s based on many different factors of their lives. Looking at previous surveys can also help to see trends in Americans’ health.
Questions
1. Does education level play a role in the mental or physical health?
2. What are some health issues that correlate to other health issues?
3: What health issues are more common among certain demographics?
4: Has COVID possibly had an effect on certain health issues?
5: Is there a link between physical health and mental health?Columns
General Health
1: Excellent
2: Very Good
3: Good
4: Fair
5: Poor
7: Refused
8: Not Ascertained
9: Don't Know2: Life Satisfaction
1: Very Satisfied
2: Satisfied
3: Dissatisfied
4: Very Dissatisfied
7: Refused
8: Not Ascertained
9: Don't Know3: General Demographics
Classification of County Lived In
1: Large central metro
2: Large fringe metro
3: Medium and small metro
4: Nonmetropolitan
Household Region
1: Northeast
2: Midwest
3: South
4: West
Age
18-84: 18-84 with number corresponding
85: 85+
97: Refused
98: Not Ascertained
99: Don't Know
Age 65+
1: Less than 65
2: 65 or older
7: Refused
8: Not Ascertained
9: Don't Know
Sex
1: Male
2: Female
7: Refused
8: Not Ascertained
9: Don't Know
Education Level
0: Never attended/Kindergarten only
1: Grade 1-11
2: 12th grade, no diploma
3: GED or equivalent
4: High School Graduate
5: Some college, no degree
6: Associate degree: occupational, technical, or vocational program
7: Associate degree: academic program
8: Bachelor's degree
9: Master's degree
10: Professional School or Doctoral degree
97: Refused
98: Not Ascertained
99: Don't KnowWeight
Person's weight in lbsHeight
Person's height in ???Medical Problems
Questions were laid out as...
Told you have (condition)?
Told you have (condition) on 2 or more visits?
Had (condition) in past 12 months?
...with the possible responses being,
1: Yes. 1 answered if respondant is taking medication to control the issue
2: No
7: Refused
8: Not Ascertained
9: Don't Know
Cancer
Types Included
1
2
3
4
Age when first told had (type) cancer?
1-84: 1-84 years, with the corresponding number
85: 85+ years
97: Refused
98: Not Ascertained
99: Don't KnowOthers
Days Missed Work
0-129: 0 to 129 with corresponding value
130: 130+ days
997: Refused
998: Not Ascertained
999: Don't Know"Unclear Data (Week 5 Assignment)
Most of the column names were unclear until I read the Codebook, however it was often easy to tell what category something fell under such as EDUCP_A, likely had something to do with education, while variable with CAN in them had to do with Cancer. I have an Excel sheet of the data where I have the columns color coded by if I know them from the codebook, if they are not in the codebook, or if I will not be using that column. Some of these unclear ones are the ones that start with DRK, PA18, MOD, VIG, and STR. I am still working on figuring those out.
Among the columns I do know, there are a few that I am unclear about. Among the cancer ones, they are asked what age were they told they have colon-rectal cancer. However, two other questions ask about colon cancer and rectal cancer, so I am trying to figure out if those are the same things, or separated.
dfColonRectal <- adult22[ , c("COLRCAGETC_A", "COLONAGETC_A", "RECTUAGETC_A")]
dfColonRectalAge <-subset(dfColonRectal, COLRCAGETC_A<="85")
#count(dfColonRectalAge) = 196
#print(dfColonRectalAge)
dfColonRectalAgeTest <-subset(dfColonRectalAge, COLRCAGETC_A==COLONAGETC_A | COLRCAGETC_A==RECTUAGETC_A)
#count(dfColonRectalAgeTest) = 196Both have 196, so that means they have the same age that they put for ColoRectal in either Colon or Rectal. So this won’t cause problems for the data, I just have to make sure I don’t include ColoRectal and Colon, or ColoRectal and Rectal as separate cancers. Such as if I am counting how many types of cancer one person has.
library(ggplot2)
# Weight
mean(adult22$WEIGHTLBTC_A)## [1] 246.2174max(adult22$WEIGHTLBTC_A)## [1] 999min(adult22$WEIGHTLBTC_A)## [1] 100# Age
mean(adult22$AGEP_A)## [1] 53.05092max(adult22$AGEP_A)## [1] 99min(adult22$AGEP_A)## [1] 18# Age65+
ggplot(adult22, aes(x = AGE65)) +
geom_bar()## Warning: Removed 27525 rows containing non-finite values (`stat_count()`).
