jforbes14/eechidna

Data package containing tables, shapefiles, etc. for the 2001-2019 Australian elections and Censuses

eechidna's Issues

use NEWS, git tags, and releases

hey @benmarwick - We want all rOpenSci pkgs to consistently keep track of changes, following https://github.com/ropensci/onboarding/blob/master/packaging_guide.md#-news

• use a NEWS or NEWS.md file to keep track of changes with each version released to CRAN
• git tag each CRAN version
• use the releases tab on this repo to include the associated NEWS items for each tag/version

Can you please do the three above items from now on?

Non-Affiliated candidates dropped from 2016 AEC dataset

I have found a discrepancy in total number of observations between the aec2016_fp dataset and an import from the AEC spreadsheets. The aec2016_fp dataset contains 83 fewer observations.

The issue appears to be two non affiliated candidates with PartyAB = NAFD and PartyNm = N/A who have been dropped from the aec2016_fp data in Longman and Lindsay.

> glimpse(datadiff)
Observations: 83
Variables: 26
$ DivisionID         <int> 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128, 128...
$ DivisionNm         <chr> "Lindsay", "Lindsay", "Lindsay", "Lindsay", "Lindsay", "Lindsay", "Lindsay", "Linds...
$ PollingPlaceID     <int> 1747, 65411, 1479, 1480, 1481, 1754, 11957, 384, 1758, 1478, 30119, 1483, 1484, 149...
$ PollingPlace       <chr> "Berkshire Park", "BLV Lindsay PPVC", "Cambridge Gardens", "Cambridge Park", "Cambr...
$ CandidateID        <int> 28025, 28025, 28025, 28025, 28025, 28025, 28025, 28025, 28025, 28025, 28025, 28025,...
$ Surname            <chr> "CORNISH", "CORNISH", "CORNISH", "CORNISH", "CORNISH", "CORNISH", "CORNISH", "CORNI...
$ GivenNm            <chr> "Marcus", "Marcus", "Marcus", "Marcus", "Marcus", "Marcus", "Marcus", "Marcus", "Ma...
$ BallotPosition     <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ Elected            <chr> "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N"...
$ HistoricElected    <chr> "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N", "N"...
$ PartyAb            <chr> "NAFD", "NAFD", "NAFD", "NAFD", "NAFD", "NAFD", "NAFD", "NAFD", "NAFD", "NAFD", "NA...
$ PartyNm            <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
$ OrdinaryVotes      <int> 8, 0, 56, 63, 55, 13, 48, 54, 116, 92, 8, 35, 71, 39, 45, 52, 13, 24, 15, 35, 19, 1...
$ Swing              <dbl> 2.12, 0.00, 2.50, 2.96, 4.43, 1.51, 2.53, 1.99, 3.40, 3.48, 1.50, 2.30, 2.90, 2.82,...
$ State              <chr> "NSW", "NSW", "NSW", "NSW", "NSW", "NSW", "NSW", "NSW", "NSW", "NSW", "NSW", "NSW",...
$ PollingPlaceTypeID <int> 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1,...
$ PollingPlaceNm     <chr> "Berkshire Park", "BLV Lindsay PPVC", "Cambridge Gardens", "Cambridge Park", "Cambr...
$ PremisesNm         <chr> "Berkshire Park Bushfire Shed", "BLV Lindsay", "Cambridge Gardens Public School", "...
$ PremisesAddress1   <chr> "Sixth Rd", "Ground Floor", "Trinity Dr", "Oxford St", "Harrow Rd", "Post Office Rd...
$ PremisesAddress2   <chr> NA, "606 High St", NA, NA, NA, NA, NA, NA, NA, NA, "606 High St", NA, NA, NA, NA, N...
$ PremisesAddress3   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,...
$ PremisesSuburb     <chr> "BERKSHIRE PARK", "PENRITH", "CAMBRIDGE GARDENS", "CAMBRIDGE PARK", "CAMBRIDGE PARK...
$ PremisesStateAb    <chr> "NSW", "NSW", "NSW", "NSW", "NSW", "NSW", "NSW", "NSW", "NSW", "NSW", "NSW", "NSW",...
$ PremisesPostCode   <int> 2765, 2750, 2747, 2747, 2747, 2749, 2747, 2760, 2749, 2749, 2750, 2750, 2750, 2750,...
$ Latitude           <dbl> -33.67690, -33.75202, -33.73733, -33.74800, -33.75230, -33.66752, -33.77375, -33.78...
$ Longitude          <dbl> 150.7810, 150.6906, 150.7230, 150.7260, 150.7340, 150.6770, 150.7461, 150.7910, 150...

