Welcome to the PSYC3361 coding W1 self test. The test assesses your ability to use the coding skills covered in the Week 1 online coding modules.

In particular, it assesses your ability to…

It is IMPORTANT to document the code that you write so that someone who is looking at your code can understand what it is doing. Above each chunk, write a few sentences outlining which packages/functions you have chosen to use and what the function is doing to your data. Where relevant, also write a sentence that interprets the output of your code.

Your notes should also document the troubleshooting process you went through to arrive at the code that worked.

For each of the challenges below, the documentation is JUST AS IMPORTANT as the code.

Good luck!!

Jenny

1. Customise your Rmd document by adding your name as the author, a table of contents and choosing a theme that you like.

added my name + selected theme

2. Load the packages you will need

Loading tidyverse into the workspace, allowing for data cleaning/anaylsis

library(tidyverse)

3. Read the birthweight data

set frames as name of data being read? - q - can we use different names is that fine? - once set put frames into console to do stuff

frames <- read.csv(file = 'birthweight_data.csv')

4. Calculate the mean birthweight separately for twins and singletons

summarise based on category, twins and singletons - attempted to use group by then filter - consistent error - unable to find singleton? - removed filter by - just group by – IT WORKED???!!! - looks like group by selects ‘columns’ - and will automatically create more categories based on those columns.

 frames_s <- read.csv(file = 'birthweight_data.csv') %>% 
group_by(plurality) %>% 
  summarise(mean_bw = mean(birthweight))%>% 
    ungroup

5. Identify the earliest (i.e. the minimum value) gestational age for each ethicity group

normal maths dictates that this follows the min + max function? q - is there a min/max function in tidyverse YES - use min/max function as per summarise

frames_gestational_age <- read.csv(file='birthweight_data.csv') %>% 
  group_by(child_ethn) %>% 
  summarise(min_gestationa_age = min(gestation_age_w)) %>% 
  ungroup 

6. Write some notes about how group_by and summarise work with the pipe below, including a link to documentation or a blog post that you think is useful

Group by is useful as it allows you to choose which data should be sorted. If you have a :sparkle: massive :sparkle: data files.

I used this source R Markdown: The Definitive Guide

7. Download a picture of a baby from the internet and insert it into your document below

8. Write the summary of mean birthweight by twins/singletons that you made in step 3 above to a new csv file

write_csv(frames_s, file = 'birthweight_meanplurality.csv')

9. Knit your document and publish the output to RPubs