Broken Links (#61)

tlyon3 · web-flow · commit 3a6250ef8601 · 2021-09-08T09:21:07.000-04:00
* 2D-histogram

* 3d-axes

* main link doesn't work

* 3d-surface-plots

* 3d-volume

* axes

* cone-plot

* fixed dot-plot link

* graphing-multiple-chart-types

* images

* interactive-html-export

* legend

* sankey-diagram

* shapes

* table fix permalink

* text-and-annotations

* time-series
diff --git a/julia/2D-Histogram.md b/julia/2D-Histogram.md
@@ -51,7 +51,7 @@ plot(
 )
 ```
 
-Density heatmaps can also be [faceted](/julia/facet-plots/):
+Density heatmaps can also be faceted:
 
 ```julia
 using PlotlyJS, CSV, DataFrames
diff --git a/julia/3d-axes.md b/julia/3d-axes.md
@@ -29,7 +29,7 @@ jupyter:
 attributes such as `xaxis`, `yaxis` and `zaxis` parameters, in order to
 set the range, title, ticks, color etc. of the axes.
 
-For creating 3D charts, see [this page](https://plotly.com/julia/3d-charts/).
+<!-- For creating 3D charts, see [this page](https://plotly.com/julia/3d-charts/). -->
 
 ```julia
 using PlotlyJS
diff --git a/julia/3d-iso-surface-plots.md b/julia/3d-iso-surface-plots.md
@@ -19,11 +19,13 @@ jupyter:
     name: 3D Isosurface Plots
     order: 10
     page_type: example_index
-    permalink: juila/3d-isosurface-plots/
+    permalink: julia/3d-isosurface-plots/
     redirect_from: julia/isosurfaces-with-marching-cubes/
     thumbnail: thumbnail/isosurface.jpg
 ---
 
+# NOTE: this permalink does not work
+
 With `go.Isosurface`, you can plot [isosurface contours](https://en.wikipedia.org/wiki/Isosurface) of a scalar field `value`, which is defined on `x`, `y` and `z` coordinates.
 
 #### Basic Isosurface
diff --git a/julia/3d-surface-plots.md b/julia/3d-surface-plots.md
@@ -71,7 +71,7 @@ plot(surface(z=z_data, x=x, y=y), layout)
 
 #### Surface Plot With Contours
 
-Display and customize contour data for each axis using the `contours` attribute ([reference](plotly.com/julia/reference/surface/#surface-contours)).
+Display and customize contour data for each axis using the `contours` attribute.
 
 ```julia
 using PlotlyJS, CSV, HTTP, DataFrames
diff --git a/julia/3d-volume.md b/julia/3d-volume.md
@@ -23,7 +23,7 @@ jupyter:
     thumbnail: thumbnail/3d-volume-plots.jpg
 ---
 
-A volume plot with `volume` shows several partially transparent isosurfaces for volume rendering. The API of `volume` is close to the one of `isosurface`. However, whereas [isosurface plots](/julia/3d-isosurface-plots/) show all surfaces with the same opacity, tweaking the `opacityscale` parameter of `volume` results in a depth effect and better volume rendering.
+A volume plot with `volume` shows several partially transparent isosurfaces for volume rendering. The API of `volume` is close to the one of `isosurface`. However, whereas isosurface plots show all surfaces with the same opacity, tweaking the `opacityscale` parameter of `volume` results in a depth effect and better volume rendering.
 
 ## Basic volume plot
 
@@ -253,6 +253,4 @@ plot(volume(
 
 See https://plotly.com/julia/reference/volume/ for more information and chart attribute options!
 
-#### See also
 
-[3D isosurface documentation](/julia/3d-isosurface-plots/)
diff --git a/julia/axes.md b/julia/axes.md
@@ -25,30 +25,27 @@ jupyter:
     thumbnail: thumbnail/axes.png
 ---
 
-This tutorial explain how to set the properties of [2-dimensional Cartesian axes](/julia/figure-structure/#2d-cartesian-trace-types-and-subplots), namely [`layout.xaxis`](/julia/reference/layout/xaxis/) and [`layout.yaxis`](julia/reference/layout/xaxis/).
+This tutorial explain how to set the properties of [2-dimensional Cartesian axes], namely [`layout.xaxis`](/julia/reference/layout/xaxis/) and [`layout.yaxis`](/julia/reference/layout/yaxis/).
 