abs data sets have inconsistent State

The State variable is not present in each of the absXXXX data sets, and the capitalisation is inconsistent between data sets.

URL in GitHub Repo incorrect

I noticed that the URL link on the GitHub repo returns a 404 error from ROpenSci.org, is that still the correct URL?

NAs in the 2019 maps

There are some weird NAs in the 2019 maps that are not in the 2016 map. Are these a problem?

library(dplyr)
library(eechidna)
nat_map16 = nat_map_download(2016)
glimpse(nat_map16)
#> Rows: 37,041
#> Columns: 9
#> $ long      <dbl> 130.5179, 130.5175, 130.5294, 130.5243, 130.5310, 130.5252,…
#> $ lat       <dbl> -12.73105, -12.72562, -12.72505, -12.73409, -12.74217, -12.…
#> $ order     <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, …
#> $ hole      <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
#> $ piece     <chr> "P.1", "P.1", "P.1", "P.1", "P.1", "P.1", "P.1", "P.1", "P.…
#> $ id        <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",…
#> $ group     <chr> "G.1.1", "G.1.1", "G.1.1", "G.1.1", "G.1.1", "G.1.1", "G.1.…
#> $ elect_div <chr> "LINGIARI", "LINGIARI", "LINGIARI", "LINGIARI", "LINGIARI",…
#> $ state     <chr> "NT", "NT", "NT", "NT", "NT", "NT", "NT", "NT", "NT", "NT",…
sum(is.na(nat_map16$elect_div))
#> [1] 0
nat_map19 = nat_map_download(2019)
glimpse(nat_map19)
#> Rows: 33,427
#> Columns: 9
#> $ long      <dbl> 149.2063, 149.1382, 149.1551, 149.1355, 149.1515, 149.1314,…
#> $ lat       <dbl> -35.34591, -35.42940, -35.43578, -35.45442, -35.49224, -35.…
#> $ order     <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, …
#> $ hole      <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
#> $ piece     <chr> "P.1", "P.1", "P.1", "P.1", "P.1", "P.1", "P.1", "P.1", "P.…
#> $ id        <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",…
#> $ group     <chr> "G.0.1", "G.0.1", "G.0.1", "G.0.1", "G.0.1", "G.0.1", "G.0.…
#> $ elect_div <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
#> $ state     <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
sum(is.na(nat_map19$elect_div))
#> [1] 92

Note that there are some geographic objects whose elect_div and state columns are NA in 2019 only.
This is different to the 2016 version, which seems to always have content there.
I'm not yet geo-savvy enough to know exactly how to parse these weird geographic data frames, but if I work this out while playing with the data I'll post more information.

erratic build error: "Value of SET_STRING_ELT() must be a 'CHARSXP' not a 'character'"

On 2 July I got this email from CRAN:

Dear maintainer,

Pls see the problems shown on
https://cran.r-project.org/web/checks/check_results_eechidna.html:

Can you pls fix as necessary?

Best,
-k

I took at look at the page referred to and I see:

And the key details are:

Quitting from lines 155-172 (exploring-election-data.Rmd) Error: processing vignette ‘exploring-election-data.Rmd’ failed with diagnostics: Value of SET_STRING_ELT() must be a 'CHARSXP' not a 'character' Execution halted

Searching online for this error I see that it's related to R's internals, maybe something in the C++ used by dplyr, no obvious fixes or workarounds are available.

