# Introduction

## Exploratory Desktop Documentation

## Introduction

Exploratory Desktop is a simple and modern UI experience for extracting data, wrangling with data, visualizing data, using statistical and machine learning algorithms to analyze data, and communicating insights with others via Dashboard, Note, and Slides.

It utilizes R as its backend and provides the best UI experience for accessing many amazing capability of R without programming.

This is a documentation about various features of Exploratory Desktop.


# Product Features


# Summary View

At the Summary View, you can view the summary information of the selected data frame.

## Row Filter

You can filter rows by the condition on Summary View. See [Row Filter](/product-features/row_filter) for more details.

## Column Filter

You can filter columns by the condition on Summary View. See [Column Filter](/product-features/column_filter) for more details.

## Regular Mode

You can view the summary of columns in the selected data frame.

![](/files/-M7oykzkUnA0pJj7DPFW)

### How to Use

You can click "Summary" tab from the top tab menus.

![](/files/-M7oykznHKvXGUJ5PHxB)

### Summary Panel

#### Header

The header area shows the column name and its data type.

![](/files/-M7oykzoqgxVBAMKBKXA)

#### Column Menu

You can click the downward button to see the column menu. It shows a list of operations you can perform against this column.

![](/files/-M7oykzpNH-s8p1_HIeN)

#### Chart

The chart area shows the data distribution of this column in a bar chart. The chart type changes depending on the data type of the column.

![](/files/-M7oykzr7FXteOsOtIk5)

#### Metrics

The metrics area shows the metrics of the column. It shows different metrics depending on the data type of the column.

![](/files/-M7oykzstVPSKM7kVCRO)

#### Create Chart

You can click this button to view the chart in the chart view.

![](/files/-M7oykztZaWbb0bdAD2n)

### Select Columns

You can select a column by clicking a panel. You can also choose multiple panels by Shift+Click or Control(Command)+Click.

After selecting columns, you can open the column menu and select an operation to perform the operation to multiple columns at a time.

![](/files/-M7oykzunN9GdVLtrlJ5)

### Search Columns

You can search columns by clicking the "Search" icon and entering a text to the search field.

![](/files/-M7oykzwi3ky5QWsAz1J)

## Highlight Mode

In Highlight mode, you can highlight the distribution of data that you are interested in.

![](/files/-M7oyl-0jhRcJzP0ZVmC)

### How to Use

Click "Highlight" button in Regular mode.

![](/files/-M7oyl-2cAQ5hrjLz1hl)

Set the condition that you want highlight in the dialog.

![](/files/-M7oyl-4FAiyUVVri34T)

You can see the data that meet the condition is highlighted in Blue color in charts.

![](/files/-M7oyl-0jhRcJzP0ZVmC)

You can click "% Ratio" to see the distribution in percentage.

![](/files/-M7oyl-7jSspeaHGjhGE)

### Exit Highlight Mode

You can click "Close" button to exit the Highlight mode.

![](/files/-M7oyl-8Dnk0Ak96_u_M)

## Correlation Mode

In Correlation mode, you can see the correlations between the target column and the other columns.

![](/files/-M7oyl-9mnqS8sGvFY9s)

### How to Use

Click "Correlate" button in Regular mode.

![](/files/-M7oyl-Acy9mn3JOsEOE)

Select a column that you want to see the correlations with other columns. You can choose either a numeric column or a logical column.

![](/files/-M7oyl-CqSG8smIlw6kC)

You can see the correlations between the target column and the other columns.

![](/files/-M7oyl-9mnqS8sGvFY9s)

### Change Sample Size

If your data has a lot of rows, it automatically apply the data sampling for performance. If you want to change the sample size, select the size from the "Sample" pulldown menu.

![](/files/-M7oyl-E9xIv_dnlihKj)

### Change Range Type

You can choose the range type for the error bars from "Range" pulldown menu. The options vary depending the data type of the target column.

Numeric target column:

* 95% Conf. Interval
* 99% Conf. Interval
* 1 Standard Deviation
* 2 Standard Deviation
* 3 Standard Deviation
* Interquartile Range (IQR)

Logical target column:

* 95% Conf. Interval
* 99% Conf. Interval

![](/files/-M7oyl-FwKAE13PKv-CR)

### Change Sort Order

You can sort columns by the metric values. You can choose which metric for sorting from "Sort" pulldown menu. The options vary depending the data type of the target column.

Numeric target column:

* R Squared
* Correlation
* P Value

Logical target column:

* AUC
* P Value

![](/files/-M7oyl-GppjA7El3CrL-)

### Create Analytics/Chart

You can create an Analytics or a Chart from the column header menu. Available Analytics and Chart types vary depending the data type of the target column and the comparing column.

#### Numeric target column

**Chart**

* Bar
* Error Bar
* Line
* Scatter (Only if a comparing column is Numeric or Date)

**Analytics**

* Linear Regression
* Decision Tree
* Random Forest
* XGBoost

**Statistical Tests**

* T Test (Only if a comparing column has 2 unique values)
* ANOVA (Only if a comparing column has 2 unique values)
* Wilcoxon Test
* Kruskal-Wallis Test

#### Logical target column

**Chart**

* Bar
* Error Bar
* Line

**Analytics**

* Logistic Regression
* Decision Tree
* Random Forest
* XGBoost

**Statistical Tests**

* Chi-Square Test

![](/files/-MNlrYmdwAqWC55Gg2Gu)

### Create Analytics with Multiple Predictor Variables

You can create an Analytics with multiple predictor variables from Summary View by the following steps.

* Choose columns for predictor variables by control(command)+click or shift+click.
* Select an Analytics type from the column header menu.

Available Analytics types vary depending the data type of the target column.

#### Numeric target column

* Linear Regression
* Decision Tree
* Random Forest
* XGBoost

#### Logical target column

* Logistic Regression
* Decision Tree
* Random Forest
* XGBoost

![](/files/-MNlrYmfDvgbwCWJrTJg)

### Exit Correlation Mode

You can click "Close" button to exit the Correlation mode.

![](/files/-M7oyl-LCkRZQzJoTelt)

## Outlier Mode

Coming soon.


# Table View

At the Table View, you can view the details information of the selected data frame.

You can view the data in each column in the selected data frame.

![](/files/-MA4_Gq34a2cGv-i7U0w)

## Row Filter

You can filter rows by the condition on Table View. See [Row Filter](/product-features/row_filter) for more details.

## Column Filter

You can filter columns by the condition on Table View. See [Column Filter](/product-features/column_filter) for more details.

## Column Metrics

You can show the column metrics by hovering the column header area and click the green "i" icon.

![](/files/JZP39gw8xsJr0TIVUlFt)

## Column Menu

You can click the downward button to see the column menu. It shows a list of operations you can perform against this column.

![](/files/-MA4_Gq7oJr_v6_gEwdc)

## Select Columns

You can select a column by clicking a panel. You can also choose multiple panels by Shift+Click or Control(Command)+Click.

After selecting columns, you can open the column menu and select an operation to perform the operation to multiple columns at a time.

![](/files/-MA4_GqCsE2M8RsRG1ki)


# Row Filter

You can filter the rows (data) to show by the condition on Summary View and Table View. You can define the condition from the "Rows" button at the top. The condition can be shared among the Summary View and Table View.

Summary View

![](/files/Kbb1SKyTvGnaS2w473VZ)

Table View

![](/files/h1RGbFF5pGkmWAfZDuFm)

If conditions are applied, the button gets highlighted. The number on the button means the number of rows after applying the filter.

![](/files/7TvXXN68ITSkZrV6PKsN)

## Create a new Condition

You can click the "Rows" button to create the very first condition. It will immediately open the Filter dialog. You can define the condition, and click the "Run" button to apply the condition.

![](/files/oCLsqBSGEQ4Hp5LKetCN)

## Update and Delete Conditions

You can click the "Rows" button to update existing conditions, add other conditions, or delete conditions.

You can add other conditions by clicking the "+ Add Filter" link.

If you have multiple conditions, you can choose how to apply those conditions. Those 2 options are available.

* AND (&)
* OR (|)

![](/files/icHewP4uXuLL7AQLJCEV)

## Reset Conditions

You can remove all the conditions at once by clicking the 'x' icon in the "Columns" button.

![](/files/oLOlTxEdLfKdOxgUXguB)


# Column Filter

You can filter the columns by the condition on Summary View and Table View. You can define the condition from the "Columns" button at the top. The condition can be shared among the Summary View and Table View.

Summary View

![](/files/dnseO99JZCPqGyv01cbp)

Table View

![](/files/tLXq1okQzswK7jThuJTk)

If conditions are applied, the button gets highlighted. The number on the button means the number of columns currently showing.

![](/files/0sNTVEHFqndGTCuxdET5)

The following filter options are available.

## Keyword

You can filter columns by the keyword. The following search options are available.

* Contains: You can show columns that contain the keyword anywhere in the column name.
* Starts With: You can show columns that contain the keyword at the beginning of the column name.
* Ends With: You can show columns that contain the keyword at the end of the column name.

![](/files/0sNTVEHFqndGTCuxdET5)

## Data Type

You can filter columns by the data type. You can check the data type to show. You can check multiple checkboxes. If you don't check anything, filtering by the data type is disabled. The following data type options are available.

* Numeric
* Character
* Factor
* Logical
* Date
* POSIXct
* Others - It matches the data types that don't match any of the data types above (Numeric, Character, Factor, Logical, Date, POSIXct).

![](/files/3dV30BxDBZczy2bqQl6D)

## Reset Conditions

You can remove all the conditions at once by clicking the 'x' icon in the "Columns" button.

![](/files/bZuQnIWgrOvuyZiwIRIr)


# Dashboard

## An Introduction to Exploratory Dashboard

This [note](https://exploratory.io/note/2ac8ae888097/0937480844541865) explains how to create a Dashboard with Exploratory Desktop and share it with your co-workers.

## Parameter

If you add charts, analytics or data frames that contain the parameters to the dashboard, those parameters will show up automatically when you run the dashboard. You can change the parameters to update charts and analytics in the dashboard on the fly.

Please see [here](/product-features/parameter) for more details about Parameter.

## Showing a Parameter Value on Dashboard

You can show the current parameter value on Dashboard using the R code chunk or R inline expression. Please see [here](/product-features/parameter#showing-the-parameter-value-on-note-dashboard) for more details.

## Row Height Setting

You can set a row height of each row if "Fit to Screen" option is disabled.

![](/files/-MHhu9sManepXT_q54lr)

## Hide the Contents Area Title

You can uncheck the "Show Title" checkbox at the "Contents Area Setting" to hide the titles of each content.

![](/files/3tL70Hpi1GdS7eLM6rsn)

![](/files/1I9gWcFwsgs2lLuMSN5Y)

## Show the Chart Detail Data

You can view the detailed data of the chart by clicking a figure of the chart such as a bar, line, circle, etc. Showing detailed data is supported on charts that you can view the detail data on the Chart View.

![](/files/824mzD9Z6QxqF5QyjqU6)

## View the Chart/Analytics in the Full-Screen Mode

You can view the chart in the full-screen mode by clicking the "Show in fullscreen" icon on each Chart/Analytics.

![](/files/2raERuLx1SrrRTPtZVZ4)

This feature is not supported on the Number chart type for now.

## Download the Image of the Chart/Analytics

You can download the chart image in either PNG or SVG file format by selecting the "Export Chart Image" menu on each Chart/Analytics.

![](/files/58Q04BZ0263jVusxLOjI)

The dimension of the image file is following.

* Width: 1,200px
* Height: 800px if Repeat By is not used in the chart. Height stretches to include all the charts if Repeat By is used.
* Pixel Ratio: 200%

The "Export Chart Image" menu is available only on dashboards published to the server.

This feature is not supported on Number, Word Cloud and Map chart types.

## Download the Data of the Chart/Analytics

You can download the chart data in the CSV format by selecting the "Export Chart Data" menu on each Chart/Analytics.

![](/files/1Q8Md8HFpyKvWDxHuKgb)

The "Export Chart Data" menu is available only on dashboards published to the server. You also need to select the "CSV" download option when you publish the dashboard.

![](/files/FZYUTgSWKhPnHC5aktks)

This feature is not supported on the Number chart type for now.

## Show the Comment of the Chart/Analytics

If your chart/analytics has a comment, you can view it on the dashboard by hovering/clicking the comment icon on each Chart/Analytics.

![](/files/9omIg2jYo4NSM1Hr99zf)

This feature is not supported on the Number chart type for now.


# Dashboard (日本語)

## Exploratoryダッシュボードの紹介

Exploratoryデスクトップを使って、ダッシュボードを作成し、他のメンバーとシェアする方法は[こちら](https://exploratory.io/note/2ac8ae888097/2211291771961907)を参照してください。

## パラメータ

ダッシュボードの中で参照されているチャート、アナリティクス、データフレームなどにパラメータがある場合、ダッシュボードを実行するとそれらのパラメータが自動的に表示されます。パラメータの値を変更することで、ダッシュボード内で使われているチャートやアナリティクスを動的に変更することができます。

パラメータの詳細に関しては[こちら](/product-features/parameter_ja)をご覧ください。

## ダッシュボード上にパラメータの値を表示する方法

ダッシュボードでは、Rコードブロックや、インラインRコードを使って、パラメーターの値をダッシュボード上に表示することができます。詳細は[こちら](/product-features/parameter_ja#ntoyadasshubdoniparamtnowosuru)をご覧ください。

## 行の高さの設定

ダッシュボードの行の高さを設定することができます。行の高さを設定するには、「画面内に収める」の機能が無効になっている必要があります。

![](/files/-MHhuC6Jukie0-xQbmCx)

## コンテンツエリアのタイトルを非表示にする

ダッシュボードの設定ダイアログで、コンテンツエリアの設定の「タイトルを表示する」のチェックボックスを外すことで、ダッシュボードのそれぞれのコンテンツのエリアのタイトルを、非表示にすることができます。

![](/files/ntQyLz6u44PTtlr3UYoM)

![](/files/wiaaHpnNR3RpNQ4tmO68)

## チャートの詳細データを表示する

チャート・ビューで詳細データを見ることができるチャートは、ダッシュボード上でもチャートをクリックすることで、同様にチャートの詳細データを確認することができます。

![](/files/I2f9uuPtqUWbUF8geiiz)

## チャート/アナリティクスを全画面で表示する

ダッシュボード上のチャートやアナリティクスの右上にある「全画面で表示」アイコンをクリックすることで、チャートやアナリティクスを全画面で表示することができます。

![](/files/dauSaYFTczu25tsm59NG)

ナンバーチャートには現在対応しておりません。

## チャート/アナリティクスの画像をダウンロードする

ダッシュボード上のチャートやアナリティクスの右上にある「チャートの画像をエクスポート」を選択すると、チャートを画像ファイル(PNGかSVG)としてダウンロードすることができます。

![](/files/7YmwT0cM7hkxOQerhXP6)

「チャートの画像をエクスポート」のメニューは、パブリッシュされたダッシュボード上でのみ利用可能です。また、ナンバーチャート、地図、ワードクラウドには現在対応しておりません。

## チャート/アナリティクスのデータをダウンロードする

ダッシュボード上のチャートやアナリティクスの右上にある「チャートのデータをエクスポート」を選択すると、チャートで使われているデータを、CSV形式でダウンロードすることができます。

![](/files/7AlcFhWrqjVyqIzZJTFV)

「チャートのデータをエクスポート」のメニューは、パブリッシュされたダッシュボード上でのみ利用可能です。また、ダッシュボードをパブリッシュする際に、ダウンロードオプションで「CSV」を選択する必要があります。

![](/files/IJi7F2YOkpjnnAFM7PZ0)

ナンバーチャートには現在対応しておりません。

## チャート/アナリティクスのコメントを見る

もし、チャートやアナリティクスにコメントがある場合、チャートやアナリティクスの右上にあるコメントアイコンをホバーかクリックすることで、コメントを確認することができます。

![](/files/L1V6whSNt5XwPglwLeCW)

ナンバーチャートには現在対応しておりません。


# Note

## An Introduction to Note with Simple Markdown Editor

This [note](https://exploratory.io/note/kanaugust/An-Introduction-to-Note-with-Simple-Markdown-Editor-5790975430740516) explains how to create a note using Simple Markdown Editor with Exploratory Desktop and share it with your co-workers.

## Parameter

If you add charts, analytics or data frames that contain the parameters to the note, those parameters will show up automatically when you run the note. You can change the parameters to update charts and analytics in the note on the fly.

Please see [here](/product-features/parameter) for more details about Parameter.

## Showing a Parameter Value on Note

You can show the current parameter value on Note using the R code chunk or R inline expression. Please see [here](/product-features/parameter#showing-the-parameter-value-on-note-dashboard) for more details.

## View Mode

### Side by Side Mode

In Side by Side mode, you can see the edit screen and the preview screen side by side in a single window. You can click the refresh button anytime to confirm the preview while you are editing.

![](/files/-M91SfCQKQ4OAr49Xl8t)

### Switching to Side by Side Mode

You can click the "Switch to Side By Side Mode" button to switch from Single View mode to Side By Side mode.

![](/files/-M91SfCT5X9o8jwV1FmI)

### Single View Mode

In Single View mode, you can show either the edit screen or the preview screen at a time. You can switch the edit screen or the preview screen by clicking "Edit" or "Run" button at the top left corner of the screen.

In Single View mode, you can use the full window area for editing and previewing. It is useful if you want to check how users actually view your note.

Edit screen

![](/files/-M91SfCVuL8m7YS0Hy79)

Preview screen

![](/files/-M91SfCXbtzziQ8PKXuM)

### Switching to Single View Mode

You can click the "Switch to Single View Mode" button to switch from ModeSide By Side mode to Single View mode.

![](/files/-M91SfC_Wl8gsj4bszKf)

## Export

You can export your note in various formats. You can access to the export menus from the Export button at the top right corner of the screen.

![](/files/-M91SfCbNEgnZknCZw0h)

### Open in Web Browser

You can preview the output in the web browser.

### Export as HTML

You can export the note output in HTML format.

### Export as Word Document

You can export the note output in Microsoft Word format.

The "Export as Word Document" feature is only available in Single View mode. You can see [here](#switching-to-single-view-mode) for how to switch to Single View mode.

### Export as EDF

You can export the note output in EDF format. The exported EDF includes not only the text in the note, but also charts, analytics, data frames and others referenced in the note. You can import that EDF anytime to reproduce the note completely.

## Show the Chart Detail Data

You can view the detailed data of the chart by clicking a figure of the chart such as a bar, line, circle, etc. Showing detailed data is supported on charts that you can view the detail data on the Chart View.

![](/files/K39I0jEBP2dx1kWWX9d9)

## View the Chart/Analytics in the Full-Screen Mode

You can view the chart in the full-screen mode by clicking the "Show in fullscreen" icon on each Chart/Analytics.

