03:00
Challenge 2: 3 mins
A) location B) leisure C) osm_id
03:00
Challenge 3: 5 mins
05:00
Challenge 4: 3 mins
03:00
Challenge 5: 5 mins
05:00
Challenge 6: 3 mins
03:00
Challenge 7: 5 mins
05:00
Challenge 8: 3 mins
03:00
Intro to raster data
The raster package
-
A plot showing the green band of an RGB aerial image of the TU Delft library
Challenge 1: 2 mins
Use describe() to determine the following about the tud-dsm-hill.tif file:
Challenge 1: 2 mins
02:00
Challenge 2: 5 mins
05:00
Challenge 3: 2 mins
View the CRS for each of these two datasets. What projection does each use?
02:00
Challenge 4: 10 mins
10:00
Challenge 4: 10 mins
Working with multi-band rasters
Reveal.initialize({ 'controlsAuto': true, 'previewLinksAuto': false, -'smaller': false, 'pdfSeparateFragments': false, 'autoAnimateEasing': "ease", 'autoAnimateDuration': 1, 'autoAnimateUnmatched': true, -'menu': {"side":"left","useTextContentForMissingTitles":true,"markers":false,"loadIcons":false,"custom":[{"title":"Tools","icon":"","content":""}],"openButton":true}, +'menu': {"side":"left","useTextContentForMissingTitles":true,"markers":false,"loadIcons":false,"custom":[{"title":"Tools","icon":"","content":""}],"openButton":true}, 'chalkboard': {"buttons":true}, 'smaller': false, @@ -723,9 +726,23 @@Working with multi-band rasters
tabsets.forEach(function(tabset) { const tabby = new Tabby('#' + tabset.id); }); + const isCodeAnnotation = (el) => { + for (const clz of el.classList) { + if (clz.startsWith('code-annotation-')) { + return true; + } + } + return false; + } const clipboard = new window.ClipboardJS('.code-copy-button', { - target: function(trigger) { - return trigger.previousElementSibling; + text: function(trigger) { + const codeEl = trigger.previousElementSibling.cloneNode(true); + for (const childEl of codeEl.children) { + if (isCodeAnnotation(childEl)) { + childEl.remove(); + } + } + return codeEl.innerText; } }); clipboard.on('success', function(e) { @@ -761,10 +778,9 @@Working with multi-band rasters
// clear code selection e.clearSelection(); }); - function tippyHover(el, contentFn) { + function tippyHover(el, contentFn, onTriggerFn, onUntriggerFn) { const config = { allowHTML: true, - content: contentFn, maxWidth: 500, delay: 100, arrow: false, @@ -773,9 +789,18 @@Working with multi-band rasters
}, interactive: true, interactiveBorder: 10, - theme: 'quarto-reveal', - placement: 'bottom-start' + theme: 'light-border', + placement: 'bottom-start', }; + if (contentFn) { + config.content = contentFn; + } + if (onTriggerFn) { + config.onTrigger = onTriggerFn; + } + if (onUntriggerFn) { + config.onUntrigger = onUntriggerFn; + } config['offset'] = [0,0]; config['maxWidth'] = 700; window.tippy(el, config); diff --git a/aio.html b/aio.html index 7048b8cf..14a0ab7b 100644 --- a/aio.html +++ b/aio.html @@ -1091,7 +1091,7 @@Exercise
Show me the solution
-
+
combined_vector <- c(abcd_vector, numeric_vector)
combined_vector
@@ -1486,8 +1486,7 @@ Exploring Data framessingle type of data (e.g., characters, numeric,
factors). For example, here is a figure depicting a data frame
comprising a numeric, a character, and a logical vector.
-
Source:
-Data
+
Source: Data
Carpentry R for Social Scientists
Reading data
@@ -2497,7 +2496,7 @@ Challenge: CRS for calculating areas
Show me the solution
-
+
Correct answer: c. EPSG:3035
@@ -2531,7 +2530,7 @@ Challenge: CRS for calculating shortest
-
+
Correct answer: b. EPSG:28992
@@ -2933,7 +2932,7 @@ Challenge: Import line and point vector
-
+
R
@@ -3298,7 +3297,7 @@ Challenge: Attributes for different spatial
-
+
- To find the number of attributes, we use the
ncol()
@@ -3539,7 +3538,7 @@ Challenge
Show me the solution
-
+
- To create the new object, we first need to see which value of the
@@ -3815,7 +3814,7 @@
Challenge: Plot line width by attribute
Show me the solution
-
+
R
@@ -3913,7 +3912,7 @@ Challenge: Plot lines by attributes
Show me the solution
-
+
R
@@ -3990,7 +3989,7 @@ Challenge: Plot polygon by attribute
Show me the solution
-
+
R
@@ -5041,7 +5040,7 @@ Challenge
Show me the solution
-
+
+units=m in the output of the code above tells us that
our data is in meters (m).
@@ -5142,7 +5141,7 @@ OUTPUT<
been working with is a single band raster. This means that there is only
one dataset stored in the raster: surface elevation in meters for one
time period.
-
A raster dataset can contain one or more bands. We can view the
+Single- and multi-band raster
A raster dataset can contain one or more bands. We can view the
number of bands in a raster using the nlyr() function.
R
@@ -5158,7 +5157,7 @@ OUTPUT<
This dataset has only 1 band. However, raster data can also be
multi-band, meaning that one raster file contains data for more than one
variable or time period for each cell. We will discuss multi-band raster
-data in a later
+data in a later
episode.
Creating a histogram of raster values
@@ -5229,7 +5228,7 @@ Challenge: Explore raster metadata
Show me the solution
-
+
R
@@ -6471,7 +6470,7 @@ Challenge: Explore CHM Raster Values
Show me the solution
-
+
R
@@ -7248,7 +7247,7 @@ Challenge: import an interactive basemap layer
-
+
R
@@ -7271,10 +7270,10 @@ R bringToFront = TRUE))
-
@@ -7633,7 +7632,7 @@ Challenge: Conservation rules have
-
+
R
diff --git a/images.html b/images.html
index f0b3b309..f3560a0b 100644
--- a/images.html
+++ b/images.html
@@ -432,8 +432,8 @@ Figure 1
Exploring Data Frames & Data frame Manipulation with dplyrExploring Data framesData frame Manipulation with dplyr
Figure 1
-
Source:
-Data
+
+
Source: Data
Carpentry R for Social Scientists
Introduction to visualisationIntroduction to VisualisationWriting data
@@ -546,7 +546,8 @@ Figure 1
Raster plot with ggplot2 using the viridis color scale
Figure 2
-
+
+Single- and multi-band raster
Figure 3
Figure 4
diff --git a/index.html b/index.html
index 19fbe1b6..65018523 100644
--- a/index.html
+++ b/index.html
@@ -345,13 +345,28 @@
This is a new lesson built with The Carpentries
Workbench.
-
-
-
-
+ -->
Overview
This workshop is designed to be run on your local machine. First, you
will need to download the data we use in the workshop. Then, you need to
@@ -432,7 +447,7 @@
GDAL, GEOS, and PROJ.4
Windows
-
+
To install the geospatial libraries, install the latest version RTools
@@ -444,7 +459,7 @@ Windows
-
+
Install the geospatial libraries individually using homebrew. Open Terminal.app and run the
following commands:
@@ -465,7 +480,7 @@ BASH
Linux
-
+
Steps for installing the geospatial libraries will vary based on
which form of Linux you are using. These instructions are adapted from
@@ -521,7 +536,7 @@
UDUNITS
Linux
-
+
Steps for installing the geospatial will vary based on which form of
Linux you are using. These instructions are adapted from the sf package’s
@@ -572,7 +587,7 @@ R
-
+
To install R, Windows users should select “Download R
for Windows” from RStudio and CRAN’s cloud download page, which will
@@ -589,7 +604,7 @@
Windows
-
+
To install R, macOS users should select “Download R for
macOS” from RStudio and CRAN’s cloud download page, which will
@@ -607,7 +622,7 @@
MacOS
-
+
To install R, Linux users should select “Download R for
Linux” from RStudio and CRAN’s cloud download page, which will
diff --git a/instructor/01-intro-to-r.html b/instructor/01-intro-to-r.html
index c723b2c5..f04acc70 100644
--- a/instructor/01-intro-to-r.html
+++ b/instructor/01-intro-to-r.html
@@ -467,7 +467,7 @@
R
Instructor Note
-
+
In interest of time, focus on one way of creating the folders. You
can showcase an alternative method with just one example.
diff --git a/instructor/02-data-structures.html b/instructor/02-data-structures.html
index cc9b9b51..08e295da 100644
--- a/instructor/02-data-structures.html
+++ b/instructor/02-data-structures.html
@@ -546,7 +546,7 @@ Exercise
Show me the solution
-
+
combined_vector <- c(abcd_vector, numeric_vector)
combined_vector
diff --git a/instructor/03-explore-data.html b/instructor/03-explore-data.html
index f73b778c..ae5adfef 100644
--- a/instructor/03-explore-data.html
+++ b/instructor/03-explore-data.html
@@ -419,8 +419,7 @@ Exploring Data framessingle type of data (e.g., characters, numeric,
factors). For example, here is a figure depicting a data frame
comprising a numeric, a character, and a logical vector.
-
Source:
-Data
+
combined_vector <- c(abcd_vector, numeric_vector)
combined_vectorExploring Data framessingle type of data (e.g., characters, numeric,
factors). For example, here is a figure depicting a data frame
comprising a numeric, a character, and a logical vector.
-
Source:
-Data
+
Source: Data
Carpentry R for Social Scientists
Reading data
@@ -2497,7 +2496,7 @@ Challenge: CRS for calculating areas
Show me the solution
-
+
Correct answer: c. EPSG:3035
@@ -2531,7 +2530,7 @@ Challenge: CRS for calculating shortest
-
+
Correct answer: b. EPSG:28992
@@ -2933,7 +2932,7 @@ Challenge: Import line and point vector
-
+
R
@@ -3298,7 +3297,7 @@ Challenge: Attributes for different spatial
-
+
- To find the number of attributes, we use the
ncol()
@@ -3539,7 +3538,7 @@ Challenge
Show me the solution
-
+
- To create the new object, we first need to see which value of the
@@ -3815,7 +3814,7 @@
Challenge: Plot line width by attribute
Show me the solution
-
+
R
@@ -3913,7 +3912,7 @@ Challenge: Plot lines by attributes
Show me the solution
-
+
R
@@ -3990,7 +3989,7 @@ Challenge: Plot polygon by attribute
Show me the solution
-
+
R
@@ -5041,7 +5040,7 @@ Challenge
Show me the solution
-
+
+units=m in the output of the code above tells us that
our data is in meters (m).
