fifty_ten_00_20
Joseph Jaiyeola
2023-10-10
Load library
Load the processed weighted data into R
library(readr)
data <- read_csv("fifty_ten_2000_20_df.csv")
# Making statefp character for easy merging
data$STATEFP<-as.character(data$STATEFP)state_and_data <- states(2021) %>%
filter(!STATEFP %in% c("02", "11", "15", "60", "66", "69", "72", "78")) %>% # Removed Alaska(02), Hawai(11), DC(15)
#filter(!STATEFP %in% c("02", "15", "60", "66", "69", "72", "78")) %>%
shift_geometry() %>%
left_join(data, by="STATEFP") %>% # join by statefp
select("STATEFP", "stname", "year_2000", "year_2020", "geometry", "stusps") #select only needed variabe## Retrieving data for the year 2021##
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|======================================================================| 100%## Warning: None of your features are in Alaska, Hawaii, or Puerto Rico, so no geometries will be shifted.
## Transforming your object's CRS to 'ESRI:102003'2000 Map
gc()## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 5014049 267.8 8389494 448.1 NA 8389494 448.1
## Vcells 9317204 71.1 17824054 136.0 16384 17754580 135.5tmap_mode("plot")## tmap mode set to plottingtm_basemap("OpenStreetMap.Mapnik")+
tm_shape(state_and_data)+
tm_polygons("year_2000",
#style="kmeans", # natural r way of scallin
title=c("Income Inequality Ratio"),
palette="Blues",
#n=8, # works with style line
breaks=c(4.2, 4.7, 5.2, 5.7, 6.2, 6.7),
legend.hist = TRUE) +
tm_text("stusps",
size = 0.5) +
tm_layout(legend.outside = TRUE,
title = "50/10 Individual Income Inequality Ratio \n US STATEs 2000",
title.size =1.5,
legend.frame = TRUE,
) + tm_compass(position = c("right","top")) + tm_format("World",
legend.position = c("left", "bottom"),
main.title.position =c("center")) + tm_scale_bar(position = c("left","bottom"))
2020 Map
gc()## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 5169375 276.1 8389494 448.1 NA 8389494 448.1
## Vcells 9630802 73.5 27202776 207.6 16384 27133029 207.1tmap_mode("plot")## tmap mode set to plottingtm_basemap("OpenStreetMap.Mapnik")+
tm_shape(state_and_data)+
tm_polygons("year_2020",
#style="kmeans",
title=c("Income Inequality Ratio"),
palette="Blues",
#n=8,
breaks=c(4.2, 4.7, 5.2, 5.7, 6.2, 6.7),
legend.hist = TRUE) +
tm_text("stusps",
size = 0.5) +
tm_layout(legend.outside = TRUE,
title = "50/10 Individual Income Inequality Ratio \n US STATEs 2020",
title.size =1.5,
legend.frame = TRUE,
) + tm_compass(position = c("right","top")) + tm_format("World",
legend.position = c("left", "bottom"),
main.title.position =c("center")) + tm_scale_bar(position = c("left","bottom"))
Additional Map
Plotting Changes from 2000 to 2020
library(dplyr)
state_and_data$year_2000<- as.numeric(state_and_data$year_2000)
state_and_data$year_2020<- as.numeric(state_and_data$year_2020)
state_and_data <- state_and_data %>%
mutate(changes = case_when(
year_2000 < year_2020 ~ "Increased",
year_2000 > year_2020 ~ "Decreased",
TRUE ~ "No Changes"
))gc()## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 5175853 276.5 8389494 448.1 NA 8389494 448.1
## Vcells 9645738 73.6 32723331 249.7 16384 27199506 207.6tmap_mode("plot")## tmap mode set to plottingtm_basemap("OpenStreetMap.Mapnik") +
tm_shape(state_and_data) +
tm_polygons("changes",
title = "Income Inequality Ratio",
palette = c("#ADD8E6", "#FFB6C1", "grey"), # Specify colors for each category
#labels = c("Decrease", "Increase", "No Change"), # Label categories
legend.show = TRUE) +
tm_text("stusps", size = 0.5) +
tm_layout(legend.outside = TRUE,
title = "50/10 Individual Income Inequality Ratio\nUS STATES 2000-2020 changes",
title.size = 1.5,
legend.frame = TRUE) +
tm_compass(position = c("right", "top")) +
tm_format("World",
legend.position = c("left", "bottom"),
main.title.position = c("center")) +
tm_scale_bar(position = c("left", "bottom"))