Lab 3
2024-11-18
packages <- c("tidyverse", "modelsummary", "forcats", "RColorBrewer",
"fst", "viridis", "knitr", "rmarkdown", "ggridges", "viridis", "questionr", "flextable", "infer")
new_packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new_packages)) install.packages(new_packages)
lapply(packages, library, character.only = TRUE)## Warning: package 'ggplot2' was built under R version 4.3.3## Warning: package 'dplyr' was built under R version 4.3.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## [26] "datasets" "methods" "base"QUESTION 1
In your own words, very briefly describe the dataset (what type of survey it is) you have chosen and why (max 200 words).
setwd("C:/Users/Erika/Desktop/SOC_202_YAY"
)
library(readr)
DoctorVisits <- read_csv("DoctorVisits.csv")## Rows: 5190 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): gender, private, freepoor, freerepat, nchronic, lchronic
## dbl (7): rownames, visits, age, income, illness, reduced, health
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.print(DoctorVisits)## # A tibble: 5,190 × 13
## rownames visits gender age income illness reduced health private freepoor
## <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
## 1 1 1 female 0.19 0.55 1 4 1 yes no
## 2 2 1 female 0.19 0.45 1 2 1 yes no
## 3 3 1 male 0.19 0.9 3 0 0 no no
## 4 4 1 male 0.19 0.15 1 0 0 no no
## 5 5 1 male 0.19 0.45 2 5 1 no no
## 6 6 1 female 0.19 0.35 5 1 9 no no
## 7 7 1 female 0.19 0.55 4 0 2 no no
## 8 8 1 female 0.19 0.15 3 0 6 no no
## 9 9 1 female 0.19 0.65 2 0 5 yes no
## 10 10 1 male 0.19 0.15 1 0 0 yes no
## # ℹ 5,180 more rows
## # ℹ 3 more variables: freerepat <chr>, nchronic <chr>, lchronic <chr>view(DoctorVisits)I chose a dataset titled “Australian Health Service Utilization Data”. This Cross-section data is derived from the Australian Health Survey 1977-78 which contains information on the number of consultations with a doctor or specialist in the two-week period before an interview The sample consists of 5190 individuals over 18 years old who answered questions related to doctor visits. The explanatory variables in the model contain 12 variables. I chose it because I found the focus of the research interesting to explore. The study’s primary focus is on the relationship between insurance levels and health care use, that is whether a high insurance level caused individuals to ‘consume’ more health care services, and on the role of income.
visits: Number of doctor visits in past 2 weeks.
Gender: Factor indicating gender.
Age:Age in years divided by 100.
Income: Annual income in tens of thousands of dollars.
Illness: Number of illnesses in past 2 weeks.
Reduced: Number of days of reduced activity in past 2 weeks due to illness or injury.
Health: General health questionnaire score using Goldberg’s method.
Private: Factor. Does the individual have private health insurance?
Freepoor: Factor. Does the individual have free government health insurance due to low income?
Freerepat: Factor. Does the individual have free government health insurance due to old age, disability or veteran status? - Basic level of insurance coverage provided free of charge to select individuals on the basis of low income , pensioner or repatriation status.
Nchronic: Is there a chronic condition not limiting activity?
Lchronic: Is there a chronic condition limiting activity?
QUESTION 2
Univariate relationships: Provide a summary table of the variables you have and describe the distributions. Maximum 2 table. Maximum 2 figures. Max 150 words explanation of the distributions.
