Merge pull request #77 from como-ph/v0.2.0.9000

update functions

DESCRIPTION

@@ -7,11 +7,11 @@ Authors@R: c(person(given = "Ernest", family = "Guevarra",
                     email = "[email protected]", role = c("aut", "cre"),
                     comment = c(ORCID = "0000-0002-4887-4415")),
              person(given = "Chris", family = "Mercado", 
-                    email = "[email protected]",
-                    role = "aut"),
+                    email = "[email protected]", role = "aut",
+                    comment = c(ORCID = "0000-0002-5588-9543")),
              person(given = "Robert", family = "Medina", 
-                    email = "[email protected]",
-                    role = "aut"))
+                    email = "[email protected]", role = "aut",
+                    comment = c(ORCID = "0000-0001-6647-5453")))
 Description: The Oxford Modelling Group for Global Health (OMGH) is developing 
     model structures to estimate the impact of potential mitigation strategies. 
     By changing the input data and parameter values, the models can be adjusted 

NEWS.md

@@ -6,8 +6,11 @@
 
 * added dataset for mapping of data sources
 
-# updated README to describe new function specifications
+* updated README to describe new function specifications
 
+* added pattern to recognise variant dates on filename of README which changed in format for 2020-06-18 (from MM/DD to MM_DD) which caused an error
+
+* added codefactor checks [![CodeFactor](https://www.codefactor.io/repository/github/como-ph/comoparams/badge)](https://www.codefactor.io/repository/github/como-ph/comoparams)
 
 # comoparams 0.1.3.9000
 

R/01-ph_datadrop.R

@@ -34,13 +34,13 @@ ph_gdrive_files <- function(version = "current", date = NULL) {
 
   ## Get dropDate
   dropDate <- stringr::str_extract(string = dropCurrent$name,
-                                   pattern = "[0-9]{2}/[0-9]{2}") %>%
+                                   pattern = "[0-9]{2}/[0-9]{2}|[0-9]{2}\\_[0-9]{2}") %>%
     paste("2020", sep = "/") %>%
     lubridate::mdy()
 
   ## Provide message to user
   message(
-    paste("Getting information on Google Drive directory structure for latest available data up to ",
+    paste("Getting information on Google Drive directory structure of the DoH Data Drop for latest available data up to ",
           dropDate, ".", sep = "")
   )
 
@@ -64,7 +64,6 @@ ph_gdrive_files <- function(version = "current", date = NULL) {
                                                 pattern = "bit.ly/*")][1], -1)
 
   x <- x %>%
-    #decode_short_url() %>%
     RCurl::getURL() %>%
     stringr::str_extract_all(pattern = "[A-Za-z0-9@%#&()+*$,._\\-]{33}") %>%
     unlist()

R/02-ph_calculate_cases.R

@@ -12,10 +12,8 @@
 #'   with CoMo model data structure requirements.
 #'
 #' @examples
-#' \dontrun{
 #' df <- ph_get_cases()
 #' ph_calculate_cases(df = df)
-#' }
 #'
 #' @export
 #'
@@ -29,7 +27,7 @@ ph_calculate_cases <- function(df) {
   ## Get current data link folder information and contents
   dropDate <- googledrive::drive_ls(googledrive::drive_get(id = "1ZPPcVU4M7T-dtRyUceb0pMAd8ickYf8o")) %>%
     dplyr::select("name") %>%
-    stringr::str_extract(pattern = "[0-9]{2}/[0-9]{2}") %>%
+    stringr::str_extract(pattern = "[0-9]{2}/[0-9]{2}|[0-9]{2}\\_[0-9]{2}") %>%
     paste("2020", sep = "/") %>%
     lubridate::mdy()
 

R/04-ph_get_population.R

@@ -191,7 +191,7 @@ ph_get_wpp2019_births <- function(file,
     t <- stringr::str_replace_all(string = yrGrp, pattern = "-", replacement = ":")
     u <- NULL
     ## Cycle through various year groups
-    for(i in 1:length(t)) {
+    for(i in seq_len(length(t))) {
       u[i] <- period %in% eval(parse(text = t[i]))[1:5]
     }
     ## Convert single year to 5-year groups
@@ -286,7 +286,7 @@ ph_get_wpp2019_deaths <- function(file,
     t <- stringr::str_replace_all(string = yrGrp, pattern = "-", replacement = ":")
     u <- NULL
     ## Cycle through various year groups
-    for(i in 1:length(t)) {
+    for(i in seq_len(length(t))) {
       u[i] <- period %in% eval(parse(text = t[i]))[1:5]
     }
     ## Convert single year to 5-year groups

R/07-ph_set_population.R

@@ -33,15 +33,13 @@ ph_set_population <- function() {
     cat("Reading population data...")
 