@@ -5142,7 +5141,7 @@ OUTPUT<
been working with is a single band raster. This means that there is only
one dataset stored in the raster: surface elevation in meters for one
time period.
-
A raster dataset can contain one or more bands. We can view the
+Single- and multi-band raster
A raster dataset can contain one or more bands. We can view the
number of bands in a raster using the nlyr() function.
R
@@ -5158,7 +5157,7 @@ OUTPUT<
This dataset has only 1 band. However, raster data can also be
multi-band, meaning that one raster file contains data for more than one
variable or time period for each cell. We will discuss multi-band raster
-data in a later
+data in a later
episode.
Creating a histogram of raster values
@@ -5229,7 +5228,7 @@ Challenge: Explore raster metadata
Show me the solution
-
+
R
@@ -6471,7 +6470,7 @@ Challenge: Explore CHM Raster Values
Show me the solution
-
+
R
@@ -7248,7 +7247,7 @@ Challenge: import an interactive basemap layer
-
+
R
@@ -7271,10 +7270,10 @@ R bringToFront = TRUE))
-
@@ -7633,7 +7632,7 @@ Challenge: Conservation rules have
-
+
R
diff --git a/images.html b/images.html
index f0b3b309..f3560a0b 100644
--- a/images.html
+++ b/images.html
@@ -432,8 +432,8 @@ Figure 1
Exploring Data Frames & Data frame Manipulation with dplyrExploring Data framesData frame Manipulation with dplyr
Figure 1
-
Source:
-Data
+
+
Source: Data
Carpentry R for Social Scientists
Introduction to visualisationIntroduction to VisualisationWriting data
@@ -546,7 +546,8 @@ Figure 1
Raster plot with ggplot2 using the viridis color scale
Figure 2
-
+
+Single- and multi-band raster
Figure 3
Figure 4
diff --git a/index.html b/index.html
index 19fbe1b6..65018523 100644
--- a/index.html
+++ b/index.html
@@ -345,13 +345,28 @@
This is a new lesson built with The Carpentries
Workbench.
-
-
-
-
+ -->
Overview
This workshop is designed to be run on your local machine. First, you
will need to download the data we use in the workshop. Then, you need to
@@ -432,7 +447,7 @@
GDAL, GEOS, and PROJ.4
Windows
-
+
To install the geospatial libraries, install the latest version RTools
@@ -444,7 +459,7 @@ Windows
-
+
Install the geospatial libraries individually using homebrew. Open Terminal.app and run the
following commands:
@@ -465,7 +480,7 @@ BASH
Linux
-
+
Steps for installing the geospatial libraries will vary based on
which form of Linux you are using. These instructions are adapted from
@@ -521,7 +536,7 @@
UDUNITS
Linux
-
+
Steps for installing the geospatial will vary based on which form of
Linux you are using. These instructions are adapted from the sf package’s
@@ -572,7 +587,7 @@ R
-
+
To install R, Windows users should select “Download R
for Windows” from RStudio and CRAN’s cloud download page, which will
@@ -589,7 +604,7 @@
Windows
-
+
To install R, macOS users should select “Download R for
macOS” from RStudio and CRAN’s cloud download page, which will
@@ -607,7 +622,7 @@
MacOS
-
+
To install R, Linux users should select “Download R for
Linux” from RStudio and CRAN’s cloud download page, which will
diff --git a/instructor/01-intro-to-r.html b/instructor/01-intro-to-r.html
index c723b2c5..f04acc70 100644
--- a/instructor/01-intro-to-r.html
+++ b/instructor/01-intro-to-r.html
@@ -467,7 +467,7 @@
R
Instructor Note
-
+
In interest of time, focus on one way of creating the folders. You
can showcase an alternative method with just one example.
diff --git a/instructor/02-data-structures.html b/instructor/02-data-structures.html
index cc9b9b51..08e295da 100644
--- a/instructor/02-data-structures.html
+++ b/instructor/02-data-structures.html
@@ -546,7 +546,7 @@ Exercise
Show me the solution
-
+
combined_vector <- c(abcd_vector, numeric_vector)
combined_vector
diff --git a/instructor/03-explore-data.html b/instructor/03-explore-data.html
index f73b778c..ae5adfef 100644
--- a/instructor/03-explore-data.html
+++ b/instructor/03-explore-data.html
@@ -419,8 +419,7 @@ Exploring Data framessingle type of data (e.g., characters, numeric,
factors). For example, here is a figure depicting a data frame
comprising a numeric, a character, and a logical vector.
