Project
library(tidyverse)## ── Attaching packages ────────────────────────────────────────── tidyverse 1.2.1 ──## ✔ ggplot2 3.2.1 ✔ purrr 0.3.3
## ✔ tibble 2.1.3 ✔ dplyr 0.8.3
## ✔ tidyr 1.0.0 ✔ stringr 1.4.0
## ✔ readr 1.3.1 ✔ forcats 0.4.0## ── Conflicts ───────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(tidycensus)
library(mapview)
census_api_key("b5e3d2da685c019db5e7c341c3949706ef5df120",install = TRUE, overwrite = TRUE)## Your original .Renviron will be backed up and stored in your R HOME directory if needed.## Your API key has been stored in your .Renviron and can be accessed by Sys.getenv("CENSUS_API_KEY").
## To use now, restart R or run `readRenviron("~/.Renviron")`## [1] "b5e3d2da685c019db5e7c341c3949706ef5df120"readRenviron("~/.Renviron")NEWJERSEY <- get_acs(geography = "state", year = 2017,
variables = c(Argentina = "B05006_149", Uruguay = "B05006_157", Chile = "B05006_152", Brazil = "B05006_151", Bolivia = "B05006_150", Colombia = "B05006_153", Ecuador = "B05006_154", Peru = "B05006_156", Venezuela = "B05006_158"), state = 34)## Getting data from the 2013-2017 5-year ACSggplot(NEWJERSEY, aes(x = reorder(variable, -estimate), y =estimate / 1000, color = variable, fill = variable))+
theme(axis.text.x=element_text(angle=90,hjust=1)) +
geom_col()+
ggtitle("PLACE OF BIRTH FOR THE FOREIGN-BORN POPULATION IN NJ")+
xlab("South American Spanish-Speaking Countries")+
ylab("Population (In Thousands)")
US <- get_acs(geography = "us", year = 2017,
variables = c(Argentina = "B05006_149", Uruguay = "B05006_157", Chile = "B05006_152", Brazil = "B05006_151", Bolivia = "B05006_150", Colombia = "B05006_153", Ecuador = "B05006_154", Peru = "B05006_156", Venezuela = "B05006_158"))## Getting data from the 2013-2017 5-year ACSggplot(US, aes(x = reorder(variable, -estimate), y =estimate / 1000, color = variable, fill = variable))+
theme(axis.text.x=element_text(angle=90,hjust=1)) +
geom_col()+
ggtitle("PLACE OF BIRTH FOR THE FOREIGN-BORN POPULATION IN THE UNITED STATES")+
xlab("South American Spanish-Speaking Countries")+
ylab("Population (In Thousands)")
test <- get_acs(geography = "state", year = 2017,state = c("new jersey", "texas","california","florida", "new york"),variables = c(very_well = "B06007_036", not_very_well = "B06007_037"), summary_var = "B06007_035")## Getting data from the 2013-2017 5-year ACShead(test)test %>%
mutate(pctvs = 100 * (estimate / summary_est)) %>%
ggplot() + geom_col(aes(x= NAME, y = pctvs, fill = variable,position="fill", stat="identity"))+
geom_text(aes (label = round(pctvs,2),y = pctvs, x = NAME), vjust = 1.5, color = "white") +
scale_fill_manual(values = c("#a1d99b", "#31a354")) ## Warning: Ignoring unknown aesthetics: position, stat
# scale_y_continuous(labels = scales::percent)englishprof <- get_acs(geography = "state", year = 2017,state = c("texas","new jersey", "new york","california","florida"),
variables = c(very_well = "B06007_036", not_very_well = "B06007_037")) %>%
filter(estimate > 4000)## Getting data from the 2013-2017 5-year ACSggplot() + geom_bar(aes(y = (estimate/1000000), x = reorder(NAME,-estimate), fill = variable), data = englishprof, stat="identity")+
theme(axis.text.x=element_text(angle=90,hjust=1))
#x axis represents millionstest <- get_acs(geography = "county", year = 2017,state = c("new jersey"),variables = c(very_well = "B06007_036", not_very_well = "B06007_037"), summary_var = "B06007_035", geometry = TRUE)## Getting data from the 2013-2017 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.##
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|=================================================================| 100%head(test)test %>%
mutate(pctvs = 100 * (estimate / summary_est)) %>%
ggplot(aes(fill = pctvs)) +
facet_wrap(~variable) +
geom_sf(color = NA) +
coord_sf(crs = 26915) +
scale_fill_viridis_c()
B05006_157 = Estimate!!Total!!Americas!!Latin America!!South America!!Uruguayyears <- lst(2012, 2013, 2014,2015,2016,2017)
Uruguay <- map_dfr(years, ~ get_acs(geography = "us",variables = "B03001_024",year = .x,survey = "acs1"), .id = "year")## Getting data from the 2012 1-year ACS## The one-year ACS provides data for geographies with populations of 65,000 and greater.## Getting data from the 2013 1-year ACS## The one-year ACS provides data for geographies with populations of 65,000 and greater.## Getting data from the 2014 1-year ACS## The one-year ACS provides data for geographies with populations of 65,000 and greater.## Getting data from the 2015 1-year ACS## The one-year ACS provides data for geographies with populations of 65,000 and greater.## Getting data from the 2016 1-year ACS## The one-year ACS provides data for geographies with populations of 65,000 and greater.## Getting data from the 2017 1-year ACS## The one-year ACS provides data for geographies with populations of 65,000 and greater.ggplot(Uruguay, aes(x = year, y = estimate, color = variable, fill = variable, group = 1))+
theme_bw(base_size = 10) +
theme(axis.text.x=element_text(angle=90,hjust=1)) +
geom_line()
nj_pop <-
get_acs(geography = "county",
variables = "B01003_001",
state = "NJ",
geometry = TRUE)## Getting data from the 2013-2017 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.mapview(nj_pop, zcol = "estimate")fill <- c("#40b8d0", "#b2d183")
p4 <- ggplot(US, aes(y = percentage, x = year, fill = product), stat="identity")
geom_bar() ## geom_bar: width = NULL, na.rm = FALSE
## stat_count: width = NULL, na.rm = FALSE
## position_stack