10 |
11 |
12 | API wrapper for extracting CMHC data out of the [CMHC Housing Market Information Portal](https://www.cmhc-schl.gc.ca/hmiportal).
13 |
14 |
15 |
16 | ## Reference
17 | Documentation is [available on the GitHub pages](https://mountainmath.github.io/cmhc/).
18 |
19 | The example vignettes contain some [common use cases](https://mountainmath.github.io/cmhc/articles/basic_usage.html).
20 |
21 | ## Installation
22 | The stable version of **cmhc** can be easily installed from CRAN.
23 | ```
24 | install.packages("cmhc")
25 | ```
26 |
27 | Alternatively, the latest development version can be installed from Github.
28 | ```
29 | remotes::install_github("mountainmath/cmhc")
30 | ```
31 |
32 | ## Usage
33 | Consult the example vignette for more information. As an example, this is how to extract time series information
34 | for vacancy rate data by bedroom type for the Vancouver Census Metropolitan Area ("59933").
35 |
36 | ```
37 | library(cmhc)
38 | vacancy_data <- get_cmhc(survey="Rms",series="Vacancy Rate",dimension="Bedroom Type",
39 | breakdown="Historical Time Periods", geo_uid="59933")
40 |
41 | ```
42 |
43 | Starting with version v.0.3.2 the package has an interactive query builder helper function `select_cmhc_table()` that interactively walks through the available data and builds parameters for `get_cmhc()` like the example above. This makes it easy to discover data and build function calls to CMHC tables.
44 |
45 | ## Contributing
46 |
47 | * We encourage contributions to improve this project. The best way is through issues and pull requests.
48 | * If you want to get in touch, we are pretty good at responding via email or via twitter at [@vb_jens](https://twitter.com/vb_jens).
49 |
50 | ## Cite **cmhc**
51 |
52 | If you wish to cite cmhc:
53 |
54 | von Bergmann, J. (2025) cmhc: R package to access, retrieve, and work with CMHC data. v0.2.10. DOI: 10.32614/CRAN.package.cmhc
55 |
56 |
57 | A BibTeX entry for LaTeX users is
58 | ```
59 | @Manual{cmhc,
60 | author = {Jens {von Bergmann}},
61 | title = {cmhc: R package to access, retrieve, and work with CMHC data},
62 | year = {2025},
63 | doi = {10.32614/CRAN.package.cmhc},
64 | note = {R package version 0.2.10},
65 | url = {https://mountainmath.github.io/cmhc/},
66 | }
67 | ```
68 |
69 | ## Related packages
70 |
71 | The cmhc package is designed to work well with the [**cancensus** package](https://mountainmath.github.io/cancensus/) working with Canadian Census data the [**cansim** package](https://mountainmath.github.io/cansim/) for regular StatCan tables, and matches the census geographies via a `GeoUID` column that is shared across these packages. The [**tongfen** package](https://mountainmath.github.io/tongfen/) facilitates making geographies from different census years that CMHC reports on comparable over time.
72 |
73 | ## CMHC Attribution
74 |
75 | Subject to the [CMHC Data License Agreement](https://www.cmhc-schl.gc.ca/about-us/terms-conditions/hmip-terms-conditions), licensed products using CMHC data should employ the following acknowledgement of source:
76 |
77 |
78 | ### Acknowledgment of Source
79 |
80 | a. You shall include the following notice on all reproductions of the Information:
81 |
82 | > Source: Canada Mortgage and Housing Corporation (CMHC), name of product or information, reference date. This information is reproduced and distributed on an “as is” basis with the permission of CMHC.
83 |
84 | b. Where any Information is contained within a Value-added Product, you shall include on such Value-added Product the following notice:
85 |
86 | > Adapted from Canada Mortgage and Housing Corporation, name of product or information, reference date. This does not constitute an endorsement by Canada Mortgage and Housing Corporation of this product.
87 | or any other notice approved in advance in writing by CMHC.
88 |
89 |
90 |
--------------------------------------------------------------------------------
/R/hex_sticker.R:
--------------------------------------------------------------------------------
1 | #' Internal function to generate hex sticker
2 | #' @keywords internal
3 | generate_cmhc_hex_sticker <- function (){
4 | library(tidyverse)
5 | library(cmhc)
6 | ggplot2::theme_set(ggplot2::theme_gray())
7 | inflation <- cansim::get_cansim_vector("v41690973") |>
8 | select(Date,CPI=val_norm) |>
9 | mutate(CPIchange=CPI/lag(CPI,order_by = Date,n=12)-1)
10 |
11 | pd1 <- get_cmhc("Rms","Vacancy Rate","Bedroom Type","Historical Time Periods","48825") |>
12 | bind_rows(get_cmhc("Rms","Vacancy Rate","Bedroom Type","Historical Time Periods","48825",filter=list("season"="April"))) |>
13 | mutate(Value=Value/100)
14 | pd2 <- get_cmhc("Rms","Average Rent Change","Bedroom Type","Historical Time Periods","48825") |>
15 | bind_rows(get_cmhc("Rms","Average Rent Change","Bedroom Type","Historical Time Periods","48825",filter=list("season"="April"))) |>
16 | mutate(Value=Value/100) |>
17 | left_join(inflation,by="Date") |>
18 | mutate(Value=Value-CPIchange)
19 |
20 | pd <- bind_rows(pd1,pd2) |>
21 | filter(`Bedroom Type`=="Total") %>%
22 | left_join(filter(.,!is.na(Value)) |>
23 | select(Series,Date) |>
24 | group_by(Series) |>
25 | mutate(diff=as.integer(Date-lag(Date,order_by=Date))),
26 | by=c("Date","Series")) |>
27 | group_by(Series) |>
28 | filter(!is.na(Value) | (!is.na(lead(diff,order_by = Date)) & lead(diff,order_by = Date)>366))
29 |
30 |
31 | read_white_image <- function(path){
32 | logo<-magick::image_read(path)
33 | img <- logo[[1]]
34 | img[1,,] <- as.raw(205)
35 | img[2,,] <- as.raw(133)
36 | img[3,,] <- as.raw(63)
37 | magick::image_read(img)
38 | }
39 |
40 | library(grid)
41 | i1 <- read_white_image(here::here("images/cityscape.png")) |> rasterGrob(interpolate=TRUE)
42 | i2 <- read_white_image(here::here("images/buildings.png")) |> rasterGrob(interpolate=TRUE)
43 | i3 <- read_white_image(here::here("images/skyline.png")) |> rasterGrob(interpolate=TRUE)
44 |
45 | crs <- "+proj=lcc +lat_1=50 +lat_2=70 +lat_0=40 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +datum=NAD83 +units=m no_defs"
46 |
47 | ca_data <- cancensus::get_census("CA21",regions=list(C="01"),geo_format='sf') %>%
48 | sf::st_transform(crs)
49 | cities <- cancensus::get_census("CA21",regions=list(CMA=c("59933","47705","35535")),geo_format='sf') |>
50 | arrange(Population) |>
51 | sf::st_transform(crs) |>
52 | select() |>
53 | sf::st_centroid() |>
54 | sf::st_coordinates() |>
55 | as_tibble()
56 | q <- ggplot2::ggplot(ca_data) +
57 | ggplot2::geom_sf(fill="#800000",size=0.01) +
58 | ggplot2::theme_void() +
59 | hexSticker::theme_transparent()
60 | bbox=sf::st_bbox(ca_data)
61 | p<-ggplot2::ggplot(pd %>% filter(!is.na(Value)),ggplot2::aes(x=Date,y=Value,color=Series)) +
62 | ggplot2::geom_point(aes(shape=Quality),size=0.8) +
63 | ggplot2::geom_line(size=0.3) +
64 | #ggplot2::scale_colour_brewer(palette="Dark2",guide="none") +
65 | ggplot2::scale_color_manual(values=c("Average Rent Change"="#f0c60a",
66 | "Vacancy Rate"="#f0430a"),
67 | guide="none") +
68 | ggplot2::scale_shape_discrete(guide="none") +
69 | ggplot2::labs(x="",y="") +
70 | ggplot2::theme_void() +
71 | hexSticker::theme_transparent()
72 |
73 | xmin <- as.numeric(bbox$xmin)
74 | xwidth <- as.numeric(bbox$xmax-bbox$xmin)
75 | ymin <- as.numeric(bbox$ymin)
76 | ywidth <- as.numeric(bbox$ymax-bbox$ymin)
77 |
78 | size=0.1
79 |
80 | pp <- q +
81 | ggplot2::annotation_custom(i3, xmin=cities$X[1]-size*xwidth,xmax=cities$X[1]+size*xwidth,
82 | ymin=cities$Y[1],ymax=cities$Y[1]+2*size*ywidth) +
83 | ggplot2::annotation_custom(i1, xmin=cities$X[2]-size*xwidth,xmax=cities$X[2]+size*xwidth,
84 | ymin=cities$Y[2],ymax=cities$Y[2]+2*size*ywidth) +
85 | ggplot2::annotation_custom(i2, xmin=cities$X[3]-size*xwidth,xmax=cities$X[3]+size*xwidth,
86 | ymin=cities$Y[3],ymax=cities$Y[3]+2*size*ywidth) +
87 | ggplot2::annotation_custom(ggplot2::ggplotGrob(p),
88 | xmin=1.5*bbox$xmin,xmax=1.5*bbox$xmax,
89 | ymin=bbox$ymin*0.8+bbox$ymax*0.2,ymax=bbox$ymax*1.1) +
90 | hexSticker::theme_transparent()
91 |
92 | hexSticker::sticker(pp, package="cmhc",
93 | p_size=12, p_y=1.5,
94 | s_x=1, s_y=0.78, s_width=1.5, s_height=1.5,
95 | h_color="#FF0000",
96 | h_fill="grey40",
97 | p_color="white",
98 | filename=here::here("logo.svg"))
99 |
100 | hexSticker::sticker(pp, package="cmhc",
101 | p_size=22, p_y=1.65,
102 | s_x=1, s_y=0.78, s_width=1.5, s_height=1.5,
103 | h_color="#FF0000",
104 | h_fill="grey40",
105 | p_color="white",
106 | filename=here::here("logo.png"))
107 |
108 | }
109 |
--------------------------------------------------------------------------------
/docs/bootstrap-toc.js:
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1 | /*!
2 | * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/)
3 | * Copyright 2015 Aidan Feldman
4 | * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */
5 | (function() {
6 | 'use strict';
7 |
8 | window.Toc = {
9 | helpers: {
10 | // return all matching elements in the set, or their descendants
11 | findOrFilter: function($el, selector) {
12 | // http://danielnouri.org/notes/2011/03/14/a-jquery-find-that-also-finds-the-root-element/
13 | // http://stackoverflow.com/a/12731439/358804
14 | var $descendants = $el.find(selector);
15 | return $el.filter(selector).add($descendants).filter(':not([data-toc-skip])');
16 | },
17 |
18 | generateUniqueIdBase: function(el) {
19 | var text = $(el).text();
20 | var anchor = text.trim().toLowerCase().replace(/[^A-Za-z0-9]+/g, '-');
21 | return anchor || el.tagName.toLowerCase();
22 | },
23 |
24 | generateUniqueId: function(el) {
25 | var anchorBase = this.generateUniqueIdBase(el);
26 | for (var i = 0; ; i++) {
27 | var anchor = anchorBase;
28 | if (i > 0) {
29 | // add suffix
30 | anchor += '-' + i;
31 | }
32 | // check if ID already exists
33 | if (!document.getElementById(anchor)) {
34 | return anchor;
35 | }
36 | }
37 | },
38 |
39 | generateAnchor: function(el) {
40 | if (el.id) {
41 | return el.id;
42 | } else {
43 | var anchor = this.generateUniqueId(el);
44 | el.id = anchor;
45 | return anchor;
46 | }
47 | },
48 |
49 | createNavList: function() {
50 | return $('');
51 | },
52 |
53 | createChildNavList: function($parent) {
54 | var $childList = this.createNavList();
55 | $parent.append($childList);
56 | return $childList;
57 | },
58 |
59 | generateNavEl: function(anchor, text) {
60 | var $a = $('');
61 | $a.attr('href', '#' + anchor);
62 | $a.text(text);
63 | var $li = $('`, then ``, etc.) that has more than one element. Defaults to 1 (for ``).
