From fae9ff2ffbbda398e701b5ce984af41ea46bff65 Mon Sep 17 00:00:00 2001 From: Jeremy Stanley Date: Sun, 5 Apr 2015 07:49:22 -0400 Subject: [PATCH 01/20] #29 first draft vignette --- .gitignore | 1 + vignettes/introduction-to-tidyjson.Rmd | 443 +++++++++++++++++++++++++ 2 files changed, 444 insertions(+) create mode 100644 vignettes/introduction-to-tidyjson.Rmd diff --git a/.gitignore b/.gitignore index 21275f5..7b16023 100644 --- a/.gitignore +++ b/.gitignore @@ -2,3 +2,4 @@ .RData .Rhistory *.swp +inst/doc diff --git a/vignettes/introduction-to-tidyjson.Rmd b/vignettes/introduction-to-tidyjson.Rmd new file mode 100644 index 0000000..b5b99af --- /dev/null +++ b/vignettes/introduction-to-tidyjson.Rmd @@ -0,0 +1,443 @@ +--- +title: "Introduction to tidyjson" +author: "Jeremy Stanley" +date: "`r Sys.Date()`" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Vignette Title} + %\VignetteEngine{knitr::rmarkdown} + %\usepackage[utf8]{inputenc} +--- + +[JSON](http://json.org/) (JavaScript Object Notation) is a lightweight data +format that is easy for humans to read and for machines to parse. It is also +incredibly flexible. JSON has become a common format used in: + +- Public APIs (e.g., [Twitter](https://dev.twitter.com/rest/public)) +- Document oriented NoSQL databases (e.g., [MongoDB](https://www.mongodb.org/)) +- Flexible JSON columns in relational databases (e.g., [PostgreSQL](http://www.postgresql.org/docs/9.4/static/datatype-json.html)) + +As such, R users are increasingly faced with JSON data sets, and need easy and +reliable ways to turn those data sets into data.frames for analysis or modeling. + +There are already several libraries for working with JSON data in R, such as +[rjson](http://cran.r-project.org/web/packages/rjson/index.html), +[rjsonio](http://cran.r-project.org/web/packages/RJSONIO/index.html) and +[jsonlite](http://cran.r-project.org/web/packages/jsonlite/index.html). Using +these libraries, you can transform JSON into a nested R list. However, working +with nested lists using base R functionality is difficult. + +The jsonlite package goes farther by automatically creating a nested R data.frame. +This is easier to work with than a list, but has two main limitations. First, the +resulting data.frame isn't [tidy](http://vita.had.co.nz/papers/tidy-data.pdf), +and so it can still be difficult to work with. Second, the structure of the +data.frame may vary as the JSON sample changes, which can happen any time you +change the database query or API call that generated the data. + +The tidyjson package takes an alternate approach to structuring JSON data into tidy +data.frames. Similar to [tidyr](http://cran.r-project.org/web/packages/tidyr/index.html), tidyjson builds +a grammar for manipulating JSON into a tidy table structure. Tidyjson is based +on the following principles: + +- Leverage other libraries for efficiently parsing JSON ([jsonlite](http://cran.r-project.org/web/packages/jsonlite/index.html)) +- Integrate with pipelines built on [dplyr](http://cran.r-project.org/web/packages/dplyr/index.html) +and the [magrittr](http://cran.r-project.org/web/packages/magrittr/index.html) `%>%` operator +- Turn arbitrarily complex and nested JSON into tidy data.frames that can be joined later +- Guarantee a deterministic data.frame column structure +- Naturally handle 'ragged' arrays and / or objects (varying lengths by document) +- Allow for extraction of data in values *or* key names +- Ensure edge cases are handled correctly (especially empty data) + +## A Simple Example + +A simple example of how tidyjson works is as follows: + +```{r, message = FALSE} +library(dplyr) # for %>% and other dplyr functions + +# Define a simple JSON array of people +people <- '[{"name": "bob", "age": 32}, {"name": "susan", "age": 54}]' + +# Structure the data +people %>% # Use the %>% pipe operator to pass json through a pipeline + as.tbl_json %>% # Parse the JSON and setup a 'tbl_json' object + gather_array %>% # Gather (stack) the array by index + spread_values( # Spread (widen) values to widen the data.frame + user.name = jstring("name"), # Extract the "name" object as a character column "user.name" + user.age = jnumber("age") # Extract the "age" object as a numeric column "user.age" + ) +``` + +In such a simple example, we can use `fromJSON` in the jsonlite package to do +this much faster: + +```{r} +library(jsonlite) +jsonlite::fromJSON(people) +``` + +However, if the structure of the data changed, so would the output from `fromJSON`. +So even in this simple example there is value in the explicit structure defined +in the tidyjson pipeline above. + +## A Complex Example + +The tidyjson package really shines in a more complex example. Consider the +following JSON, which describes three purchases of five items made by two +individuals: + +```{r} +purch_json <- ' +[ + { + "name": "bob", + "purchases": [ + { + "date": "2014/09/13", + "items": [ + {"name": "shoes", "price": 187}, + {"name": "belt", "price": 35} + ] + } + ] + }, + { + "name": "susan", + "purchases": [ + { + "date": "2014/10/01", + "items": [ + {"name": "dress", "price": 58}, + {"name": "bag", "price": 118} + ] + }, + { + "date": "2015/01/03", + "items": [ + {"name": "shoes", "price": 115} + ] + } + ] + } +]' +``` + +Suppose we want to find out how much each person has spent. + +Using jsonlite, we can parse the JSON: + +```{r} +library(jsonlite) +# Parse the JSON into a data.frame +purch_df <- jsonlite::fromJSON(purch_json) +# Examine results +purch_df +``` + +However, the resulting data structure is a complex nested data.frame: + +```{r} +str(purch_df) +``` + +This is difficult to work with, and we end up writing code like this: + +```{r} +lapply(lapply(purch_df$purchases, `[[`, "items"), lapply, `[[`, "price") +``` + +Reasoning about code like this is nearly impossible, and further, the relational +structure of the data is lost (we no longer have the name of the user). + +Using tidyjson, we can build a pipeline to turn this JSON into a tidy data.frame +where each row corresponds to a purchased item: + +```{r} +purch_items <- purch_json %>% + as.tbl_json %>% gather_array %>% + spread_values(person = jstring("name")) %>% + enter_object("purchases") %>% gather_array %>% + spread_values(purchase.date = jstring("date")) %>% + enter_object("items") %>% gather_array %>% + spread_values( + item.name = jstring("name"), + item.price = jnumber("price") + ) %>% + select(person, purchase.date, item.name, item.price) +``` + +The resulting data.frame is exactly what we want + +```{r} +purch_items +``` + +And we can easily continue the pipeline in dplyr to compute derived data + +```{r} +purch_items %>% group_by(person) %>% summarize(spend = sum(item.price)) +``` + +## Data + +The tidyjson package comes with several JSON example datasets: + +* `commits`: commit data for the dplyr repo from github API +* `issues`: issue data for the dplyr repo from github API +* `worldbank`: world bank funded projects from +[jsonstudio](http://jsonstudio.com/resources/) +* `companies`: startup company data from +[jsonstudio](http://jsonstudio.com/resources/) + +Each dataset has some example tidyjson queries in `help(commits)`, +`help(issues)`, `help(worldbank)` and `help(companies)`. + +## JSON + +(TODO: Need to describe JSON more here). + +### Create a `tbl_json` object + +The first step in using tidyjson is to convert your JSON into a `tbl_json` object. +Almost every function in tidyjson accepts a `tbl_json` object as it's first +parameter, and returns a `tbl_json` object for downstream use. `tbl_json` +inherits from `dplyr::tbl`. + +A `tbl_json` object is comprised of a `data.frame` with an additional attribute, +`JSON`, that contains a list of JSON data of the same length as the number of +rows in the `data.frame`. Each row of data in the `data.frame` corresponds to the +JSON found in the same index of the `JSON` attribute. + +The easiest way to construct a `tbl_json` object is directly from a character +string or vector. + +```{r} +# Will return a 1 row data.frame with a length 1 JSON attribute +'{"key": "value"}' %>% as.tbl_json + +# Will still return a 1 row data.frame with a length 1 JSON attribute as +# the character string is of length 1 (even though it contains a JSON array of +# length 2) +'[{"key1": "value1"}, {"key2": "value2"}]' %>% as.tbl_json + +# Will return a 2 row data.frame with a length 2 JSON attribute +c('{"key1": "value1"}', '{"key2": "value2"}') %>% as.tbl_json +``` + +Behind the scenes, `as.tbl_json()` is parsing the JSON strings and creating a +data.frame with 1 column, `document.id`, which keeps track of the character +vector position (index) where the JSON data came from. + +TODO: Need to show how to create one from a data.frame +TODO: Also need to talk about JSON lines format + +## Verbs + +The rest of tidyjson is comprised of various verbs with operate on `tbl_json` +objects and return `tbl_json` objects. They are meant to be used in a pipeline +with the `%>%` operator. + +Note that these verbs all operate on *both* the underlying data.frame and the +JSON, iteratively moving data from the JSON into the data.frame. Any +modifications of the underlying data.frame outside of these operations +may produce unintended consequences where the data.frame and JSON become out of +synch. + +The following table provides a reference of how each verb is used and what +(if any) effect it has on the data.frame rows and columns and on the associated +JSON. + +| Verb | JSON | Arguments | Row Effect | Column Effect | JSON Effect | +|:--------------------|:-------|:----------------|:------------------|:-----------------|:---------------| +| `enter_object()` | object | ... = key path | Drops without key | none | object value | +| `json_types()` | any | column.name | Duplicates rows | type column | object keys | +| `gather_array()` | array | column.name | Duplicates rows | index column | array values | +| `gather_keys()` | object | column.name | Duplicates rows | key column | object values | +| `spread_values()` | object | ... = columns | none | N value columns | none | +| `append_values_X()` | scalar | colum.name | none | column of type X | none | + +### Identify JSON structure with `json_types()` + +One of the first steps you will want to take is to investigate the structure of +your JSON data. The function `json_types()` inspects the JSON associated with +each row of the data.frame, and adds a new column (`type` by default) that +identifies the type according to the [JSON standard](http://json.org/). + +```{r} +types <- c('{"a": 1}', '[1, 2]', '"a"', '1', 'true', 'null') %>% as.tbl_json %>% + json_types +types$type +``` + +This is particularly useful for inspecting your JSON data types, and can added +after `gather_array()` (or `gather_keys()`) to inspect the types of the elements +(or values) in arrays (or objects). + +### Stack a JSON array with `gather_array()` + +Arrays are sometimes vectors (fixed or varying length integer, character or +logical vectors). But they also often contain lists of other objects (like +a list of purchases for a user). The function `gather_array()` takes JSON arrays +and duplicates the rows in the data.frame to correspond to the indices of the +array, and puts the elements of the array into the JSON attribute. +This is equivalent to "stacking" the array in the data.frame, and lets you +continue to manipulate the remaining JSON in the elements of the array. + +```{r} +'[1, "a", {"k": "v"}]' %>% as.tbl_json %>% gather_array %>% json_types +``` + +This allows you to *enter into* an array and begin processing it's elements +with other tidyjson functions. It retains the array.index in case the relative +position of elements in the array is useful information. + +### Stack a "key": object with `gather_keys()` + +Similar to `gather_array()`, `gather_keys()` takes JSON objects and duplicates +the rows in the data.frame to correspond to the keys of the object, and puts the +values of the object into the JSON attribute. + +```{r} +'{"name": "bob", "age": 32}' %>% as.tbl_json %>% gather_keys %>% json_types +``` + +This allows you to *enter into* the keys of the objects just like `gather_array` +let you enter elements of the array. + +### Create new columns with JSON values with `spread_values()` + +Adding new columns to your `data.frame` is accomplished with `spread_values()`, +which lets you dive into (potentially nested) JSON objects and extract specific +values. `spread_values()` takes `jstring()`, `jnumber()` or `jlogical()` +function calls as arguments in order to specify the type of the data that should +be captured at each desired key location + +These values can be of varying types at varying depths, e.g., + +```{r} +'{"name": {"first": "bob", "last": "jones"}, "age": 32}' %>% + as.tbl_json %>% + spread_values( + first.name = jstring("name", "first"), + age = jnumber("age") + ) +``` + +### Stack all values of a specified type with `append_values_X()` + +The `append_values_X()` functions let you take the remaining JSON and add it as +a column X (for X in "string", "number", "logical") insofar as it is of the +JSON type specified. For example: + +```{r} +'{"first": "bob", "last": "jones"}' %>% as.tbl_json %>% + gather_keys() %>% append_values_string() +``` + +Any values that do not conform to the type specified will be NA in the resulting +column. This includes other scalar types (e.g., numbers or logicals if you are +using `append_values_string()`) and *also* any rows where the JSON is still an +object or an array. + +### Dive into a specific object "key" with `enter_object()` + +For complex JSON structures, you will often need to navigate into nested objects +in order to continue structuring your data. The function `enter_object()` lets +you dive into a specific object key in the JSON attribute, so that all further +tidyjson calls happen inside that object (all other JSON data outside the object +is discarded). If the object doesn't exist for a given row / index, then that +data.frame row will be discarded. + +```{r} +c('{"name": "bob", "children": ["sally", "george"]}', '{"name": "anne"}') %>% + as.tbl_json %>% spread_values(parent.name = jstring("name")) %>% + enter_object("children") %>% + gather_array %>% append_values_string("children") +``` + +This is useful when you want to limit your data to just information found in +a specific key. + +## Strategies + +When beginning to work with JSON data, you often don't have easy access to a +schema describing what is in the JSON. One of the benefits of document oriented +data structures is that they let developers create data without having to worry +about defining the schema explicitly. + +Thus, the first step is to usually understand the structure of the JSON. A first +step can be to look at individual records with `jsonlite::prettify()`: + +```{r, message = FALSE} +library(jsonlite) +prettify(json) +``` + +Examining various random records can begin to give you a sense of what the JSON +contains and how it it structured. However, keep in mind that in many cases +documents that are missing data (either unknown or unrelevant) may omit the +entire JSON structure. + +Next, you can begin working with the data in R. + +```{r} +# assuming documents are carriage-return delimited, otherwise use readChar +# json <- readLines(file.json) # TODO: Need to change this + +# Inspect the types of objects +# json %>% json_types %>% table +``` + +Then, if you want to work with a single row of data for each JSON object, use +`spread_values()` to get at (potentially nested) key-value pairs. + +If all you care about is data from a certain sub-object, then use `enter_object()` +to dive into that object directly. Make sure you first use `spread_values()` to +capture any top level identifiers you might need for analytics, summarization or +relational uses downstream. + +If you want to access arrays, use `gather_array()` to stack their elements, and +then proceed as though you had separate documents. (Again, first spread any +top-level keys you need.) + +Finally, if you have data where information is encoded in both keys and values, +then consider using `gather_keys()` and `append_values_X()` where `X` is the type +of JSON scalar data you expect in the values. + +It's important to remember that any of the above can be combined together +iteratively to do some fairly complex data extraction. For example: + +```{r} +json <- '{ + "name": "bob", + "shopping cart": + [ + { + "date": "2014-04-02", + "basket": {"books": 2, "shirts": 0} + }, + { + "date": "2014-08-23", + "basket": {"books": 1} + } + ] +}' +json %>% as.tbl_json %>% + spread_values(customer = jstring("name")) %>% # Keep the customer name + enter_object("shopping cart") %>% # Look at their cart + gather_array %>% # Expand the data.frame and dive into each array element + spread_values(date = jstring("date")) %>% # Keep the date of the cart + enter_object("basket") %>% # Look at their basket + gather_keys("product") %>% # Expand the data.frame for each product and capture it's name + append_values_number("quantity") # Capture the values as the quantity +``` + +Note that there are often situations where there are multiple arrays or objects +of differing types that exist at the same level of the JSON hierarchy. In this +case, you need to use `enter_object()` to enter each of them in *separate* +pipelines to create *separate* `data.frames` that can then be joined +relationally. + +Finally, don't forget that once you are done with your JSON tidying, you can +use [dplyr](http://github.com/hadley/dplyr) to continue manipulating the +resulting data at your leisure! From 068159499b0878942dc9e8f8b45d122c41f19796 Mon Sep 17 00:00:00 2001 From: Jeremy Stanley Date: Sun, 5 Apr 2015 08:34:24 -0400 Subject: [PATCH 02/20] #29 restructure the introduction --- vignettes/introduction-to-tidyjson.Rmd | 23 +++++++++++++---------- 1 file changed, 13 insertions(+), 10 deletions(-) diff --git a/vignettes/introduction-to-tidyjson.Rmd b/vignettes/introduction-to-tidyjson.Rmd index b5b99af..11ebebe 100644 --- a/vignettes/introduction-to-tidyjson.Rmd +++ b/vignettes/introduction-to-tidyjson.Rmd @@ -9,16 +9,19 @@ vignette: > %\usepackage[utf8]{inputenc} --- -[JSON](http://json.org/) (JavaScript Object Notation) is a lightweight data -format that is easy for humans to read and for machines to parse. It is also -incredibly flexible. JSON has become a common format used in: +[JSON](http://json.org/) (JavaScript Object Notation) is a lightweight and +flexible data format that is easy for humans to read and for machines to parse. +JSON has become a common format used in: - Public APIs (e.g., [Twitter](https://dev.twitter.com/rest/public)) -- Document oriented NoSQL databases (e.g., [MongoDB](https://www.mongodb.org/)) -- Flexible JSON columns in relational databases (e.g., [PostgreSQL](http://www.postgresql.org/docs/9.4/static/datatype-json.html)) +- NoSQL databases as a document format (e.g., [MongoDB](https://www.mongodb.org/)) +- Relational databases as a new column type (e.g., [PostgreSQL](http://www.postgresql.org/docs/9.4/static/datatype-json.html)) -As such, R users are increasingly faced with JSON data sets, and need easy and -reliable ways to turn those data sets into data.frames for analysis or modeling. +The tidyjson package provides a grammar for turning JSON data into tidy +data.frames that are easy to work with in dplyr, ggplot2 and other modeling and +analytics packages. + +## Why use tidyjson? There are already several libraries for working with JSON data in R, such as [rjson](http://cran.r-project.org/web/packages/rjson/index.html), @@ -48,7 +51,7 @@ and the [magrittr](http://cran.r-project.org/web/packages/magrittr/index.html) ` - Allow for extraction of data in values *or* key names - Ensure edge cases are handled correctly (especially empty data) -## A Simple Example +## A simple example A simple example of how tidyjson works is as follows: @@ -80,7 +83,7 @@ However, if the structure of the data changed, so would the output from `fromJSO So even in this simple example there is value in the explicit structure defined in the tidyjson pipeline above. -## A Complex Example +## A more complex example The tidyjson package really shines in a more complex example. Consider the following JSON, which describes three purchases of five items made by two @@ -370,7 +373,7 @@ step can be to look at individual records with `jsonlite::prettify()`: ```{r, message = FALSE} library(jsonlite) -prettify(json) +prettify('[{"name": "bob", "children": ["sally", "george"]}, {"name": "anne"}]') ``` Examining various random records can begin to give you a sense of what the JSON From fd86d39524f874721fa598bea89dbed36a808b08 Mon Sep 17 00:00:00 2001 From: Jeremy Stanley Date: Sun, 5 Apr 2015 15:23:48 -0400 Subject: [PATCH 03/20] #29 add a worldbank example --- vignettes/introduction-to-tidyjson.Rmd | 110 +++++++++++++++++++++++-- 1 file changed, 101 insertions(+), 9 deletions(-) diff --git a/vignettes/introduction-to-tidyjson.Rmd b/vignettes/introduction-to-tidyjson.Rmd index 11ebebe..5040c9d 100644 --- a/vignettes/introduction-to-tidyjson.Rmd +++ b/vignettes/introduction-to-tidyjson.Rmd @@ -59,7 +59,17 @@ A simple example of how tidyjson works is as follows: library(dplyr) # for %>% and other dplyr functions # Define a simple JSON array of people -people <- '[{"name": "bob", "age": 32}, {"name": "susan", "age": 54}]' +people <- ' +[ + { + "name": "bob", + "age": 32 + }, + { + "name": "susan", + "age": 54 + } +]' # Structure the data people %>% # Use the %>% pipe operator to pass json through a pipeline @@ -183,6 +193,8 @@ purch_items %>% group_by(person) %>% summarize(spend = sum(item.price)) ## Data +### JSON included in the package + The tidyjson package comes with several JSON example datasets: * `commits`: commit data for the dplyr repo from github API @@ -195,11 +207,7 @@ The tidyjson package comes with several JSON example datasets: Each dataset has some example tidyjson queries in `help(commits)`, `help(issues)`, `help(worldbank)` and `help(companies)`. -## JSON - -(TODO: Need to describe JSON more here). - -### Create a `tbl_json` object +### Creating a `tbl_json` object The first step in using tidyjson is to convert your JSON into a `tbl_json` object. Almost every function in tidyjson accepts a `tbl_json` object as it's first @@ -231,8 +239,10 @@ Behind the scenes, `as.tbl_json()` is parsing the JSON strings and creating a data.frame with 1 column, `document.id`, which keeps track of the character vector position (index) where the JSON data came from. -TODO: Need to show how to create one from a data.frame -TODO: Also need to talk about JSON lines format +TODO + +- Need to show how to create one from a data.frame +- Also need to talk about JSON lines format ## Verbs @@ -257,7 +267,7 @@ JSON. | `gather_array()` | array | column.name | Duplicates rows | index column | array values | | `gather_keys()` | object | column.name | Duplicates rows | key column | object values | | `spread_values()` | object | ... = columns | none | N value columns | none | -| `append_values_X()` | scalar | colum.name | none | column of type X | none | +| `append_values_X()` | scalar | column.name | none | column of type X | none | ### Identify JSON structure with `json_types()` @@ -361,6 +371,88 @@ c('{"name": "bob", "children": ["sally", "george"]}', '{"name": "anne"}') %>% This is useful when you want to limit your data to just information found in a specific key. +## A real example + +Included in the tidyjson package is a `r length(worldbank)` record sample, +`worldbank`, which contains world bank funded projects from +[jsonstudio](http://jsonstudio.com/resources/). + +First, let's take a look at a single record. We can use `jsonlite::prettify` to +make the JSON easy to read. But because some of the text is very +lengthy (e.g., the abstract and many URLs), we are going to jump through some +hoops to truncate the result to 80 characters so it will fit in the vignette: + +```{r} +library(jsonlite) +library(stringr) +worldbank[1] %>% prettify %>% + str_split("\n") %>% unlist %>% + lapply(str_sub, 1, 80) %>% paste(collapse = "\n") %>% + writeLines +``` + +An interesting objects is "majorsector_percent", which appears to capture the +distribution of each project by sector. We also have several funding amounts, +such as "totalamt", which indicate how much money went into each project. + +Let's grab the "totalamt", and then gather the array of sectors and their +percent allocations. + +```{r} +amts <- worldbank %>% as.tbl_json %>% + spread_values( + total = jnumber("totalamt") + ) %>% + enter_object("majorsector_percent") %>% gather_array %>% + spread_values( + sector = jstring("Name"), + pct = jnumber("Percent") + ) %>% + select(document.id, sector, total, pct) %>% + tbl_df +amts +``` + +Let's check that the "pct" column really adds up to 100: + +```{r} +amts %>% + group_by(document.id) %>% + summarize(pct.total = sum(pct)) %>% + group_by(pct.total) %>% + tally +``` + +It appears to always add up to 100. Let's also check the distribution of +the total amounts. + +```{r} +summary(amts$total) +``` + +Many are 0, the mean is $80m and the max is over $1bn. + +Let's now aggregate by the sector and compute, on a dollar weighted basis, +where the money is going by sector + +```{r} +amts %>% + mutate( + pct = pct / 100, + spend.k = total / 1000 * pct + ) %>% + group_by(sector) %>% + summarize( + spend.k = sum(spend.k) + ) %>% + ungroup %>% + mutate(pct = spend.k / sum(spend.k)) %>% + arrange(desc(spend.k)) +``` + +It looks like in this sample of projects, "Information and Communication" is +really low on the worldbank priority list! + ## Strategies When beginning to work with JSON data, you often don't have easy access to a From 7503fdad9f34588ddddd7ef830fefd5a4f30e65a Mon Sep 17 00:00:00 2001 From: Jeremy Stanley Date: Sun, 5 Apr 2015 15:25:40 -0400 Subject: [PATCH 04/20] add call to library(tidyjson) up front --- vignettes/introduction-to-tidyjson.Rmd | 1 + 1 file changed, 1 insertion(+) diff --git a/vignettes/introduction-to-tidyjson.Rmd b/vignettes/introduction-to-tidyjson.Rmd index 5040c9d..65af39a 100644 --- a/vignettes/introduction-to-tidyjson.Rmd +++ b/vignettes/introduction-to-tidyjson.Rmd @@ -56,6 +56,7 @@ and the [magrittr](http://cran.r-project.org/web/packages/magrittr/index.html) ` A simple example of how tidyjson works is as follows: ```{r, message = FALSE} +library(tidyjson) # this library library(dplyr) # for %>% and other dplyr functions # Define a simple JSON array of people From faea8757a76466a9b6f9c746cfec37d75bdf6be6 Mon Sep 17 00:00:00 2001 From: Jeremy Stanley Date: Mon, 6 Apr 2015 13:30:09 -0400 Subject: [PATCH 05/20] #29 improve examples --- vignettes/introduction-to-tidyjson.Rmd | 65 ++++++++++++++++---------- 1 file changed, 41 insertions(+), 24 deletions(-) diff --git a/vignettes/introduction-to-tidyjson.Rmd b/vignettes/introduction-to-tidyjson.Rmd index 65af39a..0ed168a 100644 --- a/vignettes/introduction-to-tidyjson.Rmd +++ b/vignettes/introduction-to-tidyjson.Rmd @@ -73,26 +73,26 @@ people <- ' ]' # Structure the data -people %>% # Use the %>% pipe operator to pass json through a pipeline - as.tbl_json %>% # Parse the JSON and setup a 'tbl_json' object - gather_array %>% # Gather (stack) the array by index - spread_values( # Spread (widen) values to widen the data.frame - user.name = jstring("name"), # Extract the "name" object as a character column "user.name" - user.age = jnumber("age") # Extract the "age" object as a numeric column "user.age" +people %>% # %>% is the magrittr pipeline operator + as.tbl_json %>% # parse the JSON and setup a 'tbl_json' object + gather_array %>% # gather (stack) the array by index + spread_values( # spread (widen) values to widen the data.frame + name = jstring("name"), # value of "name" becomes a character column + age = jnumber("age") # value of "age" becomes a numeric column ) ``` In such a simple example, we can use `fromJSON` in the jsonlite package to do this much faster: -```{r} +```{r, message = FALSE} library(jsonlite) -jsonlite::fromJSON(people) +jsonlite::fromJSON(people, simplifyDataFrame = TRUE) ``` -However, if the structure of the data changed, so would the output from `fromJSON`. -So even in this simple example there is value in the explicit structure defined -in the tidyjson pipeline above. +However, if the structure of the JSON data changed, so would the columns output +by `fromJSON`. So even in this simple example there is value in the explicit +structure defined in the tidyjson pipeline above. ## A more complex example @@ -136,9 +136,8 @@ purch_json <- ' ]' ``` -Suppose we want to find out how much each person has spent. - -Using jsonlite, we can parse the JSON: +Suppose we want to find out how much each person has spent. Using jsonlite, we +can parse the JSON: ```{r} library(jsonlite) @@ -148,7 +147,8 @@ purch_df <- jsonlite::fromJSON(purch_json) purch_df ``` -However, the resulting data structure is a complex nested data.frame: +This looks deceptively simple, the resulting data structure is actually a +complex nested data.frame: ```{r} str(purch_df) @@ -157,27 +157,44 @@ str(purch_df) This is difficult to work with, and we end up writing code like this: ```{r} -lapply(lapply(purch_df$purchases, `[[`, "items"), lapply, `[[`, "price") +items <- lapply(purch_df$purchases, `[[`, "items") +prices <- lapply(items, lapply, `[[`, "price") +vapply(lapply(prices, unlist), sum, integer(1)) ``` Reasoning about code like this is nearly impossible, and further, the relational structure of the data is lost (we no longer have the name of the user). +We can instead try to use dplyr and the `do{}` operator to get at the +data in the nested data.frames, but this is equally challenging: + +```{r} +purch_df %>% group_by(name) %>% + do( + data.frame( + name = .$name, + items = .$purchases[[1]] %>% rowwise %>% do({.$items}), + stringsAsFactors = FALSE + ) + ) %>% + summarize(price = sum(items.price)) +``` + Using tidyjson, we can build a pipeline to turn this JSON into a tidy data.frame where each row corresponds to a purchased item: ```{r} -purch_items <- purch_json %>% - as.tbl_json %>% gather_array %>% - spread_values(person = jstring("name")) %>% - enter_object("purchases") %>% gather_array %>% - spread_values(purchase.date = jstring("date")) %>% - enter_object("items") %>% gather_array %>% - spread_values( +purch_items <- purch_json %>% as.tbl_json %>% + gather_array %>% # stack the users + spread_values(person = jstring("name")) %>% # extract the user name + enter_object("purchases") %>% gather_array %>% # stack the purchases + spread_values(purchase.date = jstring("date")) %>% # extract the purchase date + enter_object("items") %>% gather_array %>% # stack the items + spread_values( # extract item name and price item.name = jstring("name"), item.price = jnumber("price") ) %>% - select(person, purchase.date, item.name, item.price) + select(person, purchase.date, item.name, item.price) # select only what is needed ``` The resulting data.frame is exactly what we want From b0d44b86c9ea4ce7fd32b2eae2e842bb82a7fa24 Mon Sep 17 00:00:00 2001 From: Jeremy Stanley Date: Mon, 6 Apr 2015 14:00:47 -0400 Subject: [PATCH 06/20] #29 improve the complex do{} example --- vignettes/introduction-to-tidyjson.Rmd | 18 +++++++----------- 1 file changed, 7 insertions(+), 11 deletions(-) diff --git a/vignettes/introduction-to-tidyjson.Rmd b/vignettes/introduction-to-tidyjson.Rmd index 0ed168a..f98bf50 100644 --- a/vignettes/introduction-to-tidyjson.Rmd +++ b/vignettes/introduction-to-tidyjson.Rmd @@ -142,7 +142,7 @@ can parse the JSON: ```{r} library(jsonlite) # Parse the JSON into a data.frame -purch_df <- jsonlite::fromJSON(purch_json) +purch_df <- jsonlite::fromJSON(purch_json, simplifyDataFrame = TRUE) # Examine results purch_df ``` @@ -166,18 +166,14 @@ Reasoning about code like this is nearly impossible, and further, the relational structure of the data is lost (we no longer have the name of the user). We can instead try to use dplyr and the `do{}` operator to get at the -data in the nested data.frames, but this is equally challenging: +data in the nested data.frames, but this is equally challenging and confusing: ```{r} -purch_df %>% group_by(name) %>% - do( - data.frame( - name = .$name, - items = .$purchases[[1]] %>% rowwise %>% do({.$items}), - stringsAsFactors = FALSE - ) - ) %>% - summarize(price = sum(items.price)) +purch_df %>% group_by(name) %>% do({ + .$purchases[[1]] %>% rowwise %>% do({ + .$items[, "price", drop = FALSE] + }) + }) %>% summarize(price = sum(price)) ``` Using tidyjson, we can build a pipeline to turn this JSON into a tidy data.frame From 3b327c6043fa9d753216ae21b25629659893ba2a Mon Sep 17 00:00:00 2001 From: Jeremy Stanley Date: Thu, 9 Apr 2015 09:57:28 -0400 Subject: [PATCH 07/20] #29 reorder data section --- vignettes/introduction-to-tidyjson.Rmd | 66 +++++++++++++------------- 1 file changed, 32 insertions(+), 34 deletions(-) diff --git a/vignettes/introduction-to-tidyjson.Rmd b/vignettes/introduction-to-tidyjson.Rmd index f98bf50..e164a11 100644 --- a/vignettes/introduction-to-tidyjson.Rmd +++ b/vignettes/introduction-to-tidyjson.Rmd @@ -167,7 +167,6 @@ structure of the data is lost (we no longer have the name of the user). We can instead try to use dplyr and the `do{}` operator to get at the data in the nested data.frames, but this is equally challenging and confusing: - ```{r} purch_df %>% group_by(name) %>% do({ .