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

library(tidyverse)

3. Read the birthweight data

bw_data <- read_csv("data/birthweight_data.csv")
print(bw_data)

## # A tibble: 788 × 5
##    true_ID birthweight gestation_age_w child_ethn               plurality
##      <dbl>       <dbl> <chr>           <chr>                    <chr>    
##  1    3100        3030 39              Middle-Eastern           singleton
##  2    3101        3710 40              Caucasian                singleton
##  3    3102        3770 42              African/African-American singleton
##  4    3103        3660 38              Caucasian                singleton
##  5    3104        3800 39              Caucasian                singleton
##  6    3105        3540 41              Caucasian                singleton
##  7    3106        3400 37              South-East Asian         singleton
##  8    3107        3650 39              Middle-Eastern           singleton
##  9    3108        3460 39              South-East Asian         singleton
## 10    3109        3380 39              South-East Asian         singleton
## # ℹ 778 more rows

4. Calculate the mean birthweight separately for twins and singletons

mean_bw <-  
  bw_data %>%
    group_by(plurality) %>%
    summarise(mean_bw = mean(birthweight))

print(mean_bw)

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

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

bw_data %>%
  group_by(child_ethn) %>%
  summarise(min_gestation_age = min(gestation_age_w))

## # A tibble: 10 × 2
##    child_ethn                        min_gestation_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

group_by pipes the data being analysed into groups based on a specified variable. Summarise condenses the data into rows of the calculated values, piped from the grouped variables.

https://dplyr.tidyverse.org/reference/summarise.html

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

mean_bw %>%
  write_csv("mean_bw.csv")