76 | getTopLevel: function($scope) {
77 | for (var i = 1; i <= 6; i++) {
78 | var $headings = this.findOrFilter($scope, 'h' + i);
79 | if ($headings.length > 1) {
80 | return i;
81 | }
82 | }
83 |
84 | return 1;
85 | },
86 |
87 | // returns the elements for the top level, and the next below it
88 | getHeadings: function($scope, topLevel) {
89 | var topSelector = 'h' + topLevel;
90 |
91 | var secondaryLevel = topLevel + 1;
92 | var secondarySelector = 'h' + secondaryLevel;
93 |
94 | return this.findOrFilter($scope, topSelector + ',' + secondarySelector);
95 | },
96 |
97 | getNavLevel: function(el) {
98 | return parseInt(el.tagName.charAt(1), 10);
99 | },
100 |
101 | populateNav: function($topContext, topLevel, $headings) {
102 | var $context = $topContext;
103 | var $prevNav;
104 |
105 | var helpers = this;
106 | $headings.each(function(i, el) {
107 | var $newNav = helpers.generateNavItem(el);
108 | var navLevel = helpers.getNavLevel(el);
109 |
110 | // determine the proper $context
111 | if (navLevel === topLevel) {
112 | // use top level
113 | $context = $topContext;
114 | } else if ($prevNav && $context === $topContext) {
115 | // create a new level of the tree and switch to it
116 | $context = helpers.createChildNavList($prevNav);
117 | } // else use the current $context
118 |
119 | $context.append($newNav);
120 |
121 | $prevNav = $newNav;
122 | });
123 | },
124 |
125 | parseOps: function(arg) {
126 | var opts;
127 | if (arg.jquery) {
128 | opts = {
129 | $nav: arg
130 | };
131 | } else {
132 | opts = arg;
133 | }
134 | opts.$scope = opts.$scope || $(document.body);
135 | return opts;
136 | }
137 | },
138 |
139 | // accepts a jQuery object, or an options object
140 | init: function(opts) {
141 | opts = this.helpers.parseOps(opts);
142 |
143 | // ensure that the data attribute is in place for styling
144 | opts.$nav.attr('data-toggle', 'toc');
145 |
146 | var $topContext = this.helpers.createChildNavList(opts.$nav);
147 | var topLevel = this.helpers.getTopLevel(opts.$scope);
148 | var $headings = this.helpers.getHeadings(opts.$scope, topLevel);
149 | this.helpers.populateNav($topContext, topLevel, $headings);
150 | }
151 | };
152 |
153 | $(function() {
154 | $('nav[data-toggle="toc"]').each(function(i, el) {
155 | var $nav = $(el);
156 | Toc.init($nav);
157 | });
158 | });
159 | })();
160 |
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/docs/LICENSE-text.html:
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1 |
2 | License • cmhc
6 |
7 |
8 |
9 |
53 |
54 |
55 |
56 |
57 |
58 |
59 | License
60 |
61 |
62 | YEAR: 2019
63 | COPYRIGHT HOLDER: Jens von Bergmann
64 |
65 |
66 |
67 |
68 |
71 |
72 |
73 |
74 |
75 |
76 |
85 |
86 |
87 |
88 |
89 |
90 |
91 |
92 |
93 |
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/docs/articles/index.html:
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1 |
2 | Articles • cmhc
6 |
7 |
8 |
9 |
53 |
54 |
55 |
56 |
57 |
58 |
59 | Articles
60 |
61 |
62 |
63 | Basic usage
64 |
65 |
66 | - Basic usage
67 | -
68 |
- Rental Universe
69 | -
70 |
71 |
72 |
73 |
74 |
75 |
84 |
85 |
86 |
87 |
88 |
89 |
90 |
91 |
92 |
--------------------------------------------------------------------------------
/vignettes/basic_usage.Rmd:
--------------------------------------------------------------------------------
1 | ---
2 | title: "Basic usage"
3 | output: rmarkdown::html_vignette
4 | vignette: >
5 | %\VignetteIndexEntry{Basic usage}
6 | %\VignetteEngine{knitr::rmarkdown}
7 | %\VignetteEncoding{UTF-8}
8 | ---
9 |
10 | ```{r, include = FALSE}
11 | knitr::opts_chunk$set(
12 | collapse = TRUE,
13 | comment = "#>",
14 | eval = nzchar(Sys.getenv("COMPILE_VIG"))
15 | )
16 | ```
17 |
18 | ```{r setup, message=FALSE, warning=FALSE}
19 | library(cmhc)
20 | library(dplyr)
21 | library(ggplot2)
22 | ```
23 |
24 | To list the CMHC tables available via the package we can call `list_cmhc_tables`.
25 | ```{r}
26 | list_cmhc_tables() |>
27 | head() |>
28 | knitr::kable()
29 | ```
30 |
31 | It lists all possible tables by survey, series, dimension and breakdown, as well as the possible filters that can be applied. It is often convenient to run the `select_cmhc_table()` interactive query builder in the console to iteratively walk through building the parameters for the `get_cmhc()` data query.
32 |
33 | To access a time series for a region with census geographic identifier `geo_uid` we use `breakdown="Historical Time Periods"`.
34 |
35 | ```{r}
36 | vacancy_data <- get_cmhc(survey="Rms",series="Vacancy Rate",dimension="Bedroom Type",
37 | breakdown="Historical Time Periods", geo_uid="3506008")
38 |
39 | ggplot(vacancy_data,aes(x=Date,y=Value/100,colour=`Bedroom Type`)) +
40 | geom_point(aes(shape=Quality), data=~filter(.,!is.na(Value))) +
41 | geom_line() +
42 | scale_y_continuous(labels=scales::percent) +
43 | labs(title="City of Ottawa rental vacancy rate",
44 | x=NULL, y="Rental vacancy rate",
45 | caption="CMHC Rms")
46 | ```
47 |
48 | By default this fetches the data for the October survey only, in the past the Rental Market Survey was conducted twice a year. We can pass a filter option to access the April data and fold that in. We can explore the possible filter values could be used for this series via the `list_cmhc_filters` convenience function.
49 |
50 | ```{r}
51 | list_cmhc_filters("Rms","Vacancy Rate","Bedroom Type","Historical Time Periods")$Filters
52 | ```
53 |
54 | This tells us we could filter by dwelling type, with "Row" or "Apartment" as possible values, and by season, with "April" or "October" as possible values. We want the get April values to add to our existing data.
55 |
56 | ```{r}
57 | vacancy_data2 <- get_cmhc(survey="Rms",series="Vacancy Rate",dimension="Bedroom Type",
58 | breakdown="Historical Time Periods", geo_uid="3506008",filters = list("season"="April"))
59 |
60 | bind_rows(vacancy_data,vacancy_data2) |>
61 | filter(!is.na(Value)) |>
62 | ggplot(aes(x=Date,y=Value/100,colour=`Bedroom Type`)) +
63 | geom_point(aes(shape=Quality), data=~filter(.,!is.na(Value))) +
64 | geom_line() +
65 | scale_y_continuous(labels=scales::percent) +
66 | labs(title="City of Ottawa rental vacancy rate",
67 | x=NULL, y="Rental vacancy rate",
68 | caption="CMHC Rms")
69 | ```
70 |
71 | Vacancy Rate shows the number of units that are vacant and not rented out at the time. A related metric is the Availability Rate, which measures if a unit is available for rent, including units where a tenant has given notice but has not moved out yet.
72 |
73 | ```{r}
74 | availability_rate <- c("April","October") |>
75 | lapply(function(season)
76 | get_cmhc(survey="Rms",series="Availability Rate",dimension="Bedroom Type",
77 | breakdown="Historical Time Periods", geo_uid="3506008",filters = list("season"=season))) |>
78 | bind_rows()
79 |
80 | availability_rate |>
81 | filter(!is.na(Value)) |>
82 | ggplot(aes(x=Date,y=Value/100,colour=`Bedroom Type`)) +
83 | geom_point(aes(shape=Quality), data=~filter(.,!is.na(Value))) +
84 | geom_line() +
85 | scale_y_continuous(labels=scales::percent) +
86 | labs(title="City of Ottawa rental availability rate",
87 | x=NULL, y="Rental availability rate",
88 | caption="CMHC Rms")
89 | ```
90 |
91 | As expected, the availability rate is higher than the vacancy rate, unfortunately the collection of availability rates was discontinued. Another related metrics is the turnover rate, but unfortunately this data is not published in the HMIP.
92 |
93 | For data that is collected on a monthly basis, like the Survey of Starts and Completions (Scss) data, we can specify the frequency and also get annual or quarterly data. That makes things more efficient if we are just interested in annual data.
94 |
95 | ```{r}
96 | completions_data <- get_cmhc(survey="Scss",series="Completions",dimension="Dwelling Type",
97 | breakdown="Historical Time Periods", geo_uid="5915022",
98 | frequency = "Annual")
99 |
100 | ggplot(completions_data,aes(x=Date,y=Value,fill=`Dwelling Type`)) +
101 | geom_bar(stat="identity") +
102 | facet_wrap(~`Dwelling Type`,scales="free_y") +
103 | scale_fill_discrete(guide="none") +
104 | scale_y_continuous(labels=scales::comma) +
105 | labs(title="City of Vancouver housing completions",
106 | x=NULL, y="Annual completions",
107 | caption="CMHC Scss")
108 | ```
109 |
110 | To get snapshot data for sub-geographies, we can select the breakdown geography type. This can be linked back with census geographies for plotting.
111 |
112 | ```{r}
113 | under_construction_data <- get_cmhc(survey="Scss",series="Under Construction",dimension="Dwelling Type",
114 | breakdown="Census Tracts", geo_uid="5915022",
115 | year = 2022, month = 4)
116 |
117 | geo_data <- cancensus::get_census("CA16",regions=list(CSD="5915022"),geo_format = 'sf', level="CT", quiet=TRUE)
118 |
119 | geo_data |>
120 | left_join(under_construction_data |>
121 | filter(`Dwelling Type`=="All"),
122 | by="GeoUID") |>
123 | mutate(value=cut(coalesce(Value,0),
124 | breaks=c(-Inf,0,10,50,100,500,1000,1500,Inf),
125 | labels=c("0","1 to 10","11 to 50","51 to 100","101 to 500",
126 | "501 to 1000","1001 to 1500","Over 1500"))) |>
127 | ggplot(aes(fill=value)) +
128 | geom_sf() +
129 | scale_fill_viridis_d() +
130 | coord_sf(datum=NA) +
131 | labs(title="City of Vancouver housing units under construction April 2022",
132 | fill="Number of units",
133 | caption="CMCH Scss")
134 | ```
135 |
136 |
137 |
138 |
139 |
140 |
141 |
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/vignettes/rental_universe.Rmd:
--------------------------------------------------------------------------------
1 | ---
2 | title: "Rental Universe"
3 | output: rmarkdown::html_vignette
4 | vignette: >
5 | %\VignetteIndexEntry{Rental Universe}
6 | %\VignetteEngine{knitr::rmarkdown}
7 | %\VignetteEncoding{UTF-8}
8 | ---
9 |
10 | ```{r, include = FALSE}
11 | knitr::opts_chunk$set(
12 | collapse = TRUE,
13 | comment = "#>",
14 | eval = nzchar(Sys.getenv("COMPILE_VIG"))
15 | )
16 | ```
17 |
18 | ```{r setup}
19 | library(cmhc)
20 | library(ggplot2)
21 | library(dplyr)
22 | ```
23 |
24 | To understand how many purpose built rental units got constructed and torn down in each Metro Vancouver municipality query the rental universe for Metro Vancouver census subdivisions by period of construction for all years from 1990 to 2021.
25 |
26 | ```{r}
27 | rental_data <- seq(1990,2021) |>
28 | lapply(function(y)get_cmhc("Rms","Rental Universe","Year of Construction","Census Subdivision",
29 | geo_uid="59933",year=y)) %>%
30 | bind_rows()
31 | ```
32 |
33 | Computing year over year differences we can see how the stock has changed in the top municipalities. Over this time period the municipal status of some of the municipalities changed and we make the necessary adjustments. If we assume that periods did not get torn down in the same age bracked as they got built we can interpret this as gross flows.
34 |
35 | Sometimes units move in and out of the universe because of extensive renovations, and it will appear that we add old buildings. Another way how we have added old buildings to the rental universe is hotel conversions. So our proxy of using period of construction is not perfect, but ballpark estimates are about right.