-
Source:
-Data
+
Source: -Data +
Source: Data
Carpentry R for Social Scientists
Reading data
@@ -2497,7 +2496,7 @@ Challenge: CRS for calculating areas
Show me the solution
-
+
Correct answer: c. EPSG:3035
@@ -2531,7 +2530,7 @@ Challenge: CRS for calculating shortest
-
+
Correct answer: b. EPSG:28992
@@ -2933,7 +2932,7 @@ Challenge: Import line and point vector
-
+
R
@@ -3298,7 +3297,7 @@ Challenge: Attributes for different spatial
-
+
- To find the number of attributes, we use the
ncol()
@@ -3539,7 +3538,7 @@ Challenge
Show me the solution
-
+
- To create the new object, we first need to see which value of the
@@ -3815,7 +3814,7 @@
Challenge: Plot line width by attribute
Show me the solution
-
+
R
@@ -3913,7 +3912,7 @@ Challenge: Plot lines by attributes
Show me the solution
-
+
R
@@ -3990,7 +3989,7 @@ Challenge: Plot polygon by attribute
Show me the solution
-
+
R
@@ -5041,7 +5040,7 @@ Challenge
Show me the solution
-
+
+units=m in the output of the code above tells us that
our data is in meters (m).
@@ -5142,7 +5141,7 @@ OUTPUT<
been working with is a single band raster. This means that there is only
one dataset stored in the raster: surface elevation in meters for one
time period.
-
A raster dataset can contain one or more bands. We can view the
+Single- and multi-band raster
A raster dataset can contain one or more bands. We can view the
number of bands in a raster using the nlyr() function.
R
@@ -5158,7 +5157,7 @@ OUTPUT<
This dataset has only 1 band. However, raster data can also be
multi-band, meaning that one raster file contains data for more than one
variable or time period for each cell. We will discuss multi-band raster
-data in a later
+data in a later
episode.
Creating a histogram of raster values
@@ -5229,7 +5228,7 @@ Challenge: Explore raster metadata
Show me the solution
-
+
R
@@ -6471,7 +6470,7 @@ Challenge: Explore CHM Raster Values
Show me the solution
-
+
R
@@ -7248,7 +7247,7 @@ Challenge: import an interactive basemap layer
-
+
R
@@ -7271,10 +7270,10 @@ R bringToFront = TRUE))
-
@@ -7633,7 +7632,7 @@ Challenge: Conservation rules have
-
+
R
diff --git a/images.html b/images.html
index f0b3b309..f3560a0b 100644
--- a/images.html
+++ b/images.html
@@ -432,8 +432,8 @@ Figure 1
Exploring Data Frames & Data frame Manipulation with dplyrExploring Data framesData frame Manipulation with dplyr
Figure 1
-
Source:
-Data
+
+
Source: Data
Carpentry R for Social Scientists
Introduction to visualisationIntroduction to VisualisationWriting data
@@ -546,7 +546,8 @@ Figure 1
Raster plot with ggplot2 using the viridis color scale
Figure 2
-
+
+Single- and multi-band raster
Figure 3
Figure 4
diff --git a/index.html b/index.html
index 19fbe1b6..65018523 100644
--- a/index.html
+++ b/index.html
@@ -345,13 +345,28 @@
This is a new lesson built with The Carpentries
Workbench.
-
-
-
-
+ -->
Overview
This workshop is designed to be run on your local machine. First, you
will need to download the data we use in the workshop. Then, you need to
@@ -432,7 +447,7 @@
GDAL, GEOS, and PROJ.4
Windows
-
+
To install the geospatial libraries, install the latest version RTools
@@ -444,7 +459,7 @@ Windows
-
+
Install the geospatial libraries individually using homebrew. Open Terminal.app and run the
following commands:
@@ -465,7 +480,7 @@ BASH
Linux
-
+
Steps for installing the geospatial libraries will vary based on
which form of Linux you are using. These instructions are adapted from
@@ -521,7 +536,7 @@
UDUNITS
Linux
-
+
Steps for installing the geospatial will vary based on which form of
Linux you are using. These instructions are adapted from the sf package’s
@@ -572,7 +587,7 @@ R
-
+
To install R, Windows users should select “Download R
for Windows” from RStudio and CRAN’s cloud download page, which will
@@ -589,7 +604,7 @@
Windows
-
+
To install R, macOS users should select “Download R for
macOS” from RStudio and CRAN’s cloud download page, which will
@@ -607,7 +622,7 @@
MacOS
-
+
To install R, Linux users should select “Download R for
Linux” from RStudio and CRAN’s cloud download page, which will
diff --git a/instructor/01-intro-to-r.html b/instructor/01-intro-to-r.html
index c723b2c5..f04acc70 100644
--- a/instructor/01-intro-to-r.html
+++ b/instructor/01-intro-to-r.html
@@ -467,7 +467,7 @@
R
Instructor Note
-
+
In interest of time, focus on one way of creating the folders. You
can showcase an alternative method with just one example.
diff --git a/instructor/02-data-structures.html b/instructor/02-data-structures.html
index cc9b9b51..08e295da 100644
--- a/instructor/02-data-structures.html
+++ b/instructor/02-data-structures.html
@@ -546,7 +546,7 @@ Exercise
Show me the solution
-
+
combined_vector <- c(abcd_vector, numeric_vector)
combined_vector
diff --git a/instructor/03-explore-data.html b/instructor/03-explore-data.html
index f73b778c..ae5adfef 100644
--- a/instructor/03-explore-data.html
+++ b/instructor/03-explore-data.html
@@ -419,8 +419,7 @@ Exploring Data framessingle type of data (e.g., characters, numeric,
factors). For example, here is a figure depicting a data frame
comprising a numeric, a character, and a logical vector.
