Load assessment data
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## # A tibble: 55 × 7
## county school school_name population_group subgroup science_proficiency
## <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 Barbour 999 Barbour Count… Total Population Total 26.0
## 2 Berkeley 999 Berkeley Coun… Total Population Total 28.6
## 3 Boone 999 Boone County … Total Population Total 19.6
## 4 Braxton 999 Braxton Count… Total Population Total 22.6
## 5 Brooke 999 Brooke County… Total Population Total 21.1
## 6 Cabell 999 Cabell County… Total Population Total 30.8
## 7 Calhoun 999 Calhoun Count… Total Population Total 27.8
## 8 Clay 999 Clay County T… Total Population Total 23.3
## 9 Doddridge 999 Doddridge Cou… Total Population Total 31.3
## 10 Fayette 999 Fayette Count… Total Population Total 17.4
## # ℹ 45 more rows
## # ℹ 1 more variable: proficiency <dbl>
Load spending data
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## # A tibble: 55 × 8
## name enroll tfedrev tstrev tlocrev totalexp ppcstot county
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 BARBOUR CO SCH DIST 2144 7559 16584 5872 28021 11885 Barbour
## 2 BERKELEY CO SCH DIST 19722 48407 140127 86699 264253 12704 Berkeley
## 3 BOONE CO SCH DIST 3177 8194 26858 14564 48642 14663 Boone
## 4 BRAXTON CO SCH DIST 1747 5479 12748 6404 24417 13153 Braxton
## 5 BROOKE CO SCH DIST 2582 6791 17114 21352 41908 15642 Brooke
## 6 CABELL CO SCH DIST 11667 42518 88337 66699 183621 14538 Cabell
## 7 CALHOUN CO SCH DIST 861 3254 9953 3190 15154 16085 Calhoun
## 8 CLAY CO SCH DIST 1669 6157 17655 2791 25963 13825 Clay
## 9 DODDRIDGE CO SCH DIST 1082 3455 3999 31752 38493 23563 Doddrid…
## 10 FAYETTE CO SCH DIST 5594 15293 51759 23477 83373 13777 Fayette
## # ℹ 45 more rows
Load demographic data
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## Rows: 62 Columns: 5
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## Delimiter: ","
## chr (3): County, FIPS, Rank within US (of 3143 counties)
## dbl (2): Value (Percent), People (Unemployed)
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## # A tibble: 55 × 2
## county unemployed
## <chr> <dbl>
## 1 "McDowell " 15.1
## 2 "Braxton " 14.4
## 3 "Logan " 13.3
## 4 "Calhoun " 12.2
## 5 "Roane " 11.7
## 6 "Clay " 11.2
## 7 "Mingo " 11.2
## 8 "Webster " 11.1
## 9 "Monroe " 10.6
## 10 "Barbour " 10.1
## # ℹ 45 more rows
Joined data
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Correlations
library(ggcorrplot)
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t_numeric <- t %>%
select(proficiency, enroll, tfedrev, tstrev, tlocrev, totalexp)
ggcorrplot(cor(t_numeric),
colors = c('green', 'white', 'yellow'),
lab = TRUE,
title = "Correlation Matrix of Variables",
ggtheme = theme_minimal())
Linear Regression Model
m <- lm(proficiency ~ enroll + tfedrev + tstrev + tlocrev + totalexp, data = t)
summary(m)
library(usmap)
plot_usmap(data = t,
values = "proficiency",
include = 'West Virginia') +
scale_fill_continuous(name = "Proficiency",
low = 'red',
high = 'blue') +
theme(legend.position = "right") +
labs('Proficiency')