# Sex
ggplot(adult22, aes(x = SEX_A)) +
geom_bar()
# Education Level
ggplot(adult22, aes(x = EDUCP_A)) +
geom_bar()
#General Health
mean(adult22$PHSTAT_A)## [1] 2.440273ggplot(adult22, aes(x = PHSTAT_A)) +
geom_bar()
plot(adult22$AGEP_A , adult22$PHSTAT_A)
abline(lm(adult22$PHSTAT_A ~ adult22$AGEP_A), col = "red", lwd = 3)
#Weight and Health
# dfWeightFilter <- adult22[adult22$WEIGHTLBTC_A < '997', ]
dfWeightFilter <- adult22 %>%
filter(WEIGHTLBTC_A <= 900)
plot(dfWeightFilter$WEIGHTLBTC_A)
dfWHFilter <- dfWeightFilter %>%
filter(PHSTAT_A <= 6)
dfHighHealth <- dfWHFilter %>%
filter(PHSTAT_A < 3 )
dfHighWeight <- dfWHFilter %>%
filter(WEIGHTLBTC_A >= 250 )
Weight1 <- nrow(dfWeightFilter[dfWeightFilter$WEIGHTLBTC_A < '150', ])
Weight2 <- nrow(dfWeightFilter[dfWeightFilter$WEIGHTLBTC_A > '150' & dfWeightFilter$WEIGHTLBTC_A <= '200', ])
Weight3 <- nrow(dfWeightFilter[dfWeightFilter$WEIGHTLBTC_A > '200' & dfWeightFilter$WEIGHTLBTC_A <= '250', ])
Weight4 <- nrow(dfWeightFilter[dfWeightFilter$WEIGHTLBTC_A <= '250', ])
dfWeightCount <- data.frame(Weight1, Weight2, Weight3, Weight4)
print(dfWeightCount)## Weight1 Weight2 Weight3 Weight4
## 1 6451 11717 5008 24210plot(dfWHFilter$WEIGHTLBTC_A, dfWHFilter$PHSTAT_A, xlab = "Weight", ylab = "General Health")
plot(dfHighWeight$WEIGHTLBTC_A, dfHighWeight$PHSTAT_A, xlab = "Weight", ylab = "General Health")
hist(dfWeightFilter$WEIGHTLBTC_A, )
#Weight and Height
plot(adult22$WEIGHTLBTC_A, adult22$HEIGHTTC_A)
# Group_By
library(magrittr)##
## Attaching package: 'magrittr'## The following object is masked from 'package:purrr':
##
## set_names## The following object is masked from 'package:tidyr':
##
## extractlibrary(dplyr)
dfEdu <- adult22 %>% group_by(adult22$EDUCP_A)
mean(dfEdu$EDUCP_A) ## [1] 6.443528# which is an associate degree
# Probability of at least an associate degree (6, 7, 8, 9, 10)
prob_Associate_Up<- nrow(dfEdu[dfEdu$EDUCP_A >= '6' & dfEdu$EDUCP_A <= '10', ])
prob_All <- nrow(dfEdu)
prob_Associate_Up/prob_All## [1] 0# Probability of below grade 12
prob_Under_12 <- nrow(dfEdu[dfEdu$EDUCP_A <= '1', ])
prob_Under_12/prob_All## [1] 0.06802647# Probability of associate or higher and positive life satisfaction
prob_Associate_Satisfied <- nrow(dfEdu[dfEdu$EDUCP_A >= '6' & dfEdu$EDUCP_A <= '10' & dfEdu$LSATIS4_A <= '2', ])
prob_Associate_Satisfied/prob_Associate_Up## [1] NaN#Probability of below grade 12 and satisfied
prob_Under12_Satisfied <- nrow(dfEdu[dfEdu$EDUCP_A <= '1' & dfEdu$LSATIS4_A <= '2', ])
prob_Under12_Satisfied/prob_Under_12## [1] 0.917597plot## function (x, y, ...)