I took a closer look into the specific error, and the locus of the problem is in these lines, in the chunk at lines 155-172 of exploring-election-data.Rmd):

dplyr::mutate(prop_votes = round(sum_votes / sum(sum_votes), 3),
              sum_votes = as.character(prettyNum(sum_votes, ","))) 

Here's a self-contained working example that may or may not generate the error (I've split each line out as a separate code chunk in an Rmd here:

library(eechidna)
  aec2013_fp %>% 
  dplyr::filter(CandidateID != 999) %>%  #exclude informal votes
  dplyr::mutate(candidate_full_name = 
                  paste0(GivenNm, " ", Surname, " (", CandidateID, ")"))  %>% 
  dplyr::group_by(Electorate, candidate_full_name) %>% 
  dplyr::summarise(sum_votes = sum(OrdinaryVotes)) %>% 
  dplyr::mutate(prop_votes = round(sum_votes / sum(sum_votes), 3),
                sum_votes = as.character(prettyNum(sum_votes, ","))) %>% 
  dplyr::ungroup() %>% 
  dplyr::arrange(dplyr::desc(prop_votes)) %>% 
  head() %>% 
  knitr::kable()

If I try to extract sum_votes after that line, for example with my_df$sum_votes, then I get the 'CHARSXP" error.

The problem is that I can't reliably reproduce this. Fifty runs consecutive of devtools::check() gives failed builds 29/50 times, builds with warning 1/21 times, and 20 builds with no warning. Similar results with smaller scale testing on devtools::build_win() and rhub::check_for_cran(). So I don't really know what to target.

I'm not sure where to go from here, suggestions welcome!

More control to plot?

Hello all,

Thank you very much for the library, I think it is great and easy to use :)

Is it possible to add a bit more configuration to the plotting part? In particular I would like:

• Add a border
• Store the plot as a variable.

For now I'm using the eechidna:::dorling function to make Dorling Cratograms. (Maybe it is possible to do this already but I haven't found how to do it D:)

Cheers and thank you for your help,
Vanessa

Add arguments to launchApp()

• Primary use would be for controlling the panels that display:
  • Some important ones: unemployment, median income, population, christianity
  • Important ages: 20-24, 85+

Data from 2016 census to be added

I have the data for the 2016 Census collated. And R code to do this. These need to be added to the package, and then we can finish the paper.

dorling cartogram in shiny app

Get the dorling cartogram version of the electorates with a map background into app as an alternative to the hexagon - to be able to choose either on startup?

Cartogram dependency

There was some discussion between @dicook & @benmarwick on adding chxy/cartogram as a dependency.

Just FYI, if we want, we can use the Remotes: field (like this) to depend on github packages and we should still pass R CMD check (I think).

Error with eechidna::launchApp()

I just got an email from Natalia Da Silva who reported that this wasn't working.

With plotly 4.5.2 on my computer I get this error:

Error in get0(oNam, envir = ns) : 
  lazy-load database 'C:/emacs/R/win-library/3.3/shinyjs/R/shinyjs.rdb' is corrupt
In addition: Warning message:
In get0(oNam, envir = ns) : internal error -3 in R_decompress1

@cpsievert can you help?

My session info:

> devtools::session_info()
Session info ---------------------------------------------------------------------------
 setting  value                       
 version  R version 3.3.1 (2016-06-21)
 system   x86_64, mingw32             
 ui       RStudio (0.99.1280)         
 language (EN)                        
 collate  English_United States.1252  
 tz       America/Los_Angeles         
 date     2016-09-30                  