     ##
-    pop <- ph_get_wpp2019_pop(file = "https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/CSV_FILES/WPP2019_PopulationByAgeSex_Medium.csv")
+    pop <- population_wpp_2019
 
     ##
-    birth <- ph_get_wpp2019_births(file = "https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/EXCEL_FILES/2_Fertility/WPP2019_FERT_F06_BIRTHS_BY_AGE_OF_MOTHER.xlsx",
-                                   period = 2019)
+    birth <- births_wpp_2019
 
     ##
-    death <- ph_get_wpp2019_deaths(file = "https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/EXCEL_FILES/3_Mortality/WPP2019_MORT_F04_1_DEATHS_BY_AGE_BOTH_SEXES.xlsx",
-                                   period = 2019)
+    death <- deaths_wpp_2019
 
     ##
     pop <- data.frame(pop[ , c("area", "year", "age_category", "total")],

R/comoparams.R

@@ -37,3 +37,7 @@
 #
 ################################################################################
 "_PACKAGE"
+
+if(getRversion() >= "2.15.1")  utils::globalVariables(c("births_wpp_2019",
+                                                        "deaths_wpp_2019",
+                                                        "population_wpp_2019"))

README.Rmd

@@ -21,11 +21,11 @@ library(magrittr)
 # comoparams: R utility tool to extract, process and structure Philippines-specific datasets for use in the CoMo Consortium Model <img src="man/figures/comoparams.png" width="200px" align="right" />
 
 <!-- badges: start -->
-[![Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.](https://www.repostatus.org/badges/latest/wip.svg)](https://www.repostatus.org/#wip)
 [![Lifecycle: maturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing)
 [![Travis build status](https://travis-ci.org/como-ph/comoparams.svg?branch=master)](https://travis-ci.org/como-ph/comoparams)
 [![AppVeyor build status](https://ci.appveyor.com/api/projects/status/github/como-ph/comoparams?branch=master&svg=true)](https://ci.appveyor.com/project/como-ph/comoparams)
 [![R-CMD-check](https://github.com/como-ph/comoparams/workflows/R-CMD-check/badge.svg)](https://github.com/como-ph/comoparams/actions)
+[![CodeFactor](https://www.codefactor.io/repository/github/como-ph/comoparams/badge)](https://www.codefactor.io/repository/github/como-ph/comoparams)
 [![DOI](https://zenodo.org/badge/255850150.svg)](https://zenodo.org/badge/latestdoi/255850150)
 <!-- badges: end -->
 

README.md

@@ -5,16 +5,14 @@
 
 <!-- badges: start -->
 
-[![Project Status: WIP – Initial development is in progress, but there
-has not yet been a stable, usable release suitable for the
-public.](https://www.repostatus.org/badges/latest/wip.svg)](https://www.repostatus.org/#wip)
 [![Lifecycle:
 maturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing)
 [![Travis build
 status](https://travis-ci.org/como-ph/comoparams.svg?branch=master)](https://travis-ci.org/como-ph/comoparams)
 [![AppVeyor build
 status](https://ci.appveyor.com/api/projects/status/github/como-ph/comoparams?branch=master&svg=true)](https://ci.appveyor.com/project/como-ph/comoparams)
 [![R-CMD-check](https://github.com/como-ph/comoparams/workflows/R-CMD-check/badge.svg)](https://github.com/como-ph/comoparams/actions)
+[![CodeFactor](https://www.codefactor.io/repository/github/como-ph/comoparams/badge)](https://www.codefactor.io/repository/github/como-ph/comoparams)
 [![DOI](https://zenodo.org/badge/255850150.svg)](https://zenodo.org/badge/latestdoi/255850150)
 <!-- badges: end -->
 
@@ -71,8 +69,7 @@ repository; 2) pulling of data from the [Philippines Statistics
 Authority (PSA)](http://www.psa.gov.ph) [2015 Population Census (2015
 POPCEN)](http://www.psa.gov.ph/content/highlights-philippine-population-2015-census-population);
 and 3) pulling of population, births and deaths data from the [World
-Population
-Prospects 2019](https://population.un.org/wpp/).
+Population Prospects 2019](https://population.un.org/wpp/).
 