## UseMethod("plot")
## <bytecode: 0x7fe24f2e3220>
## <environment: namespace:base>#Probability of normal BMI(18.5 to 24.9) and general health
dfHealth <- adult22 %>% group_by(adult22$PHSTAT_A)
prob_NormBMI <- nrow(dfHealth[dfHealth$BMICAT_A == '2', ])
prob_NormBMI/prob_All## [1] 0.307186prob_NormBMI_GoodHealth <- nrow(dfHealth[dfHealth$BMICAT_A == '2' & dfHealth$PHSTAT_A <= '4', ])
prob_NormBMI_GoodHealth/prob_NormBMI## [1] 0.970332#Probability of overweight BMI and positive/negative health
prob_OverweightBMI <- nrow(dfHealth[dfHealth$BMICAT_A == '3', ])
prob_OverweightBMI/prob_All## [1] 0.3357926prob_OverweightBMI_GoodHealth <- nrow(dfHealth[dfHealth$BMICAT_A == '3' & dfHealth$PHSTAT_A <= '4', ])
prob_OverweightBMI_GoodHealth/prob_OverweightBMI## [1] 0.9696284prob_OverweightBMI_BadHealth <- nrow(dfHealth[dfHealth$BMICAT_A == '3' & dfHealth$PHSTAT_A == '5', ])
prob_OverweightBMI_BadHealth/prob_OverweightBMI## [1] 0.02994076prob_GoodHealth <- nrow(dfHealth[dfHealth$PHSTAT_A <= '4', ])
# How many of all BMIs considered themselves to be in good health
prob_GoodHealth/prob_All## [1] 0.9626053# About 96% of people considered themselves to be in good, or greater health. Even among different BMIs, the percent that considered themselves to be in good health was above 90%.
# Why do most people see themselves to be in good health, or were most of the survey takers healthy in general? -- Check the more specific medical issuesBMI New Column
# Sort BMI by Underweight, Normal, Overweight, Obese
adult22_raw <- adult22
adult22BMI <- adult22_raw
adult22BMI <-
adult22BMI |>
group_by(adult22BMI$BMICAT_A) |>
mutate(BMI_Status = ifelse(BMICAT_A == 1,
"Under",
ifelse(BMICAT_A == 3,
"Over",
ifelse(BMICAT_A == 4,
"Obese",
ifelse(BMICAT_A,
"Normal",
"Unknown"))))) |>
ungroup()
Normal <- nrow(adult22BMI[adult22BMI$BMI_Status == 'Normal',])# Life Satisfaction and General Health
prob_GoodLS_Health <- nrow(dfHealth[dfHealth$LSATIS4_A <= '2' & dfHealth$PHSTAT_A <= '4', ])
prob_GoodLS_Health/prob_All## [1] 0.9275976#Prob out of those who have high general health
prob_GoodLS_Health/prob_GoodHealth## [1] 0.9636323#Bad life satisfaction and bad health out of all
prob_BadLS_Health <- nrow(dfHealth[dfHealth$LSATIS4_A >= '3' & dfHealth$LSATIS4_A <=4 & dfHealth$PHSTAT_A == '5', ])
prob_BadLS_Health/prob_All## [1] 0.01182597#Bad life satisfaction among those with low health
prob_Low_LS <- nrow(dfHealth[dfHealth$PHSTAT_A == '5',])
prob_BadLS_Health/prob_Low_LS## [1] 0.3180934plot(adult22$EDUCP_A , adult22$LSATIS4_A)
abline(lm(adult22$LSATIS4_A ~ adult22$EDUCP_A), col = "red", lwd = 3)
# Because the survey was mostly multiple choice, there are not any major anomalies. The only thing that falls out of the typical range of responses are the "don't know, refuse, or not ascertained" but even those have specific values that are consistent across questions.