$purchases[[1]] %>% rowwise %>% do({ @@ -207,20 +206,6 @@ purch_items %>% group_by(person) %>% summarize(spend = sum(item.price)) ## Data -### JSON included in the package - -The tidyjson package comes with several JSON example datasets: - -* `commits`: commit data for the dplyr repo from github API -* `issues`: issue data for the dplyr repo from github API -* `worldbank`: world bank funded projects from -[jsonstudio](http://jsonstudio.com/resources/) -* `companies`: startup company data from -[jsonstudio](http://jsonstudio.com/resources/) - -Each dataset has some example tidyjson queries in `help(commits)`, -`help(issues)`, `help(worldbank)` and `help(companies)`. - ### Creating a `tbl_json` object The first step in using tidyjson is to convert your JSON into a `tbl_json` object. @@ -228,35 +213,44 @@ Almost every function in tidyjson accepts a `tbl_json` object as it's first parameter, and returns a `tbl_json` object for downstream use. `tbl_json` inherits from `dplyr::tbl`. -A `tbl_json` object is comprised of a `data.frame` with an additional attribute, -`JSON`, that contains a list of JSON data of the same length as the number of -rows in the `data.frame`. Each row of data in the `data.frame` corresponds to the -JSON found in the same index of the `JSON` attribute. - The easiest way to construct a `tbl_json` object is directly from a character -string or vector. +string: ```{r} -# Will return a 1 row data.frame with a length 1 JSON attribute -'{"key": "value"}' %>% as.tbl_json +# Using a single character string +x <- '{"key": "value"}' %>% as.tbl_json +x +attr(x, "JSON") +``` -# Will still return a 1 row data.frame with a length 1 JSON attribute as -# the character string is of length 1 (even though it contains a JSON array of -# length 2) -'[{"key1": "value1"}, {"key2": "value2"}]' %>% as.tbl_json +Behind the scenes, `as.tbl_json` is parsing the JSON string and creating a +data.frame with 1 column, `document.id`, which keeps track of the character +vector position (index) where the JSON data came from. In addition, each +`tbl_json` object has an additional attribute, `JSON`, that contains a list of +JSON data of the same length as the number of rows in the `data.frame`. + +Often times you will have many lines of JSON data that you want to work with, +in which case you can directly convert a character vector to obtain a `tbl_json` +object with the same number of rows: -# Will return a 2 row data.frame with a length 2 JSON attribute +```{r} +# Using a vector of JSON strings c('{"key1": "value1"}', '{"key2": "value2"}') %>% as.tbl_json ``` -Behind the scenes, `as.tbl_json()` is parsing the JSON strings and creating a -data.frame with 1 column, `document.id`, which keeps track of the character -vector position (index) where the JSON data came from. +### JSON included in the package + +The tidyjson package comes with several JSON example datasets: -TODO +* `commits`: commit data for the dplyr repo from github API +* `issues`: issue data for the dplyr repo from github API +* `worldbank`: world bank funded projects from +[jsonstudio](http://jsonstudio.com/resources/) +* `companies`: startup company data from +[jsonstudio](http://jsonstudio.com/resources/) -- Need to show how to create one from a data.frame -- Also need to talk about JSON lines format +Each dataset has some example tidyjson queries in `help(commits)`, +`help(issues)`, `help(worldbank)` and `help(companies)`. ## Verbs @@ -283,6 +277,10 @@ JSON. | `spread_values()` | object | ... = columns | none | N value columns | none | | `append_values_X()` | scalar | column.name | none | column of type X | none | +TODO: Add `json_lengths()` here and below +TODO: Length descriptions above +TODO: Re-order below and above to be consistent + ### Identify JSON structure with `json_types()` One of the first steps you will want to take is to investigate the structure of From b1082fa4963ca35d9ded2a8f226e0f5a904b8604 Mon Sep 17 00:00:00 2001 From: Jeremy Stanley Date: Sat, 11 Apr 2015 08:50:20 -0400 Subject: [PATCH 08/20] #29 simplify worldbank example to a smaller subset of data --- R/data-worldbank.r | 2 +- data/worldbank.rda | Bin 449400 -> 476014 bytes man/worldbank.Rd | 2 +- vignettes/introduction-to-tidyjson.Rmd | 18 ++++++++---------- 4 files changed, 10 insertions(+), 12 deletions(-) diff --git a/R/data-worldbank.r b/R/data-worldbank.r index 3f57622..73d97f4 100644 --- a/R/data-worldbank.r +++ b/R/data-worldbank.r @@ -19,7 +19,7 @@ #' name = jstring("project_name"), # Spread name #' region = jstring("regionname") # Spread region #' ) %>% -#' enter_object("sector") %>% # Enter the 'sector' object +#' enter_object("majorsector_percent") %>% # Enter the 'sector' object #' gather_array("sector.index") %>% # Gather the array #' spread_values(sector = jstring("Name")) # Spread the sector name #' diff --git a/data/worldbank.rda b/data/worldbank.rda index 2610b630b490a680b2380f7741585804f304bbb2..39accc8706330a863d734e80c68ab1766f367560 100644 GIT binary patch literal 476014 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JSON has become a common format used in: @@ -386,21 +390,15 @@ a specific key. ## A real example Included in the tidyjson package is a `r length(worldbank)` record sample, -`worldbank`, which contains world bank funded projects from -[jsonstudio](http://jsonstudio.com/resources/). +`worldbank`, which contains a subset of the JSON data describing world bank +funded projects from [jsonstudio](http://jsonstudio.com/resources/). First, let's take a look at a single record. We can use `jsonlite::prettify` to -make the JSON easy to read. But because some of the text is very -lengthy (e.g., the abstract and many URLs), we are going to jump through some -hoops to truncate the result to 80 characters so it will fit in the vignette: +make the JSON easy to read: ```{r} library(jsonlite) -library(stringr) -worldbank[1] %>% prettify %>% - str_split("\n") %>% unlist %>% - lapply(str_sub, 1, 80) %>% paste(collapse = "\n") %>% - writeLines +worldbank[1] %>% prettify ``` An interesting objects is "majorsector_percent", which appears to capture the From 8a83396abf548666b9e7e736e43b9796c9f80349 Mon Sep 17 00:00:00 2001 From: Jeremy Stanley Date: Sat, 11 Apr 2015 13:49:32 -0400 Subject: [PATCH 09/20] #29 various edits and cleanup --- vignettes/introduction-to-tidyjson.Rmd | 62 +++++++++++++++++++------- 1 file changed, 45 insertions(+), 17 deletions(-) diff --git a/vignettes/introduction-to-tidyjson.Rmd b/vignettes/introduction-to-tidyjson.Rmd index 0333129..dd78659 100644 --- a/vignettes/introduction-to-tidyjson.Rmd +++ b/vignettes/introduction-to-tidyjson.Rmd @@ -151,8 +151,8 @@ purch_df <- jsonlite::fromJSON(purch_json, simplifyDataFrame = TRUE) purch_df ``` -This looks deceptively simple, the resulting data structure is actually a -complex nested data.frame: +This looks deceptively simple, on inspection with `str()` we see that the +resulting data structure is actually a complex nested data.frame: ```{r} str(purch_df) @@ -239,9 +239,23 @@ object with the same number of rows: ```{r} # Using a vector of JSON strings -c('{"key1": "value1"}', '{"key2": "value2"}') %>% as.tbl_json +y <- c('{"key1": "value1"}', '{"key2": "value2"}') %>% as.tbl_json +y ``` +This creates a two row `tbl_json` object, where each row corresponds to an index +of the character vector. We can see the underlying parsed JSON: + +```{r} +attr(y, "JSON") +``` + +TODO: + +* Describe preservation of JSON under various operations ([, filter, etc.) +* Add sections on files, data.frames +* Show a table of methods for tbl_json + ### JSON included in the package The tidyjson package comes with several JSON example datasets: @@ -281,9 +295,11 @@ JSON. | `spread_values()` | object | ... = columns | none | N value columns | none | | `append_values_X()` | scalar | column.name | none | column of type X | none | -TODO: Add `json_lengths()` here and below -TODO: Length descriptions above -TODO: Re-order below and above to be consistent +TODO: + +* Add `json_lengths()` here and below +* Length descriptions above +* Re-order below and above to be consistent ### Identify JSON structure with `json_types()` @@ -418,12 +434,13 @@ amts <- worldbank %>% as.tbl_json %>% sector = jstring("Name"), pct = jnumber("Percent") ) %>% - select(document.id, sector, total, pct) %>% + mutate(total.m = total / 10^6) %>% + select(document.id, sector, total.m, pct) %>% tbl_df amts ``` -Let's check that the "pct" column really adds up to 100: +Let's check that the "pct" column really adds up to 100 by project: ```{r} amts %>% @@ -437,7 +454,7 @@ It appears to always add up to 100. Let's also check the distribution of the total amounts. ```{r} -summary(amts$total) +summary(amts$total.m) ``` Many are 0, the mean is $80m and the max is over $1bn. @@ -447,17 +464,13 @@ where the money is going by sector ```{r} amts %>% - mutate( - pct = pct / 100, - spend.k = total / 1000 * pct - ) %>% group_by(sector) %>% summarize( - spend.k = sum(spend.k) + spend.portion = sum(total.m * pct / 100) ) %>% ungroup %>% - mutate(pct = spend.k / sum(spend.k)) %>% - arrange(desc(spend.k)) + mutate(spend.dist = spend.portion / sum(spend.portion)) %>% + arrange(desc(spend.dist)) ``` It looks like in this sample of projects, "Information and Communication" is @@ -485,9 +498,13 @@ entire JSON structure. Next, you can begin working with the data in R. +TODO: + +* Replace below + ```{r} # assuming documents are carriage-return delimited, otherwise use readChar -# json <- readLines(file.json) # TODO: Need to change this +# json <- readLines(file.json) # Inspect the types of objects # json %>% json_types %>% table @@ -546,3 +563,14 @@ relationally. Finally, don't forget that once you are done with your JSON tidying, you can use [dplyr](http://github.com/hadley/dplyr) to continue manipulating the resulting data at your leisure! + +## Future work + +This package is still a work in progress. Significant additional features we +are contemplating include: + +- Summarizing JSON structures and visualizing them to make working with new JSON +easier +- Keeping the JSON in a parsed C++ data structure, and using rcpp to speed up +the manipulation of JSON +- Push computations to document oriented databases like MongoDB From e3c35d8aeeddd666babe5a9bd98603b71fb9a08e Mon Sep 17 00:00:00 2001 From: Jeremy Stanley Date: Sat, 11 Apr 2015 14:31:56 -0400 Subject: [PATCH 10/20] #29 remove as.tbl_json calls unless needed --- vignettes/introduction-to-tidyjson.Rmd | 27 +++++++++++++------------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/vignettes/introduction-to-tidyjson.Rmd b/vignettes/introduction-to-tidyjson.Rmd index dd78659..cb03af4 100644 --- a/vignettes/introduction-to-tidyjson.Rmd +++ b/vignettes/introduction-to-tidyjson.Rmd @@ -78,7 +78,6 @@ people <- ' # Structure the data people %>% # %>% is the magrittr pipeline operator - as.tbl_json %>% # parse the JSON and setup a 'tbl_json' object gather_array %>% # gather (stack) the array by index spread_values( # spread (widen) values to widen the data.frame name = jstring("name"), # value of "name" becomes a character column @@ -183,7 +182,7 @@ Using tidyjson, we can build a pipeline to turn this JSON into a tidy data.frame where each row corresponds to a purchased item: ```{r} -purch_items <- purch_json %>% as.tbl_json %>% +purch_items <- purch_json %>% gather_array %>% # stack the users spread_values(person = jstring("name")) %>% # extract the user name enter_object("purchases") %>% gather_array %>% # stack the purchases @@ -255,6 +254,7 @@ TODO: * Describe preservation of JSON under various operations ([, filter, etc.) * Add sections on files, data.frames * Show a table of methods for tbl_json +* Explain that you don't have to call as.tbl_json with verbs ### JSON included in the package @@ -309,7 +309,7 @@ each row of the data.frame, and adds a new column (`type` by default) that identifies the type according to the [JSON standard](http://json.org/). ```{r} -types <- c('{"a": 1}', '[1, 2]', '"a"', '1', 'true', 'null') %>% as.tbl_json %>% +types <- c('{"a": 1}', '[1, 2]', '"a"', '1', 'true', 'null') %>% json_types types$type ``` @@ -329,7 +329,7 @@ This is equivalent to "stacking" the array in the data.frame, and lets you continue to manipulate the remaining JSON in the elements of the array. ```{r} -'[1, "a", {"k": "v"}]' %>% as.tbl_json %>% gather_array %>% json_types +'[1, "a", {"k": "v"}]' %>% gather_array %>% json_types ``` This allows you to *enter into* an array and begin processing it's elements @@ -343,7 +343,7 @@ the rows in the data.frame to correspond to the keys of the object, and puts the values of the object into the JSON attribute. ```{r} -'{"name": "bob", "age": 32}' %>% as.tbl_json %>% gather_keys %>% json_types +'{"name": "bob", "age": 32}' %>% gather_keys %>% json_types ``` This allows you to *enter into* the keys of the objects just like `gather_array` @@ -360,8 +360,7 @@ be captured at each desired key location These values can be of varying types at varying depths, e.g., ```{r} -'{"name": {"first": "bob", "last": "jones"}, "age": 32}' %>% - as.tbl_json %>% +'{"name": {"first": "bob", "last": "jones"}, "age": 32}' %>% spread_values( first.name = jstring("name", "first"), age = jnumber("age") @@ -375,8 +374,9 @@ a column X (for X in "string", "number", "logical") insofar as it is of the JSON type specified. For example: ```{r} -'{"first": "bob", "last": "jones"}' %>% as.tbl_json %>% - gather_keys() %>% append_values_string() +'{"first": "bob", "last": "jones"}' %>% + gather_keys() %>% + append_values_string() ``` Any values that do not conform to the type specified will be NA in the resulting @@ -395,9 +395,10 @@ data.frame row will be discarded. ```{r} c('{"name": "bob", "children": ["sally", "george"]}', '{"name": "anne"}') %>% - as.tbl_json %>% spread_values(parent.name = jstring("name")) %>% + spread_values(parent.name = jstring("name")) %>% enter_object("children") %>% - gather_array %>% append_values_string("children") + gather_array %>% + append_values_string("children") ``` This is useful when you want to limit your data to just information found in @@ -425,7 +426,7 @@ Let's grab the "totalamt", and then gather the array of sectors and their percent allocations. ```{r} -amts <- worldbank %>% as.tbl_json %>% +amts <- worldbank %>% spread_values( total = jnumber("totalamt") ) %>% @@ -544,7 +545,7 @@ json <- '{ } ] }' -json %>% as.tbl_json %>% +json %>% spread_values(customer = jstring("name")) %>% # Keep the customer name enter_object("shopping cart") %>% # Look at their cart gather_array %>% # Expand the data.frame and dive into each array element From 2acbf543f0aa0049769d207f257bce2ce691dd57 Mon Sep 17 00:00:00 2001 From: Jeremy Stanley Date: Sun, 12 Apr 2015 09:31:42 -0400 Subject: [PATCH 11/20] #29 add json_lengths and resolve todos --- vignettes/introduction-to-tidyjson.Rmd | 67 ++++++++++++++++---------- 1 file changed, 42 insertions(+), 25 deletions(-) diff --git a/vignettes/introduction-to-tidyjson.Rmd b/vignettes/introduction-to-tidyjson.Rmd index cb03af4..e2356f4 100644 --- a/vignettes/introduction-to-tidyjson.Rmd +++ b/vignettes/introduction-to-tidyjson.Rmd @@ -11,6 +11,7 @@ vignette: > ```{r, echo = FALSE} knitr::opts_chunk$set(collapse = TRUE, comment = "#>") +options(dplyr.print_min = 4L, dplyr.print_max = 4L) ``` [JSON](http://json.org/) (JavaScript Object Notation) is a lightweight and @@ -212,9 +213,10 @@ purch_items %>% group_by(person) %>% summarize(spend = sum(item.price)) ### Creating a `tbl_json` object The first step in using tidyjson is to convert your JSON into a `tbl_json` object. -Almost every function in tidyjson accepts a `tbl_json` object as it's first -parameter, and returns a `tbl_json` object for downstream use. `tbl_json` -inherits from `dplyr::tbl`. +Almost every function in tidyjson accepts either a `tbl_json` object or a character +vector of JSON data as it's first parameter, and returns a `tbl_json` object for +downstream use. To facilitate integration with dplyr, `tbl_json` inherits from +`dplyr::tbl`. The easiest way to construct a `tbl_json` object is directly from a character string: @@ -229,7 +231,7 @@ attr(x, "JSON") Behind the scenes, `as.tbl_json` is parsing the JSON string and creating a data.frame with 1 column, `document.id`, which keeps track of the character vector position (index) where the JSON data came from. In addition, each -`tbl_json` object has an additional attribute, `JSON`, that contains a list of +`tbl_json` object has a "JSON" attribute that contains a list of JSON data of the same length as the number of rows in the `data.frame`. Often times you will have many lines of JSON data that you want to work with, @@ -249,12 +251,22 @@ of the character vector. We can see the underlying parsed JSON: attr(y, "JSON") ``` -TODO: +If your JSON data is already embedded in a data.frame, then you will need +to call `as.tbl_json` directly in order to specific which column contains +the JSON data. Note that the JSON in the data.frame should be character data, +and not a factor. Use `stringsAsFactors = FALSE` in constructing the data.frame +to avoid turning the JSON into a factor. -* Describe preservation of JSON under various operations ([, filter, etc.) -* Add sections on files, data.frames -* Show a table of methods for tbl_json -* Explain that you don't have to call as.tbl_json with verbs +```{r} +df <- data.frame( + x = 1:2, + JSON = c('{"key1": "value1"}', '{"key2": "value2"}'), + stringsAsFactors = FALSE +) +z <- df %>% as.tbl_json(json.column = "JSON") +z +attr(z, "JSON") +``` ### JSON included in the package @@ -286,18 +298,19 @@ The following table provides a reference of how each verb is used and what (if any) effect it has on the data.frame rows and columns and on the associated JSON. -| Verb | JSON | Arguments | Row Effect | Column Effect | JSON Effect | -|:--------------------|:-------|:----------------|:------------------|:-----------------|:---------------| -| `enter_object()` | object | ... = key path | Drops without key | none | object value | -| `json_types()` | any | column.name | Duplicates rows | type column | object keys | -| `gather_array()` | array | column.name | Duplicates rows | index column | array values | -| `gather_keys()` | object | column.name | Duplicates rows | key column | object values | -| `spread_values()` | object | ... = columns | none | N value columns | none | -| `append_values_X()` | scalar | column.name | none | column of type X | none | +| Verb | JSON | Arguments | Row Effect | Column Effect | JSON Effect | +|:--------------------|:-------|:----------------|:------------------|:-----------------|:--------------------| +| `enter_object()` | object | ... = key path | Drops without key | none | enter object value | +| `json_types()` | any | column.name | none | type column | none | +| `gather_array()` | array | column.name | Duplicates rows | index column | enter array values | +| `gather_keys()` | object | column.name | Duplicates rows | key column | enter object values | +| `spread_values()` | object | ... = columns | none | N value columns | none | +| `append_values_X()` | scalar | column.name | none | column of type X | none | +| `json_lengths()` | any | column.name | none | length column | none | TODO: -* Add `json_lengths()` here and below +* Add `json_lengths()` below * Length descriptions above * Re-order below and above to be consistent @@ -309,9 +322,7 @@ each row of the data.frame, and adds a new column (`type` by default) that identifies the type according to the [JSON standard](http://json.org/). ```{r} -types <- c('{"a": 1}', '[1, 2]', '"a"', '1', 'true', 'null') %>% - json_types -types$type +c('{"a": 1}', '[1, 2]', '"a"', '1', 'true', 'null') %>% json_types ``` This is particularly useful for inspecting your JSON data types, and can added @@ -404,6 +415,15 @@ c('{"name": "bob", "children": ["sally", "george"]}', '{"name": "anne"}') %>% This is useful when you want to limit your data to just information found in a specific key. +### Identify length of JSON objects with `json_lengths()` + +When investigating JSON data it can be helpful to identify the lengths of the +JSON objects or arrays, especialy when they are 'ragged' across documents: + +```{r} +c('[1, 2, 3]', '{"k1": 1, "k2": 2}', '1') %>% json_lengths +``` + ## A real example Included in the tidyjson package is a `r length(worldbank)` record sample, @@ -461,7 +481,7 @@ summary(amts$total.m) Many are 0, the mean is $80m and the max is over $1bn. Let's now aggregate by the sector and compute, on a dollar weighted basis, -where the money is going by sector +where the money is going by sector: ```{r} amts %>% @@ -474,9 +494,6 @@ amts %>% arrange(desc(spend.dist)) ``` -It looks like in this sample of projects, "Information and Communication" is -really low on the worldbank priority list! - ## Strategies When beginning to work with JSON data, you often don't have easy access to a From 84245e647f946c706dcf6bbb4b34b08e612d9972 Mon Sep 17 00:00:00 2001 From: Jeremy Stanley Date: Tue, 14 Apr 2015 06:41:15 -0400 Subject: [PATCH 12/20] #29 expand companies example --- vignettes/introduction-to-tidyjson.Rmd | 74 +++++++++++++++++++++++++- 1 file changed, 72 insertions(+), 2 deletions(-) diff --git a/vignettes/introduction-to-tidyjson.Rmd b/vignettes/introduction-to-tidyjson.Rmd index e2356f4..59c3676 100644 --- a/vignettes/introduction-to-tidyjson.Rmd +++ b/vignettes/introduction-to-tidyjson.Rmd @@ -501,8 +501,78 @@ schema describing what is in the JSON. One of the benefits of document oriented data structures is that they let developers create data without having to worry about defining the schema explicitly. -Thus, the first step is to usually understand the structure of the JSON. A first -step can be to look at individual records with `jsonlite::prettify()`: +Thus, the first step is to understand the structure of the JSON. Begin by +visually inspecting a single record with `jsonlite::prettify()`. + +```{r} +'{"key": "value", "array": [1, 2, 3]}' %>% prettify +``` + +However, for complex data or large JSON structures this can be tedious. +Alternatively, we can quickly summarize the keys using tidyjson and visualize +the results: + +```{r, fig.width = 7, fig.height = 6} +key_stats <- companies %>% + gather_keys %>% json_types %>% group_by(key, type) %>% tally +key_stats +ggplot(key_stats, aes(key, n, fill = type)) + + geom_bar(stat = "identity", position = "stack") + + coord_flip() +``` + +Suppose we are interested in exploring the funding round data. Let's examine +it's structure: + +```{r, fig.width = 7, fig.height = 2} +companies %>% + enter_object("funding_rounds") %>% + gather_array %>% + gather_keys %>% json_types %>% group_by(key, type) %>% tally %>% + ggplot(aes(key, n, fill = type)) + + geom_bar(stat = "identity", position = "stack") + + coord_flip() +``` + +Now, referencing the above visualizations, we can structure some of the data for +analysis: + +```{r} +rounds <- companies %>% + spread_values( + id = jstring("_id", "$oid"), + name = jstring("name"), + category = jstring("category_code") + ) %>% + enter_object("funding_rounds") %>% + gather_array %>% + spread_values( + round = jstring("round_code"), + raised = jnumber("raised_amount") + ) +rounds %>% glimpse +``` + +Now we can summarize by category and round how much is raised on average by +round: + +```{r, fig.width = 7, fig.height = 2} +rounds %>% + filter( + !is.na(raised), + round %in% c('a', 'b', 'c'), + category %in% c('enterprise', 'software', 'web') + ) %>% + group_by(category, round) %>% + summarize(raised = mean(raised)) %>% + ggplot(aes(round, raised / 10^6, fill = round)) + + geom_bar(stat = "identity") + + coord_flip() + + labs(y = "Raised (m)") + + facet_grid(. ~ category) +``` + +Alternatively, this is a common pattern used ```{r, message = FALSE} library(jsonlite) From c057883213be5856cc0f30710edc56669fef80a6 Mon Sep 17 00:00:00 2001 From: Jeremy Stanley Date: Tue, 14 Apr 2015 06:59:05 -0400 Subject: [PATCH 13/20] #29 reorganize the strategies section and cleanup text --- vignettes/introduction-to-tidyjson.Rmd | 152 +++++++++---------------- 1 file changed, 56 insertions(+), 96 deletions(-) diff --git a/vignettes/introduction-to-tidyjson.Rmd b/vignettes/introduction-to-tidyjson.Rmd index 59c3676..4351d87 100644 --- a/vignettes/introduction-to-tidyjson.Rmd +++ b/vignettes/introduction-to-tidyjson.Rmd @@ -424,7 +424,56 @@ JSON objects or arrays, especialy when they are 'ragged' across documents: c('[1, 2, 3]', '{"k1": 1, "k2": 2}', '1') %>% json_lengths ``` -## A real example +## Strategies + +When beginning to work with JSON data, you often don't have easy access to a +schema describing what is in the JSON. One of the benefits of document oriented +data structures is that they let developers create data without having to worry +about defining the schema explicitly. + +Thus, the first