Updating exploring.md (#3035)

* Updating Exploring, with images

Images still need some trimming. Haven't explained logs yet.

* Typos and minor fixes

* Update exploring.md

* Expanding a bit on records mode

* Adding a caution box

* Log text, link fixes, remove infobox, fix phrasing, etc

* Whoops, missed a bracket

* Expanding the log facet section, thanks @thadguidry

And fixing some date-format information.

* Splitting into three pages

* Expanding on records mode

So helpful that we call it the "key column" in the wiki!

* Updates to all three pages

* Update facets.md

* Update sort/view

* Update exploring.md

* Update exploring.md

* Update exploring.md

docs/docs/manual/exploring.md

@@ -1,102 +1,78 @@
 ---
-
 id: exploring
-
 title: Exploring data
-
+sidebar_label: Overview
-sidebar_label: Exploring data
-
 ---
 
-
 ## Overview
 
+OpenRefine is a powerful tool for learning about your dataset, even if you don’t change a single character. In this section we cover different ways for sorting through, filtering, and viewing your data. 
 
-### Cell data types
+Unlike spreadsheets, OpenRefine doesn’t store formulas and display the output of those calculations; it only shows the value inside each cell. It doesn’t support cell colors or text formatting. 
 
+## Data types
 
+Each piece of information (each cell) in OpenRefine is assigned a data type. Some file formats, when imported, can set data types that are recognized by OpenRefine. Cells without an associated data type on import will be considered a “string” at first, but you can have OpenRefine convert cell contents into other data types later. This is set at the cell level, not at the column level. 
 
-*   Automatic recognition; colours, etc.
+You can see data types in action when you preview a new project: check the box that says “Attempt to parse cell text into numbers” and cells will be converted to the “number” data type based on their contents. You’ll see numbers change from black text to green if they are recognized.
-*   [https://github.com/OpenRefine/OpenRefine/wiki/Cell-Editing](https://github.com/OpenRefine/OpenRefine/wiki/Cell-Editing) 
-*   What an error looks like
 
+The data type will determine what you can do with the value. For example, if you want to add two values together, they must both be recognized as the number type. 
 
-### Null vs empty string
+You can check data types at any time by:
+*   clicking “edit” on a single cell (where you can also edit the type)
+*   creating a Custom Text Facet on a column, and inserting `type(value)` into the “Expression” field. This will generate the data type in the preview, and you can facet by data type if you press “OK.”
 
+The data types supported are:
+*   string (one or more text characters)
+*   number (one or more characters of numbers only)
+*   boolean (values of “true” or “false”)
+*   date (ISO-8601-compliant extended format with time in UTC: YYYY-MM-DDTHH:MM:SSZ)
 
+A “date” type is created when a text column is [transformed into dates](transforming#to-date), or when individual cells are set to have the data type “date.” 
 
-*   Viewing null values
+OpenRefine recognizes two further data types as a result of its own processes:
+*   error
+*   null
 
+An “error” data type is created when the cell is storing an error generated during a transformation in OpenRefine.
 
-### Rows vs. records
+A “null” data type is a special value which basically means “this cell has no value.” It’s used to differentiate between cells that have values such as “0” or “false” - or a cell that looks empty but has, for example, spaces in it. When you use `type(value)`, it will show you that the cell’s value is “null” and its type is “undefined.” You can opt to [show “null” values](#view) to differentiate them from empty strings, by going to “All” → “View” → “Show/Hide ‘null’ values in cells.”
 
+Converting a cell's data type is not the same operation as transforming its contents. For example, using a column-wide transform such as “Transform” → “Common transforms …” → “to date” may not convert all values successfully, but going to an individual cell, clicking “edit” and changing the data type can successfully convert text to a date. These operations use different underlying code. 
 
+To transform data from one type to another, see [Transforming data](transforming#transform) for information on using common tranforms, and see [Expressions](expressions) for information on using `toString()`, `toDate()`, and other functions. 
 
