huizezhang-sherry / cubble Goto Github PK
View Code? Open in Web Editor NEWA tidy structure for spatio-temporal vector data
Home Page: https://huizezhang-sherry.github.io/cubble/
License: Other
cubble's People
Contributors
Stargazers
Watchers
cubble's Issues
should these functions be options to geom_glyph rather than separate?
geom_glyph_box() and geom_glyph_line() both might be better to be optional arguments on the geom_glyph() function, so the user doesn't need to specify the data and aes for each separate line. For example,
ggplot() +
geom_polygon(data=sth_america,
aes(x=long, y=lat, group=group),
fill="grey70") +
geom_glyph(data=nasa, aes(x_major = long, x_minor = day,
y_major = lat, y_minor = ozone)) +
geom_glyph_box(data=nasa, aes(x_major = long, x_minor = day,
y_major = lat, y_minor = ozone)) +
geom_glyph_line(data=nasa, aes(x_major = long, x_minor = day,
y_major = lat, y_minor = ozone)) +
coord_map() +
theme_map()
how to use cartogram_cont with a cubble
The function cartogram_cont from the cartogram package requires an sf object. It doesn't seem to like getting a cubble. Can you check it?
Release plans
Hi
I'm in the process of updating ggforce on CRAN and my reverse dependency checks shows an issue with the CRAN version of cubble (it doesn't suggest concaveman). This seems to be fixed already on GitHub so this is mostly an inquiry on when you plan to release the next version of cubble to CRAN?
with best wishes
Thomas
`unfold()` does not work for coords/columns called "x"
Hi Sherry,
I have been playing with the package lately, it is really a great implementation and I am looking forward to contributing to it as much as possible, so consider this my first step 😀
I am working with projected data, which when creating a cubble_df result in the columns "x" and "y" as the "coords". However, when using unfold() I get an error for the column called "x".
I traced it back to this line in new_temporal_cubble() when assigning the classes. The backtrace leads me to tsibble:::validate_index(tbl, !!qindex) but that is as far as I got.
Here is a reproducible example:
library(cubble)
library(dplyr)
climate_mel |>
rename(x = long, y = lat) |>
face_temporal() |>
unfold(x)
#> Error in `validate_index()`:
#> ! Problem while evaluating `index(x)`.
#> Caused by error in `sym()`:
#> ! Can't convert `NULL` to a symbol.
#> ---
#> Backtrace:
#> ▆
#> 1. ├─base (local) `<fn>`(x)
#> 2. ├─cubble:::print.cubble_df(x)
#> 3. │ ├─base::writeLines(format(x, width = width, ...))
#> 4. │ ├─base::format(x, width = width, ...)
#> 5. │ └─pillar:::format.tbl(x, width = width, ...)
#> 6. │ └─pillar:::format_tbl(...)
#> 7. │ └─pillar::tbl_format_setup(...)
#> 8. │ ├─pillar:::tbl_format_setup_dispatch(...)
#> 9. │ └─pillar:::tbl_format_setup.tbl(...)
#> 10. │ ├─pillar::tbl_sum(x)
#> 11. │ └─cubble:::tbl_sum.temporal_cubble_df(x)
#> 12. │ ├─tsibble::as_tsibble(as_tibble(x), key = key_vars(x), index = index(x))
#> 13. │ └─tsibble:::as_tsibble.tbl_df(...)
#> 14. │ └─tsibble::build_tsibble(...)
#> 15. │ └─tsibble:::validate_index(tbl, !!qindex)
#> 16. │ └─tidyselect::vars_pull(names, !!index)
#> 17. │ ├─tidyselect:::with_chained_errors(...)
#> 18. │ │ └─rlang::try_fetch(...)
#> 19. │ │ ├─base::tryCatch(...)
#> 20. │ │ │ └─base (local) tryCatchList(expr, classes, parentenv, handlers)
#> 21. │ │ │ └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
#> 22. │ │ │ └─base (local) doTryCatch(return(expr), name, parentenv, handler)
#> 23. │ │ └─base::withCallingHandlers(...)