![](/files/Ogfxlw7soQbQRWSmk0Nz)

This feature is not supported on the Number chart type for now.

## Download the Image of the Chart/Analytics

You can download the chart image in either PNG or SVG file format by selecting the "Export Chart Image" menu on each Chart/Analytics.

![](/files/vk3A7oPfbLvZXUxOhIJS)

The "Export Chart Image" menu is available only on notes published to the server.

This feature is not supported on Number, Word Cloud and Map chart types.

## Download the Data of the Chart/Analytics

You can download the chart data in the CSV format by selecting the "Export Chart Data" menu on each Chart/Analytics.

![](/files/jL3k6Omg7sOqi3Ka0tyS)

The "Export Chart Data" menu is available only on notes published to the server. You also need to select the "CSV" download option when you publish the note.

![](/files/FZYUTgSWKhPnHC5aktks)

This feature is not supported on the Number chart type for now.

## Show the Comment of the Chart/Analytics

If your chart/analytics has a comment, you can view it on the note by hovering/clicking the comment icon on each Chart/Analytics.

![](/files/mOG96AY887Y5qxJJGCzS)

This feature is not supported on the Number chart type for now.


# Note (日本語)

## シンプル・マークダウン・エディタによるノートの紹介

Exploratoryデスクトップでシンプル・マークダウン・エディタを使って、ノートを作成し、他のメンバーとシェアする方法は[こちら](https://exploratory.io/note/2ac8ae888097/VMX6SSV0nc)を参照してください。

## パラメータ

ノートの中で参照されているチャート、アナリティクス、データフレームなどにパラメータがある場合、ノートを実行するとそれらのパラメータが自動的に表示されます。パラメータの値を変更することで、ノート内で使われているチャートやアナリティクスを動的に変更することができます。

パラメータの詳細に関しては[こちら](/product-features/parameter_ja)をご覧ください。

## ノート上にパラメータの値を表示する方法

ノートでは、Rコードブロックや、インラインRコードを使って、パラメーターの値をノート上に表示することができます。詳細は[こちら](/product-features/parameter_ja#ntoyadasshubdoniparamtnowosuru)をご覧ください。

## ビュー・モード

### 画面分割モード

画面分割モードでは、左側に編集画面、右側にプレビュー画面が同一画面上に横並びで表示されます。画面分割モードでは、左側で編集をしながら、リフレッシュボタンを押すことで随時、出力を確認することができます。

![](/files/-M91N76rbTifMde2xp8C)

### 画面分割モードへの切り替え

シングル・ビュー・モードから画面分割モードに切り替えるには、画面左上の切り替えボタンを押します。

![](/files/-M91N76u1VdroiADsRZr)

### シングル・ビュー・モード

シングル・ビュー・モードは、編集画面とプレビュー画面が独立しています。編集画面とプレビュー画面の切り替えは、画面左上の「編集」「実行」ボタンで行うことができます。シングル・ビュー・モードは画面いっぱいに表示が行えるので、より実際のブラウザの画面に近い状態で編集およびプレビューを行うことができます。

編集画面

![](/files/-M91N76ze8_ogkfBWzgd)

プレビュー画面

![](/files/-M91N7708szZtzhMpj5E)

### シングル・ビュー・モードへの切り替え

画面分割モードからシングル・ビュー・モードに切り替えるには、画面左上の切り替えボタンを押します。

![](/files/-M91N772p0YBdSGNcIKo)

## エクスポート

ノートを様々な形でエクスポートすることができます。エクスポートのメニューは、画面右上のエクスポートボタンからアクセスすることができます。

![](/files/-M91N774wP-8oM8pDoJD)

### ブラウザで開く

プレビューをブラウザで開いて確認することができます。

### HTMLにエクスポート

ノートの出力をHTML形式でエクスポートすることができます。

### ワード文書にエクスポート

ノートの出力をワード形式の文書でエクスポートすることができます。

ワード文書にエクスポートは、シングル・ビュー・モードからしか現在行うことができません。シングル・ビュー・モードへの切替方法は、[こちら](#shingurubymdohenorie)をご覧ください。

### EDFにエクスポート

ノートをEDF形式でエクスポートすることができます。エクスポートされたEDFには、ノートと、ノートに含まれるチャート、データフレームなど、ノートを再現するのに必要なものがすべてエクスポートされます。

## チャートの詳細データを表示する

チャート・ビューで詳細データを見ることができるチャートは、ダッシュボード上でもチャートをクリックすることで、同様にチャートの詳細データを確認することができます。

![](/files/ghZYbHymOeBWthCx3sP5)

## チャート/アナリティクスを全画面で表示する

ノート上のチャートやアナリティクスの右上にある「全画面で表示」アイコンをクリックすることで、チャートやアナリティクスを全画面で表示することができます。

![](/files/CzIV7jNkNLtfbmVDcon0)

ナンバーチャートには現在対応しておりません。

## チャート/アナリティクスの画像をダウンロードする

ノート上のチャートやアナリティクスの右上にある「チャートの画像をエクスポート」を選択すると、チャートを画像(PNGかSVG)ファイルとしてダウンロードすることができます。

![](/files/xt8hqdQqKmVmpsVyGdbt)

「チャートの画像をエクスポート」のメニューは、パブリッシュされたノート上でのみ利用可能です。また、ナンバーチャート、地図、ワードクラウドには現在対応しておりません。

## チャート/アナリティクスのデータをダウンロードする

ノート上のチャートやアナリティクスの右上にある「チャートのデータをエクスポート」を選択すると、チャートで使われているデータを、CSV形式でダウンロードすることができます。

![](/files/ZSPzg2mEgbAu91xRjI5m)

「チャートのデータをエクスポート」のメニューは、パブリッシュされたノート上でのみ利用可能です。また、ノートをパブリッシュする際に、ダウンロードオプションで「CSV」を選択する必要があります。

![](/files/IJi7F2YOkpjnnAFM7PZ0)

ナンバーチャートには現在対応しておりません。

## チャート/アナリティクスのコメントを見る

もし、チャートやアナリティクスにコメントがある場合、チャートやアナリティクスの右上にあるコメントアイコンをホバーかクリックすることで、コメントを確認することができます。

![](/files/545x0wzM2VhZoe6G8LyU)

ナンバーチャートには現在対応しておりません。


# Steps (Right-hand side)

## Reproducible Steps

## Operations with Single Step

### Copy

### Paste

### Cut

### Delete

### Move

### Disable

### Cache

### Export R Script

## Operations with Multiple Steps

### Copy

### Paste

### Cut

### Delete

### Move

### Disable

### Cache


# Branch

‘Branch’ is similar to the ‘branch’ of source control management system like Git, if you are familiar with the source code management. You can create branches off of the main data frame to have different data wangling steps. All the branches share the same main data frame that they were branched off so any update on the data wangling steps in the main will be automatically reflected in all the branches. Branch can be used for aggregating the data, filtering with different conditions, running multiple experiments, building multiple models, etc.

The following blog posts talk about more detail and some use cases.

* [Introducing Branch for Running Multiple Experiments Quickly](https://blog.exploratory.io/introducing-branch-for-running-multiple-experiments-quickly-84352ce245b6#.iddruhpde)
* [Filtering Detail Data, not Aggregated, for Top10 Countries](https://blog.exploratory.io/filtering-detail-data-not-aggregated-for-top10-countries-33d5724d022f#.q35pduny2)

## Create Branch

You can click 'more (three lines)' menu in any data wrangling step at right hand side and select 'Create Branch' to create a branch.

![](/files/-M4oN4gAC7X-uk6tc2lj)

You can type a branch name in a pop-up.

![](/files/-M4oN4gC4zogj2rUrqJR)

You can start adding your data wangling step by either clicking on the "Plus" icon on the left of the command input area or clicking on the 'Plus' icon in the last Step box.

![](/files/-M4oN4gF_KU-HgeYGgGy)

## Delete Branch

You can delete a branch by clicking on 'Delete' menu under the 'more (three lines)' icon on the Data Frames tree on the lefthand-side.

![](/files/-M4oN4gHnm4S_6niTYyO)

## Join with Branch / Filter with Branch

You can join the branch data to the main data frame or to other data frames. Let's say you have created a branch to filter the data to top 10 flights for each airline carrier based on their arrival delay time, and now you want to use this data to filter the detail data in the main. You can do with two ways. One is to use one of the 'Filter Join' commands, 'semi\_join'. Another is to use the column name in 'filter' command with '%in%' operator.

Here's a Top 10 flights for each carrier in a branch 'Top10'.

![](/files/-M4oN4gJKQIsI2JWvoHO)

### Use semi\_join command to filter

```
semi_join(Top10, by=c("CARRIER" = "CARRIER", "FL_NUM" = "FL_NUM"))
```

![](/files/-M4oN4gLNMEjBohc3yWS)

### Use filter command to filter

```
filter(FL_NUM  %in% Top10$FL_NUM)
```

![](/files/-M4oN4gNKPpDtYWp-2mH)


# Parameter

With Parameter, you can make your data wrangling pipeline, Chart, Analytics, Note, Dashboard, etc. more dynamic or more interactive.

For example, you might want to make your SQL queries dynamic (Parameterized Queries), your filter step dynamic, and Dashboard to be more interactive so that the users of the Dashboard can change the data based on their interests.

## Type of Parameters

You can define following types of Parameters.

* Character
  * Text Field Input
  * Single Value Selector Input
  * Multiple Value Selector Input
* Number
  * Text Field Input
  * Slider Input
  * Single Value Selector Input
  * Multiple Value Selector Input
* Date
* DateTime (POSIXct)
* Logical (TRUE/FALSE)

## Where to Use Parameters

You can use the defined parameters from following places in your data processing, analysis, and/or visualization.

* Data Sources
  * Database Query
    * SQL Databases
    * MongoDB
  * Custom R Data Source
* Filter Step
* Mutate Step
* Summarize Step
* Chart Filter
* Analytics Filter

## What Can Be Parametrized by Parameters

By using Parameters, following Views on Exploratory Desktop or Dashboards/Documents published at exploratory.io or Collaboration Server can be parametrized.

### On Exploratory Desktop

* Summary View
* Table View
* Chart
* Analytics
* Dashboard
* Note

### Dashboards/Documents Published at exploratory.io or Collaboration Server

* Data
* Chart
* Analytics
* Dashboard
* Note

## Properties of a Parameter

You can specify the following properties for a parameter when the parameter is embedded in an SQL or MongoDB query.

* quote - Controls whether to quote the value of the parameter in the SQL or MongoDB query, and with what kind of quotation. The defaylt is double quotation.
  * `'"'` - Double quote the parameter value.
  * `"'"` - Single quote the parameter value.
  * FALSE - Do not quote the parameter value.
* escape - Controls whether to escape the value of the parameter in the SQL or MongoDB query, and for what kind of quotation. The default is escaping for double quotation.
  * `'"'` - Escape the parameter value so that it can be used with double quotation.
  * `"'"` - Escape the parameter value so that it can be used with single quotation.
  * FALSE - Do not escape the parameter value.

### Examples:

* Do not quote "search\_string" parameter value, but escape it so that it can be used within single-quotation.

```
SELECT * FROM Customers WHERE CustomerName LIKE '%@{search_string, quote=FALSE, escape="'"}%';
```

* Do not either quote or escape "email\_domain\_regex" parameter value in the MongoDB query.

```
{email: /@{email_domain_regex, quote=FALSE, escape=FALSE}$/}
```

## Showing the Parameter Value on Note / Dashboard

You can show the current parameter value on the Note / Dashboard using the `` `@{parameter_name}` `` notation like the following.

![](/files/UT10jvumkEwOuE91BJ5J)

## Parameter Pane Style on the Server View.

There are 2 types of parameter pane styles on the server view.

### Fit the parameter pane

The parameter pane fits in the same view as the insight pane.

![](/files/69Xza77WuLzN6xHkSGLm)

### Show the parameter pane on top of the pane

The parameter pane shows up on the top of the insight pane.

![](/files/eBPVDuEALJQ86f6XlNI8)

You can change the style from the Parameter Setting dialog on the server view. You can select the "Parameter Setting" from the menu to open the dialog.

![](/files/OABMcqKHPc9JtrkB4Wzt)

You can change the "Show the parameter pane on top of the pane" checkbox and click the "Update" button to change the style.

![](/files/w6YoaWQzsrQpaf6LtTqB)

## Learn More

This [note](https://exploratory.io/note/kanaugust/An-Introduction-to-Parameter-in-Exploratory-WCO4Vgn7HJ) explains how to use Parameters in Exploratory in detail.


# Parameter (日本語)

パラメーターを使うと、Exploratory Desktop上のビュー、さらには、exploratory.ioまたはCollaboration Server上に共有されたダッシュボード、ドキュメントを動的にすることができます。

例えば、ダッシュボードに表示されるチャートのためのデータを取得しているSQLクエリーや、そのデータをフィルターしているステップのフィルター条件から、パラメーターで動的に指定される値を参照することができます。 これにより、ダッシュボードを閲覧する人は、自分の興味にあわせて、ダッシュボードのデータの内容をその場で調整することができるようになります。

## パラメーターのタイプ

以下のタイプのパラメーターを定義することができます。

* 文字列
  * 文字入力
  * 単一の値が選択可能な値リスト
  * 複数の値が選択可能な値リスト
* 数値
  * 文字入力
  * スライダー
  * 単一の値が選択可能な値リスト
  * 複数の値が選択可能な値リスト
* 日付 (Date型)
* 日付・時間 (POSIXct型)
* 論理値 (TRUE/FALSE)

## パラメーターで動的に指定される値を参照できる場所

データの加工ステップ、可視化、分析中の以下の場面で、パラメーターで動的に指定される値を参照することができます、

* データ・ソース
  * Database Query
    * SQL Databases
    * MongoDB
  * Custom R Data Source
* フィルター・ステップ
* 計算の作成 (Mutate) ステップ
* 集計 (Summarize) ステップ
* チャート・フィルター
* アナリティクス・フィルター

## パラメーターを使って動的にできるもの

パラメーターを使って、以下のExploratory Desktop上のビュー、さらには、exploratory.ioまたはCollaboration Server上に共有されたダッシュボード、ドキュメントを動的にすることができます。

### Exploratory Desktop上のビュー

* サマリ・ビュー
* テーブル・ビュー
* チャート
* アナリティクス
* ダッシュボード
* ノート

### exploratory.ioまたはCollaboration Server上に共有されたダッシュボード、ドキュメント

* データ
* チャート
* アナリティクス
* ダッシュボード
* ノート

## ノートやダッシュボード上に、パラメーターの値を表示する方法

`` `@{パラメーター名}` ``の書式を使うことで、ノートやダッシュボード上に、パラメーターの値を表示することができます。

![](/files/awnpAcjFrJ9F6pFw7cBE)

## サーバー上でのパラメーターの表示方法を変更する

サーバー上でのパラメーターの表示方法は2種類あります。

### 並べて表示

パラメーターとインサイトを、並べて表示する方法です。

![](/files/84qZBbDO1xKLAVpAfqO9)

### ページ上に重ねて表示

ページ上に、パラメーターを重ねて表示する方法です。

![](/files/NDmIisjrFtCZnpSwfrlA)

表示方法の変更は、パラメーターの設定のダイアログから行うことができます。ダイアログを開くには、メニューから、「パラメーターの設定」を選択します。

![](/files/KSZV7xckZg5SCMUz70fa)

「ページの上に重ねて表示」のチェックボックスを変更して、「更新」ボタンを押すことで、表示方法の変更することができます。

![](/files/wHIkD4uaKP9lB8595HVM)

## さらに詳しく

パラメーターの詳細な使い方については[こちらのノート](https://exploratory.io/note/BWz1Bar4JF/ocn2Vts3WB)をご覧下さい。


# Export

You can export data in various formats either by clicking 'Export' button at the top of Summary / Table view or clicking on 'Export' button inside each of the data wrangling steps at the right hand side.

**At the top of Summary / Table view**

![](/files/-M4oN2YV3czXF2Ysj_tO)

**At the data wrangling step**

![](/files/-M4oN2Yb3YDOuIA9p_eq)

In this case, the exported data is based on the step you clicked the export button for.

## Standard Options

![](/files/-M4oN2YlTOWQNbdCL80S)

Following options are always available.

#### Export Data as CSV

You can export your data to a CSV file. You can choose one of the separator characters from following.

* Comma (,)
* Space ( )
* Semicolon (;)
* Dash (-)
* Tab ( )

#### Export Data as JSON

You can export your data to a JSON file. You can choose one of the formats from following.

* Rows
* Columns
* Values

#### Export Data as RDS

You can export your data to an RDS (R binary data format) file.

#### Export Data as EDF

You can export your data to an EDF (Exploratory Data Format) file. This EDF file includes not only the final data output but also the data wrangling steps to reproduce the data. This means that you can import it in another project and all the steps that would produce the data will be reproduced and shown at the right hand side. This would be useful when others want to not only see the data but also want to understand how the data wrangling has been done and want to update the steps.

#### Export Data to Clipboard

You can export your data to the Clipboard. The max data size that you can copy to the Clipboard depends on the amount of memory available on the machine for both Windows and Mac. The copied contents may be chopped in the middle if the available memory is not enough.

#### Export Data as Google Sheets

You can export your data to the Google Sheets.

#### Export Steps as R Script

You can export your reproducible steps as an R Script file.

If you want to create an environment on a different machine (Windows or Mac) where you want to run this exported R script as a standalone, you can follow the instruction in [this document](https://exploratory.io/note/exploratory/Creating-R-environment-where-R-script-from-Exploratory-can-run-tfS2dYo6).

## Chart Options

![](/files/-M4oN2Yn8fLrEgARI0tO)

In addition to the standard options, if you are in the chart view, you have chart specific options depending on the chart type.

#### Export Chart as PNG

You can export your chart to a PNG file. It is available for any chart types except Pivot and Table.

#### Export Chart as SVG

You can export your chart to an SVG file. It is available for any chart types except Map, Pivot and Table.

#### Export Chart as EDF

You can export your chart and data to an EDF (Exploratory Data Format) file. It is available for any chart types. Take a look at [Export as EDF](https://github.com/exploratory-io/book/blob/master/viz/save-as-edf.html) for more details.

## Pivot Options

![](/files/-M4oN2YvP4W2FhXfMJTc)

In addition to the standard options, if you are in a chart view and viewing a Pivot Table, you have following Pivot Table specific options.

#### Export Pivot Table as EDF

You can export your Pivot Table and data to an EDF (Exploratory Data Format) file.

#### Export Pivot Table as CSV

You can export your Pivot Table to an CSV file. You can choose one of the separator characters from following.

* Comma (,)
* Space ( )
* Semicolon (;)
* Dash (-)
* Tab ( )

#### Export Pivot Table as JSON

You can export your Pivot Table to a JSON file. You can choose one of the formats from following.

* Rows
* Columns
* Values

#### Export Pivot Table to Clipboard

You can export your Pivot Table to the Clipboard. The max data size that you can copy to the Clipboard depends on the amount of memory available on the machine for both Windows and Mac. The copied contents may be chopped in the middle if the available memory is not enough.


# Share


# Share Type

You can publish the following content to Exploratory Server and share with others.

* Dashboard
* Note
* Chart
* Analytics
* Data
* Project

There are three types of the sharing.

* Private
* URL
* Public

Please take a look at this note for more details.

* [How Sharing Dashboard, Note, Chart, Data, Project at Exploratory Server Works?](https://exploratory.io/note/exploratory/How-Sharing-Dashboard-Note-Chart-and-Data-at-Exploratory-Server-Works-QTd6dEH6nP)


# Chart / Analytics

## Sharing Chart/Analytics

You can quickly share your chart or analytics you have created in Exploratory Desktop with others by simply clicking on 'Publish' button. Since not only you are sharing the chart itself but also you are sharing the underlying data and the data wrangling steps to produce the data, other people whom you share with will have much easier time to understand where the data originally came from and how it was prepared, and improve or fix the data wangling steps collaboratively. By importing the shared chart anybody can easily reproduce and validate the steps to reproduce the chart step by step.

## How to Share Chart or Analytics with Data and Steps

You can simply click 'Publish' button in the Chart or Analytics view to start sharing your data visualization.

![](/files/-MTatuM5PUrfCbEh1R1w)

It opens up the dialog. You can set the followings.

* Title: Title of the chart.
* Description: Description of the chart.
* Tags: Tag words separated by comma. It is useful for other people to search this chart by the keywords.
* Publish in private mode: If you check this checkbox, this chart will be published and shared privately. Only the people that you invite can see the published chart on the server.
* Download Options
  * EDF: If you check this, it enables the EDF file download from the published chart page on the server. EDF stands for "Exploratory Data Format". It is a data exchange format between Exploratory Desktops. It includes metadata information to reproduce the data and visualizations.
  * CSV: If you check this, it enables the CSV file download and CSV API from the published chart page on the server.
* URL: This is a read-only field. The URL for the published chart will show up here once you publish the chart.

Click the "Publish" button to publish it.

![](/files/-MTatuM9VKE1iMV5kCpZ)

You can republish or stop publishing by the buttons at the bottom of the dialog once you publish it.

![](/files/-MTatuMDsW9MKyQwoa0G)

## Schedule

Once you publish your chart, you can setup a schedule to refresh your chart periodically. See the [document](/product-features/schedule/manage) for more details about how to manage the schedule.


# Data

## Sharing Data

You can quickly share data you have built or transformed in Exploratory Desktop with others at any step of the data wrangling at the left hand side. Since not only you are sharing the data but also you are sharing the wrangling steps to produce the data, other people whom you share with will have much easier time to understand where the data originally came from and how it was prepared, and improve or fix the data wangling steps collaboratively. By importing the shared data anybody can easily reproduce and validate each step of the data wrangling and analysis.

## How to Share Data with Steps

You can simply click 'Publish' button in any Step.

![](/files/-MTatu88kCVEVtMb54Gh)

It opens up the dialog. You can set the followings.

* Title: Title of the data.
* Description: Description of the data.
* Tags: Tag words separated by comma. It is useful for other people to search this data by the keywords.
* Publish in private mode: If you check this checkbox, this data will be published and shared privately. Only the people that you invite can see the published data on the server.
* Download Options
  * EDF: If you check this, it enables the EDF file download from the published data page on the server. EDF stands for "Exploratory Data Format". It is a data exchange format between Exploratory Desktops. It includes metadata information to reproduce the data.
  * CSV: If you check this, it enables the CSV file download and CSV API from the published chart page on the server.
* URL: This is a read-only field. The URL for the published data will show up here once you publish the data.

Click the "Publish" button to publish it.

![](/files/-MTatu8HtZTCt7ftCx9s)

You can republish or stop publishing by the buttons at the bottom of the dialog once you publish it.

![](/files/-MTatu8SYk568T2o7SVo)

## Sharing Branch Data or Joined Data

What if you want to share data that is branched off or joined with other data frames? Exploratory Desktop takes care of the data dependency automatically. The shared data includes all the necessary data wangling steps and data to reproduce the data so that the imported data will have the branch or joined data frames if required.

## Schedule

Once you publish your data, you can setup a schedule to refresh your data periodically. See the [document](/product-features/schedule/manage) for more details about how to manage the schedule.


# Report (Note / Dashboard)

## How to Share Report (Note or Dashboard)

You can simply click 'Publish' button in the report (Note or Dashboard).

![](/files/-MTatv4KTkFRJGjpK58I)

It opens up the dialog. You can set the followings.

* Title: Title of the report.
* Description: Description of the report.
* Tags: Tag words separated by comma. It is useful for other people to search this report by the keywords.
* Publish in private mode: If you check this checkbox, this report will be published and shared privately. Only the people that you invite can see the published report on the server.
* Download Options
  * EDF: If you check this, it enables the EDF file download from the published report page on the server. EDF stands for "Exploratory Data Format". It is a data exchange format between Exploratory Desktops. It includes metadata information to reproduce the report.
* URL: This is a read-only field. The URL for the published report will show up here once you publish your report.

Click the "Publish" button to publish it.

![](/files/-MTatv4NTBSuw5A8_Po9)

You can republish or stop publishing by the buttons at the bottom of the dialog once you publish it.

![](/files/-MTatv4PV4EYZrmM-466)

## Schedule

Once you publish your Note or Dashboard, you can setup a schedule to refresh your Note or Dashboard periodically. See the [document](/product-features/schedule/manage) for more details about how to manage the schedule.


# Notification

You can set the notification for the team members if the insight is shared as a team. If you want to set up the notification, select the "Notification Setting" from the menu.

![](/files/YqIbfKdkHbHNkCjMFyO9)

In the Notification dialog, you can set up when the notification is delivered to the team members.

* Notify when it is published/republished.
* Notify when it is updated by scheduling.

You can click the "Update" button to update the notification setting.

![](/files/g684sHhCoh5kjZ6wR3o4)


# Version History

You can check the version history from the "Version History" menu.

![](/files/BYgHluCQ1bshF11LTPNT)

In the "Version History" dialog, you can check when this insight was published/republished. You can also view the screenshot image when it was published by clicking the "View" button if available.

You can also restore the previous version. See the [Restore Old Version](/product-features/share/restore) for more detail.

![](/files/47QMJwPnnLZWPEiEQKv3)


# Restore Older Version

You can restore the old version of the insight by clicking the "Restore" button in the "Version History" dialog.

![](/files/47QMJwPnnLZWPEiEQKv3)

If you click the "Restore" button, the confirmation dialog shows up. You can click the "Restore" button in the confirmation dialog to perform the restoring.

![](/files/7ODNKKREO1PAgGNnt8Pa)

The restoring process doesn't affect the existing version history. You can still access all the histories after the restoring.


# CSV API

If you publish your insight such as Chart, Data, Analytics with the download option enabled, you can download the data from the server by using the CSV API. You can check the CSV API URL by selecting "API - CSV data" from the download button on the server insight page.

Example: [Exchange Rate - Japanese Yen vs U.S. Dollar](https://exploratory.io/viz/kanaugust/Exchange-Rate-Japanese-Yen-vs-U-S-Dollar-Fdt2eDO7CC?lang=en)

![](/files/loKF8uyLaTAsVzQGr3gJ)

![](/files/7haIvv2HYHJJOXwczkyw)

## Specification

The API specification is following. You need the API Key only if the insight is published privately.

```
https://exploratory.io/public/api/{User ID}/{Insight ID}/data?api_key={API Key}
```


# Share (日本語)


# 共有のタイプ

Exploratoryデスクトップで作成した以下のものをExploratoryサーバーにパブリッシュして、他の人達と共有することができます。

ダッシュボード ノート チャート アナリティクス データ プロジェクト

デフォルトではプライベート・モードでパブリッシュされますので、自分（あなた）以外は誰もアクセスできないようになっています。

共有の設定はExploratoryサーバー上で行うことができ、以下の3つの共有タイプがサポートされています。

* プライベート
* パブリック
* URL

詳細については以下のノートをご参照下さい。

* [パブリッシュしたコンテンツの共有方法について](https://exploratory.io/note/exploratory/SrX0DBF5Ad)


# チャート / アナリティクス

## チャート/アナリティクスの共有

You can quickly share your chart or analytics you have created in Exploratory Desktop with others by simply clicking on 'Publish' button. Since not only you are sharing the chart itself but also you are sharing the underlying data and the data wrangling steps to produce the data, other people whom you share with will have much easier time to understand where the data originally came from and how it was prepared, and improve or fix the data wangling steps collaboratively. By importing the shared chart anybody can easily reproduce and validate the steps to reproduce the chart step by step.

## How to Share Chart or Analytics with Data and Steps

You can simply click 'Publish' button in the Chart or Analytics view to start sharing your data visualization.

![](/files/-MTatuM5PUrfCbEh1R1w)

It opens up the dialog. You can set the followings.

* Title: Title of the chart.
* Description: Description of the chart.
* Tags: Tag words separated by comma. It is useful for other people to search this chart by the keywords.
* Publish in private mode: If you check this checkbox, this chart will be published and shared privately. Only the people that you invite can see the published chart on the server.
* Download Options
  * EDF: If you check this, it enables the EDF file download from the published chart page on the server. EDF stands for "Exploratory Data Format". It is a data exchange format between Exploratory Desktops. It includes metadata information to reproduce the data and visualizations.
  * CSV: If you check this, it enables the CSV file download and CSV API from the published chart page on the server.
* URL: This is a read-only field. The URL for the published chart will show up here once you publish the chart.

Click the "Publish" button to publish it.

![](/files/-MTatuM9VKE1iMV5kCpZ)

You can republish or stop publishing by the buttons at the bottom of the dialog once you publish it.

![](/files/-MTatuMDsW9MKyQwoa0G)

## Schedule

Once you publish your chart, you can setup a schedule to refresh your chart periodically. See the [document](/product-features/schedule_ja/manage_ja) for more details about how to manage the schedule.


# データ

## データの共有

You can quickly share data you have built or transformed in Exploratory Desktop with others at any step of the data wrangling at the left hand side. Since not only you are sharing the data but also you are sharing the wrangling steps to produce the data, other people whom you share with will have much easier time to understand where the data originally came from and how it was prepared, and improve or fix the data wangling steps collaboratively. By importing the shared data anybody can easily reproduce and validate each step of the data wrangling and analysis.

## How to Share Data with Steps

You can simply click 'Publish' button in any Step.

![](/files/-MTatu88kCVEVtMb54Gh)

It opens up the dialog. You can set the followings.

* Title: Title of the data.
* Description: Description of the data.
* Tags: Tag words separated by comma. It is useful for other people to search this data by the keywords.
* Publish in private mode: If you check this checkbox, this data will be published and shared privately. Only the people that you invite can see the published data on the server.
* Download Options
  * EDF: If you check this, it enables the EDF file download from the published data page on the server. EDF stands for "Exploratory Data Format". It is a data exchange format between Exploratory Desktops. It includes metadata information to reproduce the data.
  * CSV: If you check this, it enables the CSV file download and CSV API from the published chart page on the server.
* URL: This is a read-only field. The URL for the published data will show up here once you publish the data.

Click the "Publish" button to publish it.

![](/files/-MTatu8HtZTCt7ftCx9s)

You can republish or stop publishing by the buttons at the bottom of the dialog once you publish it.

![](/files/-MTatu8SYk568T2o7SVo)

## Sharing Branch Data or Joined Data

What if you want to share data that is branched off or joined with other data frames? Exploratory Desktop takes care of the data dependency automatically. The shared data includes all the necessary data wangling steps and data to reproduce the data so that the imported data will have the branch or joined data frames if required.

## Schedule

Once you publish your data, you can setup a schedule to refresh your data periodically. See the [document](/product-features/schedule_ja/manage_ja) for more details about how to manage the schedule.


# レポート (ノート / ダッシュボード)

## レポート (ノート, ダッシュボード) の共有

You can simply click 'Publish' button in the report (Note, Slides or Dashboard).

![](/files/-MTatv4KTkFRJGjpK58I)

It opens up the dialog. You can set the followings.

* Title: Title of the report.
* Description: Description of the report.
* Tags: Tag words separated by comma. It is useful for other people to search this report by the keywords.
* Publish in private mode: If you check this checkbox, this report will be published and shared privately. Only the people that you invite can see the published report on the server.
* Download Options
  * EDF: If you check this, it enables the EDF file download from the published report page on the server. EDF stands for "Exploratory Data Format". It is a data exchange format between Exploratory Desktops. It includes metadata information to reproduce the report.
* URL: This is a read-only field. The URL for the published report will show up here once you publish your report.

Click the "Publish" button to publish it.

![](/files/-MTatv4NTBSuw5A8_Po9)

You can republish or stop publishing by the buttons at the bottom of the dialog once you publish it.

![](/files/-MTatv4PV4EYZrmM-466)

## Schedule

Once you publish your Note or Dashboard, you can setup a schedule to refresh your Note or Dashboard periodically. See the [document](/product-features/schedule_ja/manage_ja) for more details about how to manage the schedule.


# 通知

チームに対して共有されたインサイトは、チームのメンバーに対して通知の設定を行うことができます。通知の設定を行うには、メニューから「通知の設定」を選択します。

![](/files/1OK6Vsp9UDrgRC2iYiEJ)

通知の設定のダイアログでは、どのような場合にチームのメンバーに対して通知を行うかを設定することができます。

* パブリッシュ/再パブリッシュされたときに通知する。
* スケジュールで更新されたときに通知する。

「更新」ボタンを押すことで、通知の設定を変更することができます。

![](/files/n39nCO7eqMsxQKdnJmuV)


# バージョンの履歴

「バージョンの履歴」のメニューから、これまでのスケジュールの履歴を確認することができます。

![](/files/p2gVK4WfZFWef4WkCdtf)

バージョンの履歴のダイアログでは、パブリッシュ/再パブリッシュされた日時や、その時のコメントを確認することができます。また、パブリッシュされたときの画像がある場合は、「見る」ボタンを押すことで確認することができます。

また、インサイトを以前のバージョンに戻すことが可能です。詳しくは[古いバージョンの復元](/product-features/share_ja/restore_ja)をご覧ください。

![](/files/yOt1uWapnLpFGfrUeOTY)


# 古いバージョンの復元

「バージョンの履歴」のダイアログで、戻したいバージョンの「復元」ボタンを押すことで、インサイトを以前のバージョンに戻すことができます。

![](/files/yOt1uWapnLpFGfrUeOTY)

ボタンを押すと、確認のダイアログが現れます。復元を実行するときは、確認のダイアログの中の「復元」ボタンを押してください。

![](/files/bziaRUDhqbqFdgmFRsxA)

復元を行っても、これまでのバージョンの履歴はすべて保存されており、なくなることはありません。


# CSV API

ダウンロードオプションを有効にしてパブリッシュされたデータ、チャート、アナリティクスなどのインサイトのデータは、CSV APIを用いてダウンロードすることができます。CSV APIのURLは、サーバのインサイトのページのダウンロードボタンから、「API - CSVデータ」を選択することで確認することができます。

例: [Exchange Rate - Japanese Yen vs U.S. Dollar](https://exploratory.io/viz/kanaugust/Exchange-Rate-Japanese-Yen-vs-U-S-Dollar-Fdt2eDO7CC?lang=ja)

![](/files/ZqzNdqqA0qXfxagcR63X)

![](/files/Batm7tZUnxoRB4JvAksr)

## 仕様

APIの仕様は以下の通りです。APIキーは、インサイトがプライベートで共有されている場合のみ必要です。

```
https://exploratory.io/public/api/{ユーザーID}/{インサイトID}/data?api_key={APIキー}
```


# Schedule

Please check out [the scheduling troubleshooting guide](https://exploratory.io/note/exploratory/Trouble-Shooting-Guide-for-Scheduling-Data-Analysis-at-exploratory-io-ujG7iHR4) for the popular problems and solutions.


# Manage Schedules

## Publish Insights

In order to schedule your insight, you need to publish your insight to the server first. See the [document](/product-features/share) for more details.

## Manage Schedules

You can click the clock icon to open the scheduling dialog for managing schedules.

If the insight is not capable for scheduling, the clock icon doesn't show up. The popular reasons are the following.

* Data is coming from your local file. The data should be accessible from the network for scheduling.
* Data source type doesn't support scheduling such as "R Script" data source.

![](/files/N2fjGQwImi83oDPBZfIV)

You can change the scheduling frequency from the "Repeat By" dropdown. The following options are available.

* Every Week
* Every Day
* Every Hour
* Every N Minutes (Available only for Business plan users)

![](/files/9h81j9f3pSQoDBXKAS0g)

Once you choose the Repeat By option, you see the scheduling details. Change the setting to your needs.

You can check the "Subscribe Notification Email" checkbox to receive the notification for every scheduling run.

Click the "Save Configuration" button at the bottom once the setting is done.

![](/files/JK6rto5Djaw3QbXmO0iU)

If the insight is either Data, Chart or Analytics, you can also save the refreshed data to the Google Spreadsheet. The following options are available.

* Save it as a new spreadsheet.
* Add a new tab in an existing spreadsheet.
* Override the data in an existing tab in an existing spreadsheet.
* Append the data to an existing tab in an existing spreadsheet.

![](/files/HgCJVL4C68R45GSJ1DDo)

## Run Immediately

If you just want to run it once immediately to refresh the data without setting up a schedule, you can click the "Run Immediately" button.

![](/files/Ub2nncQugHIRxfLfrHJX)

## FAQ / Troubleshooting

Please check out [the scheduling troubleshooting guide](https://exploratory.io/note/exploratory/Trouble-Shooting-Guide-for-Scheduling-Data-Analysis-at-exploratory-io-ujG7iHR4) for the popular problems and solutions.


# Notification

You can receive notifications by email when the insight is updated by the scheduling. If you want to set up the notification, select the "Subscribe" from the menu.

![](/files/B11lCkkY31MfYda5umKl)

Then the Confirmation dialog shows up. You can check the scheduling frequency here. If you want notifications, click "OK".

![](/files/gCOMMAOttrglKZYUaZr0)

You can select "Unsubscribe" from the menu to stop the notification. ![](/files/5YjrqYGDh7X3PE4qhELz)


# Scheduling History

You can check the scheduling history from the "Scheduling History" menu.

![](/files/4O3SCVNLubcSGCeaWcCc)

In the "Scheduling History" dialog, you can check the date/time, status, and output (screenshot images) of each schedule run.

