Soy un analísta de datos de Overland Park. Crecí acá, dentro el centro del país, en un lugar en que la mayoría de gente asuma que es, decimos, culturalmente directo. Una lengua, una cultura, uno tipo de fan, uno tipo de historía.
Quiero contarte que existe alguien que quiere hablar dentro tu lengua, dentro la lengua de analíticas.
Comienzo ProPlotFits, un blog de analíticas de béisbol, y vive al cruce de dos cosas que yo amo: números y lenguas.
Hay demasiado analíticas de béisbol, pero, en mi opinion, no existe un outlet de explicación de estas analíticas en tu lengua.
La comunidad latina en Kansas City as más grande que piensa la mayoría. Esta mapa demonstra esto:
Code
library(tidyverse)
Warning: package 'tidyverse' was built under R version 4.5.2
Warning: package 'ggplot2' was built under R version 4.5.3
Warning: package 'tibble' was built under R version 4.5.2
Warning: package 'tidyr' was built under R version 4.5.3
Warning: package 'readr' was built under R version 4.5.3
Warning: package 'dplyr' was built under R version 4.5.2
Warning: package 'stringr' was built under R version 4.5.3
Warning: package 'lubridate' was built under R version 4.5.3
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.2.0 ✔ readr 2.2.0
✔ forcats 1.0.1 ✔ stringr 1.6.0
✔ ggplot2 4.0.2 ✔ tibble 3.3.1
✔ lubridate 1.9.5 ✔ tidyr 1.3.2
✔ purrr 1.1.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
Code
library(tidycensus)
Warning: package 'tidycensus' was built under R version 4.5.3
Code
library(sf)
Warning: package 'sf' was built under R version 4.5.3
Linking to GEOS 3.14.1, GDAL 3.12.1, PROJ 9.7.1; sf_use_s2() is TRUE
Code
library(tigris)
Warning: package 'tigris' was built under R version 4.5.2
To enable caching of data, set `options(tigris_use_cache = TRUE)`
in your R script or .Rprofile.
Code
options(tigris_use_cache =TRUE)# Pull state boundaries for MO and KSstate_borders <-states(cb =TRUE, year =2020) %>%filter(STUSPS %in%c("MO", "KS")) %>%st_transform(crs =4326)# -------------------------------------------------------------# Census variable: B16001_003E# "Speak Spanish" — from table B16001 (Language Spoken at Home)# ACS 5-year estimates# -------------------------------------------------------------# Set your Census API key if not already done:# census_api_key("my_key", install = TRUE)kc_counties <-list(MO =c("Jackson", "Clay", "Platte", "Cass"),KS =c("Johnson", "Wyandotte", "Leavenworth"))pull_hispanic_pop <-function(state, counties) {get_decennial(geography ="tract",variables =c(hispanic_pop ="P2_002N"),state = state,county = counties,year =2020,sumfile ="dhc",geometry =TRUE )}kc_mo <-pull_hispanic_pop("MO", kc_counties$MO)
Getting data from the 2020 decennial Census
Using the Demographic and Housing Characteristics File
Note: 2020 decennial Census data use differential privacy, a technique that
introduces errors into data to preserve respondent confidentiality.
ℹ Small counts should be interpreted with caution.
ℹ See https://www.census.gov/library/fact-sheets/2021/protecting-the-confidentiality-of-the-2020-census-redistricting-data.html for additional guidance.
Code
kc_ks <-pull_hispanic_pop("KS", kc_counties$KS)
Getting data from the 2020 decennial Census
Using the Demographic and Housing Characteristics File
Code
# Dissolve county tracts to state-level outlines (only your 7 counties)mo_outline <- kc_mo %>%st_union()ks_outline <- kc_ks %>%st_union()kc_metro <-bind_rows(kc_mo, kc_ks) %>%st_transform(crs =4326)# -------------------------------------------------------------# Royal Blue palette# Official KC Royals Royal Blue: #174B8B# Build a sequential ramp from near-white to full Royal Blue# -------------------------------------------------------------royal_blue_dark <-"#174B8B"royal_blue_light <-"#D6E4F5"ggplot(kc_metro) +geom_sf(aes(fill = value),color ="white",linewidth =0.15 ) +geom_sf(data = mo_outline,fill =NA,color = royal_blue_dark,linewidth =0.6 ) +geom_sf(data = ks_outline,fill =NA,color = royal_blue_dark,linewidth =0.6 ) +scale_fill_gradient(low = royal_blue_light,high = royal_blue_dark,na.value ="#EEEEEE",name ="Hispanic Population",labels = scales::comma ) +labs(title ="Population of Hispanic Descent across the KC metro by Census Tract",subtitle ="",caption ="Source: 2020 Census · Table P2_002N · ProPlotFits" ) +theme_void(base_family ="sans") +theme(plot.title =element_text(size =16, face ="bold",color = royal_blue_dark, margin =margin(b =6)),plot.subtitle =element_text(size =10, color ="#555555", margin =margin(b =12)),plot.caption =element_text(size =8, color ="#888888", margin =margin(t =10)),legend.position ="right",legend.title =element_text(size =9, color ="#333333"),legend.text =element_text(size =8, color ="#555555"),plot.margin =margin(16, 16, 16, 16) )
Entiendo que mi estilo de español no es “corecto”.
Sin embargo, esto es mi efuerza de hablar analíticas en una lengua afuera de mis padres.
Esto es para los hispanohablantes que aman esto deporte y quieren conocer mas las analiticas, los datos, los conocimientos que conoces.
Esto es una carta de amor dentro datos, dentro dos lenguas, dentro del orgullo que llevas cada vez que te pones el uniforme de los Royals.
Te uniste en 2011, cuando tenia 13, y ahorita tengo 28. Eres mi capitan, y quiero darte el amor de este region de mi pais a tuyo.
Gracias, Salvy. For showing up. For staying. For being exactly who you are in exactly this place.
We’re just trying to do the same thing with a microphone and a dataset.