-*   Functions that matter to rows/records distinction
+## Rows vs. records
 
+A row is a simple way to organize data: a series of cells, one cell per column. Sometimes there are multiple pieces of information in one cell, such as when a survey respondent can select more than one response. In cases where there is more than one value for a single column in one or more rows, you may wish to use OpenRefine’s records mode: this defines a single record (a survey response, for example) as potentially containing more than one row. From there you can transform cells into multiple rows, each cell containing one value you’d like to work with. 
 
-## Facets
+Generally, when you import some data, OpenRefine reads that data in row mode. From there you can convert the project into records mode. OpenRefine remembers this action and will present you with records mode each time you open the project from then on. 
 
+OpenRefine understands records based on the content of the first column, what we call the "key column." Splitting a row into a multi-row record will base all association on the first column in your dataset. If you have more than one column to split out into multiple rows, OpenRefine will keep your data associated with its original record: you can imagine this structure as a tree with many branches, all leading back to the same trunk. 
 
-### Overview 
+For example, your key column may be a film or television show, with multiple cast members identified by name, associated to that work. You may have one or more roles listed for each person. The roles are linked to the actors, which are linked to the title.
 
-(Types of Facet views and their common controls such as dragging Facets, organizing, sub elements like include/exclude, edit, sort by, change, reset, etc.)
+|Work|Actor|Role|
+|---|---|---|
+|The Wizard of Oz|Judy Garland|Dorothy Gale|
+||Ray Bolger|"Hunk"|
+|||The Scarecrow|
+||Jack Haley|"Hickory"|
+|||The Tin Man|
+||Bert Lahr|"Zeke"|
+|||The Cowardly Lion|
+||Frank Morgan|Professor Marvel|
+|||The Gatekeeper|
+|||The Carriage Driver|
+|||The Guard|
+|||The Wizard of Oz|
+||Margaret Hamilton|Miss Almira Gulch|
+|||The Wicked Witch of the West|
 
+Once you are in records mode, you can still move columns around, but if you move a column to the beginning, you may find your data becomes misaligned. The new key column will sort into records based on empty cells, and values in the old key column will be assigned to the last row in the old record (the key value sitting above those values). 
 
-### Text facet
+OpenRefine assigns a unique key behind the scenes, so your records don’t need a unique identifier in the key column (but you will likely have one, to ensure data stays properly sorted). You can keep track of which rows are assigned to which record by the record number that appears under the “All” column.
 
+To [split multi-valued cells](transforming#split-multi-valued-cells) and apply other operations that take advantage of records mode, see [Transforming data](transforming). 
 
-### Numeric facet
+Be careful when in records mode that you do not accidentally delete rows based on being blank in one column where there is a value in another. 
-
-
-### Timeline facet
-
-
-### Scatterplot facet
-
-
-### Custom facets:
-
-
-
-*   Word facet
-*   Duplicates facet
-*   Numeric log facet
-*   Text-length facet
-
-
-### Facet by star or blank or flag
-
-
-## Filtering 
-
-
-### Overview
-
-Applying (case sensitivity; applying multiple in succession), inverting, and removing
-
-
-### Permalink function
-
-
-### Export data function
-
- (only current view)
-
-
-### Regular expressions
-
-
-## Sort and View
-
-
-### Sort
-
-
-### View: Collapsing (and expanding)
-
-
-### Show/hide “null” 
-
-
-### Reordering columns