Packages -------------------------------------------------------------------------------
 package     * version date       source                                
 assertthat    0.1     2013-12-06 CRAN (R 3.2.0)                        
 base64enc     0.1-3   2015-07-28 CRAN (R 3.2.2)                        
 colorspace    1.2-6   2015-03-11 CRAN (R 3.2.4)                        
 curl          2.1     2016-09-22 CRAN (R 3.3.1)                        
 DBI           0.5-1   2016-09-10 CRAN (R 3.3.1)                        
 devtools      1.11.1  2016-04-21 CRAN (R 3.2.5)                        
 digest        0.6.10  2016-08-02 CRAN (R 3.3.1)                        
 dplyr         0.5.0   2016-06-24 CRAN (R 3.3.1)                        
 eechidna    * 0.1     2016-09-30 Github (ropenscilabs/eechidna@f8fd0d2)
 ggplot2       2.1.0   2016-03-01 CRAN (R 3.2.4)                        
 ggthemes      3.2.0   2016-07-11 CRAN (R 3.3.1)                        
 git2r         0.15.0  2016-05-11 CRAN (R 3.2.5)                        
 gridExtra     2.2.1   2016-02-29 CRAN (R 3.2.3)                        
 gtable        0.2.0   2016-02-26 CRAN (R 3.2.3)                        
 htmltools     0.3.5   2016-03-21 CRAN (R 3.2.4)                        
 htmlwidgets   0.7     2016-08-02 CRAN (R 3.3.1)                        
 httpuv        1.3.3   2015-08-04 CRAN (R 3.2.3)                        
 httr          1.2.1   2016-07-03 CRAN (R 3.3.1)                        
 jsonlite      1.1     2016-09-14 CRAN (R 3.3.1)                        
 knitr         1.14.4  2016-09-23 Github (yihui/knitr@54ea64f)          
 lazyeval      0.2.0   2016-06-12 CRAN (R 3.3.1)                        
 magrittr      1.5     2014-11-22 CRAN (R 3.2.0)                        
 memoise       1.0.0   2016-01-29 CRAN (R 3.2.4)                        
 mime          0.5     2016-07-07 CRAN (R 3.3.1)                        
 miniUI        0.1.1   2016-01-15 CRAN (R 3.2.5)                        
 munsell       0.4.3   2016-02-13 CRAN (R 3.2.3)                        
 plotly        4.5.2   2016-09-26 CRAN (R 3.3.1)                        
 plyr          1.8.4   2016-06-08 CRAN (R 3.3.1)                        
 R6            2.1.3   2016-08-19 CRAN (R 3.3.1)                        
 Rcpp          0.12.7  2016-09-05 CRAN (R 3.3.1)                        
 rsconnect     0.4.3   2016-05-02 CRAN (R 3.2.5)                        
 scales        0.4.0   2016-02-26 CRAN (R 3.2.3)                        
 shiny         0.14    2016-09-10 CRAN (R 3.3.1)                        
 shinyjs       0.7     2016-08-21 CRAN (R 3.3.1)                        
 tibble        1.2     2016-08-26 CRAN (R 3.3.1)                        
 tidyr         0.6.0   2016-08-12 CRAN (R 3.3.1)                        
 viridis       0.3.4   2016-03-12 CRAN (R 3.2.4)                        
 withr         1.0.1   2016-02-04 CRAN (R 3.2.4)                        
 xtable        1.8-2   2016-02-05 CRAN (R 3.2.4)                        
> 

The `area_sqkm` is incorrect for 2019 map data?

library(tidyverse)
library(eechidna)

# 2016 (looks correct)

nat_map16 <- nat_map_download(2016) 
nat_data16 <- nat_data_download(2016)

nat_map16 %>% 
  left_join(nat_data16, by = "elect_div") %>% 
  ggplot() + 
  geom_polygon(aes(x = long, y = lat, group = group, fill = area_sqkm), color = "black") +
  theme(legend.position="bottom") + theme_void() + coord_equal() 

# 2019 (something wrong)

nat_map19 <- nat_map_download(2019) 
nat_data19 <- nat_data_download(2019)

nat_map19 %>% 
  left_join(nat_data19, by = "elect_div") %>% 
  ggplot() + 
  geom_polygon(aes(x = long, y = lat, group = group, fill = area_sqkm), color = "black") +
  theme(legend.position="bottom") + theme_void() + coord_equal() 

^{Created on 2020-04-18 by the reprex package (v0.3.0)}

colourInput() has been moved to the 'colourpicker' package

library(eechidna)
launchApp()