#> 24. │ └─rlang::eval_tidy(expr, set_names(seq_along(vars), vars))
#> 25. └─cubble::index(x)
#> 26. └─rlang::sym(index_var(data))When trying for "y" or even when calling the coordinates "X" and "Y" (capital letters) then everythinh works.
climate_mel |>
rename(x = long, y = lat) |>
face_temporal() |>
unfold(y)
#> # cubble: key: id [3], index: date, long form
#> # temporal: 2020-01-01 -- 2020-01-10 [1D], no gaps
#> # spatial: x [dbl], y [dbl], elev [dbl], name [chr], wmo_id [dbl]
#> id date prcp tmax tmin y
#> <chr> <date> <dbl> <dbl> <dbl> <dbl>
#> 1 ASN00086038 2020-01-01 0 26.8 11 -37.7
#> 2 ASN00086038 2020-01-02 0 26.3 12.2 -37.7
#> 3 ASN00086038 2020-01-03 0 34.5 12.7 -37.7
#> 4 ASN00086038 2020-01-04 0 29.3 18.8 -37.7
#> 5 ASN00086038 2020-01-05 18 16.1 12.5 -37.7
#> 6 ASN00086038 2020-01-06 104 17.5 11.1 -37.7
#> 7 ASN00086038 2020-01-07 14 20.7 12.1 -37.7
#> 8 ASN00086038 2020-01-08 0 26.4 16.4 -37.7
#> 9 ASN00086038 2020-01-09 0 33.1 17.4 -37.7
#> 10 ASN00086038 2020-01-10 0 34 19.6 -37.7
#> # ℹ 20 more rows
climate_mel |>
rename(X = long, Y = lat) |>
face_temporal() |>
unfold(X, Y)
#> # cubble: key: id [3], index: date, long form
#> # temporal: 2020-01-01 -- 2020-01-10 [1D], no gaps
#> # spatial: X [dbl], Y [dbl], elev [dbl], name [chr], wmo_id [dbl]
#> id date prcp tmax tmin X Y
#> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 ASN00086038 2020-01-01 0 26.8 11 145. -37.7
#> 2 ASN00086038 2020-01-02 0 26.3 12.2 145. -37.7
#> 3 ASN00086038 2020-01-03 0 34.5 12.7 145. -37.7
#> 4 ASN00086038 2020-01-04 0 29.3 18.8 145. -37.7
#> 5 ASN00086038 2020-01-05 18 16.1 12.5 145. -37.7
#> 6 ASN00086038 2020-01-06 104 17.5 11.1 145. -37.7
#> 7 ASN00086038 2020-01-07 14 20.7 12.1 145. -37.7
#> 8 ASN00086038 2020-01-08 0 26.4 16.4 145. -37.7
#> 9 ASN00086038 2020-01-09 0 33.1 17.4 145. -37.7
#> 10 ASN00086038 2020-01-10 0 34 19.6 145. -37.7
#> # ℹ 20 more rows
Release cubble 0.2.0
Prepare for release:
- Check current CRAN check results
- Polish NEWS
-
devtools::build_readme() -
urlchecker::url_check() -
devtools::check(remote = TRUE, manual = TRUE) -
devtools::check_win_devel() -
rhub::check_for_cran() -
revdepcheck::revdep_check(num_workers = 4) - Update
cran-comments.md - Review pkgdown reference index for, e.g., missing topics
- Draft blog post
Submit to CRAN:
-
usethis::use_version('minor') -
devtools::submit_cran() - Approve email
Wait for CRAN...