docs/docs/manual/facets.md

@@ -0,0 +1,289 @@
+---
+id: facets
+title: Exploring facets
+sidebar_label: Facets
+---
+
+## Overview
+
+Facets are one of OpenRefine’s strongest features - that’s where the diamond logo comes from! Faceting allows you to look for patterns and trends. Facets are essentially aspects or angles of data variance in a given column. For example, if you had survey data where respondents indicated one of five responses from “Strongly agree” to “Strongly disagree,” those five responses make up a text facet, showing how many people selected each option.  
+
+Faceted browsing gives you a big-picture look at your data (do they agree or disagree?) and also allows you to filter down to a specific subset to explore it more (what do people who disagree say in other responses?). 
+
+Typically, you create a facet on a particular column. That facet selection appears on the left, in the Facet/Filter tab, and you can click on a displayed facet to view all the records that match. You can also “exclude” the facet, to view every record that does _not_ match, and you can select more than one facet by clicking “include.”
+
+
+### An example
+
+You can learn about facets and filtering with the following example. 
+
+We collected a list of the [10 most populous cities from Wikidata](https://w.wiki/3Em), using an example query of theirs. We removed the GPS coordinates and added the country. 
+
+| cityLabel | population | countryLabel |
+|-|-|-|
+| Shanghai | 23390000 | People's Republic of China |
+| Beijing | 21710000 | People's Republic of China |
+| Lagos | 21324000 | Nigeria |
+| Dhaka | 16800000 | Bangladesh |
+| Mumbai | 15414288 | India |
+| Istanbul | 14657434 | Turkey |
+| Tokyo | 13942856 | Japan |
+| Tianjin | 13245000 | People's Republic of China |
+| Guangzhou | 13080500 | People's Republic of China |
+| São Paulo | 12106920 | Brazil |
+
+If we want to see which countries have the most populous cities, we can create a “text facet” on the “countryLabel” column and OpenRefine will generate a list of all the different strings used in these cells. 
+
+We will see in the sidebar that the countries identified are displayed, along with the number of matches (the “count”). We can sort this list alphabetically or by the count. If you sort by count, you’ll learn which countries hold the most populous cities. 
+
+|Facet|Count|
+|---|---|
+|People's Republic of China|4|
+|Bangladesh|1|
+|Brazil|1|
+|India|1|
+|Japan|1|
+|Nigeria|1|
+|Turkey|1|
+
+If we want to learn more about a particular country, we can click on its appearance in the facet sidebar. This narrows our dataset down temporarily to only rows matching that facet.
+
+You’ll see the “10 rows” notification change to “4 matching rows (10 total)” if you click on “People’s Republic of China”. In the data grid, you’ll see the same number of rows, but only the ones matching your current filter. Each row will maintain its original numbering, though - in this case, rows #1, 2, and 8.
+
+If you want to go back to the original dataset, click “reset” or “exclude.” If you want to view the most populous cities in both China and India, click “include” next to each facet. Now you’ll see 5 rows - #1, 2, 5, 8, 9.
+
+We can also explore our data using the population information. In this case, because population is a number, we can create a numeric facet. This will give us the ability to explore by range rather than by exact matching values. 
+
+With the numeric facet, we are given a scale from the smallest to the largest value in the column. We can drag the range minimum and maximum to narrow the results. In this case, if we narrow down to only cities with more than 20 million in population, we get 3 matching rows out of the original 10. 
+
+When you look at the facet display of countries, you should see a smaller list with a reduced count: OpenRefine is now displaying the facets of the 3 matching rows, not the total dataset of 10 rows. 
+
+We can combine these facets - say, by narrowing to only the Chinese cities with populations greater than 20 million - simply by clicking in both. You should see 2 matching rows for both these criteria. 
+
+### Things to know about facets
+
+When you have facets applied, you will see “matching rows” in the [project grid header](running#project-grid-header). If you press “Export” and copy your data out of OpenRefine while facets are active, you will only export the matching rows, not all the rows in your project. 
+
+OpenRefine has several default facets, which you’ll learn about below. The most powerful facets are the ones designed by you - custom facets, written using [expressions](expressions) to transform the data behind the scenes and help you narrow down to precisely what you’re looking for. 
+
+Facets are not saved in the project along with the data. But you can save a link to the current state of the application. Find the "permalink" next to the project’s name.
+
+You can modify any facet expression by clicking the “change” button to the right of the column name in the facet sidebar.
+
+Facet boxes that appear in the sidebar can be resized and rearranged. You can drag and drop the title bar of each box to reorder them, and drag on the bottom bar of text facet boxes. 
+
+## Text facet
+
+A text facet can be generated on any column with the “text” data type. Select the column dropdown and go to “Facet” → “Text facet”. The created facet will be sorted alphabetically, and can be sorted by count. 
+
+A text facet is very simple: it takes the total contents of the cells of the column in question and matches them up. It does no guessing about typos or near-matches. 
