updates to examples

Author: thegargiulian

R/combine_estimates.R

@@ -26,7 +26,7 @@
 #' \insertRef{BDA3}{verdata}
 #'
 #' @examples
-#' \dontrun{
+#' \donttest{
 #' set.seed(19481210)
 #'
 #' library(dplyr)

R/filter_standard_cev.R

@@ -18,61 +18,61 @@
 #' @importFrom dplyr %>%
 #'
 #' @examples
-#' \dontrun{
+#' \donttest{
 #' local_dir <- system.file("extdata", "right", package = "verdata")
-#' replicates_data <- read_replicates(local_dir, "reclutamiento", 1, 2)
+#' replicates_data <- read_replicates(local_dir, "reclutamiento", c(1, 2), version = "v1")
 #' filter_standard_cev(replicates_data, "reclutamiento", perp_change = TRUE)
 #' }
 filter_standard_cev <- function(replicates_data, violation, perp_change = TRUE) {
 
-  if (!is.data.frame(replicates_data)) {
-    stop("The argument 'replicates_data' must be a data frame")
-  }
+    if (!is.data.frame(replicates_data)) {
+        stop("The argument 'replicates_data' must be a data frame")
+    }
 
     if (!(violation %in% c("homicidio", "secuestro", "reclutamiento", "desaparicion"))) {
 
-      stop("Violation argument incorrectly specified. Please put any of the following
+        stop("Violation argument incorrectly specified. Please put any of the following
          violations (in quotes and in lower case): homicidio, secuestro,
          reclutamiento or desaparicion")
 
     }
 
     data_filter <- replicates_data %>%
         dplyr::mutate(edad_minors = dplyr::case_when(edad_jep == "INFANCIA" ~ "MENOR",
-                                                edad_jep == "ADOLESCENCIA" ~ "MENOR",
-                                                edad_jep == "ADULTEZ" ~ "ADULTO",
+                                                     edad_jep == "ADOLESCENCIA" ~ "MENOR",
+                                                     edad_jep == "ADULTEZ" ~ "ADULTO",
+                                                     TRUE ~ NA_character_)) %>%
+        dplyr::mutate(edad_minors_imputed = dplyr::if_else(edad_jep_imputed == FALSE, FALSE, TRUE)) %>%
+        dplyr::mutate(etnia2 = dplyr::case_when(etnia %in% c('MESTIZO') ~ "MESTIZO",
+                                                etnia %in% c('INDIGENA','NARP','ROM') ~ "ETNICO",
                                                 TRUE ~ NA_character_)) %>%
-      dplyr::mutate(edad_minors_imputed = dplyr::if_else(edad_jep_imputed == FALSE, FALSE, TRUE)) %>%
-      dplyr::mutate(etnia2 = dplyr::case_when(etnia %in% c('MESTIZO') ~ "MESTIZO",
-                                       etnia %in% c('INDIGENA','NARP','ROM') ~ "ETNICO",
-                                       TRUE ~ NA_character_)) %>%
-      dplyr::mutate(etnia2 = as.character(etnia2)) %>%
-      dplyr::mutate(etnia2_imputed = dplyr::if_else(etnia_imputed == FALSE, FALSE, TRUE)) %>%
-      dplyr::mutate(quinquenio = dplyr::case_when(yy_hecho >= 1985 & yy_hecho <= 1989 ~ "1985_1989",
-                                                  yy_hecho >= 1990 & yy_hecho <= 1994 ~ "1990_1994",
-                                                  yy_hecho >= 1995 & yy_hecho <= 1999 ~ "1995_1999",
-                                                  yy_hecho >= 2000 & yy_hecho <= 2004 ~ "2000_2004",
-                                                  yy_hecho >= 2005 & yy_hecho <= 2009 ~ "2005_2009",
-                                                  yy_hecho >= 2010 & yy_hecho <= 2014 ~ "2010_2014",
-                                                  yy_hecho >= 2015 & yy_hecho <= 2019 ~ "2015_2019",
-                                                  TRUE ~ NA_character_)) %>%
-      dplyr::mutate(muni_code_hecho = as.character(muni_code_hecho)) %>%
-      dplyr::mutate(dplyr::across(muni_code_hecho,
-                    ~ dplyr::case_when(. == "91236" ~ "91263",
-                                TRUE ~ .))) %>%
-      dplyr::mutate(muni_code_hecho = as.numeric(muni_code_hecho)) %>%
-      assertr::verify(!is.na(quinquenio))
+        dplyr::mutate(etnia2 = as.character(etnia2)) %>%
+        dplyr::mutate(etnia2_imputed = dplyr::if_else(etnia_imputed == FALSE, FALSE, TRUE)) %>%
+        dplyr::mutate(quinquenio = dplyr::case_when(yy_hecho >= 1985 & yy_hecho <= 1989 ~ "1985_1989",
+                                                    yy_hecho >= 1990 & yy_hecho <= 1994 ~ "1990_1994",
+                                                    yy_hecho >= 1995 & yy_hecho <= 1999 ~ "1995_1999",
+                                                    yy_hecho >= 2000 & yy_hecho <= 2004 ~ "2000_2004",
+                                                    yy_hecho >= 2005 & yy_hecho <= 2009 ~ "2005_2009",
+                                                    yy_hecho >= 2010 & yy_hecho <= 2014 ~ "2010_2014",
+                                                    yy_hecho >= 2015 & yy_hecho <= 2019 ~ "2015_2019",
+                                                    TRUE ~ NA_character_)) %>%
+        dplyr::mutate(muni_code_hecho = as.character(muni_code_hecho)) %>%
+        dplyr::mutate(dplyr::across(muni_code_hecho,
+                                    ~ dplyr::case_when(. == "91236" ~ "91263",
+                                                       TRUE ~ .))) %>%
+        dplyr::mutate(muni_code_hecho = as.numeric(muni_code_hecho)) %>%
+        assertr::verify(!is.na(quinquenio))
 
