ganalytics.Rmd

---
title: "ganalytics - Advanced dynamic segment and Filter examples"
author: "Johann de Boer"
---

[`ganalytics`](https://CRAN.R-project.org/package=ganalytics) provides functions that makes it easy to define filters and segments using natural R language comparison and logical operators. This example demonstrates how to define dynamic segments using functions from the `ganalytics` package and using those segments with the `googleAnalyticsR` package. You need `googleAnalyticsR>=0.6.0` and `ganalytics>=0.10.6`.

More examples are available at the [ganalytics README](https://github.com/jdeboer/ganalytics).

## Setup

Load the `ganalytics` library as well as `googleAnalyticsR` to make use of the new syntax.

```r
library(googleAnalyticsR)
library(ganalytics)

# authenticate
ga_auth()

# set to your view Id
view_id <- 81416156
```

## Advanced Filter syntax

In this example, we'll define the following filters:

* Device category is desktop or tablet - a dimension filter using an OR condition.
* New visitors using either a desktop or tablet device - a dimension filter involving both an AND and an OR condition.
* At least one goal completion or transaction - a metric filter using an OR condition.

The above list of filters will be defined using `ganalytics` expressions as follows:

```r
# Device category is desktop or tablet - a dimension filter using an OR condition.
desktop_or_mobile <- Expr(~deviceCategory == "desktop") | Expr(~deviceCategory == "tablet")

# New visitors using either a desktop or tablet device - a dimension filter involving both an AND and an OR condition.
new_desktop_and_mobile_visitors <- Expr(~userType == "new") & desktop_or_mobile

# At least one goal completion or transaction - a metric filter using an OR condition.
at_least_one_conversion <- Expr(~goalCompletionsAll > 0) | Expr(~transactions > 0)
```

We can now use `googleAnalyticsR` to query the data with the above filters:

```r
results <- google_analytics(
  viewId = view_id,
  date_range = c("30daysAgo", "yesterday"),
  metrics = c("users", "sessions", "goalCompletionsAll"),
  dimensions = c("deviceCategory", "userType"),
  dim_filters = new_desktop_and_mobile_visitors,
  met_filters = at_least_one_conversion
)

results
#  deviceCategory    userType users sessions goalCompletionsAll
#1        desktop New Visitor  2721     2726                600
#2         tablet New Visitor    67       67                 13            
```

## Advanced Segment Syntax

In this example, we'll define a list of six segments:

* Bounced sessions: Sessions where the bounces metric is not zero.
* Mobile or tablet sessions: Sessions by mobile and tablet users.
* Multi-session users: Users who have visited more than once during the defined date range.

The above list of dynamic segments is defined using `ganalytics` expressions as follows:

```r
bounces <- Expr(~bounces != 0)

mobile_or_tablet <- Expr(~deviceCategory %in% c("mobile", "tablet"))

multi_session_users <- Include(PerUser(Expr(~sessions > 1)), scope = "users")

my_segment_list <- list(
    bounced_sessions = PerSession(bounces),
    mobile_or_tablet = mobile_or_tablet,
    multi_session_users = multi_session_users)

results <- google_analytics(
    viewId = view_id,
    date_range = c("30daysAgo", "yesterday"),
    metrics = c("users", "sessions"),
    dimensions = c("segment"),
    segments = Segments(my_segment_list)
  )

results
#              segment users sessions
#1    bounced_sessions  3080     4070
#2    mobile_or_tablet   631      899
#3 multi_session_users    45      84
```

### More than 4 segments

The Google Analytics Reporting API can only be used to query 4 segments at a time, so if you have more than 4 you need to break the list segments into chunks:

```r
segment_chunks <- split(my_segment_list, (seq_along(my_segment_list) - 1L) %/% 4L)
```

We can now use `googleAnalyticsR` to query each chunk of segments and bind the results into a single `data.frame`. For each segment, we will request a count of users and sessions.

```r
library(purrr)
library(dplyr)

results <- map_df(segment_chunks, function(chunk) {
  google_analytics(
    viewId = view_id,
    date_range = c(start_date, end_date),
    metrics = c("users", "sessions"),
    dimensions = c("segment"),
    segments = Segments(chunk)
  )
})
```