36 |
37 | ```{r}
38 | rental_data %>%
39 | filter(!is.na(`Census Subdivision`)) %>%
40 | mutate(Name=gsub("\\(C\\)","(CY)",`Census Subdivision`)) %>%
41 | mutate(Name=recode(Name,
42 | "Burnaby (DM)"="Burnaby (CY)",
43 | "Coquitlam (DM)"="Coquitlam (CY)",
44 | "Surrey (DM)"="Surrey (CY)")) %>%
45 | filter(Name %in% (filter(.,`Year of Construction`=="Total",Date==max(Date)) %>%
46 | slice_max(Value,n=6) %>%
47 | pull(Name))) %>%
48 | filter(`Year of Construction`!="Total") %>%
49 | group_by(Name,`Year of Construction`) %>%
50 | mutate(Change=Value-lag(Value,order_by = Date)) %>%
51 | mutate(`Year of Construction`=factor(`Year of Construction`,levels=rev(levels(`Year of Construction`)))) %>%
52 | ungroup() %>%
53 | filter(Date!=min(Date)) %>%
54 | ggplot(aes(x=Date,y=Change,fill=`Year of Construction`)) +
55 | geom_bar(stat="identity") +
56 | facet_wrap(~Name,scales="free_y") +
57 | theme(legend.position = "bottom") +
58 | labs(title="Year over year change in rental stock",
59 | y="Number of units",
60 | x=NULL,
61 | fill="Period of construction",
62 | caption="CMHC Rms")
63 | ```
64 |
65 | We can also look at the total change over this timeframe to understand gross flows.
66 |
67 | ```{r fig.width=8.5}
68 | rental_data %>%
69 | filter(!is.na(`Census Subdivision`)) %>%
70 | mutate(Name=gsub("\\(C\\)","(CY)",`Census Subdivision`)) %>%
71 | mutate(Name=recode(Name,
72 | "Burnaby (DM)"="Burnaby (CY)",
73 | "Coquitlam (DM)"="Coquitlam (CY)",
74 | "Surrey (DM)"="Surrey (CY)")) %>%
75 | filter(Name %in% (filter(.,`Year of Construction`=="Total",Date==max(Date)) %>%
76 | slice_max(Value,n=20) %>%
77 | pull(Name))) %>%
78 | filter(Date==max(Date)|Date==min(Date)) %>%
79 | group_by(Name,`Year of Construction`) %>%
80 | mutate(Change=coalesce(Value,0)-coalesce(lag(Value,order_by = Date),0)) %>%
81 | ungroup() %>%
82 | filter(Date==max(Date)) %>%
83 | mutate(Name=factor(Name,levels=filter(.,`Year of Construction`=="Total") |> arrange(Change) |> pull(Name))) %>%
84 | mutate(`Year of Construction`=factor(`Year of Construction`,levels=rev(levels(`Year of Construction`)))) %>%
85 | ggplot(aes(y=Name,x=Change,fill=`Year of Construction`)) +
86 | geom_bar(data=~filter(.,`Year of Construction`!="Total"), stat="identity") +
87 | geom_point(data=~filter(.,`Year of Construction`=="Total"),
88 | aes(colour=`Year of Construction`),fill="black") +
89 | theme(legend.position = "bottom") +
90 | scale_colour_manual(values=c("Total"="black")) +
91 | scale_x_continuous(labels=scales::comma) +
92 | labs(title="Change in rental stock 1990-2021",
93 | x="Number of units",
94 | y=NULL,colour=NULL,
95 | fill="Period of construction",
96 | caption="CMHC Rms")
97 | ```
98 |
99 | Over this timeframe the City of Vancouver has added the most rental units, and Burnaby has torn down the most.
100 |
101 |
102 | Another way to slice the data is to look at the net change by number of bedrooms.
103 |
104 | ```{r}
105 | rental_data <- c(1990,2021) |>
106 | lapply(function(y)get_cmhc("Rms","Rental Universe","Bedroom Type","Census Subdivision",
107 | geo_uid="59933",year=y)) %>%
108 | bind_rows()
109 |
110 | rental_data %>%
111 | filter(!is.na(`Census Subdivision`)) %>%
112 | mutate(Name=gsub("\\(C\\)","(CY)",`Census Subdivision`)) %>%
113 | mutate(Name=recode(Name,
114 | "Burnaby (DM)"="Burnaby (CY)",
115 | "Coquitlam (DM)"="Coquitlam (CY)",
116 | "Surrey (DM)"="Surrey (CY)")) %>%
117 | filter(Name %in% (filter(.,`Bedroom Type`=="Total",Date==max(Date)) %>%
118 | slice_max(Value,n=20) %>%
119 | pull(Name))) %>%
120 | filter(Date==max(Date)|Date==min(Date)) %>%
121 | group_by(Name,`Bedroom Type`) %>%
122 | mutate(Change=coalesce(Value,0)-coalesce(lag(Value,order_by = Date),0)) %>%
123 | ungroup() %>%
124 | filter(Date==max(Date)) %>%
125 | mutate(Name=factor(Name,levels=filter(.,`Bedroom Type`=="Total") |> arrange(Change) |> pull(Name))) %>%
126 | ggplot(aes(y=Name,x=Change,fill=`Bedroom Type`)) +
127 | geom_bar(data=~filter(.,`Bedroom Type`!="Total"), stat="identity") +
128 | geom_point(data=~filter(.,`Bedroom Type`=="Total"),
129 | aes(colour=`Bedroom Type`),fill="black") +
130 | theme(legend.position = "bottom") +
131 | scale_colour_manual(values=c("Total"="black")) +
132 | scale_x_continuous(labels=scales::comma) +
133 | labs(title="Change in rental stock 1990-2021",
134 | x="Number of units",
135 | y=NULL,colour=NULL,
136 | fill="Bedroom Type",
137 | caption="CMHC Rms")
138 | ```
139 |
140 | There are no radical shifts in bedroom types, most municipalities that overall lost rental units lost units of all bedroom types, and municipalities that added rental units added units of all types. On net Vancouver gained mostly 1 and 2 bedroom units, Burnaby lost units of all bedroom types.
141 |
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/docs/404.html:
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1 |
2 | MIT License • cmhc
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58 |
59 | MIT License
60 |
61 |
62 |
63 |
64 | Copyright (c) 2019 Jens von Bergmann
65 | Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
66 | The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
67 | THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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1 |
2 | Authors and Citation • cmhc
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/docs/reference/cmhc_ct_translation_data.html:
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1 |
2 | A dataset with geographic identifiers for CMHC and Census at the CT level — cmhc_ct_translation_data • cmhc
6 |
7 |
8 |
9 |
53 |
54 |
55 |
56 |
57 |
58 |
59 | A dataset with geographic identifiers for CMHC and Census at the CT level
60 | Source: R/helpers.R
61 | cmhc_ct_translation_data.Rd
62 |
63 |
64 |
65 | A dataset with geographic identifiers for CMHC and Census at the CT level
66 |
67 |
68 |
69 |
70 | Format
71 | A tibble with Census and CMHC geographic identifiers
72 |
73 |
74 | Source
75 | Custom data extract from CMHC
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1 |
2 | A dataset with geographic identifiers for CMHC and Census at the CMA level — cmhc_cma_translation_data • cmhc
6 |
7 |
8 |
9 |
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55 |
56 |
57 |
58 |
59 | A dataset with geographic identifiers for CMHC and Census at the CMA level
60 | Source: R/helpers.R
61 | cmhc_cma_translation_data.Rd
62 |
63 |
64 |
65 | A dataset with geographic identifiers for CMHC and Census at the CMA level
66 |
67 |
68 |
69 |
70 | Format
71 | A tibble with Census and CMHC geographic identifiers
72 |
73 |
74 | Source
75 | Custom data extract from CMHC
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1 |
2 | A dataset with geographic identifiers for CMHC and Census at the CSD level — cmhc_csd_translation_data • cmhc
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8 |
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56 |
57 |
58 |
59 | A dataset with geographic identifiers for CMHC and Census at the CSD level
60 | Source: R/helpers.R
61 | cmhc_csd_translation_data.Rd
62 |
63 |
64 |
65 | A dataset with geographic identifiers for CMHC and Census at the CSD level
66 |
67 |
68 |
69 |
70 | Format
71 | A tibble with Census and CMHC geographic identifiers
72 |
73 |
74 | Source
75 | Custom data extract from CMHC
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1 |
2 | A dataset with geographic identifiers for CMHC and Census at the CSD level for 2023 data portal version — cmhc_csd_translation_data_2023 • cmhc
6 |
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58 |
59 | A dataset with geographic identifiers for CMHC and Census at the CSD level for 2023 data portal version
60 | Source: R/helpers.R
61 | cmhc_csd_translation_data_2023.Rd
62 |
63 |
64 |
65 | A dataset with geographic identifiers for CMHC and Census at the CSD level for 2023 data portal version
66 |
67 |
68 |
69 |
70 | Format
71 | A tibble with Census CSD and CMHC METID geographic identifiers
72 |
73 |
74 | Source
75 | Custom data extract from CMHC
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/docs/reference/show_cmhc_cache_path.html:
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2 | View saved cache directory path — show_cmhc_cache_path • cmhc
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58 |
59 | View saved cache directory path
60 | Source: R/user_settings.R
61 | show_cmhc_cache_path.Rd
62 |
63 |
64 |
65 | View saved cache path
66 |
67 |
68 |
69 | show_cmhc_cache_path()
70 |
71 |
72 |
73 | Value
74 |
75 |
76 | a character string with the CMHC cache path
77 |
78 |
79 |
80 | Examples
81 | show_cmhc_cache_path()
82 | #> [1] "/Users/jens/data/cmhc.data"
83 |
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/docs/reference/cmhc_quality_labels.html:
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1 |
2 | A list of CMHC quality indicators — cmhc_quality_labels • cmhc
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60 |
65 |
66 |
67 | Data obtained via this package will automatically translate internal CMHC quality labels
68 | using this translation vector, this named vector is useful when working with CMHC data obtained from other
69 | sources like sporadic excel sheets or data scraped from PDF reports.
70 |
71 |
72 |
73 | cmhc_quality_labels
74 |
75 |
76 |
77 | Format
78 | A named vector to translate internal CMHC quality indicators to plain text.
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2 | Interactive table selector — select_cmhc_table • cmhc
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65 | Interactive table selector
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68 |
69 | select_cmhc_table()
70 |
71 |
72 |
73 | Value
74 |
75 |
76 | A string containing the function call to access the selected table
77 |
78 |
79 |
80 | Examples
81 | if (FALSE) {
82 | select_cmhc_table()
83 | }
84 |
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/docs/reference/list_cmhc_surveys.html:
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2 | List available CMHC surveys — list_cmhc_surveys • cmhc
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64 |
65 | List available CMHC surveys
66 |
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68 |
69 | list_cmhc_surveys()
70 |
71 |
72 |
73 | Value
74 |
75 |
76 | A data frame with available survey names.
77 |
78 |
79 |
80 | Examples
81 | list_cmhc_surveys()
82 | #> # A tibble: 6 × 1
83 | #> Survey
84 | #> <chr>
85 | #> 1 Scss
86 | #> 2 Rms
87 | #> 3 Srms
88 | #> 4 Seniors
89 | #> 5 Census
90 | #> 6 Core Housing Need
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2 | List available CMHC series — list_cmhc_series • cmhc
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63 |
64 |
65 | List available CMHC series
66 |
67 |
68 |
69 | list_cmhc_series(survey = NULL)
70 |
71 |
72 |
73 | Arguments
74 | - survey
75 | Optional survey to filter by
78 |
79 | Value
80 |
81 |
82 | A data frame with survey names, and available series names.