- Accepted 🎉
-
usethis::use_github_release() -
usethis::use_dev_version() - Finish blog post
- Tweet
- Add link to blog post in pkgdown news menu
Why does the cubble lose it's geometry here
covid <- read_csv("https://raw.githubusercontent.com/numbats/eda/master/data/melb_lga_covid.csv") %>%
mutate(Buloke = as.numeric(ifelse(Buloke == "null", "0", Buloke))) %>%
mutate(Hindmarsh = as.numeric(ifelse(Hindmarsh == "null", "0", Hindmarsh))) %>%
mutate(Towong = as.numeric(ifelse(Towong == "null", "0", Towong))) %>%
pivot_longer(cols = Alpine:Yarriambiack, names_to="NAME", values_to="cases") %>%
mutate(Date = ydm(paste0("2020/",Date))) %>%
mutate(cases=replace_na(cases, 0))
covid <- covid %>%
group_by(NAME) %>%
mutate(new_cases = cases - dplyr::lag(cases)) %>%
na.omit()
lga <- strayr::read_absmap("lga2018") |>
rename(lga = lga_name_2018) |>
filter(state_name_2016 == "Victoria")
covid <- covid %>%
select(-cases) %>%
rename(lga = NAME, date=Date, cases = new_cases)
covid_ts <- as_tsibble(covid, key=lga, index=date)
covid_matching <- check_key(spatial = lga, temporal = covid_ts)
lga <- lga %>%
mutate(lga = ifelse(lga == "Colac-Otway (S)", "Colac Otway (S)", lga)) %>%
filter(!(lga %in% covid_matching$others$spatial))
covid_matching <- check_key(spatial = lga, temporal = covid_ts)
covid_cb <- make_cubble(
spatial = lga, temporal = covid_ts,
potential_match = covid_matching, index = date)
covid_tot <- covid_cb %>%
face_temporal() %>%
summarise(totcases = sum(cases, na.rm = TRUE))
> covid_tot
# A tibble: 79 × 2
lga totcases
<chr> <dbl>
1 Alpine (S) 0
2 Ararat (RC) 2
3 Ballarat (C) 46
4 Banyule (C) 350
....
Error on mismatched sizes in making a cubble
library(tidyverse)
library(tsibble)
library(cubble)
library(ozmaps)
# Read and handle missing
covid <- read_csv("https://raw.githubusercontent.com/numbats/eda/master/data/melb_lga_covid.csv") %>%
mutate(Buloke = as.numeric(ifelse(Buloke == "null", "0", Buloke))) %>%
mutate(Hindmarsh = as.numeric(ifelse(Hindmarsh == "null", "0", Hindmarsh))) %>%
mutate(Towong = as.numeric(ifelse(Towong == "null", "0", Towong))) %>%
pivot_longer(cols = Alpine:Yarriambiack, names_to="NAME", values_to="cases") %>%
mutate(Date = ydm(paste0("2020/",Date))) %>%
mutate(cases=replace_na(cases, 0))
# Create a tsibble
covid_ts <- as_tsibble(covid, key=NAME, index=Date)
# Get spatial polygons from ozmaps
data("abs_lga")
# Create cubble
covid_cb <- as_cubble(
list(spatial = abs_lga, temporal = covid_ts),
key = NAME, index = Date,
output = "unmatch")
gives the following error:
Error:
! Tibble columns must have compatible sizes.
• Size 84: Existing data.
• Size 78: Column `temporal`.
ℹ Only values of size one are recycled.
Run `rlang::last_error()` to see where the error occurred.
All the time series are of the same length according to my check.
Release cubble 0.3.0
Prepare for release:
-
git pull - Check current CRAN check results
- Check if any deprecation processes should be advanced, as described in Gradual deprecation
- Polish NEWS
-
devtools::build_readme() -
urlchecker::url_check() -
devtools::check(remote = TRUE, manual = TRUE) -
devtools::check_win_devel() -
rhub::check_for_cran() -
revdepcheck::revdep_check(num_workers = 4) - Update
cran-comments.md -
git push - Draft blog post
Submit to CRAN:
-
usethis::use_version('minor') -
devtools::submit_cran() - Approve email
Wait for CRAN...
- Accepted 🎉
-
git push -
usethis::use_github_release() -
usethis::use_dev_version() -
git push - Finish blog post
- Tweet
- Add link to blog post in pkgdown news menu
Should `add_geometry_column()` add a subclass `sf` to a `cubble_df` object?
Hi @huizezhang-sherry and @edzer,
currently, add_geometry_column() does not add the sf subclass to the cubble_df object. Is this desired behavior?
