Homework_09

Functions and outputs

# David C. Green
# 2022-03-23
# Homework 9

# Functions

# ======================================================================
# FUNCTION sim_data_norm
# Purpose: simulates data for a normal distribution
# Input: size, mean, sd
# Output: data vector
# --------------------------------------------------------------------
sim_data_norm <- function(size = 100, mean = 50, sd  = 2) {
  dat <- rnorm(n = size, mean = mean, sd = sd)
}

# ======================================================================
# FUNCTION create_dataframe
# Purpose: puts data into a data frame
# Input: group 1 to 4
# Output: dataframe
# --------------------------------------------------------------------
create_dataframe<- function(g1 = NULL, g2 = NULL, g3 = NULL, g4 = NULL) {
  if(is.null(g1) & is.null(g2) & is.null(g3) & is.null(g4)) {
    message('No data has been entered') 
    nd <- TRUE
  } else
    if (is.null(g2) & is.null(g3) & is.null(g4)) {
      data1 <- data.frame(g1)
      nd <- FALSE
    } else
      if(is.null(g3) & is.null(g4)) {
        data1 <- data.frame(g1, g2)
        nd <- FALSE
      } else
        if(is.null(g4)) {
          data1 <- data.frame(g1, g2, g3)
          nd <- FALSE
        } else 
          data1 <- data.frame(g1, g2, g3, g4)
          nd <- FALSE
  return(data1)
}

# ======================================================================
# FUNCTION analyze_data
# Purpose:
# Input:
# Output:
# --------------------------------------------------------------------
analyze_data <- function(data = data.frame(rnorm(100), rnorm(100))) {
  t_test <- t.test(data[1], data[2])
  print(t_test)
  boxplot(data)
}

# ---------------------------------------------------------------------
# Body

control <- sim_data_norm(28, 17, 2)
latd <- sim_data_norm(28, 7, 2)
df <- create_dataframe(control, latd)
analyze_data(df)

## 
##  Welch Two Sample t-test
## 
## data:  data[1] and data[2]
## t = 19.891, df = 50.579, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##   9.432522 11.550788
## sample estimates:
## mean of x mean of y 
## 17.644871  7.153216

Reworked Function

# David C. Green
# 2022-03-23
# Homework 9
# ---------------------------------------------------------------------

# Functions

# ======================================================================
# FUNCTION sim_data_pois
# Purpose: simulates data for a poisson distribution
# Input: size, lambda
# Output: data vector
# --------------------------------------------------------------------
sim_data_norm <- function(size = 1:100, lambda = 3) {
  dat <- dpois(x =  size, lambda = lambda)
}

# ======================================================================
# FUNCTION create_dataframe
# Purpose: puts data into a data frame
# Input: group 1 to 4
# Output: dataframe
# --------------------------------------------------------------------
create_dataframe<- function(g1 = NULL, g2 = NULL, g3 = NULL, g4 = NULL) {
  if(is.null(g1) & is.null(g2) & is.null(g3) & is.null(g4)) {
    message('No data has been entered') 
    nd <- TRUE
  } else
    if (is.null(g2) & is.null(g3) & is.null(g4)) {
      data1 <- data.frame(g1)
      nd <- FALSE
    } else
      if(is.null(g3) & is.null(g4)) {
        data1 <- data.frame(g1, g2)
        nd <- FALSE
      } else
        if(is.null(g4)) {
          data1 <- data.frame(g1, g2, g3)
          nd <- FALSE
        } else 
          data1 <- data.frame(g1, g2, g3, g4)
          nd <- FALSE
  return(data1)
}

# ======================================================================
# FUNCTION analyze_data
# Purpose:
# Input:
# Output:
# --------------------------------------------------------------------
analyze_data <- function(data = data.frame(rnorm(100), rnorm(100))) {
  t_test <- t.test(data[1], data[2])
  print(t_test)
  boxplot(data)
}

# ---------------------------------------------------------------------
# Body

control <- sim_data_norm(1:40, 3)
latd <- sim_data_norm(1:40, 1)
df <- create_dataframe(control, latd)
analyze_data(df)

## 
##  Welch Two Sample t-test
## 
## data:  data[1] and data[2]
## t = 0.56893, df = 77.611, p-value = 0.571
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.01987749  0.03578211
## sample estimates:
##  mean of x  mean of y 
## 0.02375532 0.01580301