summary(DoctorVisits)## rownames visits gender age
## Min. : 1 Min. :0.0000 Length:5190 Min. :0.1900
## 1st Qu.:1298 1st Qu.:0.0000 Class :character 1st Qu.:0.2200
## Median :2596 Median :0.0000 Mode :character Median :0.3200
## Mean :2596 Mean :0.3017 Mean :0.4064
## 3rd Qu.:3893 3rd Qu.:0.0000 3rd Qu.:0.6200
## Max. :5190 Max. :9.0000 Max. :0.7200
## income illness reduced health
## Min. :0.0000 Min. :0.000 Min. : 0.0000 Min. : 0.000
## 1st Qu.:0.2500 1st Qu.:0.000 1st Qu.: 0.0000 1st Qu.: 0.000
## Median :0.5500 Median :1.000 Median : 0.0000 Median : 0.000
## Mean :0.5832 Mean :1.432 Mean : 0.8619 Mean : 1.218
## 3rd Qu.:0.9000 3rd Qu.:2.000 3rd Qu.: 0.0000 3rd Qu.: 2.000
## Max. :1.5000 Max. :5.000 Max. :14.0000 Max. :12.000
## private freepoor freerepat nchronic
## Length:5190 Length:5190 Length:5190 Length:5190
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## lchronic
## Length:5190
## Class :character
## Mode :character
##
##
## str(DoctorVisits)## spc_tbl_ [5,190 × 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ rownames : num [1:5190] 1 2 3 4 5 6 7 8 9 10 ...
## $ visits : num [1:5190] 1 1 1 1 1 1 1 1 1 1 ...
## $ gender : chr [1:5190] "female" "female" "male" "male" ...
## $ age : num [1:5190] 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 0.19 ...
## $ income : num [1:5190] 0.55 0.45 0.9 0.15 0.45 0.35 0.55 0.15 0.65 0.15 ...
## $ illness : num [1:5190] 1 1 3 1 2 5 4 3 2 1 ...
## $ reduced : num [1:5190] 4 2 0 0 5 1 0 0 0 0 ...
## $ health : num [1:5190] 1 1 0 0 1 9 2 6 5 0 ...
## $ private : chr [1:5190] "yes" "yes" "no" "no" ...
## $ freepoor : chr [1:5190] "no" "no" "no" "no" ...
## $ freerepat: chr [1:5190] "no" "no" "no" "no" ...
## $ nchronic : chr [1:5190] "no" "no" "no" "no" ...
## $ lchronic : chr [1:5190] "no" "no" "no" "no" ...
## - attr(*, "spec")=
## .. cols(
## .. rownames = col_double(),
## .. visits = col_double(),
## .. gender = col_character(),
## .. age = col_double(),
## .. income = col_double(),
## .. illness = col_double(),
## .. reduced = col_double(),
## .. health = col_double(),
## .. private = col_character(),
## .. freepoor = col_character(),
## .. freerepat = col_character(),
## .. nchronic = col_character(),
## .. lchronic = col_character()
## .. )
## - attr(*, "problems")=<externalptr>dim(DoctorVisits)## [1] 5190 13summary(DoctorVisits)## rownames visits gender age
## Min. : 1 Min. :0.0000 Length:5190 Min. :0.1900
## 1st Qu.:1298 1st Qu.:0.0000 Class :character 1st Qu.:0.2200
## Median :2596 Median :0.0000 Mode :character Median :0.3200
## Mean :2596 Mean :0.3017 Mean :0.4064
## 3rd Qu.:3893 3rd Qu.:0.0000 3rd Qu.:0.6200
## Max. :5190 Max. :9.0000 Max. :0.7200
## income illness reduced health
## Min. :0.0000 Min. :0.000 Min. : 0.0000 Min. : 0.000
## 1st Qu.:0.2500 1st Qu.:0.000 1st Qu.: 0.0000 1st Qu.: 0.000
## Median :0.5500 Median :1.000 Median : 0.0000 Median : 0.000
## Mean :0.5832 Mean :1.432 Mean : 0.8619 Mean : 1.218
## 3rd Qu.:0.9000 3rd Qu.:2.000 3rd Qu.: 0.0000 3rd Qu.: 2.000
## Max. :1.5000 Max. :5.000 Max. :14.0000 Max. :12.000
## private freepoor freerepat nchronic
## Length:5190 Length:5190 Length:5190 Length:5190
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## lchronic
## Length:5190
## Class :character
## Mode :character
##
##
## ggplot(DoctorVisits, aes(x = age)) +
geom_histogram(fill = "lightblue", color = "black") +
labs(title = "Distribution of Age (Years)", x = "Age (Years)", y = "Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
The histogram of age reveals a right-skewed distribution, suggesting a concentration of younger individuals in the dataset. The majority of individuals fall within the age range of 0.2 to 0.3, with a smaller proportion in the older age groups. This distribution indicates a young demographic in the analyzed population (population is likely younger, with fewer older individuals represented).