Output is available for weekly or daily schedules for Charts, Analytics, and Dashboards.

![](/files/KyaxyaSpGmsd9ZLssUMn)


# Schedule (日本語)

スケジュールに関するよくある問題と解決法については、[こちら](https://exploratory.io/note/exploratory/iMN6asA6)をご覧ください。


# スケジュールの設定

## インサイトのパブリッシュ

スケジュールを設定するには、まず、インサイト(ダッシュボード、チャートなど)をサーバーにパブリッシュする必要があります。パブリッシュについての詳細は[こちら](/product-features/share_ja)を参照して下さい。

## スケジュールの管理

時計アイコンをクリックして、スケジュールを管理するためダイアログを開くことができます。

このインサイトがスケジュールを設定できない場合、時計アイコンは表示されません。一般的な理由は次のとおりです。

* データがローカルファイルから取得されている場合。スケジュールを行うためには、データはネットワークからアクセスできる必要があります。
* データソースのタイプが、スケジューリングをサポートていない場合。「Rスクリプト」データソースなど。

![](/files/N2fjGQwImi83oDPBZfIV)

「繰り返しのタイプ」のドロップダウンから、スケジュールの頻度を変更できます。次のオプションが利用可能です。

* 毎週
* 毎日
* 毎時間
* N分おき（Businessプランのユーザーのみが利用可能）

![](/files/4XK0YKjTkJpcNXIdS832)

繰り返しのタイプを選択すると、スケジュールの詳細が表示されます。必要に応じて設定を変更してください。

「通知メールを購読」チェックボックスをオンにすると、スケジュールの実行ごとに通知を受け取ることができます。

![](/files/pqrdkm3YKqnpvxN2iyVm)

インサイトがデータ、チャート、アナリティクスのいずれかである場合は、更新されたデータをGoogleスプレッドシートに保存することもできます。保存方法には以下のオプションがあります。

* 新しいスプレッドシートとして保存
* 既存のスプレッドシートに新しいタブを追加
* 既存のスプレッドシートの既存のタブのデータを上書き
* 既存のスプレッドシートの既存のタブにデータを追加

![](/files/u9rrcH7yzmGfYtYtd1BZ)

設定が完了したら、「設定を保存」ボタンをクリックします。

## 今すぐ実行

繰り返しを設定せずに、ただスケジュールを1回だけ実行してデータを更新したい場合は、「今すぐ実行」ボタンをクリックします。

![](/files/AIejg5nWmb9pHoCcHv5k)

## スケジュールに関するよくある問題と解決法

スケジュールに関するよくある問題と解決法については、[こちら](https://exploratory.io/note/exploratory/iMN6asA6)をご覧ください。


# 通知

スケジュールによってインサイトが更新されたときに、メールで通知を受け取ることができます。通知を受け取るには、メニューから「通知メールの購読」を選択します。

![](/files/kUi7Qjm4N3HTbHCDC9cK)

すると、確認のダイアログが開きます。スケジュールの頻度などを確認して、通知を希望するなら「OK」をクリックします。

![](/files/iN070W9qyl0qy6cgdV0l)

通知を解除する場合には、メニューから「購読をやめる」を選択します。

![](/files/yIKCHaX5RNAEYFEkv2TV)


# スケジュールの履歴

「スケジュールの履歴」のメニューから、これまでのスケジュールの履歴を確認することができます。

![](/files/BJBAcCQzOfzUwJTBHcMZ)

スケジュールの履歴のダイアログでは、実行の日時、ステータス、アウトプット（画像）などを確認することができます。アウトプットは、繰り返しのタイプが毎週または毎日の、チャート、アナリティクス、およびダッシュボードの場合のみ表示されます。

![](/files/yPvWfUKUmhfOAkS6SaDB)


# Team


# Manage Teams

You can manage teams at the [Account Setting](https://exploratory.io/account/settings) page.

You can manage teams only if you subscribe the Business plan now.

![](/files/S9T8htKOGYeuXuaTebz4)

## Ceating a Team

You can click the "Add New" link to create a new team.

![](/files/WgD53BY2s7X0rVCsKlL5)

Then, the screen for creating a team will be displayed. You can create a new team by entering the following information and clicking the "Create" button.

* Team name (required): The name of the team used in the URL. Only lowercase alphanumers, hyphens, and underscores can be used in the team name. Once created, the team name cannot be changed.
* Display Name (Optional): The name used for the display. If not entered, the team name will be used automatically.
* Overview (optional): An overview of the team.
* Members: Members of this team.

![](/files/ka3FWH4gY7tvIdpIlLw2)

### Adding Members to the Team

To add a member of your team, enter the email address of the member you want to add in the Add Member input field. Members must be registered on exploratory.io.

![](/files/E1TJ1xaPirKRN6yft6Yg)

After entering the email address, enter ',' to confirm the email address. You can add multiple members at once by entering multiple email addresses.

![](/files/TG6BdxV5p6dGYtXpn225)

When you're done, press the add button. Then, the member you just added will be displayed at the bottom.

![](/files/qrj5UTXmeck9qZpZeFtt)

### Deleting Members from the Team

To delete a member, click the "x" button next to the member.

![](/files/4xuJufrBGHvnzDHVPQYA)

## Updating a Team

To change a team, click the team you want to change from the team management screen.

![](/files/dOTLGt1Kr8YWZAJEUGFi)

The team settings screen is displayed. You can change the following information.

* Display name (optional): The name used for display. If not entered, the team name will be used automatically.
* Overview (optional): An overview of the team.
* Members: Members of this team.

When you have finished making changes, press the "Update" button to confirm your changes.

![](/files/ygVSzYbJnunjyEFKLI0I)

## Deleting a Team

To change a team, click the team you want to change from the team management screen.

![](/files/dOTLGt1Kr8YWZAJEUGFi)

The team settings screen is displayed. You can delete a team by pressing the "Delete Team" button at the bottom of the screen.

Once a team is deleted, it cannot be undone later. Deleting a team also removes all insights published as a team from the server.

![](/files/wrMYxDIwnvGc71QTFBTi)


# Team (日本語)


# チームの設定

チームの管理は、[アカウントの設定](https://exploratory.io/account/settings)から行うことができます。

チームの管理は、現在Businessプランでのみサポートされています。

![](/files/p9TdGcei6Eeq6qIaON8F)

## チームの作成

新しくチームを作成するには「新規作成」のリンクをクリックします。

![](/files/UuxeYBXQMtSHZp8PotEd)

すると、チーム作成の画面が表示されます。以下の情報を入力して、「作成」ボタンをクリックすることで、新規のチームを作成することができます。

* チーム名 (必須): URLに使われるチームの名前になります。チーム名には小文字の英数字、ハイフン、アンダースコアのみが使用できます。一度作成するとチーム名は変更できません。
* 表示名 (オプショナル): 表示に使われる名前です。入力しない場合は、チーム名が自動的に使用されます。
* 概要 (オプショナル): チームの概要です。
* メンバー: このチームのメンバーです。

![](/files/L1CqhsS5vcOK6WDouUEC)

### チームのメンバーの追加

チームのメンバーを追加するには、「メンバーを追加」の入力フィールドに、追加したいメンバーのメールアドレスを入力します。

![](/files/kvikwNBT3Pw1orBLIimt)

このときメンバーは、exploratory.io上に登録されている（Exploratoryのアカウントを持っている）必要があります。Exploratoryのアカウントの作成（サインアップ）方法の詳細は[こちら](https://exploratory.io/note/exploratory/Exploratory-Tue5umi8)をご覧ください。

なお、チームのメンバーには無料プラン（Public）の方も追加が可能です。

メールアドレスを入力したら、','を入力することでメールアドレスが確定されます。続けてメールアドレスを入力することで、複数のメンバーを一度に追加することができます。

![](/files/2m9tJGYZ5uGgZWIGSOmI)

入力が終わったら、追加ボタンを押します。すると、下の方に今追加したメンバーが表示されます。

![](/files/qUwlMuhqmBNciBRqfhQf)

### チームのメンバーの削除

メンバーを削除するには、メンバーの横にある「ｘ」ボタンをクリックしてください。

![](/files/5jt8GJnsY2xTT7FHfMP9)

## チームの変更

チームの変更を行うには、チームの管理画面から、変更を行いたいチームをクリックしてください。

![](/files/uHviJfmytkNZoJvcvhlL)

チームの設定画面が表示されます。以下の情報を変更することができます。

* 表示名 (オプショナル): 表示に使われる名前です。入力しない場合は、チーム名が自動的に使用されます。
* 概要 (オプショナル): チームの概要です。
* メンバー: このチームのメンバーです。

変更が終わったら、「変更」ボタンを押して、変更を確定します。

![](/files/PaiMcYdEImgfr0005yPP)

## チームの削除

チームの変更を行うには、チームの管理画面から、変更を行いたいチームをクリックしてください。

![](/files/uHviJfmytkNZoJvcvhlL)

チームの設定画面が表示されます。画面一番下にある「チームの削除」のボタンを押すことで、チームを削除することができます。

チームは削除してしまうと、後でもとに戻すことはできません。また、チームを削除すると、チームとしてパブリッシュされた全てのインサイトもサーバーから削除されます。

![](/files/iyYCbM5Uhtz4nakZgJU2)


# Project


# Import

You can import a project that is exported as a zip file. Click import button on right hand side top of the Project List page.

![](/files/-M4oN0k_9He2pZrtdzBm)

And select the project zip file that you want to import.

![](/files/-M4oN0kbW9MZPjk5SszM)


# Export

Click menu at the right hand side end of the project that you want to export and select `Export`

![](/files/-M4oMyou-nuXYeAABbvZ)

And save the project as zip file.

![](/files/-M4oMyowtK-BqJ1xKAMV)


# Search

You can search the project by typing in a keyword at the search text input.

![](/files/-MYkRVnr98wEiOm7AYRw)

If you type in a keyword, only the objects that match the keyword show up in the tree on the left-hand side. The matched keyword will be highlighted. The search is case-insensitive.

The search targets are the following.

* Data Frame name
* Chart name
* Chart comment
* Analytics name
* Analytics comment
* Report (Note, Slide Dashboard) name
* Report (Note, Slide Dashboard) description
* Script name

## How to exit the search

You can click the 'x' icon to quit the search.

![](/files/-MYkRVnvDdUWuSFA41iQ)


# AI


# AI Prompt Function


# AI Prompt Function (日本語)


# Data Import

1. Import Data
2. Update Data Import Setting
3. Re-Import (Re-load) Data

## File import

* CSV - Comma Separated Files
* TSV - Tab Separated Files
* Delimited Files
* Excel
* Statistical Files - SPSS / SAS / STATA
* R Data Files - RDS, RData
* JSON

## Databases

* Amazon Redshift
* PostgreSQL
* HP Vertica
* MySQL
* MariaDB
* Google BigQuery
* MongoDB

## Cloud Apps

* Google Analytics
* Google Spreadsheet
* Twitter
* Github Issues

## Others

* REST APIs / Remote JSON
* Web Page Scraping

## Custom Data with R Script

You can create your own custom data sources by writing R Script or using R packages like quantmod for financial data, gtrend for Google Trend, Rfacebook.

## User Defined Plugin

You can hook up your custom data source to Exploratory's Data Import Dialog UI by writing a plugin definition file (with data source definition like input parameters, underlying R function name, etc) with JSON format and corresponding R Script.


# File Data


# CSV / Delimited File

You can import both local and remote CSV / Delimited File Data quickly.

## 1. Select CSV / Delimited File Data Menu

Click '+' button next to 'Data Frames' and select 'Import File Data'.

![](/files/-M4oMx1vuumKACr0xqof)

If you import CSV / Delimited File on your desktop or laptop, make sure Local tab is selected. If you import a file from remote server like Amazon S3, Dropbox, etc make sure Remote tab is selected. Click 'Text File (CSV, delimited)'

![](/files/-M4oMxqdAVwKnXlWbXbM)

## 2. Select File.

If you select local, file picker shows up so select a file to import.

### 2.1 Local File

![](/files/-M4oMxqfcKxvC5uDTPun)

### 2.2 Remote File

if you select remote, small dialog shows up so enter a URL for the file.

![](/files/-M4oMxqh46T_lHi_Af5I)

## 3. Input parameters

Input parameters are same for local and remote file import.

![](/files/-M4oMxqjDoQ4fleBbzAT)

Set these parameters.

* Encoding - File Encoding. By default, it shows best guessed encoding for you. you can manually select a correct one from pulldown list if default guessed encoding is not correct.
* Separator - Single character used to separate fields within a record.
* Decimal mark - Decimal mark used for numeric values in the CSV / Delimited File.
* Thousand mark - Thousand mark used in numeric vales in the CSV/ Delimited File
* Quote - Single character used to quote strings. Either double or Single
* Skip first N rows - Number of lines to skip before reading data.
* Treat these as NA - String(s) to use for missing values. Use comma(,) to enter multiple Strings. (e.g: N/A, Not Applicable)
* First Row as Header - Either TRUE or FALSE. If TRUE, the first row of the input will be used as the column names, and will not be included in the data frame. If FALSE, column names will be generated automatically: X1, X2, X3 etc.
* Comment Character - A string used to identify comments. Any text after the comment characters will be silently ignored.
* Trim White Space - Should leading and trailing whitespace be trimmed from each field before parsing it?
* Column Types - See below section for details.

## 4. Column Types

you can override column type by passing column name and data type pair with `columnName = 'type'` syntax. For Example, if `value` column is detected as integer but you want to import it as double, you can set `value = 'd'`. (d is for double). If you want to override multiple column types, you can do so by separating each pair with comma(,) like this. `key = 'i', value = 'd'`

Supported data types for overrides are:

* c (character)
* i (integer)
* n (number)
* d (double)
* l (logical)
* D (date)
* T (Date time)
* t (time)

**Note :** To specify Column Types, you need to disable "Auto Detect Data Type" like the screenshot below.

![](/files/-M4oMxqlj4taqumO4MTr)

Also, if the column name contains special characters, use backtick \` to escape column names like \`Column A\` = 'c'

## 5. Column Selection

On top of input parameters, you can also use checkboxes on the preview table to include/exclude columns. if you uncheck the checkboxes on the columns, these columns are excluded when you save the data.

## 6. Preview and Import

Click Preview button to see the data in CSV / Delimited File. You can limit number of rows for the preview by selecting number from pulldown list on the preview table. If it looks ok, then you can click 'Import' to import the whole CSV / Delimited data into Exploratory.

## 7. Column Data Type Auto-Detection

When previewing the data, Exploratory uses the number of rows from preview data to detect column data types. It means if you set 50 rows for your preview, column data types are detect with the 50 rows. When you actually import full data, it will use up to 1,000 rows to detect column data types so you might see different column data types in the imported data than in the preview.


# Amazon S3

## Import CSV / Delimited File or Excel File or Parquet File Data from Amazon S3

You can import CSV / Delimited File or Excel File or Parquet File from Amazon S3.

## 1. Create Amazon S3 connection

When you try to import data from Amazon S3 for the first time, it shows you below Create Connection Dialog forAmazon S3.

Type in AWS Region, Access Key ID, and Access Key Secret. ![](/files/-MhVeF8Jnrq2xc0jbeDX)

## 2. Select Amazon S3 from File Data

![](/files/-MhVeF8OgpHTNMdKh2Gl)

### 2.1 Select files to import

![](/files/-MhVeF8STA9hQqiw9Gw5)

### 3. Input Parameters

Input parameters are same Text File (CSV, delimited) import, Excel File import, and Parquet File Import.

### 4. Preview and import

Click the Preview button to see the data in CSV / Delimited or Excel File or Parquet File. You can limit number of rows for the preview by selecting number from pulldown list on the preview table. If it looks ok, then you can click 'Import' to import the whole CSV / Delimited or Excel or Parquet data into Exploratory.


# Google Drive

## Import CSV / Delimited File or Excel File Data from Google Drive

You can import CSV / Delimited File or Excel File from Google Drive.

## 1. Create Google Drive connection

## 2. Select Google Drive from Cloud Apps Data Source

### 2.1 Select files to import

### 3. Input Parameters

Input parameters are same Text File (CSV, delimited) import and Excel File import.

### 4. Preview and import

Click the Preview button to see the data in CSV / Delimited or Excel File. You can limit number of rows for the preview by selecting number from pulldown list on the preview table. If it looks ok, then you can click 'Import' to import the whole CSV / Delimited or Excel data into Exploratory.


# Google Cloud Storage

## Import CSV / Delimited File or Excel File Data from Google Cloud Storage (To be Introduced)

You can import CSV / Delimited File or Excel File from Google Cloud Storage.

## 1. Create Google Drive connection

## 2. Select Google Drive from File

### 2.1 Select files to import

### 3. Input Parameters

Input parameters are same Text File (CSV, delimited) import and Excel File import.

### 4. Preview and import

Click the Preview button to see the data in CSV / Delimited or Excel File. You can limit number of rows for the preview by selecting number from pulldown list on the preview table. If it looks ok, then you can click 'Import' to import the whole CSV / Delimited or Excel data into Exploratory.


# Excel

You can import both local and remote Excel File Data quickly.

## 1. Select Excel File Data Menu

Click '+' button next to 'Data Frames' and select 'Import File Data'.

![](/files/-M4oMx1vuumKACr0xqof)

If you import Excel File on your desktop or laptop, make sure Local tab is selected. If you import a file from remote server like Amazon S3, Dropbox, etc make sure Remote tab is selected. Click 'Excel File'

![](/files/-M4oMzKrvvf6K6yx8xSX)

## 2. Select File.

If you select local, file picker shows up so select a file to import.

### 2.1 Local File

![](/files/-M4oMzKtENoSIiizdJLu)

### 2.2 Remote File

if you select remote, small dialog shows up so enter a URL for the file.

![](/files/-M4oMzKvAXPQgwaZRVZC)

## 3. Input parameters

Input parameters are same for local and remote file import.

![](/files/-M4oMzKx-oErlKMEsGHx)

Set these parameters.

* Sheet - Sheet name of the Excel File that you want to import.
* Treat this as NA - String to use for missing values.
* First Row as Header - Either TRUE or FALSE. If TRUE, the first row of the input will be used as the column names, and will not be included in the data frame. If FALSE, column names will be generated automatically: X1, X2, X3 etc.
* Skip first N rows - Number of lines to skip before reading data. enter multiple Strings. (e.g: N/A, Not Applicable)
* Trim White Space - Should leading and trailing whitespace be trimmed from each field before parsing it?

## 4. Column Selection

On top of input parameters, you can also use checkboxes on the preview table to include/exclude columns. if you uncheck the checkboxes on the columns, these columns are excluded when you save the data.

