W1 self test

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…

choose packages/functions
read in data
group_by and summarise
make notes using RMarkdown
insert pictures in an Rmd document
write data to csv

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.

I adjusted the “output” part of the YAML section while minding the indentations.

2. load the packages you will need

I used tidyverse for the package.

library(tidyverse)

3. read the birthweight data

I copied the birthweight data from “data” folder to the “project” folder before continuing with the “frames” command

frames <- read_csv(file = "birthweight_data.csv")

4. calculate the mean birthweight separately for twins and singletons

I grouped the babies by plurality and then summarised the mean of the birthweight. In the R Script file, after clicking “Source”, I proceeded to click “Run” to get the output.

frames %>% 
  group_by(plurality) %>% 
  summarise(
    mean_birthweight = mean(birthweight)) %>% 
ungroup()

## # A tibble: 2 × 2
##   plurality mean_birthweight
##   <chr>                <dbl>
## 1 singleton            3248.
## 2 twin                 2311.

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

I grouped the babies by ethnicity and then ‘filtered’ the minimum gestation age. In the R Script file, after clicking “Source”, I proceeded to click “Run” to get the output.

frames %>% 
  group_by(child_ethn) %>% 
  summarise(
    minimum_age = min(gestation_age_w)) %>% 
ungroup()

## # A tibble: 10 × 2
##    child_ethn                        minimum_age
##    <chr>                             <chr>      
##  1 Aboriginal/Torres Strait Islander 33         
##  2 African/African-American          26         
##  3 Caucasian                         26         
##  4 East Asian                        33         
##  5 Hispanic/Latino                   37         
##  6 Middle-Eastern                    28         
##  7 Missing                           36         
##  8 Polynesian/Melanesian             28         
##  9 South Asian                       28         
## 10 South-East Asian                  29

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

Piping (%>%) data using “group_by” is effective when we need to look into only certain variables, which then works in tandem with “summarise” to get specific means and to filter certain values (i.e. minimum gestation age value). It gives us separate descriptive summaries of our data. More can be seen here:

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

This is a big baby.

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

I used the same code with an added pipe after “ungroup” and used the write_csv command, which automatically creates the file in the “project” folder.

frames %>% 
  group_by(plurality) %>% 
  summarise(
    mean_birthweight = mean(birthweight)) %>% 
ungroup() %>% 
write_csv("birthweight_by_plurality")