-
Source:
-Data
+
Show me the solution
-Correct answer: c. EPSG:3035
Challenge: CRS for calculating shortest
-
+
Correct answer: b. EPSG:28992
@@ -2933,7 +2932,7 @@ Challenge: Import line and point vector
-
+
R
@@ -3298,7 +3297,7 @@ Challenge: Attributes for different spatial
-
+
- To find the number of attributes, we use the
ncol()
@@ -3539,7 +3538,7 @@ Challenge
Show me the solution
-
+
- To create the new object, we first need to see which value of the
@@ -3815,7 +3814,7 @@
Challenge: Plot line width by attribute
Show me the solution
-
+
R
@@ -3913,7 +3912,7 @@ Challenge: Plot lines by attributes
Show me the solution
-
+
R
@@ -3990,7 +3989,7 @@ Challenge: Plot polygon by attribute
Show me the solution
-
+
R
@@ -5041,7 +5040,7 @@ Challenge
Show me the solution
-
+
+units=m in the output of the code above tells us that
our data is in meters (m).
@@ -5142,7 +5141,7 @@ OUTPUT<
been working with is a single band raster. This means that there is only
one dataset stored in the raster: surface elevation in meters for one
time period.
-
A raster dataset can contain one or more bands. We can view the
+Single- and multi-band raster
A raster dataset can contain one or more bands. We can view the
number of bands in a raster using the nlyr() function.
R
@@ -5158,7 +5157,7 @@ OUTPUT<
This dataset has only 1 band. However, raster data can also be
multi-band, meaning that one raster file contains data for more than one
variable or time period for each cell. We will discuss multi-band raster
-data in a later
+data in a later
episode.
Creating a histogram of raster values
@@ -5229,7 +5228,7 @@ Challenge: Explore raster metadata
Show me the solution
-
+
R
@@ -6471,7 +6470,7 @@ Challenge: Explore CHM Raster Values
Show me the solution
-
+
R
@@ -7248,7 +7247,7 @@ Challenge: import an interactive basemap layer
-
+
R
@@ -7271,10 +7270,10 @@ R bringToFront = TRUE))
-
@@ -7633,7 +7632,7 @@ Challenge: Conservation rules have
-
+
R
diff --git a/images.html b/images.html
index f0b3b309..f3560a0b 100644
--- a/images.html
+++ b/images.html
@@ -432,8 +432,8 @@ Figure 1
Exploring Data Frames & Data frame Manipulation with dplyrExploring Data framesData frame Manipulation with dplyr
Figure 1
-
Source:
-Data
+
+
Source: Data
Carpentry R for Social Scientists
Introduction to visualisationIntroduction to VisualisationWriting data
@@ -546,7 +546,8 @@ Figure 1
Raster plot with ggplot2 using the viridis color scale
Figure 2
-
+
+Single- and multi-band raster
Figure 3
Figure 4
diff --git a/index.html b/index.html
index 19fbe1b6..65018523 100644
--- a/index.html
+++ b/index.html
@@ -345,13 +345,28 @@
This is a new lesson built with The Carpentries
Workbench.
-
-
-
-
+ -->
Overview
This workshop is designed to be run on your local machine. First, you
will need to download the data we use in the workshop. Then, you need to
@@ -432,7 +447,7 @@
GDAL, GEOS, and PROJ.4
Windows
-
+
To install the geospatial libraries, install the latest version RTools
@@ -444,7 +459,7 @@ Windows
-
+
Install the geospatial libraries individually using homebrew. Open Terminal.app and run the
following commands:
@@ -465,7 +480,7 @@ BASH
Linux
-
+
Steps for installing the geospatial libraries will vary based on
which form of Linux you are using. These instructions are adapted from
@@ -521,7 +536,7 @@
UDUNITS
Linux
-
+
Steps for installing the geospatial will vary based on which form of
Linux you are using. These instructions are adapted from the sf package’s
@@ -572,7 +587,7 @@ R
-
+
To install R, Windows users should select “Download R
for Windows” from RStudio and CRAN’s cloud download page, which will
@@ -589,7 +604,7 @@
Windows
-
+
To install R, macOS users should select “Download R for
macOS” from RStudio and CRAN’s cloud download page, which will
@@ -607,7 +622,7 @@
MacOS
-
+
To install R, Linux users should select “Download R for
Linux” from RStudio and CRAN’s cloud download page, which will
diff --git a/instructor/01-intro-to-r.html b/instructor/01-intro-to-r.html
index c723b2c5..f04acc70 100644
--- a/instructor/01-intro-to-r.html
+++ b/instructor/01-intro-to-r.html
@@ -467,7 +467,7 @@
R
Instructor Note
-
+
In interest of time, focus on one way of creating the folders. You
can showcase an alternative method with just one example.
diff --git a/instructor/02-data-structures.html b/instructor/02-data-structures.html
index cc9b9b51..08e295da 100644
--- a/instructor/02-data-structures.html
+++ b/instructor/02-data-structures.html
@@ -546,7 +546,7 @@ Exercise
Show me the solution
-
+
combined_vector <- c(abcd_vector, numeric_vector)
combined_vector
diff --git a/instructor/03-explore-data.html b/instructor/03-explore-data.html
index f73b778c..ae5adfef 100644
--- a/instructor/03-explore-data.html
+++ b/instructor/03-explore-data.html
@@ -419,8 +419,7 @@ Exploring Data framessingle type of data (e.g., characters, numeric,
factors). For example, here is a figure depicting a data frame
comprising a numeric, a character, and a logical vector.