+
+You can edit any entry that appears in the facet display, by hovering over the facet and clicking the “edit” button that appears. You can then type in a new value manually. This will mass-edit every identical cell in your data. This is a great way to fix typos, whitespace, and other issues that may be affecting the way facets appear. You can also automate the cleanup of facets by using [clustering](transforming#cluster-and-edit), with the “Cluster” button displayed within the facet window. It may be most efficient to cluster cells to one value, and then mass-edit that value to your desired string within the clustering operation window. 
+
+Each text facet shows up to 2,000 choices by default. You can [increase this limit on the Preferences screen](running#preferences) if you need to, which will increase the processing work required by your browser. If your applied facet has more choices than the current limit, you'll be offered the option to increase the limit, which will edit that preference for you. 
+
+The choices and counts displayed in each facet can be copied as tab-separated values. To do so, click on the "X choices" link near the top left corner of the facet.
+
+## Numeric facet
+
+![A screenshot of an example numeric facet.](/img/numericfacet.png)
+
+Whereas a text facet groups unique text values into groups, a numeric facet sorts numbers by their range - smallest to biggest. This displays visually as a histogram, and allows you to set a custom facet within that range. You can drag the minimum and maximum range markers to set a range. OpenRefine snaps to some basic equal-sized divisions - 19 in the example set above. 
+
+You will be offered the option to include blank, non-numeric, and error values in your numeric visualization; these will appear in the visual range as “0” values.
+
+## Timeline facet
+
+![A screenshot of an example timeline facet.](/img/timelinefacet.png)
+
+Much like a numeric facet, a timeline facet will display as a small histogram with the values sorted: in this case, chronologically. A timeline facet only works on dates formatted as “date” data types (e.g. by [using the `toDate()` function](expressions#dates) to transform text into dates, or by manually setting the [data type](#cell-data-types) on individual cells) and in the structure of the ISO-8601-compliant extended format with time in UTC: **YYYY**-**MM**-**DD**T**HH**:**MM**:**SS**Z. 
+
+## Scatterplot facet
+
+A scatterplot facet can be generated on any number-formatted column. You require two or more number columns to generate scatterplots. 
+
+You have the option to generate linear scatterplots (where the X and Y axes show continuous increases) or logarithmic scatterplots (where the X and Y axes show exponential or scaled increases). You can also rotate the plot by 45 degrees in either direction, and you can choose the size of the dot indicating a datapoint. You can make these choices in both the preview and in the facet display. 
+
+Selecting “Facet” → “Scatterplot facet” will create a preview of data plotted from every number-formatted column in your dataset, comparing every column against every other column. Each scatterplot will show in its own square, allowing you to choose which data comparison you would like to analyze further. 
+
+When you click on your desired square, that two-column comparison will appear in the facets sidebar. From here, you can drag your mouse to draw a rectangle inside the scatterplot, which will narrow down to just the rows matching the points plotted inside that rectangle. This rectangle can be resized by dragging any of the four edges. To draw a new rectangle, simply click and drag your mouse again. To add more scatterplots to the facet sidebar, re-run this process and select a different square. 
+
+If you have multiple facets applied, plotted points in your scatterplot displays will be greyed out if they are not part of the current matching data subset. If the rectangle you have drawn within a scatterplot display only includes grey dots, you will see no matching rows.
+
+If you would like to export a scatterplot, OpenRefine will open a new tab with a generated PNG image that you can save. 
+
+## Custom text facet
+
+You may want to explore your textual data in a way that doesn’t involve modifying it but does require being more selective about what gets considered. Creating custom text facets will load your column into memory, transform the data, and store those transformations inside the facet. 
+
+You can also use text facets to analyze numerical data, such as by analyzing a number as a string, or by creating a test that will return “true” and false” as values. 
+
+If you would like to build your own version of a text facet, you can use the “Custom Text Facet” option. Clicking on “Facets” → “Custom text facet…” will bring up an [expressions](expressions) window where you can enter in a GREL, Python or Jython, or Clojure expression to modify how the facet works. 
+
+A custom text facet operates just like a [text facet](#text-facet) by default. Unlike a text facet, however, you cannot edit the facets that appear in the sidebar and change the matching cells in your dataset. 
+
+For example, you may wish to analyze only the first word in a text field - perhaps the first name in a column of “[First Name] [Last Name]” entries. In this case, you can tell OpenRefine to facet only on the information that comes before the first space:
+
+```value.split(" ")[0]```
+