 Other kinds of subplots and axes are described in other tutorials:
 
 - [3D axes](/julia/3d-axes) The axis object is [`layout.Scene`](/julia/reference/layout/scene/)
 - [Polar axes](/julia/polar-chart/). The axis object is [`layout.Polar`](/julia/reference/layout/polar/)
-- [Ternary axes](/julia/ternary-plots). The axis object is [`layout.Ternary`](/julia/reference/layout/ternary/)
-- [Geo axes](/julia/map-configuration/). The axis object is [`layout.Geo`](/julia/reference/layout/geo/)
-- [Mapbox axes](/julia/mapbox-layers/). The axis object is [`layout.Mapbox`](/julia/reference/layout/mapbox/)
-- [Color axes](/julia/colorscales/). The axis object is [`layout.Coloraxis`](/julia/reference/layout/coloraxis/).
 
-**See also** the tutorials on [facet plots](/julia/facet-plots/), [subplots](/julia/subplots) and [multiple axes](/julia/multiple-axes/).
+
+**See also** the tutorials on [subplots](/julia/subplots) and [multiple axes](/julia/multiple-axes/).
 
 ### 2-D Cartesian Axis Types and Auto-Detection
 
 The different types of Cartesian axes are configured via the `xaxis.type` or `yaxis.type` attribute, which can take on the following values:
 
 - `'linear'` as described in this page
-- `'log'` (see the [log plot tutorial](/julia/log-plots/))
+- `'log'` 
 - `'date'` (see the [tutorial on timeseries](/julia/time-series/))
-- `'category'` (see the [categorical axes tutorial](/julia/categorical-axes/))
-- `'multicategory'` (see the [categorical axes tutorial](/julia/categorical-axes/))
+- `'category'`
+- `'multicategory'`
 
-The axis type is auto-detected by looking at data from the first [trace](/julia/figure-structure/) linked to this axis:
+The axis type is auto-detected by looking at data from the first [trace] linked to this axis:
 
 - First check for `multicategory`, then `date`, then `category`, else default to `linear` (`log` is never automatically selected)
 - `multicategory` is just a shape test: is the array nested?
diff --git a/julia/cone-plot.md b/julia/cone-plot.md
@@ -24,7 +24,7 @@ jupyter:
     thumbnail: thumbnail/3dcone.png
 ---
 
-A cone plot is the 3D equivalent of a 2D [quiver plot](/julia/quiver-plots/), i.e., it represents a 3D vector field using cones to represent the direction and norm of the vectors. 3-D coordinates are given by `x`, `y` and `z`, and the coordinates of the vector field by `u`, `v` and `w`.
+A cone plot is the 3D equivalent of a 2D [quiver plot], i.e., it represents a 3D vector field using cones to represent the direction and norm of the vectors. 3-D coordinates are given by `x`, `y` and `z`, and the coordinates of the vector field by `u`, `v` and `w`.
 
 ### Basic 3D Cone
 
diff --git a/julia/dot-plots.md b/julia/dot-plots.md
@@ -25,7 +25,7 @@ jupyter:
 
 #### Basic Dot Plot
 
-Dot plots (also known as [Cleveland dot plots](<https://en.wikipedia.org/wiki/Dot_plot_(statistics)>)) are [scatter plots](https://plotly.com/julia/line-and-scatter/) with one categorical axis and one continuous axis. They can be used to show changes between two (or more) points in time or between two (or more) conditions. Compared to a [bar chart](/julia/bar-charts/), dot plots can be less cluttered and allow for an easier comparison between conditions.
+Dot plots (also known as [Cleveland dot plots](https://en.wikipedia.org/wiki/Dot_plot_(statistics))) are [scatter plots](https://plotly.com/julia/line-and-scatter/) with one categorical axis and one continuous axis. They can be used to show changes between two (or more) points in time or between two (or more) conditions. Compared to a [bar chart](/julia/bar-charts/), dot plots can be less cluttered and allow for an easier comparison between conditions.
 
