Session Overview

Part 1: Course Overview

• What even is R & why are you making me learn it?

• Introduction to the fundRmentals course

• The R-cult: why R is ⭐magical⭐

Part 2: Getting StaRted

• Tips for success

• Next steps

• Installing R & RStudio

What even is R anyway? 🤔

print("Hi LearneRs!")

## [1] "Hi LearneRs!"

Introduction to fundRmentals

The R-cult: why R is ⭐magical⭐

• Makes complicated things easy to do

• Super efficient

• Collaboration

• Reproducibility

• Visualisations

• Modelling

• Reporting

• Presenting

• & sooo many other cool things!!

A very simple regression

• 1 line of code, much quicker than clicking in SPSS:

lm(outcome ~ predictor, data = data)

"But Dan, what if I have 10 predictors?"

• Still just 1 line of code...

lm(outcome ~ predictor_1 + predictor_2 + predictor_3 + predictor_4 + predictor_5 + predictor_6 +
     predictor_7 + predictor_8 + predictor_9 + predictor_10, data = data)

A very simple regression

• 1 line of code, much quicker than clicking in SPSS:

lm(outcome ~ predictor, data = data)

"But Dan, what if I want to do a t-test?"

t.test(outcome ~ grouping_variable, data = data)

"Or a correlation?"

cor.test(~ variable_1 + variable_2, data = data)

A very simple regression

• 1 line of code, much quicker than clicking in SPSS:

lm(outcome ~ predictor, data = data)

"But Dan, what if I have a factorial design?"

• Still just 1 line of code...

lm(outcome ~ predictor_1*predictor_2, data = data)

A very simple regression

• 1 line of code, much quicker than clicking in SPSS:

lm(outcome ~ predictor, data = data)

"But Dan, what if I have a stupidly complex model?"

A Gross Latent Growth Model

model <- "i =~ 1*sat140 + 1*ks2_mat + 1*ks3_mat + 1*ks4_maths
s =~ -4*sat140 + 0*ks2_mat + 3*ks3_mat + 5*ks4_maths
i ~ sex + int_sdq_11 + iq_cent + wm_cent + ses_cent + cse_voc + cse_olevel + cse_alevel + cse_degree + par_mh_fa + mum_home_int + part_home_int + cai_cent + psai_cent +
home_teach_1_al + home_teach_1_nu + schl_supp_sa + schl_supp_se + eas_harmony + eas_control
s ~ sex + int_sdq_11 + iq_cent + wm_cent + ses_cent + cse_voc + cse_olevel + cse_alevel + cse_degree + par_mh_fa + mum_home_int + part_home_int + cai_cent + psai_cent +
home_teach_1_al + home_teach_1_nu + schl_supp_sa + schl_supp_se + eas_harmony + eas_control"
growth(model, data = data, estimator = "MLR")

Data Viz: A Pretty, Plain, APA Scatterplot

ggplot(data, aes(x = flipper_length_mm, y = body_mass_g)) +
  geom_point() + 
    labs(x = "Flipper Length (mm)", y = "Body Mass (g)") + 
      theme_apa()

Data Viz: Grouped Scatterplot

ggplot(data, aes(x = flipper_length_mm, y = body_mass_g, colour = island)) +
  geom_point() + 
    labs(x = "Flipper Length (mm)", y = "Body Mass (g)", colour = "Island") + 
      theme_apa()

Data Viz: sCATterplot

ggplot(data, aes(x = flipper_length_mm, y = body_mass_g)) +
 geom_cat(cat = "lil_bub", size = 2) + 
  labs(x = "Catnip Consumed", y = "Happiness") + 
    theme_apa()

RMarkdown

Inline Code

• Allows you to include code within the main text of your document

• Can be used with test results, values from tables etc.

• Any values will be automatically updated when you render the file

• Incredibly useful & efficient

• Helps you avoid tYpos & rounding erroRs

Tips for Success

For Supervision...

Number 1 rule: don't panic! We've all got this!!

• Students have access to everything we've covered thus far on Canvas
• They are equipped to be able to do their analyses - they just don't know it
• They'll also have access to an R helpdesk next semester & two analyses workshops

For Yourself...

• Practice as often as you can
• Be kind to yourself - it feels really slow & confusing when you first start
• Ask questions, often someone else explaining it slightly differently makes everything click
• There's a huge online community of R users, for any problems/errors you're having the solution can usually be found quite easily by searching
• Google is your best friend

Next Steps

Complete the tutorial before our first HappyHouR next week, which covers:

• Installation & tour of RStudio

• Working in RStudio

• Installing & loading packages

• RMarkdown

• Getting data into RStudio

• Tables in RMarkdown