ggplot(DoctorVisits, aes(x = visits)) +
geom_histogram(fill = "skyblue", color = "black") +
labs(title = "Distribution of Doctor Visits", x = "Number of Visits", y = "Frequency")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(DoctorVisits, aes(x = gender, y = age, fill = gender)) +
geom_boxplot() +
labs(title = "Age Distribution by Gender", x = "Gender", y = "Age") +
scale_fill_manual(values = c("pink", "lightblue"))
The boxplot illustrates the age distribution by gender. We can observe that the median age for females is slightly higher than for males. The box for females is also slightly wider, indicating a larger spread in their ages compared to males. Both genders exhibit a similar range, with a few outliers present at the higher end. Overall, the plot suggests a slight difference in age distribution between genders, with females tending to be slightly older on average.
QUESTION 3
Bivariate relationship: Identify your dependent variable (outcome) and 2-3 independent variables. Explore the relationship between your dependent variable and each of your independent variables using appropriate methods and visualizations. Use a maximum of 1 table and/or 1 figure for each relationship explored. Max 100 words explanation of each relationship.
ggplot(DoctorVisits, aes(x = age, y = visits)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "Doctor Visits by Age", x = "Age", y = "Number of Visits")## `geom_smooth()` using formula = 'y ~ x'
ggplot(DoctorVisits, aes(x = income, y = visits)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
labs(title = "Doctor Visits by Income", x = "Income", y = "Number of Visits")## `geom_smooth()` using formula = 'y ~ x'
The scatter plot shows a weak positive relationship between age and the number of doctor visits. As age increases, there is a slight tendency for the number of visits to increase as well. However, the relationship is not very strong, indicating that other factors may also influence the number of doctor visits.
The scatter plot illustrates a weak positive relationship between income and the number of doctor visits. As income increases, there is a slight tendency for the number of visits to increase as well. However, the relationship is not very strong, indicating that other factors may also influence the number of doctor visits. It’s possible that individuals with higher incomes may have better access to healthcare, leading to more frequent visits, or they may have more complex health conditions requiring more medical attention.
QUESTION 4
- Clearly identify the dependent and independent variables. You choose how many independent variables you want to include
Dependent Variable: visits (number of doctor visits)
Independent Variables: age, income, illness, reduced, health, gender
- Create a DAG diagram of the relationship you expect between the variables and briefly explain it.
The DAG illustrates causal relationships between the variables. For instance, age is expected to directly influence the number of doctor visits and indirectly through health. Higher age might lead to more health issues, resulting in more visits. Similarly, income might directly affect visits and indirectly through reduced access to healthcare or better health insurance. Illness is expected to directly impact visits. Reduced access to healthcare might limit visits, and health status would likely influence the number of visits. Gender might influence access to healthcare and health behaviors, thus indirectly affecting visits.
library(tidyverse)
library(ggdag)## Warning: package 'ggdag' was built under R version 4.3.3##
## Attaching package: 'ggdag'## The following object is masked from 'package:stats':
##
## filterlibrary(dagitty)## Warning: package 'dagitty' was built under R version 4.3.3simple_dag <- dagify(
Visits ~ Health + Age + Income + Illness + Reduced + Gender,
Health ~ Age + Income + Illness,
Income ~ Health + Reduced,
Illness ~ Age + Health,
Reduced ~ Income + Health,
Gender ~ Age + Income,
outcome = "Visits"
)
set.seed(1234)
ggdag_status(simple_dag)
- Identify the type of regression (linear or logistic) and write out the regression formula, identifying each component of the formula in a brief text.
Type of Regression: Linear Regression
Formula:
visits = β₀ + β₁age + β₂income + β₃illness + β₄reduced + β₅health + β₆gender + ε
Components:
visits: The dependent variable representing the number of doctor visits. β₀: The intercept term, representing the expected number of visits when all independent variables are zero.
β₁, β₂, …, β₆: Regression coefficients representing the change in the number of visits associated with a one-unit increase in each independent variable, holding other variables constant.
age, income, illness, reduced, health, and gender: The independent variables.
ε: The error term, representing the unexplained variation in the number of visits.
QUESTION 5
Run the identified regression model in R. Provide a brief interpretation of the cofficients. A regression table. Interpretation of the coefficients and confidence interval. Write a short summary (Max 250 words) of the findings from the regression.
library(tidyverse)
model <- lm(visits ~ age + income + illness + reduced + health + gender, data = DoctorVisits)
summary(model)##
## Call:
## lm(formula = visits ~ age + income + illness + reduced + health +
## gender, data = DoctorVisits)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1188 -0.2592 -0.1442 -0.0437 7.0590
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.058517 0.034578 1.692 0.09064 .
## age 0.210117 0.052486 4.003 6.33e-05 ***
## income -0.037128 0.028731 -1.292 0.19633
## illness 0.062424 0.007941 7.860 4.62e-15 ***
## reduced 0.103850 0.003616 28.721 < 2e-16 ***
## health 0.016918 0.005152 3.284 0.00103 **
## gendermale -0.041744 0.021225 -1.967 0.04926 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7141 on 5183 degrees of freedom
## Multiple R-squared: 0.2004, Adjusted R-squared: 0.1995
## F-statistic: 216.5 on 6 and 5183 DF, p-value: < 2.2e-16Coefficient Interpretation:
The intercept is 0.058157 and is statistically significant. This means that when all independent variables are zero, the predicted number of doctor visits is 0.058157 (p<0.09064, 95% CI: 0.034578, 1.692).
The coefficient for age is 0.210117 and is statistically significant. This indicates that for every one-unit increase in age, the number of doctor visits is expected to increase by 0.0210117, holding all other variables constant (p<6.33e-05, 95% CI: 0.052486, 4.003). The small p-value indicates that it is highly unlikely to observe a strong relationship between age and doctor visits by chance.
The coefficient for income is -0.037128 and is statistically significant. This suggests that for every one-unit increase in income, the number of doctor visits is expected to decrease by 0.037128, holding other variables constant (p<0.19633, 95% CI: 0.028731, -1.292).
The coefficient for illness is 0.062424 and is highly statistically significant. This implies that for every one-unit increase in illness, the number of doctor visits is expected to increase by 0.062424, holding other variables constant (p<4.62e-15, 95% CI: 0.007941, 7.860). The extremely small p-value indicates a very high level of confidence rejecting null hypothesis. This means that there is strong evidence to suggest that the variable “illness” has a significant impact on the number of doctor visits.
The coefficient for reduced is 0.103850 and is highly statistically significant. This suggests that for every one-unit increase in reduced access to healthcare, the number of doctor visits is expected to increase by 0.103850, holding other variables constant (p<2e-16, 95% CI: 0.003616, 28.721). We can reject null hypothesis as there is a strong relationship between the “reduced” variable and the number of doctor visits.
The coefficient for health is 0.016918 and is highly statistically significant. This indicates that for every one-unit increase in health, the number of doctor visits is expected to increase by 0.016918, holding other variables constant(p<0.00103, 95% CI: 0.005152, 3.284).
The coefficient for gender (male) is -0.041744 and is statistically significant. This suggests that males are expected to have 0.041744 fewer doctor visits than females, on average, holding all other variables constant (p<0.04926, 95% CI: 0.021225, -1.967).
Findings:
The regression model suggests that age, income, illness, reduced access to healthcare, health status, and gender are all significantly associated with the number of doctor visits. Older individuals, those with higher incomes, and those with poorer health tend to have more visits. Reduced access to healthcare is surprisingly associated with increased visits, possibly due to delayed care leading to more urgent needs. Females tend to have more visits than males, potentially due to differences in health behaviors or healthcare-seeking habits. However, it’s important to note that these associations are not necessarily causal relationships.