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getCMS <- function(distributionId, | |
columns = "*", | |
where = NULL, | |
limit = 0) | |
{ | |
require(magrittr) | |
require(httr) | |
require(jsonlite) | |
root <- "https://data.cms.gov/provider-data/api/1/datastore/sql" | |
if (is.null(where)) { |
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pal <- c("#005595", # Medium Electric Blue | |
"#5E7F24", # Sap Green | |
"#D87E1A", # Ochre | |
"#007FAD", # Celadon Blue | |
"#A01C3F", # Deep Carmine | |
"#F2F2F2", # Anti-Flash White | |
"#949494", # Spanish Gray | |
"#606060", # Granite Gray | |
# Sequential Blue | |
"#6799BF", "#4779B1", "#005595", "#00406F", "#002B4A", |
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# US Census ACS 5-year API documentation: https://www.census.gov/data/developers/data-sets/acs-5year.html | |
# tidycensus documentation: https://walker-data.com/tidycensus/ | |
Sys.getenv("CENSUS_API_KEY") # load pre-installed Census API key | |
getACSbyZCTA <- function(lookup, table = NULL, variables = NULL, dataset = "acs5/profile", year = 2022) { | |
require(magrittr) | |
require(dplyr) | |
require(tidyr) | |
require(tidycensus) |
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search <- function(file, str) { | |
text <- readLines(file.path(path, file), warn = FALSE) | |
df <- data.frame(file = file, | |
line = 1:length(text), | |
regex = str, | |
result = grepl(str, text), | |
text) | |
df[df$result == TRUE, ] | |
} |
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# Reference: https://www.bls.gov/developers/api_r.htm | |
library(magrittr) | |
library(dplyr) | |
library(devtools) | |
install_github("mikeasilva/blsAPI") # https://github.com/mikeasilva/blsAPI | |
library(rjson) | |
library(blsAPI) | |
payload <- list("seriesid" = c("CUUR0000SA0L1E"), | |
"startyear" = 2016, |
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c(rgb( 26, 26, 26, maxColorValue = 255), | |
rgb( 51, 51, 51, maxColorValue = 255), | |
rgb( 77, 77, 77, maxColorValue = 255), | |
rgb(102, 102, 102, maxColorValue = 255), | |
rgb(127, 127, 127, maxColorValue = 255), | |
rgb(153, 153, 153, maxColorValue = 255), | |
rgb(179, 179, 179, maxColorValue = 255), | |
rgb(204, 204, 204, maxColorValue = 255), | |
rgb(230, 230, 230, maxColorValue = 255)) |
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library(magrittr) | |
library(dplyr) | |
c("Alice", | |
"Bob", | |
"Carol", | |
"Dave") %>% | |
unique() %>% | |
sample(1e6, replace = TRUE) %>% | |
data.frame(names = .) %>% |
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library(magrittr) | |
library(readr) | |
library(tidyr) | |
library(censusapi) | |
name <- "acs/acs5" # See https://api.census.gov/data.html for list of tables for the name argument for listCensusMetadata() | |
vintage <- 2012 | |
metadata <- | |
listCensusMetadata(name = name, vintage = vintage) %>% |
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# Ref: https://en.wikipedia.org/wiki/Birthday_problem | |
# Ref: https://www.scientificamerican.com/article/bring-science-home-probability-birthday-paradox/ | |
library(magrittr) | |
library(dplyr) | |
people <- 23 | |
simulations <- 1e5 | |
expand.grid(id = 1:people, | |
sim = 1:simulations) %>% | |
mutate(bday = sample(365, simulations * people, replace = TRUE)) %>% | |
group_by(sim) %>% |
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library(magrittr) | |
library(dplyr) | |
buildSample <- function(hosp, pop = 500, samp = 100) { | |
data.frame(hosp = hosp, | |
id = sample(pop, samp, replace = TRUE), | |
stringsAsFactors = FALSE) %>% | |
mutate(z = rnorm(nrow(.))) | |
} | |
df1 <- buildSample("A") | |
df2 <- buildSample("B") |
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