It may cause somewhat unintuitive things like:
library(cubble)
library(sf)
#> Linking to GEOS 3.9.3, GDAL 3.5.2, PROJ 8.2.1; sf_use_s2() is TRUE
library(tibble)
library(dplyr)
#> Warning: package 'dplyr' was built under R version 4.2.2
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
# create cubble_df from an sf object and a tibble
stations_sf <-
cubble::climate_subset |>
dplyr::select(-ts) |>
sf::st_as_sf(coords = c("long", "lat"), crs = 4283, remove = FALSE)
ts <-
cubble::climate_aus |>
cubble::face_temporal() |>
tibble::as_tibble() |>
dplyr::filter(id %in% stations_sf$id)
weather1 <-
cubble::as_cubble(
list(spatial = stations_sf, temporal = ts),
key = id, index = date, coords = c(long, lat)
)
class(weather1)
#> [1] "cubble_df" "sf" "rowwise_df" "tbl_df" "tbl"
#> [6] "data.frame"
# create a cubble object without sf and use `add_geometry_column()`
weather2 <-
cubble::climate_subset |>
cubble::add_geometry_column()
#> CRS missing: using OGC:CRS84 (WGS84) as default
class(weather2)
#> [1] "cubble_df" "rowwise_df" "tbl_df" "tbl" "data.frame"Created on 2023-05-03 by the reprex package (v2.0.1)
Session info
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.2.0 (2022-04-22 ucrt)
#> os Windows 10 x64 (build 22621)
#> system x86_64, mingw32
#> ui RTerm
#> language (EN)
#> collate German_Germany.utf8
#> ctype German_Germany.utf8
#> tz Europe/Berlin
#> date 2023-05-03
#> pandoc 2.19.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date (UTC) lib source
#> anytime 0.3.9 2020-08-27 [1] CRAN (R 4.2.3)
#> assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.2.1)
#> class 7.3-20 2022-01-16 [2] CRAN (R 4.2.0)
#> classInt 0.4-3 2020-04-07 [1] CRAN (R 4.2.0)
#> cli 3.4.1 2022-09-23 [1] CRAN (R 4.2.1)
#> crayon 1.5.1 2022-03-26 [1] CRAN (R 4.2.0)
#> cubble * 0.2.1 2023-05-03 [1] Github (huizezhang-sherry/cubble@d6610f4)
#> DBI 1.1.2 2021-12-20 [1] CRAN (R 4.2.0)
#> digest 0.6.29 2021-12-01 [1] CRAN (R 4.2.0)
#> dplyr * 1.0.10 2022-09-01 [1] CRAN (R 4.2.2)
#> e1071 1.7-9 2021-09-16 [1] CRAN (R 4.2.0)
#> ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.0)
#> evaluate 0.15 2022-02-18 [1] CRAN (R 4.2.0)
#> fansi 1.0.3 2022-03-24 [1] CRAN (R 4.2.0)
#> fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.2.0)
#> fs 1.5.2 2021-12-08 [1] CRAN (R 4.2.0)
#> generics 0.1.2 2022-01-31 [1] CRAN (R 4.2.0)
#> geosphere 1.5-18 2022-11-15 [1] CRAN (R 4.2.3)
#> glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.0)
#> highr 0.9 2021-04-16 [1] CRAN (R 4.2.0)
#> htmltools 0.5.2 2021-08-25 [1] CRAN (R 4.2.0)
#> KernSmooth 2.23-20 2021-05-03 [2] CRAN (R 4.2.0)
#> knitr 1.39 2022-04-26 [1] CRAN (R 4.2.0)
#> lattice 0.20-45 2021-09-22 [1] CRAN (R 4.2.0)
#> lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.2.2)
#> lubridate 1.8.0 2021-10-07 [1] CRAN (R 4.2.0)
#> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.0)
#> ncdf4 1.21 2023-01-07 [1] CRAN (R 4.2.2)
#> pillar 1.7.0 2022-02-01 [1] CRAN (R 4.2.0)
#> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.0)
#> proxy 0.4-26 2021-06-07 [1] CRAN (R 4.2.0)
#> purrr 0.3.4 2020-04-17 [1] CRAN (R 4.2.0)
#> R.cache 0.15.0 2021-04-30 [1] CRAN (R 4.2.0)
#> R.methodsS3 1.8.1 2020-08-26 [1] CRAN (R 4.2.0)
#> R.oo 1.24.0 2020-08-26 [1] CRAN (R 4.2.0)
#> R.utils 2.11.0 2021-09-26 [1] CRAN (R 4.2.0)
#> R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0)
#> Rcpp 1.0.8.3 2022-03-17 [1] CRAN (R 4.2.0)
#> reprex 2.0.1 2021-08-05 [1] CRAN (R 4.2.0)
#> rlang 1.0.6 2022-09-24 [1] CRAN (R 4.2.1)
#> rmarkdown 2.14 2022-04-25 [1] CRAN (R 4.2.0)
#> rstudioapi 0.14 2022-08-22 [1] CRAN (R 4.2.1)
#> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0)
#> sf * 1.0-10 2022-12-07 [1] Github (r-spatial/sf@9534e4a)
#> sp 1.5-0 2022-06-12 [1] Github (edzer/sp@cfe9cdb)
#> stringi 1.7.6 2021-11-29 [1] CRAN (R 4.2.0)
#> stringr 1.4.0 2019-02-10 [1] CRAN (R 4.2.0)
