### Instructions
#' Please read everything carefully.
#' Anything following '#' is a comment that is meant for a person to read. 
#' Everything else is syntax that can be ran in R.
#' Your task is to follow the steps below.
#' Throughout this document you will be adding in your own code to answer the questions.
#' You will then email this script to Garrett and relabel the script: LastName_FirstName_PSY392_HW1
#' I will run your code to see if it works. 
#' Some questions ask you to provide a written response, do so with comments using #


#' This homework needs to be sent to Garrett by 5:00 pm on Friday, September 6 ***


### Step 0 - Download this file
#' If you left-click on the Homework 1 link it will open a tab in your browser that displays text.
#' You can copy and paste the content of that page into a new R script.
#' Or you can right click on the Homework 1 link and click save as then select save as R script.

### Step 1 - Download the data
#' For this homework you will be working with the class data from STATLAB - Speeded Reaction Time.
#' You can download this data from the class website: http://www2.psych.purdue.edu/~gfrancis/Classes/PSY392/indexF19.html
#' Once at the website, navigate to the row containing August 26.
#' The data is located in the far right column of this row.
#' Download the data by clicking on '(class data)'.
#' If you are having trouble downloading the data you may need to right click on the class data link and select save link as.

### Step 2 - Set up working directories
#' Now that the data is downloaded it is important to set up a working directory.
#' You can do this however you like, but this is how I typically set up my folder structures for classes. i.e., a folder for PSY 392
#' Inside my PSY 392 folder I create a folder for homework - i.e., 'HW'
#' Place this R script and the data file into this homework subfolder within your class folder.
#' When you use this R script in RStudio you will need to tell it what the working directory is.
#' To do this click on Session within the top menu bar.
#' Then click on Set Working Directory => Choose Directory.
#' Then navigate to your HW subfolder that you created, which is where this Rscript and the data should now be.
#' The point of all of this is to tell R where necessary files are located on your computer so that R can work with them.


### Step 3 - Read in data
#' Now you can check if your working directory has been set correctly.
getwd() # what is my current working directory?

#' Is the data file in your current working directory
list.files() # what are the files in my current working directory?

# read in the csv file that contains the class data - be sure to specify the correct file!  
HW1data <-read.csv("SimpleRT.csv") # we are going to store the data into a variable called HW1data

### Step 4 - Getting to know the data
#' You should now see HW1data in the top right portion of RStudio in the Global Enivronment window
#' It tells you that there are 600 observations in 2 columns
names(HW1data) # what are the names of the column in my data?

#' Uh oh! We do not have a subject number column.
#' To fix this we can create a new column that creates an id number that repeats for the number of trials each participant completed
#' We know that each participant completed 50 trials. Thus, our subject id variable will repeat 50 times for each participant before changing.
#' We also know that we have 600 total observations. 600 total observations divided by 50 observations per person = 12 people.
#' So, we will create 12 subject numbers that each repeat 50 times.
#' We then create a new column in our data frame for subjectNumbers
HW1data$subjectNumber <- rep(1:12, each = 50) 
view(HW1data) # look at data and see that subject numbers repeat 50 times and then increases


### Step 5 - Descriptive statistics
#' If you are not sure how to do this in R then you should search for the information online.
#' An important part of programming is learning how to learning program on your own.
#' Of course, if you are struggling please contact us or come to office hours.

#' Compute the following statistcs for ReactionTime_ms:
#' Mean
#' Median
#' Mode
#' Standard deviation
#' Variance
#' Minimum value
#' Maximum value 
#' Range

### Step 6 - Data visualization
#' Try your best to create a visualization of the data
#' Here is a link to some simple plots in R - https://www.statmethods.net/graphs/index.html
#' Provide reasoning for the type of plot you chose
#' Describe your visualization

### Step 7 - Data tidying at the subject level
#' So, far we have looked at the data for every trial for every person.
#' However, we frequently care about how individual people or groups do.
#' Thus, we need to average across every trial for each participant to get the average reaction time for each person.

#' To do this I am going to use the tidyverse package
install.packages("tidyverse") # install the package we need -- you only need to do this once
library(tidyverse) # make sure the package is loaded.

#' The tidyverse uses the pipe symbol '%>%'
#' The shortcut for this is control + shift + m on pc or command + shift + m on mac

subDataHW1 <- HW1data %>%                         # create a new data frame from the HW1data
  group_by(subjectNumber) %>%                     # that groups the data by subject number
  summarize(meanSubRT_ms = mean(ReactionTime_ms)) # then summarize the data by calculating the mean RT for each subject

### Step 8
#' Who was the fastest subject?
#' Who was the slowest subject?
#' Create a visualization of the distribution of meanRTs at the subject level
#' Provide reasoning for the type of plot you chose
#' Describe your visualization