# There were a few strange ones among these, such as a few people putting "don't know/not ascertained" for their age, which is something they should know. Probably a wrong click or just not paying attention?#Education Dataframe Sample
dfEduSample <- dfEdu[ , c("EDUCP_A")]
dfEdu1 <- sample_n(dfEduSample,100, replace = TRUE)
dfEdu2 <- sample_n(dfEduSample,100, replace = TRUE)
dfEdu3 <- sample_n(dfEduSample,100, replace = TRUE)
dfEdu4 <- sample_n(dfEduSample,100, replace = TRUE)
dfEdu5 <- sample_n(dfEduSample,100, replace = TRUE)
print(dfEdu1)## # A tibble: 100 × 1
## EDUCP_A
## <int>
## 1 8
## 2 9
## 3 10
## 4 4
## 5 8
## 6 7
## 7 4
## 8 9
## 9 1
## 10 7
## # ℹ 90 more rowsprint(mean(dfEdu1$EDUCP_A))## [1] 6.64print(dfEdu2)## # A tibble: 100 × 1
## EDUCP_A
## <int>
## 1 9
## 2 3
## 3 10
## 4 4
## 5 4
## 6 8
## 7 9
## 8 4
## 9 4
## 10 8
## # ℹ 90 more rowsprint(mean(dfEdu2$EDUCP_A))## [1] 5.58print(dfEdu3)## # A tibble: 100 × 1
## EDUCP_A
## <int>
## 1 5
## 2 8
## 3 6
## 4 4
## 5 4
## 6 7
## 7 7
## 8 99
## 9 10
## 10 8
## # ℹ 90 more rowsprint(mean(dfEdu3$EDUCP_A))## [1] 6.98print(dfEdu4)## # A tibble: 100 × 1
## EDUCP_A
## <int>
## 1 4
## 2 5
## 3 4
## 4 5
## 5 8
## 6 9
## 7 5
## 8 8
## 9 8
## 10 7
## # ℹ 90 more rowsprint(mean(dfEdu4$EDUCP_A))## [1] 7.48print(dfEdu5)## # A tibble: 100 × 1
## EDUCP_A
## <int>
## 1 4
## 2 4
## 3 1
## 4 5
## 5 4
## 6 10
## 7 4
## 8 8
## 9 4
## 10 4
## # ℹ 90 more rowsprint(mean(dfEdu5$EDUCP_A))## [1] 5.56# The average tends to be between 5 (some college) and 8 (Bachelor's degree), among all the samples. However if any sample ends up with the 97,98, or 99 that correspond with "don't know", then the sample will be greatly skewed.dfWeightHeightSample <- dfHealth[ , c("WEIGHTLBTC_A", "HEIGHTTC_A")]
dfWH1 <- sample_n(dfWeightHeightSample,100, replace = TRUE)
dfWH2 <- sample_n(dfWeightHeightSample,100, replace = TRUE)
dfWH3 <- sample_n(dfWeightHeightSample,100, replace = TRUE)
dfWH4 <- sample_n(dfWeightHeightSample,100, replace = TRUE)
dfWH5 <- sample_n(dfWeightHeightSample,100, replace = TRUE)
print(dfWH1)## # A tibble: 100 × 2
## WEIGHTLBTC_A HEIGHTTC_A
## <int> <int>
## 1 128 65
## 2 250 72
## 3 143 64
## 4 215 71
## 5 997 63
## 6 996 96
## 7 124 64
## 8 996 96
## 9 160 63
## 10 215 74
## # ℹ 90 more rowsprint(dfWH2)## # A tibble: 100 × 2
## WEIGHTLBTC_A HEIGHTTC_A
## <int> <int>
## 1 160 64
## 2 240 70
## 3 200 72
## 4 180 72
## 5 110 62
## 6 180 64
## 7 996 96
## 8 215 69
## 9 240 69
## 10 200 70
## # ℹ 90 more rowsprint(dfWH3)## # A tibble: 100 × 2
## WEIGHTLBTC_A HEIGHTTC_A
## <int> <int>
## 1 130 59
## 2 175 71
## 3 180 65
## 4 150 71
## 5 999 65
## 6 155 71
## 7 140 65
## 8 215 63
## 9 996 96
## 10 190 62
## # ℹ 90 more rowsprint(dfWH4)## # A tibble: 100 × 2
## WEIGHTLBTC_A HEIGHTTC_A
## <int> <int>
## 1 122 63
## 2 131 64
## 3 996 96
## 4 110 60
## 5 150 69
## 6 144 63
## 7 168 64
## 8 180 64
## 9 178 68
## 10 165 63
## # ℹ 90 more rowsprint(dfWH5)## # A tibble: 100 × 2
## WEIGHTLBTC_A HEIGHTTC_A
## <int> <int>
## 1 195 73
## 2 170 66
## 3 138 63
## 4 170 70
## 5 140 71
## 6 175 71
## 7 210 63
## 8 129 68
## 9 120 64
## 10 996 96
## # ℹ 90 more rowsplot(dfWH1$WEIGHTLBTC_A,dfWH1$HEIGHTTC_A,type="p",main="Normal Distribution",xlab="Weight(lbs)",ylab="Height")
points(dfWH2$WEIGHTLBTC_A,dfWH2$HEIGHTTC_A, col="green")
