Activity 3 : upload to Rpubs Completed for DATA101

HW1 Data 101

This presentation demonstrates an interactive Plotly visualization created in R Markdown using the Gapminder dataset. It explores the relationship between GDP per capita, life expectancy, and population across countries in 2007.

activity 3

library(dplyr) library(stringr) library(sf) library(tmap) library(scales) rbind(load_variables(2023, "acs5", cache = TRUE) %>% .[,1:3], load_variables(2023, "acs5/profile", cache = TRUE), load_variables(2023, "acs5/subject", cache = TRUE), load_variables(2023, "acs5/cprofile", cache = TRUE)) %>% as.data.frame(.) ->all_acs_vars lrgv_24<- get_acs(geography = "tract", county = c("Hidalgo", "Cameron", "Willacy", "Starr"), table = "C27016", state = "TX", year = 2024, 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") %>% separate_wider_delim(., cols = "variable", delim = "_", names_sep="", too_few = "align_start") %>% mutate(variable2 = as.numeric(variable2)) lrgv_24 %>% filter(grepl("205.05; H|206; H|207.29; H|207.30; H;| 207.24; H", NAME)) %>% dplyr::select(NAME, geometry) %>% unique(.) %>% st_as_sf(.) %>% st_union(.) %>% st_boundary() -> hoon_boundary rbind(load_variables(2023, "acs5", cache = TRUE) %>% .[,1:3], load_variables(2023, "acs5/profile", cache = TRUE), load_variables(2023, "acs5/subject", cache = TRUE), load_variables(2023, "acs5/cprofile", cache = TRUE)) %>% as.data.frame(.) ->all_acs_vars lrgv_24<- get_acs(geography = "tract", county = c("Hidalgo", "Cameron", "Willacy", "Starr"), table = "C27016", state = "TX", year = 2024, 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") %>% separate_wider_delim(., cols = "variable", delim = "_", names_sep="", too_few = "align_start") %>% mutate(variable2 = as.numeric(variable2)) lrgv_24 %>% mutate(over_under = case_when( str_detect(label3, "1.00|1.38") ~ "UNDER", str_detect(label3, "2.00|4.00") ~ "OVER", TRUE ~ "SUMMARY_VAR")) %>% group_by(NAME, label5, over_under) %>% mutate(grouped_ests = sum(estimate)) %>% filter(variable2 != 1 & !is.na(label4) & !is.na(label5)) %>% ungroup() %>% dplyr::select(NAME, label5, over_under, grouped_ests, geometry) %>% unique() %>% group_by(NAME) %>% mutate(tract_est = sum(grouped_ests), pct = grouped_ests/tract_est) %>% ungroup() %>% filter(over_under != "OVER" & !is.na(pct)) %>% group_by(NAME) %>% mutate(pct_nocoverage = sum(pct) %>% scales::percent(.)) %>% filter(grepl("Hidalgo", NAME)) %>% mutate(pct_two = pct*100, pct = pct %>% scales::percent(.), ) %>% dplyr::rename(., `Tract Name`=1, `Insurance Coverage`=2, `Under 2.0x Poverty Threshold?`=3, `Number Uninsured`=4, `Tract Pop. Size`=6, `Percent Uninsured under 2.0x Threshold` = 7, `Percent Under 2.0x Poverty Threshold`=8, `Under 2.0x Poverty Threshold (%)`=9) %>% filter(grepl("No", `Insurance Coverage`)) %>% st_as_sf() %>% tm_shape() + tm_polygons( fill = "Under 2.0x Poverty Threshold (%)", fill_alpha = 0.35, popup.vars = c("Tract Name", "Percent Under 2.0x Poverty Threshold", "Percent Uninsured under 2.0x Threshold", "Number Uninsured", "Tract Pop. Size"), fill.scale = tm_scale_intervals( style = "jenks" )) + tm_shape(hoon_boundary) + tm_lines(col = "red") + tm_basemap("OpenStreetMap")

landfire vegetation data, simplified/re-classified into main habitat classes

Nura Uddin's Learning R and Python with Heatmaps