lrgv_23_grandparents <- get_acs(geography = "tract", county = c("Cameron"), table = "S1002", state = "TX", year = 2023, survey = "acs5", geometry = TRUE) %>% left_join(., all_acs_vars, by = c("variable"="name")) %>% separate_wider_delim(., cols = "label", delim = "!!", names_sep="", too_few = "align_start") lrgv_23_grandparents %>% filter(variable == "S1002_C01_001") %>% st_as_sf(.) %>% tm_shape(.) + tm_polygons(col = c("estimate"), popup.vars = c("Name" = "label3", "NAME", "Estimate"="estimate"), alpha = 0.5, style = "jenks") + tm_basemap("OpenStreetMap")

lrgv_23_grandparents <- get_acs(geography = "tract", county = c("Cameron"), table = "S1002", state = "TX", year = 2023, survey = "acs5", geometry = TRUE) %>% left_join(., all_acs_vars, by = c("variable"="name")) %>% separate_wider_delim(., cols = "label", delim = "!!", names_sep="", too_few = "align_start") custom_Bville<- read_sf(dsn = "C:/Users/daniel.pinon/Downloads/my_custom_brownsville_shape/map.geojson") %>% as.data.frame(.) %>% mutate(location = c("inner_Brownsville", "outer_Brownsville")) %>% st_as_sf(.) %>% st_transform(., crs = 4269) lrgv_23_grandparents %>% filter(variable == "S1002_C01_001") %>% st_as_sf(.) %>% st_intersection(., st_buffer(cam_places %>% filter(NAME == "Brownsville"), dist = 10)) %>% tm_shape(.) + tm_polygons(col = c("estimate"), popup.vars = c("Name" = "label3", "NAME", "Estimate"="estimate"), alpha = 0.5, style = "jenks") + tm_basemap("OpenStreetMap")

lrgv_23_grandparents <- get_acs(geography = "tract", county = c("Cameron"), table = "S1002", state = "TX", year = 2023, survey = "acs5", geometry = TRUE) %>% left_join(., all_acs_vars, by = c("variable"="name")) %>% separate_wider_delim(., cols = "label", delim = "!!", names_sep="", too_few = "align_start") custom_Bville<- read_sf(dsn = "C:/Users/daniel.pinon/Downloads/my_custom_brownsville_shape/map.geojson") %>% as.data.frame(.) %>% mutate(location = c("inner_Brownsville", "outer_Brownsville")) %>% st_as_sf(.) %>% st_transform(., crs = 4269) lrgv_23_grandparents %>% filter(variable == "S1002_C01_001") %>% st_as_sf(.) %>% st_intersection(., st_buffer(st_union(custom_Bville), dist = 10)) %>% tm_shape(.) + tm_polygons(col = c("estimate"), popup.vars = c("Name" = "label3", "NAME", "Estimate"="estimate"), alpha = 0.5, style = "jenks") + tm_basemap("OpenStreetMap")

Testing R dashboards with flexdashboard

Modello Statistico per la Previsione del Peso Neonatale

This RMarkdown contains the code used for downstream analysis of the data for the publication "Genome-wide gene expression responses to experimental manipulation of Saccharomyces cerevisiae repressor activator protein 1 (Rap1) expression level".

Thematic convergence between Colombia and Peru in biofuels is moderate and focused, with a predominance of the waste management and energy efficiency clusters. Similarities in bioprocesses and thermal conversion are limited, suggesting opportunities to strengthen common areas in bioenergy, waste treatment, and decentralized production. Overall, this relationship provides an initial basis for developing future collaborations focuscused on sustainable waste utilization and emerging conversion technologies.