week1_inclass_exercise_script.R

## Warning: the code below contains deliberate errors.
## Also it sometimes contains "???" which you need to replace with appropriate text.


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## first line of code is to clear R's memory
rm(list=ls())
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## First we load some required add-on package
## (you need to install these if you haven't already)
library(readr)
library(dplyr)
library(ggplot2)
library(skimr)
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## Now read in the data, using the read_csv() function.
## First we should assign, using the assignment arrow,
## the URL of the published version of the google sheet data into an object.
the_URL - "https://docs.google.com/spreadsheets/d/e/2PACX-1vQFgYX1QhF9-UXep22XmPow1ZK5nbFHix9nkQIa0DzqUhPtZRxH1HtY-hsno32zDiuIHiLb2Hvphk1L/pub?gid=1188775314&single=true&output=csv"
## then we use the read_csv function to read in the data from that URL
class_RTs <- read_cvs(the_URL)
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## DO NOT USE read.csv above!!!

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## Have a look at the data in R, does it look OK?
clas_RTs
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## Now we need to do some data wrangling (cleaning and tidying)
## Clean up the column / variable names:
## Must be very careful to get the next line right!!! Really important!!!
## Otherwise columns will have the wrong names, which would be very confusing
names(class_RTs) <- c("Timestamp", "ID", "Gender", "Pref_Reaction_time_1"
                      "Verbal_memory_score", "Number_memory_score",
                      "Visual_memory_score",
                      "Weight_kgs", "Handed", "Nonpref_Reaction_time_ave",
                      "Pref_Reaction_time_2", "Pref_Reaction_time_3", 
                      "Pref_Reaction_time_4", "Pref_Reaction_time_5",
                      "Pref_Reaction_time", "Random_number")
## check the variable names are what we just tried to set them to be
class_RTs
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## Check the variable types are correct
## (they should be in this case, but checking is a good habit.)
## Timestamp should be a character
## ID should be a character
## Gender should be a character
## Handed should be character
## The remaining variables should be numeric (<dbl> if fractional, <int> if whole numbers).
class_RTs
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## Correct or exclude problematic data
## If we have problems here, with variables of the wrong type,
## it probably means some of the data entry is a bit messed up.
## the skim() function is a really nice one for looking at the data,
## including if any variables have missing values (NAs)
skim(class_RT)


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## and the number of observations of each gender
### Check numbers of data points in each gender
table(class_RTs$Gender)
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## Now make a figure containing the histogram of reaction times
ggplot(data=class_RTs, aes(x=???)) + geom_histogram()

## Now make a figure containing two histograms histograms (i.e. two "facets"), one for each gender
ggplot(data=class_RTs, aes(x=???)) + geom_histogram() + facet_grid(~Gender)

## And a box and whisker plot
ggplot(data=class_RTs, aes(x=???, y=???)) + geom_boxplot()

## Or just the data points (with some jitter, to separate overlapping points):
ggplot(data=class_RTs, aes(x=???, y=???)) + geom_point() + geom_jitter(width=0.05)
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## Do you think there is a difference in reaction times between females and males?
## What is the effect size (i.e. the magnitude of the difference?)
## Is this likely to be of practical significance?
## Look at your graphs and assess assumptions:
## - Do you think the residuals will be normally distributed?
## - Do the two groups have similar variance?
## - Do there seem to be any outliers?
## - Are data points independent? (You don't get this from the graph, but rather from knowing how the data were collected.)
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## Do the t test and assign the outcome to an object:
my_ttest <- t.test(??? ~ ???, data=class_RTs, var.equal=TRUE)
## look at the result of the t-test
t.test
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## Critical thinking
# How might the work be flawed?
# How might the analysis be flawed (assumptions violated)?
# Is the difference (i.e. effect size) small, medium, large, relative to differences caused by other factors?
# How general might be the finding?
# How do the qualitative and quantitative findings compare to those in previous studies?
# What could have been done better?
# What are the implications of the findings?
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## Report and communicate the results
## Write a sentence that gives the direction and extent of difference,
## and a measure of certainty / uncertainty in that finding.
## Make a beautiful graph that very clearly communicates the findings!
ggplot(data=???, aes(x=???, y=???)) + geom_boxplot() +
  ylab("Reaction time (seconds)")