Error: colourInput() has been moved to the 'colourpicker' package.

library(colourpicker)
 launchApp()

Error: colourInput() has been moved to the 'colourpicker' package.

cartogram example code, creates 3 duplicated electorates

In the plot resulting from this code you can see three electorates, one in Vic and two in Australia, that are darker, there are two points overlaid suggesting an error in the data. This is fixed manually in the nat_data_cart.rda data provided with the package.

library(dplyr)
library(ggplot2)
data(nat_map)
data(nat_data)
cities <- list(c(151.2, -33.8), # Sydney
c(153.0, -27.5), # Brisbane
c(145.0, -37.8), # Melbourne
c(138.6, -34.9), # Adelaide,
c(115.9, -32.0)) # Perth
expand <- list(c(2,3.8), c(2,3), c(2.6,4.1), c(4,3), c(12,6))
nat_carto <- purrr::map2(.x=cities, .y=expand,
.f=aec_extract_f, aec_data=nat_data) %>%
purrr::map_df(aec_carto_f) %>%
mutate(region=as.integer(as.character(region))) %>%
rename(id=region)
nat_data_cart <- aec_carto_join_f(nat_data, nat_carto)

Map theme

theme_map <- theme_bw()
theme_map$line <- element_blank()
theme_map$strip.text <- element_blank()
theme_map$axis.text <- element_blank()
theme_map$plot.title <- element_blank()
theme_map$axis.title <- element_blank()
theme_map$panel.border <- element_rect(colour = "grey90", size=1, fill=NA)

ggplot(data=nat_map) +
geom_polygon(aes(x=long, y=lat, group=group, order=order),
fill="grey90", colour="white") +
geom_point(data=nat_data_cart, aes(x=x, y=y), size=2, alpha=0.4,
colour="#572d2c") +
geom_text(data=nat_data_cart, aes(x=x, y=y, label=id), size=0.5) +
coord_equal() + theme_map

nat_map_download() doesn't download map

Hi team!

It looks like there's a little problem with the nat_map_download().

Running nat_map16 <- nat_map_download(2016), returns instructions to run the same thing again when running nat_map16.

Cheers,
Quang

dependency on retiring spatial infrastructure packages

You will be aware, for example from:
https://r-spatial.org/r/2022/04/12/evolution.html,
https://r-spatial.org/r/2022/12/14/evolution2.html,
https://r-spatial.org/r/2023/04/10/evolution3.html and
https://rsbivand.github.io/csds_jan23/bivand_csds_ssg_230117.pdf and
perhaps view https://www.youtube.com/watch?v=TlpjIqTPMCA&list=PLzREt6r1NenmWEidssmLm-VO_YmAh4pq9&index=1
that rgdal, rgeos and maptools will be retired this
year, in October 2023.

eechidna imports rgeos, rgdal and seems from pkgapi to use rgdal::ogrListLayers rgdal::readOGR rgeos::gArea rgeos::gContains rgeos::gIntersection rgeos::gIntersects rgeos::gSimplify. Please refer to links in https://r-spatial.org/r/2023/04/10/evolution3.html for hints on replacements, especially the renderings of ASDAR using sf. Please update best by June, latest October 2023.

Try running CMD check under a scenario using sp evolution status 2 (substitute use of rgdal with sf for projection/transformation/CRS) and absence of retiring packages from the library path.

Imputing electorates - Error when calling weighted_avg_census_sa1

Hello,

Firstly, thanks for creating this awesome package. It really makes working with electoral data and census data easy and enjoyable!

I am planning to add some additional variables from the Census data to my data frames, which currently consist of eechidna abs and some variations of the aec variables. For the years when there was an election but no census, I have decided to follow the imputation strategy that you have introduced. Hence, I tested whether I can replicate the code on imputation vignette. I got an error message (Error: object 'CD' not found) after calling weighted_avg_census_sa1 on the mapping. See the code and error message below. I also got warnings after calling allocate_electorate, which I have attached below. I tried the weighted_avg_census_sa1 also with a data frame consisting of one additional variable from census data 2011, but I got the same error message as I got with the abs2011 data.

