EDA for CWS(Agency) Dataset document

Práctica de árboles de decisión empleando una base de datos del Titani para predecir si alguien sobrevive o no

EDA FOR CWS

This is a set of maps that show which parts of Cameron County have the highest proportion of people aged 45 and older; these would be most relevant to secondary prevention and, to some extent, tertiary prevention. The top map, "Percentage (%) of Population age 45 and over", illustrates the proportion of people who are 65 and older as divided by the population in that tract, multiplied by 100. The bottom map, "Raw # of Population age 45 and over", shows the numerator for that proportion calculation; essentially, how many people 65 and older are *actually* living in that Census tract. These can be considered a map of the 'high risk' people with respect to ADRD in Cameron County when considering age alone. .. (you'll need 'tidyverse', 'tm', 'tidycensus', 'sf', and 'cwi' from DataHaven for ACS labeling) Code:: get_acs("tract", table = "B01001", state = "TX", county = "Cameron", year= 2022, survey = "acs5", geometry = TRUE) %>% label_acs(.) %>% 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)) %>% filter(variable2 != "1" & variable2 != "2" & variable2!= "26") %>% filter(variable2 > 6 & variable2 < 26 | variable2 > 30 & variable2 <=49) %>% transform(., tract_pop_sum = ave(.$estimate, .$NAME, FUN=sum)) %>% mutate(tract_pop_prop = estimate/tract_pop_sum, test = if_else(variable2 > 14 & variable2 < 26 | variable2>38 & variable2<=49,"atrisk","norm")) %>% group_by(test, NAME) %>% mutate(atrisk_sum = sum(estimate)) %>% transform(., tract_pop_sum = ave(.$estimate, .$NAME, FUN=sum)) %>% mutate(tract_pop_prop = estimate/tract_pop_sum, test = if_else(variable2 > 14 & variable2 < 26 | variable2>38 & variable2<=49,"atrisk","norm")) %>% group_by(test, NAME) %>% mutate(atrisk_sum = sum(estimate), prop_atrisk = 100 * atrisk_sum/tract_pop_sum) -> cam_pop_tracts_votingage cam_pop_tracts_votingage %>% filter(test == "atrisk") %>% st_as_sf(.) %>% dplyr::select(prop_atrisk, test, atrisk_sum, geometry, NAME) %>% drop_na(.) %>% unique(.) %>% mutate(prop_atrisk = round(prop_atrisk, 1)) %>% dplyr::rename(., "Percentage (%) of Population age 45 and over"=1, "Raw # of Population age 45 and over"=3) %>% tm_shape(.) + tm_polygons(col=c("Percentage (%) of Population age 45 and over", "Raw # of Population age 45 and over"), alpha=0.6, style="jenks", palette="Reds") + tm_layout(legend.outside = "TRUE") + tm_facets(nrow = 2, sync = TRUE)

Rancangan Acak kelompok adalah suatu rancangan acak yang dilakukan dengan mengelompokkan satuan percobaan ke dalam grup-grup yang homogen yang dinamakan kelompok dan kemudian menentukan perlakuan secara acak di dalam masing-masing kelompok.