In tth40/FinalProject3: What the Package Does (One Line, Title Case)

FinalProject 3 is my package for use in Data Analysis with R.

library(FinalProject3)
head(BodyFat)

Vignette sample dataset is "BodyFat" which lists the percentage body fats of observations along with several predictor variables.

1st step: read in the data BodyFat is a dataset that has included percent body fat as the response variable and includes predictor variables such as age, weight, and waist. This dataset was "clean" before use, so before you use the functions in this package, please make sure that your data is formatted properly.

#BodyFat <- read.delim("~/Desktop/BIO 1 Cred/Data Analysis with R/FinalProject3/data/BodyFat.txt")

#BodyFat1<-read.delim("~/FinalProject3/data/BodyFat.txt")

Example 1: To find the mean and median of a given parameter, use the center function. This function will also compare the two and describe if there may be a skewed right or skewed left distribution

center(BodyFat$Weight) #parameter here is weight (continous)

Output shows that the mean is slightly greater than median.

example 2: Find partial R-squared

lm1<-lm(BodyFat$PctBF~BodyFat$Age+BodyFat$Weight)#model with age and weight

lm2<-lm(BodyFat$PctBF~BodyFat$Weight)#model without age
#generate summaries of each linear model to acquire r-squared values
summary(lm1)
summary(lm2)
r1<-0.4741 #r-squared value for the full model
r2<-0.3811 #r-squared value for the model with only weight
partialR2(r1, r2) #output is the partial r-squared value for age

[1] 0.1502666... this means that this is the amount of variation covered by age in the linear model.

example 3: creating basic plots and simple linear regression models

basic(BodyFat$PctBF, BodyFat$Age)#response variable first, following by predictor variable.

As seen in the output, a scatterplot of the response vs. predictor variable is produced. Similarly, the summary reveals that both the intercept and coefficient have significant values in this case. This is also further supported by the fact that neither confidence interval covers 0.

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

tth40/FinalProject3 documentation built on Dec. 31, 2020, 9:38 a.m.