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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) |
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