#> styler 1.7.0 2022-03-13 [1] CRAN (R 4.2.0)
#> tibble * 3.1.7 2022-05-03 [1] CRAN (R 4.2.0)
#> tidyr 1.2.0 2022-02-01 [1] CRAN (R 4.2.0)
#> tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.2)
#> tsibble 1.1.3 2022-10-09 [1] CRAN (R 4.2.3)
#> units 0.8-1 2022-12-10 [1] CRAN (R 4.2.3)
#> utf8 1.2.2 2021-07-24 [1] CRAN (R 4.2.0)
#> vctrs 0.5.1 2022-11-16 [1] CRAN (R 4.2.2)
#> withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.0)
#> xfun 0.30 2022-03-02 [1] CRAN (R 4.2.0)
#> yaml 2.3.5 2022-02-21 [1] CRAN (R 4.2.0)
#>
#> [1] C:/Users/henni/AppData/Local/R/win-library/4.2
#> [2] C:/Program Files/R/R-4.2.0/library
#>
#> ──────────────────────────────────────────────────────────────────────────────Need range01 function
In converting from
temp.gly <- glyphs(nasa, "long", "day", "lat",
"ozone", height=2.5,
y_scale = range01, polar = TRUE)
ggplot(temp.gly, ggplot2::aes(gx, gy, group = gid)) +
geom_polygon(data=sth_america,
aes(x=long, y=lat, group=group),
fill="grey70") +
add_ref_lines(temp.gly, color = "grey90") +
add_ref_boxes(temp.gly, color = "grey90") +
geom_path() +
labs(x = "", y = "") +
coord_map() +
theme_map()
to
ggplot() +
geom_polygon(data=sth_america,
aes(x=long, y=lat, group=group),
fill="#014221", alpha=0.5, colour="#ffffff") +
geom_glyph_box(data=nasa, aes(x_major = long, x_minor = day,
y_major = lat, y_minor = ozone), fill=NA) +
geom_glyph_line(data=nasa, aes(x_major = long, x_minor = day,
y_major = lat, y_minor = ozone)) +
geom_glyph(data=nasa, aes(x_major = long, x_minor = day,
y_major = lat, y_minor = ozone),
y_scale = range01, polar = TRUE) +
theme_map()
will throw an error. cubble is missing the range01 function, that will scale each spatial location data to range between 0 and 1, necessary to make polar glyphs with local scale.
conversion to/from stars
Hi, nice project! It might be interesting to have converters between cubble and stars objects, see https://github.com/r-spatial/stars as that also can hold vector data cubes.
Suggested improvements for add_missing_prct()
Line 19 in e97069c
This code assumes that ... is of length 1 (since expr() only accepts one argument).
Instead, you can introduce a new argument for the function cols which supports tidyselect (much like tidyr::pivot_longer() and others), then capture the inputted expression with rlang::enexpr(cols).
Alternatively, you could probably directly use tidyselect::eval_select({{cols}}, face_temporal(data)). Note that I've removed the pipe as it complicates the evaluation environment and is a slight slow down.
Line 31 in e97069c
Using
quo() here is capturing the environment of your mapped function, and the contents of the add_missing_prct() environment. Since you just want to evaluate elements from your data, it is simpler to use expr() here.
Line 33 in e97069c
Since you have a list of expressions (or quosures) you can apply them across multiple function arguments with
!!!, avoiding the conflict with variable name missing and the need to unnest_wider().cubble coerced from raster `stars` object does not inherit CRS
Raster data cubes do not inherit the CRS when coerced to cubble. This is because the cubble creation is extracting the coordinate values as integers, losing the geometry here.
library(stars)
library(cubble)
tif = system.file("tif/L7_ETMs.tif", package = "stars")
(x = stars::read_stars(tif))
#> stars object with 3 dimensions and 1 attribute
#> attribute(s):
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> L7_ETMs.tif 1 54 69 68.91242 86 255
#> dimension(s):
#> from to offset delta refsys point x/y
#> x 1 349 288776 28.5 SIRGAS 2000 / UTM zone 25S FALSE [x]
#> y 1 352 9120761 -28.5 SIRGAS 2000 / UTM zone 25S FALSE [y]
#> band 1 6 NA NA NA NA
(t = x |> as_cubble())
#> ℹ More than 10,000 keys: only use the first key to test spatial &
#> temporal variables.