points(dfWH3$WEIGHTLBTC_A,dfWH3$HEIGHTTC_A,col="blue")
points(dfWH4$WEIGHTLBTC_A,dfWH4$HEIGHTTC_A,col="red")
points(dfWH5$WEIGHTLBTC_A,dfWH5$HEIGHTTC_A,col="yellow")
# Among the samples, they tend to stay in the same corner/area for weight and height. They also tend to have around the same number of outliers(97,98,99 for "don't know").dfGenHealthSample <- dfHealth[ , c("PHSTAT_A")]
dfGH1 <- sample_n(dfGenHealthSample,100, replace = TRUE)
dfGH2 <- sample_n(dfGenHealthSample,100, replace = TRUE)
dfGH3 <- sample_n(dfGenHealthSample,100, replace = TRUE)
dfGH4 <- sample_n(dfGenHealthSample,100, replace = TRUE)
dfGH5 <- sample_n(dfGenHealthSample,100, replace = TRUE)
# Average
print(mean(dfGH1$PHSTAT_A))## [1] 2.34print(mean(dfGH2$PHSTAT_A))## [1] 2.27print(mean(dfGH3$PHSTAT_A))## [1] 2.18print(mean(dfGH4$PHSTAT_A))## [1] 2.45print(mean(dfGH5$PHSTAT_A))## [1] 2.41# The average tends to be between 2 and 3, which makes sense because the general health among all survey takers is often a 2 (Very good) or 3 (Good).Looking at data among cancer types
Types:
BLADDCAN_A BLOODCAN_A BONECAN_A BRAINCAN_A BREASCAN_A CERVICAN_A ESOPHCAN_A GALLBCAN_A LARYNCAN_A LEUKECAN_A LIVERCAN_A LUNGCAN_A LYMPHCAN_A MELANCAN_A MOUTHCAN_A OVARYCAN_A PANCRCAN_A PROSTCAN_A SKNMCAN_A SKNNMCAN_A SKNDKCAN_A STOMACAN_A THROACAN_A THYROCAN_A UTERUCAN_A HDNCKCAN_A COLRCCAN_A OTHERCANP_A
Number of reported cancers: NUMCAN_A
Age Told has Cancer
BLADDAGETC_A BLOODAGETC_A BONEAGETC_A BRAINAGETC_A BREASAGETC_A CERVIAGETC_A COLONAGETC_A ESOPHAGETC_A GALLBAGETC_A LARYNAGETC_A LEUKEAGETC_A LIVERAGETC_A LUNGAGETC_A LYMPHAGETC_A MELANAGETC_A MOUTHAGETC_A OVARYAGETC_A PANCRAGETC_A PROSTAGETC_A SKNMAGETC_A SKNNMAGETC_A SKNDKAGETC_A STOMAAGETC_A THROAAGETC_A THYROAGETC_A UTERUAGETC_A HDNCKAGETC_A COLRCAGETC_A OTHERAGETC_A
# Cancers df
dfCancer <- adult22 %>%
filter(NUMCAN_A > 0 & NUMCAN_A < 7)
ggplot(dfCancer, aes(x = NUMCAN_A)) +
geom_bar()
ggplot(dfCancer, aes(NUMCAN_A, LSATIS4_A, colour=NUMCAN_A)) +
geom_line() +
geom_point()
ggplot(dfCancer, aes(NUMCAN_A, AGEP_A, colour=NUMCAN_A)) +
geom_line() +
geom_point()
plot(dfCancer$AGEP_A, dfCancer$NUMCAN_A)
abline(lm(dfCancer$NUMCAN_A ~ dfCancer$AGEP_A), col = "red", lwd = 3)
# Age CI of those with cancer
resultCAN <- t.test(dfCancer$AGEP_A)
confidence_intervalCAN <- resultCAN$conf.int
confidence_intervalCAN## [1] 68.11267 68.97246
## attr(,"conf.level")
## [1] 0.95mean(dfCancer$AGEP_A)## [1] 68.54257# Age CI of those without cancer
dfNoCancer <- adult22 %>%
filter(NUMCAN_A == 0)
# Age CI of those with no cancer
resultNOCAN <- t.test(dfNoCancer$AGEP_A)
confidence_intervalNONE <- resultNOCAN$conf.int
confidence_intervalNONE## [1] 50.60551 51.06352
## attr(,"conf.level")
## [1] 0.95mean(dfNoCancer$AGEP_A)## [1] 50.83452# Age CI of all
result <- t.test(adult22$AGEP_A)
confidence_interval <- result$conf.int
confidence_interval## [1] 52.83239 53.26945
## attr(,"conf.level")
## [1] 0.95mean(adult22$AGEP_A)## [1] 53.05092