Unemployment rates in California
Joe Long
7/15/2020
Unemployment rates using different congressional district boundaries
- B23025_005E Unemployed Estimate In Civilian labor force
- B23025_003E Total Estimate In Civilian labor force
- B23025_004E Employed Estimate In Civilian labor force
v18 <- load_variables(2018, "acs5", cache = TRUE)
# B25064_001 Estimate!!Median gross rent
View(v18)
Var<-c("B23025_003E","B23025_004E","B23025_005E")
## c(Median household Income, Median Housing Value)
ca_df <- get_acs(geography = "county",
variables = Var,
year = 2018,
survey = "acs5",
state = "06",
output = "wide")## Getting data from the 2014-2018 5-year ACS## Warning: `funs()` is deprecated as of dplyr 0.8.0.
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated. #geometry = TRUE)
ca_df <- ca_df %>%
rename("Total Labor Force"=B23025_003E,
"Employed"=B23025_004E,
"Unemployed"=B23025_005E)
ca_df$rate <- 100*ca_df$Unemployed/(ca_df$`Total Labor Force`)
colnames(ca_df)## [1] "GEOID" "NAME" "Total Labor Force"
## [4] "B23025_003M" "Employed" "B23025_004M"
## [7] "Unemployed" "B23025_005M" "rate" library(DT)
datatable(cbind('County Name' = ca_df[[2]],
"Unemployment rate" = ca_df[[9]]) )ca_df <- get_acs(geography = "county",
variables = Var,
year = 2018,
survey = "acs5",
state = "06",
output = "wide", geometry = T)## Getting data from the 2014-2018 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.##
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|======================================================================| 100%ca_df <- ca_df %>%
rename("Total Labor Force"=B23025_003E,
"Employed"=B23025_004E,
"Unemployed"=B23025_005E)
ca_df$rate <- 100*ca_df$Unemployed/(ca_df$`Total Labor Force`)
colnames(ca_df)## [1] "GEOID" "NAME" "Total Labor Force"
## [4] "B23025_003M" "Employed" "B23025_004M"
## [7] "Unemployed" "B23025_005M" "geometry"
## [10] "rate" ggplot(ca_df) +
geom_sf(aes(fill = rate), color=NA) +
#coord_sf(datum=NA) +
labs(title = "2018 Unemployment rate",
caption = "Source: Census ACS 5-year, 2014-2018",
fill = "rate") +
scale_fill_viridis(direction=-1)
B23025_005E Unemployed Estimate !!Total!!In labor force!!Civilian labor force!!
B23025_003E Total Estimate!!Total!!In labor force!!Civilian labor force
B23025_004E Employed Estimate!!Total!!In labor force!!Civilian labor force!!Employed
ca_cd <- get_acs(geography = "congressional district", variables = Var, state = "CA",
year = 2018, output = "wide")## Getting data from the 2014-2018 5-year ACSca_cd <- ca_cd %>%
rename("Total Labor Force"=B23025_003E,
"Employed"=B23025_004E,
"Unemployed"=B23025_005E)
ca_assembly <- get_acs(geography = "state legislative district (lower chamber)",
variables = Var, output = "wide",
state = "CA",year = 2018)## Getting data from the 2014-2018 5-year ACSca_assembly <- ca_assembly %>%
rename("Total Labor Force"=B23025_003E,
"Employed"=B23025_004E,
"Unemployed"=B23025_005E)
ca_senate <- get_acs(geography = "state legislative district (upper chamber)",
variables = Var, output = "wide",
state = "CA",year = 2018)## Getting data from the 2014-2018 5-year ACSca_senate <- ca_senate %>%
rename("Total Labor Force"=B23025_003E,
"Employed"=B23025_004E,
"Unemployed"=B23025_005E)cleaning up
numextract <- function(string){
str_extract(string, "\\-*\\d+\\.*\\d*")}
ca_cd$NAME <- gsub("115th Congress", "", ca_cd$NAME)
ca_cd$NAME <- numextract(ca_cd$NAME) %>% as.numeric(ca_cd$NAME)
str(ca_cd)## Classes 'tbl_df', 'tbl' and 'data.frame': 53 obs. of 8 variables:
## $ GEOID : chr "0601" "0603" "0605" "0606" ...
## $ NAME : num 1 3 5 6 8 10 11 13 15 16 ...
## $ Total Labor Force: num 307057 341148 383690 366051 298355 ...
## $ B23025_003M : num 2500 2409 2832 2860 3013 ...
## $ Employed : num 284276 317261 361052 336654 267817 ...
## $ B23025_004M : num 2359 2614 2995 2870 3046 ...
## $ Unemployed : num 22781 23887 22638 29397 30538 ...
## $ B23025_005M : num 1008 1040 1258 1260 1535 ...ca_cd$rate <- 100*ca_cd$Unemployed/(ca_cd$Employed+ca_cd$Unemployed)
ca_cd %>%
ggplot(aes(x = rate, y = reorder(NAME,rate ))) +
geom_point(color = "brown", size = 1) +
labs(title = "2018 Unemployment rate by Congressional District in California",
subtitle = "Census Variable = B23025_005E",
y = "",
x = "2018 Median Unemployment rate", caption = "Tidycensus extraction by Joe Long")+
theme_economist(base_size = 7)
ca_cd[,c(2,9)] %>%
rename(Congressional_District = NAME, Unemployment_rate = rate) %>%
kable() %>%
kable_styling(bootstrap_options = c("hover", "condensed",full_width = F))| Congressional_District | Unemployment_rate |
|---|---|
| 1 | 7.4 |
| 3 | 7.0 |
| 5 | 5.9 |
| 6 | 8.0 |
| 8 | 10.2 |
| 10 | 9.5 |
| 11 | 6.2 |
| 13 | 5.8 |
| 15 | 4.3 |
| 16 | 11.6 |
| 18 | 3.9 |
| 20 | 6.0 |
| 21 | 11.3 |
| 23 | 8.9 |
| 24 | 5.5 |
| 26 | 6.1 |
| 28 | 6.8 |
| 29 | 7.2 |
| 31 | 8.5 |
| 33 | 5.4 |
| 34 | 7.4 |
| 36 | 9.5 |
| 38 | 5.8 |
| 39 | 5.7 |
| 41 | 9.1 |
| 42 | 7.5 |
| 44 | 9.2 |
| 46 | 5.5 |
| 47 | 5.8 |
| 49 | 4.8 |
| 51 | 11.3 |
| 52 | 5.1 |
| 2 | 5.7 |
| 4 | 5.9 |
| 7 | 6.8 |
| 9 | 8.6 |
| 12 | 4.4 |
| 14 | 4.6 |
| 25 | 6.8 |
| 17 | 4.4 |
| 19 | 5.5 |
| 22 | 7.7 |
| 27 | 5.0 |
| 30 | 6.4 |
| 32 | 6.8 |
| 35 | 8.2 |
| 37 | 6.9 |
| 40 | 8.1 |
| 43 | 7.3 |
| 45 | 4.8 |
| 48 | 4.7 |
| 50 | 6.0 |
| 53 | 6.6 |
ca_assembly$rate <- 100*ca_assembly$Unemployed/(ca_assembly$Employed+ca_assembly$Unemployed)
#--------------------------------
ca_assembly %>%
mutate(NAME = gsub("2018", "",NAME)) %>%
mutate(NAME = numextract(NAME)) %>%
ggplot(aes(x = rate, y = reorder(NAME, rate))) +
geom_point(color = "brown", size = 1) +
labs(title = "2018 Unemployment rate by Assembly District in California",
subtitle = "American Community Survey",
y = "Assembly District ",
x = "Rate")+
theme_economist(base_size = 6.5)
ca_assembly[,c(2,9)] %>%
mutate(NAME = gsub("2018", "",NAME)) %>%
mutate(NAME = numextract(NAME)) %>%
rename(Assembly_District = NAME, Unemployment_rate = rate) %>%