## 5. Preview and Import

Click Preview button to see the data in Excel File. You can limit number of rows for the preview by selecting number from pulldown list on the preview table. If it looks ok, then you can click 'Import' to import the whole Excel data into Exploratory.

## 6. Column Data Type Auto-Detection

When previewing the data, Exploratory uses the number of rows from preview data to detect column data types. It means if you set 50 rows for your preview, column data types are detect with the 50 rows. When you actually import full data, it will use up to 1,000 rows to detect column data types so you might see different column data types in the imported data than in the preview.


# JSON

You can import both local and remote JSON File Data quickly.

## 1. Select JSON File Data Menu

Click '+' button next to 'Data Frames' and select 'Import File Data'.

![](/files/-M4oMx1vuumKACr0xqof)

If you import JSON File on your desktop or laptop, make sure Local tab is selected. If you import a JSON file from remote server like Amazon S3, Dropbox, etc or accessing REST APIs that return JSON data, make sure Remote tab is selected. Click 'JSON File'

![](/files/-M4oN7OgwNBdFir-_0io)

## 2. Select File.

If you select local, file picker shows up so select a file to import.

### 2.1 Local File

![](/files/-M4oN7OihiZ3zwzxraj-)

### 2.2 Remote File

if you select remote, small dialog shows up so enter a URL for the file. Enter either json file URL or REST API endpoint URL that returns JSON as response.

![](/files/-M4oN7OkmsJ6Fw9qJF6e)

## 3. JSON Tree

JSON Tree and Preview sections are same for both local and remote.

![](/files/-M4oN7OmlelhRBipl4LP)

Select node on JSON Tree.

On the left hand side, you an see JSON Tree and you can select red table icon for importing data.

You can configure the following properties for the JSON Tree.

* Max Tree Depth: How deep you want to show the tree structure. If your JSON tree structure is deep and you want to see the deeper levels, increase this. If you increase this, it may take time to load the tree.
* Max List Size: How many list items to show in the tree. If you want to see more list items in the list data type in your JSON tree, increase this. If you increase this, it may take time to load the tree.

## 4. Column Selection

On top of input parameters, you can also use checkboxes on the preview table to include/exclude columns. if you uncheck the checkboxes on the columns, these columns are excluded when you save the data.

## 5. Preview and Import

Select red table node on the tree (or Click Get Data button) to see the data in JSON File (or REST API). If it looks ok, then you can click 'Import' to import the JSON data into Exploratory.

## 5. More details about JSON

* [Analyzing JSON data by taking advantage of what makes it JSON in R](https://blog.exploratory.io/analyzing-json-data-by-taking-advantage-of-the-structure-in-r-1e0508e30117)


# Log File

You can import both local and remote Log File Data quickly.

## 1. Select Log File Data Menu

Click '+' button next to 'Data Frames' and select 'Import File Data'.

![](/files/-M4oMx1vuumKACr0xqof)

If you import Log File on your desktop or laptop, make sure Local tab is selected. If you import a file from remote server like Amazon S3, Dropbox, etc make sure Remote tab is selected. Click 'Log File'

![](/files/-M4oMx2-s5BETGaoYs8h)

## 2. Select File.

If you select local, file picker shows up so select a file to import.

### 2.1 Local File

![](/files/-M4oMx21-mFn5rsvH5o2)

### 2.2 Remote File

if you select remote, small dialog shows up so enter a URL for the file.

![](/files/-M4oMx23DqVOlaVJyYO4)

## 3. Input parameters

Input parameters are same for local and remote file import.

![](/files/-M4oMx25VCh3MJCB0wJs)

Set these parameters.

* Number of rows to skip - Number of lines to skip before reading data.

## 4. Column Selection

On top of input parameters, you can also use checkboxes on the preview table to include/exclude columns. if you uncheck the checkboxes on the columns, these columns are excluded when you save the data.

## 5. Preview and Import

Click Preview button to see the data in Log File. You can limit number of rows for the preview by selecting number from pulldown list on the preview table. If it looks ok, then you can click 'Import' to import Log data into Exploratory.


# Microsoft Azure

Please refer [this note](https://exploratory.io/note/exploratory/How-to-import-multiple-CSV-or-Excel-files-to-Exploratory-XDU6lij0) for Microsoft Azure Data Import.


# Stats - SAS / SPSS / STATA

You can import both local and remote Stats (SAS / SPSS / STAT) File Data quickly.

## 1. Select Stats (SAS / SPSS / STAT) File Data Menu

Click '+' button next to 'Data Frames' and select 'Import File Data'.

![](/files/-M4oMx1vuumKACr0xqof)

If you import Stats (SAS / SPSS / STAT) File on your desktop or laptop, make sure Local tab is selected. If you import a file from remote server like Amazon S3, Dropbox, etc make sure Remote tab is selected. Click 'Stats File (SPSS, SAS, STATA)'

![](/files/-M4oN9ZYPDu2zgZLvBF1)

## 2. Select File.

If you select local, file picker shows up so select a file to import.

### 2.1 Local File

![](/files/-M4oN9Z_IFaWvvSGF3qP)

### 2.2 Remote File

if you select remote, small dialog shows up so enter a URL for the file. And select File Type from Pulldown list.

![](/files/-M4oN9Zbagz1ILiYPe1w)

## 3. Input parameters

* Encoding (Optional) : The character encoding used for the data file.

![](/files/-M4oN9ZdN5rDA3om_wDk)

## 4. Column Selection

On top of input parameters, you can also use checkboxes on the preview table to include/exclude columns. if you uncheck the checkboxes on the columns, these columns are excluded when you save the data.

## 5. Preview and Import

Click Preview button to see the data in Stats (SAS / SPSS / STAT) File. If it looks ok, then you can click 'Import' to import the whole Stats (SAS / SPSS / STAT) data into Exploratory.


# RData / RDS

You can import both local and remote R File (RDATA, RDS) File Data quickly.

## 1. Select R File (RDATA, RDS) File Data Menu

Click '+' button next to 'Data Frames' and select 'Import File Data'.

![](/files/-M4oMx1vuumKACr0xqof)

If you import R File (RDATA, RDS) File on your desktop or laptop, make sure Local tab is selected. If you import a file from remote server like Amazon S3, Dropbox, etc make sure Remote tab is selected. Click 'R File (RDATA, RDS)'

![](/files/-M4oN8QrmPTrjKmwDB5g)

## 2. Select File.

If you select local, file picker shows up so select a file to import.

### 2.1 Local File

![](/files/-M4oN8QtNoVMLWiM2spF)

### 2.2 Remote File

if you select remote, small dialog shows up so enter a URL for the file. And select File Type, either RData for Single R Data(RDS), from pulldown list.

![](/files/-M4oN8QwaVhLJELAnFQU)

## 3. Input parameters

### 3.1 RDS File

![](/files/-M4oN8QyxMBz9AAC14rM)

No Input Parameters for RDS

### 3.2 RDA File

![](/files/-M4oN8R-2oldrE_eQLpw)

Set one parameter.

* Data Frame - Since RDA file can have multiple data frames in it, please select a Data Frame that you want to import

## 4. Column Selection

On top of input parameters, you can also use checkboxes on the preview table to include/exclude columns. if you uncheck the checkboxes on the columns, these columns are excluded when you save the data.

## 5. Preview and Import

Click Preview button to see the data in R File (RDATA, RDS) File. If it looks ok, then you can click 'Import' to import the whole R File (RDATA, RDS) data into Exploratory.


# Parquet File

You can import both local and remote Parquet File Data quickly.

## 1. Select Parquet File Data Menu

Click '+' button next to 'Data Frames' and select 'Import File Data'.

![](/files/-M4oMx1vuumKACr0xqof)

If you import CSV / Delimited File on your desktop or laptop, make sure Local tab is selected. If you import a file from remote server like Amazon S3, Dropbox, etc make sure Remote tab is selected. Click 'Text File (CSV, delimited)'

![](/files/pk6cHyQan0QypsdPPITo)

## 2. Select File.

If you select local, file picker shows up so select a file to import.

### 2.1 Local File

![](/files/btrrZNW7OKssNZi1eizC)

### 2.2 Remote File

if you select remote, small dialog shows up so enter a URL for the file.

![](/files/onTeZrDzbdFkQ0IMGTri)

## 3. Preview

Preview the Parquet file import.

![](/files/xOytauVFWgrSFbGVGK6h)

## 5. Column Selection

On top of input parameters, you can also use checkboxes on the preview table to include/exclude columns. if you uncheck the checkboxes on the columns, these columns are excluded when you save the data.

## 6. Import

If it looks ok, then you can click 'Import' to import the whole CSV / Delimited data into Exploratory.


# EDF - Exploratory

You can import local Exploratory (EDF) File Data.

## 1. Select Exploratory (EDF) File Data Menu

Click '+' button next to 'Data Frames' and select 'Import EDF'.

![](/files/-M4oN014lGHfbyDANT6Z)

## 2. Select File.

EDF import is supported for local so select a file to import on File Picker.

![](/files/-M4oN0182tOGaUp4JovS)

## 3. Input parameters

No Input Parameters or EDF file import.

![](/files/-M4oN01AC9-ORfL5sUc6)

## 4. Preview and Import

If it looks ok, then you can click 'Save' to import the whole Exploratory (EDF) data into Exploratory.


# Database Data


# Create Connection

You can register database connection configuration.

## 1. Open Connection dialog

Open your configuration dropdown and click "Connection" there.

![](/files/-M4oN83-IsGzJoNzP2no)

Click "Add" to create a new connection or "Edit" to modify existing connection.

![](/files/-M4oN831VLOuuLCHADPD)

Choose the type of data source.

![](/files/-M4oN8338DMXl8Dbq-MA)

## 2. Set parameters

Set parameters for the database.

![](/files/-M4oN835ngg3vRnXspbR)

You can check the parameters from "Test Connection" button. You will see the result on top of the dialog.

![](/files/-M4oN837JQ5WX-L7RExc)

If you are okay with the configuration, click "Add" Button.

![](/files/-M4oN8392NUY6JZwm-ua)

You will see the connection from the dialog.

![](/files/-M4oN83Bl3q9qbMtC_84)

## 3. Import data from connection.

You can choose those registered connection from Remote Data Frame Import dialog.

![](/files/-M4oN83D9-phrVs5QOk9)


# Amazon Athena

Please refer [this note](https://exploratory.io/note/2ac8ae888097/Connect-to-Amazon-Athena-with-Exploratory-with-ODBC-Data-Source-9617908787989852) for Athena Data Import.\
日本語版は[こちらのノート](https://exploratory.io/note/exploratory/Exploratory-Amazon-Athena-aIA9wSL1)を参照してください。


# Amazon Aurora

You can quickly import data from your Amazon Aurora Database into Exploratory.

## 1. AWS Security Group Setup

Make sure you added your client PC's IP address to your DB Security Group associated with the Aurora database instance.

![](/files/-M4oN8VdKzRdaPgEHSxA)

## 2. Create a Connection to use

Create a connection following [this instruction](https://docs.exploratory.io/data_import/database-data/connection).

* Whether the connection is SSL or not is automatically detected.

## 3. Open Aurora Import dialog

Click '+' button next to 'Data Frames' and select 'Import Database Data'.

![](/files/-M4oN-qBMezITjny_pQQ)

Click Amazon Aurora.

![](/files/-M4oNAntBMX2oV0PByXc)

## 4. Preview and Import

1. Select connection from left box in the dialog.
2. Write a query in the input field.
3. Click "Preview" button. Then you will see the preview of the data.
4. Click "Save" button to import.

![](/files/-M4oNAnv0G4vPoa5cuTy)

## 4. Using Parameters in SQL

First, click Parameter link on the SQL Data Import Dialog.

![](/files/-M4oN2yAtTsqxNt3r0_M)

Second, define a parameter and click Save button.

![](/files/-M4oN2yC4K_GmMFFjzoo)

Finally, you can use @{} to surround a variable name inside the query like below.

```
select *
from airline_2016_01
where carrier = @{carrier}
```

If you type @ then it suggests parameters like below.

![](/files/-M4oN2yEXevqQptAWUEl)

Here's a [blog post](https://exploratory.io/note/kanaugust/An-Introduction-to-Parameter-in-Exploratory-WCO4Vgn7HJ) for more detail.

## 5. Number of rows

From performance point of view, we no longer show actual number of rows which can be only fetched by executing whole query again.

![](/files/-M4oN66xWkcCxqo19jUZ)

If you still want to show the actual number of query for your query, you can do so by setting System Configuration.

![](/files/-M4oN66zCHttnANqtQHx)

Then set "Yes" For "Show Actual Number of Rows on SQL Data Import Dialog"

![](/files/-M4oN670aVGCuTHA4oG4)

This will show you Actual Number of Rows like below.

![](/files/-M4oN6725Fzzj5A208wn)


# Amazon Redshift

You can quickly import data from your Amazon Redshift Database into Exploratory.

Here is a [blog post](https://blog.exploratory.io/exploratory-data-analysis-for-amazon-redshift-with-r-dplyr-9a14441020eb#.aqcbfa6h8) introducing this support in detail.

## 1. Create a Connection to use

Create a connection following [this instruction](https://docs.exploratory.io/data_import/database-data/connection).

## 2. Open Redshift Import dialog

Click '+' button next to 'Data Frames' and select 'Database Data'.

![](/files/-M4oN-qBMezITjny_pQQ)

Click Amazon Redshift to select.

![](/files/-M4oN66k4YJQfHTwMU5I)

## 3. Preview and Import

Click Preview button to see the data back from your Redshift db.

![](/files/-M4oN66mzrfS3x8GJpWw)

If it looks ok, then you can click 'Import' to import the data into Exploratory.

## 4. Querying Random Sample Data

You might want to take a random sample of the data that would be reasonable size for your analysis.

You can use [md5](http://docs.aws.amazon.com/redshift/latest/dg/r_MD5.html) function to get random number generated and use it like below to get the random sample of the data.

```
SELECT *
   FROM airline_2016_01
   ORDER BY md5('randomSeed' || flight_num)
   LIMIT 100000
```

## 5. Using Parameters in SQL

First, click a parameter link on the SQL Data Import Dialog.

![](/files/-M4oN2yAtTsqxNt3r0_M)

Second, define a parameter and click Save button.

![](/files/-M4oN2yC4K_GmMFFjzoo)

Finally, you can use @{} to surround a variable name inside the query like below.

```
select *
from airline_2016_01
where carrier = @{carrier}
```

If you type @ then it suggests parameters like below.

![](/files/-M4oN2yEXevqQptAWUEl)

Here's a [blog post](https://exploratory.io/note/kanaugust/An-Introduction-to-Parameter-in-Exploratory-WCO4Vgn7HJ) for more detail.

## 6. AWS Security Group Setup

![](/files/-M4oN66v_0i1voeo7Yp-)

If you encounter a database connection error, please go to AWS console and make sure you added your client PC's IP address to your Security Group (Inbound) associated with the Redshift cluster.

## 7. Number of rows

From performance point of view, we no longer show actual number of rows which can be only fetched by executing whole query again.

![](/files/-M4oN66xWkcCxqo19jUZ)

If you still want to show the actual number of query for your query, you can do so by setting System Configuration.

![](/files/-M4oN66zCHttnANqtQHx)

Then set "Yes" For "Show Actual Number of Rows on SQL Data Import Dialog"

![](/files/-M4oN670aVGCuTHA4oG4)

This will show you Actual Number of Rows like below.

![](/files/-M4oN6725Fzzj5A208wn)

## 8. Exploratory Data Analysis for Amazon Redshift with R & dplyr

Here is the link to the blog post [Exploratory Data Analysis for Amazon Redshift with R & dplyr](https://blog.exploratory.io/exploratory-data-analysis-for-amazon-redshift-with-r-dplyr-9a14441020eb)


# Amazon Redshift (日本語)

Amazon Redshift DatabaseからExploratoryに素早くデータをインポートすることができます。

Redshiftについて詳しく紹介した[ブログ記事](https://blog.exploratory.io/exploratory-data-analysis-for-amazon-redshift-with-r-dplyr-9a14441020eb#.aqcbfa6h8)はこちらです。

## 1. 使用するデータコネクションを作成する

[この手順](https://docs.exploratory.io/data_import/database-data/connection)に従って、接続を作成します。

## 2. Redshift インポートダイアログを開く

データフレームの横にある「＋」ボタンをクリックし、「Database Data」を選択します。

![](/files/-M4oN-qBMezITjny_pQQ)をクリックします。

Amazon Redshiftをクリックして選択します。

![](/files/-M4oN66k4YJQfHTwMU5I)

## 3. プレビューとインポート

実行ボタンをクリックすると、Redshift データベースから取得してきたデータを確認することができます。

![](/files/-M4oN66mzrfS3x8GJpWw)

問題ないようであれば、「インポート」をクリックしてExploratoryにデータをインポートします。

## 4. ランダムサンプルデータのクエリ

分析に適したサイズのデータからランダムにサンプルを取りたい場合があります。

[md5](http://docs.aws.amazon.com/redshift/latest/dg/r_MD5.html)関数を使って乱数を生成し、以下のように使用することでデータのランダムサンプルを得ることができます。

```
SELECT *
   FROM airline_2016_01
   ORDER BY md5('randomSeed' || flight_num)
   LIMIT 100000
```

## 5. SQLでパラメータを使用する

まず、SQL データインポートダイアログのパラメータリンクをクリックします。

![](/files/-M4oN2yAtTsqxNt3r0_M)をクリックします。

次に、パラメータを定義し、\[保存]ボタンをクリックします。

![](/files/-M4oN2yC4K_GmMFFjzoo)

最後に、クエリ内の変数名を@{}で囲むと、以下のようになります。

```
select *
from airline_2016_01
where carrier = @{carrier}
```

を入力すると、以下のようなパラメータが表示されます。

![](/files/-M4oN2yEXevqQptAWUEl)のようになります。

詳しくはこちらの[ブログ記事](https://exploratory.io/note/kanaugust/An-Introduction-to-Parameter-in-Exploratory-WCO4Vgn7HJ)をご覧ください。

## 6. AWSセキュリティグループの設定

![](/files/-M4oN66v_0i1voeo7Yp-)

データベース接続エラーが発生した場合は、AWSコンソールにアクセスし、Redshiftクラスタに関連付けられたセキュリティグループ（Inbound側に）にクライアントPCのIPアドレスが追加されていることを確認してください。

## 7. 実際の行数

パフォーマンスの観点から、クエリ全体を再実行しないと取得できない、実際の行数を表示しないようにしました。

![](/files/-M4oN66xWkcCxqo19jUZ)

それでもクエリの結果の実数を表示したい場合は、システム設定により表示させることができます。

![](/files/-M4oN66zCHttnANqtQHx)

そして、"SQLデータインポートダイアログで実際の行数を表示"を「はい」に設定します。

![](/files/-M4oN670aVGCuTHA4oG4)

これにより、以下のように実際の行数が表示されます。

![](/files/-M4oN6725Fzzj5A208wn)

## 8. Rとdplyrを使ったAmazon Redshiftの探索的データ分析

ブログ記事R & dplyrによるAmazon Redshiftの探索的データ分析への[リンクはこちらです](https://blog.exploratory.io/exploratory-data-analysis-for-amazon-redshift-with-r-dplyr-9a14441020eb)。


# Google BigQuery

## 1. Before you start

To use Google BigQuery with Exploratory Desktop, you need to create a project on Google Cloud Platform and a dataset on Google BigQuery.

