Assignment 2 LIS4273

 


Assignment 2: myMean

This assignment featured a relatively simple R function that does a few things in succession: Declares a data set, Declares a function that manipulates or points out a certain trend in the data (in this case we are taking a statistical mean), and finally, declares a result variable to the function and prints the resultant mean. My code is as follows


#Declare data set and define as assignment2

assignment2 <- c(6,18,14,22,27,17,22,20,22)

#Write function for the data set, which involves taking the mean of the data set

myMean <- function(assignment2) {return(sum(assignment2)/length(assignment2))}

# Declare result variable to be presented as the mean of assignment2 (line 6) Resultant Mean of the assignment2 data set (line 8) using the print function on the newly defined 'result.mean' variable

result.myMean <- mean(assignment2)

#Result

print(result.myMean)

[1] 18.66666666667


My resultant mean for this data set was 18.66666666667


Comments

Post a Comment

Popular posts from this blog

R Package: pfStat

Image
 Final Project: pfStat, a personal finance mini-suite in R GitHub URL: https://github.com/tahousex/pfStat What is pfStat? This package is entitled pfStat, and is a small package with a few functions dedicated to helping a user calculate different personal finance metrics. The functions include the ability to derive Returns on Interest, Tax Rates based on Income, Taxable Income and Home Equity, and the purpose behind choosing these kinds of metrics was to emphasize the importance of knowing where one puts their wallet. It goes without being entirely sanctimonious that the dollar does not go as far as it once did, and knowing where one stands amongst their own finances and parameters in the financial world will assist them in making more calculated decisions.  The Functions: The taxrate() income receives input from the user income and state tax percentage represented as a decimal percentage (0-1)   and gives the user output of a dollar amount of the taxes owed based on...
Read more

Module 7 Assignment

Module 7 Assignment  R Code: # Module 7 Assignment #1.1 #First define the data set required, I/O values x <- c(16, 17, 13, 18, 12, 14, 19, 11, 11, 10) y <- c(63, 81, 56, 91, 47, 57, 76, 72, 62, 48) #Then we must create the relationship (correlation) cor(x,y) #This value 0.728 that is in the console tells us a few things #This relationship is correlated and is increasing linearly #I may choose to plot these findings using plot() plot(x,y) #See the lower righthand window for a composite plot for this data, as the regression trednline would positively increase through the points #1.2 #This settion of code will get the value for the coefficients of the linear regression for this data Regression_model=lm(y~x) Regression_model$coefficients #2.1 #This problem will go over the aquisition of similar values with a different data set discharge <- c(3.600,1.800,3.333,2.283,4.533,2.883) waiting <- c(79,54,74,62,85,55) plot(discharge,waiting) #2.1 Regression_model_1=lm(waiting~discha...
Read more

Visual Analytics Final Project: Portuguese Wine and Which Components Correlate to Higher Quality

Image
  Visual Analytics Final Project Tyler House 2023-04-27 library (ggplot2) library (ggcorrplot) library (ggmap) library (ggraph) library (ggpubr) library (tidyverse) library (ggthemes) library (dplyr) library (readr) library (tinytex) library (latexpdf) library (stats) winedata <- read_csv ( "C: \\ Users \\ tyler \\ Desktop \\ Spring 2023 \\ Visual Analytics \\ Final Project \\ winequality-red.csv" ) str (winedata) ## spc_tbl_ [1,599 × 12] (S3: spec_tbl_df/tbl_df/tbl/data.frame) ##   $ fixed acidity        : num [1:1599] 7.4 7.8 7.8 11.2 7.4 7.4 7.9 7.3 7.8 7.5 ... ##   $ volatile acidity     : num [1:1599] 0.7 0.88 0.76 0.28 0.7 0.66 0.6 0.65 0.58 0.5 ... ##   $ citric acid          : num [1:1599] 0 0 0.04 0.56 0 0 0.06 0 0.02 0.36 ... ##   $ residual sugar       : num [1:1599] 1.9 2.6 2.3 1.9 1.9 1.8 1.6 1.2 2 6.1 ... ## ...
Read more