     if (perp_change == TRUE) {
 
-      data_filter <- data_filter %>%
-      dplyr::mutate(p_str = as.character(p_str)) %>%
-        dplyr::mutate(p_str = base::ifelse(yy_hecho > 2016 & p_str == "GUE-FARC",
-                                           "GUE-OTRO", p_str))
+        data_filter <- data_filter %>%
+            dplyr::mutate(p_str = as.character(p_str)) %>%
+            dplyr::mutate(p_str = base::ifelse(yy_hecho > 2016 & p_str == "GUE-FARC",
+                                               "GUE-OTRO", p_str))
 
     } else {
 
-      logger::log_info("Not change in perp's argument")
+        logger::log_info("Not change in perp's argument")
 
     }
 
@@ -111,19 +111,19 @@ filter_standard_cev <- function(replicates_data, violation, perp_change = TRUE)
                 is_forced_dis == 1 & is_conflict == 1 ~ 1))
 
     } else if (violation == "reclutamiento") {
-      # apply additional filters for reclutamiento
-      data_filter <- data_filter %>%
-        dplyr::mutate(periodo_pres = dplyr::case_when(yy_hecho >= 1990 & yy_hecho <= 1993 ~ "1990_1993",
-                                                      yy_hecho >= 1994 & yy_hecho <= 1997 ~ "1994_1997",
-                                                      yy_hecho >= 1998 & yy_hecho <= 2001 ~ "1998_2001",
-                                                      yy_hecho >= 2002 & yy_hecho <= 2005 ~ "2002_2005",
-                                                      yy_hecho >= 2006 & yy_hecho <= 2009 ~ "2006_2009",
-                                                      yy_hecho >= 2010 & yy_hecho <= 2013 ~ "2010_2013",
-                                                      yy_hecho >= 2014 & yy_hecho <= 2017 ~ "2014_2017",
-                                                      TRUE ~ NA_character_)) %>%
-        assertr::verify(!is.na(periodo_pres)) %>%
-        dplyr::filter(edad_jep == "INFANCIA" |
-                      edad_jep == "ADOLESCENCIA")
+        # apply additional filters for reclutamiento
+        data_filter <- data_filter %>%
+            dplyr::mutate(periodo_pres = dplyr::case_when(yy_hecho >= 1990 & yy_hecho <= 1993 ~ "1990_1993",
+                                                          yy_hecho >= 1994 & yy_hecho <= 1997 ~ "1994_1997",
+                                                          yy_hecho >= 1998 & yy_hecho <= 2001 ~ "1998_2001",
+                                                          yy_hecho >= 2002 & yy_hecho <= 2005 ~ "2002_2005",
+                                                          yy_hecho >= 2006 & yy_hecho <= 2009 ~ "2006_2009",
+                                                          yy_hecho >= 2010 & yy_hecho <= 2013 ~ "2010_2013",
+                                                          yy_hecho >= 2014 & yy_hecho <= 2017 ~ "2014_2017",
+                                                          TRUE ~ NA_character_)) %>%
+            assertr::verify(!is.na(periodo_pres)) %>%
+            dplyr::filter(edad_jep == "INFANCIA" |
+                              edad_jep == "ADOLESCENCIA")
     }
 
     return(data_filter)

R/summary_observed.R

@@ -16,7 +16,7 @@
 #' `summary_observed`.
 #' @export
 #' @examples
-#' \dontrun{
+#' \donttest{
 #' local_dir <- system.file("extdata", "right", package = "verdata")
 #' replicates_data <- read_replicates(local_dir, "reclutamiento", c(1, 2), version = "v1")
 #' tab_observed <- summary_observed("reclutamiento", replicates_data,

README.md

@@ -81,7 +81,7 @@ Para el uso de este paquete es necesario haber descargado previamente los datos
 
 ### Datos imputados:
 
-* La función `combine_replicates` usa la aproximación normal usando las reglas de total expectativa y varianza para combinar las réplicas, lo que permite obtener un intervalo de la imputación. Ver sección 18.2 de [*Bayesian Data Analysis*](https://www.stat.columbia.edu/~gelman/book/) para más información.
+* La función `combine_replicates` usa la aproximación normal usando las reglas de total expectativa y varianza para combinar las réplicas, lo que permite obtener un intervalo de la imputación. Ver sección 18.2 de [*Bayesian Data Analysis*](https://sites.stat.columbia.edu/gelman/book/) para más información.
 
 ### Datos estimados:
 

inst/docs/README-en.md

@@ -82,7 +82,7 @@ To use this package, it is necessary to have previously downloaded the data from
 
 ### Imputed data
 
-* The `combine_replicates` function uses the Normal approximation using the laws of total expectation and variance to combine the replicates, yielding a 95% confidence interval and a point estimate of the mean number of documented victims taking the imputation uncertainty into consideration. See Section 18.2 of [*Bayesian Data Analysis*](http://www.stat.columbia.edu/~gelman/book/) for more information.
+* The `combine_replicates` function uses the Normal approximation using the laws of total expectation and variance to combine the replicates, yielding a 95% confidence interval and a point estimate of the mean number of documented victims taking the imputation uncertainty into consideration. See Section 18.2 of [*Bayesian Data Analysis*](https://sites.stat.columbia.edu/gelman/book/) for more information.
 
 ### Estimates