| Type: | Package |
| Title: | Time-Based Joins to Analyze Sequences of Events |
| Version: | 0.2.0 |
| Depends: | R (≥ 2.10) |
| Maintainer: | Emily Robinson <robinson.es@gmail.com> |
| Description: | Time-based joins to analyze sequence of events, both in memory and out of memory. after_join() joins two tables of events, while funnel_start() and funnel_step() join events in the same table. With the type argument, you can switch between different funnel types, like first-first and last-firstafter. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| Suggests: | testthat, knitr, rmarkdown, tibble |
| RoxygenNote: | 7.2.1 |
| Imports: | dplyr, glue, magrittr, broom, purrr, rlang, tidyr, methods, forcats |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2023-03-21 19:35:45 UTC; emilyrobinson |
| Author: | Emily Robinson [aut, cre], Anthony Baker [aut], David Robinson [aut], Jacqueline Nolis [art] |
| Repository: | CRAN |
| Date/Publication: | 2023-03-21 20:00:02 UTC |
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
|
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
|
min_gap |
Optional: the maximum gap allowed between events. Can be a
integer representing the number of seconds or a difftime object, such as
|
gap_col |
Whether to include a numeric column, |
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 |
moment_types |
For |
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.
Objects exported from other packages
Description
These objects are imported from other packages. Follow the links below to see their documentation.
- dplyr
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