step is to understand the structure of the JSON. Begin by +visually inspecting a single record with `jsonlite::prettify()`. + +```{r} +'{"key": "value", "array": [1, 2, 3]}' %>% prettify +``` + +However, for complex data or large JSON structures this can be tedious. Instead, +use `gather_keys`, `json_types` and `json_lengths` to summarize the data: + +```{r} +'{"key": "value", "array": [1, 2, 3]}' %>% + gather_keys %>% json_types %>% json_lengths +``` + +You can repeat this as you move through the JSON data using `enter_object()` to +summarize nested structures as well. + +Once you have an understanding of how you'd like the data to be assembled, begin +creating your tidyjson pipeline. Use `enter_objects()` and `gather_array()` to +navigate the JSON and stack any arrays, and use `spread_values()` to get at +(potentially nested) key-value pairs along the way. + +Before entering any objects, make sure you first use `spread_values()` to +capture any top level identifiers you might need for analytics, summarization or +relational uses downstream. If an identifier doesn't exist, then you can always +fall back on the `as.tbl_json` generated document.id column. + +If you encounter data where information is encoded in both keys and values, +then consider using `gather_keys()` and `append_values_X()` where `X` is the type +of JSON scalar data you expect in the values. + +Note that there are often situations where there are multiple arrays or objects +of differing types that exist at the same level of the JSON hierarchy. In this +case, you need to use `enter_object()` to enter each of them in *separate* +pipelines to create *separate* `data.frames` that can then be joined +relationally. + +Finally, don't forget that once you are done with your JSON tidying, you can +use [dplyr](http://github.com/hadley/dplyr) to continue manipulating the +resulting data. + +### World bank example Included in the tidyjson package is a `r length(worldbank)` record sample, `worldbank`, which contains a subset of the JSON data describing world bank @@ -494,23 +543,14 @@ amts %>% arrange(desc(spend.dist)) ``` -## Strategies +### Companies example -When beginning to work with JSON data, you often don't have easy access to a -schema describing what is in the JSON. One of the benefits of document oriented -data structures is that they let developers create data without having to worry -about defining the schema explicitly. +Also included in the tidyjson package is a `r length(companies)` record sample, +`companies`, which contains a subset of the JSON data describing startups from +[jsonstudio](http://jsonstudio.com/resources/). -Thus, the first step is to understand the structure of the JSON. Begin by -visually inspecting a single record with `jsonlite::prettify()`. - -```{r} -'{"key": "value", "array": [1, 2, 3]}' %>% prettify -``` - -However, for complex data or large JSON structures this can be tedious. -Alternatively, we can quickly summarize the keys using tidyjson and visualize -the results: +Instead of using `jsonlite::prettify`, let's quickly summarize the keys using +tidyjson and visualize the results: ```{r, fig.width = 7, fig.height = 6} key_stats <- companies %>% @@ -572,86 +612,6 @@ rounds %>% facet_grid(. ~ category) ``` -Alternatively, this is a common pattern used - -```{r, message = FALSE} -library(jsonlite) -prettify('[{"name": "bob", "children": ["sally", "george"]}, {"name": "anne"}]') -``` - -Examining various random records can begin to give you a sense of what the JSON -contains and how it it structured. However, keep in mind that in many cases -documents that are missing data (either unknown or unrelevant) may omit the -entire JSON structure. - -Next, you can begin working with the data in R. - -TODO: - -* Replace below - -```{r} -# assuming documents are carriage-return delimited, otherwise use readChar -# json <- readLines(file.json) - -# Inspect the types of objects -# json %>% json_types %>% table -``` - -Then, if you want to work with a single row of data for each JSON object, use -`spread_values()` to get at (potentially nested) key-value pairs. - -If all you care about is data from a certain sub-object, then use `enter_object()` -to dive into that object directly. Make sure you first use `spread_values()` to -capture any top level identifiers you might need for analytics, summarization or -relational uses downstream. - -If you want to access arrays, use `gather_array()` to stack their elements, and -then proceed as though you had separate documents. (Again, first spread any -top-level keys you need.) - -Finally, if you have data where information is encoded in both keys and values, -then consider using `gather_keys()` and `append_values_X()` where `X` is the type -of JSON scalar data you expect in the values. - -It's important to remember that any of the above can be combined together -iteratively to do some fairly complex data extraction. For example: - -```{r} -json <- '{ - "name": "bob", - "shopping cart": - [ - { - "date": "2014-04-02", - "basket": {"books": 2, "shirts": 0} - }, - { - "date": "2014-08-23", - "basket": {"books": 1} - } - ] -}' -json %>% - spread_values(customer = jstring("name")) %>% # Keep the customer name - enter_object("shopping cart") %>% # Look at their cart - gather_array %>% # Expand the data.frame and dive into each array element - spread_values(date = jstring("date")) %>% # Keep the date of the cart - enter_object("basket") %>% # Look at their basket - gather_keys("product") %>% # Expand the data.frame for each product and capture it's name - append_values_number("quantity") # Capture the values as the quantity -``` - -Note that there are often situations where there are multiple arrays or objects -of differing types that exist at the same level of the JSON hierarchy. In this -case, you need to use `enter_object()` to enter each of them in *separate* -pipelines to create *separate* `data.frames` that can then be joined -relationally. - -Finally, don't forget that once you are done with your JSON tidying, you can -use [dplyr](http://github.com/hadley/dplyr) to continue manipulating the -resulting data at your leisure! - ## Future work This package is still a work in progress. Significant additional features we From 1f388af2308a7d245ad7bf4f2d3ec826bba6d491 Mon Sep 17 00:00:00 2001 From: Jeremy Stanley Date: Tue, 14 Apr 2015 09:52:35 -0400 Subject: [PATCH 14/20] #29 remove TODOs and clean up table --- vignettes/introduction-to-tidyjson.Rmd | 28 ++++++++++---------------- 1 file changed, 11 insertions(+), 17 deletions(-) diff --git a/vignettes/introduction-to-tidyjson.Rmd b/vignettes/introduction-to-tidyjson.Rmd index 4351d87..793fa8b 100644 --- a/vignettes/introduction-to-tidyjson.Rmd +++ b/vignettes/introduction-to-tidyjson.Rmd @@ -298,21 +298,15 @@ The following table provides a reference of how each verb is used and what (if any) effect it has on the data.frame rows and columns and on the associated JSON. -| Verb | JSON | Arguments | Row Effect | Column Effect | JSON Effect | -|:--------------------|:-------|:----------------|:------------------|:-----------------|:--------------------| -| `enter_object()` | object | ... = key path | Drops without key | none | enter object value | -| `json_types()` | any | column.name | none | type column | none | -| `gather_array()` | array | column.name | Duplicates rows | index column | enter array values | -| `gather_keys()` | object | column.name | Duplicates rows | key column | enter object values | -| `spread_values()` | object | ... = columns | none | N value columns | none | -| `append_values_X()` | scalar | column.name | none | column of type X | none | -| `json_lengths()` | any | column.name | none | length column | none | - -TODO: - -* Add `json_lengths()` below -* Length descriptions above -* Re-order below and above to be consistent +| Verb | Use | Row Effect | Column Effect | JSON Effect | +|:--------------------|:-------------------------------|:--------------------|:-----------------|:-------------| +| `json_types()` | Identify JSON structure | none | type column | none | +| `gather_array()` | Stack JSON array | Repeats rows | index column | enter array | +| `gather_keys()` | Stack a {"key": value} object | Repeats rows | key column | enter object | +| `spread_values()` | Create new columns from values | none | N value columns | none | +| `append_values_X()` | Append all values of a type | none | column of type X | none | +| `enter_object()` | Dive into an object "key" | Keeps rows with key | none | enter object | +| `json_lengths()` | Identify JSON length | none | length column | none | ### Identify JSON structure with `json_types()` @@ -347,7 +341,7 @@ This allows you to *enter into* an array and begin processing it's elements with other tidyjson functions. It retains the array.index in case the relative position of elements in the array is useful information. -### Stack a "key": object with `gather_keys()` +### Stack a {"key": value} object with `gather_keys()` Similar to `gather_array()`, `gather_keys()` takes JSON objects and duplicates the rows in the data.frame to correspond to the keys of the object, and puts the @@ -378,7 +372,7 @@ These values can be of varying types at varying depths, e.g., ) ``` -### Stack all values of a specified type with `append_values_X()` +### Append all values of a specified type with `append_values_X()` The `append_values_X()` functions let you take the remaining JSON and add it as a column X (for X in "string", "number", "logical") insofar as it is of the From 065b82edfa7f5b046888afefa3bd43fa8c5b6c80 Mon Sep 17 00:00:00 2001 From: Jeremy Stanley Date: Tue, 14 Apr 2015 10:25:09 -0400 Subject: [PATCH 15/20] #29 add a note about manipulating with other dplyr verbs --- vignettes/introduction-to-tidyjson.Rmd | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/vignettes/introduction-to-tidyjson.Rmd b/vignettes/introduction-to-tidyjson.Rmd index 793fa8b..b3f6222 100644 --- a/vignettes/introduction-to-tidyjson.Rmd +++ b/vignettes/introduction-to-tidyjson.Rmd @@ -465,7 +465,10 @@ relationally. Finally, don't forget that once you are done with your JSON tidying, you can use [dplyr](http://github.com/hadley/dplyr) to continue manipulating the -resulting data. +resulting data. `dplyr::filter`, `dplyr::arrange` and `dplyr::mutate` can be +used and will preserve the JSON attribute for further tidyjson manipulation. +The same is true for the `[` operator. Other `dplyr` functions will destroy +the JSON attribute, so you will no longer be able to manipulate the JSON data. ### World bank example From f2d13a0391883846595f941cccde50f29ad05e3e Mon Sep 17 00:00:00 2001 From: Jeremy Stanley Date: Tue, 14 Apr 2015 11:11:35 -0400 Subject: [PATCH 16/20] #29 missing call to ggplot2 --- vignettes/introduction-to-tidyjson.Rmd | 1 + 1 file changed, 1 insertion(+) diff --git a/vignettes/introduction-to-tidyjson.Rmd b/vignettes/introduction-to-tidyjson.Rmd index b3f6222..cb6dc52 100644 --- a/vignettes/introduction-to-tidyjson.Rmd +++ b/vignettes/introduction-to-tidyjson.Rmd @@ -550,6 +550,7 @@ Instead of using `jsonlite::prettify`, let's quickly summarize the keys using tidyjson and visualize the results: ```{r, fig.width = 7, fig.height = 6} +library(ggplot2) key_stats <- companies %>% gather_keys %>% json_types %>% group_by(key, type) %>% tally key_stats From 003968cdc143436d7db244be5c538efcd3e69560 Mon Sep 17 00:00:00 2001 From: Jeremy Stanley Date: Tue, 14 Apr 2015 15:57:58 -0400 Subject: [PATCH 17/20] #38 update append_values_x documentation --- R/append_values.r | 16 +++++++++++++++- man/append_values.Rd | 18 ++++++++++++++++-- 2 files changed, 31 insertions(+), 3 