Package 'funneljoin'

Join tables based on one event happening after another

Description

Join two tables based on observations in one table happening after observations in the other. Each table must have a user_id column, which must always match for two observations to be joined, and a time column, which must be greater in y than in x for the two to be joined. Supports all types of dplyr joins (inner, left, anti, etc.) and requires a type argument to specify which observations in a funnel get kept (see details for supported types).

Usage

after_join(
  x,
  y,
  by_time,
  by_user,
  mode = "inner",
  type = "first-first",
  max_gap = NULL,
  min_gap = NULL,
  gap_col = FALSE,
  suffix = c(".x", ".y")
)

after_inner_join(
  x,
  y,
  by_time,
  by_user,
  type,
  max_gap = NULL,
  min_gap = NULL,
  gap_col = FALSE,
  suffix = c(".x", ".y")
)

after_left_join(
  x,
  y,
  by_time,
  by_user,
  type,
  max_gap = NULL,
  min_gap = NULL,
  gap_col = FALSE,
  suffix = c(".x", ".y")
)

after_right_join(
  x,
  y,
  by_time,
  by_user,
  type,
  max_gap = NULL,
  min_gap = NULL,
  gap_col = FALSE,
  suffix = c(".x", ".y")
)

after_full_join(
  x,
  y,
  by_time,
  by_user,
  type,
  max_gap = NULL,
  min_gap = NULL,
  gap_col = FALSE,
  suffix = c(".x", ".y")
)

after_anti_join(
  x,
  y,
  by_time,
  by_user,
  type,
  max_gap = NULL,
  min_gap = NULL,
  gap_col = FALSE,
  suffix = c(".x", ".y")
)

after_semi_join(
  x,
  y,
  by_time,
  by_user,
  type,
  max_gap = NULL,
  min_gap = NULL,
  gap_col = FALSE,
  suffix = c(".x", ".y")
)

Arguments

x	A tbl representing the first event to occur in the funnel.
y	A tbl representing an event to occur in the funnel.
by_time	A character vector to specify the time columns in x and y. This would typically be a datetime or a date column. These columns are used to filter for time y being after time x.
by_user	A character vector to specify the user or identity columns in x and y.
mode	The method used to join: "inner", "full", "anti", "semi", "right", "left". Each also has its own function, such as after_inner_join.
type	The type of funnel used to distinguish between event pairs, such as "first-first", "last-first", or "any-firstafter". See details for more.
max_gap	Optional: the maximum gap allowed between events. Can be a integer representing the number of seconds or a difftime object, such as as.difftime(2, units = "hours").
min_gap	Optional: the maximum gap allowed between events. Can be a integer representing the number of seconds or a difftime object, such as as.difftime(2, units = "hours").
gap_col	Whether to include a numeric column, .gap, with the time difference in seconds between the events.
suffix	If there are non-joined duplicate variables in x and y, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2.

Details

type can be any combination of first, last, any, lastbefore, firstwithin with first, last, any, firstafter. Some common ones you may use include:

first-first

Take the earliest x and y for each user before joining. For example, you want the first time someone entered an experiment, followed by the first time someone ever registered. If they registered, entered the experiment, and registered again, you do not want to include that person.

first-firstafter

Take the first x, then the first y after that. For example, you want when someone first entered an experiment and the first course they started afterwards. You don't care if they started courses before entering the experiment.

lastbefore-firstafter

First x that's followed by a y before the next x. For example, in last click paid ad attribution, you want the last time someone clicked an ad before the first subscription they did afterward.

any-firstafter

Take all Xs followed by the first Y after it. For example, you want all the times someone visited a homepage and their first product page they visited afterwards.

any-any

Take all Xs followed by all Ys. For example, you want all the times someone visited a homepage and all the product pages they saw afterward.

Examples

library(dplyr)
landed <- tribble(
  ~user_id, ~timestamp,
  1, "2018-07-01",
  2, "2018-07-01",
  2, "2018-07-01",
  3, "2018-07-02",
  4, "2018-07-01",
  4, "2018-07-04",
  5, "2018-07-10",
  5, "2018-07-12",
  6, "2018-07-07",
  6, "2018-07-08"
) %>%
  mutate(timestamp = as.Date(timestamp))

registered <- tribble(
  ~user_id, ~timestamp,
  1, "2018-07-02",
  3, "2018-07-02",
  4, "2018-06-10",
  4, "2018-07-02",
  5, "2018-07-11",
  6, "2018-07-10",
  6, "2018-07-11",
  7, "2018-07-07"
) %>%
 mutate(timestamp = as.Date(timestamp))

after_inner_join(landed, registered, by_user = "user_id",
           by_time = "timestamp", type = "first-first")

# You can use different methods of joining:
after_left_join(landed, registered, by_user = "user_id",
           by_time = "timestamp", type = "first-first")

after_anti_join(landed, registered, by_user = "user_id",
           by_time = "timestamp", type = "any-any")

---

View result for each type of afterjoin

Description

View result for each type of afterjoin

Usage

after_join_all(x, y, by_user, by_time, mode = "inner", ...)

