restructure prep folder so ready for importing other years of data

README.Rmd

@@ -197,8 +197,8 @@ electorates <- c(1,0,0,27,0,1,41,1,1,0)
 
 polls %>%
     filter(MidDate > "2014-12-30" & MidDate < "2017-10-1") %>%
-    mutate(wt_p = weight_polls(MidDate, method = "pundit"),
-           wt_c = weight_polls(MidDate, method = "curia")) %>%
+    mutate(wt_p = weight_polls(MidDate, method = "pundit", refdate = as.Date("2017-09-22")),
+           wt_c = weight_polls(MidDate, method = "curia", refdate = as.Date("2017-09-22"))) %>%
     group_by(Party) %>%
     summarise(pundit_perc = round(sum(VotingIntention * wt_p, na.rm = TRUE) / sum(wt_p) * 100, 1),
               curia_perc = round(sum(VotingIntention * wt_c, na.rm = TRUE) / sum(wt_c) * 100, 1)) %>%

README.md

@@ -141,17 +141,17 @@ GE2014 %>%
 ##  1          Gisborne Region      14342              0.351
 ##  2            Nelson Region      18754              0.398
 ##  3         Northland Region      53688              0.427
-##  4        Wellington Region     165207              0.430
+##  4        Wellington Region     164913              0.430
 ##  5 Manawatu-Wanganui Region      78841              0.447
 ##  6             Otago Region      75933              0.447
-##  7                     <NA>     934589              0.451
+##  7                     <NA>     935376              0.451
 ##  8       Hawke's Bay Region      53833              0.460
 ##  9            Tasman Region      17935              0.465
 ## 10        West Coast Region      12226              0.465
 ## 11     Bay of Plenty Region      89065              0.473
-## 12          Auckland Region     478760              0.486
+## 12          Auckland Region     478724              0.486
 ## 13           Waikato Region     134511              0.512
-## 14        Canterbury Region     192577              0.520
+## 14        Canterbury Region     192120              0.520
 ## 15       Marlborough Region      17474              0.520
 ## 16         Southland Region      36158              0.528
 ## 17          Taranaki Region      42586              0.552
@@ -168,16 +168,19 @@ GE2014 %>%
 ```
 
 ```
-## # A tibble: 7 x 2
-##                                               VotingPlace TotalVotes
-##                                                     <chr>      <dbl>
-## 1 Chatham Islands Council Building, 9 Tuku Road, Waitangi         90
-## 2                       Ordinary Votes BEFORE polling day     630775
-## 3          Overseas Special Votes including Defence Force      38316
-## 4                        Special Votes BEFORE polling day      71362
-## 5                            Special Votes On polling day     151530
-## 6                            Votes Allowed for Party Only      40986
-## 7        Voting places where less than 6 votes were taken       1530
+## # A tibble: 10 x 2
+##                                                VotingPlace TotalVotes
+##                                                      <chr>      <dbl>
+##  1 Chatham Islands Council Building, 9 Tuku Road, Waitangi         90
+##  2  Mount Pleasant Community Centre, 3 Mccormacks Bay Road        457
+##  3                       Ordinary Votes BEFORE polling day     630775
+##  4               Otaki Surf Lifesaving Club, Marine Parade        294
+##  5          Overseas Special Votes including Defence Force      38316
+##  6              Port Fitzroy Aotea Centre (Nurses Cottage)         36
+##  7                        Special Votes BEFORE polling day      71362
+##  8                            Special Votes On polling day     151530
+##  9                            Votes Allowed for Party Only      40986
+## 10        Voting places where less than 6 votes were taken       1530
 ```
 
 ```r
@@ -301,8 +304,8 @@ electorates <- c(1,0,0,27,0,1,41,1,1,0)
 
 polls %>%
     filter(MidDate > "2014-12-30" & MidDate < "2017-10-1") %>%
-    mutate(wt_p = weight_polls(MidDate, method = "pundit"),
-           wt_c = weight_polls(MidDate, method = "curia")) %>%
+    mutate(wt_p = weight_polls(MidDate, method = "pundit", refdate = as.Date("2017-09-22")),
+           wt_c = weight_polls(MidDate, method = "curia", refdate = as.Date("2017-09-22"))) %>%
     group_by(Party) %>%
     summarise(pundit_perc = round(sum(VotingIntention * wt_p, na.rm = TRUE) / sum(wt_p) * 100, 1),
               curia_perc = round(sum(VotingIntention * wt_c, na.rm = TRUE) / sum(wt_c) * 100, 1)) %>%
@@ -364,7 +367,7 @@ ggplot(Meshblocks2013, aes(x = WGS84Longitude, y = WGS84Latitude, colour = Media
 ```
 
 ```
-## Warning: Removed 13 rows containing missing values (geom_point).
+## Warning: Removed 642 rows containing missing values (geom_point).
 ```
 
 ![plot of chunk unnamed-chunk-10](figure/unnamed-chunk-10-1.png)

build.R

@@ -26,33 +26,33 @@ library(grid)
 # repeating expensive downloads
 
 # About 1MB worth of voting results:
-# source("prep/download_votingplace_results.R")
+# source("prep/election_results/download_votingplace_results.R")
 
 # About 130MB of shapefiles / maps, used for locating voting places in areas:
-# source("prep/download_map_shapefiles.R")
+# source("prep/census_related/download_map_shapefiles.R")
 
-# source("prep/download_census2013.R")
+# source("prep/census_related/download_census2013.R")
 
 # download.file("http://www.electionresults.govt.nz/electionresults_2014/2014_Voting_Place_Co-ordinates.xls",
 #              destfile = "downloads/elect2014/vp_coordinates.xls", mode = "wb")
 
 #----------tidying----------------
 # import all the voting results CVS and amalgamate into a single object
-source("prep/tidy_votingplace_results.R") # 30 seconds
+source("prep/election_results/tidy_votingplace_results.R") # 30 seconds
 
 # download and import the actual locations.  Includes a 575KB download.
 # This script also calls ./prep/match_locations_to_areas.R from within itself 
 # (takes a few minutes to run because of importing shapefiles, downloaded earlier):
-source("prep/import_votingplace_locations.R") # 3 minutes
+source("prep/election_results/import_votingplace_locations.R") # 3 minutes
 
 # Import and tidy up census data
-source("prep/import_census.R") # 30 seconds
+source("prep/census_related/import_census.R") # 30 seconds
 
 # Match census data to shapefiles so we have lat and long
-source("prep/add_locations_census.R")
+source("prep/census_related/add_locations_census.R")
 
 # Create cartograms
-# go to "prep/create-cartograms.R" and run by hand; requires some manual steps with ScapeToad
+# go to "prep/census_related/create-cartograms.R" and run by hand; requires some manual steps with ScapeToad
 
 #-------opinion polls and related-------------------
 # Load in the existing version of the polls to facilitate checking and seeing
@@ -61,12 +61,12 @@ load("pkg1/data/polls.rda")
 oldpolls <- polls
 rm(polls)
 
-source("prep/download_polls_2005.R")
-source("prep/download_polls_2008.R")
-source("prep/download_polls_2011.R")
-source("prep/download_polls_2014.R")
-source("prep/download_polls_2017.R")
-source("prep/combine_polls.R")
+source("prep/polls/download_polls_2005.R")
+source("prep/polls/download_polls_2008.R")
+source("prep/polls/download_polls_2011.R")
+source("prep/polls/download_polls_2014.R")
+source("prep/polls/download_polls_2017.R")
+source("prep/polls/combine_polls.R")
 
 expect_equal(oldpolls, polls[1:nrow(oldpolls), ])
 polls_both <- cbind(oldpolls, polls[1:nrow(oldpolls), ])
@@ -99,7 +99,7 @@ knit("README.Rmd", "README.md")
 
 # run CRAN checks
 check("pkg1") 
-check("pkg2") # one note from exceeding 5MB
+check("pkg2") # one note from exceeding 5MB, and the cartograms need helpfiles
 
 # create vignettes for actual builds
 build_vignettes("pkg1")