kable() %>%
kable_styling(bootstrap_options = c("hover", "condensed"))| Assembly_District | Unemployment_rate |
|---|---|
| 1 | 6.4 |
| 2 | 7.0 |
| 3 | 8.7 |
| 4 | 5.8 |
| 5 | 7.2 |
| 6 | 4.8 |
| 7 | 7.4 |
| 8 | 7.6 |
| 9 | 8.0 |
| 10 | 4.4 |
| 11 | 7.0 |
| 12 | 8.6 |
| 13 | 9.3 |
| 14 | 6.9 |
| 15 | 5.8 |
| 16 | 3.7 |
| 17 | 4.6 |
| 18 | 6.3 |
| 19 | 4.9 |
| 20 | 4.8 |
| 21 | 12.6 |
| 22 | 3.8 |
| 23 | 7.9 |
| 24 | 4.1 |
| 25 | 4.4 |
| 29 | 5.5 |
| 26 | 9.3 |
| 27 | 6.2 |
| 28 | 4.1 |
| 30 | 5.8 |
| 31 | 11.1 |
| 32 | 11.7 |
| 33 | 11.3 |
| 34 | 8.3 |
| 35 | 5.6 |
| 36 | 8.8 |
| 37 | 5.6 |
| 38 | 5.6 |
| 39 | 6.3 |
| 40 | 8.4 |
| 41 | 5.1 |
| 42 | 8.6 |
| 43 | 6.9 |
| 44 | 6.2 |
| 45 | 6.0 |
| 46 | 8.0 |
| 47 | 9.3 |
| 48 | 7.0 |
| 49 | 5.2 |
| 50 | 6.0 |
| 51 | 8.7 |
| 52 | 7.8 |
| 53 | 6.9 |
| 54 | 6.4 |
| 55 | 5.7 |
| 56 | 11.9 |
| 57 | 6.0 |
| 58 | 6.3 |
| 59 | 9.1 |
| 60 | 7.1 |
| 61 | 9.4 |
| 62 | 7.1 |
| 63 | 8.5 |
| 64 | 8.8 |
| 65 | 5.8 |
| 66 | 5.0 |
| 67 | 9.0 |
| 68 | 4.8 |
| 69 | 5.6 |
| 70 | 6.5 |
| 71 | 7.6 |
| 72 | 5.2 |
| 73 | 4.3 |
| 74 | 4.7 |
| 75 | 5.5 |
| 76 | 5.2 |
| 77 | 5.1 |
| 78 | 4.9 |
| 79 | 7.5 |
| 80 | 9.8 |
ca_senate$rate <- 100*ca_senate$Unemployed/(ca_senate$Employed+ca_senate$Unemployed)
ca_senate %>%
mutate(NAME = gsub("2018", "",NAME)) %>%
mutate(NAME = numextract(NAME)) %>%
ggplot(aes(x = rate, y = reorder(NAME, rate))) +
geom_point(color = "darkgreen", size = 2) +
labs(title = "2018 Unemployment rate by State Senate District in California",
subtitle = "American Community Survey",
y = "",
x = "")+
theme_economist()
ca_senate[,c(2,9)] %>%
mutate(NAME = gsub("2018", "",NAME)) %>%
mutate(NAME = numextract(NAME)) %>%
rename(State_Senate_District = NAME, '2018 Unemployment rate' = rate) %>%
kable() %>%
kable_styling(bootstrap_options = c("hover", "condensed"))| State_Senate_District | 2018 Unemployment rate |
|---|---|
| 1 | 5.7 |
| 2 | 6.0 |
| 3 | 6.0 |
| 4 | 7.5 |
| 5 | 8.9 |
| 6 | 8.1 |
| 7 | 5.4 |
| 8 | 8.0 |
| 9 | 6.0 |
| 10 | 4.6 |
| 11 | 4.8 |
| 12 | 9.7 |
| 13 | 3.9 |
| 14 | 12.0 |
| 15 | 5.1 |
| 16 | 8.6 |
| 17 | 5.4 |
| 18 | 7.2 |
| 19 | 6.2 |
| 20 | 8.6 |
| 21 | 8.9 |
| 22 | 6.2 |
| 23 | 8.5 |
| 24 | 7.6 |
| 25 | 5.9 |
| 26 | 5.6 |
| 27 | 5.6 |
| 28 | 8.6 |
| 29 | 5.8 |
| 33 | 7.8 |
| 30 | 7.8 |
| 31 | 8.2 |
| 32 | 5.8 |
| 34 | 5.3 |
| 35 | 8.0 |
| 36 | 4.7 |
| 37 | 4.7 |
| 38 | 6.1 |
| 39 | 5.3 |
| 40 | 10.2 |