From 700bea837e09416d8e1b42136f1a06af7b312f8e Mon Sep 17 00:00:00 2001 From: "Daniel E. de la Rosa" Date: Fri, 1 Dec 2023 17:55:10 -0400 Subject: [PATCH] doc: better general doc and readme file --- R/data.R | 9 ++ README.Rmd | 62 ++++++++- README.md | 149 +++++++++++++++++++++- data-raw/Remesas_PR.xlsx | Bin 0 -> 12345 bytes data-raw/remesas_provincias.R | 11 ++ data/remesas_provincias.rda | Bin 0 -> 507 bytes man/DR_DM.Rd | 2 +- man/DR_MUN.Rd | 2 +- man/DR_PROV.Rd | 2 +- man/DR_REG.Rd | 2 +- man/DR_SEC.Rd | 2 +- man/dr_municipal_district.Rd | 2 +- man/dr_municipality.Rd | 2 +- man/dr_province.Rd | 2 +- man/dr_region.Rd | 2 +- man/dr_section.Rd | 2 +- man/figures/README-unnamed-chunk-28-1.png | Bin 0 -> 10735 bytes man/figures/README-unnamed-chunk-3-1.png | Bin 0 -> 10735 bytes man/figures/README-unnamed-chunk-31-1.png | Bin 0 -> 10304 bytes man/figures/README-unnamed-chunk-32-1.png | Bin 0 -> 10999 bytes man/figures/README-unnamed-chunk-6-1.png | Bin 0 -> 10304 bytes man/figures/README-unnamed-chunk-7-1.png | Bin 0 -> 10999 bytes man/remesas_provincias.Rd | 25 ++++ 23 files changed, 258 insertions(+), 18 deletions(-) create mode 100644 R/data.R create mode 100644 data-raw/Remesas_PR.xlsx create mode 100644 data-raw/remesas_provincias.R create mode 100644 data/remesas_provincias.rda create mode 100644 man/figures/README-unnamed-chunk-28-1.png create mode 100644 man/figures/README-unnamed-chunk-3-1.png create mode 100644 man/figures/README-unnamed-chunk-31-1.png create mode 100644 man/figures/README-unnamed-chunk-32-1.png create mode 100644 man/figures/README-unnamed-chunk-6-1.png create mode 100644 man/figures/README-unnamed-chunk-7-1.png create mode 100644 man/remesas_provincias.Rd diff --git a/R/data.R b/R/data.R new file mode 100644 index 0000000..9217bb7 --- /dev/null +++ b/R/data.R @@ -0,0 +1,9 @@ +#' Percentage distribution by provinces of remittances received by the Dominican Republic +#' +#' @format ## `remesas_provincias` +#' \describe{ +#' \item{province}{Full name of the province} +#' \item{percentage}{Percentage of the total remittances received in 2010} +#' } +#' @source +'remesas_provincias' diff --git a/README.Rmd b/README.Rmd index 2fbaed0..4048e0f 100644 --- a/README.Rmd +++ b/README.Rmd @@ -27,17 +27,69 @@ The goal of sfDR is to provide a comprehensive suite of simple feature (sf) obje You can install the development version of sfDR from r-universe with: -```r -install.packages('sfDR', repos = c('https://adatar-do.r-universe.dev', 'https://cloud.r-project.org')) +```{r} +if (!require('sfDR')) install.packages('sfDR', repos = c('https://adatar-do.r-universe.dev')) +if (!require('dplyr')) install.packages('dplyr') +if (!require('ggplot2')) install.packages('ggplot2') ``` ## Example This is a basic example showing how to use sfDR to plot a Dominican Republic province map: -```r +```{r} library(sfDR) +library(dplyr) -# Plotting the map of a specific province -plot(DR_PROV) +DR_PROV_SF <- sf::st_as_sf(DR_PROV) + +plot(DR_PROV_SF) +``` + +Suppose you have a dataset showing the percentage distribution of remittances received in each province of the Dominican Republic for a specific year. To visualize this data using a graph with sfDR, you first need to combine the metadata with the simple features. + + +```{r} +DR_PROV_SF <- DR_PROV_SF |> + left_join(dr_province, by = join_by(PROV_ID)) + +DR_PROV_SF %>% + sf::st_drop_geometry() +``` + +The original simple features dataset only includes the PROV_ID variable. Additional variables are incorporated through the dr_province object. + +```{r} + datos <- DR_PROV_SF |> + left_join(remesas_provincias, by = join_by('PROV_NAME' == 'province')) + +datos %>% + sf::st_drop_geometry() ``` + +It's important to note that non-ASCII characters can become corrupted when stored in the package, which may affect the merging of the data sets. + +> We recommend cleaning any errors before performing the merge. We will soon release another package with functions designed to facilitate this process. + +```{r} +datos |> + select(percentage) %>% + plot() +``` + +To get the desired graph, just apply the plot function to your data. + +Next, we show how to achieve a similar result using ggplot2. This serves to demonstrate how to create maps with both libraries and to highlight the simplicity of the process by integrating everything in one step. + +```{r} +library(ggplot2) + +DR_PROV %>% + sf::st_as_sf() %>% + left_join(dr_province) %>% + left_join(remesas_provincias, by = join_by('PROV_NAME' == 'province')) %>% + ggplot() + + geom_sf(aes(fill = percentage)) + + theme_void() +``` + diff --git a/README.md b/README.md index 997c3c9..df11f46 100644 --- a/README.md +++ b/README.md @@ -22,7 +22,9 @@ Republic, facilitating geospatial analysis and mapping. You can install the development version of sfDR from r-universe with: ``` r -install.packages('sfDR', repos = c('https://adatar-do.r-universe.dev', 'https://cloud.r-project.org')) +if (!require('sfDR')) install.packages('sfDR', repos = c('https://adatar-do.r-universe.dev')) +if (!require('dplyr')) install.packages('dplyr') +if (!require('ggplot2')) install.packages('ggplot2') ``` ## Example @@ -32,7 +34,148 @@ Republic province map: ``` r library(sfDR) +library(dplyr) -# Plotting the map of a specific province -plot(DR_PROV) +DR_PROV_SF <- sf::st_as_sf(DR_PROV) + +plot(DR_PROV_SF) +``` + + + +Suppose you have a dataset showing the percentage distribution of +remittances received in each province of the Dominican Republic for a +specific year. To visualize this data using a graph with sfDR, you first +need to combine the metadata with the simple features. + +``` r +DR_PROV_SF <- DR_PROV_SF |> + left_join(dr_province, by = join_by(PROV_ID)) + +DR_PROV_SF %>% + sf::st_drop_geometry() +#> PROV_ID PROV_CODE PROV_NAME +#> 1 21 SC San Cristóbal +#> 2 14 MTS María Trinidad Sánchez +#> 3 17 PER Peravia +#> 4 20 SAM Samaná +#> 5 18 PP Puerto Plata +#> 6 11 LA La Altagracia +#> 7 07 EP Elías Piña +#> 8 10 IND Independencia +#> 9 16 PED Pedernales +#> 10 03 BAH Baoruco +#> 11 03 BAH Bahoruco +#> 12 04 BAR Barahona +#> 13 26 SRO Santiago Rodríguez +#> 14 27 VAL Valverde +#> 15 19 HMI Hermanas Mirabal +#> 16 19 SAL Salcedo +#> 17 24 SRA Sánchez Ramírez +#> 18 13 LV La Vega +#> 19 28 MN Monseñor Nouel +#> 20 31 SJO San José de Ocoa +#> 21 22 SJ San Juan +#> 22 22 SJ San Juan de la Maguana +#> 23 02 AZU Azua +#> 24 25 SAN Santiago +#> 25 25 SAN Santiago de los Caballeros +#> 26 06 DUA Duarte +#> 27 09 ESP Espaillat +#> 28 29 MP Monte Plata +#> 29 30 HMA Hato Mayor +#> 30 12 LR La Romana +#> 31 23 SPM San Pedro de Macorís +#> 32 08 ES El Seibo +#> 33 08 ES El Seybo +#> 34 01 DN Distrito Nacional +#> 35 05 DAJ Dajabón +#> 36 32 SD Santo Domingo +#> 37 15 MC Monte Cristi ``` + +The original simple features dataset only includes the PROV_ID variable. +Additional variables are incorporated through the dr_province object. + +``` r + datos <- DR_PROV_SF |> + left_join(remesas_provincias, by = join_by('PROV_NAME' == 'province')) + +datos %>% + sf::st_drop_geometry() +#> PROV_ID PROV_CODE PROV_NAME percentage +#> 1 21 SC San Cristóbal NA +#> 2 14 MTS María Trinidad Sánchez NA +#> 3 17 