+In this case, `split()` is creating an array of text strings based on every space in the cells - in this case, one space, so two values in the array. Because arrays number their entries starting with 0, we want the first value, so we ask for `[0]`. We can do the same splitting and ask for the last name with 
+
+```value.split(" ")[1]``` 
+
+You may want to create a facet that references several columns. For example, let’s say you have two columns, "First Name" and "Last Name", and you want out how many people have the same initial letter for both names (e.g., Marilyn Monroe, Steven Segal). To do so, create a custom text facet on either column and enter the expression
+
+```cells["First Name"].value[0] == cells["Last Name"].value[0]```
+
+That expression will facet your rows into `true` and `false`. 
+
+You can learn more about text-modification functions on the [Expressions page](expressions). 
+
+## Custom numeric facet
+
+You may want to explore your numerical data in a way that doesn’t involve modifying it but does require being more selective about what gets considered. You can also use custom numeric facets to analyze textual data, such as by getting the length of text strings (with `value.length()`), or by analyzing it as though it were formatted as numbers (with `toNumber(value)`). 
+
+If you would like to build your own version of a numeric facet, you can use the “Custom Numeric Facet” option. Clicking on “Facets” → “Custom Numeric Facet…” will bring up an [expressions](expressions) window where you can enter in a GREL, Python or Jython, or Clojure expression to modify how the facet works. A custom numeric facet operates just like a [numeric facet](#numeric-facet) by default.
+
+For example, you may wish to create a numeric facet that rounds your value to the nearest integer, enter
+
+```round(value)```
+
+If you have two columns of numbers and for each row you wish to create a numeric facet only on the larger of the two, enter
+
+```max(cells["Column1"].value, cells[“Column2”].value)```
+
+If the numeric values in a column are drawn from a power law distribution, then it's better to group them by their logs:
+
+```value.log()```
+
+If the values are periodic you could take the modulus by the period to understand if there's a pattern:
+
+```mod(value, 7)```
+
+You can learn more about numeric-modification functions on the [Expressions page](expressions). 
+
+## Customized facets
+
+Customized facets have been added to expand the number of default facets users can apply with a single click. They represent some common and useful functions you shouldn’t have to work out using an [expression](expressions).
+
+All facets that display in the “Facet/Filter” sidebar can be edited by clicking on the “change” button to the right of the column title. This brings up the expressions window that will allow you to modify and preview the expression being used. 
+
+### Word facet
+
+Word facet is a simple version of a text facet: it splits up the content of the cells based on spaces, and outputs each character string as a facet:
+
+```value.split(" ")```
+
+This can be useful for exploring the language used in a corpus, looking for common first and last names or titles, or seeing what’s in multi-valued cells you don’t wish to split up. 
+
+Word facet is case-sensitive and only splits by spaces, not by line breaks or other natural divisions. 
+
+### Duplicates facet
+
+A duplicates facet will return only rows that have non-unique values in the column you’ve selected. It will create a facet of “true” and “false” values - true being cells that are not unique, and “false” being cells that are. The actual expression being used is
+
+```facetCount(value, 'value', '[Column]') > 1```
+
+Duplicates facets are case-sensitive and you may wish to filter out things like leading and trailing whitespace or other hard-to-see issues. You can modify the facet expression, for example, with:
+
+```facetCount(trim(toLowercase(value)), 'trim(toLowercase(value))', 'cityLabel') > 1```
+
+### Numeric log facet 
+
+Logarithmic scales reduce wide-ranging quantities to more compact and manageable ranges. A log transformation can be used to make highly skewed distributions less skewed. If your numerical data is unevenly distributed (say, lots of values in one range, and then a long tail extending off into different magnitudes), a numeric log facet can represent that range better than a simple numeric facet. It will break these values down into more navigable segments than the buckets of a numeric facet. This facet can make patterns in your data more visible.
+
+OpenRefine uses a base-10 log, the "common logarithm."
+
+For example, we can look at [this data about the body weight of various mammals](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_Brain2BodyWeight):
+
+|Species|BodyWeight (kg)|
+|---|---|
+| Newborn_Human | 3.2 |
+| Adult_Human | 73 |
+| Pithecanthropus_Man | 70 |
+| Squirrel | 0.8 |
+| Hamster | 0.15 |
+| Chimpanzee | 50 |
+| Rabbit | 1.4 |
+| Dog_(Beagle) | 10 |
+| Cat | 4.5 |
+| Rat | 0.4 |
+| Sperm_Whale | 35000 |
+| Turtle | 3 |
+| Alligator | 270 |
+
+Most values will be clustered in the 0-100 range, but 35,000 is many magnitudes above that. A numeric facet will create 36 equal buckets of 1,000 each - containing almost all the cells in the first bucket. A numeric log facet will instead display the data more evenly across the visual range.
+
+![A screenshot of a numeric facet first and a numeric log facet second.](/img/numericlogfacet.png)
+