#> # cubble: key: id [122848], index: time, nested form
#> # spatial: [288790.500000803, 9110743.00002899, 298708.50000055,
#> # 9120746.50002874], Missing CRS!
#> # temporal: band [int], L7_ETMs.tif [dbl]
#> x y id ts
#> <dbl> <dbl> <int> <list>
#> 1 288791. 9120747. 122500 <tibble [6 × 2]>
#> 2 288819. 9120747. 122501 <tibble [6 × 2]>
#> 3 288848. 9120747. 122502 <tibble [6 × 2]>
#> 4 288876. 9120747. 122503 <tibble [6 × 2]>
#> 5 288905. 9120747. 122504 <tibble [6 × 2]>
#> 6 288933. 9120747. 122505 <tibble [6 × 2]>
#> 7 288962. 9120747. 122506 <tibble [6 × 2]>
#> 8 288990. 9120747. 122507 <tibble [6 × 2]>
#> 9 289019. 9120747. 122508 <tibble [6 × 2]>
#> 10 289047. 9120747. 122509 <tibble [6 × 2]>
#> # ℹ 122,838 more rowsOne option would be to coerce the tibble to sf before coercing to cubble before line 192, to recover the CRS:
as_tibble(data) |>
mutate(id = as.integer(interaction(!!sym(longlat[[1]]),
!!sym(longlat[[2]])))) |>
st_as_sf(crs = st_crs(x), coords = longlat) |>
as_cubble(key = id, index = time)
or to add make_spatial_sf() after line 192:
as_tibble(data) |>
mutate(id = as.integer(interaction(!!sym(longlat[[1]]),
!!sym(longlat[[2]])))) |>
as_cubble(key = id, index = time, coords = longlat) |>
make_spatial_sf(crs = sf::st_crs(data))
But that is not such a fast approach and also adds an extra geometry column that the user might find unexpected. So I am not sure what would be the best approach here.
Recommend keep magrittr pipe
I'd recommend keeping the magrittr pipe %>% (it's part of tibble anyway so won't make difference). The native pipe is only available for R version >=4.1.0 which was only released last year. There are going to be a number of people who won't be able to use it so it's just adding a barrier for people to use your package.
For the automatic matching can we choose which label to use
covid <- read_csv("https://raw.githubusercontent.com/numbats/eda/master/data/melb_lga_covid.csv") %>%
mutate(Buloke = as.numeric(ifelse(Buloke == "null", "0", Buloke))) %>%
mutate(Hindmarsh = as.numeric(ifelse(Hindmarsh == "null", "0", Hindmarsh))) %>%
mutate(Towong = as.numeric(ifelse(Towong == "null", "0", Towong))) %>%
pivot_longer(cols = Alpine:Yarriambiack, names_to="NAME", values_to="cases") %>%
mutate(Date = ydm(paste0("2020/",Date))) %>%
mutate(cases=replace_na(cases, 0))
covid <- covid %>%
group_by(NAME) %>%
mutate(new_cases = cases - dplyr::lag(cases)) %>%
na.omit()
lga <- strayr::read_absmap("lga2018") |>
rename(lga = lga_name_2018) |>
filter(state_name_2016 == "Victoria")
covid <- covid %>%
select(-cases) %>%
rename(lga = NAME, date=Date, cases = new_cases)
covid_ts <- as_tsibble(covid, key=lga, index=date)
covid_matching <- check_key(spatial = lga, temporal = covid_ts)
lga <- lga %>%
mutate(lga = ifelse(lga == "Colac-Otway (S)", "Colac Otway (S)", lga)) %>%
filter(!(lga %in% covid_matching$others$spatial))
covid_matching <- check_key(spatial = lga, temporal = covid_ts)
covid_cb <- make_cubble(
spatial = lga, temporal = covid_ts,
potential_match = covid_matching, index = date)
It uses the key from the spatial data, but the temporal one would be better.
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.