deletions(-) diff --git a/R/append_values.r b/R/append_values.r index aa6ae38..0aeaf81 100644 --- a/R/append_values.r +++ b/R/append_values.r @@ -1,4 +1,13 @@ -#' Append keys to a new column +#' Appends all values with a specified type as a new column +#' +#' The append_values_X functions let you take any remaining JSON and add it as +#' a column X (for X in "string", "number", "logical") insofar as it is of the +#' JSON type specified. +#' +#' Any values that do not conform to the type specified will be NA in the resulting +#' column. This includes other scalar types (e.g., numbers or logicals if you are +#' using append_values_string) and *also* any rows where the JSON is still an +#' object or an array. #' #' @name append_values #' @param x a tbl_json object @@ -6,6 +15,11 @@ #' under #' @param force parameter that determines if the variable type should be computed or not #' if force is FALSE, then the function may take more memory +#' @examples +#' library(magrittr) +#' '{"first": "bob", "last": "jones"}' %>% +#' gather_keys() %>% +#' append_values_string() NULL #' Creates the append_values_* functions diff --git a/man/append_values.Rd b/man/append_values.Rd index 28230e5..26857a7 100644 --- a/man/append_values.Rd +++ b/man/append_values.Rd @@ -5,7 +5,7 @@ \alias{append_values_logical} \alias{append_values_number} \alias{append_values_string} -\title{Append keys to a new column} +\title{Appends all values with a specified type as a new column} \usage{ append_values_string(x, column.name = type, force = TRUE) @@ -23,6 +23,20 @@ under} if force is FALSE, then the function may take more memory} } \description{ -Append keys to a new column +The append_values_X functions let you take any remaining JSON and add it as +a column X (for X in "string", "number", "logical") insofar as it is of the +JSON type specified. +} +\details{ +Any values that do not conform to the type specified will be NA in the resulting +column. This includes other scalar types (e.g., numbers or logicals if you are +using append_values_string) and *also* any rows where the JSON is still an +object or an array. +} +\examples{ +library(magrittr) +'{"first": "bob", "last": "jones"}' \%>\% + gather_keys() \%>\% + append_values_string() } From 6fab1b8fd76818660f953eeae569d996e39c063e Mon Sep 17 00:00:00 2001 From: Jeremy Stanley Date: Tue, 21 Apr 2015 07:04:19 -0400 Subject: [PATCH 18/20] #38 update enter_object documentation --- R/append_values.r | 2 +- R/enter_object.r | 21 ++++++++++++++++++++- man/append_values.Rd | 2 +- man/enter_object.Rd | 23 +++++++++++++++++++++-- 4 files changed, 43 insertions(+), 5 deletions(-) diff --git a/R/append_values.r b/R/append_values.r index 0aeaf81..a974113 100644 --- a/R/append_values.r +++ b/R/append_values.r @@ -16,7 +16,7 @@ #' @param force parameter that determines if the variable type should be computed or not #' if force is FALSE, then the function may take more memory #' @examples -#' library(magrittr) +#' library(dplyr) #' '{"first": "bob", "last": "jones"}' %>% #' gather_keys() %>% #' append_values_string() diff --git a/R/enter_object.r b/R/enter_object.r index 8afb351..08033a5 100644 --- a/R/enter_object.r +++ b/R/enter_object.r @@ -1,8 +1,27 @@ -#' Selects an object by key and filters rows to just those with matching keys +#' Dive into a specific object "key" +#' +#' JSON can contain nested objects, such as {"key1": {"key2": [1, 2, 3]}}. The +#' function enter_object() can be used to access the array nested under "key1" +#' and "key2". After using enter_object(), all further tidyjson calls happen +#' inside the referenced object (all other JSON data outside the object +#' is discarded). If the object doesn't exist for a given row / index, then that +#' data.frame row will be discarded. +#' +#' This is useful when you want to limit your data to just information found in +#' a specific key. Use the ... to specific a sequence of keys that you want to +#' enter into. Keep in mind that any rows with JSON that do not contain the key +#' will be discarded by this function. #' #' @param x a tbl_json object #' @param ... path to filter #' @export +#' @examples +#' library(dplyr) +#' c('{"name": "bob", "children": ["sally", "george"]}', '{"name": "anne"}') %>% +#' spread_values(parent.name = jstring("name")) %>% +#' enter_object("children") %>% +#' gather_array %>% +#' append_values_string("children") enter_object <- function(x, ...) { if (!is.tbl_json(x)) x <- as.tbl_json(x) diff --git a/man/append_values.Rd b/man/append_values.Rd index 26857a7..6f8e8ab 100644 --- a/man/append_values.Rd +++ b/man/append_values.Rd @@ -34,7 +34,7 @@ using append_values_string) and *also* any rows where the JSON is still an object or an array. } \examples{ -library(magrittr) +library(dplyr) '{"first": "bob", "last": "jones"}' \%>\% gather_keys() \%>\% append_values_string() diff --git a/man/enter_object.Rd b/man/enter_object.Rd index 42c44ae..6947125 100644 --- a/man/enter_object.Rd +++ b/man/enter_object.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/enter_object.r \name{enter_object} \alias{enter_object} -\title{Selects an object by key and filters rows to just those with matching keys} +\title{Dive into a specific object "key"} \usage{ enter_object(x, ...) } @@ -12,6 +12,25 @@ enter_object(x, ...) \item{...}{path to filter} } \description{ -Selects an object by key and filters rows to just those with matching keys +JSON can contain nested objects, such as {"key1": {"key2": [1, 2, 3]}}. The +function enter_object() can be used to access the array nested under "key1" +and "key2". After using enter_object(), all further tidyjson calls happen +inside the referenced object (all other JSON data outside the object +is discarded). If the object doesn't exist for a given row / index, then that +data.frame row will be discarded. +} +\details{ +This is useful when you want to limit your data to just information found in +a specific key. Use the ... to specific a sequence of keys that you want to +enter into. Keep in mind that any rows with JSON that do not contain the key +will be discarded by this function. +} +\examples{ +library(dplyr) +c('{"name": "bob", "children": ["sally", "george"]}', '{"name": "anne"}') \%>\% + spread_values(parent.name = jstring("name")) \%>\% + enter_object("children") \%>\% + gather_array \%>\% + append_values_string("children") } From cf6c9cbcaaabef94a098b2c83d27cacb9bdd5351 Mon Sep 17 00:00:00 2001 From: Jeremy Stanley Date: Tue, 21 Apr 2015 07:11:18 -0400 Subject: [PATCH 19/20] #38 update documentation for gather_array() --- R/gather_array.r | 22 +++++++++++++++++++++- man/gather_array.Rd | 24 ++++++++++++++++++++++-- 2 files changed, 43 insertions(+), 3 deletions(-) diff --git a/R/gather_array.r b/R/gather_array.r index 28362e9..835e090 100644 --- a/R/gather_array.r +++ b/R/gather_array.r @@ -1,10 +1,30 @@ -#' Expands a tbl_json to span the indices of a JSON array +#' Stack a JSON array +#' +#' Given a JSON array, such as [1, 2, 3], gather_array will "stack" the array in +#' the tbl_json data.frame, by replicating each row of the data.frame by the +#' length of the corresponding JSON array. A new column (by default called +#' "array.index") will be added to keep track of the referenced position in the +#' array for each row of the resuling data.frame. +#' +#' JSON can contain arrays of data, which can be simple vectors (fixed or varying +#' length integer, character or logical vectors). But they also often contain +#' lists of other objects (like a list of purchases for a user). The function +#' gather_array() takes JSON arrays and duplicates the rows in the data.frame to +#' correspond to the indices of the array, and puts the elements of +#' the array into the JSON attribute. This is equivalent to “stacking” the array +#' in the data.frame, and lets you continue to manipulate the remaining JSON +#' in the elements of the array. For simple arrays, use append_values_* to +#' capture all of the values of the array. For more complex arrays (where the +#' values are themselves objects or arrays), continue using other tidyjson +#' functions to structure the data as needed. #' #' @param x a tbl_json whose JSON attribute should always be an array #' @param column.name the name to give to the array index column created #' @return a tbl_json with a new column (column.name) that captures the array #' index and JSON attribute extracted from the array #' @export +#' @examples +#' '[1, "a", {"k": "v"}]' %>% gather_array %>% json_types gather_array <- function(x, column.name = "array.index") { if (!is.tbl_json(x)) x <- as.tbl_json(x) diff --git a/man/gather_array.Rd b/man/gather_array.Rd index 0f0cb3c..0be8e46 100644 --- a/man/gather_array.Rd +++ b/man/gather_array.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/gather_array.r \name{gather_array} \alias{gather_array} -\title{Expands a tbl_json to span the indices of a JSON array} +\title{Stack a JSON array} \usage{ gather_array(x, column.name = "array.index") } @@ -16,6 +16,26 @@ a tbl_json with a new column (column.name) that captures the array index and JSON attribute extracted from the array } \description{ -Expands a tbl_json to span the indices of a JSON array +Given a JSON array, such as [1, 2, 3], gather_array will "stack" the array in +the tbl_json data.frame, by replicating each row of the data.frame by the +length of the corresponding JSON array. A new column (by default called +"array.index") will be added to keep track of the referenced position in the +array for each row of the resuling data.frame. +} +\details{ +JSON can contain arrays of data, which can be simple vectors (fixed or varying +length integer, character or logical vectors). But they also often contain +lists of other objects (like a list of purchases for a user). The function +gather_array() takes JSON arrays and duplicates the rows in the data.frame to +correspond to the indices of the array, and puts the elements of +the array into the JSON attribute. This is equivalent to “stacking” the array +in the data.frame, and lets you continue to manipulate the remaining JSON +in the elements of the array. For simple arrays, use append_values_* to +capture all of the values of the array. For more complex arrays (where the +values are themselves objects or arrays), continue using other tidyjson +functions to structure the data as needed. +} +\examples{ +'[1, "a", {"k": "v"}]' \%>\% gather_array \%>\% json_types } From d45e9647ebc3bb84b4b7d57b97208335b11cb2ef Mon Sep 17 00:00:00 2001 From: Jeremy Stanley Date: Tue, 21 Apr 2015 10:11:53 -0400 Subject: [PATCH 20/20] #38 finish documentation for all tidyjson verbs --- R/append_values.r | 2 +- R/enter_object.r | 2 +- R/gather_array.r | 3 ++- R/gather_keys.r | 16 ++++++++++++++-- R/json_lengths.r | 12 +++++++++++- R/json_types.r | 14 +++++++++++++- R/spread_values.r | 16 +++++++++++++++- man/append_values.Rd | 2 +- man/enter_object.Rd | 2 +- man/gather_array.Rd | 1 + man/gather_keys.Rd | 16 ++++++++++++++-- man/json_lengths.Rd | 13 +++++++++++-- man/json_types.Rd | 14 +++++++++++++- man/spread_values.Rd | 16 ++++++++++++++-- vignettes/introduction-to-tidyjson.Rmd | 2 +- 15 files changed, 113 insertions(+), 18 deletions(-) diff --git a/R/append_values.r b/R/append_values.r index a974113..9bfd493 100644 --- a/R/append_values.r +++ b/R/append_values.r @@ -16,7 +16,7 @@ #' @param force parameter that determines if the variable type should be computed or not #' if force is FALSE, then the function may take more memory #' @examples -#' library(dplyr) +#' library(magrittr) # for %>% #' '{"first": "bob", "last": "jones"}' %>% #' gather_keys() %>% #' append_values_string() diff --git a/R/enter_object.r