### Create a project for Google BigQuery

* Open [Google Cloud Platform Console](https://console.cloud.google.com/)
* Click "Create Project" menu at the right hand side top

![](/files/-M4oN2xpc-zKJ1oGxJ_Q)

* Select a Project name and click "Create" button

![](/files/-M4oN2xrIboyi60dQz_g)

* Make sure that you enable BigQuery API for you Project by clicking "Enable and manager APIs " menu under Use Google APIs section

![](/files/-M4oN2xtRVrLbY0tyzE0)

### Create a dataset on Google BigQuery

* Open [Google BigQuery Web ui](https://bigquery.cloud.google.com/)
* Click down arrow icon next to your project name and select "Create new dataset" menu.

![](/files/-M4oN2xvUg1RMzf5XEYD)

* Enter Dataset id and Click "OK" button

![](/files/-M4oN2xxgKuUFCeTDdLA)

## 2. Select Google BigQuery Data Menu

* Select 'Import Database Data' from Add Data Frames dropdown

![](/files/-M4oN-qBMezITjny_pQQ)

* Click 'Google BigQuery'

![](/files/-M4oN2xzIppUuIfJCRXH)

## 3. Authentication with Google OAuth

Select an account you want to use for your Google BigQuery and click 'Allow' button to allow Exploratory to extract your Google BigQuery data based on the parameters you are going to set up in the next step.

![](/files/-M4oN2y0w0m9yox9thnO)

![](/files/-M4oN2y2-whjF2KWQh-X)

## 4. Write SQL Query

### 4.1 Preview Data

* Type Data Frame Name
* Select Google BigQuery Project from the dropdown menu
* Enter query to SQL Query editor
* Click Run button to preview data.

![](/files/-M4oN2y4qYjNglLOAIQW)

## 5. Standard SQL

You can now use Standard SQL by clicking the "Standard SQL Mode" checkbox on Google BigQuery Configuration Dialog.

Click the Edit button to open the Google BigQuery Configuration Dialog. ![](/files/vRWRhz7Lkg0MV12DOXWZ)

On the Google BigQuery Configuration Dialog, you can set the Standard SQL Mode.

![](/files/-M4oN2y679hwa4t4yF-b)

BigQuery standard SQL is compliant with the SQL 2011 standard and has extensions that support querying nested and repeated data.

Standard SQL has several advantages over legacy SQL, including:

* Composability using WITH clauses and SQL functions
* Subqueries in the SELECT list and WHERE clause
* Correlated subqueries
* ARRAY and STRUCT data types
* COUNT(DISTINCT \<expr>) is exact and scalable, providing the accuracy of EXACT\_COUNT\_DISTINCT without its limitations
* Automatic predicate push-down through JOINs
* Complex JOIN predicates, including arbitrary expressions

### 5.1 Composability using WITH clauses and SQL functions

Now you can use `WITH` clause which enables extraction or reuse of named subqueries. For example:

```sql
WITH SUBQ AS (
  SELECT score FROM UNNEST([50, 60, 40, 50]) AS score
)
SELECT score / (SELECT SUM(score) FROM SUBQ) AS weighted_score
FROM SUBQ;
```

![](/files/-M4oN2y8WIb_BP0L5Tog)

For Migration from legacy SQL, Please refer [Migrating to Standard SQL](https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql)

## 6. Page Size

When importing data from Google BigQuery, if you specify a lot of columns in your SQL query, the query may return missing results. If this is the case, you might want to reduce the page size.

Also, when your query result contains many list columns, you also want to reduce page size so that you can import the query result.

Please note that decreasing the page size slows the importing data process.

![](/files/3HN1WITkH9GtZYV54Rtv)

You can override the Page Size at the Import Dialog too.

![](/files/AYrPK7wkNnDAsdp4xTVe)

## 7. Using Parameters in SQL

First, click Parameter link on the SQL Data Import Dialog.

![](/files/-M4oN2yAtTsqxNt3r0_M)

Second, define a parameter and click Save button.

![](/files/-M4oN2yC4K_GmMFFjzoo)

Finally, you can use @{} to surround a variable name inside the query like below.

```
select *
from airline_2016_01
where carrier = @{carrier}
```

If you type @ then it suggests parameters like below.

![](/files/-M4oN2yEXevqQptAWUEl)

Here's a [blog post](https://exploratory.io/note/kanaugust/An-Introduction-to-Parameter-in-Exploratory-WCO4Vgn7HJ) for more detail.

## 8. Import

Click 'Import' button

If the data in the preview table look ok, then click 'Import' button to import the data into Exploratory.

## 9. Switch Billing Project

When you want to switch your Billing Project, you can click select a Billing Project on Google BigQuery Configuration dialog.

![](/files/inh1psWWQ2r5HilgQ7zQ)


# HP Vertica

You can quickly import data from your HP Vertica Database into Exploratory.

## 1. Create a Connection to use

Create a connection following [this instruction](https://docs.exploratory.io/data_import/database-data/connection).

## 2. Open HP Vertica Import dialog

Click '+' button next to 'Data Frames' and select 'Import Database Data'.

![](/files/-M4oN83D9-phrVs5QOk9)

Click HP Vertica to select.

![](/files/-M4oNA4QlzUrtCkpxq2N)

## 3. Import

1. Put data frame name.
2. Select connection from left dialog box.
3. Write a query in the input field.
4. Click "Preview" button. Then you will see the preview of the data.
5. Click "Import" button.

![](/files/-M4oNA4TVgsrEkWpL_x8)

If it looks ok, then you can click 'Import' to import the data into Exploratory.

## 4. Using Variables in SQL

First, create a custom R script.

![](/files/-M4oNA4VSoPDigL48Qrt)

Second, define a variable in the R script and save.

![](/files/-M4oNA4X41Hk5MMomwsC)

```
cutoff_date <- "\'2016-01-15\'"
```

Note that the ‘\’ (backslash) symbols are used to escape the single quotes, which are required to be used for characters in SQL queries.

Finally, you can use @{} to surround a variable name inside the query like below.

```
select *
from airline_2016_01
where fl_date > @{cutoff_date}
```

Here's a [blog post](https://blog.exploratory.io/using-variables-in-sql-query-2740924d9f20#.bdcn5v68x) for more detail.


# MariaDB / MySQL DB

You can quickly import data from your MySQL Database into Exploratory.

## 1. Create a Connection to use

Create a connection following [this instruction](https://docs.exploratory.io/data_import/database-data/connection).

* Whether the connection is SSL or not is automatically detected.

## 2. Open MariaDB (MySQL) Import dialog

Click '+' button next to 'Data Frames' and select 'Import Database Data'.

![](/files/-M4oN83D9-phrVs5QOk9)

Click MySQL.

![](/files/-M4oN8V6_llEgk0jx7MC)

## 3. Import

1. Select connection from left dialog box.
2. Write a query in the input field.
3. Click "Preview" button. Then you will see the preview of the data.
4. Click "Import" button.

![](/files/-M4oN8VUicjMpImTugpd)

## 4. Using Parameters in SQL

First, click Parameter link on the SQL Data Import Dialog.

![](/files/-M4oN2yAtTsqxNt3r0_M)

Second, define a parameter and click Save button.

![](/files/-M4oN2yC4K_GmMFFjzoo)

Finally, you can use @{} to surround a variable name inside the query like below.

```
select *
from airline_2016_01
where carrier = @{carrier}
```

If you type @ then it suggests parameters like below.

![](/files/-M4oN2yEXevqQptAWUEl)

Here's a [blog post](https://exploratory.io/note/kanaugust/An-Introduction-to-Parameter-in-Exploratory-WCO4Vgn7HJ) for more detail.

## 5. AWS Security Group Setup (Only for Amazon RDS)

![](/files/-M4oN8VdKzRdaPgEHSxA)

If you are using Amazon RDS as your MySQL database, and encounter a database connection error, please go to AWS console and make sure you added your client PC's IP address to an inbound rule for your DB Security Group associated with the MySQL database instance.

## 6. Number of rows

From performance point of view, we no longer show actual number of rows which can be only fetched by executing whole query again.

![](/files/-M4oN66xWkcCxqo19jUZ)

If you still want to show the actual number of query for your query, you can do so by setting System Configuration.

![](/files/-M4oN66zCHttnANqtQHx)

Then set "Yes" For "Show Actual Number of Rows on SQL Data Import Dialog"

![](/files/-M4oN670aVGCuTHA4oG4)

This will show you Actual Number of Rows like below.

![](/files/-M4oN6725Fzzj5A208wn)


# MariaDB / MySQL DB (日本語)

MariaDB (MySQL) データベースからExploratoryに素早くデータをインポートすることができます。

## 1. 使用するデータコネクションを作成する

[この手順](https://docs.exploratory.io/data_import/database-data/connection)に従って、データコネクションを作成します。

* SSL接続かどうかは自動的に判別されます。

## 2. MariaDB (MySQL) インポートダイアログを開く

データフレームの横の「＋」ボタンをクリックし、「データベースデータ」を選択します。

![](/files/-M4oN83D9-phrVs5QOk9)

MariaDB (MySQL) をクリックします。

![](/files/-M4oN8V6_llEgk0jx7MC)

## 3. インポート

1. 左のダイアログボックスから接続を選択します。
2. 入力フィールドにクエリを記述します。
3. "実行 "ボタンをクリックします。すると、データのプレビューが表示されます。
4. "保存"ボタンをクリックします。

![](/files/-M4oN8VUicjMpImTugpd)

## 4. SQLでパラメーターを使用する

まず、SQLデータインポートダイアログの「パラメーター」リンクをクリックします。

![](/files/-M4oN2yAtTsqxNt3r0_M)

次に、パラメーターを定義し、\[保存]ボタンをクリックします。

![](/files/-M4oN2yC4K_GmMFFjzoo)

最後に、クエリ内の変数名を@{}で囲むと、以下のようになります。

```
select *
from airline_2016_01
where carrier = @{carrier}
```

を入力すると、以下のようなパラメーターが表示されます。

![](/files/-M4oN2yEXevqQptAWUEl)のようになります。

詳しくはこちらの[ブログ記事](https://exploratory.io/note/kanaugust/An-Introduction-to-Parameter-in-Exploratory-WCO4Vgn7HJ)をご覧ください。

## 5. AWSセキュリティグループの設定 (Amazon RDSのみ)

![](/files/-M4oN8VdKzRdaPgEHSxA)

MySQLのデータベースとしてAmazon RDSを使用していて、データベース接続エラーが発生した場合、AWSコンソールにアクセスし、MySQLデータベースインスタンスに関連付けられたDBセキュリティグループのルール (インバウンド) にクライアントPCのIPアドレスが追加されていることを確認してください。

## 6. 実際の行数

パフォーマンスの観点から、クエリ全体を再実行しないと取得できない、実際の行数を表示しないようにしました。

![](/files/-M4oN66xWkcCxqo19jUZ)

それでもクエリの結果の実数を表示したい場合は、システム設定により表示させることができます。

![](/files/-M4oN66zCHttnANqtQHx)

そして、"SQLデータインポートダイアログで実際の行数を表示"を「はい」に設定します。

![](/files/-M4oN670aVGCuTHA4oG4)

これにより、以下のように実際の行数が表示されます。

![](/files/-M4oN6725Fzzj5A208wn)


# Microsoft Access

You can quickly import data from your MS Access into Exploratory.

Please refer [How to import Data from Microsoft Access Database with ODBC](https://exploratory.io/note/exploratory/How-to-import-Data-from-Microsoft-Access-Database-with-ODBC-zIJ2bjs2) for more details.


# MongoDB

You can quickly import data from your MongoDB into Exploratory.

## 1. Create a Connection to use

In Exploratory, there are three types of MongoDB connections.

* Connection string
* Single Host
* Multiple Hosts

### 1.1 Connection String

If you want to connect to your MongoDB with DNS Seedlist Connection Format or if you already have a working MongoDB Connection String, you can use this "Connection String" type. Simply copy and paste the Connection String to the text area and click the Test Connection button. If the connection test went well, click the Add button to save it.

You can find MongoDB connection Sting details [here](https://docs.mongodb.com/manual/reference/connection-string/).

![](/files/-M4oN9YV1qH2etAvWohw)

### 1.2 Single Host

If you prefer to connection to your MongoDB by supplying Host, Port, Database, Username, and Password from the Connection Dialog, you can use this "Single Host" type. Like other Database connections, type in required fields then click Test Connection button. If the connection test went well, click Add to save it.

![](/files/-M4oN9YXd7VfDD-61H4s)

### 1.3 Multiple Hosts

If you prefer to connection to your MongoDB (Sharded Cluster) by supplying Cluster, Database, Username, and Password from the Connection Dialog, you can use this "Multiple Hosts" type. Type in Cluster and other required fields, then click the Test Connection button. If the connection test went well, click Add button to save it.

![](/files/-M4oN9YaF6HJwJ1c9_iw)

## 2. Open MongoDB Import dialog

Click '+' button next to 'Data Frames' and select 'Import Database Data'.

![](/files/-M4oN-qBMezITjny_pQQ)

Click MongoDB to select.

![](/files/-M4oN9Ye8svCaBd1ZWZ0)

## 3. Preview Data

### 3.1 Find

![](/files/BElKnYBDqQLAEcBnOLdi)

1. Select connection from left dialog box.
2. Select collection from left dialog box. Then you will see the preview of the data.

You can use Input Parameters to fine tune your query result.

See [An Introduction to MongoDB Query for Beginners](https://blog.exploratory.io/an-introduction-to-mongodb-query-for-beginners-bd463319aa4c) for each Input Parameter details.

#### 3.1.1 Input Parameters

* Query - You can set the filtering condition.
* Fields - You can select the columns (or fields) by either keeping them or excluding them explicitly.
* Sort - You can use this to set the sorting rules.
* Limit - 100 by default, which means it will query only the first 100 rows (documents) of the data. You can change this to other values and eventually you want to set it to ‘All’ once you finalize your queries and are ready for importing the data into Exploratory.

### 3.2 Aggregate

![](/files/-M4oN9Yl0IQbAEMjWehu)

The aggregation pipeline has an internal optimization phase that provides improved performance for certain sequences of operators. For details, see [Aggregation Pipeline Optimization](https://docs.mongodb.com/manual/core/aggregation-pipeline-optimization/)

## 4. Using Parameters in Find

First, click Parameter link on the MongoDB Data Import Dialog.

![](/files/-M4oN9YoCumpPG45xy4u)

Second, define a parameter and click Save button.

![](/files/-M4oN9YrrE-iEdo6QnLN)

Finally, you can use @{} to surround a variable name inside the query like below.

```
{borough: @{borough}}
```

If you type @ then it suggests parameters like below. And now it shows restaurants in Manhattan.

![](/files/-M4oN9YuyCaVQKJRh6h-)

Here's a [blog post](https://exploratory.io/note/kanaugust/An-Introduction-to-Parameter-in-Exploratory-WCO4Vgn7HJ) for more detail.

## 5. Import

If it looks ok, then you can click 'Import' to import the data into Exploratory.


# ODBC

You can quickly import data from your ODBC Database Connection into Exploratory.

## 1. Install ODBC Drivers and Set Up Data Source Name (DSN)

First, install ODBC Drivers and set up Data Source Name (DSN) for the database you want to connect Exploratory to. Following are instructions to create ODBC DSN for some of the types of databases we verified that they work.

* [Snowflake](https://exploratory.io/note/2ac8ae888097/How-to-import-data-from-Snowflake-nhP3FTm5ZM) (for Mac/Windows)
* [Oracle](https://exploratory.io/note/exploratory/Connecting-to-Oracle-Database-through-ODBC-from-Windows-with-Oracle-Instance-Client-LmR8nHr3) (for Windows)
* [Oracle](https://exploratory.io/note/exploratory/Connecting-to-Oracle-Database-through-ODBC-from-Mac-uvi2DuT5) (for Mac)
* [Oracle (Japanese)](https://exploratory.io/note/BWz1Bar4JF/Oracle-Instance-Client-Windows-ODBC-Oracle-Database-AGi5wIz7QG) (for Windows)
* [Oracle (Japanese)](https://exploratory.io/note/BWz1Bar4JF/ODBC-Mac-Oracle-Database-baR1tUK7KO) (for Mac)
* [Microsoft SQL Server](https://community.exploratory.io/t/connecting-to-ms-sql-server-through-odbc-from-windows/341) (for Windows)
* [Microsoft SQL Server](https://community.exploratory.io/t/connecting-to-ms-sql-server-through-odbc-from-mac/339) (for Mac)

## 2. Create a Connection to use

Select the "Data Connection" menu from the menu right next to the project name.

![](/files/-MRgc-0mZMh4oasxjBRw)

Click the "Add" button.

![](/files/-MRgc-0om4eHBH7DieAc)

Select the "ODBC" icon.

![](/files/-MRgc-0pMLyb2f3EMhsP)

Select the DSN created at Step 1 and click the Test and make sure it works.\
For Mac, we support both [iODBC](http://www.iodbc.org/dataspace/doc/iodbc/wiki/iodbcWiki/ODBCMacOSX) based and [unixODBC](http://www.unixodbc.org/drivers.html) based ODBC Drivers and DSN.

![](/files/-MRgc-0sYFU-aNdcDMRQ)

Click the "Add" button to save the connection.

## 3. Open ODBC Import dialog

Click '+' button next to 'Data Frames' and select 'Database Data'.

![](/files/-M4oN-qBMezITjny_pQQ)

Click ODBC to select.

![](/files/-M4oN8dOXQmEdeuOsh0X)

## 4. Preview and Import

Click Preview button to see the data from your ODBC database connection.

![](/files/-M4oN8dQejGt1jX7I0An)

If it looks ok, then you can click 'Import' to import the data into Exploratory.

## 5. Using Parameters in SQL

First, click a parameter link on the SQL Data Import Dialog.

![](/files/-M4oN2yAtTsqxNt3r0_M)

Second, define a parameter and click Save button.

![](/files/-M4oN2yC4K_GmMFFjzoo)

Finally, you can use @{} to surround a variable name inside the query like below.

```
select *
from airline_2016_01
where carrier = @{carrier}
```

If you type @ then it suggests parameters like below.