+A 1-bounded numeric log facet can be used if you'd like to exclude all the values below 1 (including zero and negative numbers). 
+
+### Text-length facet
+
+The text-length facet returns a numerical value for each cell and plots it on a numeric facet chart. The expression used is
+
+```value.length()```
+
+This can be useful to, for example, look for values that did not successfully split on an earlier split operation, or to validate that data is a certain expected length (such as whether a date, as YYYY/MM/DD, is eight to ten characters). 
+
+You can also employ a log of text-length facet that allows you to navigate more easily a wide range of string lengths. This can be useful in the case of web-scraping, where lots of textual data is loaded into single cells and needs to be parsed out. 
+
+
+### Unicode character-code facet
+
+![A screenshot of the Unicode facet.](/img/unicodefacet.png)
+
+The Unicode facet identifies and returns [Unicode decimal values](https://en.wikipedia.org/wiki/List_of_Unicode_characters). It generates a list of the Unicode numerical values of each character used in each text cell, which allows you to narrow down and search for special characters, punctuation, and other data formatting issues.
+
+This facet creates a numerical chart, which offers you the ability to narrow down to a range of numbers. For example, lowercase characters are numbers 97-122, uppercase characters are numbers 65-90, and numerical digits are numbers 48-57. 
+
+### Facet by error
+
+An error is a data type created by OpenRefine in the process of transforming data. For example, say you had converted a column to the number data type. If one cell had text characters in it, OpenRefine could either output the original text string unchanged or output an error. If you allow errors to be created, you can facet by them later to search for them and fix them. 
+
+![A view of the expressions window with an error converting a string to a number.](/img/error.png)
+
+To store errors in cells, ensure that you have “store error” selected for the “On error” option in the expressions window. 
+
+### Facet by null, empty, or blank
+
+Any column can be faceted for [null and/or empty cells](#cell-data-types). These can help you find cells where you want to manually enter content. “Blank” means both null values and empty values. All three facets will generate “true” and “false” facets, “true” being blank. 
+
+An empty cell is a cell that is set to contain a string, but doesn’t have any characters in it (a zero-length string). This can be a leftover from an operation that removed characters, or from manually editing a cell and deleting its contents.
+
+### Facet by star or flag
+
+Stars and flags offer you the opportunity to mark specific rows for yourself for later focus. Stars and flags persist through closing and opening your project, and thus can provide a different function than using a permalink to persist your facets. Stars and flags can be used in any way you want, although they are designed to help you flag errors and star rows of particular importance. 
+
+You can manually star or flag rows simply by clicking on the icons to the left of each row. You can also apply stars or flags to all matching rows by using the “All” dropdown menu and selecting “Edit rows” → “Star rows” or “Flag rows.” These operations will modify all matching rows in your current subset. You can unstar or unflag them as well. 
+
+You may wish to create a custom subset of your data through a series of separate faceting activities (rather than successively narrowing down with multiple facets applied). For example, you may wish to: 
+*   apply a facet
+*   star all the matching rows
+*   remove that facet 
+*   apply another, unrelated facet
+*   star all the new matching rows (which will not modify already-starred rows)
+*   remove that facet
+*   and then work with all of the cumulative starred rows. 
+
+You can use the dropdown menu on the “All” column and selecting “Facet by star” or “Facet by flag.” This will create “true” and “false” facets in the facet sidebar.
+
+You can also create a text facet on any column with the expression `row.starred` or `row.flagged`.
+
+## Text filter
+
+Filters allow you to narrow down your data based on whether a given column includes a text string. 
+
+When you choose “Text filter” a box appears in the “Facet/Filter” sidebar that allows you to enter in text. Matching rows will narrow dynamically with every character you enter. You can set the search to be case-sensitive or not, and you can use this box to enter in a regular expression. 
+
+For example, you can enter in "side" as a text filter, and it will return all cells in that column containing "side," "sideways," "offside," etc. 
+
+The text filter field supports [Java's regular expression language](http://download.oracle.com/javase/tutorial/essential/regex/). For example, you can employ a regular expression to view all properly-formatted emails:
+
+```([a-zA-Z0-9_\-\.\+]+)@([a-zA-Z0-9\-\.]+)\.([a-zA-Z0-9\-]{2,15})```
+
+You can press “invert” on this facet to then see blank cells or invalid email addresses. 
+
+This filter works differently than facets because it is always active as long as it appears in the sidebar. If you “reset” it, you will delete all the text or expression you have entered. 
+
+You can apply multiple text filters in succession, which will successively narrow your data subset. This can be useful if you apply multiple inverted filters, such as to filter out all rows that respond “yes” or “maybe” and only look at the remaining responses.

