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preschool_maps.R
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preschool_maps.R
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# ## Export data for clusters
# cp_clusters_pre <- st_read("Centros poblados/CP_P.shp") %>%
# as.data.frame %>%
# select(XGD, YGD, CODCP) %>%
# mutate(across(matches('(CODCP)'), as.character))
#
# write.arff(cp_clusters_pre, file = 'Centros poblados/clustering.arff')
#
## Import clusters with different measures
# ccpp_clusters <- c(
# "Centros poblados/cluster_min2/clustering_result025/",
# "Centros poblados/cluster_min2/clustering_result050/",
# "Centros poblados/cluster_min2/clustering_result075/",
# "Centros poblados/cluster_min2/clustering_result125/",
# "Centros poblados/cluster_min2/clustering_result1/",
# 'Centros poblados/cluster_min2/clustering_result15/',
# 'Centros poblados/cluster_min2/clustering_result175/',
# "Centros poblados/cluster_min2/clustering_result2/"
# ) %>%
# map(import_clustering) %>%
# reduce(full_join, by = 'codccpp') %>%
# select(codccpp, matches('id'), matches('noise'))
#
# write_csv(ccpp_clusters, file = 'Centros poblados/ccpp_clusters.csv')
# ccpp_clusters <- read_csv("Centros poblados/ccpp_clusters.csv") %>%
# mutate(across(where(is.numeric), as.integer))
#
## Import centros poblados info
# corrected_minedu <- read_csv('Centros poblados/corrected_geo-minedu.csv') %>%
# bind_rows(read_csv('Centros poblados/corrected_geo-minedu2.csv')) %>%
# mutate(across(where(is.numeric), as.integer64))
#
centros_poblados <- readRDS('Centros poblados/centros_poblados.rds')
#
## Create cluster areas
# cluster_list <- inner_join(centros_poblados %>% select(codccpp), ccpp_clusters) %>%
# mutate(
# id025 = if_else(noise025 == TRUE, 0L, id025),
# id050 = if_else(noise050 == TRUE, 0L, id050),
# id075 = if_else(noise075 == TRUE, 0L, id075),
# id1 = if_else(noise1 == TRUE, 0L, id1),
# id125 = if_else(noise1 == TRUE, 0L, id125),
# id15 = if_else(noise15 == TRUE, 0L, id15),
# id175 = if_else(noise15 == TRUE, 0L, id175),
# id2 = if_else(noise2 == TRUE, 0L, id2)
# ) %>%
# select(-matches('noise'))
# #
# cluster_areas <- c('id2', 'id175', 'id15','id125', 'id1', 'id075', 'id050', 'id025') %>%
# map(add_area, data = cluster_list) %>%
# map(right_join, y = cluster_list) %>%
# map(~ ungroup(.x) %>% select(codccpp, starts_with('area'))) %>%
# reduce(inner_join) %>%
# replace_na(value = 0L)
#
# Save
# write_csv(cluster_areas, 'Centros poblados/cluster_areas.csv')
# write_csv(cluster_list, 'Centros poblados/cluster_list.csv')
#
cluster_areas <- read_csv("Centros poblados/cluster_areas.csv") %>%
mutate(
across(matches("area"), ~ units::set_units(.x, value = km^2)),
codccpp = as.integer(codccpp)
)
cluster_list <- read_csv("Centros poblados/cluster_list.csv") %>%
mutate(across(where(is.numeric), as.integer))
cluster_errors <- readRDS("Centros poblados/cluster_errors.rds") %>%
filter(area_cluster > units::set_units(100, value = km^2)) %>%
select(codccpp)
identical_ccpp <- centros_poblados %>%
as_tibble() %>%
select(-geometry) %>%
group_by(ubigeo, nomcp) %>%
filter(n() > 1) %>%
mutate(group_id = cur_group_id()) %>%
ungroup() %>%
select(codccpp, group_id) %>%
anti_join(cluster_errors)
############## DONT RUN ###############################################
## identical_ccpp.1 <- centros_poblados %>%
## as_tibble() %>%
## select(-geometry) %>%
## group_by(ubigeo, nomcp) %>%
## filter(n() > 1) %>%
## mutate(group_id.1 = cur_group_id(), n.1 = n()) %>%
## ungroup() %>%
## select(codccpp, group_id.1) %>%
## anti_join(cluster_errors)
##
## identical_ccpp.2 <- centros_poblados %>%
## as_tibble() %>%
## select(-geometry) %>%
## filter(!is.na(idccpp)) %>%
## group_by(idccpp) %>%
## filter(n() > 1) %>%
## mutate(group_id.2 = cur_group_id(), n.2 = n()) %>%
## ungroup() %>%
## select(codccpp, group_id.2)
##
## identical_ccpp <- full_join(identical_ccpp.1, identical_ccpp.2) %>%
## absorb_groups(group_id.1, group_id.2)
#######################################################################
# clusters <- inner_join(cluster_list %>% as_tibble() %>% select(-geometry), cluster_areas) %>%
# select_id(var = "id025", start = TRUE) %>%
# select_id(var = "id050") %>%
# select_id(var = "id075") %>%
# select_id(var = "id1") %>%
# select_id(var = "id125") %>%
# select_id(var = "id15") %>%
# select_id(var = "id175") %>%
# select_id(var = "id2") %>%
# left_join(identical_ccpp) %>%
# recodify_id()
#
# areas <- clusters %>%
# inner_join(centros_poblados %>% select(codccpp)) %>%
# add_area('cluster', data = .)
#
# clusters %<>% left_join(areas)
#
# saveRDS(clusters, file = 'Centros poblados/cluster_errors.rds') # RUN WITHOUT DOING ANTIJOIN OF CLUSTER ERRORS
# saveRDS(clusters, file = 'Centros poblados/clusters.rds')
clusters <- readRDS(file = "Centros poblados/clusters.rds")
#
# Merge all
# centros_poblados %<>% inner_join(clusters)
#
# correction_sisfoh <- read_csv("Centros poblados/correction_ccpp-sisfoh.csv") %>%
# left_join(clusters %>% select(-area_cluster)) %>%
# mutate(ubigeo = as.integer(ubigeo), idccpp = as.integer64(idccpp)) %>%
# select(-codccpp)
#
# conversion_table <- centros_poblados %>% as_tibble() %>%
# select(cluster, ubigeo, idccpp, matches('nomcp')) %>%
# pivot_longer(cols = matches('nomcp'), values_to = 'nomcp', values_drop_na = TRUE) %>%
# select(-name) %>%
# bind_rows(correction_sisfoh) %>%
# mutate(
# ubigeo = str_pad(ubigeo, 6, pad = "0"),
# idccpp = str_pad(idccpp, 10, pad = "0"),
# nomcp = stri_enc_toutf8(nomcp, validate = TRUE),
# )
#
# dbWriteTable(db_conn, name = "conversion_ccpp", conversion_table, temporary = FALSE, overwrite = TRUE)
#
# cluster_summary <- centros_poblados %>%
# as_tibble() %>%
# select(-geometry) %>%
# ungroup() %>%
# mutate(across(!matches("(^idccpp$)|(^nregion$)|(^area_cluster$)|(^geometry$)|(^nomcp)"), as.integer)) %>%
# group_by(cluster) %>%
# mutate(n = n(), group = if_else(n == 1, 0L, cluster), .before = 1) %>%
# group_by(idccpp) %>%
# arrange(n) %>%
# mutate(across(matches("(2015)|(2017)"), ~ if_else(row_number() == 1, ., 0L, 0L))) %>%
# group_by(cluster) %>%
# summarise(
# across(matches("(2015)|(2017)"), sum, na.rm = TRUE),
# across(matches("(nregion)|(rural)|(category)"), calculate_mode, na.rm = TRUE),
# height = mean(height),
# capital = if_else(is.na(capital), 0L, capital) %>% max(),
# area_cluster = head(area_cluster, 1),
# group = head(group, 1),
# n = head(n, 1),
# )
#
# cluster_summary.geo <- centros_poblados %>%
# select(cluster, codccpp) %>%
# left_join(cluster_summary)
#
# saveRDS(cluster_summary, file = 'Centros poblados/cluster_summary.rds')
# saveRDS(cluster_summary.geo, file = 'Centros poblados/cluster_summary-geo.rds')
# dbWriteTable(db_conn, name = "clusters", cluster_summary %>% mutate(area_cluster = as.numeric(area_cluster)), temporary = FALSE, overwrite = TRUE)
cluster_summary.geo <- readRDS(file = "Centros poblados/cluster_summary-geo.rds")
peru_sf <- ne_states(country = "Peru", returnclass = "sf") %>%
select(geometry) %>%
st_transform(crs = "+proj=moll +datum=WGS84")
#colours <- seq_gradient_pal("#132B43", "#56B1F7")(seq(0, 1, length.out = 12))
colours <- seq_gradient_pal("#003f5c", "#ffa600")(seq(0, 1, length.out = 12))
# All points plot
ggsave("Centros poblados/clusters.png",
plot =
ggplot() +
geom_sf(data = peru_sf, fill = "#f7fcfd", alpha = 0.75, lwd = 0.25) +
geom_sf(data = cluster_summary.geo, aes(color = group), size = 0.1, alpha = 0.5) +
continuous_scale("color", "noise_clusters", highlight_palette(colours, range = range(cluster_summary.geo$group), target = c(-0.25, 0.25)),
guide = guide_colourbar(nbin = max(cluster_summary.geo$group) / 100) # Give guide plenty bins
) +
theme_bw(),
device = "png", height = 13333.3333333, width = 10000, units = "px"
)
# Noise plot
ggsave("Centros poblados/clusters_noise.png",
plot =
ggplot() +
geom_sf(data = peru_sf, fill = "#f7fcfd", alpha = 0.75, lwd = 0.25) +
geom_sf(data = cluster_summary.geo %>% filter(group == 0), aes(color = group), size = 0.1, alpha = 0.5) +
continuous_scale("color", "noise_clusters", highlight_palette(colours, range = range(cluster_summary.geo$group), target = c(-0.25, 0.25)),
guide = guide_colourbar(nbin = max(cluster_summary.geo$group) / 100) # Give guide plenty bins
) +
theme_bw(),
device = "png", height = 13333.3333333, width = 10000, units = "px"
)
# One per group plot
ggsave("Centros poblados/oneincluster.png",
plot =
ggplot() +
geom_sf(data = peru_sf, fill = "#f7fcfd", alpha = 0.75, lwd = 0.25) +
geom_sf(data = cluster_summary.geo %>% arrange(cluster, idccpp) %>% filter(row_number() == 1) %>% mutate(area_cluster = as.numeric(area_cluster)),
aes(color = group, size = area_cluster), alpha = 0.5) +
continuous_scale("color", "noise_clusters", highlight_palette(colours, range = range(cluster_summary.geo$group), target = c(-0.25, 0.25)),
guide = guide_colourbar(nbin = max(cluster_summary.geo$group) / 100) # Give guide plenty bins
) +
theme_bw(),
device = "png", height = 13333.3333333, width = 10000, units = "px"
)
ggsave("Centros poblados/bigclusters.png",
plot =
ggplot() +
geom_sf(data = peru_sf, fill = "#f7fcfd", alpha = 0.75, lwd = 0.25) +
geom_sf(data = cluster_summary.geo %>% arrange(cluster, idccpp) %>% mutate(area_cluster = as.numeric(area_cluster)) %>% filter(between(area_cluster, 1000, 3000)),
aes(color = area_cluster)) +
continuous_scale("color", "noise_clusters", highlight_palette(colours, range = range(cluster_summary.geo$group), target = c(-0.25, 0.25)),
guide = guide_colourbar(nbin = max(cluster_summary.geo$group) / 100) # Give guide plenty bins
) +
theme_bw(),
device = "png", height = 13333.3333333, width = 10000, units = "px"
)