All Images

Day 1: Starting with DataWorking Environment R BasicsLoad Data

Figure 1

RStudio interface screenshot showing four panes.

Figure 2

RStudio preferences screenshot showing the workspace saving option set to Never.

Figure 3

Screenshot of the expected working directory structure.

Figure 4

Diagram of the file structure to recreate.

Figure 5

Screenshot of the RStudio help interface.

Figure 6

Diagram showing a data frame with numeric, character, and logical vector columns.

Figure 7

Bar plot showing the number of females, males, and undetermined individuals captured.

Figure 8

Bar plot showing female, male, and undetermined individuals in the specified order.

Day 2: Manipulating DataData Manipulation using `dplyr` and `tidyr` Exporting data

Figure 1

Diagram illustrating the pivot_wider transformation.

Figure 2

Diagram illustrating the pivot_longer transformation.

Day 3: Visualising Data

Figure 1

A blank ggplot canvas with no axes or data plotted.

Figure 2

A ggplot canvas with weight on the x-axis and hindfoot_length on the y-axis but no data points.

Figure 3

Scatter plot of weight versus hindfoot length for all species in the surveys_complete dataset.

Figure 4

Scatter plot of weight versus hindfoot length generated by adding geom_point to a stored ggplot object.

Figure 5

Basic scatter plot of weight versus hindfoot length.

Figure 6

Scatter plot of weight versus hindfoot length with transparent points to show overplotting.

Figure 7

Scatter plot of weight versus hindfoot length with all points coloured blue.

Figure 8

Scatter plot of weight versus hindfoot length with points coloured by species ID.

Figure 9

Scatter plot of weight by species ID with points coloured by plot type.

Figure 10

Boxplot showing the distribution of weight for each species ID.

Figure 11

Boxplot with jittered data points overlaid in tomato red showing the distribution of weight by species ID.

Figure 12

Line plot of counts over years with all genera combined into a single line.

Figure 13

Line plot of counts over years with a separate line for each genus.

Figure 14

Line plot of counts over years with each genus shown in a different colour.

Figure 15

Line plot of counts over years by genus created by piping data into ggplot.

Figure 16

Line plot of counts over years by genus created by chaining data manipulation and ggplot with pipes.

Figure 17

Faceted line plots showing counts over years for each genus in a separate panel.

Figure 18

Faceted line plots showing counts over years by genus, with lines coloured by sex.

Figure 19

Grid of faceted line plots with sex in rows and genus in columns.

Figure 20

Faceted line plots arranged in a single column with one panel per genus.

Figure 21

Faceted line plots arranged in a single row with one panel per genus.

Figure 22

Faceted line plots by genus and sex using the black and white theme.

Figure 23

Faceted line plots showing average weight over years for each species ID using the black and white theme.

Figure 24

Faceted line plots by genus and sex with descriptive axis labels and a title.

Figure 25

Faceted line plots by genus and sex with increased font size on all text elements.

Figure 26

Faceted line plots by genus and sex with rotated x-axis labels for readability.

Figure 27

Boxplot of hindfoot length by species ID with the custom grey theme applied.

Figure 28

Two plots arranged side by side: a boxplot of species weights on the left and a line plot of genus abundance over time on the right.

Figure 29

Combined plot saved with ggsave showing the weight boxplot and abundance line plot side by side.