1511 render

_freeze/materials/04-plotting/execute-results/html.json

@@ -0,0 +1,17 @@
+{
+  "hash": "d4a131e91826a1b7c0f7e8f521c61159",
+  "result": {
+    "engine": "knitr",
+    "markdown": "---\ntitle: Plotting data\n---\n\n\n\n\n::: {.callout-tip}\n#### Learning objectives\n\n- \n:::\n\n## Context\nWe now have a good grasp of how data is commonly structured, with variables in columns and observations in rows. This is the perfect format for visualising data.\n\n## Introducing plotting\n\nTo create a plot we'll need three things:\n\n1. data\n2. mapping aesthetics\n3. specify the type of plot\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code  code-line-numbers=\"2\"}\nlibrary(tidyverse)\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\n── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──\n✔ dplyr     1.1.4     ✔ readr     2.1.5\n✔ forcats   1.0.0     ✔ stringr   1.5.1\n✔ ggplot2   3.5.1     ✔ tibble    3.2.1\n✔ lubridate 1.9.3     ✔ tidyr     1.3.1\n✔ purrr     1.0.2     \n── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n✖ dplyr::filter() masks stats::filter()\n✖ dplyr::lag()    masks stats::lag()\nℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors\n```\n\n\n:::\n\n```{.r .cell-code  code-line-numbers=\"2\"}\nsurveys <- read_csv(\"data/surveys.csv\")\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\nRows: 34786 Columns: 13\n── Column specification ────────────────────────────────────────────────────────\nDelimiter: \",\"\nchr (6): species_id, sex, genus, species, taxa, plot_type\ndbl (7): record_id, month, day, year, plot_id, hindfoot_length, weight\n\nℹ Use `spec()` to retrieve the full column specification for this data.\nℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.\n```\n\n\n:::\n\n```{.r .cell-code  code-line-numbers=\"2\"}\nggplot(data = surveys,\n       mapping = aes(x = weight, y = hindfoot_length)) +\n  geom_point()\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\nWarning: Removed 4048 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n\n\n:::\n\n::: {.cell-output-display}\n![](04-plotting_files/figure-html/unnamed-chunk-1-1.png){width=672}\n:::\n:::\n\n\n\n\n## Python\n\n:::\n\n### Start plotting\n* LO: plotting with ggplot2\n\n### Building up plots\n* LO: layering plots\n\n### Changing plots\n* LO: changing aesthetics (colour, fill, transparency, size)\n\n\n\n## Summary\n\n::: {.callout-tip}\n#### Key points\n\n- \n:::\n",
+    "supporting": [
+      "04-plotting_files"
+    ],
+    "filters": [
+      "rmarkdown/pagebreak.lua"
+    ],
+    "includes": {},
+    "engineDependencies": {},
+    "preserve": {},
+    "postProcess": true
+  }
+}

materials/04-plotting.qmd

@@ -13,6 +13,20 @@ We now have a good grasp of how data is commonly structured, with variables in c
 
 ## Introducing plotting
 
+To create a plot we'll need three things:
+
+1. data
+2. mapping aesthetics
+3. specify the type of plot
+
+::: {.panel-tabset group="language"}
+## R
+
+
+## Python
+
+:::
+
 ### Start plotting
 * LO: plotting with ggplot2
 

materials/04-plotting_files/execute-results/html.json

@@ -0,0 +1,17 @@
+{
+  "hash": "d4a131e91826a1b7c0f7e8f521c61159",
+  "result": {
+    "engine": "knitr",
+    "markdown": "---\ntitle: Plotting data\n---\n\n\n\n\n::: {.callout-tip}\n#### Learning objectives\n\n- \n:::\n\n## Context\nWe now have a good grasp of how data is commonly structured, with variables in columns and observations in rows. This is the perfect format for visualising data.\n\n## Introducing plotting\n\nTo create a plot we'll need three things:\n\n1. data\n2. mapping aesthetics\n3. specify the type of plot\n\n::: {.panel-tabset group=\"language\"}\n## R\n\n\n\n\n::: {.cell}\n\n```{.r .cell-code  code-line-numbers=\"2\"}\nlibrary(tidyverse)\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\n── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──\n✔ dplyr     1.1.4     ✔ readr     2.1.5\n✔ forcats   1.0.0     ✔ stringr   1.5.1\n✔ ggplot2   3.5.1     ✔ tibble    3.2.1\n✔ lubridate 1.9.3     ✔ tidyr     1.3.1\n✔ purrr     1.0.2     \n── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n✖ dplyr::filter() masks stats::filter()\n✖ dplyr::lag()    masks stats::lag()\nℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors\n```\n\n\n:::\n\n```{.r .cell-code  code-line-numbers=\"2\"}\nsurveys <- read_csv(\"data/surveys.csv\")\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\nRows: 34786 Columns: 13\n── Column specification ────────────────────────────────────────────────────────\nDelimiter: \",\"\nchr (6): species_id, sex, genus, species, taxa, plot_type\ndbl (7): record_id, month, day, year, plot_id, hindfoot_length, weight\n\nℹ Use `spec()` to retrieve the full column specification for this data.\nℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.\n```\n\n\n:::\n\n```{.r .cell-code  code-line-numbers=\"2\"}\nggplot(data = surveys,\n       mapping = aes(x = weight, y = hindfoot_length)) +\n  geom_point()\n```\n\n::: {.cell-output .cell-output-stderr}\n\n```\nWarning: Removed 4048 rows containing missing values or values outside the scale range\n(`geom_point()`).\n```\n\n\n:::\n\n::: {.cell-output-display}\n![](04-plotting_files/figure-html/unnamed-chunk-1-1.png){width=672}\n:::\n:::\n\n\n\n\n## Python\n\n:::\n\n### Start plotting\n* LO: plotting with ggplot2\n\n### Building up plots\n* LO: layering plots\n\n### Changing plots\n* LO: changing aesthetics (colour, fill, transparency, size)\n\n\n\n## Summary\n\n::: {.callout-tip}\n#### Key points\n\n- \n:::\n",
+    "supporting": [
+      "04-plotting_files"
+    ],
+    "filters": [
+      "rmarkdown/pagebreak.lua"
+    ],
+    "includes": {},
+    "engineDependencies": {},
+    "preserve": {},
+    "postProcess": true
+  }
+}