 For the same data, we show below how to create a dot plot using `scatter`.
 
diff --git a/julia/graphing-multiple-chart-types.md b/julia/graphing-multiple-chart-types.md
@@ -25,7 +25,7 @@ jupyter:
 
 ### Chart Types versus Trace Types
 
-Plotly figures support defining [subplots](/julia/subplots/) of various types (e.g. cartesian, [polar](/julia/polar-chart/), [3-dimensional](/julia/3d-charts/), [maps](/julia/maps/) etc) with attached traces of various compatible types (e.g. scatter, bar, choropleth, surface etc). This means that **Plotly figures are not constrained to representing a fixed set of "chart types"** such as scatter plots only or bar charts only or line charts only: any subplot can contain multiple traces of different types.
+Plotly figures support defining [subplots](/julia/subplots/) of various types (e.g. cartesian, [polar](/julia/polar-chart/), [3-dimensional], [maps] etc) with attached traces of various compatible types (e.g. scatter, bar, choropleth, surface etc). This means that **Plotly figures are not constrained to representing a fixed set of "chart types"** such as scatter plots only or bar charts only or line charts only: any subplot can contain multiple traces of different types.
 
 
 ### Multiple Trace Types
diff --git a/julia/images.md b/julia/images.md
@@ -25,7 +25,7 @@ jupyter:
 
 #### Add a Background Image
 
-In this page we explain how to add static, non-interactive images as background, logo or annotation images to a figure. For exploring image data in interactive charts, see the [tutorial on displaying image data](/julia/imshow).
+In this page we explain how to add static, non-interactive images as background, logo or annotation images to a figure. For exploring image data in interactive charts, see the [tutorial on displaying image data].
 
 A background image can be added to the layout of a figure with the `images` parameter of `gLayout`. The
 `source` attribute of a `layout.Image` should be the URL of the image.
diff --git a/julia/interactve-html-export.md b/julia/interactve-html-export.md
@@ -27,7 +27,7 @@ jupyter:
 
 ### Interactive vs Static Export
 
-Plotly figures are interactive when viewed in a web browser: you can hover over data points, pan and zoom axes, and show and hide traces by clicking or double-clicking on the legend. You can export figures either to [static image file formats like PNG, JPEG, SVG or PDF](/julia/static-image-export/) or you can export them to HTML files which can be opened in a browser. This page explains how to do the latter.
+Plotly figures are interactive when viewed in a web browser: you can hover over data points, pan and zoom axes, and show and hide traces by clicking or double-clicking on the legend. You can export figures either to [static image file formats like PNG, JPEG, SVG or PDF] or you can export them to HTML files which can be opened in a browser. This page explains how to do the latter.
 
 ### Saving to an HTML file
 
diff --git a/julia/legend.md b/julia/legend.md
@@ -26,11 +26,11 @@ jupyter:
 
 ### Trace Types, Legends and Color Bars
 
-[Traces](/julia/figure-structure) of most types can be optionally associated with a single legend item in the [legend](/julia/legend/). Whether or not a given trace appears in the legend is controlled via the `showlegend` attribute. Traces which are their own subplots (see above) do not support this, with the exception of traces of type `pie` and `funnelarea` for which every distinct color represented in the trace gets a separate legend item. Users may show or hide traces by clicking or double-clicking on their associated legend item. Traces that support legend items also support the `legendgroup` attribute, and all traces with the same legend group are treated the same way during click/double-click interactions.
+[Traces] of most types can be optionally associated with a single legend item in the [legend](/julia/legend/). Whether or not a given trace appears in the legend is controlled via the `showlegend` attribute. Traces which are their own subplots (see above) do not support this, with the exception of traces of type `pie` and `funnelarea` for which every distinct color represented in the trace gets a separate legend item. Users may show or hide traces by clicking or double-clicking on their associated legend item. Traces that support legend items also support the `legendgroup` attribute, and all traces with the same legend group are treated the same way during click/double-click interactions.
 
-The fact that legend items are linked to traces means that when using [discrete color](/julia/discrete-color/), a figure must have one trace per color in order to get a meaningful legend.
+The fact that legend items are linked to traces means that when using [discrete color], a figure must have one trace per color in order to get a meaningful legend.
 
-Traces which support [continuous color](/julia/colorscales/) can also be associated with color axes in the layout via the `coloraxis` attribute. Multiple traces can be linked to the same color axis. Color axes have a legend-like component called color bars. Alternatively, color axes can be configured within the trace itself.
+Traces which support [continuous color] can also be associated with color axes in the layout via the `coloraxis` attribute. Multiple traces can be linked to the same color axis. Color axes have a legend-like component called color bars. Alternatively, color axes can be configured within the trace itself.
 
