canadaHCDx Coverage

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##' @title Find a Historical Climate Data station (advanced)
##'
##' @description Search for stations in the Historical Climate Data inventory name, available data, and/or distance to a target.
##'
##' @param name character; optional character string or a regular expression to be matched against known station names. See \code{\link{grep}} for details.
##' @param ignore.case logical; by default the search for station names is not case-sensitive.
##' @param glob logical; use wildcards in station name as detailed in \code{link{glob2rx}}.
##' @param province character; optional character string to filter by a given province. Use full name or two-letter code, e.g. ON for Ontario.
##' @param baseline vector; optional vector with a start and end year for a desired baseline.
##' @param type character; type of data to search for. Only used if a baseline is specified. Defaults to "hourly".
##' @param target numeric; optional numeric value of the target (reference) station, or a vector of length 2 containing latitude and longitude (in that order).
##' @param dist numeric; vector with a range of distance from the target in km. Only used if a target is specified. (default is 0:100)
##' @param sort Boolean; if TRUE (default), will sort the resultant table by distance from `target`. Only used if a target is specified.
##' @param ... Additional arguments passed to \code{\link{grep}}.
##'
##' @return A \code{tbl_df}, containing details of any matching HCD stations.
##'
##' @importFrom geosphere distGeo
##' @importFrom utils glob2rx
##'
##' @export
##'
##' @author Conor I. Anderson
##'
##' @examples
##' # Find all stations with names beginning in  "Reg".
##' find_station_adv("Reg*", glob = TRUE)
##'
##' # Find stations named "Yellowknife", with hourly data available from 1971 to 2000.
##' find_station_adv("Yellowknife", baseline = c(1971, 2000), type = "hourly")
##'
##' # Find all stations between 0 and 100 km from Station No. 5051.
##' find_station_adv(target = 5051, dist = 0:100)
##'

`find_station_adv` <- function(name = NULL, ignore.case = TRUE, glob = FALSE, province = NULL, baseline = NULL, type = "daily", target = NULL, dist = 0:100, sort = TRUE, ...) {

  # If `name` is not NULL, filter by name
  if (!is.null(name)) {
    if (glob) {
      name <- glob2rx(name)
    }
    take <- grep(name, station_data$Name, ignore.case = ignore.case, ...)
  } else {
    take <- 1:nrow(station_data)
  }

  # Next, set the data we are interested in, if necessary
  if (!is.null(baseline)) {
    if (length(baseline) < 2 | baseline[1] > baseline[length(baseline)]) stop("error: check baseline format")
    if (type == "hourly") data_vars <- c("HourlyFirstYr", "HourlyLastYr")
    if (type == "daily") data_vars <- c("DailyFirstYr", "DailyLastYr")
    if (type == "monthly") data_vars <- c("MonthlyFirstYr", "MonthlyLastYr")
  } else {
    data_vars <- NULL
  }

  # Make a table with the info we want
  vars_wanted <- c("Name", "Province", "StationID", "LatitudeDD", "LongitudeDD", data_vars)
  df <- station_data[take, vars_wanted]

  # If `province` is not NULL, we will filter by province
  if (!is.null(province)) {
    # Identify all stations outside of our baseline
    if (nchar(province) == 2L) {
      province <- levels(as.factor(station_data$Province))[which(c("AB", "BC", "MB", "NB", "NL", "NT", "NS", "NU", "ON", "PE", "QC", "SK", "YT") == province)]
      if (length(province) == 0L) {
        stop("Incorrect province code provided.")
      }
    }
    province <- toupper(province)
    df <- df[df$Province == province,]
    if (nrow(df) == 0) {
      stop("No data found for that province. Did you spell it correctly?")
    }
  }

  # If `baseline` is not NULL, filter by available data
  if (!is.null(baseline)) {
    index = NULL
    # Identify all stations outside of our baseline
    for (i in 1:nrow(df)) {
      if (is.na(df[i,6]) | df[i,6] > baseline[1]) index <- c(index, i)
      else if (is.na(df[i,7]) | df[i,7] < baseline[length(baseline)]) index <- c(index, i)
    }
    # Delete those stations
    if (!is.null(index)) df <- df[-index,]
  }