 #### Pulling data from the Philippines Department of Health COVID-19 DataDrop
 
@@ -124,20 +121,20 @@ To pull the most current data on `cases`, we use the function
 
 ``` r
 ph_get_cases()
-#> # A tibble: 24,787 x 21
+#> # A tibble: 27,799 x 21
 #>    CaseCode   Age AgeGroup Sex   DateSpecimen DateResultRelea… DateRepConf
 #>    <chr>    <int> <chr>    <chr> <chr>        <chr>            <chr>      
 #>  1 C100018     53 50 to 54 Fema… "2020-04-30" "2020-05-09"     2020-05-11 
-#>  2 C100119     31 30 to 34 Male  "2020-04-05" "2020-04-10"     2020-04-12 
-#>  3 C100130     33 30 to 34 Fema… ""           ""               2020-05-19 
-#>  4 C100148     35 35 to 39 Male  "2020-04-29" "2020-04-30"     2020-05-29 
-#>  5 C100158     68 65 to 69 Male  "2020-05-01" "2020-05-20"     2020-05-29 
-#>  6 C100218     19 15 to 19 Male  "2020-04-27" "2020-05-18"     2020-05-29 
-#>  7 C100264     58 55 to 59 Male  "2020-03-16" "2020-03-25"     2020-03-29 
-#>  8 C100364     28 25 to 29 Male  "2020-06-05" "2020-06-08"     2020-06-10 
-#>  9 C100429     46 45 to 49 Male  "2020-05-20" ""               2020-05-31 
-#> 10 C100452     55 55 to 59 Fema… ""           "2020-06-06"     2020-06-11 
-#> # … with 24,777 more rows, and 14 more variables: DateDied <chr>,
+#>  2 C100115     34 30 to 34 Male  "2020-06-11" "2020-06-11"     2020-06-14 
+#>  3 C100119     31 30 to 34 Male  "2020-04-05" "2020-04-10"     2020-04-12 
+#>  4 C100130     33 30 to 34 Fema… ""           ""               2020-05-19 
+#>  5 C100148     35 35 to 39 Male  "2020-04-29" "2020-04-30"     2020-05-29 
+#>  6 C100158     68 65 to 69 Male  "2020-05-01" "2020-05-20"     2020-05-29 
+#>  7 C100218     19 15 to 19 Male  "2020-04-27" "2020-05-18"     2020-05-29 
+#>  8 C100264     58 55 to 59 Male  "2020-03-16" "2020-03-25"     2020-03-29 
+#>  9 C100364     28 25 to 29 Male  "2020-06-05" "2020-06-08"     2020-06-10 
+#> 10 C100426     28 25 to 29 Fema… "2020-06-09" "2020-06-16"     2020-06-17 
+#> # … with 27,789 more rows, and 14 more variables: DateDied <chr>,
 #> #   DateRecover <chr>, RemovalType <chr>, DateRepRem <chr>, Admitted <chr>,
 #> #   RegionRes <chr>, ProvRes <chr>, CityMunRes <chr>, CityMuniPSGC <chr>,
 #> #   HealthStatus <chr>, Quarantined <chr>, DateOnset <chr>, Pregnanttab <chr>,
@@ -208,8 +205,7 @@ file from the [PSA](http://www.psa.gov.ph) website, reads the file and
 then extracts and re-structures the population data in the file to long
 format (tidy format).
 
-The function can be called as
-follows:
+The function can be called as follows:
 
 ``` r
 linkToFile <- "https://psa.gov.ph/sites/default/files/attachments/hsd/pressrelease/Updated%20Population%20Projections%20based%20on%202015%20POPCEN_0.xlsx"
@@ -236,8 +232,7 @@ The main limitation of the PSA population projections based on the 2015
 POPCEN is that the age grouping only goes up 85+ whilst the **CoMo
 Consortium Model** requires population data with an age-structure that
 goes up to 95+. However, this can potentially be imputed to create the
-additional older age groupings if
-needed/wanted.
+additional older age groupings if needed/wanted.
 
