Merge pull request #40 from getwilds/documentation

modified documentation

NAMESPACE

@@ -1,7 +1,7 @@
 # Generated by roxygen2: do not edit by hand
 
 export("%>%")
-export(demo_crowding)
+export(area)
 export(demo_education)
 export(demo_food)
 export(demo_income)
@@ -19,7 +19,7 @@ export(risk_colorectal_screening)
 export(risk_diet_exercise)
 export(risk_smoking)
 export(risk_vaccines)
-export(risk_whealth)
+export(risk_women_health)
 importFrom(cdlTools,fips)
 importFrom(cli,cli_abort)
 importFrom(dplyr,filter)

R/demo-crowding.R

@@ -34,6 +34,12 @@
 #'               areatype = "hsa",
 #'               race = "black")
 #' }
+#' 
+
+area = "wa"
+areatype = "county"
+race = "all races (includes hispanic)"
+
 demo_crowding <- function(area, areatype, race) {
 
   crowding = "00027"

R/handle-screening.R

@@ -27,7 +27,7 @@ handle_screening <- function(screening) {
     "had colonoscopy in past 10 years, ages 50-75" = "v302",
 
     "home blood stool test in the past year, ages 45-75" = "v520",
-    "receieved at least one recommended crc test, ages 45-75" = "v521"
+    "received at least one recommended crc test, ages 45-75" = "v521"
 
     #removed from scp
     # "ever had colorectal endoscopy (sigmoidoscopy or colonoscopy), ages 50+" = "v09",

R/handle-whealth.R → R/handle-women_health.R

@@ -2,7 +2,7 @@
 #' 
 #' This function returns a matching code value for a Women's Health for the api to use to get data from State Cancer Profiles
 #'
-#' @param whealth Either "mammogram in past 2 years, ages 50-74", "mammogram in past 2 years, ages 40+",
+#' @param women_health Either "mammogram in past 2 years, ages 50-74", "mammogram in past 2 years, ages 40+",
 #'                       "pap smear in past 3 years, no hysterectomy, ages 21-65",
 #'                       "pap smear in past 3 years, no hysterectomy, ages 18+"
 #'                       
@@ -16,8 +16,8 @@
 #' \dontrun{
 #' handle_whealth("mammogram in past 2 years, ages 50-74")
 #' }
-handle_whealth <- function(whealth) {
-  whealth <- tolower(whealth)
+handle_women_health <- function(women_health) {
+  women_health <- tolower(women_health)
 
   whealth_mapping <- c(
     "mammogram in past 2 years, ages 50-74" = "v05",
@@ -29,14 +29,11 @@ handle_whealth <- function(whealth) {
     # "pap smear in past 3 years, no hysterectomy, ages 18+" = "v11"
   )
 
-  whealth_code <- whealth_mapping[whealth]
+  whealth_code <- whealth_mapping[women_health]
 
   if (is.na(whealth_code)) {
     stop("Invalid womens health input, please check the documentation for valid inputs")
   }
 
   return(as.character(whealth_code))
 }
-
-
-handle_whealth("pap smear in past 3 years, no hysteroetomy, ages 21-65")

R/incidence-cancer.R

@@ -17,7 +17,7 @@
 #' @param stage Either "all stages" or "late stage (regional & distant)"
 #' @param year Either "latest 5 year average", "latest single year (us by state)"
 #' 
-#' @returns A data frame with the following columns Areatype, Area Code, "Age Adjusted Incidence Rate", "Lower 95% CI",
+#' @returns A data frame with the following columns: Area Type, Area Code, "Age Adjusted Incidence Rate", "Lower 95% CI",
 #'          "Upper 95% CI", "CI Rank", "Lower CI Rank", "Upper CI Rank", "Annual Average Count", "Recent Trend", 
 #'          "Recent 5 Year Trend", "Trend Lower 95% CI", "Trend Upper 95% CI"
 #' 

R/mortality-cancer.R

@@ -18,7 +18,7 @@
 #' @param age Either "all ages", "ages <50", "ages 50+", "ages <65", "ages 65+", ages <15, ages <20
 #' @param year Either "latest 5 year average", "latest single year (us by state)"
 #' 
-#' @returns A data frame with the following columns Areatype, Area Code, "Met Healthy People Objective of ***?",
+#' @returns A data frame with the following columns: Area Type, Area Code, "Met Healthy People Objective of ***?",
 #'          "Age Adjusted Death Rate", "Lower 95% CI Rate", "Upper 95% CI Rate", 
 #'          "CI Rank", "Lower CI Rank", "Upper CI Rank", "Annual Average Count", 
 #'          "Recent Trend", "Recent 5 Year Trend", "Lower 95% CI Trend", "Upper 95% CI Trend"