  # If `target` is not NULL, filter by distance to target
  if (!is.null(target)) {
    if (length(target) == 1L) {
      p1 <- c(station_data$LongitudeDD[grep(paste0("\\b", as.character(target), "\\b"), station_data$StationID)], station_data$LatitudeDD[grep(paste0("\\b", as.character(target), "\\b"), station_data$StationID)])
    }
    else if (length(target) == 2L) {
      p1 <- c(target[2], target[1])
    } else stop("error: check target format")
    df$Dist <- rep(NA, nrow(df))
    for (j in 1:nrow(df)) {
      df$Dist[j] <- (distGeo(p1, c(df$LongitudeDD[j], df$LatitudeDD[j]))/1000)
    }
    df <- df[(!is.na(df$Dist) & (df$Dist >= min(dist)) & (df$Dist <= max(dist))),]
    if (sort == TRUE) df <- df[order(df$Dist),]
  }
  class(df) <- c("hcd_station_list", class(df))
  df
}

##' @title Map canadaHCD stations on an interactive map
##'
##' @description Show the stations of interest on an interactive map using Leaflet. Zoom levels are guessed based on an RStudio plot window.
##'
##' @param station character; one or more station ID numbers to show on the map.
##' @param zoom numeric; the level to zoom the map to.
##'
##' @importFrom leaflet "%>%" leaflet addTiles setView addMarkers
##' @importFrom tibble tibble
##'
##' @export
##'
##' @author Conor I. Anderson
##'
##' @examples
##' # Make a map of all the stations named "Yellowknife".
##' map_stations(find_station_adv(name = "Yellowknife"))
##' # Make a map of all stations within 50km of Toronto Station 5051.
##' map_stations(find_station_adv(target = 5051, dist = 0:50))

map_stations <- function(station, zoom) {

  if (inherits(station, "data.frame")) {
    station <- station$StationID
  }

  station <- as.character(station)

  poi <- NULL
  for (i in station) {
    row <- which(as.character(station_data$StationID) == i)
    if (!is.na(station_data$LatitudeDD[row]) && !is.na(station_data$LongitudeDD)[row]) {
      poi <- c(poi, row)
    }
  }
  poi <- station_data[poi,]

  if (nrow(poi) == 1L){
    lats <- poi$LatitudeDD
    lons <- poi$LongitudeDD
    latrng <- 0
  } else {
    hilat <- ceiling(max(poi$LatitudeDD))
    lolat <- floor(min(poi$LatitudeDD))
    hilon <- ceiling(max(poi$LongitudeDD))
    lolon <- floor(min(poi$LongitudeDD))
    lats <- (hilat + lolat)/2
    lons <- (hilon + lolon)/2
    latrng <- (hilat - lolat)
  }

  if (missing(zoom)) {
    zoomlevels <- tibble(range = c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16), zoom = c(10,8,7,7,7,6,6,6,5,5,5,5,5,5,5,5,4))
    zoom <- zoomlevels$zoom[which(zoomlevels$range == min(as.integer(latrng), 16L))]
  }

  leaflet() %>% addTiles() %>%
    setView(lng = lons, lat = lats, zoom = zoom)  %>%
    addMarkers(poi, lng = poi$LongitudeDD, lat = poi$LatitudeDD,
               popup = paste0(poi$StationID, " - ", poi$Name))
}

##' @title Perform a quick audit for missing values at a given station
##'
##' @description Returns a tibble with the proportion of missing values at a station for a given year and variables.
##'
##' @param datain A \code{tbl_df} containing the data to process
##' @param variables numeric or character; By default, all variables will be included. Pass a numeric vector to specify columns to include, or pass a character vector to try to match column names.
##' @param by character; whether values should be reported annually (\code{by = year}), or monthly (\code{by = month}).
##' @param reverse Boolean; if true, will show proportion present instead of proportion missing.
##'
##' @importFrom tibble tibble add_column
##' @importFrom zoo as.yearmon
##'
##' @export
##'
##' @author Conor I. Anderson
##'
##' @examples
##'
##' city <- hcd_daily(5051, 1981:2010)
##' quick_audit(city, "MaxTemp", by = "year", reverse = TRUE)
##' quick_audit(city, "MaxTemp", by = "month")
##'

quick_audit <- function(datain, variables, by = "year", reverse = FALSE) {

  dat <- datain

  if (missing(variables)) {
    variables <- 2:ncol(dat)
  } else {
    if (inherits(variables, "character")) {
      for (var in seq_along(variables)) {
        variables[var] <- as.numeric(grep(variables[var], names(dat), ignore.case = TRUE))
      }
    }
    variables <- as.numeric(variables)
  }