-
Source:
-Data
+
Correct answer: b. EPSG:28992
Challenge: Import line and point vector
-
+
R
@@ -3298,7 +3297,7 @@ Challenge: Attributes for different spatial
-
+
- To find the number of attributes, we use the
ncol()
@@ -3539,7 +3538,7 @@ Challenge
Show me the solution
-
+
- To create the new object, we first need to see which value of the
@@ -3815,7 +3814,7 @@
Challenge: Plot line width by attribute
Show me the solution
-
+
R
@@ -3913,7 +3912,7 @@ Challenge: Plot lines by attributes
Show me the solution
-
+
R
@@ -3990,7 +3989,7 @@ Challenge: Plot polygon by attribute
Show me the solution
-
+
R
@@ -5041,7 +5040,7 @@ Challenge
Show me the solution
-
+
+units=m in the output of the code above tells us that
our data is in meters (m).
@@ -5142,7 +5141,7 @@ OUTPUT<
been working with is a single band raster. This means that there is only
one dataset stored in the raster: surface elevation in meters for one
time period.
-
A raster dataset can contain one or more bands. We can view the
+Single- and multi-band raster
A raster dataset can contain one or more bands. We can view the
number of bands in a raster using the nlyr() function.
R
@@ -5158,7 +5157,7 @@ OUTPUT<
This dataset has only 1 band. However, raster data can also be
multi-band, meaning that one raster file contains data for more than one
variable or time period for each cell. We will discuss multi-band raster
-data in a later
+data in a later
episode.
Creating a histogram of raster values
@@ -5229,7 +5228,7 @@ Challenge: Explore raster metadata
Show me the solution
-
+
R
@@ -6471,7 +6470,7 @@ Challenge: Explore CHM Raster Values
Show me the solution
-
+
R
@@ -7248,7 +7247,7 @@ Challenge: import an interactive basemap layer
-
+
R
@@ -7271,10 +7270,10 @@ R bringToFront = TRUE))
-
@@ -7633,7 +7632,7 @@ Challenge: Conservation rules have
-
+
R
diff --git a/images.html b/images.html
index f0b3b309..f3560a0b 100644
--- a/images.html
+++ b/images.html
@@ -432,8 +432,8 @@ Figure 1
Exploring Data Frames & Data frame Manipulation with dplyrExploring Data framesData frame Manipulation with dplyr
Figure 1
-
Source:
-Data
+
+
Source: Data
Carpentry R for Social Scientists
Introduction to visualisationIntroduction to VisualisationWriting data
@@ -546,7 +546,8 @@ Figure 1
Raster plot with ggplot2 using the viridis color scale
Figure 2
-
+
+Single- and multi-band raster
Figure 3
Figure 4
diff --git a/index.html b/index.html
index 19fbe1b6..65018523 100644
--- a/index.html
+++ b/index.html
@@ -345,13 +345,28 @@
This is a new lesson built with The Carpentries
Workbench.
-
-
-
-
+ -->
Overview
This workshop is designed to be run on your local machine. First, you
will need to download the data we use in the workshop. Then, you need to
@@ -432,7 +447,7 @@
GDAL, GEOS, and PROJ.4
Windows
-
+
To install the geospatial libraries, install the latest version RTools
@@ -444,7 +459,7 @@ Windows
-
+
Install the geospatial libraries individually using homebrew. Open Terminal.app and run the
following commands:
@@ -465,7 +480,7 @@ BASH
Linux
-
+
Steps for installing the geospatial libraries will vary based on
which form of Linux you are using. These instructions are adapted from
@@ -521,7 +536,7 @@
UDUNITS
Linux
-
+
Steps for installing the geospatial will vary based on which form of
Linux you are using. These instructions are adapted from the sf package’s
@@ -572,7 +587,7 @@ R
-
+
To install R, Windows users should select “Download R
for Windows” from RStudio and CRAN’s cloud download page, which will
@@ -589,7 +604,7 @@
Windows
-
+
To install R, macOS users should select “Download R for
macOS” from RStudio and CRAN’s cloud download page, which will
@@ -607,7 +622,7 @@
MacOS
-
+
To install R, Linux users should select “Download R for
Linux” from RStudio and CRAN’s cloud download page, which will
diff --git a/instructor/01-intro-to-r.html b/instructor/01-intro-to-r.html
index c723b2c5..f04acc70 100644
--- a/instructor/01-intro-to-r.html
+++ b/instructor/01-intro-to-r.html
@@ -467,7 +467,7 @@
R
Instructor Note
-
+
In interest of time, focus on one way of creating the folders. You
can showcase an alternative method with just one example.
diff --git a/instructor/02-data-structures.html b/instructor/02-data-structures.html
index cc9b9b51..08e295da 100644
--- a/instructor/02-data-structures.html
+++ b/instructor/02-data-structures.html
@@ -546,7 +546,7 @@ Exercise
Show me the solution
-
+
combined_vector <- c(abcd_vector, numeric_vector)
combined_vector
diff --git a/instructor/03-explore-data.html b/instructor/03-explore-data.html
index f73b778c..ae5adfef 100644
--- a/instructor/03-explore-data.html
+++ b/instructor/03-explore-data.html
@@ -419,8 +419,7 @@ Exploring Data framessingle type of data (e.g., characters, numeric,
factors). For example, here is a figure depicting a data frame
comprising a numeric, a character, and a logical vector.