R/process-response.R

@@ -34,7 +34,6 @@ process_response <- function(resp) {
     paste(collapse = "\n") %>% 
     (\(x) read.csv(textConnection(x), header=TRUE, colClasses = "character"))()
 
-
   column <- c("Health.Service.Area", "County", "State")[c("Health.Service.Area", "County", "State") %in% colnames(resp)]
 
   resp <- resp %>% 

R/risk-alcohol.R

@@ -8,15 +8,16 @@
 #'              "asian or pacific islander (includes hispanic)","hispanic (any race)
 #' @param sex Either "both sexes", "male", "female"
 #' 
-#' @returns A data frame with the following columns "State", "FIPS", "Percent", "Lower 95% CI", "Upper 95% CI", "Number of Respondents"
+#' @returns A data frame with the following columns: Area Type, Area Code, "Percent", "Lower 95% CI", "Upper 95% CI", "Number of Respondents"
 #' 
 #' @export
 #' 
 #' @examples
 #' \dontrun{
-#' risk_alcohol(paste("binge drinking (4+ drinks on one occasion for women,", 
-#'                    "5+ drinks for one occasion for men), ages 21+"), 
-#'                    "all races (includes hispanic)", "both sexes")
+#' risk_alcohol(alcohol = paste("binge drinking (4+ drinks on one occasion for women,", 
+#'                              "5+ drinks for one occasion for men), ages 21+"), 
+#'              race = "all races (includes hispanic)", 
+#'              sex = "both sexes")
 #' }
 risk_alcohol <- function(alcohol, race, sex) {
 

R/risk-colorectal-screening.R

@@ -2,41 +2,46 @@
 #' 
 #' This function returns a data frame from Colorectal Screening in State Cancer Profiles
 #'
-#' @param screening One of the following values: "home blood stool test in the past year, ages 45-75",
-#'                                               "receieved at least one recommended crc test, ages 45-75",
-#'                                               "ever had fobt, ages 50-75",
+#' @param screening One of the following values: "ever had fobt, ages 50-75",
 #'                                               "guidance sufficient crc, ages 50-75",
 #'                                               "had colonoscopy in past 10 years, ages 50-75"
+#'                                               "home blood stool test in the past year, ages 45-75"
+#'                                               "received at least one recommended crc test, ages 45-75"
 #' @param race One of the following values: "All Races (includes Hispanic)", "white (includes hispanic)",
 #'              "white non-hispanic","black","amer. indian/alaskan native (includes hispanic)",
 #'              "asian or pacific islander (includes hispanic)","hispanic (any race)
 #' @param sex Either "both sexes", "male", "female"
 #' @param area A state/territory abbreviation or USA.
 #' 
-#' @returns A data frame with the following columns "County", "FIPS", "Percent", "People Unemployed", "Rank"
+#' @returns A data frame with the following columns: Area Type, Area Code, "Percent", "People Unemployed", "Rank"
 #' 
 #' @export
 #' 
 #' @examples
 #' \dontrun{
-#' risk_colorectal_screening("home blood stool test in the past year, ages 45-75",
-#'                           "all races (includes hispanic)","both sexes")
-#' risk_colorectal_screening("ever had fobt, ages 50-75", area="usa")
-#' risk_colorectal_screening("ever had fobt, ages 50-75", area="wa")
+#' risk_colorectal_screening(screening = "home blood stool test in the past year, ages 45-75",
+#'                           race = "all races (includes hispanic)",
+#'                           sex = "both sexes")
+#'                           
+#' risk_colorectal_screening(screening = "ever had fobt, ages 50-75",
+#'                           area="usa")
+#' risk_colorectal_screening(screening = "received at least one recommended crc test, ages 45-75",
+#'                           race = "all races (includes hispanic)",
+#'                           sex = "both sexes")
 #' }
 risk_colorectal_screening <- function(screening, race=NULL, sex=NULL, area=NULL) {
 
   req <- create_request("risk")
 
   screening_type_1 = c("home blood stool test in the past year, ages 45-75",
-                       "receieved at least one recommended crc test, ages 45-75")
+                       "received at least one recommended crc test, ages 45-75")
 
   screening_type_2 = c("ever had fobt, ages 50-75",
                        "guidance sufficient crc, ages 50-75",
                        "had colonoscopy in past 10 years, ages 50-75")
 
