linelist provides a safe entry point to the Epiverse software ecosystem, adding a foundational layer through tagging, validation, and safeguarding epidemiological data, to help make data pipelines more straightforward and robust.
Our stable versions are released on CRAN, and can be installed using:
install.packages("linelist", build_vignettes = TRUE)The development version of linelist can be installed from GitHub with:
if (!require(remotes)) {
install.packages("remotes")
}
remotes::install_github("epiverse-trace/linelist", build_vignettes = TRUE)
linelist works by tagging key epidemiological data in a data.frame
or a tibble to facilitate and strengthen data pipelines. The resulting
object is a linelist object, which extends data.frame (or tibble)
by providing three types of features:
-
a tagging system to identify key data, enabling access to these data using their tags rather than actual names, which may change over time and across datasets
-
validation of the tagged variables (making sure they are present and of the right type/class)
-
safeguards against accidental losses of tagged variables in common data handling operations
The short example below illustrates these different features. See the
Documentation section for more in-depth examples and
details about linelist objects.
# load packages and a dataset for the example
# -------------------------------------------
library(pacman)
p_load(dplyr)
#> Installing package into '/Users/runner/work/_temp/Library'
#> (as 'lib' is unspecified)
#> also installing the dependencies 'ellipsis', 'purrr', 'generics', 'tidyselect'
#>
#> The downloaded binary packages are in
#> /var/folders/24/8k48jl6d249_n_qfxwsl6xvm0000gn/T//RtmpPI9gSu/downloaded_packages
#>
#> dplyr installed
p_load(magrittr)
p_load(outbreaks)
#> Installing package into '/Users/runner/work/_temp/Library'
#> (as 'lib' is unspecified)
#>
#> The downloaded binary packages are in
#> /var/folders/24/8k48jl6d249_n_qfxwsl6xvm0000gn/T//RtmpPI9gSu/downloaded_packages
#>
#> outbreaks installed
p_load(incidence2)
#> Installing package into '/Users/runner/work/_temp/Library'
#> (as 'lib' is unspecified)
#> also installing the dependencies 'tzdb', 'cpp11', 'data.table', 'grates', 'clock'
#>
#> The downloaded binary packages are in
#> /var/folders/24/8k48jl6d249_n_qfxwsl6xvm0000gn/T//RtmpPI9gSu/downloaded_packages
#>
#> incidence2 installed
p_load(linelist)
dataset <- outbreaks::mers_korea_2015$linelist
head(dataset)
#> id age age_class sex place_infect reporting_ctry
#> 1 SK_1 68 60-69 M Middle East South Korea
#> 2 SK_2 63 60-69 F Outside Middle East South Korea
#> 3 SK_3 76 70-79 M Outside Middle East South Korea
#> 4 SK_4 46 40-49 F Outside Middle East South Korea
#> 5 SK_5 50 50-59 M Outside Middle East South Korea
#> 6 SK_6 71 70-79 M Outside Middle East South Korea
#> loc_hosp dt_onset dt_report
#> 1 Pyeongtaek St. Mary, Hospital, Pyeongtaek, Gyeonggi 2015-05-11 2015-05-19
#> 2 Pyeongtaek St. Mary, Hospital, Pyeongtaek, Gyeonggi 2015-05-18 2015-05-20
#> 3 Pyeongtaek St. Mary, Hospital, Pyeongtaek, Gyeonggi 2015-05-20 2015-05-20
#> 4 Pyeongtaek St. Mary, Hospital, Pyeongtaek, Gyeonggi 2015-05-25 2015-05-26
#> 5 365 Yeollin Clinic, Seoul 2015-05-25 2015-05-27
#> 6 Pyeongtaek St. Mary, Hospital, Pyeongtaek, Gyeonggi 2015-05-24 2015-05-28
#> week_report dt_start_exp dt_end_exp dt_diag outcome dt_death
#> 1 2015_21 2015-04-18 2015-05-04 2015-05-20 Alive <NA>
#> 2 2015_21 2015-05-15 2015-05-20 2015-05-20 Alive <NA>
#> 3 2015_21 2015-05-16 2015-05-16 2015-05-21 Dead 2015-06-04
#> 4 2015_22 2015-05-16 2015-05-20 2015-05-26 Alive <NA>
#> 5 2015_22 2015-05-17 2015-05-17 2015-05-26 Alive <NA>
#> 6 2015_22 2015-05-15 2015-05-17 2015-05-28 Dead 2015-06-01
# check known tagged variables
# ----------------------------
tags_names()
#> [1] "id" "date_onset" "date_reporting" "date_admission"
#> [5] "date_discharge" "date_outcome" "date_death" "gender"
#> [9] "age" "location" "occupation" "hcw"
#> [13] "outcome"
# build a linelist
# ----------------
x <- dataset %>%
tibble() %>%
make_linelist(date_onset = "dt_onset", # date of onset
date_reporting = "dt_report", # date of reporting
occupation = "age" # mistake
)
x
#>
#> // linelist object
#> # A tibble: 162 × 15
#> id age age_class sex place_inf…¹ repor…² loc_h…³ dt_onset dt_report
#> <chr> <int> <chr> <fct> <fct> <fct> <fct> <date> <date>
#> 1 SK_1 68 60-69 M Middle East South … Pyeong… 2015-05-11 2015-05-19
#> 2 SK_2 63 60-69 F Outside Mi… South … Pyeong… 2015-05-18 2015-05-20
#> 3 SK_3 76 70-79 M Outside Mi… South … Pyeong… 2015-05-20 2015-05-20
#> 4 SK_4 46 40-49 F Outside Mi… South … Pyeong… 2015-05-25 2015-05-26
#> 5 SK_5 50 50-59 M Outside Mi… South … 365 Ye… 2015-05-25 2015-05-27
