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# Load package

library(pwr)

# Perform power analysis

pwr.t2n.test(sig.level = 0.05, n1= 32,n2= 23, power = 0.8,
             alternative="two.sided")

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# Load TOSTER package

library(TOSTER)

# Perform equivalence test

TOSTtwo(m1=37.33,m2=39.65 ,sd1=13.40,sd2=15.91,n1=23,n2=32,
        low_eqbound_d=-0.26,high_eqbound_d=0.26,
        plot = TRUE)

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# Write function

t.test2 <- function(m1,m2,s1,s2,n1,n2,m0=0,equal.variance=FALSE)
{
  if( equal.variance==FALSE ) 
  {
    se <- sqrt( (s1^2/n1) + (s2^2/n2) )
    # welch-satterthwaite df
    df <- ( (s1^2/n1 + s2^2/n2)^2 )/( (s1^2/n1)^2/(n1-1) + (s2^2/n2)^2/(n2-1) )
  } else

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### Contour-enhanced funnel plots using metafor ###

# Load metafor package

library("metafor")

# Load dataset

dat <- get(data(dat.molloy2014)) 

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library(synthpop) 
library(tidyverse) 
library(cowplot)

h_dat <- read_csv("help.csv")

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h_dat_s <- syn(h_dat, m = 1, seed = 1969)

compare(h_dat_s, h_dat,
        stat = "counts", # Selecting counts instead of percentage
        cols = c("#62B6CB", "#1B4965")) # Changing colours

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> m1 = lm(happy_o ~ 1 + drugcond + emocond, 
        data = h_dat) # Model 1 with main effects only

> m2 = lm(happy_o ~ 1 + drugcond + emocond + 
        drugcond:emocond, data = h_dat) # Model with main effects and interaction

> summary(m2) # Summary of model 2 (with same p-value as original ANOVA for the interaction)

Call:
lm(formula = happy_o ~ 1 + drugcond + emocond + drugcond:emocond, 

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> help_glm_h <- lm(happy_o ~ 1 + drugcond + 
                 emocond + drugcond:emocond,
               data = h_dat) # Model from observed data
               
> help_syn_glm_h <- lm.synds(happy_o ~ 1 + drugcond + 
                           emocond + drugcond:emocond,
                         data = h_dat_s) # Model from synthesized data


> compare(help_syn_glm_h, h_dat) # A comparison of the models

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library(shiny)
library(synthpop)
library(DT)

ui <- fluidPage(
  titlePanel("Creating synthetic data"),
  sidebarLayout(
    sidebarPanel(
      
      p("This is an application that creates default data synthesis using the 'synthpop' package. Upload some data and inspect the synthesized data. Your file must be in csv format to upload."),

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install.packages("europepmc")
install.packages("cowplot")
install.packages("tidyverse")

library(europepmc)
library(cowplot)
library(tidyverse)

hrv_trend <- europepmc::epmc_hits_trend(query = "heart rate variability", 
                                       period = 1978:2018)