mydata <- read.csv2("Blood pressure.csv")

head(mydata)
##   Pressure_A Pressure_B
## 1         82         90
## 2         80         90
## 3         88         99
## 4        108        118
## 5        116        123
## 6        136        137

Description of the variables:

library(pastecs)
round(stat.desc(mydata), 2)
##              Pressure_A Pressure_B
## nbr.val           27.00      27.00
## nbr.null           0.00       0.00
## nbr.na             0.00       0.00
## min               63.00      76.00
## max              136.00     139.00
## range             73.00      63.00
## sum             2608.00    2754.00
## median            94.00      99.00
## mean              96.59     102.00
## SE.mean            3.46       3.06
## CI.mean.0.95       7.12       6.30
## var              323.87     253.62
## std.dev           18.00      15.93
## coef.var           0.19       0.16
mydata$Difference <- mydata$Pressure_A - mydata$Pressure_B
library(ggplot2)
ggplot(mydata, aes(x = Difference)) +
  geom_histogram(position = "identity", binwidth = 3, colour = "black") +
  ylab("Frequency") +
  xlab("Difference")

shapiro.test(mydata$Difference)
## 
##  Shapiro-Wilk normality test
## 
## data:  mydata$Difference
## W = 0.95383, p-value = 0.2652
t.test(mydata$Pressure_A, mydata$Pressure_B,
       paired = TRUE,
       alternative = "two.sided")
## 
##  Paired t-test
## 
## data:  mydata$Pressure_A and mydata$Pressure_B
## t = -4.6867, df = 26, p-value = 7.686e-05
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
##  -7.779049 -3.035766
## sample estimates:
## mean difference 
##       -5.407407
library(effectsize)
cohens_d(mydata$Difference)
## Cohen's d |         95% CI
## --------------------------
## -0.90     | [-1.34, -0.45]
interpret_cohens_d(0.90, rules = "sawilowsky2009")
## [1] "large"
## (Rules: sawilowsky2009)