83 |
84 |
85 |
86 | Examples
87 | list_cmhc_series("Rms")
88 | #> # A tibble: 7 × 2
89 | #> Survey Series
90 | #> <chr> <chr>
91 | #> 1 Rms Vacancy Rate
92 | #> 2 Rms Availability Rate
93 | #> 3 Rms Average Rent
94 | #> 4 Rms Average Rent Change
95 | #> 5 Rms Median Rent
96 | #> 6 Rms Rental Universe
97 | #> 7 Rms Summary Statistics
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1 | /* Sticky footer */
2 |
3 | /**
4 | * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/
5 | * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css
6 | *
7 | * .Site -> body > .container
8 | * .Site-content -> body > .container .row
9 | * .footer -> footer
10 | *
11 | * Key idea seems to be to ensure that .container and __all its parents__
12 | * have height set to 100%
13 | *
14 | */
15 |
16 | html, body {
17 | height: 100%;
18 | }
19 |
20 | body {
21 | position: relative;
22 | }
23 |
24 | body > .container {
25 | display: flex;
26 | height: 100%;
27 | flex-direction: column;
28 | }
29 |
30 | body > .container .row {
31 | flex: 1 0 auto;
32 | }
33 |
34 | footer {
35 | margin-top: 45px;
36 | padding: 35px 0 36px;
37 | border-top: 1px solid #e5e5e5;
38 | color: #666;
39 | display: flex;
40 | flex-shrink: 0;
41 | }
42 | footer p {
43 | margin-bottom: 0;
44 | }
45 | footer div {
46 | flex: 1;
47 | }
48 | footer .pkgdown {
49 | text-align: right;
50 | }
51 | footer p {
52 | margin-bottom: 0;
53 | }
54 |
55 | img.icon {
56 | float: right;
57 | }
58 |
59 | /* Ensure in-page images don't run outside their container */
60 | .contents img {
61 | max-width: 100%;
62 | height: auto;
63 | }
64 |
65 | /* Fix bug in bootstrap (only seen in firefox) */
66 | summary {
67 | display: list-item;
68 | }
69 |
70 | /* Typographic tweaking ---------------------------------*/
71 |
72 | .contents .page-header {
73 | margin-top: calc(-60px + 1em);
74 | }
75 |
76 | dd {
77 | margin-left: 3em;
78 | }
79 |
80 | /* Section anchors ---------------------------------*/
81 |
82 | a.anchor {
83 | display: none;
84 | margin-left: 5px;
85 | width: 20px;
86 | height: 20px;
87 |
88 | background-image: url(./link.svg);
89 | background-repeat: no-repeat;
90 | background-size: 20px 20px;
91 | background-position: center center;
92 | }
93 |
94 | h1:hover .anchor,
95 | h2:hover .anchor,
96 | h3:hover .anchor,
97 | h4:hover .anchor,
98 | h5:hover .anchor,
99 | h6:hover .anchor {
100 | display: inline-block;
101 | }
102 |
103 | /* Fixes for fixed navbar --------------------------*/
104 |
105 | .contents h1, .contents h2, .contents h3, .contents h4 {
106 | padding-top: 60px;
107 | margin-top: -40px;
108 | }
109 |
110 | /* Navbar submenu --------------------------*/
111 |
112 | .dropdown-submenu {
113 | position: relative;
114 | }
115 |
116 | .dropdown-submenu>.dropdown-menu {
117 | top: 0;
118 | left: 100%;
119 | margin-top: -6px;
120 | margin-left: -1px;
121 | border-radius: 0 6px 6px 6px;
122 | }
123 |
124 | .dropdown-submenu:hover>.dropdown-menu {
125 | display: block;
126 | }
127 |
128 | .dropdown-submenu>a:after {
129 | display: block;
130 | content: " ";
131 | float: right;
132 | width: 0;
133 | height: 0;
134 | border-color: transparent;
135 | border-style: solid;
136 | border-width: 5px 0 5px 5px;
137 | border-left-color: #cccccc;
138 | margin-top: 5px;
139 | margin-right: -10px;
140 | }
141 |
142 | .dropdown-submenu:hover>a:after {
143 | border-left-color: #ffffff;
144 | }
145 |
146 | .dropdown-submenu.pull-left {
147 | float: none;
148 | }
149 |
150 | .dropdown-submenu.pull-left>.dropdown-menu {
151 | left: -100%;
152 | margin-left: 10px;
153 | border-radius: 6px 0 6px 6px;
154 | }
155 |
156 | /* Sidebar --------------------------*/
157 |
158 | #pkgdown-sidebar {
159 | margin-top: 30px;
160 | position: -webkit-sticky;
161 | position: sticky;
162 | top: 70px;
163 | }
164 |
165 | #pkgdown-sidebar h2 {
166 | font-size: 1.5em;
167 | margin-top: 1em;
168 | }
169 |
170 | #pkgdown-sidebar h2:first-child {
171 | margin-top: 0;
172 | }
173 |
174 | #pkgdown-sidebar .list-unstyled li {
175 | margin-bottom: 0.5em;
176 | }
177 |
178 | /* bootstrap-toc tweaks ------------------------------------------------------*/
179 |
180 | /* All levels of nav */
181 |
182 | nav[data-toggle='toc'] .nav > li > a {
183 | padding: 4px 20px 4px 6px;
184 | font-size: 1.5rem;
185 | font-weight: 400;
186 | color: inherit;
187 | }
188 |
189 | nav[data-toggle='toc'] .nav > li > a:hover,
190 | nav[data-toggle='toc'] .nav > li > a:focus {
191 | padding-left: 5px;
192 | color: inherit;
193 | border-left: 1px solid #878787;
194 | }
195 |
196 | nav[data-toggle='toc'] .nav > .active > a,
197 | nav[data-toggle='toc'] .nav > .active:hover > a,
198 | nav[data-toggle='toc'] .nav > .active:focus > a {
199 | padding-left: 5px;
200 | font-size: 1.5rem;
201 | font-weight: 400;
202 | color: inherit;
203 | border-left: 2px solid #878787;
204 | }
205 |
206 | /* Nav: second level (shown on .active) */
207 |
208 | nav[data-toggle='toc'] .nav .nav {
209 | display: none; /* Hide by default, but at >768px, show it */
210 | padding-bottom: 10px;
211 | }
212 |
213 | nav[data-toggle='toc'] .nav .nav > li > a {
214 | padding-left: 16px;
215 | font-size: 1.35rem;
216 | }
217 |
218 | nav[data-toggle='toc'] .nav .nav > li > a:hover,
219 | nav[data-toggle='toc'] .nav .nav > li > a:focus {
220 | padding-left: 15px;
221 | }
222 |
223 | nav[data-toggle='toc'] .nav .nav > .active > a,
224 | nav[data-toggle='toc'] .nav .nav > .active:hover > a,
225 | nav[data-toggle='toc'] .nav .nav > .active:focus > a {
226 | padding-left: 15px;
227 | font-weight: 500;
228 | font-size: 1.35rem;
229 | }
230 |
231 | /* orcid ------------------------------------------------------------------- */
232 |
233 | .orcid {
234 | font-size: 16px;
235 | color: #A6CE39;
236 | /* margins are required by official ORCID trademark and display guidelines */
237 | margin-left:4px;
238 | margin-right:4px;
239 | vertical-align: middle;
240 | }
241 |
242 | /* Reference index & topics ----------------------------------------------- */
243 |
244 | .ref-index th {font-weight: normal;}
245 |
246 | .ref-index td {vertical-align: top; min-width: 100px}
247 | .ref-index .icon {width: 40px;}
248 | .ref-index .alias {width: 40%;}
249 | .ref-index-icons .alias {width: calc(40% - 40px);}
250 | .ref-index .title {width: 60%;}
251 |
252 | .ref-arguments th {text-align: right; padding-right: 10px;}
253 | .ref-arguments th, .ref-arguments td {vertical-align: top; min-width: 100px}
254 | .ref-arguments .name {width: 20%;}
255 | .ref-arguments .desc {width: 80%;}
256 |
257 | /* Nice scrolling for wide elements --------------------------------------- */
258 |
259 | table {
260 | display: block;
261 | overflow: auto;
262 | }
263 |
264 | /* Syntax highlighting ---------------------------------------------------- */
265 |
266 | pre, code, pre code {
267 | background-color: #f8f8f8;
268 | color: #333;
269 | }
270 | pre, pre code {
271 | white-space: pre-wrap;
272 | word-break: break-all;
273 | overflow-wrap: break-word;
274 | }
275 |
276 | pre {
277 | border: 1px solid #eee;
278 | }
279 |
280 | pre .img, pre .r-plt {
281 | margin: 5px 0;
282 | }
283 |
284 | pre .img img, pre .r-plt img {
285 | background-color: #fff;
286 | }
287 |
288 | code a, pre a {
289 | color: #375f84;
290 | }
291 |
292 | a.sourceLine:hover {
293 | text-decoration: none;
294 | }
295 |
296 | .fl {color: #1514b5;}
297 | .fu {color: #000000;} /* function */
298 | .ch,.st {color: #036a07;} /* string */
299 | .kw {color: #264D66;} /* keyword */
300 | .co {color: #888888;} /* comment */
301 |
302 | .error {font-weight: bolder;}
303 | .warning {font-weight: bolder;}
304 |
305 | /* Clipboard --------------------------*/
306 |
307 | .hasCopyButton {
308 | position: relative;
309 | }
310 |
311 | .btn-copy-ex {
312 | position: absolute;
313 | right: 0;
314 | top: 0;
315 | visibility: hidden;
316 | }
317 |
318 | .hasCopyButton:hover button.btn-copy-ex {
319 | visibility: visible;
320 | }
321 |
322 | /* headroom.js ------------------------ */
323 |
324 | .headroom {
325 | will-change: transform;
326 | transition: transform 200ms linear;
327 | }
328 | .headroom--pinned {
329 | transform: translateY(0%);
330 | }
331 | .headroom--unpinned {
332 | transform: translateY(-100%);
333 | }
334 |
335 | /* mark.js ----------------------------*/
336 |
337 | mark {
338 | background-color: rgba(255, 255, 51, 0.5);
339 | border-bottom: 2px solid rgba(255, 153, 51, 0.3);
340 | padding: 1px;
341 | }
342 |
343 | /* vertical spacing after htmlwidgets */
344 | .html-widget {
345 | margin-bottom: 10px;
346 | }
347 |
348 | /* fontawesome ------------------------ */
349 |
350 | .fab {
351 | font-family: "Font Awesome 5 Brands" !important;
352 | }
353 |
354 | /* don't display links in code chunks when printing */
355 | /* source: https://stackoverflow.com/a/10781533 */
356 | @media print {
357 | code a:link:after, code a:visited:after {
358 | content: "";
359 | }
360 | }
361 |
362 | /* Section anchors ---------------------------------
363 | Added in pandoc 2.11: https://github.com/jgm/pandoc-templates/commit/9904bf71
364 | */
365 |
366 | div.csl-bib-body { }
367 | div.csl-entry {
368 | clear: both;
369 | }
370 | .hanging-indent div.csl-entry {
371 | margin-left:2em;
372 | text-indent:-2em;
373 | }
374 | div.csl-left-margin {
375 | min-width:2em;
376 | float:left;
377 | }
378 | div.csl-right-inline {
379 | margin-left:2em;
380 | padding-left:1em;
381 | }
382 | div.csl-indent {
383 | margin-left: 2em;
384 | }
385 |
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1 |
2 | List available CMHC dimensions — list_cmhc_dimensions • cmhc
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63 |
64 |
65 | List available CMHC dimensions
66 |
67 |
68 |
69 | list_cmhc_dimensions(survey = NULL, series = NULL)
70 |
71 |
72 |
73 | Arguments
74 | - survey
75 | Optional survey to filter by
- series
79 | Optional series to filter by
82 |
83 | Value
84 |
85 |
86 | A data frame with survey names, series names, and available dimension names.
87 |
88 |
89 |
90 | Examples
91 | list_cmhc_dimensions("Rms","Vacancy Rate")
92 | #> # A tibble: 5 × 3
93 | #> Survey Series Dimension
94 | #> <chr> <chr> <chr>
95 | #> 1 Rms Vacancy Rate Bedroom Type
96 | #> 2 Rms Vacancy Rate Year of Construction
97 | #> 3 Rms Vacancy Rate Structure Size
98 | #> 4 Rms Vacancy Rate Rent Ranges
99 | #> 5 Rms Vacancy Rate Rent Quartiles
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/docs/reference/list_cmhc_filters.html:
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2 | List available CMHC filters — list_cmhc_filters • cmhc
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64 |
65 | List available CMHC filters
66 |
67 |
68 |
69 | list_cmhc_filters(
70 | survey = NULL,
71 | series = NULL,
72 | dimension = NULL,
73 | breakdown = NULL
74 | )
75 |
76 |
77 |
78 | Arguments
79 | - survey
80 | Optional survey to filter by
- series
84 | Optional series to filter by
- dimension
88 | Optional dimension to filter by
- breakdown
92 | Optional breakdown to filter by
95 |
96 | Value
97 |
98 |
99 | A data frame with available filters
100 |
101 |
102 |
103 | Examples
104 | list_cmhc_filters("Rms","Vacancy Rate","Bedroom Type","Historical Time Periods")
105 | #> # A tibble: 1 × 5
106 | #> Survey Series Dimension Breakdown Filters
107 | #> <chr> <chr> <chr> <chr> <list>
108 | #> 1 Rms Vacancy Rate Bedroom Type Historical Time Periods <named list [2]>
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2 | Set persistent cmhc cache location for geographic data — set_cmhc_cache_path • cmhc
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60 |
61 | Set persistent cmhc cache location for geographic data
62 | Source: R/user_settings.R
63 | set_cmhc_cache_path.Rd
64 |
65 |
66 |
67 | The cmhc package provides access to custom cmhc geographies, these are large files and should be
68 | stored in a permanent location. This function sets the CMHC_CACHE_PATH environment variable and optionally
69 | installs it in the .Renviron file for future use. This is only needed when using the `get_cmhc_geography()` function.