docs/docs/manual/sortview.md

@@ -0,0 +1,35 @@
+---
+id: sortview
+title: Sort and view
+sidebar_label: Sort and view
+---
+
+## Sort
+
+You can temporarily sort your rows by one column. You can sort:
+*   text alphabetically or reverse
+*   numbers by largest or smallest
+*   dates by earliest or latest
+*   boolean values by false first or true first
+
+You can also choose where to place errors and blank cells in the sorting. Text can be case-sensitive or not: cells that start with lowercase characters will appear ahead of uppercase.
+
+![A screenshot of the Sort window.](/img/sort.png)
+
+After you apply a sorting method, you can make it permanent, remove it, reverse it, or apply a subsequent sorting. You’ll find “Sort” in the project grid header to the right of the rows-display setting, which will show all current sorting settings. 
+
+If you have multiple sorting methods applied, they will work in the order you applied them (represented in order in the "Sort" menu). For example, you can sort an "authors" column alphabetically, and then sort books by publication date, for those authors that have more than one book. If you apply those in a different order -  sort all the publication dates in the dataset first, and then alphabetically by author - your dataset will look different. 
+
+![Temporarily sorted rows.](/img/sort2.png) 
+
+When the sorting method you've applied is temporary, you will see that the rows retain their original numbering. When you make that sorting method permanent, by selecting "Reorder rows permanently," the row numbers will change and the "Sort" menu in the project grid header will disappear. This will apply all current sorting methods. 
+
+## View
+
+You can control what data you view in the grid. On each column, you can “collapse” that specific column, all other columns, all columns to the left, and all columns to the right. Using the “All” column’s dropdown menu, you can collapse all columns, and expand all the columns that you previously collapsed.
+
+### Show/hide “null” 
+
+You can also use the “All” dropdown to show and hide [“null” values](#cell-data-types). A small grey “null” will appear in each applicable cell. 
+
+![A screenshot of what a null value looks like.](/img/null.png)

docs/sidebars.js

@@ -5,7 +5,11 @@ module.exports = {
       'manual/installing',
       'manual/running',
       'manual/starting',
-      'manual/exploring',
+	        {
+      type: 'category',
+      label: 'Exploring data',
+      items: ['manual/exploring', 'manual/facets', 'manual/sortview'],
+    },
       'manual/transforming',
       'manual/expressions',
       'manual/wikidata',