 ### Legends with DataFrame
 
@@ -55,7 +55,7 @@ plot(
 
 ### Legend Order
 
-By default, Plotly lays out legend items in the order in which values appear in the underlying data. Every function also includes a `category_orders` keyword argument which can be used to control [the order in which categorical axes are drawn](/julia/categorical-axes/), but beyond that can also control the order in which legend items appear, and [the order in which facets are laid out](/julia/facet-plots/).
+By default, Plotly lays out legend items in the order in which values appear in the underlying data. Every function also includes a `category_orders` keyword argument which can be used to control [the order in which categorical axes are drawn], but beyond that can also control the order in which legend items appear, and [the order in which facets are laid out].
 
 ```julia
 using PlotlyJS, CSV, DataFrames
@@ -153,7 +153,7 @@ plot(
 
 ### Legend Positioning
 
-Legends have an anchor point, which can be set to a point within the legend using `layout.legend.xanchor` and `layout.legend.yanchor`. The coordinate of the anchor can be positioned with `layout.legend.x` and `layout.legend.y` in [paper coordinates](/julia/figure-structure/). Note that the plot margins will grow so as to accommodate the legend. The legend may also be placed within the plotting area.
+Legends have an anchor point, which can be set to a point within the legend using `layout.legend.xanchor` and `layout.legend.yanchor`. The coordinate of the anchor can be positioned with `layout.legend.x` and `layout.legend.y` in [paper coordinates]. Note that the plot margins will grow so as to accommodate the legend. The legend may also be placed within the plotting area.
 
 ```julia
 using PlotlyJS, DataFrames, CSV
diff --git a/julia/sankey-diagram.md b/julia/sankey-diagram.md
@@ -129,7 +129,7 @@ plot(
 
 ### Hovertemplate and customdata of Sankey diagrams
 
-Links and nodes have their own hovertemplate, in which link- or node-specific attributes can be displayed. To add more data to links and nodes, it is possible to use the `customdata` attribute of `link` and `nodes`, as in the following example. For more information about hovertemplate and customdata, please see the [tutorial on hover text](/julia/hover-text-and-formatting/).
+Links and nodes have their own hovertemplate, in which link- or node-specific attributes can be displayed. To add more data to links and nodes, it is possible to use the `customdata` attribute of `link` and `nodes`, as in the following example. For more information about hovertemplate and customdata, please see the [tutorial on hover text].
 
 ```julia
 using PlotlyJS
diff --git a/julia/shapes.md b/julia/shapes.md
@@ -253,7 +253,7 @@ p = plot(
 
 #### Highlighting Time Series Regions with Rectangle Shapes
 
-_Note:_ there are [special methods `add_hline`, `add_vline`, `add_hrect` and `add_vrect` for the common cases of wanting to draw horizontal or vertical lines or rectangles](/julia/horizontal-vertical-shapes/) that are fixed to data coordinates in one axis and absolutely positioned in another.
+_Note:_ there are [special methods `add_hline`, `add_vline`, `add_hrect` and `add_vrect` for the common cases of wanting to draw horizontal or vertical lines or rectangles] that are fixed to data coordinates in one axis and absolutely positioned in another.
 
 ```julia
 using PlotlyJS
@@ -542,7 +542,7 @@ fig
 
 You can create layout shapes programmatically, but you can also draw shapes manually by setting the `dragmode` to one of the shape-drawing modes: `'drawline'`,`'drawopenpath'`, `'drawclosedpath'`, `'drawcircle'`, or `'drawrect'`. If you need to switch between different shape-drawing or other dragmodes (panning, selecting, etc.), [modebar buttons can be added](/julia/configuration-options#add-optional-shapedrawing-buttons-to-modebar) in the `config` to select the dragmode. If you switch to a different dragmode such as pan or zoom, you will need to select the drawing tool in the modebar to go back to shape drawing.
 
-This shape-drawing feature is particularly interesting for annotating graphs, in particular [image traces](/julia/imshow) or [layout images](/julia/images).
+This shape-drawing feature is particularly interesting for annotating graphs, in particular [image traces] or [layout images](/julia/images).
 