  if (reverse) {
    ctl = 1
  } else {
    ctl = 0
  }

  years <- min(format(dat$Date, format = "%Y")):max(format(dat$Date, format = "%Y"))

  if (by == "month") {
    months <- min(format(dat$Date, format = "%m")):max(format(dat$Date, format = "%m"))
    yearmons <- apply(expand.grid(sprintf("%02d", months), years), 1, function(x) paste(x[2], x[1], sep = "-"))

    out <- tibble(`Year-month` = yearmons)

    for (var in variables) {
      integrity <- NULL
      for (yearmon in yearmons) {
        colname <- colnames(dat)[var]
        yearmonly <- dat[format(dat$Date, format = "%Y-%m") == yearmon,]
        obs <- nrow(yearmonly)
        missing <- sum(is.na(yearmonly[[var]]))
        integrity <- c(integrity, abs(ctl-(missing/obs)))
      }
      out <- add_column(out, newcol = integrity)
      names(out)[which(names(out) == "newcol")] <- colname
    }
    out$`Year-month` <- as.yearmon(out$`Year-month`)
    out <- add_column(out, Year = format(out$`Year-month`, format = "%Y"), Month = format(out$`Year-month`, format = "%m"), .before = 1)
  } else {
    if (by == "year") {
      out <- tibble(Year = years)

      for (var in variables) {
        integrity <- NULL
        for (year in years) {
          colname <- colnames(dat)[var]
          yearly <- dat[format(dat$Date, format = "%Y") == year,]
          obs <- nrow(yearly)
          missing <- sum(is.na(yearly[[var]]))
          integrity <- c(integrity, abs(ctl-(missing/obs)))
        }
        out <- add_column(out, newcol = integrity)
        names(out)[which(names(out) == "newcol")] <- colname
      }
    } else {
      stop("You must choose by \"month\", or by \"year\".")
    }
  }

  return(out)
}

##' @title Find a Historical Climate Data station (GUI)
##'
##' @description A function to launch a shiny web app to search for Historical Climate Data stations.
##'
##' @param stations optional \code{tbl_df} of stations such as a search result from \code{find_station()}.
##'
##' @return none
##'
##' @author Conor I. Anderson
##'
##' @importFrom shiny br column em fluidPage fluidRow runApp selectInput shinyApp shinyUI titlePanel
##' @importFrom DT datatable dataTableOutput renderDataTable
##'
##' @export
##'
##' @examples
##' \dontrun{find_station_GUI()}

find_station_GUI <- function(stations = NULL) {

  if (is.null(stations)) {
    data <- station_data
  } else {
    data <- stations
  }

  app <- shinyApp(
    shinyUI(
      fluidPage(
        titlePanel("canadaHCD data inventory"),

        # Create a new Row in the UI for selectInputs
        fluidRow(
          column(4,
                 selectInput("prov",
                             "Province:",
                             c("All",
                               unique(as.character(data$Province))))
          )
        ),
        # Create a new row for the table.
        fluidRow(
          dataTableOutput("table")),
        fluidRow(br(em(comment(data))))
      )
    ),
    server = function(input, output) {
      output$table <- renderDataTable(datatable({
        if (input$prov != "All") {
          data <- data[data$Province == input$prov,]
        }
        data
      }))
    })
  runApp(app)
}