b/R/enter_object.r index 08033a5..2d2be9e 100644 --- a/R/enter_object.r +++ b/R/enter_object.r @@ -16,7 +16,7 @@ #' @param ... path to filter #' @export #' @examples -#' library(dplyr) +#' library(magrittr) # for %>% #' c('{"name": "bob", "children": ["sally", "george"]}', '{"name": "anne"}') %>% #' spread_values(parent.name = jstring("name")) %>% #' enter_object("children") %>% diff --git a/R/gather_array.r b/R/gather_array.r index 835e090..780fd22 100644 --- a/R/gather_array.r +++ b/R/gather_array.r @@ -23,7 +23,8 @@ #' @return a tbl_json with a new column (column.name) that captures the array #' index and JSON attribute extracted from the array #' @export -#' @examples +#' @examples +#' library(magrittr) # for %>% #' '[1, "a", {"k": "v"}]' %>% gather_array %>% json_types gather_array <- function(x, column.name = "array.index") { diff --git a/R/gather_keys.r b/R/gather_keys.r index d41b123..c397003 100644 --- a/R/gather_keys.r +++ b/R/gather_keys.r @@ -1,10 +1,22 @@ -#' Gathers every key from the top level of the json and stacks them -#' +#' Stack a JSON {"key": value} object +#' +#' Given a JSON key value structure, like {"key1": 1, "key2": 2}, the +#' gather_keys() function duplicates the rows of the tbl_json data.frame for +#' every key, adds a new column (default name "key") to capture the key names, +#' and then dives into the JSON values to enable further manipulation with +#' downstream tidyjson functions. +#' +#' This allows you to *enter into* the keys of the objects just like `gather_array` +#' let you enter elements of the array. +#' #' @param x a tbl_json whose JSON attribute should always be an object #' @param column.name the name to give to the column of key names created #' @return a tbl_json with a new column (column.name) that captures the keys #' and JSON attribute of the associated value data #' @export +#' @examples +#' library(magrittr) # for %>% +#' '{"name": "bob", "age": 32}' %>% gather_keys %>% json_types gather_keys <- function(x, column.name = "key") { if (!is.tbl_json(x)) x <- as.tbl_json(x) diff --git a/R/json_lengths.r b/R/json_lengths.r index f2fe042..5761735 100644 --- a/R/json_lengths.r +++ b/R/json_lengths.r @@ -1,9 +1,19 @@ -#' Add a column that tells the 'length' of the data in the root of the JSON +#' Add a column that contains the length of the JSON data +#' +#' When investigating JSON data it can be helpful to identify the lengths of the +#' JSON objects or arrays, especialy when they are 'ragged' across documents. The +#' json_lengths() function adds a column (default name "length") that contains +#' the 'length' of the JSON associated with each row. For objects, this will +#' be equal to the number of keys. For arrays, this will be equal to the length +#' of the array. All scalar values will be of length 1. #' #' @param x a tbl_json object #' @param column.name the name to specify for the length column #' @return a tbl_json object with column.name column that tells the length #' @export +#' @examples +#' library(magrittr) # for %>% +#' c('[1, 2, 3]', '{"k1": 1, "k2": 2}', '1', {}) %>% json_lengths json_lengths <- function(x, column.name = "length") { if (!is.tbl_json(x)) x <- as.tbl_json(x) diff --git a/R/json_types.r b/R/json_types.r index 9040441..7edd0e7 100644 --- a/R/json_types.r +++ b/R/json_types.r @@ -1,9 +1,21 @@ #' Add a column that tells the 'type' of the data in the root of the JSON -#' +#' +#' The function json_types() inspects the JSON associated with +#' each row of the tbl_json data.frame, and adds a new column ("type" by +#' default) that identifies the type according to the +#' JSON standard at http://json.org/. +#' +#' This is particularly useful for inspecting your JSON data types, and can added +#' after gather_array() (or gather_keys()) to inspect the types of the elements +#' (or values) in arrays (or objects). +#' #' @param x a tbl_json object #' @param column.name the name to specify for the type column #' @return a tbl_json object with column.name column that tells the type #' @export +#' @examples +#' library(magrittr) # for %>% +#' c('{"a": 1}', '[1, 2]', '"a"', '1', 'true', 'null') %>% json_types json_types <- function(x, column.name = "type") { if (!is.tbl_json(x)) x <- as.tbl_json(x) diff --git a/R/spread_values.r b/R/spread_values.r index 22720fd..7bb099b 100644 --- a/R/spread_values.r +++ b/R/spread_values.r @@ -1,9 +1,23 @@ -#' Extracts values from JSON refereced by a sequence of keys +#' Create new columns with JSON values +#' +#' The spread_values() function lets you dive into (potentially nested) JSON +#' objects and extract specific values. spread_values() takes jstring(), +#' jnumber() or jlogical() named function calls as arguments in order to specify +#' the type of the data that should be captured at each desired key location. +#' These values can be of varying types at varying depths. +#' #' @param x tbl_json object #' @param ... column=value list where 'column' will be the column name created #' and 'value' must be a call to jstring(), jnumber() or jlogical() specifying #' the path to get the value (and the type implicit in the function name) #' @export +#' @examples +#' library(magrittr) # for %>% +#' '{"name": {"first": "bob", "last": "jones"}, "age": 32}' %>% +#' spread_values( +#' first.name = jstring("name", "first"), +#' age = jnumber("age") +#' ) spread_values <- function(x, ...) { if (!is.tbl_json(x)) x <- as.tbl_json(x) diff --git a/man/append_values.Rd b/man/append_values.Rd index 6f8e8ab..30d556b 100644 --- a/man/append_values.Rd +++ b/man/append_values.Rd @@ -34,7 +34,7 @@ using append_values_string) and *also* any rows where the JSON is still an object or an array. } \examples{ -library(dplyr) +library(magrittr) # for \%>\% '{"first": "bob", "last": "jones"}' \%>\% gather_keys() \%>\% append_values_string() diff --git a/man/enter_object.Rd b/man/enter_object.Rd index 6947125..04f3fdc 100644 --- a/man/enter_object.Rd +++ b/man/enter_object.Rd @@ -26,7 +26,7 @@ enter into. Keep in mind that any rows with JSON that do not contain the key will be discarded by this function. } \examples{ -library(dplyr) +library(magrittr) # for \%>\% c('{"name": "bob", "children": ["sally", "george"]}', '{"name": "anne"}') \%>\% spread_values(parent.name = jstring("name")) \%>\% enter_object("children") \%>\% diff --git a/man/gather_array.Rd b/man/gather_array.Rd index 0be8e46..09c06ab 100644 --- a/man/gather_array.Rd +++ b/man/gather_array.Rd @@ -36,6 +36,7 @@ values are themselves objects or arrays), continue using other tidyjson functions to structure the data as needed. } \examples{ +library(magrittr) # for \%>\% '[1, "a", {"k": "v"}]' \%>\% gather_array \%>\% json_types } diff --git a/man/gather_keys.Rd b/man/gather_keys.Rd index a66fcf1..b413f6d 100644 --- a/man/gather_keys.Rd +++ b/man/gather_keys.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/gather_keys.r \name{gather_keys} \alias{gather_keys} -\title{Gathers every key from the top level of the json and stacks them} +\title{Stack a JSON {"key": value} object} \usage{ gather_keys(x, column.name = "key") } @@ -16,6 +16,18 @@ a tbl_json with a new column (column.name) that captures the keys and JSON attribute of the associated value data } \description{ -Gathers every key from the top level of the json and stacks them +Given a JSON key value structure, like {"key1": 1, "key2": 2}, the +gather_keys() function duplicates the rows of the tbl_json data.frame for +every key, adds a new column (default name "key") to capture the key names, +and then dives into the JSON values to enable further manipulation with +downstream tidyjson functions. +} +\details{ +This allows you to *enter into* the keys of the objects just like `gather_array` +let you enter elements of the array. +} +\examples{ +library(magrittr) # for \%>\% +'{"name": "bob", "age": 32}' \%>\% gather_keys \%>\% json_types } diff --git a/man/json_lengths.Rd b/man/json_lengths.Rd index 31c53bc..018c9a3 100644 --- a/man/json_lengths.Rd +++ b/man/json_lengths.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/json_lengths.r \name{json_lengths} \alias{json_lengths} -\title{Add a column that tells the 'length' of the data in the root of the JSON} +\title{Add a column that contains the length of the JSON data} \usage{ json_lengths(x, column.name = "length") } @@ -15,6 +15,15 @@ json_lengths(x, column.name = "length") a tbl_json object with column.name column that tells the length } \description{ -Add a column that tells the 'length' of the data in the root of the JSON +When investigating JSON data it can be helpful to identify the lengths of the +JSON objects or arrays, especialy when they are 'ragged' across documents. The +json_lengths() function adds a column (default name "length") that contains +the 'length' of the JSON associated with each row. For objects, this will +be equal to the number of keys. For arrays, this will be equal to the length +of the array. All scalar values will be of length 1. +} +\examples{ +library(magrittr) # for \%>\% +c('[1, 2, 3]', '{"k1": 1, "k2": 2}', '1', {}) \%>\% json_lengths } diff --git a/man/json_types.Rd b/man/json_types.Rd index 85d93be..d66b89f 100644 --- a/man/json_types.Rd +++ b/man/json_types.Rd @@ -15,6 +15,18 @@ json_types(x, column.name = "type") a tbl_json object with column.name column that tells the type } \description{ -Add a column that tells the 'type' of the data in the root of the JSON +The function json_types() inspects the JSON associated with +each row of the tbl_json data.frame, and adds a new column ("type" by +default) that identifies the type according to the +JSON standard at http://json.org/. +} +\details{ +This is particularly useful for inspecting your JSON data types, and can added +after gather_array() (or gather_keys()) to inspect the types of the elements +(or values) in arrays (or objects). +} +\examples{ +library(magrittr) # for \%>\% +c('{"a": 1}', '[1, 2]', '"a"', '1', 'true', 'null') \%>\% json_types } diff --git a/man/spread_values.Rd b/man/spread_values.Rd index 97f0d87..c65383c 100644 --- a/man/spread_values.Rd +++ b/man/spread_values.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/spread_values.r \name{spread_values} \alias{spread_values} -\title{Extracts values from JSON refereced by a sequence of keys} +\title{Create new columns with JSON values} \usage{ spread_values(x, ...) } @@ -14,6 +14,18 @@ and 'value' must be a call to jstring(), jnumber() or jlogical() specifying the path to get the value (and the type implicit in the function name)} } \description{ -Extracts values from JSON refereced by a sequence of keys +The spread_values() function lets you dive into (potentially nested) JSON +objects and extract specific values. spread_values() takes jstring(), +jnumber() or jlogical() named function calls as arguments in order to specify +the type of the data that should be captured at each desired key location. +These values can be of varying types at varying depths. +} +\examples{ +library(magrittr) # for \%>\% +'{"name": {"first": "bob", "last": "jones"}, "age": 32}' \%>\% + spread_values( + first.name = jstring("name", "first"), + age = jnumber("age") + ) } diff --git a/vignettes/introduction-to-tidyjson.Rmd b/vignettes/introduction-to-tidyjson.Rmd index cb6dc52..07d8efc 100644 --- a/vignettes/introduction-to-tidyjson.Rmd +++ b/vignettes/introduction-to-tidyjson.Rmd @@ -415,7 +415,7 @@ When investigating JSON data it can be helpful to identify the lengths of the JSON objects or arrays, especialy when they are 'ragged' across documents: ```{r} -c('[1, 2, 3]', '{"k1": 1, "k2": 2}', '1') %>% json_lengths +c('[1, 2, 3]', '{"k1": 1, "k2": 2}', '1', {}) %>% json_lengths ``` ## Strategies