70 |
71 |
72 |
73 | set_cmhc_cache_path(cache_path, overwrite = FALSE, install = FALSE)
74 |
75 |
76 |
77 | Arguments
78 | - cache_path
79 | a local directory to use for saving cached data
- overwrite
83 | Option to overwrite any existing cache path already stored locally.
- install
87 | Option to install permanently for use across sessions.
90 |
91 | Value
92 |
93 |
94 | a character string with the CMHC cache path
95 |
96 |
97 |
98 | Examples
99 | if (FALSE) {
100 | # This sets the cache path for the duration of the current session
101 | set_cmhc_cache_path("~/cmhc_cache")
102 |
103 | # This will set the cache path permanently until overwritten again
104 | set_cmhc_cache_path("~/cmhc_cache", install = TRUE)
105 | }
106 |
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76 | getTopLevel: function($scope) {
77 | for (var i = 1; i <= 6; i++) {
78 | var $headings = this.findOrFilter($scope, 'h' + i);
79 | if ($headings.length > 1) {
80 | return i;
81 | }
82 | }
83 |
84 | return 1;
85 | },
86 |
87 | // returns the elements for the top level, and the next below it
88 | getHeadings: function($scope, topLevel) {
89 | var topSelector = 'h' + topLevel;
90 |
91 | var secondaryLevel = topLevel + 1;
92 | var secondarySelector = 'h' + secondaryLevel;
93 |
94 | return this.findOrFilter($scope, topSelector + ',' + secondarySelector);
95 | },
96 |
97 | getNavLevel: function(el) {
98 | return parseInt(el.tagName.charAt(1), 10);
99 | },
100 |
101 | populateNav: function($topContext, topLevel, $headings) {
102 | var $context = $topContext;
103 | var $prevNav;
104 |
105 | var helpers = this;
106 | $headings.each(function(i, el) {
107 | var $newNav = helpers.generateNavItem(el);
108 | var navLevel = helpers.getNavLevel(el);
109 |
110 | // determine the proper $context
111 | if (navLevel === topLevel) {
112 | // use top level
113 | $context = $topContext;
114 | } else if ($prevNav && $context === $topContext) {
115 | // create a new level of the tree and switch to it
116 | $context = helpers.createChildNavList($prevNav);
117 | } // else use the current $context
118 |
119 | $context.append($newNav);
120 |
121 | $prevNav = $newNav;
122 | });
123 | },
124 |
125 | parseOps: function(arg) {
126 | var opts;
127 | if (arg.jquery) {
128 | opts = {
129 | $nav: arg
130 | };
131 | } else {
132 | opts = arg;
133 | }
134 | opts.$scope = opts.$scope || $(document.body);
135 | return opts;
136 | }
137 | },
138 |
139 | // accepts a jQuery object, or an options object
140 | init: function(opts) {
141 | opts = this.helpers.parseOps(opts);
142 |
143 | // ensure that the data attribute is in place for styling
144 | opts.$nav.attr('data-toggle', 'toc');
145 |
146 | var $topContext = this.helpers.createChildNavList(opts.$nav);
147 | var topLevel = this.helpers.getTopLevel(opts.$scope);
148 | var $headings = this.helpers.getHeadings(opts.$scope, topLevel);
149 | this.helpers.populateNav($topContext, topLevel, $headings);
150 | }
151 | };
152 |
153 | $(function() {
154 | $('nav[data-toggle="toc"]').each(function(i, el) {
155 | var $nav = $(el);
156 | Toc.init($nav);
157 | });
158 | });
159 | })();
160 |
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1 |
2 | License • cmhc
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54 |
55 |
56 |
57 |
58 |
59 | License
60 |
61 |
62 | YEAR: 2019
63 | COPYRIGHT HOLDER: Jens von Bergmann
64 |
65 |
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68 |
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/docs/articles/index.html:
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2 | Articles • cmhc
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55 |
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57 |
58 |
59 | Articles
60 |
61 |
62 |
63 | Basic usage
64 |
65 |
66 | - Basic usage
67 | -
68 |
- Rental Universe
69 | -
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/vignettes/basic_usage.Rmd:
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1 | ---
2 | title: "Basic usage"
3 | output: rmarkdown::html_vignette
4 | vignette: >
5 | %\VignetteIndexEntry{Basic usage}
6 | %\VignetteEngine{knitr::rmarkdown}
7 | %\VignetteEncoding{UTF-8}
8 | ---
9 |
10 | ```{r, include = FALSE}
11 | knitr::opts_chunk$set(
12 | collapse = TRUE,
13 | comment = "#>",
14 | eval = nzchar(Sys.getenv("COMPILE_VIG"))
15 | )
16 | ```
17 |
18 | ```{r setup, message=FALSE, warning=FALSE}
19 | library(cmhc)
20 | library(dplyr)
21 | library(ggplot2)
22 | ```
23 |
24 | To list the CMHC tables available via the package we can call `list_cmhc_tables`.
25 | ```{r}
26 | list_cmhc_tables() |>
27 | head() |>
28 | knitr::kable()
29 | ```
30 |
31 | It lists all possible tables by survey, series, dimension and breakdown, as well as the possible filters that can be applied. It is often convenient to run the `select_cmhc_table()` interactive query builder in the console to iteratively walk through building the parameters for the `get_cmhc()` data query.
32 |
33 | To access a time series for a region with census geographic identifier `geo_uid` we use `breakdown="Historical Time Periods"`.
34 |
35 | ```{r}
36 | vacancy_data <- get_cmhc(survey="Rms",series="Vacancy Rate",dimension="Bedroom Type",
37 | breakdown="Historical Time Periods", geo_uid="3506008")
38 |
39 | ggplot(vacancy_data,aes(x=Date,y=Value/100,colour=`Bedroom Type`)) +
40 | geom_point(aes(shape=Quality), data=~filter(.,!is.na(Value))) +
41 | geom_line() +
42 | scale_y_continuous(labels=scales::percent) +
43 | labs(title="City of Ottawa rental vacancy rate",
44 | x=NULL, y="Rental vacancy rate",
45 | caption="CMHC Rms")
46 | ```
47 |
48 | By default this fetches the data for the October survey only, in the past the Rental Market Survey was conducted twice a year. We can pass a filter option to access the April data and fold that in. We can explore the possible filter values could be used for this series via the `list_cmhc_filters` convenience function.
49 |
50 | ```{r}
51 | list_cmhc_filters("Rms","Vacancy Rate","Bedroom Type","Historical Time Periods")$Filters
52 | ```
53 |
54 | This tells us we could filter by dwelling type, with "Row" or "Apartment" as possible values, and by season, with "April" or "October" as possible values. We want the get April values to add to our existing data.
55 |
56 | ```{r}
57 | vacancy_data2 <- get_cmhc(survey="Rms",series="Vacancy Rate",dimension="Bedroom Type",
58 | breakdown="Historical Time Periods", geo_uid="3506008",filters = list("season"="April"))
59 |
60 | bind_rows(vacancy_data,vacancy_data2) |>
61 | filter(!is.na(Value)) |>
62 | ggplot(aes(x=Date,y=Value/100,colour=`Bedroom Type`)) +
63 | geom_point(aes(shape=Quality), data=~filter(.,!is.na(Value))) +
64 | geom_line() +
65 | scale_y_continuous(labels=scales::percent) +
66 | labs(title="City of Ottawa rental vacancy rate",
67 | x=NULL, y="Rental vacancy rate",
68 | caption="CMHC Rms")
69 | ```
70 |
71 | Vacancy Rate shows the number of units that are vacant and not rented out at the time. A related metric is the Availability Rate, which measures if a unit is available for rent, including units where a tenant has given notice but has not moved out yet.
72 |
73 | ```{r}
74 | availability_rate <- c("April","October") |>
75 | lapply(function(season)
76 | get_cmhc(survey="Rms",series="Availability Rate",dimension="Bedroom Type",
77 | breakdown="Historical Time Periods", geo_uid="3506008",filters = list("season"=season))) |>
78 | bind_rows()
79 |
80 | availability_rate |>
81 | filter(!is.na(Value)) |>
82 | ggplot(aes(x=Date,y=Value/100,colour=`Bedroom Type`)) +
83 | geom_point(aes(shape=Quality), data=~filter(.,!is.na(Value))) +
84 | geom_line() +
85 | scale_y_continuous(labels=scales::percent) +
86 | labs(title="City of Ottawa rental availability rate",
87 | x=NULL, y="Rental availability rate",
88 | caption="CMHC Rms")
89 | ```
90 |
91 | As expected, the availability rate is higher than the vacancy rate, unfortunately the collection of availability rates was discontinued. Another related metrics is the turnover rate, but unfortunately this data is not published in the HMIP.
92 |
93 | For data that is collected on a monthly basis, like the Survey of Starts and Completions (Scss) data, we can specify the frequency and also get annual or quarterly data. That makes things more efficient if we are just interested in annual data.
94 |
95 | ```{r}
96 | completions_data <- get_cmhc(survey="Scss",series="Completions",dimension="Dwelling Type",
97 | breakdown="Historical Time Periods", geo_uid="5915022",
98 | frequency = "Annual")
99 |
100 | ggplot(completions_data,aes(x=Date,y=Value,fill=`Dwelling Type`)) +
101 | geom_bar(stat="identity") +
102 | facet_wrap(~`Dwelling Type`,scales="free_y") +
103 | scale_fill_discrete(guide="none") +
104 | scale_y_continuous(labels=scales::comma) +
105 | labs(title="City of Vancouver housing completions",
106 | x=NULL, y="Annual completions",
107 | caption="CMHC Scss")
108 | ```
109 |
110 | To get snapshot data for sub-geographies, we can select the breakdown geography type. This can be linked back with census geographies for plotting.
111 |
112 | ```{r}
113 | under_construction_data <- get_cmhc(survey="Scss",series="Under Construction",dimension="Dwelling Type",
114 | breakdown="Census Tracts", geo_uid="5915022",
115 | year = 2022, month = 4)
116 |
117 | geo_data <- cancensus::get_census("CA16",regions=list(CSD="5915022"),geo_format = 'sf', level="CT", quiet=TRUE)
118 |
119 | geo_data |>
120 | left_join(under_construction_data |>
121 | filter(`Dwelling Type`=="All"),
122 | by="GeoUID") |>
123 | mutate(value=cut(coalesce(Value,0),
124 | breaks=c(-Inf,0,10,50,100,500,1000,1500,Inf),
125 | labels=c("0","1 to 10","11 to 50","51 to 100","101 to 500",
126 | "501 to 1000","1001 to 1500","Over 1500"))) |>
127 | ggplot(aes(fill=value)) +
128 | geom_sf() +
129 | scale_fill_viridis_d() +
130 | coord_sf(datum=NA) +
131 | labs(title="City of Vancouver housing units under construction April 2022",
132 | fill="Number of units",
133 | caption="CMCH Scss")
134 | ```
135 |
136 |
137 |
138 |
139 |
140 |
141 |
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/vignettes/rental_universe.Rmd:
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1 | ---
2 | title: "Rental Universe"
3 | output: rmarkdown::html_vignette
4 | vignette: >
5 | %\VignetteIndexEntry{Rental Universe}
6 | %\VignetteEngine{knitr::rmarkdown}
7 | %\VignetteEncoding{UTF-8}
8 | ---
9 |
10 | ```{r, include = FALSE}
11 | knitr::opts_chunk$set(
12 | collapse = TRUE,
13 | comment = "#>",
14 | eval = nzchar(Sys.getenv("COMPILE_VIG"))
15 | )
16 | ```
17 |
18 | ```{r setup}
19 | library(cmhc)
20 | library(ggplot2)
21 | library(dplyr)
22 | ```
23 |
24 | To understand how many purpose built rental units got constructed and torn down in each Metro Vancouver municipality query the rental universe for Metro Vancouver census subdivisions by period of construction for all years from 1990 to 2021.
25 |
26 | ```{r}
27 | rental_data <- seq(1990,2021) |>
28 | lapply(function(y)get_cmhc("Rms","Rental Universe","Year of Construction","Census Subdivision",
29 | geo_uid="59933",year=y)) %>%
30 | bind_rows()
31 | ```
32 |
33 | Computing year over year differences we can see how the stock has changed in the top municipalities. Over this time period the municipal status of some of the municipalities changed and we make the necessary adjustments. If we assume that periods did not get torn down in the same age bracked as they got built we can interpret this as gross flows.
34 |
35 | Sometimes units move in and out of the universe because of extensive renovations, and it will appear that we add old buildings. Another way how we have added old buildings to the rental universe is hotel conversions. So our proxy of using period of construction is not perfect, but ballpark estimates are about right.