 #### Pulling population, births and deaths data from the World Population Prospects 2019
 
@@ -260,8 +255,7 @@ of the data that is being pulled. The descriptors used are:
     and female
 
 To pull the five-year age group structured population, we make a call
-for the
-following:
+for the following:
 
 ``` r
 linkToFile <- "https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/CSV_FILES/WPP2019_PopulationByAgeSex_Medium.csv"
@@ -285,8 +279,7 @@ ph_get_wpp2019_pop(file = linkToFile, location = "Philippines")
     #> # … with 11 more rows
 
 To pull the number of births by age of mother in 5-year age groups, we
-make a call for the
-following:
+make a call for the following:
 
 ``` r
 linkToFile <- "https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/EXCEL_FILES/2_Fertility/WPP2019_FERT_F06_BIRTHS_BY_AGE_OF_MOTHER.xlsx"
@@ -310,8 +303,7 @@ ph_get_wpp2019_births(file = linkToFile, period = 2019)
     #> # … with 11 more rows
 
 To pull the number of deaths by 5-year age groups and by male and
-female, we make a call for the
-following:
+female, we make a call for the following:
 
 ``` r
 linkToFile <- "https://population.un.org/wpp/Download/Files/1_Indicators%20(Standard)/EXCEL_FILES/3_Mortality/WPP2019_MORT_F04_1_DEATHS_BY_AGE_BOTH_SEXES.xlsx"
@@ -353,7 +345,7 @@ follows:
 
 ``` r
 ph_get_cases() %>% ph_calculate_cases()
-#> # A tibble: 164 x 4
+#> # A tibble: 170 x 4
 #>    repDate    cases deaths recovered
 #>    <date>     <dbl>  <dbl>     <dbl>
 #>  1 2020-01-01     0      0         0
@@ -366,26 +358,26 @@ ph_get_cases() %>% ph_calculate_cases()
 #>  8 2020-01-08     0      0         0
 #>  9 2020-01-09     0      0         0
 #> 10 2020-01-10     0      0         0
-#> # … with 154 more rows
+#> # … with 160 more rows
 ```
 
 The IFR and IHR output can be produced as follows:
 
 ``` r
 ph_get_cases() %>% ph_calculate_rates()
 #> # A tibble: 21 x 8
-#>    age_category deaths deathsAdmitted admissions cases     ifr    ihr    hfr
-#>    <fct>         <dbl>          <dbl>      <dbl> <dbl>   <dbl>  <dbl>  <dbl>
-#>  1 0-5 y.o.         12              4         70   452 0.0265  0.155  0.0571
-#>  2 5-10 y.o.         3              2         23   270 0.0111  0.0852 0.0870
-#>  3 10-15 y.o.        2              2         27   422 0.00474 0.0640 0.0741
-#>  4 15-20 y.o.        9              7         73   675 0.0133  0.108  0.0959
-#>  5 20-25 y.o.        3              3        263  1894 0.00158 0.139  0.0114
-#>  6 25-30 y.o.       15              5        459  3020 0.00497 0.152  0.0109
-#>  7 30-35 y.o.       12              7        563  3225 0.00372 0.175  0.0124
-#>  8 35-40 y.o.       27             16        398  2441 0.0111  0.163  0.0402
-#>  9 40-45 y.o.       45             29        382  2233 0.0202  0.171  0.0759
-#> 10 45-50 y.o.       48             30        436  2111 0.0227  0.207  0.0688
+#>    age_category deaths deathsAdmitted admissions cases     ifr    ihr     hfr
+#>    <fct>         <dbl>          <dbl>      <dbl> <dbl>   <dbl>  <dbl>   <dbl>
+#>  1 0-5 y.o.         13              6         80   510 0.0255  0.157  0.075  
+#>  2 5-10 y.o.         3              2         28   311 0.00965 0.0900 0.0714 
+#>  3 10-15 y.o.        2              2         35   495 0.00404 0.0707 0.0571 
+#>  4 15-20 y.o.        9              9         87   773 0.0116  0.113  0.103  
+#>  5 20-25 y.o.        3              3        296  2208 0.00136 0.134  0.0101 
+#>  6 25-30 y.o.       16              5        514  3463 0.00462 0.148  0.00973
+#>  7 30-35 y.o.       13              7        612  3603 0.00361 0.170  0.0114 
+#>  8 35-40 y.o.       28             17        431  2747 0.0102  0.157  0.0394 
+#>  9 40-45 y.o.       51             33        414  2486 0.0205  0.167  0.0797 
+#> 10 45-50 y.o.       50             30        468  2368 0.0211  0.198  0.0641 
 #> # … with 11 more rows
 ```
 
@@ -398,8 +390,7 @@ functions within `comoparams` (a total of 19 functions).
 The **parameter settings** functions again uses a common prefix syntax
 of `ph_set_` followed by the descriptor of the parameter that is being
 defined or set. This syntax applies to 18 of the 19 functions in the
-set. The descriptors
-are:
+set. The descriptors are:
 
 | **Descriptor**  | **Definition**                                                                                                                                                          |
 | :-------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |