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.

This has been done above.

2. load the packages you will need

Will load the tidyverse package and the here package.The tidyverse package has function that can read the read_csv data and create grouped summaries. The here package allows us to tell R where the data is when reading it in.

library(tidyverse)
library(here)

3. read the birthweight data

To read the birthweight data in, use read_csv as the file is in .csv format. The here function tells R to find the data in the data folder. R will also make a new object labelled babies.

babies <- read_csv(here("data", "Sample_BirthWeight_GestAge.csv"))

4. calculate the mean birthweight separately for twins and singletons

To calculate the mean birthweight of twins and singletons separately, I will use group_by and summarise. Ungroup is used in case I choose to pipe more operations onto the list.

babies %>% 
  group_by(plurality) %>% 
  summarise(mean_bw = mean(birthweight)) %>% 
  ungroup()

## # 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

To identify the earliest gestational age for each ethnicity group, I will use group_by and summarise to identify the minimum birthweight baby and use the min() function. I will continue to ungroup.

babies %>% 
  group_by(child_ethn) %>% 
  summarise(min_ga = min(gestation_age_w)) %>% 
  ungroup()

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

The pipe allows for several code operations to be brought together into a sequence of actions. Piping can be useful to produce descriptive summaries for each separate group in the data set. If you use the dataframe, pipe it to group_by, and then pipe it to summarise, the means can be calculated separately for each group. A blog that was helpful was on github pages.

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

Summary of the mean birthweight by plurarity is made into a new csv file labelled “bw_by_plurality.csv”

babies %>%
  group_by(plurality) %>%
  summarise(mean_bw = mean(birthweight)) %>%
  ungroup() %>%
  write_csv("bw_by_plurality.csv")