1	##' @title Find a Historical Climate Data station (advanced)
2	##'
3	##' @description Search for stations in the Historical Climate Data inventory name, available data, and/or distance to a target.
4	##'
5	##' @param name character; optional character string or a regular expression to be matched against known station names. See \code{\link{grep}} for details.
6	##' @param ignore.case logical; by default the search for station names is not case-sensitive.
7	##' @param glob logical; use wildcards in station name as detailed in \code{link{glob2rx}}.
8	##' @param province character; optional character string to filter by a given province. Use full name or two-letter code, e.g. ON for Ontario.
9	##' @param baseline vector; optional vector with a start and end year for a desired baseline.
10	##' @param type character; type of data to search for. Only used if a baseline is specified. Defaults to "hourly".
11	##' @param target numeric; optional numeric value of the target (reference) station, or a vector of length 2 containing latitude and longitude (in that order).
12	##' @param dist numeric; vector with a range of distance from the target in km. Only used if a target is specified. (default is 0:100)
13	##' @param sort Boolean; if TRUE (default), will sort the resultant table by distance from `target`. Only used if a target is specified.
14	##' @param ... Additional arguments passed to \code{\link{grep}}.
15	##'
16	##' @return A \code{tbl_df}, containing details of any matching HCD stations.
17	##'
18	##' @importFrom geosphere distGeo
19	##' @importFrom utils glob2rx
20	##'
21	##' @export
22	##'
23	##' @author Conor I. Anderson
24	##'
25	##' @examples
26	##' # Find all stations with names beginning in "Reg".
27	##' find_station_adv("Reg*", glob = TRUE)
28	##'
29	##' # Find stations named "Yellowknife", with hourly data available from 1971 to 2000.
30	##' find_station_adv("Yellowknife", baseline = c(1971, 2000), type = "hourly")
31	##'
32	##' # Find all stations between 0 and 100 km from Station No. 5051.
33	##' find_station_adv(target = 5051, dist = 0:100)
34	##'
35
36	`find_station_adv` <- function(name = NULL, ignore.case = TRUE, glob = FALSE, province = NULL, baseline = NULL, type = "daily", target = NULL, dist = 0:100, sort = TRUE, ...) {
37
38	# If `name` is not NULL, filter by name
39	if (!is.null(name)) {	13x
40	if (glob) {	5x
41	name <- glob2rx(name)	2x
42	}	5x
43	take <- grep(name, station_data$Name, ignore.case = ignore.case, ...)	5x
44	} else {	13x
45	take <- 1:nrow(station_data)	8x
46	}	13x
47
48	# Next, set the data we are interested in, if necessary
49	if (!is.null(baseline)) {	13x
50	if (length(baseline) < 2 \| baseline[1] > baseline[length(baseline)]) stop("error: check baseline format")	2x
51	if (type == "hourly") data_vars <- c("HourlyFirstYr", "HourlyLastYr")	2x
52	if (type == "daily") data_vars <- c("DailyFirstYr", "DailyLastYr")	2x
53	if (type == "monthly") data_vars <- c("MonthlyFirstYr", "MonthlyLastYr")	2x
54	} else {	13x
55	data_vars <- NULL	11x
56	}	13x
57
58	# Make a table with the info we want
59	vars_wanted <- c("Name", "Province", "StationID", "LatitudeDD", "LongitudeDD", data_vars)	13x
60	df <- station_data[take, vars_wanted]	13x
61
62	# If `province` is not NULL, we will filter by province
63	if (!is.null(province)) {	13x
64	# Identify all stations outside of our baseline
65	if (nchar(province) == 2L) {	3x
66	province <- levels(as.factor(station_data$Province))[which(c("AB", "BC", "MB", "NB", "NL", "NT", "NS", "NU", "ON", "PE", "QC", "SK", "YT") == province)]	2x
67	if (length(province) == 0L) {	2x
68	stop("Incorrect province code provided.")	1x
69	}	2x
70	}	3x
71	province <- toupper(province)	2x
72	df <- df[df$Province == province,]	2x
73	if (nrow(df) == 0) {	2x
74	stop("No data found for that province. Did you spell it correctly?")	1x
75	}	2x
76	}	13x
77
78	# If `baseline` is not NULL, filter by available data
79	if (!is.null(baseline)) {	11x
80	index = NULL	2x
81	# Identify all stations outside of our baseline
82	for (i in 1:nrow(df)) {	2x
83	if (is.na(df[i,6]) \| df[i,6] > baseline[1]) index <- c(index, i)	8741x
84	else if (is.na(df[i,7]) \| df[i,7] < baseline[length(baseline)]) index <- c(index, i)	8741x
85	}	2x
86	# Delete those stations
87	if (!is.null(index)) df <- df[-index,]	2x
88	}	11x
89
90	# If `target` is not NULL, filter by distance to target
91	if (!is.null(target)) {	11x
92	if (length(target) == 1L) {	4x
93	p1 <- c(station_data$LongitudeDD[grep(paste0("\\b", as.character(target), "\\b"), station_data$StationID)], station_data$LatitudeDD[grep(paste0("\\b", as.character(target), "\\b"), station_data$StationID)])	3x
94	}	4x
95	else if (length(target) == 2L) {	4x
96	p1 <- c(target[2], target[1])	1x
97	} else stop("error: check target format")	4x
98	df$Dist <- rep(NA, nrow(df))	4x
99	for (j in 1:nrow(df)) {	4x
100	df$Dist[j] <- (distGeo(p1, c(df$LongitudeDD[j], df$LatitudeDD[j]))/1000)	34940x
101	}	4x
102	df <- df[(!is.na(df$Dist) & (df$Dist >= min(dist)) & (df$Dist <= max(dist))),]	4x
103	if (sort == TRUE) df <- df[order(df$Dist),]	4x
104	}	11x
105	class(df) <- c("hcd_station_list", class(df))	11x
106	df	11x
107	}

1	##' @title Map canadaHCD stations on an interactive map
2	##'
3	##' @description Show the stations of interest on an interactive map using Leaflet. Zoom levels are guessed based on an RStudio plot window.
4	##'
5	##' @param station character; one or more station ID numbers to show on the map.
6	##' @param zoom numeric; the level to zoom the map to.
7	##'
8	##' @importFrom leaflet "%>%" leaflet addTiles setView addMarkers
9	##' @importFrom tibble tibble
10	##'
11	##' @export
12	##'
13	##' @author Conor I. Anderson
14	##'
15	##' @examples
16	##' # Make a map of all the stations named "Yellowknife".
17	##' map_stations(find_station_adv(name = "Yellowknife"))
18	##' # Make a map of all stations within 50km of Toronto Station 5051.
19	##' map_stations(find_station_adv(target = 5051, dist = 0:50))
20
21	map_stations <- function(station, zoom) {
22
23	if (inherits(station, "data.frame")) {	4x
24	station <- station$StationID	3x
25	}	4x
26
27	station <- as.character(station)	4x
28
29	poi <- NULL	4x
30	for (i in station) {	4x
31	row <- which(as.character(station_data$StationID) == i)	254x
32	if (!is.na(station_data$LatitudeDD[row]) && !is.na(station_data$LongitudeDD)[row]) {	254x
33	poi <- c(poi, row)	254x
34	}	254x
35	}	4x
36	poi <- station_data[poi,]	4x
37
38	if (nrow(poi) == 1L){	4x
39	lats <- poi$LatitudeDD	1x
40	lons <- poi$LongitudeDD	1x
41	latrng <- 0	1x
42	} else {	4x
43	hilat <- ceiling(max(poi$LatitudeDD))	3x
44	lolat <- floor(min(poi$LatitudeDD))	3x
45	hilon <- ceiling(max(poi$LongitudeDD))	3x
46	lolon <- floor(min(poi$LongitudeDD))	3x
47	lats <- (hilat + lolat)/2	3x
48	lons <- (hilon + lolon)/2	3x
49	latrng <- (hilat - lolat)	3x
50	}	4x
51
52	if (missing(zoom)) {	4x
53	zoomlevels <- tibble(range = c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16), zoom = c(10,8,7,7,7,6,6,6,5,5,5,5,5,5,5,5,4))	4x
54	zoom <- zoomlevels$zoom[which(zoomlevels$range == min(as.integer(latrng), 16L))]	4x
55	}	4x
56
57	leaflet() %>% addTiles() %>%	4x
58	setView(lng = lons, lat = lats, zoom = zoom) %>%	4x
59	addMarkers(poi, lng = poi$LongitudeDD, lat = poi$LatitudeDD,	4x
60	popup = paste0(poi$StationID, " - ", poi$Name))	4x
61	}

1	##' @title Perform a quick audit for missing values at a given station
2	##'
3	##' @description Returns a tibble with the proportion of missing values at a station for a given year and variables.
4	##'
5	##' @param datain A \code{tbl_df} containing the data to process
6	##' @param variables numeric or character; By default, all variables will be included. Pass a numeric vector to specify columns to include, or pass a character vector to try to match column names.
7	##' @param by character; whether values should be reported annually (\code{by = year}), or monthly (\code{by = month}).
8	##' @param reverse Boolean; if true, will show proportion present instead of proportion missing.
9	##'
10	##' @importFrom tibble tibble add_column
11	##' @importFrom zoo as.yearmon
12	##'
13	##' @export
14	##'
15	##' @author Conor I. Anderson
16	##'
17	##' @examples
18	##'
19	##' city <- hcd_daily(5051, 1981:2010)
20	##' quick_audit(city, "MaxTemp", by = "year", reverse = TRUE)
21	##' quick_audit(city, "MaxTemp", by = "month")
22	##'
23
24	quick_audit <- function(datain, variables, by = "year", reverse = FALSE) {
25
26	dat <- datain	6x
27
28	if (missing(variables)) {	6x
29	variables <- 2:ncol(dat)	2x
30	} else {
31	if (inherits(variables, "character")) {	4x
32	for (var in seq_along(variables)) {	4x
33	variables[var] <- as.numeric(grep(variables[var], names(dat), ignore.case = TRUE))	8x
34	}
35	}
36	variables <- as.numeric(variables)	4x
37	}
38
39	if (reverse) {	6x
40	ctl = 1	2x
41	} else {
42	ctl = 0	4x
43	}
44
45	years <- min(format(dat$Date, format = "%Y")):max(format(dat$Date, format = "%Y"))	6x
46
47	if (by == "month") {	6x
48	months <- min(format(dat$Date, format = "%m")):max(format(dat$Date, format = "%m"))	2x
49	yearmons <- apply(expand.grid(sprintf("%02d", months), years), 1, function(x) paste(x[2], x[1], sep = "-"))	2x
50
51	out <- tibble(`Year-month` = yearmons)	2x
52
53	for (var in variables) {	2x
54	integrity <- NULL	4x
55	for (yearmon in yearmons) {	4x
56	colname <- colnames(dat)[var]	432x
57	yearmonly <- dat[format(dat$Date, format = "%Y-%m") == yearmon,]	432x
58	obs <- nrow(yearmonly)	432x
59	missing <- sum(is.na(yearmonly[[var]]))	432x
60	integrity <- c(integrity, abs(ctl-(missing/obs)))	432x
61	}
62	out <- add_column(out, newcol = integrity)	4x
63	names(out)[which(names(out) == "newcol")] <- colname	4x
64	}
65	out$`Year-month` <- as.yearmon(out$`Year-month`)	2x
66	out <- add_column(out, Year = format(out$`Year-month`, format = "%Y"), Month = format(out$`Year-month`, format = "%m"), .before = 1)	2x
67	} else {
68	if (by == "year") {	4x
69	out <- tibble(Year = years)	3x
70
71	for (var in variables) {	3x
72	integrity <- NULL	16x
73	for (year in years) {	16x
74	colname <- colnames(dat)[var]	60x
75	yearly <- dat[format(dat$Date, format = "%Y") == year,]	60x
76	obs <- nrow(yearly)	60x
77	missing <- sum(is.na(yearly[[var]]))	60x
78	integrity <- c(integrity, abs(ctl-(missing/obs)))	60x
79	}
80	out <- add_column(out, newcol = integrity)	16x
81	names(out)[which(names(out) == "newcol")] <- colname	16x
82	}
83	} else {
84	stop("You must choose by \"month\", or by \"year\".")	1x
85	}
86	}
87
88	return(out)	5x
89	}

1	##' @title Find a Historical Climate Data station (GUI)
2	##'
3	##' @description A function to launch a shiny web app to search for Historical Climate Data stations.
4	##'
5	##' @param stations optional \code{tbl_df} of stations such as a search result from \code{find_station()}.
6	##'
7	##' @return none
8	##'
9	##' @author Conor I. Anderson
10	##'
11	##' @importFrom shiny br column em fluidPage fluidRow runApp selectInput shinyApp shinyUI titlePanel
12	##' @importFrom DT datatable dataTableOutput renderDataTable
13	##'
14	##' @export
15	##'
16	##' @examples
17	##' \dontrun{find_station_GUI()}
18
19	find_station_GUI <- function(stations = NULL) {
20
21	if (is.null(stations)) {	!
22	data <- station_data	!
23	} else {	!
24	data <- stations	!
25	}	!
26
27	app <- shinyApp(	!
28	shinyUI(	!
29	fluidPage(	!
30	titlePanel("canadaHCD data inventory"),	!
31
32	# Create a new Row in the UI for selectInputs
33	fluidRow(	!
34	column(4,	!
35	selectInput("prov",	!
36	"Province:",	!
37	c("All",	!
38	unique(as.character(data$Province))))	!
39	)	!
40	),	!
41	# Create a new row for the table.
42	fluidRow(	!
43	dataTableOutput("table")),	!
44	fluidRow(br(em(comment(data))))	!
45	)	!
46	),	!
47	server = function(input, output) {	!
48	output$table <- renderDataTable(datatable({	!
49	if (input$prov != "All") {	!
50	data <- data[data$Province == input$prov,]	!
51	}	!
52	data	!
53	}))	!
54	})	!
55	runApp(app)	!
56	}