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Lunchtime Basics-Basic Statistics using R/Basic Stats with R.R

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+###### CU Data Week ######
+
+# Load Data
+bweight <- read.csv("~/Downloads/bweight.csv")
+birthwgt <- read.csv("~/Downloads/birthwgt.csv")
+cars <- read.csv("~/Downloads/cars.csv")
+class <- read.csv("~/Downloads/class.csv")
+
+########## Hypothesis 1 ########################################################
+# Birthweight and Smoking 
+################################################################################
+
+# Summarize the data 
+library(tidyverse)
+
+# Difference in weights 
+bweight %>% group_by(MomSmoke) %>% summarise(Mean = mean(Weight), SD = sd(Weight))
+# 1 = Smoking; 0  = No Smoking 
+
+# Other categories 
+library(table1)
+
+table1(~ MomWtGain + factor(MomEdLevel) + factor(Boy) | factor(MomSmoke), data = bweight)
+
+
+########## Test 1: Relationship between Low birthweight and Maternal Smoking ###
+table1(~ LowBirthWgt | Smoking, data = birthwgt)
+
+# We have some missing data, let's remove it 
+birthwgt <- birthwgt %>% filter(Smoking != "")
+
+table(birthwgt$LowBirthWgt, birthwgt$Smoking)
+
+# Chi-Square test
+chisq.test(birthwgt$LowBirthWgt, birthwgt$Smoking, correct = F)
+# We can say that low birth weigh and smoking are significantly associated 
+
+############## Test 2: Relationship between birthweight and Maternal Smoking ###
+
+# How does the data look?
+boxplot(bweight$Weight ~ bweight$MomSmoke)
+
+# Any missingness?
+summary(bweight) # No!
+
+# What about the distribution?
+
+## qq plot
+qqnorm(bweight %>% filter(MomSmoke == 1) %>% pull(Weight), pch = 1, frame = FALSE)
+qqline(bweight %>% filter(MomSmoke == 1) %>% pull(Weight), col = "steelblue", lwd =1)
+
+qqnorm(bweight %>% filter(MomSmoke == 0) %>% pull(Weight), pch = 1, frame = FALSE)
+qqline(bweight %>% filter(MomSmoke == 0) %>% pull(Weight), col = "steelblue", lwd =1)
+
+# Not happy about that QQ plot, let's go non-parametric
+
+wilcox.test(bweight$Weight ~ bweight$MomSmoke)
+# Significant difference in birth weight 
+
+# How is the t-test different?
+
+# We assume normality, we assume equal variance 
+var.test(bweight$Weight ~ bweight$MomSmoke)
+
+# By default, R will assume unequal variance
+t.test(bweight$Weight ~ bweight$MomSmoke, var.equal = F)
+
+
+########## Hypothesis 2 ########################################################
+# Cars and MPG
+################################################################################
+
+############## Test 1: Relationship between City MPG and Highway MPG ###
+
+hist(cars$MPG_Highway - cars$MPG_City)
+
+qqnorm(cars$MPG_Highway - cars$MPG_City, pch = 1, frame = FALSE)
+qqline(cars$MPG_Highway - cars$MPG_City, col = "steelblue", lwd =1)
+
+wilcox.test(cars$MPG_Highway, cars$MPG_City, paired = T)
+
+# These are the same 
+t.test(cars$MPG_Highway, cars$MPG_City, paired = T)
+t.test(cars$MPG_Highway - cars$MPG_City)
+summary(lm(cars$MPG_Highway - cars$MPG_City ~ 1))
+
+
+########## Hypothesis 3 ########################################################
+# Weight and Height
+################################################################################
+
+############## Test 1: Relationship between weight and height ###
+
+# Look at the data 
+plot(class$Weight ~ class$Height)
+
+# normal?
+hist(class$Weight) 
+hist(class$Height)
+
+# Fit a linear model 
+LM1 <- lm(class$Weight ~ class$Height)
+summary(LM1)
+
+# Check our model
+plot(LM1)
+hist(LM1$residuals)
+
+# same results
+cor.test(class$Weight, class$Height)
+
+# Not sure about the distribution?
+cor.test(class$Weight, class$Height, method = "spearman")
+
+