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library("dlstats")
library("ggplot2")

pack <- cran_stats(c("metafor", "meta", "rmeta", "psychmeta"))

if (!is.null(pack)) {
  print(head(pack))
  ggplot(pack, aes(end, downloads, group=package, color=package)) +
    geom_line() + 
    geom_point() +

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

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

svb_trend <- europepmc::epmc_hits_trend(query = "sympathovagal balance", 
                                       period = 2008:2021)

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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)

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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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> 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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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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> summary(aov(happy_o ~drugcond*emocond,data=h_dat)) # Interaction effect on happiness

                 Df Sum Sq Mean Sq F value   Pr(>F)    
drugcond          1   1.70    1.70   1.436   0.2352    
emocond           1  34.21   34.21  28.950 1.16e-06 ***
drugcond:emocond  1   4.40    4.40   3.726   0.0581 .  
Residuals        63  74.44    1.18                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
49 observations deleted due to missingness

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

h_dat <- read_csv("help.csv")

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

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

ot_trend <- europepmc::epmc_hits_trend(query = "oxytocin", 
                                       period = 2008:2018)