 Once you have drawn shapes, you can select and modify an existing shape by clicking on its boundary (note the arrow pointer). Its fillcolor turns to pink to highlight the activated shape and then you can
 
diff --git a/julia/table.md b/julia/table.md
@@ -19,7 +19,7 @@ jupyter:
     name: Tables
     order: 11
     page_type: example_index
-    permalink: julia-/table/
+    permalink: julia/table/
     thumbnail: thumbnail/table.gif
 ---
 
diff --git a/julia/text-and-annotations.md b/julia/text-and-annotations.md
@@ -106,7 +106,7 @@ plot([trace1, trace2, trace3])
 
 ### Controlling text fontsize with uniformtext
 
-For the [pie](/julia/pie-charts), [bar](/julia/bar-charts), [sunburst](/julia/sunburst-charts) and [treemap](/julia/treemap-charts) traces, it is possible to force all the text labels to have the same size thanks to the `uniformtext` layout parameter. The `minsize` attribute sets the font size, and the `mode` attribute sets what happens for labels which cannot fit with the desired fontsize: either `hide` them or `show` them with overflow.
+For the [pie](/julia/pie-charts), [bar](/julia/bar-charts), [sunburst] and [treemap] traces, it is possible to force all the text labels to have the same size thanks to the `uniformtext` layout parameter. The `minsize` attribute sets the font size, and the `mode` attribute sets what happens for labels which cannot fit with the desired fontsize: either `hide` them or `show` them with overflow.
 
 ```julia
 using PlotlyJS, CSV, DataFrames
@@ -148,7 +148,7 @@ plot(trace, layout)
 
 ### Controlling text fontsize with textfont
 
-The `textfont_size` parameter of the the [pie](/julia/pie-charts), [bar](/julia/bar-charts), [sunburst](/julia/sunburst-charts) and [treemap](/julia/treemap-charts) traces can be used to set the **maximum font size** used in the chart. Note that the `textfont` parameter sets the `insidetextfont` and `outsidetextfont` parameter, which can also be set independently.
+The `textfont_size` parameter of the the [pie](/julia/pie-charts), [bar](/julia/bar-charts), [sunburst] and [treemap] traces can be used to set the **maximum font size** used in the chart. Note that the `textfont` parameter sets the `insidetextfont` and `outsidetextfont` parameter, which can also be set independently.
 
 ```julia
 using PlotlyJS, CSV, DataFrames
@@ -396,7 +396,7 @@ plot(trace, layout)
 
 ### Positioning Text Annotations Absolutely
 
-By default, text annotations have `xref` and `yref` set to `"x"` and `"y"`, respectively, meaning that their x/y coordinates are with respect to the axes of the plot. This means that panning the plot will cause the annotations to move. Setting `xref` and/or `yref` to `"paper"` will cause the `x` and `y` attributes to be interpreted in [paper coordinates](/julia/figure-structure/#positioning-with-paper-container-coordinates-or-axis-domain-coordinates).
+By default, text annotations have `xref` and `yref` set to `"x"` and `"y"`, respectively, meaning that their x/y coordinates are with respect to the axes of the plot. This means that panning the plot will cause the annotations to move. Setting `xref` and/or `yref` to `"paper"` will cause the `x` and `y` attributes to be interpreted in [paper coordinates].
 
 
 Try panning or zooming in the following figure:
@@ -528,7 +528,7 @@ plot(trace)
 
 ### Set Date in Text Template
 
-The following example shows how to show date by setting [axis.type](https://plotly.com/julia/reference/layout/yaxis/#layout-yaxis-type) in [funnel charts](https://plotly.com/julia/funnel-charts/).
+The following example shows how to show date by setting [axis.type](https://plotly.com/julia/reference/layout/yaxis/#layout-yaxis-type) in [funnel charts].
 As you can see [textinfo](https://plotly.com/julia/reference/funnel/#funnel-textinfo) and [texttemplate](https://plotly.com/julia/reference/funnel/#funnel-texttemplate) have the same functionality when you want to determine 'just' the trace information on the graph.
 
 ```julia
diff --git a/julia/time-series.md b/julia/time-series.md

Original file line number	Diff line number	Diff line change
`@@ -51,7 +51,7 @@ plot(`
`51`	`51`	`)`
`52`	`52`	```
`53`	`53`
`54`		`-Density heatmaps can also be [faceted](/julia/facet-plots/):`
	`54`	`+Density heatmaps can also be faceted:`
`55`	`55`
`56`	`56`	```julia
`57`	`57`	`using PlotlyJS, CSV, DataFrames`