36 |
37 | ```{r}
38 | rental_data %>%
39 | filter(!is.na(`Census Subdivision`)) %>%
40 | mutate(Name=gsub("\\(C\\)","(CY)",`Census Subdivision`)) %>%
41 | mutate(Name=recode(Name,
42 | "Burnaby (DM)"="Burnaby (CY)",
43 | "Coquitlam (DM)"="Coquitlam (CY)",
44 | "Surrey (DM)"="Surrey (CY)")) %>%
45 | filter(Name %in% (filter(.,`Year of Construction`=="Total",Date==max(Date)) %>%
46 | slice_max(Value,n=6) %>%
47 | pull(Name))) %>%
48 | filter(`Year of Construction`!="Total") %>%
49 | group_by(Name,`Year of Construction`) %>%
50 | mutate(Change=Value-lag(Value,order_by = Date)) %>%
51 | mutate(`Year of Construction`=factor(`Year of Construction`,levels=rev(levels(`Year of Construction`)))) %>%
52 | ungroup() %>%
53 | filter(Date!=min(Date)) %>%
54 | ggplot(aes(x=Date,y=Change,fill=`Year of Construction`)) +
55 | geom_bar(stat="identity") +
56 | facet_wrap(~Name,scales="free_y") +
57 | theme(legend.position = "bottom") +
58 | labs(title="Year over year change in rental stock",
59 | y="Number of units",
60 | x=NULL,
61 | fill="Period of construction",
62 | caption="CMHC Rms")
63 | ```
64 |
65 | We can also look at the total change over this timeframe to understand gross flows.
66 |
67 | ```{r fig.width=8.5}
68 | rental_data %>%
69 | filter(!is.na(`Census Subdivision`)) %>%
70 | mutate(Name=gsub("\\(C\\)","(CY)",`Census Subdivision`)) %>%
71 | mutate(Name=recode(Name,
72 | "Burnaby (DM)"="Burnaby (CY)",
73 | "Coquitlam (DM)"="Coquitlam (CY)",
74 | "Surrey (DM)"="Surrey (CY)")) %>%
75 | filter(Name %in% (filter(.,`Year of Construction`=="Total",Date==max(Date)) %>%
76 | slice_max(Value,n=20) %>%
77 | pull(Name))) %>%
78 | filter(Date==max(Date)|Date==min(Date)) %>%
79 | group_by(Name,`Year of Construction`) %>%
80 | mutate(Change=coalesce(Value,0)-coalesce(lag(Value,order_by = Date),0)) %>%
81 | ungroup() %>%
82 | filter(Date==max(Date)) %>%
83 | mutate(Name=factor(Name,levels=filter(.,`Year of Construction`=="Total") |> arrange(Change) |> pull(Name))) %>%
84 | mutate(`Year of Construction`=factor(`Year of Construction`,levels=rev(levels(`Year of Construction`)))) %>%
85 | ggplot(aes(y=Name,x=Change,fill=`Year of Construction`)) +
86 | geom_bar(data=~filter(.,`Year of Construction`!="Total"), stat="identity") +
87 | geom_point(data=~filter(.,`Year of Construction`=="Total"),
88 | aes(colour=`Year of Construction`),fill="black") +
89 | theme(legend.position = "bottom") +
90 | scale_colour_manual(values=c("Total"="black")) +
91 | scale_x_continuous(labels=scales::comma) +
92 | labs(title="Change in rental stock 1990-2021",
93 | x="Number of units",
94 | y=NULL,colour=NULL,
95 | fill="Period of construction",
96 | caption="CMHC Rms")
97 | ```
98 |
99 | Over this timeframe the City of Vancouver has added the most rental units, and Burnaby has torn down the most.
100 |
101 |
102 | Another way to slice the data is to look at the net change by number of bedrooms.
103 |
104 | ```{r}
105 | rental_data <- c(1990,2021) |>
106 | lapply(function(y)get_cmhc("Rms","Rental Universe","Bedroom Type","Census Subdivision",
107 | geo_uid="59933",year=y)) %>%
108 | bind_rows()
109 |
110 | rental_data %>%
111 | filter(!is.na(`Census Subdivision`)) %>%
112 | mutate(Name=gsub("\\(C\\)","(CY)",`Census Subdivision`)) %>%
113 | mutate(Name=recode(Name,
114 | "Burnaby (DM)"="Burnaby (CY)",
115 | "Coquitlam (DM)"="Coquitlam (CY)",
116 | "Surrey (DM)"="Surrey (CY)")) %>%
117 | filter(Name %in% (filter(.,`Bedroom Type`=="Total",Date==max(Date)) %>%
118 | slice_max(Value,n=20) %>%
119 | pull(Name))) %>%
120 | filter(Date==max(Date)|Date==min(Date)) %>%
121 | group_by(Name,`Bedroom Type`) %>%
122 | mutate(Change=coalesce(Value,0)-coalesce(lag(Value,order_by = Date),0)) %>%
123 | ungroup() %>%
124 | filter(Date==max(Date)) %>%
125 | mutate(Name=factor(Name,levels=filter(.,`Bedroom Type`=="Total") |> arrange(Change) |> pull(Name))) %>%
126 | ggplot(aes(y=Name,x=Change,fill=`Bedroom Type`)) +
127 | geom_bar(data=~filter(.,`Bedroom Type`!="Total"), stat="identity") +
128 | geom_point(data=~filter(.,`Bedroom Type`=="Total"),
129 | aes(colour=`Bedroom Type`),fill="black") +
130 | theme(legend.position = "bottom") +
131 | scale_colour_manual(values=c("Total"="black")) +
132 | scale_x_continuous(labels=scales::comma) +
133 | labs(title="Change in rental stock 1990-2021",
134 | x="Number of units",
135 | y=NULL,colour=NULL,
136 | fill="Bedroom Type",
137 | caption="CMHC Rms")
138 | ```
139 |
140 | There are no radical shifts in bedroom types, most municipalities that overall lost rental units lost units of all bedroom types, and municipalities that added rental units added units of all types. On net Vancouver gained mostly 1 and 2 bedroom units, Burnaby lost units of all bedroom types.
141 |
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98 | Content not found. Please use links in the navbar.
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/docs/LICENSE.html:
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1 |
2 | MIT License • cmhc
6 |
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55 |
56 |
57 |
58 |
59 | MIT License
60 |
61 |
62 |
63 |
64 | Copyright (c) 2019 Jens von Bergmann
65 | Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
66 | The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
67 | THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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/docs/authors.html:
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1 |
2 | Authors and Citation • cmhc
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7 |
8 |
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/docs/reference/cmhc_ct_translation_data.html:
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1 |
2 | A dataset with geographic identifiers for CMHC and Census at the CT level — cmhc_ct_translation_data • cmhc
6 |
7 |
8 |
9 |
53 |
54 |
55 |
56 |
57 |
58 |
59 | A dataset with geographic identifiers for CMHC and Census at the CT level
60 | Source: R/helpers.R
61 | cmhc_ct_translation_data.Rd
62 |
63 |
64 |
65 | A dataset with geographic identifiers for CMHC and Census at the CT level
66 |
67 |
68 |
69 |
70 | Format
71 | A tibble with Census and CMHC geographic identifiers
72 |
73 |
74 | Source
75 | Custom data extract from CMHC
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/docs/reference/cmhc_cma_translation_data.html:
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1 |
2 | A dataset with geographic identifiers for CMHC and Census at the CMA level — cmhc_cma_translation_data • cmhc
6 |
7 |
8 |
9 |
53 |
54 |
55 |
56 |
57 |
58 |
59 | A dataset with geographic identifiers for CMHC and Census at the CMA level
60 | Source: R/helpers.R
61 | cmhc_cma_translation_data.Rd
62 |
63 |
64 |
65 | A dataset with geographic identifiers for CMHC and Census at the CMA level
66 |
67 |
68 |
69 |
70 | Format
71 | A tibble with Census and CMHC geographic identifiers
72 |
73 |
74 | Source
75 | Custom data extract from CMHC
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/docs/reference/cmhc_csd_translation_data.html:
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1 |
2 | A dataset with geographic identifiers for CMHC and Census at the CSD level — cmhc_csd_translation_data • cmhc
6 |
7 |
8 |
9 |
53 |
54 |
55 |
56 |
57 |
58 |
59 | A dataset with geographic identifiers for CMHC and Census at the CSD level
60 | Source: R/helpers.R
61 | cmhc_csd_translation_data.Rd
62 |
63 |
64 |
65 | A dataset with geographic identifiers for CMHC and Census at the CSD level
66 |
67 |
68 |
69 |
70 | Format
71 | A tibble with Census and CMHC geographic identifiers
72 |
73 |
74 | Source
75 | Custom data extract from CMHC
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/docs/reference/cmhc_csd_translation_data_2023.html:
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1 |
2 | A dataset with geographic identifiers for CMHC and Census at the CSD level for 2023 data portal version — cmhc_csd_translation_data_2023 • cmhc
6 |
7 |
8 |
9 |
53 |
54 |
55 |
56 |
57 |
58 |
59 | A dataset with geographic identifiers for CMHC and Census at the CSD level for 2023 data portal version
60 | Source: R/helpers.R
61 | cmhc_csd_translation_data_2023.Rd
62 |
63 |
64 |
65 | A dataset with geographic identifiers for CMHC and Census at the CSD level for 2023 data portal version
66 |
67 |
68 |
69 |
70 | Format
71 | A tibble with Census CSD and CMHC METID geographic identifiers
72 |
73 |
74 | Source
75 | Custom data extract from CMHC
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/docs/reference/show_cmhc_cache_path.html:
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1 |
2 | View saved cache directory path — show_cmhc_cache_path • cmhc
6 |
7 |
8 |
9 |
53 |
54 |
55 |
56 |
57 |
58 |
59 | View saved cache directory path
60 | Source: R/user_settings.R
61 | show_cmhc_cache_path.Rd
62 |
63 |
64 |
65 | View saved cache path
66 |
67 |
68 |
69 | show_cmhc_cache_path()
70 |
71 |
72 |
73 | Value
74 |
75 |
76 | a character string with the CMHC cache path
77 |
78 |
79 |
80 | Examples
81 | show_cmhc_cache_path()
82 | #> [1] "/Users/jens/data/cmhc.data"
83 |
84 |
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/docs/reference/cmhc_quality_labels.html:
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1 |
2 | A list of CMHC quality indicators — cmhc_quality_labels • cmhc
8 |
9 |
10 |
11 |
55 |
56 |
57 |
58 |
59 |
60 |
65 |
66 |
67 | Data obtained via this package will automatically translate internal CMHC quality labels
68 | using this translation vector, this named vector is useful when working with CMHC data obtained from other
69 | sources like sporadic excel sheets or data scraped from PDF reports.
70 |
71 |
72 |
73 | cmhc_quality_labels
74 |
75 |
76 |
77 | Format
78 | A named vector to translate internal CMHC quality indicators to plain text.
79 |
80 |
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/docs/reference/select_cmhc_table.html:
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1 |
2 | Interactive table selector — select_cmhc_table • cmhc
6 |
7 |
8 |
9 |
53 |
54 |
55 |
56 |
57 |
58 |
63 |
64 |
65 | Interactive table selector
66 |
67 |
68 |
69 | select_cmhc_table()
70 |
71 |
72 |
73 | Value
74 |
75 |
76 | A string containing the function call to access the selected table
77 |
78 |
79 |
80 | Examples
81 | if (FALSE) {
82 | select_cmhc_table()
83 | }
84 |
85 |
86 |
87 |
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/docs/reference/list_cmhc_surveys.html:
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1 |
2 | List available CMHC surveys — list_cmhc_surveys • cmhc
6 |
7 |
8 |
9 |
53 |
54 |
55 |
56 |
57 |
58 |
63 |
64 |
65 | List available CMHC surveys
66 |
67 |
68 |
69 | list_cmhc_surveys()
70 |
71 |
72 |
73 | Value
74 |
75 |
76 | A data frame with available survey names.
77 |
78 |
79 |
80 | Examples
81 | list_cmhc_surveys()
82 | #> # A tibble: 6 × 1
83 | #> Survey
84 | #> <chr>
85 | #> 1 Scss
86 | #> 2 Rms
87 | #> 3 Srms
88 | #> 4 Seniors
89 | #> 5 Census
90 | #> 6 Core Housing Need
91 |
92 |
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/docs/reference/list_cmhc_series.html:
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1 |
2 | List available CMHC series — list_cmhc_series • cmhc
6 |
7 |
8 |
9 |
53 |
54 |
55 |
56 |
57 |
58 |
63 |
64 |
65 | List available CMHC series
66 |
67 |
68 |
69 | list_cmhc_series(survey = NULL)
70 |
71 |
72 |
73 | Arguments
74 | - survey
75 | Optional survey to filter by
78 |
79 | Value
80 |
81 |
82 | A data frame with survey names, and available series names.