![](/files/-M4oN2yEXevqQptAWUEl)

Here's a [blog post](https://exploratory.io/note/kanaugust/An-Introduction-to-Parameter-in-Exploratory-WCO4Vgn7HJ) for more detail.

## 6. Number of rows

From performance point of view, we no longer show actual number of rows which can be only fetched by executing whole query again.

![](/files/-M4oN66xWkcCxqo19jUZ)

If you still want to show the actual number of query for your query, you can do so by setting System Configuration.

![](/files/-M4oN66zCHttnANqtQHx)

Then set "Yes" For "Show Actual Number of Rows on SQL Data Import Dialog"

![](/files/-M4oN670aVGCuTHA4oG4)

This will show you Actual Number of Rows like below.

![](/files/-M4oN6725Fzzj5A208wn)

## 7. Known Issues

* [Importing multi-byte characters from Teradata Aster fails](https://exploratory.io/note/exploratory/How-to-workaround-known-issues-when-connectiong-to-Teradata-Aster-with-ODBC-BTG0Tmz9)
* [Schema and Tables are not shown for Kintone](https://exploratory.io/note/exploratory/How-to-workaround-known-issues-when-connecting-to-Kintone-with-ODBC-hjq9Deg4)
* [Unable to Connect to IBM DB2 with ODBC and it shows error message SQLAllocHandle failed at SQL\_HANDLE\_ENV.](https://community.exploratory.io/t/unable-to-connect-to-ibm-db2-with-odbc-and-it-shows-error-message-sqlallochandle-failed-at-sql-handle-env/3888)


# Oracle

To be updated.


# PostgreSQL

## PostgreSQL Database Data Import

You can quickly import data from your PostgreSQL Database into Exploratory.

### 1. Create a Connection to use

Create a connection following [this instruction](https://docs.exploratory.io/data_import/database-data/connection).

* Whether the connection is SSL or not is automatically detected.

### 2. Open PostgreSQL Import dialog

Click '+' button next to 'Data Frames' and select 'Database Data'.

![](/files/-M4oN-qBMezITjny_pQQ)

Click PostgreSQL to select.

![](/files/-M4oNAP9G3lWC5ppN1PQ)

### 3. Preview and Import

Click Preview button to see the data back from your PostgreSQL db.

![](/files/-M4oNAPBx4_aTFq4j4bc)

If it looks ok, then you can click 'Import' to import the data into Exploratory.

### 4. Using Parameters in SQL

First, click Parameter link on the SQL Data Import Dialog.

![](/files/-M4oN2yAtTsqxNt3r0_M)

Second, define a parameter and click Save button.

![](/files/-M4oN2yC4K_GmMFFjzoo)

Finally, you can use @{} to surround a variable name inside the query like below.

```
select *
from airline_2016_01
where carrier = @{carrier}
```

If you type @ then it suggests parameters like below.

![](/files/-M4oN2yEXevqQptAWUEl)

Here's a [blog post](https://exploratory.io/note/kanaugust/An-Introduction-to-Parameter-in-Exploratory-WCO4Vgn7HJ) for more detail.

### 5. Number of rows

From performance point of view, we no longer show actual number of rows which can be only fetched by executing whole query again.

![](/files/-M4oN66xWkcCxqo19jUZ)

If you still want to show the actual number of query for your query, you can do so by setting System Configuration.

![](/files/-M4oN66zCHttnANqtQHx)

Then set "Yes" For "Show Actual Number of Rows on SQL Data Import Dialog"

![](/files/-M4oN670aVGCuTHA4oG4)

This will show you Actual Number of Rows like below.

![](/files/-M4oN6725Fzzj5A208wn)

### 6. AWS Security Group Setup (Only for Amazon RDS)

![](/files/-M4oN8VdKzRdaPgEHSxA)

If you are using Amazon RDS as your MySQL database, and encounter a database connection error, please go to AWS console and make sure you added your client PC's IP address to an inbound rule for your DB Security Group associated with the MySQL database instance.

## 7. Known Issues for geometry column.

When you try to import data from PostgreSQL instance, you might get the below error if your SQL query contains columns whose data type is geometry which is not supported in Exploratory.

Error : No method asJSON S3 class: pq\_geometry

Please refer to [this community post](https://community.exploratory.io/t/error-no-method-asjson-s3-class-pq-geometry-is-raised-when-try-to-import-data-from-postgresql/2102) for the workaround.


# PostgreSQL (日本語)

## PostgreSQLデータベースからデータをインポートする

PostgreSQLデータベースからExploratoryに素早くデータをインポートすることができます。

### 1. 使用するデータコネクションを作成する

[この手順](https://docs.exploratory.io/data_import/database-data/connection)に従って、データコネクションを作成します。

* SSL接続かどうかは自動的に判別されます。

### 2. PostgreSQLインポートダイアログを開く

データフレームの横の「＋」ボタンをクリックし、データベースデータを選択します。

![](/files/-M4oN-qBMezITjny_pQQ)

PostgreSQLをクリックして選択します。

![](/files/-M4oNAP9G3lWC5ppN1PQ)

### 3. プレビューとインポート

実行ボタンをクリックすると、PostgreSQLのデータベースから取得してきたデータを見ることができます。

![](/files/-M4oNAPBx4_aTFq4j4bc)

問題ないようであれば、「保存」ボタンをクリックしてExploratoryにデータをインポートすることができます。

### 4. SQLでパラメータを使う

まず、SQLデータインポートダイアログの「パラメーター」リンクをクリックします。

![](/files/-M4oN2yAtTsqxNt3r0_M)をクリックします。

次に、パラメーターを定義し、\[保存]ボタンをクリックします。

![](https://github.com/exploratory-io/book/blob/master/import/画像/define_parameter.png)

最後に、クエリ内の変数名を@{}で囲むと、以下のようになります。

```
select *
from airline_2016_01
where carrier = @{carrier}
```

を入力すると、以下のようなパラメータが表示されます。

![](/files/-M4oN2yEXevqQptAWUEl)のようになります。

詳しくはこちらの[ブログ記事](https://exploratory.io/note/kanaugust/An-Introduction-to-Parameter-in-Exploratory-WCO4Vgn7HJ)をご覧ください。

### 5. 行数

パフォーマンスの観点から、クエリ全体を再実行しないと取得できない、実際の行数を表示しないようにしました。

![](/files/-M4oN66xWkcCxqo19jUZ)

それでもクエリの結果の実数を表示したい場合は、システム設定により表示させることができます。

![](/files/-M4oN66zCHttnANqtQHx)

そして、"SQLデータインポートダイアログで実際の行数を表示"を「はい」に設定します。

![](/files/-M4oN670aVGCuTHA4oG4)

これにより、以下のように実際の行数が表示されます。

![](/files/-M4oN6725Fzzj5A208wn)

### 6. AWSセキュリティグループの設定(Amazon RDSのみ)

![](/files/-M4oN8VdKzRdaPgEHSxA)

MySQLのデータベースとしてAmazon RDSを使用していて、データベース接続エラーが発生した場合、AWSコンソールに移動し、MySQLデータベースインスタンスに関連付けられたDBセキュリティグループ（インバウンド）のルールにクライアントPCのIPアドレスが追加されていることを確認してください。

## 7. ジオメトリカラムの既知の問題

PostgreSQL インスタンスからデータをインポートしようとした場合、SQL クエリに Exploratory でサポートされていないデータ型が geometry のカラムが含まれていると、以下のエラーが表示されることがあります。

Error : No method asJSON S3 class: pq\_geometry

回避策としては、[このコミュニティの投稿](https://community.exploratory.io/t/error-no-method-asjson-s3-class-pq-geometry-is-raised-when-try-to-import-data-from-postgresql/2102)を参照してください。


# Presto

You can quickly import data from your Presto into Exploratory.

## 1. Create a connection for Presto

First, you want to create a connection for Presto.

![](/files/-M4oN-q7KC77-cS90ots)

After filling the database information, click ‘Test Connection’ button to make sure the information is correct, before you save it.

![](/files/-M4oN-q9tcAotpE5_3yz)

## 2. Open Presto Import dialog

Select 'Import Database Data' from Add New Data Frame menu.

![](/files/-M4oN-qBMezITjny_pQQ)

Click Presto to select.

![](/files/-M4oN-qDwYtoZaCBx3V-)

## 3. Preview and Import

Click "Get Data" button to see the data back from your Presto.

![](/files/-M4oN-qK-GMd0SUXtX2B)

If it looks ok, then you can click 'Import' to import the data into Exploratory.


# Snowflake

You can quickly import data from your Snowflake into Exploratory.

## Prerequisite: ODBC Drivers

To use the Snowflake Data Source, you need to install ODBC driver.

If you use Mac with Apple Silicon (M1/M2), you need to install the ODBC driver for architecture macaarch64.

![](/files/IWoxXKG4j9EcQIMrWHll)

### Install ODBC Driver for Windows

To Install ODBC Driver for Snowflake for Windows, please follow the [instruction](https://docs.snowflake.com/en/user-guide/odbc-windows.html)

### Install ODBC Driver for Mac

First, you need to install homebrew to manage your packages and you have the necessary admin rights on your machine.

Homebrew installation: [link](http://brew.sh/)

1. Install unixodbc Install with your shell/terminal:

```sh
brew install unixodbc
```

Then follow the [instruction](https://docs.snowflake.com/en/user-guide/odbc-mac.html) to install Mac ODBC Driver. Once installed, now you need to register the ODBC driver.

Since now you installed the unixODBC, you can use `odbcinst` command from Terminal. And if you try `odbcinst -j`, you can know the location of driver registration file (odbcinst.ini) like below.

```sh
➜  ~ odbcinst -j
unixODBC 2.3.9
DRIVERS............: /usr/local/etc/odbcinst.ini
SYSTEM DATA SOURCES: /usr/local/etc/odbc.ini
FILE DATA SOURCES..: /usr/local/etc/ODBCDataSources
USER DATA SOURCES..: /Users/exploratory/.odbc.ini
SQLULEN Size.......: 8
SQLLEN Size........: 8
SQLSETPOSIROW Size.: 8
```

if you use Mac with Apple Silicon (M1/M2), you might see slight different location like below.

```sh
unixODBC 2.3.11
DRIVERS............: /opt/homebrew/etc/odbcinst.ini
SYSTEM DATA SOURCES: /opt/homebrew/etc/odbc.ini
FILE DATA SOURCES..: /opt/homebrew/etc/ODBCDataSources
USER DATA SOURCES..: /Users/hidekoji/.odbc.ini
SQLULEN Size.......: 8
SQLLEN Size........: 8
SQLSETPOSIROW Size.: 8
```

So add the below to the odbcinst.ini file

```sh
[SnowflakeDSIIDriver]
APILevel=1
ConnectFunctions=YYY
Description=Snowflake DSII
Driver=/opt/snowflake/snowflakeodbc/lib/universal/libSnowflake.dylib
DriverODBCVer=03.52
SQLLevel=1
```

if you use Mac with Apple Silicon (M1/M2), driver path is different so it looks like this.

```sh
[SnowflakeDSIIDriver]
APILevel=1
ConnectFunctions=YYY
Description=Snowflake DSII
Driver=/opt/snowflake/snowflakeodbc/lib/libSnowflake.dylib
DriverODBCVer=03.52
SQLLevel=1
```

Open the Snowflake ODBC config file located at `/opt/snowflake/snowflakeodbc/lib/universal/simba.snowflake.ini`

For Mac with Apple Silicon (M1/M2), the location is `/opt/snowflake/snowflakeodbc/lib/simba.snowflake.ini`

Make sure that unixODBC section points to the `libodbcinst.dylib` file which is installed with homebrew. Please note the location of the `libodbcinst.dylib` varies per installation.

```sh
#   unixODBC
#ODBCInstLib=libodbcinst.a(libodbcinst.so.1)
ODBCInstLib=/usr/local/lib/libodbcinst.dylib

```

For Mac with Apple Silicon (M1/M2), the library file location is `/opt/homebrew/lib/libodbcinst.dylib` by default.

```sh
#   unixODBC
ODBCInstLib=/opt/homebrew/lib/libodbcinst.dylib
```

## 1. Create a connection for Snowflake

First, you want to create a connection for Snowflake.

![](/files/-MR62twH0lkMvh8e5N8O)

After filling the below database information:

* Host - Snowflake server
* Warehouse - Warehouse Name
* Database - Database Name
* ODBC Driver Name for Snowflake - It shows list of installed drivers. Regardless of the driver selected here, when you publish a Data source that uses the connection, "/usr/lib64/snowflake/odbc/lib/libSnowflake.so" is used for Scheduling. (Both exploratory.io and Collaboration Server have /usr/lib64/snowflake/odbc/lib/libSnowflake.so)
* Username - Username of the Snowflake
* Password - Password of the Snowflake

Click ‘Test Connection’ button to make sure the information is correct, before you save it.

## 2. Open Snowflake Import dialog

Select 'Import Database Data' from Add New Data Frame menu.

![](/files/-M4oN-qBMezITjny_pQQ)

Click Snowflake to select.

![](/files/-MR62twM8Opm-6ZNc4gI)

## 3. Preview and Import

Click "Run" button to see the data back from Snowflake.

![](/files/-MR62twPnAFEevGbr7Ib)

If it looks ok, then you can click 'Import' to import the data into Exploratory.

## 4. Known Issues

Currently, Snowflake does not support Snowflake ODBC drive on Apple M1 Mac.

ref: <https://stackoverflow.com/questions/69652761/error-loading-snowflake-odbc-driver-on-mac-m1-from-erlang-call-odbc-connect>


# SQLServer (DSN)

You can quickly import data from your SQLServer into Exploratory.

## 1. Create a connection for SQL Server

First, you want to create a connection for SQL Server.

![](/files/-M4oN-q7KC77-cS90ots)

After filling the database information, click ‘Test Connection’ button to make sure the information is correct, before you save it.

## 2. Open SQLServer Import dialog

Select 'Import Database Data' from Add New Data Frame menu.

![](/files/-M4oN-qBMezITjny_pQQ)

Click SQLServer to select.

## 3. Preview and Import

Click "Get Data" button to see the data back from your Presto.

If it looks ok, then you can click 'Import' to import the data into Exploratory.


# SQLServer

You can quickly import data from your SQL Server into Exploratory.

## 1. Create a connection for MS SQL Server

First, you want to create a connection for SQL Server.

![](/files/-M4oNBanWuZTrXjv8wd4)

After filling the below database information:

* Host - Host name of the SQL Server
* Port - Port number for the SQL Server.
* Database - Database Name
* MS SQL Server ODBC Driver - It shows list of installed drivers. Regardless of the driver selected here, when you publish a Data source that uses the connection, ODBC Driver 17 for SQL Server is used for Scheduling. (Both exploratory.io and Collaboration Server have ODBC Driver 17 for SQL Server)
* Username - Username of the SQL Server
* Password - Password of the SQL Server

To Install ODBC Driver for SQL Server, please follow the [instruction](https://docs.microsoft.com/en-us/sql/connect/odbc/download-odbc-driver-for-sql-server?view=sql-server-ver15).

Click ‘Test Connection’ button to make sure the information is correct, before you save it.

## Troubleshooting

If you use Mac and installed ODBC Driver 17 for SQL Server version 17.8 or older, you might see the below error when connection to your SQL Server

```r
[08001][Microsoft][ODBC Driver 17 for SQL Server]SSL Provider: [OpenSSL library could not be loaded, make sure OpenSSL 1.0 or 1.1 is installed]
```

or

```r
[08000][Microsoft][ODBC Driver 17 for SQL Server]TCP Provider: Error code 0x2746 [Microsoft][ODBC Driver 17 for SQL Server]Client unable to establish connection
```

If this is the case, try the below steps: It assume you use brew to install openssl and brew prefix is `/usr/local`. (you can check it with `brew --prefix`)\
Please note `1.1.1s` might vary for the last character.

```
$ rm -rf /usr/local/opt/openssl

$ ln -s /usr/local/Cellar/openssl@1.1/1.1.1s /usr/local/opt/openssl
```

Then restart Exploratory.

## 2. Open SQL Server Import dialog

Select 'Import Database Data' from Add New Data Frame menu.

![](/files/-M4oN-qBMezITjny_pQQ)

Click SQL Server to select.

## 3. Preview and Import

Click "Run" button to see the data back from your SQL Server.

![](/files/-M4oNBavM2AkQ_8NxFOE)

If it looks ok, then you can click 'Import' to import the data into Exploratory.


# Teradata

You can quickly import data from your Teradata into Exploratory.

## 1. Create a connection for Teradata

First, you want to create a connection for Teradata.

![](/files/-M4oN-q7KC77-cS90ots)

Click the Teradata Icon.

![](/files/QDKDwjRQIqgbcOXoe9W2)

After filling the database information, click ‘Test Connection’ button to make sure the information is correct, before you save it.

![](/files/X12sVk3fUHhqQtlzSEOk)

## 2. Open Teradata Import dialog

Select 'Import Database Data' from Add New Data Frame menu.

![](/files/-M4oN-qBMezITjny_pQQ)

Click Teradata to select.

![](/files/JDFyFoi4n2VEyLxzstRR)

## 3. Preview and Import

Click "Get Data" button to see the data back from your Presto.

If it looks ok, then you can click 'Import' to import the data into Exploratory.

### 4. Known Issue

[Teradata: How to workaround the Syntax error: All expressions in a driver table must have an explicit name](https://community.exploratory.io/t/teradata-how-to-workaround-the-syntax-error-all-expressions-in-a-driver-table-must-have-an-explicit-name/2117)


# Treasure Data

## Treasure Data Import

Please refer [this note](https://exploratory.io/note/2ac8ae888097/How-to-access-Treasure-Data-from-Exploratory-kWs8Gid5gk) for Treasure Data Import.

## Known issues (Query exceeded distributed user memory limit of xxGB)

You might see the "Query exceeded distributed user memory limit of xxGB" error when you try to import data from Treasure Data with a SQL that uses following operators.

* DISTINCT
* UNION
* ORDER BY
* GROUP BY (of many columns)
* JOINS

To workaround the issue, please refer below documents.

<https://docs.treasuredata.com/display/public/PD/How+can+I+fix+the+Exceeded+max+local+memory+xxGB+error> (English) <https://docs.treasuredata.com/pages/releaseview.action?pageId=328022> (Japanese)


# Cloud Apps Data


# Create Connection

* [How to Setup OAuth Cloud Apps Connections in Exploratory](https://blog.exploratory.io/how-to-setup-oauth-cloud-apps-connections-in-exploratory-a5c20d18e7c7)


# FRED - Federal Reserve of Economic Data

## 1. Obtain API Key.

Go to <https://research.stlouisfed.org/docs/api/api\\_key.html> and obtain your API Key to use FRED APIs.

