This assignment is based on some of the basic methods presented in the Datacamp course on census data.
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --
## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.0.6 v dplyr 1.0.4
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(tidycensus)
library(scales)
##
## Attaching package: 'scales'
## The following object is masked from 'package:purrr':
##
## discard
## The following object is masked from 'package:readr':
##
## col_factor
From the ACS questionnaire identify a topic that interests you. Copy the text of the question that produces the data you want and paste it here.
“Which FUEL is used most for heating this house, apartment, or mobile home?”
Gas: from underground pipes serving the neighborhood
Gas: bottled, tank, or LP
Electricity
Fuel oil, Kerosene, etc
Coal or coke
Wood
Solar energy
Other fuel
No fuel used
Note that you can’t have View() in a knitted document. Use head() instead.
v19_5=load_variables(year = 2019,"acs5",cache=TRUE)
v19_5%>%
filter(str_detect(str_to_lower(label),"gas"))%>%
head()
v19_5%>%
filter(str_detect(str_to_lower(label),"electricity"))%>%
head()
v19_5%>%
filter(str_detect(str_to_lower(label),"fuel"))%>%
head()
v19_5%>%
filter(str_detect(str_to_lower(label),"coal"))%>%
head()
v19_5%>%
filter(str_detect(str_to_lower(label),"wood"))%>%
head()
v19_5%>%
filter(str_detect(str_to_lower(label),"solar"))%>%
head()
Creating a vector of fuel variables
fuel_vars = c(pipeGas ="B25040_002", tankGas="B25040_003", electricity ="B25040_004" , fuel="B25040_005", coal="B25040_006",wood="B25040_007",solar="B25040_008", noFuel="B25040_010" ,otherFuel="B25040_009")
Finding a variable for total housing units to use as summary variable
v19_5%>%
filter(str_detect(str_to_lower(label),"housing units"))%>%
head()
Wa_state_housing_units=get_acs(geography = "county",state="WA", variables = "B25001_001",geometry=TRUE)
## Getting data from the 2015-2019 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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head(Wa_state_housing_units)
Use get_acs() to obtain the values of your selected variable from the 2015-2019 file.
Getting data from the 2015-2019 5-year ACS
Wa_state_fuel=get_acs(geography = "county",state="WA", variables = fuel_vars,summary_var="B25001_001", geometry=TRUE)
## Getting data from the 2015-2019 5-year ACS
## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
head(Wa_state_fuel)
Get the percent per county of each fuel type
Wa_state_fuel_pct= Wa_state_fuel%>%
mutate(pct=100*(estimate/summary_est))%>%
select(NAME,variable,pct)
head(Wa_state_fuel_pct)
Determining the most used fuel type by county
most_used_fuel =Wa_state_fuel%>%
group_by(GEOID)%>%
filter(estimate==max(estimate))%>%
select(NAME,variable,estimate)
head(most_used_fuel)
Getting a count of the most used fuel
most_used_fuel%>%
group_by(variable)%>%
tally()
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
## although coordinates are longitude/latitude, st_union assumes that they are planar
Do a dotplot of the variable you selected.
We can look specifically at wood because it is the least “clean” fuel source of the top three listed above
Wa_state_fuel_wood=get_acs(geography = "county",state="WA", variables = "B25040_007", geometry=TRUE)
## Getting data from the 2015-2019 5-year ACS
## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
Wa_state_fuel_wood%>%
ggplot(aes(x=estimate,y=reorder(NAME, estimate)))+
geom_point()+
labs(x="Households using mostly wood for wood for heating", y = "County")+
scale_x_continuous()
Let’s see how/if the use of wood as the primary heating has changed over time
wood_fuel=map_df(2015:2019,function(x){
get_acs(geography = "county",variables =c(wood="B25040_007"),state= "WA",survey = "acs1",year=x,geometry =TRUE)%>%
mutate(year=x)})
## The 1-year ACS provides data for geographies with populations of 65,000 and greater.
## Getting data from the 2015 1-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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## The 1-year ACS provides data for geographies with populations of 65,000 and greater.
## Getting data from the 2016 1-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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## The 1-year ACS provides data for geographies with populations of 65,000 and greater.
## Getting data from the 2017 1-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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## The 1-year ACS provides data for geographies with populations of 65,000 and greater.
## Getting data from the 2018 1-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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## The 1-year ACS provides data for geographies with populations of 65,000 and greater.
## Getting data from the 2019 1-year ACS
## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
wood_fuel%>%
arrange(NAME,year)
Create a map for your variable using geom_sf()
ggplot(Wa_state_fuel_wood)+
geom_sf(aes(fill=estimate))+
geom_sf_label(aes(label=NAME))
## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
Lets have a look at how it has changed over the years
ggplot(wood_fuel,aes(fill=estimate,color=estimate))+
geom_sf()+
coord_sf(datum=NA)+
facet_wrap(~year)