-
Source:
-Data
+
R
@@ -3298,7 +3297,7 @@ Challenge: Attributes for different spatial
-
+
- To find the number of attributes, we use the
ncol()
@@ -3539,7 +3538,7 @@ Challenge
Show me the solution
-
+
- To create the new object, we first need to see which value of the
@@ -3815,7 +3814,7 @@
Challenge: Plot line width by attribute
Show me the solution
-
+
R
@@ -3913,7 +3912,7 @@ Challenge: Plot lines by attributes
Show me the solution
-
+
R
@@ -3990,7 +3989,7 @@ Challenge: Plot polygon by attribute
Show me the solution
-
+
R
@@ -5041,7 +5040,7 @@ Challenge
Show me the solution
-
+
+units=m in the output of the code above tells us that
our data is in meters (m).
@@ -5142,7 +5141,7 @@ OUTPUT<
been working with is a single band raster. This means that there is only
one dataset stored in the raster: surface elevation in meters for one
time period.
-
A raster dataset can contain one or more bands. We can view the
+Single- and multi-band raster
A raster dataset can contain one or more bands. We can view the
number of bands in a raster using the nlyr() function.
R
@@ -5158,7 +5157,7 @@ OUTPUT<
This dataset has only 1 band. However, raster data can also be
multi-band, meaning that one raster file contains data for more than one
variable or time period for each cell. We will discuss multi-band raster
-data in a later
+data in a later
episode.
Creating a histogram of raster values
@@ -5229,7 +5228,7 @@ Challenge: Explore raster metadata
Show me the solution
-
+
R
@@ -6471,7 +6470,7 @@ Challenge: Explore CHM Raster Values
Show me the solution
-
+
R
@@ -7248,7 +7247,7 @@ Challenge: import an interactive basemap layer
-
+
R
@@ -7271,10 +7270,10 @@ R bringToFront = TRUE))
-
@@ -7633,7 +7632,7 @@ Challenge: Conservation rules have
-
+
R
diff --git a/images.html b/images.html
index f0b3b309..f3560a0b 100644
--- a/images.html
+++ b/images.html
@@ -432,8 +432,8 @@ Figure 1
Exploring Data Frames & Data frame Manipulation with dplyrExploring Data framesData frame Manipulation with dplyr
Figure 1
-
Source:
-Data
+
+
Source: Data
Carpentry R for Social Scientists
Introduction to visualisationIntroduction to VisualisationWriting data
@@ -546,7 +546,8 @@ Figure 1
Raster plot with ggplot2 using the viridis color scale
Figure 2
-
+
+Single- and multi-band raster
Figure 3
Figure 4
diff --git a/index.html b/index.html
index 19fbe1b6..65018523 100644
--- a/index.html
+++ b/index.html
@@ -345,13 +345,28 @@
This is a new lesson built with The Carpentries
Workbench.
-
-
-
-
+ -->
Overview
This workshop is designed to be run on your local machine. First, you
will need to download the data we use in the workshop. Then, you need to
@@ -432,7 +447,7 @@
GDAL, GEOS, and PROJ.4
Windows
-
+
To install the geospatial libraries, install the latest version RTools
@@ -444,7 +459,7 @@ Windows
-
+
Install the geospatial libraries individually using homebrew. Open Terminal.app and run the
following commands:
@@ -465,7 +480,7 @@ BASH
Linux
-
+
Steps for installing the geospatial libraries will vary based on
which form of Linux you are using. These instructions are adapted from
@@ -521,7 +536,7 @@
UDUNITS
Linux
-
+
Steps for installing the geospatial will vary based on which form of
Linux you are using. These instructions are adapted from the sf package’s
@@ -572,7 +587,7 @@ R
-
+
To install R, Windows users should select “Download R
for Windows” from RStudio and CRAN’s cloud download page, which will
@@ -589,7 +604,7 @@
Windows
-
+
To install R, macOS users should select “Download R for
macOS” from RStudio and CRAN’s cloud download page, which will
@@ -607,7 +622,7 @@
MacOS
-
+
To install R, Linux users should select “Download R for
Linux” from RStudio and CRAN’s cloud download page, which will
diff --git a/instructor/01-intro-to-r.html b/instructor/01-intro-to-r.html
index c723b2c5..f04acc70 100644
--- a/instructor/01-intro-to-r.html
+++ b/instructor/01-intro-to-r.html
@@ -467,7 +467,7 @@
R
Instructor Note
-
+
In interest of time, focus on one way of creating the folders. You
can showcase an alternative method with just one example.
diff --git a/instructor/02-data-structures.html b/instructor/02-data-structures.html
index cc9b9b51..08e295da 100644
--- a/instructor/02-data-structures.html
+++ b/instructor/02-data-structures.html
@@ -546,7 +546,7 @@ Exercise
Show me the solution
-
+
combined_vector <- c(abcd_vector, numeric_vector)
combined_vector
diff --git a/instructor/03-explore-data.html b/instructor/03-explore-data.html
index f73b778c..ae5adfef 100644
--- a/instructor/03-explore-data.html
+++ b/instructor/03-explore-data.html
@@ -419,8 +419,7 @@ Exploring Data framessingle type of data (e.g., characters, numeric,
factors). For example, here is a figure depicting a data frame
comprising a numeric, a character, and a logical vector.
