What were the goals for this week?
The goal for this week was to get the team onto GitHub! This would ensure that we would be able to work as efficiently as possible, without duplicating our efforts and making sure that everyone always had the most up to date version of the code. I also wanted to work on my visualisation skills in order to make sure I wasn’t forgetting anything.
How did I achieve these goals?
The first thing I did this week was attempt the QnA activities. Here’s the plots that I made based on the Palmer’s Penguins data.
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.1.2 v dplyr 1.0.6
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(palmerpenguins)
#Taking our dataset and filtering for our plot
data1 <- penguins %>%
filter(species == "Gentoo" & sex != "NA")
# Constructing the graph
plot1 <- ggplot(data1, aes(bill_length_mm, body_mass_g)) +
geom_point() +
geom_smooth()
# Styling the graph
plot1 <- plot1 +
scale_x_continuous(
name = "Bill Length (mm)"
) +
scale_y_continuous(
name = "Body Mass"
) +
ggtitle("Relationship between bill length and body mass of Gentoo penguins") +
theme(legend.position = "none")
plot(plot1)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#Taking our dataset and filtering for our plot
data1 <- penguins %>%
filter(species == "Gentoo" & sex != "NA")
# Constructing the graph
plot1 <- ggplot(data1, aes(bill_length_mm, body_mass_g, color = sex)) +
geom_point() +
geom_smooth() +
facet_wrap(vars(sex), scales = 'free_x')
# Styling the graph
plot1 <- plot1 +
scale_x_continuous(
name = "Bill Length (mm)"
) +
scale_y_continuous(
name = "Body Mass"
) +
ggtitle("Relationship between bill length and body mass of Gentoo penguins",
subtitle = "Divided and colored by sex") +
theme(legend.position = "none")
plot(plot1)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Unfortunately I was not able to reproduce the 3rd plot. For some reason, my plot was not displaying the same number of penguins as Jenny’s plot.
The next thing I did was to learn how to use GitHub with RStudio. I followed along several tutorials online and figured out how to use the inbuilt Github client in RStudio. It is actually very intuitive and works well, so I was quite pleased with it. My team called on Saturday night in order to get everyone onto Github and we got everyone’s RStudio working with it! We all had a go at making a test repository, staging, committing, and pushing changes to the main branch. I also created a document explaining how to create new branches, and opening pull requests in order for my teammates to get acquainted with how GitHub works. We also all joined the repo that Jenny invited us to, and I made changes to the repo to set us up for getting a start on reproducing the data.
Next steps
Next week we will finally be getting a start at attempting to reproduce some of the descriptives in the Nichols et al. paper. Although this may seem a bit overdue, we prioritized getting everyone onto Github as we knew it would save us a lot of time and hairpulling in the future. We will also need to continue to check in with each other to make sure we are all using Github correctly.