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.

2. load the packages you will need

#load packages 
library(tidyverse)

3. read the birthweight data

#read data 
birthweight <- read.csv(file = "data/birthweight_data.csv")

4. calculate the mean birthweight separately for twins and singletons

#Mean birthweights for singeltons and twins 
my_birthweightsumplu <- birthweight %>% 
  group_by(plurality) %>% 
  summarise(
    birthweight = mean(birthweight)
  )
  ungroup(my_birthweightsumplu)

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

print(my_birthweightsumplu)

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

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

#Miniumum gestational age for each ethnicity 
my_mingestationalage <- birthweight %>% 
  group_by(child_ethn) %>% 
  summarise(
    mingestationalage = min(gestation_age_w)
  )
  ungroup(my_mingestationalage)

## # A tibble: 10 × 2
##    child_ethn                        mingestationalage
##    <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

print(my_mingestationalage)

## # A tibble: 10 × 2
##    child_ethn                        mingestationalage
##    <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

Put very simply, to summarise work you can use the group_by function which will mean you will have a result for each of the levels within the varible you are grouping by and then can use the summarise function to find any descriptive stats you want to know about that group_by

Here is a link to a blog post, it discusses the summarise and group by function using dplyr and has some useful info about all the function compatible will the summarise function.

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

Here is a cute picture of a baby

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

#Mean birthweights for singeltons and twins 
my_birthweightsumplu <- birthweight %>% 
  group_by(plurality) %>% 
  summarise(
    birthweight = mean(birthweight)
  )
  ungroup(my_birthweightsumplu)

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

write_csv(my_birthweightsumplu, file = "my_birthweightsumplu.csv")