Introduction

Preliminaries

# Data for live-coding exercies
# (cntrl = control = ambient sounds)
# (dist = disturbed = sound disturbance)
#
# 6 rows of data
# telomere length for control (in kilobases)
# telomere length for sound disturbance (in kilobases)
# corticosterone  concentration control
# corticosterone concentration distrubrance

## How to enter these data into vectors?

# Task 1) The lines of data below need to be formatted for loading into vectors
# name them as follows and load the data into vectors.
# "telos.cntrl"
# "telos.dist"
# "cort.cntrl"
# "cort.dist
# "trt.cntrl"
# "trt.dist

telos.cntrl <- c(1.202, 1.135, 1.116, 1.339, 0.948, 1.194, 1.179)
telos.dist <- c(0.829, 1.121, 1.128, 1.002, 0.866, 1.087, 0.935, 0.929, 1.012)
cort.cntrl <- c(5.93,  10.45, 1.84,  8.89,  6.32,  1.95,  1.47)
cort.dist <- c(7.38,  0.85,  4.76,  7.26,  5.39,  1.36, 18.35,  5.73,  4.22)
trt.cntrl <- c("cntrl", "cntrl", "cntrl", "cntrl", "cntrl", "cntrl", "cntrl")
trt.dist <- c("dist",  "dist", "dist",  "dist",  "dist",  "dist",  "dist",  "dist",  "dist")
telos <- c(telos.cntrl, telos.dist)
trt <- c(trt.cntrl, trt.dist)

# Task 2) How do you calculate the mean for the telomeres? Standard deviation? Sample size?

#Mean
mean.telo.cntrl <- mean(telos.cntrl)
mean.telo.dist <- mean(telos.dist)
print(mean.telo.cntrl)
## [1] 1.159
print(mean.telo.dist)
## [1] 0.9898889
#Standard Deviation
sd.telo.cntrl <- sd(telos.cntrl)
sd.telo.dist <- sd(telos.dist)
print(sd.telo.cntrl)
## [1] 0.1174876
print(sd.telo.cntrl)
## [1] 0.1174876
#Sample Size
n.telo.cntrl <- length(telos.cntrl)
n.telo.dist <- length(telos.dist)


#Standard Error
  #Standard Error = SD/sqrt(N)

se.telo.cntrl <- sd.telo.cntrl/sqrt(n.telo.cntrl)
se.telo.dist <- sd.telo.dist/sqrt(n.telo.dist)

#Confidence Intervals
  #One CI 'Arm' = 2*SE
upper.arm.cntrl <- 2* se.telo.cntrl + mean.telo.cntrl
lower.arm.cntrl <- mean.telo.cntrl - 2*se.telo.cntrl

upper.arm.dist <- 2*se.telo.dist + mean.telo.dist
lower.arm.dist <- mean.telo.dist - 2*se.telo.dist

#Use the lowbrow_errbars function

#lowbrow_errbars(means = c(mean.telo.cntrl, mean.telo.dist),
#                SEs   = c(se.telo.cntrl, se.telo.dist),
#                categories = c("Control", "Disturbed"),
#                x.axis.label = "Experimental Treatment",
#                y.axis.label = "Telomere Length (kb)")

#Create A Dataframe

df <- data.frame(telos, trt)
df

T-Test

#T test
  #Telomere length depends on experimental treatment
  #y = x
  #telo ~ treatment

t.test(telos ~ trt, data = df)
## 
##  Welch Two Sample t-test
## 
## data:  telos by trt
## t = 2.9522, df = 12.485, p-value = 0.01165
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.04483683 0.29338539
## sample estimates:
## mean in group cntrl  mean in group dist 
##           1.1590000           0.9898889