-  if (screening %in% screening_type_1 && (is.null(race) || is.null(sex))) {
-    cli_abort("For this screening type, Race and Sex must not be NULL")
+  if (screening %in% screening_type_1 && ((is.null(race) || is.null(sex)) || !is.null(area))) {
+    cli_abort("For this screening type, Race and Sex must not be NULL, and Area must be NULL")
   } else if (screening %in% screening_type_2 && (is.null(area) || (!is.null(race) || !is.null(sex)))) {
     cli_abort("for this screening type, area must NOT be NULL and Race and Sex must be NULL")
   }

R/risk-diet-exercise.R

@@ -11,14 +11,16 @@
 #'              "asian or pacific islander (includes hispanic)","hispanic (any race)
 #' @param sex Either "both sexes", "male", "female"
 #' 
-#' @returns A data frame with the following columns "State", "FIPS", "Percent", "Lower 95% CI", "Upper 95% CI", "Number of Respondents"
+#' @returns A data frame with the following columns: Area Type, Area Code, "Percent", "Lower 95% CI", "Upper 95% CI", "Number of Respondents"
 #' 
 #' @export
 #' 
 #' @examples
 #' \dontrun{
-#' risk_diet_exercise("bmi is healthy, ages 20+", "all races (includes hispanic)", "both sexes")
-#' risk_diet_exercise("bmi is obese, high school survey", "all races (includes hispanic)", "males")
+#' risk_diet_exercise(diet_exercise = "bmi is healthy, ages 20+",
+#'                    race = "all races (includes hispanic)", 
+#'                    sex = "both sexes")
+#' risk_diet_exercise(diet_exer"bmi is obese, high school survey", "all races (includes hispanic)", "males")
 #' }
 risk_diet_exercise <- function(diet_exercise, race, sex) {
 

R/risk-smoking.R

@@ -1,6 +1,8 @@
 #' Access to Smoking Data
 #' 
 #' This function returns a data frame from Smoking in State Cancer Profiles
+#' 
+#' Please note that this function requires very specific arguments for each smoking type
 #'
 #' @param smoking Either "smoking laws (any)", "smoking laws (bars)", "smoking laws (restaurants)", 
 #'                       "smoking laws (workplace)", "smoking laws (workplace; restaurant; & bar)", 
@@ -21,23 +23,28 @@
 #' @importFrom cli cli_abort
 #' @importFrom stats setNames
 #' 
-#' @returns A data frame with the following columns #"State", "FIPS", "Percent", "Lower CI 95%", "Upper CI 95%", "Number of Respondents"
+#' @returns A data frame with the following columns: Area Type, Area Code, "Percent", "Lower CI 95%", "Upper CI 95%", "Number of Respondents"
 #' 
 #' @export
 #' 
 #' @examples
 #' \dontrun{
-#' risk_smoking("smoking laws (any)")
-#' risk_smoking("smokers (stopped for 1 day or longer)", sex="both sexes", 
-#'               datatype="county level modeled estimates", area="wa")
-#' risk_smoking("smoking not allowed at work (current smokers)", sex="both sexes", 
-#'               datatype="direct estimates")
-#' risk_smoking("former smoker; ages 18+", sex="both sexes", 
-#'               datatype="county level modeled estimates", area="ca")
-#' risk_smoking("smokers (ever); ages 18+", race="hispanic (any race)", sex="both sexes", 
-#'               datatype="direct estimates")
-#' risk_smoking("smokers (current); ages 18+", race="all races (includes hispanic)", 
-#'               sex="both sexes", datatype="county level modeled estimates", area="wa")
+#' risk_smoking(smoking = "smoking laws (any)")
+#' 
+#' risk_smoking(smoking = "smokers (stopped for 1 day or longer)",
+#'              sex = "both sexes", 
+#'              datatype = "county level modeled estimates",
+#'              area = "wa")
+#'              
+#' risk_smoking(smoking = "smoking not allowed at work (current smokers)",
+#'              sex = "both sexes", 
+#'              datatype = "direct estimates")
+#'              
+#' risk_smoking(smoking = "smokers (current); ages 18+",
+#'              race = "all races (includes hispanic)",
+#'              sex = "both sexes",
+#'              datatype = "county level modeled estimates",
+#'              area="wa")
 #' }
 risk_smoking <- function(smoking, race=NULL, sex=NULL, datatype=NULL, area=NULL) {
 

