---
title: 'Lecture 3: Importing and handling data II'
author: "Dr Nicolaas Puts and Dr Lucas França"
date: "17 October 2022"
output: html_document
---

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

# Functions and pipe operators

Functions in R can be difficult to read. For example.

```{r}
a <- min(abs(count(x)))
```

Tidyverse packages introduced a novel approach to nest function:

```{r}
library(tidyverse)
```

```{r}
a <- x %>% 
  count() %>% 
  abs() %>% 
  min()
```

# Filtering data

First we load a dataset in a similar way we did last time:

```{r}
urlfile <- 'https://raw.githubusercontent.com/HeJasonL/BIG-TAC/main/TutorialData.csv' #this loads a csv file called TutorialData and puts it into urlfile

data <- read.csv(urlfile) %>% 
  as_tibble()
```


```{r}
data %>% 
  filter(Sex == 'M')
```

# Merging data (and filter)

First we need to create two datasets 

```{r}
sensory <- data %>% 
  select(Sub_ID, GABA, Sensory1)
```

```{r}
IDs <- data %>% 
  select(Sub_ID, Group, Age, Sex)
```

And we can merge those:

```{r}
new_data <- inner_join(IDs, sensory)
```

# Summarising data and descriptives

```{r}
summary(data)
```

We need to use factors in this case.

```{r}
data$Group <- as.factor(data$Group)
data$Sex <- as.factor(data$Sex)
```

```{r}
summary(data)
```

# The functions group_by() and summarise()

```{r}
data %>% 
  dplyr::group_by(Sex) %>% 
  dplyr::summarise(mean_GABA = mean(GABA))
```

```{r}
data %>% 
  dplyr::group_by(Sex) %>% 
  dplyr::summarise(mean_GABA = mean(GABA), 
                   sd_GABA = sd(GABA))
```

# The function mutate()

```{r}
data %>% 
  dplyr::mutate(sensory_norm = Sensory1/34)
```