-
Source:
-Data
+
- To find the number of attributes, we use the
ncol()@@ -3539,7 +3538,7 @@Challenge
Show me the solution
-+- To create the new object, we first need to see which value of the
@@ -3815,7 +3814,7 @@
Challenge: Plot line width by attribute
Show me the solution
-+R @@ -3913,7 +3912,7 @@
Challenge: Plot lines by attributes
Show me the solution
-+R @@ -3990,7 +3989,7 @@
Challenge: Plot polygon by attribute
Show me the solution
-+R @@ -5041,7 +5040,7 @@
Challenge
Show me the solution
-+
@@ -5142,7 +5141,7 @@+units=min the output of the code above tells us that our data is in meters (m).OUTPUT< been working with is a single band raster. This means that there is only one dataset stored in the raster: surface elevation in meters for one time period. -
A raster dataset can contain one or more bands. We can view the +
Single- and multi-band raster A raster dataset can contain one or more bands. We can view the number of bands in a raster using the
nlyr()function.R @@ -5158,7 +5157,7 @@
OUTPUT<
This dataset has only 1 band. However, raster data can also be multi-band, meaning that one raster file contains data for more than one variable or time period for each cell. We will discuss multi-band raster -data in a later +data in a later episode.
Creating a histogram of raster values
@@ -5229,7 +5228,7 @@Challenge: Explore raster metadata
Show me the solution
-+R @@ -6471,7 +6470,7 @@
Challenge: Explore CHM Raster Values
Show me the solution
-+R @@ -7248,7 +7247,7 @@
Challenge: import an interactive basemap layer -
+@@ -7633,7 +7632,7 @@-R @@ -7271,10 +7270,10 @@
R bringToFront = TRUE))
Challenge: Conservation rules have -
+R diff --git a/images.html b/images.html index f0b3b309..f3560a0b 100644 --- a/images.html +++ b/images.html @@ -432,8 +432,8 @@
Figure 1
Exploring Data Frames & Data frame Manipulation with dplyrExploring Data framesData frame Manipulation with dplyr
Figure 1
-
Source: -Data + +
Source: Data Carpentry R for Social ScientistsIntroduction to visualisationIntroduction to VisualisationWriting data
@@ -546,7 +546,8 @@Figure 1
Raster plot withggplot2using the viridis color scale
Figure 2
-
+ +Single- and multi-band raster
Figure 3
Figure 4
diff --git a/index.html b/index.html index 19fbe1b6..65018523 100644 --- a/index.html +++ b/index.html @@ -345,13 +345,28 @@This is a new lesson built with The Carpentries Workbench.
- - - -+ -->Overview
This workshop is designed to be run on your local machine. First, you will need to download the data we use in the workshop. Then, you need to @@ -432,7 +447,7 @@
GDAL, GEOS, and PROJ.4
Windows
-+@@ -444,7 +459,7 @@To install the geospatial libraries, install the latest version RTools
Windows
-+Install the geospatial libraries individually using homebrew. Open Terminal.app and run the following commands:
@@ -465,7 +480,7 @@BASH
Linux
-+Steps for installing the geospatial libraries will vary based on which form of Linux you are using. These instructions are adapted from @@ -521,7 +536,7 @@
UDUNITS
Linux
-+Steps for installing the geospatial will vary based on which form of Linux you are using. These instructions are adapted from the
sfpackage’s @@ -572,7 +587,7 @@R -
+To install
R, Windows users should select “Download R for Windows” from RStudio and CRAN’s cloud download page, which will @@ -589,7 +604,7 @@Windows
-+To install
R, macOS users should select “Download R for macOS” from RStudio and CRAN’s cloud download page, which will @@ -607,7 +622,7 @@MacOS
-+To install
R, Linux users should select “Download R for Linux” from RStudio and CRAN’s cloud download page, which will diff --git a/instructor/01-intro-to-r.html b/instructor/01-intro-to-r.html index c723b2c5..f04acc70 100644 --- a/instructor/01-intro-to-r.html +++ b/instructor/01-intro-to-r.html @@ -467,7 +467,7 @@R
-Instructor Note+In interest of time, focus on one way of creating the folders. You can showcase an alternative method with just one example.
diff --git a/instructor/02-data-structures.html b/instructor/02-data-structures.html index cc9b9b51..08e295da 100644 --- a/instructor/02-data-structures.html +++ b/instructor/02-data-structures.html @@ -546,7 +546,7 @@Exercise
Show me the solution
-+
diff --git a/instructor/03-explore-data.html b/instructor/03-explore-data.html index f73b778c..ae5adfef 100644 --- a/instructor/03-explore-data.html +++ b/instructor/03-explore-data.html @@ -419,8 +419,7 @@combined_vector <- c(abcd_vector, numeric_vector) combined_vectorExploring Data framessingle type of data (e.g., characters, numeric, factors). For example, here is a figure depicting a data frame comprising a numeric, a character, and a logical vector. -
Source: -Data +
- To create the new object, we first need to see which value of the
@@ -3815,7 +3814,7 @@