I'm currently using github installation of the package but got the same error message with r-cran version last week.

Thanks for help!
Onerva M.

Code below:

shapefile containing electoral boundaries from the

2013 federal election

sF_13 <- sF_download(year=2013)
trying URL 'https://github.com/ropenscilabs/eechidna/raw/master/extra-data/sF_13.rda'
Content type 'application/octet-stream' length 306828 bytes (299 KB)
downloaded 299 KB
data(abs2011)

load the centroids of 2011 SA1 polygons

load("centroids_sa1_2011.rda")

allocate electorate to allocate each SA1

to the electoral boundary in which it sits

mapping_c11_e13 <- allocate_electorate(centroids_ls = centroids_sa1_2011, electorates_sf = sF_13, census_year = "2011", election_year = "2013")
[1] 2000
[1] 4000
[1] 6000
[1] 8000
[1] 10000
[1] 12000
[1] 14000
[1] 16000
[1] 18000
[1] 20000
[1] 22000
[1] 24000
[1] 26000
[1] 28000
[1] 30000
[1] 32000
[1] 34000
[1] 36000
[1] 38000
[1] 40000
[1] 42000
[1] 44000
[1] 46000
[1] 48000
[1] 50000
[1] 52000
[1] 54000
There were 50 or more warnings (use warnings() to see the first 50)
warnings()
Warning messages:
1: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
2: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
3: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
4: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
5: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
6: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
7: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
8: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
9: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
10: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
11: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
12: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
13: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
14: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
15: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
16: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
17: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
18: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
19: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
20: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
21: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
22: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
23: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
24: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
25: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
26: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
27: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
28: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
29: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
30: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
31: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
32: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
33: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
34: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
35: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
36: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
37: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
38: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
39: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
40: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
41: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
42: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
43: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
44: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
45: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
46: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
47: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
48: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
49: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
50: In RGEOSBinPredFunc(spgeom1, spgeom2, byid, func) :
spgeom1 and spgeom2 have different proj4 strings
> census_aec13_11 <- weighted_avg_census_sa1(mapping_df = mapping_c11_e13, abs_df = abs2011)
Error: object 'CD' not found

color scheme for shiny app

1b9e77 "forest"

e7298a "pink"

e6ab02 "yellow"

66a61e "green"

7570b3 "violet"

d95f02 "orange"

3690c0 "blue"

pkgdown build fails

Hey @bryceroney could you check out the logs for the gh-pages pkgdown build? Build seems to be failing.

Links broken on readme.md

On readme.md, the following is not true (in that there is no articles folder anymore):

We have many vignettes that show how to access these data in the package, and demonstrate how to analyse the data using R. These can be found in the articles tab at the top of this page.

And as such, the links are broken in this section.

2019 data

Over the summer holidays 'd like to pull in the 2019 election results to the project. Before I do so, is there anything I should know about best practices for this? The format should, I suppose be much like the 2016 results.

Vignettes not building

Vignettes are not building on Appveyor and Travis because of small changes made to data on the git repo. When there are other changes to warrant a new release these will be pushed through. No action required.

R CMD check

Check the package build again, that example code delimited by \dontrun, might be made to run, in the package submission to CRAN.

rmapshaper now on CRAN

@benmarwick Asked me to let you folks know when rmapshaper landed on CRAN. It was accepted and released this morning with version 0.1.0. Thanks for the nudge along, otherwise I probably wouldn't have done it by this point. :)

Don't ignore NEWS.md file

I think this line https://github.com/ropenscilabs/eechidna/blob/master/.Rbuildignore#L1 is ignoring all .md files - including NEWS.md - can you not rbuildignore news.md so that it shows up on the CRAN page?

also, can you update NEWS each time a version goes to CRAN?

thx!

Add another view to shiny app

We have space for at least one more view in the top row of the shiny app (the same row as the map and the proportions by party). Any ideas on what we should show?