My Coding Goals This Week

  1. Work through Week 2’s Data Visualisation playlist including taking notes and completing her exercises and examples as I follow along
  2. Test out what I have learnt this week by producing a plot using everything we learnt in the video tutorials

How I Made Progress Towards My Goals

After completing Danielle’s tutorial videos, I attempted to implement everything I learnt with the ChickWeight data on R. The steps and codes I have used are as below:

Loading Packages

library(tidyverse)
── Attaching packages ──────────────────────────────────────────── tidyverse 1.3.1 ──
✓ ggplot2 3.3.3     ✓ purrr   0.3.4
✓ tibble  3.1.2     ✓ dplyr   1.0.6
✓ tidyr   1.1.3     ✓ stringr 1.4.0
✓ readr   1.4.0     ✓ forcats 0.5.1
── Conflicts ─────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

Plotting the data set

picture <- ggplot(
  data = ChickWeight
) +
  geom_point(
    mapping = aes(
      x = Time ,
      y = weight,
    )
  )

plot(picture)

This tells us that over time, chicks’ weights are increasing - but not that informative… So let’s try to add the diet variable to find out which diet is most effective.

picture <- ggplot(
  data = ChickWeight
) +
  geom_point(
    mapping = aes(
      x = Time,
      y = weight,
      colour = Diet
    )
  ) +
  theme_minimal (
  ) +
  scale_x_discrete (
    name = "Time",
    ) +
  scale_y_continuous(
    name = "Chick Weight"
  ) +
  ggtitle(
    label = "Effect of Type of Diet on Chick Weight"
  ) 

plot(picture)

Still a little confusing, so I am going to attempt to do this with a boxplot!

picture <- ggplot(
  data = ChickWeight
) +
  geom_boxplot(
    mapping = aes(
      x = Diet,
      y = weight,
      fill = Diet
    )
  ) +
  theme_minimal (
  ) +
  scale_x_discrete (
    labels = NULL,
    name = NULL
    ) +
  scale_y_continuous(
    name = "Chick Weight"
  ) +
  ggtitle(
    label = "Effect of Type of Diet on Chick Weight"
  ) +
  scale_fill_viridis_d(
    alpha = 0.5,
  ) +
  facet_wrap(vars(Time))

plot(picture)

This seems more interpretable!

On first glance: Diet has a more observable effect on chicks’ weights at later days. It looks like diets 3 and 4 produce heavier chicks than 1 and 2.

Challenges

I had to Google a lot today, and had a lot of errors come up that I had to fix! But - I was also pleasantly surprised with the final outcome and how interpretable it was (positive reinforcement)!

Next Steps in My Coding Journey

This is actually quite fun! While it took me a long time to get my head around all the different things (and is still very much a work in progress), I look forward to practicing more of this and getting better at it.

Next up, I’m planning to watch the next playlist for Week 3 and get started on it this long weekend!