83 |
84 |
85 |
86 | Examples
87 | list_cmhc_series("Rms")
88 | #> # A tibble: 7 × 2
89 | #> Survey Series
90 | #> <chr> <chr>
91 | #> 1 Rms Vacancy Rate
92 | #> 2 Rms Availability Rate
93 | #> 3 Rms Average Rent
94 | #> 4 Rms Average Rent Change
95 | #> 5 Rms Median Rent
96 | #> 6 Rms Rental Universe
97 | #> 7 Rms Summary Statistics
98 |
99 |
100 |
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/docs/pkgdown.css:
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1 | /* Sticky footer */
2 |
3 | /**
4 | * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/
5 | * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css
6 | *
7 | * .Site -> body > .container
8 | * .Site-content -> body > .container .row
9 | * .footer -> footer
10 | *
11 | * Key idea seems to be to ensure that .container and __all its parents__
12 | * have height set to 100%
13 | *
14 | */
15 |
16 | html, body {
17 | height: 100%;
18 | }
19 |
20 | body {
21 | position: relative;
22 | }
23 |
24 | body > .container {
25 | display: flex;
26 | height: 100%;
27 | flex-direction: column;
28 | }
29 |
30 | body > .container .row {
31 | flex: 1 0 auto;
32 | }
33 |
34 | footer {
35 | margin-top: 45px;
36 | padding: 35px 0 36px;
37 | border-top: 1px solid #e5e5e5;
38 | color: #666;
39 | display: flex;
40 | flex-shrink: 0;
41 | }
42 | footer p {
43 | margin-bottom: 0;
44 | }
45 | footer div {
46 | flex: 1;
47 | }
48 | footer .pkgdown {
49 | text-align: right;
50 | }
51 | footer p {
52 | margin-bottom: 0;
53 | }
54 |
55 | img.icon {
56 | float: right;
57 | }
58 |
59 | /* Ensure in-page images don't run outside their container */
60 | .contents img {
61 | max-width: 100%;
62 | height: auto;
63 | }
64 |
65 | /* Fix bug in bootstrap (only seen in firefox) */
66 | summary {
67 | display: list-item;
68 | }
69 |
70 | /* Typographic tweaking ---------------------------------*/
71 |
72 | .contents .page-header {
73 | margin-top: calc(-60px + 1em);
74 | }
75 |
76 | dd {
77 | margin-left: 3em;
78 | }
79 |
80 | /* Section anchors ---------------------------------*/
81 |
82 | a.anchor {
83 | display: none;
84 | margin-left: 5px;
85 | width: 20px;
86 | height: 20px;
87 |
88 | background-image: url(./link.svg);
89 | background-repeat: no-repeat;
90 | background-size: 20px 20px;
91 | background-position: center center;
92 | }
93 |
94 | h1:hover .anchor,
95 | h2:hover .anchor,
96 | h3:hover .anchor,
97 | h4:hover .anchor,
98 | h5:hover .anchor,
99 | h6:hover .anchor {
100 | display: inline-block;
101 | }
102 |
103 | /* Fixes for fixed navbar --------------------------*/
104 |
105 | .contents h1, .contents h2, .contents h3, .contents h4 {
106 | padding-top: 60px;
107 | margin-top: -40px;
108 | }
109 |
110 | /* Navbar submenu --------------------------*/
111 |
112 | .dropdown-submenu {
113 | position: relative;
114 | }
115 |
116 | .dropdown-submenu>.dropdown-menu {
117 | top: 0;
118 | left: 100%;
119 | margin-top: -6px;
120 | margin-left: -1px;
121 | border-radius: 0 6px 6px 6px;
122 | }
123 |
124 | .dropdown-submenu:hover>.dropdown-menu {
125 | display: block;
126 | }
127 |
128 | .dropdown-submenu>a:after {
129 | display: block;
130 | content: " ";
131 | float: right;
132 | width: 0;
133 | height: 0;
134 | border-color: transparent;
135 | border-style: solid;
136 | border-width: 5px 0 5px 5px;
137 | border-left-color: #cccccc;
138 | margin-top: 5px;
139 | margin-right: -10px;
140 | }
141 |
142 | .dropdown-submenu:hover>a:after {
143 | border-left-color: #ffffff;
144 | }
145 |
146 | .dropdown-submenu.pull-left {
147 | float: none;
148 | }
149 |
150 | .dropdown-submenu.pull-left>.dropdown-menu {
151 | left: -100%;
152 | margin-left: 10px;
153 | border-radius: 6px 0 6px 6px;
154 | }
155 |
156 | /* Sidebar --------------------------*/
157 |
158 | #pkgdown-sidebar {
159 | margin-top: 30px;
160 | position: -webkit-sticky;
161 | position: sticky;
162 | top: 70px;
163 | }
164 |
165 | #pkgdown-sidebar h2 {
166 | font-size: 1.5em;
167 | margin-top: 1em;
168 | }
169 |
170 | #pkgdown-sidebar h2:first-child {
171 | margin-top: 0;
172 | }
173 |
174 | #pkgdown-sidebar .list-unstyled li {
175 | margin-bottom: 0.5em;
176 | }
177 |
178 | /* bootstrap-toc tweaks ------------------------------------------------------*/
179 |
180 | /* All levels of nav */
181 |
182 | nav[data-toggle='toc'] .nav > li > a {
183 | padding: 4px 20px 4px 6px;
184 | font-size: 1.5rem;
185 | font-weight: 400;
186 | color: inherit;
187 | }
188 |
189 | nav[data-toggle='toc'] .nav > li > a:hover,
190 | nav[data-toggle='toc'] .nav > li > a:focus {
191 | padding-left: 5px;
192 | color: inherit;
193 | border-left: 1px solid #878787;
194 | }
195 |
196 | nav[data-toggle='toc'] .nav > .active > a,
197 | nav[data-toggle='toc'] .nav > .active:hover > a,
198 | nav[data-toggle='toc'] .nav > .active:focus > a {
199 | padding-left: 5px;
200 | font-size: 1.5rem;
201 | font-weight: 400;
202 | color: inherit;
203 | border-left: 2px solid #878787;
204 | }
205 |
206 | /* Nav: second level (shown on .active) */
207 |
208 | nav[data-toggle='toc'] .nav .nav {
209 | display: none; /* Hide by default, but at >768px, show it */
210 | padding-bottom: 10px;
211 | }
212 |
213 | nav[data-toggle='toc'] .nav .nav > li > a {
214 | padding-left: 16px;
215 | font-size: 1.35rem;
216 | }
217 |
218 | nav[data-toggle='toc'] .nav .nav > li > a:hover,
219 | nav[data-toggle='toc'] .nav .nav > li > a:focus {
220 | padding-left: 15px;
221 | }
222 |
223 | nav[data-toggle='toc'] .nav .nav > .active > a,
224 | nav[data-toggle='toc'] .nav .nav > .active:hover > a,
225 | nav[data-toggle='toc'] .nav .nav > .active:focus > a {
226 | padding-left: 15px;
227 | font-weight: 500;
228 | font-size: 1.35rem;
229 | }
230 |
231 | /* orcid ------------------------------------------------------------------- */
232 |
233 | .orcid {
234 | font-size: 16px;
235 | color: #A6CE39;
236 | /* margins are required by official ORCID trademark and display guidelines */
237 | margin-left:4px;
238 | margin-right:4px;
239 | vertical-align: middle;
240 | }
241 |
242 | /* Reference index & topics ----------------------------------------------- */
243 |
244 | .ref-index th {font-weight: normal;}
245 |
246 | .ref-index td {vertical-align: top; min-width: 100px}
247 | .ref-index .icon {width: 40px;}
248 | .ref-index .alias {width: 40%;}
249 | .ref-index-icons .alias {width: calc(40% - 40px);}
250 | .ref-index .title {width: 60%;}
251 |
252 | .ref-arguments th {text-align: right; padding-right: 10px;}
253 | .ref-arguments th, .ref-arguments td {vertical-align: top; min-width: 100px}
254 | .ref-arguments .name {width: 20%;}
255 | .ref-arguments .desc {width: 80%;}
256 |
257 | /* Nice scrolling for wide elements --------------------------------------- */
258 |
259 | table {
260 | display: block;
261 | overflow: auto;
262 | }
263 |
264 | /* Syntax highlighting ---------------------------------------------------- */
265 |
266 | pre, code, pre code {
267 | background-color: #f8f8f8;
268 | color: #333;
269 | }
270 | pre, pre code {
271 | white-space: pre-wrap;
272 | word-break: break-all;
273 | overflow-wrap: break-word;
274 | }
275 |
276 | pre {
277 | border: 1px solid #eee;
278 | }
279 |
280 | pre .img, pre .r-plt {
281 | margin: 5px 0;
282 | }
283 |
284 | pre .img img, pre .r-plt img {
285 | background-color: #fff;
286 | }
287 |
288 | code a, pre a {
289 | color: #375f84;
290 | }
291 |
292 | a.sourceLine:hover {
293 | text-decoration: none;
294 | }
295 |
296 | .fl {color: #1514b5;}
297 | .fu {color: #000000;} /* function */
298 | .ch,.st {color: #036a07;} /* string */
299 | .kw {color: #264D66;} /* keyword */
300 | .co {color: #888888;} /* comment */
301 |
302 | .error {font-weight: bolder;}
303 | .warning {font-weight: bolder;}
304 |
305 | /* Clipboard --------------------------*/
306 |
307 | .hasCopyButton {
308 | position: relative;
309 | }
310 |
311 | .btn-copy-ex {
312 | position: absolute;
313 | right: 0;
314 | top: 0;
315 | visibility: hidden;
316 | }
317 |
318 | .hasCopyButton:hover button.btn-copy-ex {
319 | visibility: visible;
320 | }
321 |
322 | /* headroom.js ------------------------ */
323 |
324 | .headroom {
325 | will-change: transform;
326 | transition: transform 200ms linear;
327 | }
328 | .headroom--pinned {
329 | transform: translateY(0%);
330 | }
331 | .headroom--unpinned {
332 | transform: translateY(-100%);
333 | }
334 |
335 | /* mark.js ----------------------------*/
336 |
337 | mark {
338 | background-color: rgba(255, 255, 51, 0.5);
339 | border-bottom: 2px solid rgba(255, 153, 51, 0.3);
340 | padding: 1px;
341 | }
342 |
343 | /* vertical spacing after htmlwidgets */
344 | .html-widget {
345 | margin-bottom: 10px;
346 | }
347 |
348 | /* fontawesome ------------------------ */
349 |
350 | .fab {
351 | font-family: "Font Awesome 5 Brands" !important;
352 | }
353 |
354 | /* don't display links in code chunks when printing */
355 | /* source: https://stackoverflow.com/a/10781533 */
356 | @media print {
357 | code a:link:after, code a:visited:after {
358 | content: "";
359 | }
360 | }
361 |
362 | /* Section anchors ---------------------------------
363 | Added in pandoc 2.11: https://github.com/jgm/pandoc-templates/commit/9904bf71
364 | */
365 |
366 | div.csl-bib-body { }
367 | div.csl-entry {
368 | clear: both;
369 | }
370 | .hanging-indent div.csl-entry {
371 | margin-left:2em;
372 | text-indent:-2em;
373 | }
374 | div.csl-left-margin {
375 | min-width:2em;
376 | float:left;
377 | }
378 | div.csl-right-inline {
379 | margin-left:2em;
380 | padding-left:1em;
381 | }
382 | div.csl-indent {
383 | margin-left: 2em;
384 | }
385 |
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/docs/reference/list_cmhc_dimensions.html:
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1 |
2 | List available CMHC dimensions — list_cmhc_dimensions • cmhc
6 |
7 |
8 |
9 |
53 |
54 |
55 |
56 |
57 |
58 |
63 |
64 |
65 | List available CMHC dimensions
66 |
67 |
68 |
69 | list_cmhc_dimensions(survey = NULL, series = NULL)
70 |
71 |
72 |
73 | Arguments
74 | - survey
75 | Optional survey to filter by
- series
79 | Optional series to filter by
82 |
83 | Value
84 |
85 |
86 | A data frame with survey names, series names, and available dimension names.