PER Peravia 0.005948295 +#> 4 20 SAM Samaná NA +#> 5 18 PP Puerto Plata 0.018996422 +#> 6 11 LA La Altagracia NA +#> 7 07 EP Elías Piña NA +#> 8 10 IND Independencia NA +#> 9 16 PED Pedernales NA +#> 10 03 BAH Baoruco NA +#> 11 03 BAH Bahoruco NA +#> 12 04 BAR Barahona NA +#> 13 26 SRO Santiago Rodríguez NA +#> 14 27 VAL Valverde 0.004422258 +#> 15 19 HMI Hermanas Mirabal NA +#> 16 19 SAL Salcedo NA +#> 17 24 SRA Sánchez Ramírez NA +#> 18 13 LV La Vega 0.014144396 +#> 19 28 MN Monseñor Nouel NA +#> 20 31 SJO San José de Ocoa NA +#> 21 22 SJ San Juan NA +#> 22 22 SJ San Juan de la Maguana NA +#> 23 02 AZU Azua NA +#> 24 25 SAN Santiago 0.039947667 +#> 25 25 SAN Santiago de los Caballeros NA +#> 26 06 DUA Duarte 0.008876070 +#> 27 09 ESP Espaillat 0.004489833 +#> 28 29 MP Monte Plata NA +#> 29 30 HMA Hato Mayor NA +#> 30 12 LR La Romana 0.008441885 +#> 31 23 SPM San Pedro de Macorís NA +#> 32 08 ES El Seibo NA +#> 33 08 ES El Seybo NA +#> 34 01 DN Distrito Nacional 0.090322521 +#> 35 05 DAJ Dajabón NA +#> 36 32 SD Santo Domingo 0.098682428 +#> 37 15 MC Monte Cristi NA +``` + +It’s important to note that non-ASCII characters can become corrupted +when stored in the package, which may affect the merging of the data +sets. + +> We recommend cleaning any errors before performing the merge. We will +> soon release another package with functions designed to facilitate +> this process. + +``` r +datos |> + select(percentage) %>% + plot() +``` + + + +To get the desired graph, just apply the plot function to your data. + +Next, we show how to achieve a similar result using ggplot2. This serves +to demonstrate how to create maps with both libraries and to highlight +the simplicity of the process by integrating everything in one step. + +``` r +library(ggplot2) + +DR_PROV %>% + sf::st_as_sf() %>% + left_join(dr_province) %>% + left_join(remesas_provincias, by = join_by('PROV_NAME' == 'province')) %>% + ggplot() + + geom_sf(aes(fill = percentage)) + + theme_void() +#> Joining with `by = join_by(PROV_ID)` +``` + + diff --git a/data-raw/Remesas_PR.xlsx b/data-raw/Remesas_PR.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..b773f4a8c1f0a06fed5c895b5094432b3c1c6dc2 GIT binary patch literal 12345 zcmb_?WmFtX+b! zFGn*MeMV0^+Xl5!g&)i)&&flYNzJ=~DAp_Bv1Dps0^LiQ0~IE^>vmReIWgcc>f*Gd z9eqyb?&b?4-i=Fc)k`8G_`VD!^I{mAZp;fc)|Zc0N(ND2(q4sn7fMn5d0Jx@OiwUc zj(|a(da+9hnK=emxJr~Bwwd5Lnk_q>;j$YYfRKR#rKSqB;%0e~f#{fp$5dHN;gNlB9R%l)P%OKc-=8f8sDWpQ`9ayBWke8g}ocm7+92cdQ z`Bqg>=r<+X32-K0X5S$5Ce*zEJZ3K_?&WCw6<^hNCx(B;EjW-=RYqr12sy-W1YA#k 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b/data-raw/remesas_provincias.R new file mode 100644 index 0000000..9bc818e --- /dev/null +++ b/data-raw/remesas_provincias.R @@ -0,0 +1,11 @@ +library(dplyr) +library(tidyr) + +remesas_provincias <- readxl::read_excel('data-raw/Remesas_PR.xlsx', skip = 6) %>% + select(1:2) %>% + drop_na() %>% + setNames(c('province', 'percentage')) %>% + filter(percentage != 1) + + +usethis::use_data(remesas_provincias, overwrite = TRUE) diff --git a/data/remesas_provincias.rda b/data/remesas_provincias.rda new file mode 100644 index 0000000000000000000000000000000000000000..c37a7ae8205bae69362f9c4652b4a270df3c5e36 GIT binary patch literal 507 zcmVZ9oQqWHivw9;Sz)8RVf6r97$a z6V)~*rk;=(fB*mu00000&^=6^pa-Y`0LYY&)QpX&&@=!50MG+K^#*`6&;SEK0001> zOt}7CWuBn`MMOZy6@luApRu2lD1oWd^wtti4qB33b_jVY*2O@X&NYt_vUD;}fNBsY zK_LMq0)V73RA!(AzCt`uYoH`S2sF@#Z4@2E1XfKzjmz&Ia1;S_q9N}=d4C@s(Vg8t zxxqe^{V61FGkI9Dm}`Z)(25MNCM8J_`_wbXiwRMbah%71TD4>A6yyl}40ssLP3_Gw 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