From 2a23779ef913476c47c0b76e9b9eff8e18076506 Mon Sep 17 00:00:00 2001 From: Zarrar Khan Date: Sat, 22 Jan 2022 18:03:45 +0500 Subject: [PATCH] Updating county files with FIPS https://github.com/openjournals/joss-reviews/issues/4015#issuecomment-1018420402 --- cheatsheet.pdf | Bin 0 -> 2188520 bytes data/mapIntersectGCAMBasinUS52County.rda | Bin 849515 -> 868215 bytes data/mapIntersectGCAMBasinUS52Countydf.rda | Bin 896794 -> 999411 bytes data/mapUS49County.rda | Bin 717338 -> 737573 bytes data/mapUS49Countydf.rda | Bin 562627 -> 628543 bytes data/mapUS52County.rda | Bin 783228 -> 799130 bytes data/mapUS52CountyCompact.rda | Bin 804462 -> 821409 bytes data/mapUS52CountyCompactdf.rda | Bin 646159 -> 714553 bytes data/mapUS52Countydf.rda | Bin 581505 -> 651576 bytes inst/extras/devTests.R | 16 +++- inst/extras/saveDataFiles.R | 105 ++++++++++++++++++++- inst/extras/saveDataFilesDF.R | 27 +----- 12 files changed, 113 insertions(+), 35 deletions(-) create mode 100644 cheatsheet.pdf diff 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zp+_l76m=A#M{!CNVktt8<|r#9%4;n+y_0|gbU*kX_S^sfKq5c|;CsXu{r}Vm0000s z04D$#XlMWc4FCWD0AZn^001-q0001rku>z0WK9g288l=XV1o$WsWEoSkvKpBQ6)6` zr7MebWf$!rAgAUUIq;8H?B1~Ko^LXab8@V@swkEt+=v2@KmkZF1VK#DL;zD3{+a+4 z6Jn^MWBA(Fdw!2#1w`6wHqysjH|=+QGpdRvt&r% # Will give you the relevant plot but multiple counties # See how you can define your own columns as arguments -rmap::map(mydata) +rmap::map(mydata, + subRegCol = "NAME", + valueCol = "HR60") -# Here you can see all the multiple counties. rmap appends the State for you as it recognizes this. +# Here you can see all the multiple counties labelled. +# rmap appends the State for you as it recognizes this. +# You can also see some of the other features of rmap here +# such as underLayer, zoom, labels etc. rmap::map(mydata, + subRegCol="NAME", + valueCol="HR60", labels = T, labelSize = 3, labelRepel = T, - subRegCol="NAME", - valueCol="HR60", - underLayer = rmap::mapUS49) + underLayer = rmap::mapUS49, + zoom=-2) diff --git a/inst/extras/saveDataFiles.R b/inst/extras/saveDataFiles.R index efeaeb2..27689ad 100644 --- a/inst/extras/saveDataFiles.R +++ b/inst/extras/saveDataFiles.R @@ -128,6 +128,20 @@ if(redoMaps){ mapStates@data <- mapStates@data %>% dplyr::mutate(name="mapStates") use_data(mapStates, version=3, overwrite=T) + + mapx <- rmap::mapStates + mapx <- mapx[!is.na(mapx@data$subRegion),] + mapx@data <- mapx@data%>%droplevels() + mapx@data %>% filter(is.na(subRegion)) + nrow(mapx) + mapx@data <- mapx@data %>% + dplyr::mutate(subRegion = if_else(is.na(subRegion),subRegionAlt,subRegion)) %>% + dplyr::filter(!is.na(subRegion)); + mapx@data %>% filter(is.na(subRegion)) + nrow(mapx) + mapStates <- mapx + use_data(mapStates, version=3, overwrite=T) + } #----------------- @@ -772,6 +786,27 @@ if(redoMaps){ mapUS52County@data <- mapUS52County@data %>% dplyr::mutate(name="mapUS52County") use_data(mapUS52County, version=3, overwrite=T) + + # Create FIPS + mapx <- rmap::mapUS52County + mapx@data <- mapx@data %>% + dplyr::mutate(subRegion_cc = paste0(subRegion,COUNTYCODE)) + subRegions_duplicated <- (mapx@data$subRegion)[(mapx@data$subRegion)%>%duplicated()]; subRegions_duplicated + countycodes_remove <- mapx@data %>% + dplyr::select(subRegion,subRegionAlt,COUNTYCODE, area_sqkm, subRegion_cc) %>% + dplyr::filter(subRegion %in% subRegions_duplicated)%>% + dplyr::group_by(subRegionAlt) %>% + dplyr::mutate(max_area = max(area_sqkm)) %>% + dplyr::filter(area_sqkm != max_area); countycodes_remove + + mapx@data <- mapx@data %>% + dplyr::mutate( + subRegion = if_else(subRegion_cc %in% countycodes_remove$subRegion_cc,paste0(subRegion,"_city"),subRegion), + FIPS = paste0(STATEFP,COUNTYCODE)) + mapUS52County <- mapx + subRegions_duplicated <- (mapx@data$subRegion)[(mapx@data$subRegion)%>%duplicated()]; subRegions_duplicated + use_data(mapUS52County, version=3, overwrite=T) + } # US 52 Counties with Alaska (AK), Hawaii (HI) and Puerto Rico (PR) shrunken and shifted @@ -794,19 +829,19 @@ if(redoMaps){ alaska <- elide(alaska, scale=max(apply(bbox(alaska), 1, diff)) / 2.3) alaska <- elide(alaska, shift=c(-2100000, -2500000)) proj4string(alaska) <- proj4string(us_aea) - rmap::rmap::map(alaska) + rmap::map(alaska) # extract, then rotate & shift hawaii hawaii <- us_aea[us_aea$STATENAME=="Hawaii",] hawaii <- elide(hawaii, rotate=-35) hawaii <- elide(hawaii, shift=c(5400000, -1400000)) proj4string(hawaii) <- proj4string(us_aea) - rmap::rmap::map(hawaii) + rmap::map(hawaii) # extract, then rotate & shift Puerto Rico pr <- us_aea[us_aea$STATENAME=="Puerto Rico",] #pr <- elide(pr, rotate=-35) pr <- elide(pr, shift=c(-2500000,0)) proj4string(pr) <- proj4string(us_aea); - rmap::rmap::map(pr) + rmap::map(pr) # remove old states and put new ones back in; note the different order # we're also removing puerto rico in this example but you can move it # between texas and florida via similar methods to the ones we just used @@ -821,6 +856,27 @@ if(redoMaps){ mapUS52CountyCompact@data <- mapUS52CountyCompact@data %>% dplyr::mutate(name="mapUS52CountyCompact") use_data(mapUS52CountyCompact, version=3, overwrite=T) + + # Create FIPS + mapx <- rmap::mapUS52CountyCompact + mapx@data <- mapx@data %>% + dplyr::mutate(subRegion_cc = paste0(subRegion,COUNTYCODE)) + subRegions_duplicated <- (mapx@data$subRegion)[(mapx@data$subRegion)%>%duplicated()]; subRegions_duplicated + countycodes_remove <- mapx@data %>% + dplyr::select(subRegion,subRegionAlt,COUNTYCODE, area_sqkm, subRegion_cc) %>% + dplyr::filter(subRegion %in% subRegions_duplicated)%>% + dplyr::group_by(subRegionAlt) %>% + dplyr::mutate(max_area = max(area_sqkm)) %>% + dplyr::filter(area_sqkm != max_area); countycodes_remove + + mapx@data <- mapx@data %>% + dplyr::mutate( + subRegion = if_else(subRegion_cc %in% countycodes_remove$subRegion_cc,paste0(subRegion,"_city"),subRegion), + FIPS = paste0(STATEFP,COUNTYCODE)) + mapUS52CountyCompact <- mapx + subRegions_duplicated <- (mapx@data$subRegion)[(mapx@data$subRegion)%>%duplicated()]; subRegions_duplicated + use_data(mapUS52CountyCompact, version=3, overwrite=T) + } # US 49 Counties @@ -842,7 +898,28 @@ if(redoMaps){ mapUS49County@data <- mapUS49County@data %>% dplyr::mutate(name="mapUS49County") use_data(mapUS49County, version=3, overwrite=T) -} + + # Create FIPS + mapx <- rmap::mapUS49County + mapx@data <- mapx@data %>% + dplyr::mutate(subRegion_cc = paste0(subRegion,COUNTYCODE)) + subRegions_duplicated <- (mapx@data$subRegion)[(mapx@data$subRegion)%>%duplicated()]; subRegions_duplicated + countycodes_remove <- mapx@data %>% + dplyr::select(subRegion,subRegionAlt,COUNTYCODE, area_sqkm, subRegion_cc) %>% + dplyr::filter(subRegion %in% subRegions_duplicated)%>% + dplyr::group_by(subRegionAlt) %>% + dplyr::mutate(max_area = max(area_sqkm)) %>% + dplyr::filter(area_sqkm != max_area); countycodes_remove + + mapx@data <- mapx@data %>% + dplyr::mutate( + subRegion = if_else(subRegion_cc %in% countycodes_remove$subRegion_cc,paste0(subRegion,"_city"),subRegion), + FIPS = paste0(STATEFP,COUNTYCODE)) + mapUS49County <- mapx + subRegions_duplicated <- (mapx@data$subRegion)[(mapx@data$subRegion)%>%duplicated()]; subRegions_duplicated + use_data(mapUS49County, version=3, overwrite=T) + + } # Merge @@ -1381,3 +1458,23 @@ if(F){ } +#----------- +# Test for duplicate subRegions +#---------- + +if(F){ + library(rmap); + + # Plotting + #------------- + # World + for(i in c("mapCountries","mapStates","mapGCAMReg32","mapGCAMBasins","mapUS52","mapUS52County", + "mapUS52CountyCompact","mapUS49","mapUS49County","mapGCAMReg32US52", + "mapIntersectGCAMBasinCountry")){ + mapx = get(i) + subRegions_duplicated <- (mapx@data$subRegion)[(mapx@data$subRegion)%>%duplicated()]; subRegions_duplicated + print(i) + print(subRegions_duplicated) + } + +} diff --git a/inst/extras/saveDataFilesDF.R b/inst/extras/saveDataFilesDF.R index b4feb35..18da30c 100644 --- a/inst/extras/saveDataFilesDF.R +++ b/inst/extras/saveDataFilesDF.R @@ -211,6 +211,7 @@ mapUS52Countydf <- rmap::tidy_shape(shape=mapx, shapeColum="subRegion") %>% mapUS52Countydf %>% head() use_data(mapUS52Countydf, version=3, overwrite=T) + # US 52 Counties with Alaska (AK), Hawaii (HI) and Puerto Rico (PR) shrunken and shifted #------------------- mapx <- rmap::mapUS52CountyCompact @@ -294,14 +295,6 @@ mapIntersectGCAMBasinUS52Countydf <- rmap::tidy_shape(shape=mapx, shapeColum="su mapIntersectGCAMBasinUS52Countydf %>% head() use_data(mapIntersectGCAMBasinUS52Countydf, version=3, overwrite=T) -# Intersection of GCAM Basins and US 52 County -mapx <- rmap::mapIntersectGCAMBasinUS52County -mapIntersectGCAMBasinUS52Countydf <- rmap::tidy_shape(shape=mapx, shapeColum="subRegion") %>% - dplyr::rename(subRegion=id)%>% - dplyr::inner_join(mapx@data, by="subRegion") %>% dplyr::rename(lon=long) %>% dplyr::mutate(name = paste0(name,"df")); -mapIntersectGCAMBasinUS52Countydf %>% head() -use_data(mapIntersectGCAMBasinUS52Countydf, version=3, overwrite=T) - # Intersection of GCAM Basins and US 52 States mapx <- rmap::mapIntersectGCAMBasin32RegUruguay mapIntersectGCAMBasin32RegUruguaydf <- rmap::tidy_shape(shape=mapx, shapeColum="subRegion") %>% @@ -403,15 +396,6 @@ if(T){ "subRegGCAMBasinsUS52" = tolower(rmap::mapGCAMBasinsUS52@data$subRegion %>% unique() %>% as.character %>% sort()), - "subRegGCAMLand" = - tolower(rmap::mapGCAMLand@data$subRegion %>% unique() %>% as.character %>% - sort()), - "subRegGCAMLandUS49" = - tolower(rmap::mapGCAMLandUS49@data$subRegion %>% unique() %>% as.character %>% - sort()), - "subRegGCAMLandUS52" = - tolower(rmap::mapGCAMLandUS52@data$subRegion %>% unique() %>% as.character %>% - sort()), "subRegUS49HUC2" = tolower(rmap::mapUS49HUC2@data$subRegion %>% unique() %>% as.character %>% sort()), @@ -475,15 +459,6 @@ if(T){ "subRegGCAMBasinsUS52Alt" = tolower(rmap::mapGCAMBasinsUS52@data$subRegionAlt %>% unique() %>% as.character %>% sort()), - "subRegGCAMLandAlt" = - tolower(rmap::mapGCAMLand@data$subRegionAlt %>% unique() %>% as.character %>% - sort()), - "subRegGCAMLandUS49Alt" = - tolower(rmap::mapGCAMLandUS49@data$subRegionAlt %>% unique() %>% as.character %>% - sort()), - "subRegGCAMLandUS52Alt" = - tolower(rmap::mapGCAMLandUS52@data$subRegionAlt %>% unique() %>% as.character %>% - sort()), "subRegUS49HUC2Alt" = tolower(rmap::mapUS49HUC2@data$subRegionAlt %>% unique() %>% as.character %>% sort()),

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