87 |
88 |
89 |
90 | Examples
91 | list_cmhc_dimensions("Rms","Vacancy Rate")
92 | #> # A tibble: 5 × 3
93 | #> Survey Series Dimension
94 | #> <chr> <chr> <chr>
95 | #> 1 Rms Vacancy Rate Bedroom Type
96 | #> 2 Rms Vacancy Rate Year of Construction
97 | #> 3 Rms Vacancy Rate Structure Size
98 | #> 4 Rms Vacancy Rate Rent Ranges
99 | #> 5 Rms Vacancy Rate Rent Quartiles
100 |
101 |
102 |
103 |
104 |
107 |
108 |
109 |
110 |
119 |
120 |
121 |
122 |
123 |
124 |
125 |
126 |
127 |
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/docs/reference/list_cmhc_filters.html:
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1 |
2 | List available CMHC filters — list_cmhc_filters • cmhc
6 |
7 |
8 |
9 |
53 |
54 |
55 |
56 |
57 |
58 |
63 |
64 |
65 | List available CMHC filters
66 |
67 |
68 |
69 | list_cmhc_filters(
70 | survey = NULL,
71 | series = NULL,
72 | dimension = NULL,
73 | breakdown = NULL
74 | )
75 |
76 |
77 |
78 | Arguments
79 | - survey
80 | Optional survey to filter by
- series
84 | Optional series to filter by
- dimension
88 | Optional dimension to filter by
- breakdown
92 | Optional breakdown to filter by
95 |
96 | Value
97 |
98 |
99 | A data frame with available filters
100 |
101 |
102 |
103 | Examples
104 | list_cmhc_filters("Rms","Vacancy Rate","Bedroom Type","Historical Time Periods")
105 | #> # A tibble: 1 × 5
106 | #> Survey Series Dimension Breakdown Filters
107 | #> <chr> <chr> <chr> <chr> <list>
108 | #> 1 Rms Vacancy Rate Bedroom Type Historical Time Periods <named list [2]>
109 |
110 |
111 |
112 |
113 |
116 |
117 |
118 |
119 |
128 |
129 |
130 |
131 |
132 |
133 |
134 |
135 |
136 |
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/docs/reference/set_cmhc_cache_path.html:
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1 |
2 | Set persistent cmhc cache location for geographic data — set_cmhc_cache_path • cmhc
8 |
9 |
10 |
11 |
55 |
56 |
57 |
58 |
59 |
60 |
61 | Set persistent cmhc cache location for geographic data
62 | Source: R/user_settings.R
63 | set_cmhc_cache_path.Rd
64 |
65 |
66 |
67 | The cmhc package provides access to custom cmhc geographies, these are large files and should be
68 | stored in a permanent location. This function sets the CMHC_CACHE_PATH environment variable and optionally
69 | installs it in the .Renviron file for future use. This is only needed when using the `get_cmhc_geography()` function.
70 |
71 |
72 |
73 | set_cmhc_cache_path(cache_path, overwrite = FALSE, install = FALSE)
74 |
75 |
76 |
77 | Arguments
78 | - cache_path
79 | a local directory to use for saving cached data
- overwrite
83 | Option to overwrite any existing cache path already stored locally.
- install
87 | Option to install permanently for use across sessions.
90 |
91 | Value
92 |
93 |
94 | a character string with the CMHC cache path
95 |
96 |
97 |
98 | Examples
99 | if (FALSE) {
100 | # This sets the cache path for the duration of the current session
101 | set_cmhc_cache_path("~/cmhc_cache")
102 |
103 | # This will set the cache path permanently until overwritten again
104 | set_cmhc_cache_path("~/cmhc_cache", install = TRUE)
105 | }
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62 | License
60 |YEAR: 2019 63 | COPYRIGHT HOLDER: Jens von Bergmann 64 |65 | 66 |
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62 | Articles
60 |
63 |
71 | Basic usage
64 | 65 | 66 |- Basic usage 67 |
- 68 |
- Rental Universe 69 |
- 70 |
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98 | Content not found. Please use links in the navbar.
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100 | Page not found (404)
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62 | MIT License
60 |
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69 |
70 | Copyright (c) 2019 Jens von Bergmann
65 |Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
66 |The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
67 |THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
68 |
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88 |
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59 |
62 |
63 |
64 | A dataset with geographic identifiers for CMHC and Census at the CT level
60 | Source:R/helpers.R
61 | cmhc_ct_translation_data.Rd
65 |
67 |
68 |
69 | A dataset with geographic identifiers for CMHC and Census at the CT level
66 |
70 |
73 | Format
71 |A tibble with Census and CMHC geographic identifiers
72 |
74 |
77 |
81 |
82 | Source
75 |Custom data extract from CMHC
76 |
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87 |
88 |
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83 |
86 |
59 |
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63 |
64 | A dataset with geographic identifiers for CMHC and Census at the CMA level
60 | Source:R/helpers.R
61 | cmhc_cma_translation_data.Rd
65 |
67 |
68 |
69 | A dataset with geographic identifiers for CMHC and Census at the CMA level
66 |
70 |
73 | Format
71 |A tibble with Census and CMHC geographic identifiers
72 |
74 |
77 |
81 |
82 | Source
75 |Custom data extract from CMHC
76 |
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87 |
88 |
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83 |
86 |
59 |
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63 |
64 | A dataset with geographic identifiers for CMHC and Census at the CSD level
60 | Source:R/helpers.R
61 | cmhc_csd_translation_data.Rd
65 |
67 |
68 |
69 | A dataset with geographic identifiers for CMHC and Census at the CSD level
66 |
70 |
73 | Format
71 |A tibble with Census and CMHC geographic identifiers
72 |
74 |
77 |
81 |
82 | Source
75 |Custom data extract from CMHC
76 |
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88 |
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83 |
86 |
59 |
62 |
63 |
64 | A dataset with geographic identifiers for CMHC and Census at the CSD level for 2023 data portal version
60 | Source:R/helpers.R
61 | cmhc_csd_translation_data_2023.Rd
65 |
67 |
68 |
69 | A dataset with geographic identifiers for CMHC and Census at the CSD level for 2023 data portal version
66 |
70 |
73 | Format
71 |A tibble with Census CSD and CMHC METID geographic identifiers
72 |
74 |
77 |
81 |
82 | Source
75 |Custom data extract from CMHC
76 |
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87 |
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59 |
62 |
63 |
64 | View saved cache directory path
60 | Source:R/user_settings.R
61 | show_cmhc_cache_path.Rd
65 |
67 |
68 | View saved cache path
66 |
69 |
70 |
71 |
72 | show_cmhc_cache_path()
73 |
78 |
79 | Value
74 | 75 | 76 |a character string with the CMHC cache path
77 |
80 |
85 |
86 | Examples
81 |show_cmhc_cache_path()
82 | #> [1] "/Users/jens/data/cmhc.data"
83 |
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87 |
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71 |
72 | Data obtained via this package will automatically translate internal CMHC quality labels 68 | using this translation vector, this named vector is useful when working with CMHC data obtained from other 69 | sources like sporadic excel sheets or data scraped from PDF reports.
70 |
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74 |
75 |
76 | cmhc_quality_labels
77 |
80 |
81 | Format
78 |A named vector to translate internal CMHC quality indicators to plain text.
79 |
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64 |
87 |
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67 |
68 | Interactive table selector
66 |
69 |
70 |
71 |
72 | select_cmhc_table()
73 |
78 |
79 | Value
74 | 75 | 76 |A string containing the function call to access the selected table
77 |
80 |
85 |
86 | Examples
81 |if (FALSE) {
82 | select_cmhc_table()
83 | }
84 |
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99 |
100 |
101 |
58 |
63 |
64 |
95 |
98 |
65 |
67 |
68 | List available CMHC surveys
66 |
69 |
70 |
71 |
72 | list_cmhc_surveys()
73 |
78 |
79 | Value
74 | 75 | 76 |A data frame with available survey names.
77 |
80 |
93 |
94 | Examples
81 |list_cmhc_surveys()
82 | #> # A tibble: 6 × 1
83 | #> Survey
84 | #> <chr>
85 | #> 1 Scss
86 | #> 2 Rms
87 | #> 3 Srms
88 | #> 4 Seniors
89 | #> 5 Census
90 | #> 6 Core Housing Need
91 |
92 |
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63 |
64 |
102 |
105 |
65 |
67 |
68 | List available CMHC series
66 |
69 |
70 |
71 |
72 | list_cmhc_series(survey = NULL)
73 |
78 | Arguments
74 |- survey 75 |
Optional survey to filter by
76 |
77 |
79 |
84 |
85 | Value
80 | 81 | 82 |A data frame with survey names, and available series names.
83 |
86 |
100 |
101 | Examples
87 |list_cmhc_series("Rms")
88 | #> # A tibble: 7 × 2
89 | #> Survey Series
90 | #> <chr> <chr>
91 | #> 1 Rms Vacancy Rate
92 | #> 2 Rms Availability Rate
93 | #> 3 Rms Average Rent
94 | #> 4 Rms Average Rent Change
95 | #> 5 Rms Median Rent
96 | #> 6 Rms Rental Universe
97 | #> 7 Rms Summary Statistics
98 |
99 |
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108 |
109 |
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58 |
63 |
64 |
104 |
107 |
65 |
67 |
68 | List available CMHC dimensions
66 |
69 |
70 |
71 |
72 | list_cmhc_dimensions(survey = NULL, series = NULL)
73 |
82 | Arguments
74 |- survey 75 |
Optional survey to filter by
76 |
77 |
78 | - series 79 |
Optional series to filter by
80 |
81 |
83 |
88 |
89 | Value
84 | 85 | 86 |A data frame with survey names, series names, and available dimension names.
87 |
90 |
102 |
103 | Examples
91 |list_cmhc_dimensions("Rms","Vacancy Rate")
92 | #> # A tibble: 5 × 3
93 | #> Survey Series Dimension
94 | #> <chr> <chr> <chr>
95 | #> 1 Rms Vacancy Rate Bedroom Type
96 | #> 2 Rms Vacancy Rate Year of Construction
97 | #> 3 Rms Vacancy Rate Structure Size
98 | #> 4 Rms Vacancy Rate Rent Ranges
99 | #> 5 Rms Vacancy Rate Rent Quartiles
100 |
101 |
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118 |
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58 |
63 |
64 |
113 |
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65 |
67 |
68 | List available CMHC filters
66 |
69 |
75 |
76 |
77 | list_cmhc_filters(
70 | survey = NULL,
71 | series = NULL,
72 | dimension = NULL,
73 | breakdown = NULL
74 | )
78 |
95 | Arguments
79 |- survey 80 |
Optional survey to filter by
81 |
82 |
83 | - series 84 |
Optional series to filter by
85 |
86 |
87 | - dimension 88 |
Optional dimension to filter by
89 |
90 |
91 | - breakdown 92 |
Optional breakdown to filter by
93 |
94 |
96 |
101 |
102 | Value
97 | 98 | 99 |A data frame with available filters
100 |
103 |
111 |
112 | Examples
104 |list_cmhc_filters("Rms","Vacancy Rate","Bedroom Type","Historical Time Periods")
105 | #> # A tibble: 1 × 5
106 | #> Survey Series Dimension Breakdown Filters
107 | #> <chr> <chr> <chr> <chr> <list>
108 | #> 1 Rms Vacancy Rate Bedroom Type Historical Time Periods <named list [2]>
109 |
110 |
59 |
114 |
115 |
116 |
60 |
110 |
113 |
61 |
64 |
65 |
66 | Set persistent cmhc cache location for geographic data
62 | Source:R/user_settings.R
63 | set_cmhc_cache_path.Rd
67 |
71 |
72 | The cmhc package provides access to custom cmhc geographies, these are large files and should be 68 | stored in a permanent location. This function sets the CMHC_CACHE_PATH environment variable and optionally 69 | installs it in the .Renviron file for future use. This is only needed when using the `get_cmhc_geography()` function.
70 |
73 |
74 |
75 |
76 | set_cmhc_cache_path(cache_path, overwrite = FALSE, install = FALSE)
77 |
90 | Arguments
78 |- cache_path 79 |
a local directory to use for saving cached data
80 |
81 |
82 | - overwrite 83 |
Option to overwrite any existing cache path already stored locally.
84 |
85 |
86 | - install 87 |
Option to install permanently for use across sessions.
88 |
89 |
91 |
96 |
97 | Value
92 | 93 | 94 |a character string with the CMHC cache path
95 |
98 |
108 |
109 | Examples
99 |if (FALSE) {
100 | # This sets the cache path for the duration of the current session
101 | set_cmhc_cache_path("~/cmhc_cache")
102 |
103 | # This will set the cache path permanently until overwritten again
104 | set_cmhc_cache_path("~/cmhc_cache", install = TRUE)
105 | }
106 |
107 |