R/risk-vaccines.R

@@ -6,14 +6,17 @@
 #'                       "percent with up to date hpv vaccination coverage, ages 13-17"
 #' @param sex Either "both sexes", "males", "females"
 #' 
-#' @returns A data frame with the following columns "State", "FIPS", "Percent", "Lower 95% CI", "Upper 95% CI", "Number of Respondents"
+#' @returns A data frame with the following columns: Area Type, Area Code, "Percent", "Lower 95% CI", "Upper 95% CI", "Number of Respondents"
 #' 
 #' @export
 #' 
 #' @examples
 #' \dontrun{
-#' risk_vaccines("percent with up to date hpv vaccination coverage, ages 13-15", "both sexes")
-#' risk_vaccines("percent with up to date hpv vaccination coverage, ages 13-17", "females")
+#' risk_vaccines(vaccine = "percent with up to date hpv vaccination coverage, ages 13-15",
+#'               sex = "both sexes")
+#'               
+#' risk_vaccines(vaccine = "percent with up to date hpv vaccination coverage, ages 13-17",
+#'               sex = "females")
 #' }
 risk_vaccines <- function(vaccine, sex) {
 

R/risk-womens-health.R

@@ -2,27 +2,33 @@
 #' 
 #' This function returns a data frame from Women's Health in State Cancer Profiles
 #'
-#' @param whealth Either "mammogram in past 2 years, ages 50-74", "mammogram in past 2 years, ages 40+", 
+#' @param women_health Either "mammogram in past 2 years, ages 50-74", "mammogram in past 2 years, ages 40+", 
 #'                       "pap smear in past 3 years, no hysterectomy, ages 21-65"
 #' @param race One of the following values: "all races (includes hispanic)", "white (non-hispanic)", 
 #'                                          "black (non-hispanic)", "amer. indian / ak native (non-hispanic)", 
 #'                                          "asian / pacific islander (non-hispanic)","hispanic (any race)"
 #' @param datatype Either "direct estimates" or "county level modeled estimates"
 #' @param area A state/territory abbreviation or USA.
 #' 
-#' @returns A data frame with the following columns "County", "FIPS", "Percent", "People Unemployed", "Rank"
+#' @returns A data frame with the following columns: Area Type, Area Code, "Percent", "People Unemployed", "Rank"
 #' 
 #' @export
 #' 
 #' @examples
 #' \dontrun{
-#' risk_whealth("mammogram in past 2 years, ages 50-74", 
-#'              "all races (includes hispanic)", "direct estimates")
-#' risk_whealth("pap smear in past 3 years, no hysterectomy, ages 21-65", 
-#'              "all races (includes hispanic)", "county level modeled estimates", "wa")
-#' risk_whealth("pap smear in past 3 years, no hysteroetomy, ages 21-65", "black (non-hispanic)")
+#' risk_women_health(women_health = "mammogram in past 2 years, ages 50-74", 
+#'                   race = "all races (includes hispanic)",
+#'                   datatype = "direct estimates")
+#'                   
+#' risk_women_health(women_health = "pap smear in past 3 years, no hysterectomy, ages 21-65", 
+#'                   race = "all races (includes hispanic)",
+#'                   datatype = "county level modeled estimates",
+#'                   area = "wa")
+#'                   
+#' risk_women_health(women_health = "pap smear in past 3 years, no hysteroetomy, ages 21-65",
+#'                   race = "black (non-hispanic)")
 #' }
-risk_whealth <- function(whealth, race, datatype="direct estimates", area=NULL) {
+risk_women_health <- function(women_health, race, datatype="direct estimates", area=NULL) {
 
   req <- create_request("risk")
 
@@ -41,7 +47,7 @@ risk_whealth <- function(whealth, race, datatype="direct estimates", area=NULL)
   resp <- req %>%
     req_url_query(
       topic="women",
-      risk=handle_whealth(whealth),
+      risk=handle_women_health(women_health),
       race=handle_race(race),
       type="risk",
       sortVariableName="percent",
@@ -65,7 +71,7 @@ risk_whealth <- function(whealth, race, datatype="direct estimates", area=NULL)
   resp <- process_screening(resp)
 
   if (datatype == "county level modeled estimates") {
-    if(whealth == "pap smear in past 3 years, no hysterectomy, ages 21-65") {
+    if(women_health == "pap smear in past 3 years, no hysterectomy, ages 21-65") {
       resp %>% 
         setNames(c("State", "FIPS", "Percent", "Lower_95%_CI", "Upper_95%_CI", "Number_of_Respondents"))
     } else {