Arguments

x	A tbl representing the first event to occur in the funnel.
y	A tbl representing an event to occur in the funnel.
by_user	A character vector to specify the user or identity columns in x and y.
by_time	A character vector to specify the time columns in x and y. This would typically be a datetime or a date column. These columns are used to filter for time y being after time x.
mode	The method used to join: "inner", "full", "anti", "semi", "right", "left".
...	any additional arguments

---

Title

Description

Title

Usage

as_seconds(x, sql = FALSE)

Arguments

x	a difftime object
sql	set to TRUE if you're working with remote tables and using dbplyr

Value

a difftime object in seconds

---

Distinct events

Description

Distinct events

Usage

distinct_events(.data, time_col, user_col, type)

Arguments

.data	a dataset, either local or remote
time_col	the name of the time column
user_col	the name of the user identifying column
type	the type of after_join ("first-first", "first-firstafter", etc.)

---

Start a funnel

Description

Start a funnel

Usage

funnel_start(tbl, moment_type, moment, tstamp, user)

Arguments

tbl	A table of different moments and timestamps
moment_type	The first moment in the funnel
moment	The name of the column with the moment_type
tstamp	The name of the column with the timestamps of the moments
user	The name of the column indicating the user who did the moment

Examples

library(dplyr)

activity <- tibble::tribble(
  ~ "user_id", ~ "event", ~ "timestamp",
  1, "landing", "2019-07-01",
  1, "registration", "2019-07-02",
  1, "purchase", "2019-07-07",
  1, "purchase", "2019-07-10",
  2, "landing", "2019-08-01",
  2, "registration", "2019-08-15",
  3, "landing", "2019-05-01",
  3, "registration", "2019-06-01",
  3, "purchase", "2019-06-04",
  4, "landing", "2019-06-13")

activity %>%
  funnel_start(moment_type = "landing",
               moment = "event",
               tstamp = "timestamp",
               user = "user_id")

---

Continue to funnel

Description

Continue to funnel

Usage

funnel_step(tbl, moment_type, type, name = moment_type, optional = FALSE, ...)

funnel_steps(tbl, moment_types, type, ...)

Arguments

tbl	A table of different moments and timestamps
moment_type	The next moment in the funnel
type	The type of after_join (e.g. "first-first", "any-any")
name	If you want a custom name instead of the moment_type; needed if the moment type is already in the sequence
optional	Whether this step in the funnel should be optional. If so, the following step will also try joining in a way that skips this step. Note that multiple optional steps in a row aren't supported.
...	Extra arguments passed on to after_left_join. For funnel_steps, these are passed on to funnel_step.
moment_types	For funnel_steps, a character vector of moment types, which are applied in order

Examples

library(dplyr)

activity <- tibble::tribble(
  ~ "user_id", ~ "event", ~ "timestamp",
  1, "landing", "2019-07-01",
  1, "registration", "2019-07-02",
  1, "purchase", "2019-07-07",
  1, "purchase", "2019-07-10",
  2, "landing", "2019-08-01",
  2, "registration", "2019-08-15",
  3, "landing", "2019-05-01",
  3, "registration", "2019-06-01",
  3, "purchase", "2019-06-04",
  4, "landing", "2019-06-13")

activity %>%
  funnel_start(moment_type = "landing",
               moment = "event",
               tstamp = "timestamp",
               user = "user_id")  %>%
funnel_step(moment_type = "registration",
           type = "first-firstafter")

---

Example dataset of landing events

Description

An example dataset for trying out after_join. The variables are as follows:

Usage

landed

Format

A data frame with 9 rows and 2 variables:

user_id

A numeric column for identifying people

timestamp

A date column for the date the landing happened

---

Copy class and attributes from the original version of an object to a modified version.

Description

Copied over from https://github.com/tidyverse/dplyr/issues/719

Usage

reclass(x, result)

Arguments

x	The original object, which has a class/attributes to copy
result	The modified object, which is / might be missing the class/attributes.

Value

result, now with class/attributes restored.

---

Example dataset of registration events

Description

An example dataset for trying out after_join. The variables are as follows:

Usage

registered

Format

A data frame with 8 rows and 2 variables:

user_id

A numeric column for identifying people

timestamp

A date column for the date the registration happened

---

Summarize Left-joined table into conversion count

Description

Summarize Left-joined table into conversion count

Usage

summarize_conversions(x, converted)

Arguments

x	A tbl with one row per user
converted	The name of the column representing whether the user converted (treated as FALSE if NA or FALSE, otherwise TRUE)

Value

A table with columns for your groups, along with 'nb_users', 'nb_conversions', and 'pct_converted'

---

Summarize after funnel start and funnel step(s)

Description

Summarize after funnel start and funnel step(s)

Usage

summarize_funnel(tbl_funnel)

Arguments

tbl_funnel

a table from funnel start and funnel step(s)

Value

A tibble with one row for each moment_type and grouping variable, with columns:

nb_step

The number of users who reached this moment

pct_cumulative

The percentage of original users who reached this moment

pct_step

The percentage of users who reached the last step reaching this moment

---

Summarise after join funnel with proportion test

Description

Summarise after join funnel with proportion test

Usage

summarize_prop_tests(
  x,
  alternative_name = alternative.name,
  base_level = "control",
  ...,
  ungroup = TRUE
)

Arguments

x	a data.frame with columns nb_conversions and nb_users
alternative_name	the name of the column indicating the experiment group
base_level	the name of the control experiment group
...	any additional arguments
ungroup	whether the table needs to be ungrouped

Value

a data.frame with proportion test results

---