gill-wiehl_et_al_Figure_S1.Rmd

---
title: "Gill-Wiehl et al. Figure S1"
output: html_notebook
---

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```{r}
df_by_project_v2 <- read_excel("df_by_project_pub.xlsx")
df_project_overcrediting_v2 <- read_excel("df_project_overcrediting.xlsx")
#df_by_project_v2$protocol_id<-df_by_project_v2$`Row Labels`
df_regress=na.omit(df_by_project_v2)
df_regress <- left_join(df_project_overcrediting_v2,df_by_project_v2)
df_regress <- df_regress[,c('protocol_id','0','total_verified_credits','log_total_verified_credits')]
df_regress <- unique(df_regress)
df_regress <- df_regress[df_regress$total_verified_credits>0,]
df_regress$log_total_verified_credits <- as.numeric(df_regress$log_total_verified_credits)
df_regress$project_mean_overcrediting=df_regress$'0'
model=lm(df_regress$project_mean_overcrediting~df_regress$log_total_verified_credits, data=df_regress)
summary(model)
```

```{r}
library(ggplot2)
coeff<-coefficients(model)          
intercept<-coeff[1]
slope<- coeff[2]
ggp <- ggplot(df_regress, aes(log_total_verified_credits, project_mean_overcrediting,color=log_total_verified_credits)) +         
  geom_point()+theme_classic()+geom_abline(intercept = intercept, slope = slope, color="red", 
               linetype="dashed", size=1.5)+xlab('Log (Total verified credits)')+ylab('Mean amount of over-crediting (from Monte Carlo Method)')+geom_text(x=14, y=130, label="Linear-log fit: y ~ 70 - 3.2x")+geom_text(x=14, y=100, label="p - values: 0.2")+geom_text(x=14, y=70, label="Adjusted R-squared:0.01")
ggp
```



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