test_analysis.Rmd

---
title: "Data-wrangling"
author: "Matt"
date: "12/14/2018"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# data source

[https://osf.io/uzrge/](https://osf.io/uzrge/)

# Load data

```{r}
library(data.table)
stroop_data <- fread("data/stroopDataV2.csv")

```

# run analysis

```{r}
library(dplyr)
subject_means <- stroop_data %>%
                 filter(acc==1) %>%
                 group_by(pNo,condition,congruency) %>%
                 summarize(mean_rt = mean(rt,na.rm=TRUE))

subject_means$pNo<-as.factor(subject_means$pNo)
subject_means$condition<-as.factor(subject_means$condition)
subject_means$congruency<-as.factor(subject_means$congruency)

aov_out <- aov(mean_rt ~ condition*congruency +
                 Error(pNo/(condition*congruency)),
               subject_means)

summary(aov_out)

library(ggplot2)

plot_df <- subject_means %>%
            group_by(condition,congruency) %>%
            summarize(all_mean_rt = mean(mean_rt),
                      SEM = sd(mean_rt)/sqrt(length(mean_rt)))

ggplot(plot_df, aes(x=condition, y=all_mean_rt, group=congruency))+
  geom_line()+
  geom_point()+
  geom_errorbar(aes(ymin=all_mean_rt-SEM,
                    ymax=all_mean_rt+SEM),
                width=.2)+
  theme_classic()

```


# pre-processing

# are their weird rts
```{r}
hist(stroop_data$rt)
length(stroop_data$rt[stroop_data$rt>2000])
max(stroop_data$rt)
```

# include < 2000

```{r}
library(Crump)
subject_means <- stroop_data %>%
                 filter(acc==1,
                        rt <=10000) %>%
                 group_by(pNo,condition,congruency) %>%
                 summarize(mean_rt = mean(modified_recursive_moving(rt)$restricted))

subject_means$pNo<-as.factor(subject_means$pNo)
subject_means$condition<-as.factor(subject_means$condition)
subject_means$congruency<-as.factor(subject_means$congruency)

aov_out <- aov(mean_rt ~ condition*congruency +
                 Error(pNo/(condition*congruency)),
               subject_means)

summary(aov_out)

plot_df <- subject_means %>%
            group_by(condition,congruency) %>%
            summarize(all_mean_rt = mean(mean_rt),
                      SEM = sd(mean_rt)/sqrt(length(mean_rt)))

ggplot(plot_df, aes(x=condition, y=all_mean_rt, group=congruency))+
  geom_line()+
  geom_point()+
  geom_errorbar(aes(ymin=all_mean_rt-SEM,
                    ymax=all_mean_rt+SEM),
                width=.2)+
  theme_classic()

```