## 2. Open FRED Import Dialog

Click '+' button next to 'Data Frames' and select 'Import Cloud Apps Data'.

![](/files/-M4oN0X39XzG_qmQv2-F)

Click 'FRED (Economic Data)'.

![](/files/-MhVeF_ytrlwvOesmRAh)

Create connection from here if you don't have.

Type connection name and FRED API Key. Test connection and if it works ok, click "Add" button.

![](/files/-MhVeFa34BjNPxr72RRz)

Type in Series ID, e.g. UNRATE for unemployment rate. You can set multiple Series ID by separating them by comma(,). For example, you can type in MIXRSA, NYXRSA, SFXRSA as Series IDs to get Real State Price Index for Miami, New York, and San Francisco.

You can search Series ID in <https://fred.stlouisfed.org/tags/series>

Start Date and End Date are optional. If you leave them blank, it will get all data available.

![](/files/-MhVeFa7EYY5qZnGe5yL)

Click the "Run" button to preview data. If the preview looks okay, click the "Save" button.

![](/files/-MhVeFa9m7hKQXi06pO7)


# Github Issues

For Basic Authentication, you can simply type your Github username and password.

### Steps

Click '+' button next to 'Data Frames' and select 'Import Cloud Apps Data'.

![](/files/-M4oN0X39XzG_qmQv2-F)

Click 'Github Issues'.

![](/files/-M4oN1hweArAQhdK8m01)

Create connection from here if you don't have.

![](/files/-M4oN1hyxTg4fpflnTvD)

Select "GitHub Issues".

![](/files/-M4oN1i-yt6gIYZN8YSK)

Type connection name, Github username and password. Then, "Add" button.

![](/files/-M4oN1i1F4-6QAqO0GaD)

Type Owner and Repository and click "Get Data". If the preview is okay, click "Import".

![](/files/-M4oN1i3Nlrr_i2V30mN)

Repository Owner and Repository Name are something like below. (just in case!)

![](/files/-M4oN1i5wLWjGCgSX8uw)


# Google Analytics

## 1. Select Google Analytics Data Menu

Click '+' button next to 'Data Frames' and select 'Cloud Apps Data'.

![](/files/-M4oN0X39XzG_qmQv2-F)

Click Google Analytics

![](/files/-M4oNBgnUkob8VwDeG_v)

## 2. OAuth Setting

Create a connection following [this instruction](https://blog.exploratory.io/how-to-setup-oauth-cloud-apps-connections-in-exploratory-a5c20d18e7c7).

## 3. Set Parameters

* Select Account, Property, and View from the dropdown menu.
* Set Period. You can select either of the followings:
  * From This Date
  * Today
  * Yesterday
  * This Week to Yesterday
  * This Month to Yesterday
  * This Year to Yesterday
  * Last Week to Yesterday
  * Last Month to Yesterday
  * Last Year to Yesterday
  * Last N Days
  * Last N Days (Exclude Today)
  * Last N Weeks
  * Last N Weeks (Exclude This Week)
  * Last N Months
  * Last N Months (Exclude This Month)
  * Last N Months (Exclude This Week)
  * Last N Months (Exclude Today)
  * Last N Quarters
  * Last N Quarters (Exclude This Quarter)
  * Last N Quarters (Exclude This Month)
  * Last N Quarters (Exclude This Week)
  * Last N Quarters (Exclude Today)
  * Last N Years
  * Last N Years (Exclude This Year)
  * Last N Years (Exclude This Quarter)
  * Last N Years (Exclude This Month)
  * Last N Years (Exclude This Week)
  * Last N Years (Exclude Today)
* Select Dimensions and Metrics from the List. You might want to take a look at [Query Parameter reference page](https://developers.google.com/analytics/devguides/reporting/core/v3/reference) for more detail on Dimension and Measures. Also, [Google Analytics Query Explorer tool page](https://ga-dev-tools.appspot.com/query-explorer/) is helpful for you to explore different parameters that Google Analytics support.
* Segments: You can select both Custom segments as well as predefined segments from list of values. For segments details, please refer[About Segments](https://support.google.com/analytics/answer/3123951) and [Core Reporting API - Segments](https://developers.google.com/analytics/devguides/reporting/core/v3/segments)

![](/files/-M4oNBgwUVnfVd0x69oF)

## 4. Preview and Import

Click 'Run' button to preview the data from Google Analytics. If it looks ok, then you can click 'Save' button to import the data into Exploratory.


# Google Analytics (日本語)

## 1. Google Analytics Dataのメニューを選択

データフレームの隣にある'+' ボタンをクリックして、クラウドアプリケーションデータを選択します。

![](/files/-M4oNAMZ-0AXyO_Edqbc)

Google Analyticsをクリック

![](/files/-M4oNAMaOYv8WRN8ddRn)

## 2. OAuthの設定

[この説明](https://blog.exploratory.io/how-to-setup-oauth-cloud-apps-connections-in-exploratory-a5c20d18e7c7)にしたがってコネクションを作成

## 3. パラメタを設定

* ドロップダウンメニューからアカウント、プロパティ、ビューを選択します。
* 期間を以下のオプションから選択します。
  * 今日
  * 昨日
  * 今週 (昨日まで)
  * 今月 (昨日まで)
  * 今年 (昨日まで)
  * 直近N日
  * 直近N週間
  * 直近Nヶ月
  * 直近N年
  * この日より後
* リストから、ディメンションと指標を選びます。ディメンションと指標についての詳細は、[Query Parameter reference page (英語)](https://developers.google.com/analytics/devguides/reporting/core/v3/reference)や[ディメンションと指標(日本語)](https://support.google.com/analytics/answer/1033861?hl=ja)を参照してください。
* セグメント: セグメントを使っている場合はこちらのリストから選ぶことができます。セグメントの詳細については[セグメントについて(日本語)](https://support.google.com/analytics/answer/3123951?hl=ja)や[Core Reporting API - Segments(英語)](https://developers.google.com/analytics/devguides/reporting/core/v3/segments)を参照してください。

![](/files/-M4oNAMdi1IkKZql85-F)

## 4. プレビューとインポート

'実行'ボタンをクリックして、Google Analyticsからのデータを確認します。 よければ'保存'ボタンをクリックしてデータをExploratoryに取り込みます。


# Google Spreadsheet

## 1. Select Google Spreadsheet Data Menu

Click '+' button next to 'Data Frames' and select 'Import Cloud Apps Data'.

![](/files/-M4oN0X39XzG_qmQv2-F)

Click 'Google Sheets'

![](/files/-M4oN98xMZoWA0l7F4hn)

## 2. OAuth Setting

Create a connection following [this instruction](https://blog.exploratory.io/how-to-setup-oauth-cloud-apps-connections-in-exploratory-a5c20d18e7c7).

## 3. Set Parameters

* Select a Drive if you have Shared Drives. If you don't have Shared Drives, it only shows defualt drive which is My Drive.
* Select Google Spreadsheet name from the dropdown menu
* Select Worksheet name from the dropdown menu
* Set parameters for how to read the data.

Now you can set below parameters:

1. First row as header - Set Yes if the first row is the header row.
2. Skip first N rows - Number of lines to skip before reading data
3. Timezone - Timezone you want to use for the POSIXct columns
4. Treat these as NA - Character vector of strings to interpret as missing values.

![](/files/-M4oN990HzGvKC9AyYPq)

## 4. Preview and Import

Click 'Get Data' button

If the data in the preview table look ok, then click 'Save' button to import the data into Exploratory.


# Salesforce

You can quickly import data from your Salesforce into Exploratory.

## 1. Open Salesforce Import dialog

Select 'Cloud Apps Data' from Add New Data Frame menu.

![](/files/-M4oN0X39XzG_qmQv2-F)

Click Snowflake to select.

![](/files/-Mao5S9CrsjST2aknPdo)

If this is your first time to use Salesforce for the first time, below Dialog shows up.

![](/files/-McNro1Itm_EpaboV4ki)

## 2. Preview and Import

Click "Run" button to see the data back from Salesforce. You can add filters to limit your query results.

![](/files/-Mao5S9FheEG1Z845Acw)

## 3. Using Parameters inside Filters.

First, click the Add Filter Button.

![](/files/-Mao5S9JbDI0n6dsDQr8)

Click the Create Parameter button.

![](/files/-Mao5S9SJugbrK-oLtIk)

Then define a parameter and click Save button.

![](/files/-Mao5S9aVv77qZNY2hq5)

Finally, you can select the parameter for the filter like below.

![](/files/-Mao5S9gtNqZwfypJhWr)

Click the Parameter link to open the parameter pane.

![](/files/-Mao5S9mBBWejUgeNJO_)

Select the parameter value and click the Run button, then you can previewed the data filtered by the parameter.

![](/files/-Mao5S9t4f8Y51vXJbFh)

If it looks ok, then you can click 'Save' to import the data into Exploratory.

## 4 Restriction

To use Salesforce Data Source, your Salesforce edition needs to be one of the followings:

* Enterprise Edition
* Unlimited Edition
* Developer Edition
* Performance Edition

[Salesforce editions with API access](https://help.salesforce.com/articleView?id=000326486\&type=1\&mode=1)


# Twitter Search

NOTE: Currently, this data source is not supported in Exploratory Desktop.

You can query and extract Twitter's tweet data.

* [Quick Video: How to Query and Extract Twitter Data](https://exploratory.io/tutorial#querying-extracting-twitter-data)

Note that due to the Twitter Search API’s restriction, the data is supposed to be only for the last 7 days, though it usually returns 6 to 9 days of the data in most cases.

Number of tweets returned will often be less than what was specified by the user. This can happen because (a) the search query did not return many results (the search pool is already thinned out from the population of tweets to begin with), (b) because user hitting rate limit for a given token, or (c) of recent activity (either more tweets, which affect pagination in returned results or deletion of tweets).

## 1. Select Twitter Search Data Menu

Click '+' button next to 'Data Frames' and select 'Import Cloud Apps Data'.

![](/files/-M4oN0X39XzG_qmQv2-F)

Click 'Twitter Search'

![](/files/-M4oN0X5Gi6WS5npfC92)

## 2. OAuth Setting

Create a connection following [this instruction](https://github.com/exploratory-io/book/blob/master/import/oauth-connection.html).

## 3. Input parameters

* Max # of tweets - Set max number of the tweets to return.
* Language - Set a language code if you want to limit the tweets to certain languages. The code is [ISO 639-1 alpha-2](https://en.wikipedia.org/wiki/ISO_639-1).
* Last N Days - Set number of the past days for the search data.
* Search Text - Type text to search.

Here's a list of the examples of how you can query tweets.

| Operator                          | Finds Tweets...                                                                               |
| --------------------------------- | --------------------------------------------------------------------------------------------- |
| watching now                      | containing both “watching” and “now”. This is the default operator.                           |
| “happy hour”                      | containing the exact phrase “happy hour”.                                                     |
| love OR hate                      | containing either “love” or “hate” (or both).                                                 |
| beer -root                        | containing “beer” but not “root”.                                                             |
| #haiku                            | containing the hashtag “haiku”.                                                               |
| from:interior                     | sent from Twitter account “interior”.                                                         |
| list:NASA/astronauts-in-space-now | sent from a Twitter account in the NASA list astronauts-in-space-now                          |
| to:NASA                           | a Tweet authored in reply to Twitter account “NASA”.                                          |
| @NASA                             | mentioning Twitter account “NASA”.                                                            |
| politics filter:safe              | containing “politics” with Tweets marked as potentially sensitive removed.                    |
| puppy filter:media                | containing “puppy” and an image or video.                                                     |
| puppy -filter:retweets            | containing “puppy”, filtering out retweets                                                    |
| puppy filter:native\_video        | containing “puppy” and an uploaded video, Amplify video, Periscope, or Vine.                  |
| puppy filter:periscope            | containing “puppy” and a Periscope video URL.                                                 |
| puppy filter:vine                 | containing “puppy” and a Vine.                                                                |
| puppy filter:images               | containing “puppy” and links identified as photos, including third parties such as Instagram. |
| puppy filter:twimg                | containing “puppy” and a pic.twitter.com link representing one or more photos.                |
| hilarious filter:links            | containing “hilarious” and linking to URL.                                                    |
| puppy url:amazon                  | containing “puppy” and a URL with the word “amazon” anywhere within it.                       |
| superhero since:2015-12-21        | containing “superhero” and sent since date “2015-12-21” (year-month-day).                     |
| puppy until:2015-12-21            | containing “puppy” and sent before the date “2015-12-21”.                                     |
| movie -scary :)                   | containing “movie”, but not “scary”, and with a positive attitude.                            |
| flight :(                         | containing “flight” and with a negative attitude.                                             |
| traffic ?                         | containing “traffic” and asking a question.                                                   |

For more details, please visit [Twitter's Tweets Search page](https://developer.twitter.com/en/docs/tweets/search/guides/standard-operators).

## 4. Preview and Import

Click Preview button to see the data back from Twitter. If it looks ok, then you can click 'Import' to import the data into Exploratory.


# Stripe

You can get your payment data from Stripe quickly. Take a look at this [blog post](https://blog.exploratory.io/importing-stripe-payment-data-visualize-and-schedule-with-exploratory-3b1422a9cf8b) for the details.

## 1. Select Stripe Data Menu

Click '+' button next to 'Data Frames' and select 'Import Cloud Apps Data'.

![](/files/-M4oN0X39XzG_qmQv2-F)

Click 'Stripe'

![](/files/-M4oNBwu0z79UNzghTXl)

## 2. OAuth Setting

Create a connection following [this instruction](https://blog.exploratory.io/how-to-setup-oauth-cloud-apps-connections-in-exploratory-a5c20d18e7c7).

Note that your Stripe account needs to have [the Administrator role](https://stripe.com/docs/dashboard/teams) in allowing Exploratory to read your Stripe data.

## 3. Input parameters

Set these parameters.

* Data Type - Select a type of Stripe data set.
  * Balance History - Returns a list of transactions that have contributed to the Stripe account balance (e.g., charges, transfers, and so forth).
  * Charges - Returns a list of charges you’ve previously created.
  * Customers - Returns a list of your customers.
  * Disputes - Returns a list of your disputes. A dispute occurs when a customer questions your charge with their bank or credit card company. When a customer disputes your charge, you're given the opportunity to respond to the dispute with evidence that shows the charge is legitimate.
  * Events - List events, going back up to 30 days. A new Event data is created when an interesting event occurs. For example, when a charge succeeds, a charge.succeeded event is created.
  * File Uploads - Returns a list of the files that you have uploaded to Stripe.
  * Payouts - Returns a list of existing payouts sent to third-party bank accounts or that Stripe has sent you. A payout data is created when you receive funds from Stripe or you initiate a payout to either a bank account or debit card of a connected Stripe account.
  * Refunds - Returns a list of all refunds you’ve previously created.
  * Transfers - Returns a list of existing transfers sent to connected accounts. A transfer data is created when you move funds between Stripe accounts as part of connect.
  * Subscriptions - Returns a list of subscriptions that have not been canceled. Subscriptions allow you to charge a customer's card on a recurring basis. A subscription ties a customer to a particular plan you've created.
* Date Range - Select a type of date filtering. If you choose 'After This Date', you can indicate explicit date like '2015-11-03' in 'Value for Date Range'. If you choose 'Last N \~', you can indicate a positive integer in 'Value for Date Range' and the data is filtered by the number with the time unit in the option.
* Value for Date Range - If this is empty, all data is imported. If 'Date Range' is 'After This Date', this should be explicit date like '2015-11-03'. If 'Date Range' is 'Last N \~', this should be a positive integer.

## 4. Custom Query String

To filter Stripe data, you can pass query string as `field=value` fashion. For example, if you want to filter `charges` data whose currency is jpy, you can set `currency=jpy` in this field. You can also set multiple conditions by concatenating condition with `&`. Let's say you want to fetch charges data whose currency is jpy and amount greater than 100,000, you can set following as your query string.

`currency=jpy&amount[gt]=100000`

In here `[gt]` is called child argument and `gt` means greater than. As you'd guess there are other operator available for child argument per column. For example, as for amount column, following child arguments are available.

* gt (Greater Than)
* gte (Grater Than or Equal To)
* lt (Less Than)
* lte (Less Than or Equal To)

Here is the list of available child arguments per resource/column

### Balance

#### available\_on

* gt (Greater Than)
* gte (Grater Than or Equal To)
* lt (Less Than)
* lte (Less Than or Equal To)

#### created

* gt (Greater Than)
* gte (Grater Than or Equal To)
* lt (Less Than)
* lte (Less Than or Equal To)

### Charges

#### created

* gt (Greater Than)
* gte (Grater Than or Equal To)
* lt (Less Than)
* lte (Less Than or Equal To)

#### source

* object (available options are `all`, `alipay_account`, `bank_account`, `bitcoin_receiver`, or `card`)

### Customers

#### created

* gt (Greater Than)
* gte (Grater Than or Equal To)
* lt (Less Than)
* lte (Less Than or Equal To)

### Disputes

#### created

* gt (Greater Than)
* gte (Grater Than or Equal To)
* lt (Less Than)
* lte (Less Than or Equal To)

### Events

#### created

* gt (Greater Than)
* gte (Grater Than or Equal To)
* lt (Less Than)
* lte (Less Than or Equal To)

### File Uploads

#### created

* gt (Greater Than)
* gte (Grater Than or Equal To)
* lt (Less Than)
* lte (Less Than or Equal To)

### Payouts

#### arrival\_date

* gt (Greater Than)
* gte (Grater Than or Equal To)
* lt (Less Than)
* lte (Less Than or Equal To)

#### created

* gt (Greater Than)
* gte (Grater Than or Equal To)
* lt (Less Than)
* lte (Less Than or Equal To)

NOTE: Not all columns are available for query string. Please refer Stripe [API Doc](https://stripe.com/docs/api/node) for supported columns for each resource. For example, you can check [Charges](https://stripe.com/docs/api/node#charges) if you want to query against Charge.

## 5. Preview and Import

Click Preview button to see the data back from Stripe. If it looks ok, then you can click 'Import' to import the data into Exploratory.


# Weather Data

You can quickly import historical weather data in Exploratory. Check out the note below for more details.

* How to Import Weather Data (English) - [Link](https://exploratory.io/note/exploratory/How-to-Import-Weather-Data-PhK4oCM0)
* 気象データをインポートする方法 (Japanese) - [Link](https://exploratory.io/note/exploratory/nsf9gdF3)


# Stock Price Data

You can quickly import historical stock price data in Exploratory. Check out the note below for more details.

* How to Import Stock Price Data (English) - [Link](https://exploratory.io/note/exploratory/How-to-Import-Stock-Price-Data-lBz9rHF3)
* 株価のデータをインポートする方法 (Japanese) - [Link](https://exploratory.io/note/exploratory/FCk6jAK7)




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