Client for the 'KoboToolbox' API.

robotoolbox

robotoolbox is an R client to access data from KoboToolbox.

Installation

This package is not on yet on CRAN and to install it, you will need the remotes package. You can get robotoolbox from Gitlab or Github (mirror)

## install.packages("remotes")
remotes::install_gitlab("dickoa/robotoolbox")

robotoolbox: A quick tutorial

The robotoolbox package is a client to KoboToolbox API v2. You will need to set your API token and specify the KoboToolbox server URL. The easiest way to set up robotoolbox is to store the token and the URL in your .Renviron file, which is automatically read by R on startup.

Getting the API token

You can retrieve your API token by following the instruction in the official API documentation: https://support.kobotoolbox.org/api.html.

You can also get your token directly from R using the kobo_token function.

The following examples will utilize UNHCR KoboToolbox server url (https://kobo.unhcr.org/). You can replace this URL with <https://kobo.humanitarianresponse.info/ or any other KoboToolbox server URL you typically use.

kobo_token(username = "xxxxxxxxx",
           password = "xxxxxxxxx",
           url = "https://kobo.unhcr.org")

Setting up Your Session

You can either edit directly the .Renviron file or access it by calling usethis::edit_r_environ() (assuming you have the usethis package installed) and entering the following two lines:

KOBOTOOLBOX_URL="https://kobo.unhcr.org/"
KOBOTOOLBOX_TOKEN=xxxxxxxxxxxxxxxxxxxxxxxxxx

Or use directly the kobo_setup function

kobo_setup(url = "https://kobo.unhcr.org",
           token = "xxxxxxxxxxxxxxxxxxxxxxxxxx")

You can check the settings using the kobo_settings function.

library("robotoolbox")
kobo_settings()
## <robotoolbox settings>
##    KoboToolbox URL: https://kobo.unhcr.org/
##    KoboToolbox API Token: xxxxxxxxxxxxxxxxxxxxxxxxxx

With the settings done, it is possible to list all assets (e.g surveys, questions, etc) for the account associated to the token and URL.

Accessing data

library("dplyr")
l <- kobo_asset_list()
l
# A tibble: 24 x 7
   uid     name     asset_type owner_username date_created
   <chr>   <chr>    <chr>      <chr>          <dttm>
 1 b9kgvd… Proj_A1… survey     xxxxxxxxxxxxx… 2020-04-27 20:34:23
 2 aRFJMp… Proj_A2… survey     xxxxxxxxxxxxx… 2020-04-27 21:21:12
 3 a6qMG7… Proj_A3… survey     xxxxxxxxxxxxx… 2021-05-25 16:59:08
 4 azhrVs… Proj_A4… survey     xxxxxxxxxxxxx… 2021-05-25 13:59:46
 5 aReR58… Proj_A5… survey     xxxxxxxxxxxxx… 2021-06-07 09:15:53
 6 aWaoqy… Proj_A6… survey     xxxxxxxxxxxxx… 2021-05-29 10:46:09
 7 aABU3C… Proj_A7… survey     xxxxxxxxxxxxx… 2020-11-28 15:00:10
 8 aaznyX… Proj_A9… survey     xxxxxxxxxxxxx… 2020-11-28 14:28:48
 9 aCVr2Q… Proj_A9… survey     xxxxxxxxxxxxx… 2021-05-25 13:30:24
10 aPxNao… Proj_A10… survey    xxxxxxxxxxxxx… 2020-04-27 11:37:34
# … with 14 more rows, and 3 more variables:
#   date_modified <dttm>, submissions <int>
glimpse(l)
$ uid            <chr> "b9kgvd7AXQCmo5qyUOBEl", "aRfJMpTSGRLzZ…"
$ name           <chr> "Proj_A1", "Proj_A2", "Proj_A3", "Proj_A…"
$ asset_type     <chr> "survey", "survey", "survey", "survey", …
$ owner_username <chr> "xxxxxxxxxxxxxx", "xxxxxxxxxxxxxxx", "xx…"
$ date_created   <dttm> 2020-04-27 20:34:23, 2020-04-27 21:21:1…
$ date_modified  <dttm> 2021-06-17 01:52:57, 2021-06-17 01:52:5…
$ submissions    <int> 2951, 2679, 2, 1, 0, 0, 287, 73, 0, 274,…

The list of assets is a tibble, you can filter it to select the form unique identifier uid that uniquely identify the API asset you want to open. The function kobo_asset can then be used to get the asset from the uid.

uid <- l |>
  filter(name == "proj_A1") |>
  pull(uid) |>
  first()
uid
## b9agvd9AXQCmo5qyUOBEl

asset <- kobo_asset(uid)
asset
## <robotoolbox asset>  b9agvd9AXQCmo5qyUOBEl
##   Asset Name: proj_A1
##   Asset Type: survey
##   Created: 2021-05-10 07:47:53
##   Last modified: 2021-08-16 12:35:50
##   Submissions: 941

Now with the selected asset, we can extract the submissions using the kobo_submissions function. The kobo_data can also be used, it’s an alias of kobo_submissions.

df <- kobo_submissions(asset) ## or df <-  kobo_data(asset)
glimpse(df)
## Rows: 941
## Columns: 17
## $ id                                                         <int> …
## $ start                                                      <dttm> …
## $ end                                                        <dttm> …
## $ today                                                      <date> …
## $ deviceid                                                   <chr> …
## $ test                                                       <chr+lbl> …
## $ round                                                      <date> …
## $ effective_date                                             <date> …
## $ collect_type                                               <chr+lbl> …
## $ covid_module                                               <chr+lbl> …
## $ country                                                    <chr+lbl> …
## $ interviewer_id                                             <chr> …
## $ respondent_is_major                                        <chr+lbl> …
## $ consent                                                    <chr+lbl> …
## $ admin_level_1                                              <chr+lbl> …
## $ admin_level_2                                              <chr+lbl> …
## $ admin_level_3                                              <chr+lbl> …

Multi-Languages Survey forms

robotoolbox uses the R package labelled to provide tools to manipulate variable labels and value labels. You can learn more about this here. You can learn more about this here

Repeating groups

Repeating groups associate multiple records to a single record in the main table. It’s used to group questions that need to be answered repeatedly. The package dm is used to model such relationship and allow you to safely query and join such linked data for your analysis. Learn more about it here

Spatial data

KoboToolbox provides three types of question to record spatial data: geopoint for points, geotrace for lines and geoshape to map close polygons. robotoolbox associates to each spatial column a WKT column. It provides a simple way to use it with various GIS software and R package for spatial data analysis. The sf package is the standard for spatial vector data handling and visualization. Learn more about it here

Audit logging data

KoboToolbox comes with a feature that records all activities related to a form submission in a log file. The audit logging metadata is useful for data quality control, security and workflow management. The kobo_audit function allow you to read KoboToolbox audit logs file. Learn more in the following vignette: Audit Data

I’m collecting data using ODK

OpenDataKit (ODK) is an open-source tool for collecting data. Similar to KoboToolbox, ODK utilizes the XLSForm standard for form creation. Both tools offer similar features and functionality, and data collected using KoboToolbox can be collected using ODK Collect as well.

If you are using ODK in conjunction with R, the ruODK package is an excellent resource. The ruODK R package served as the primary inspiration for robotoolbox and provides similar functionality for interacting with ODK data.

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• License: MIT
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