#> 6 SK_6 71 70-79 M Outside Mi… South … Pyeong… 2015-05-24 2015-05-28
#> 7 SK_7 28 20-29 F Outside Mi… South … Pyeong… 2015-05-21 2015-05-28
#> 8 SK_8 46 40-49 F Outside Mi… South … Seoul … 2015-05-26 2015-05-29
#> 9 SK_9 56 50-59 M Outside Mi… South … Pyeong… NA 2015-05-29
#> 10 SK_10 44 40-49 M Outside Mi… China Pyeong… 2015-05-21 2015-05-29
#> # … with 152 more rows, 6 more variables: week_report <fct>,
#> # dt_start_exp <date>, dt_end_exp <date>, dt_diag <date>, outcome <fct>,
#> # dt_death <date>, and abbreviated variable names ¹place_infect,
#> # ²reporting_ctry, ³loc_hosp
#>
#> // tags: date_onset:dt_onset, date_reporting:dt_report, occupation:age
tags(x) # check available tags
#> $date_onset
#> [1] "dt_onset"
#>
#> $date_reporting
#> [1] "dt_report"
#>
#> $occupation
#> [1] "age"
# validation of tagged variables
# ------------------------------
## (this flags a likely mistake: occupation should not be an integer)
validate_linelist(x)
#> Error in validate_types(x, ref_types): Issue when checking class of tag `occupation`:
#> Must inherit from class 'character'/'factor', but has class 'integer'
# change tags: fix mistakes, add new ones
# ---------------------------------------
x <- x %>%
set_tags(occupation = NULL, # tag removal
gender = "sex", # new tag
outcome = "outcome"
)
# safeguards against actions losing tags
# --------------------------------------
## attemping to remove geographical info but removing dates by mistake
x_no_geo <- x %>%
select(-(5:8))
#> Warning in prune_tags(out, lost_action): The following tags have lost their variable:
#> date_onset:dt_onset
## for stronger pipelines, trigger errors upon loss
x_no_geo <- x %>%
lost_tags_action("error") %>%
select(-(5:8))
#> Error in prune_tags(out, lost_action): The following tags have lost their variable:
#> date_onset:dt_onset
x_no_geo <- x %>%
select(-(5:7))
## to revert to default behaviour (warning upon error)
lost_tags_action()
#> Lost tags will now issue a warning.
# access content by tags, and build downstream pipelines
# ------------------------------------------------------
x_no_geo %>%
select_tags(date_onset, outcome)
#> # A tibble: 162 × 2
#> date_onset outcome
#> <date> <fct>
#> 1 2015-05-11 Alive
#> 2 2015-05-18 Alive
#> 3 2015-05-20 Dead
#> 4 2015-05-25 Alive
#> 5 2015-05-25 Alive
#> 6 2015-05-24 Dead
#> 7 2015-05-21 Alive
#> 8 2015-05-26 Alive
#> 9 NA Alive
#> 10 2015-05-21 Alive
#> # … with 152 more rows
x_no_geo %>%
tags_df()
#> # A tibble: 162 × 4
#> date_onset date_reporting gender outcome
#> <date> <date> <fct> <fct>
#> 1 2015-05-11 2015-05-19 M Alive
#> 2 2015-05-18 2015-05-20 F Alive
#> 3 2015-05-20 2015-05-20 M Dead
#> 4 2015-05-25 2015-05-26 F Alive
#> 5 2015-05-25 2015-05-27 M Alive
#> 6 2015-05-24 2015-05-28 M Dead
#> 7 2015-05-21 2015-05-28 F Alive
#> 8 2015-05-26 2015-05-29 F Alive
#> 9 NA 2015-05-29 M Alive
#> 10 2015-05-21 2015-05-29 M Alive
#> # … with 152 more rows
x_no_geo %>%
tags_df() %>%
incidence("date_onset", groups = c("gender", "outcome")) %>%
facet_plot(facets = "gender", fill = outcome)
More detailed documentation can be found at: https://epiverse-trace.github.io/linelist/
In particular:
To ask questions or give us some feedback, please use the github issues system.
Case line lists may contain personally identifiable information (PII). While linelist provides a way to store this data in R, it does not currently provide tools for data anonymization. The user is responsible for respecting individual privacy and ensuring PII is handled with the required level of confidentiality, in compliance with applicable laws and regulations for storing and sharing PII.
Note that PII is rarely needed for common analytics tasks, so that in many instances it may be advisable to remove PII from the data before sharing them with analytics teams.
This package is currently experimental, as defined by the RECON software lifecycle. This means that essential features and mechanisms should not change drastically, but depending on user feedback, functions may be renamed, arguments may change, some functionalities may be added, etc.
Contributions are welcome via pull requests.
Contributors to the project include:
- Thibaut Jombart (author)
- David Mascarina (logo)
- Emma Marty (communication)
Please note that the linelist project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
This package is a reboot of the RECON package linelist. Unliked its predecessor, the new package focuses on the implementation of a linelist class. The data cleaning features of the original package will eventually be re-implemented for linelist objects, albeit likely in a separate package.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent