Visualization 2

library(tidyverse)
library(rio)
library(Hmisc)
library(jtools)
library(table1)
library(plotly)

setwd("C:/Users/yoonh/OneDrive - The University of Chicago/Courses/Winter 2025/SOCI 30617 - Intro to Applied Stats and Data Science/Assignments/Assignment 2/Data")
gss <- import("GSS2018cleaned.rdata")

gss$Female <- ifelse(gss$Sex=="Female", 1, 0) 
gss$Black <- ifelse(gss$Race=="Black", 1, 0)
model5viz2 <- lm(OccPres ~ DadSES + Black + Female + Age + FamIncome10k, data=gss)
model5viz2

## 
## Call:
## lm(formula = OccPres ~ DadSES + Black + Female + Age + FamIncome10k, 
##     data = gss)
## 
## Coefficients:
##  (Intercept)        DadSES         Black        Female           Age  
##     31.63198       0.12324      -0.91260       0.23434       0.08694  
## FamIncome10k  
##      1.15957

new_data_3d <- expand.grid(
  DadSES = seq(min(gss$DadSES, na.rm = TRUE), max(gss$DadSES, na.rm = TRUE), length.out = 100),
  Age = seq(min(gss$Age, na.rm = TRUE), max(gss$Age, na.rm = TRUE), length.out = 100),
  Black = mean(gss$Black, na.rm = TRUE),
  Female = mean(gss$Female, na.rm = TRUE), 
  FamIncome10k = mean(gss$FamIncome10k, na.rm = TRUE)
)

new_data_3d$Predicted_OccPres <- predict(model5viz2, newdata = new_data_3d)

plot_ly(new_data_3d, x = ~DadSES, y = ~Age, z = ~Predicted_OccPres, type = "scatter3d", mode = "markers")