SATData
2022-11-01
Observations
In Snider’s comparison set, it is apparent that the school with the highest percentage of tested students at the SAT Math benchmark has the lowest percentage of students relying on Free/Reduced Lunch. Attica High School has the highest SAT Math scoring rate, with 31% of students at the SAT Math benchmark and the lowest percentage of students relying on Free/Reduced Lunch, with a rate of 46%.
Correspondingly, in North Side’s comparison set, it is notable that the schools with the lowest percentage of tested students at the SAT Math benchmark have the highest rate of students relying on Free/Reduced Lunch. Fort Wayne Virtual Academy has the highest percentage of students using Free/Reduced Lunch, with a rate of 69%, and the second-lowest SAT Math scoring rate, with 5% of students at the SAT Math benchmark. Emmerich Manual High School has the second-highest percentage of students relying on Free/Reduced Lunch, with a rate of 68%, and the lowest SAT Math scoring rate, with 2.3% of students at the SAT Math benchmark.
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──## ✔ ggplot2 3.4.0 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ stringr 1.4.0
## ✔ tidyr 1.2.1 ✔ forcats 0.5.2
## ✔ readr 2.1.3 ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(DT)library(readxl)
SAT_2022_Grade11_Final_School_v2_1_ <- read_excel("SAT-2022-Grade11-Final-School-v2 (1).xlsx")
SATEnglish <- SAT_2022_Grade11_Final_School_v2_1_library(readxl)
Copy_of_SAT_2022_Grade11_Final_School_v2 <- read_excel("Copy of SAT-2022-Grade11-Final-School-v2.xlsx")
SATMath <- Copy_of_SAT_2022_Grade11_Final_School_v2library(readxl)
Copy_of_Copy_of_SAT_2022_Grade11_Final_School_v2 <- read_excel("Copy of Copy of SAT-2022-Grade11-Final-School-v2.xlsx")
Combined <- Copy_of_Copy_of_SAT_2022_Grade11_Final_School_v2library(readxl)
school_enrollment_ethnicity_and_free_reduced_price_meal_status_2006_22_2_ <- read_excel("school-enrollment-ethnicity-and-free-reduced-price-meal-status-2006-22 (2).xlsx")
Enrollment <- school_enrollment_ethnicity_and_free_reduced_price_meal_status_2006_22_2_
#mutate(SATMath)Combined <-
Enrollment %>%
mutate(Diversity = `Free/Reduced Price Meals`/`TOTAL ENROLLMENT`)%>%
select(`School Name`, Diversity) %>%
filter(Diversity > .43 & Diversity < .53)SATMath%>%
left_join(Combined)%>%
filter(Diversity > .46 & Diversity < .50)## Joining, by = "School Name"## # A tibble: 25 × 10
## `Corp ID` `Corp Name` Schoo…¹ Schoo…² Math\…³ Math …⁴ Math …⁵ Math\…⁶ Math\…⁷
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr>
## 1 0235 Fort Wayne… 0102 R Nels… 218 99 99 416 0.2379…
## 2 0775 Pioneer Re… 0645 Pionee… 39 19 18 76 0.2368…
## 3 1000 Clarksvill… 0833 Clarks… 61 18 13 92 0.1413…
## 4 1730 Greensburg… 1268 Greens… 75 34 40 149 0.2684…
## 5 2305 Elkhart Co… 1754 Elkhar… 429 135 123 687 0.1790…
## 6 2315 Goshen Com… 1821 Goshen… 222 110 111 443 0.2505…
## 7 2400 New Albany… 2408 NAFC V… 11 6 4 21 0.1904…
## 8 2435 Attica Con… 2053 Attica… 22 11 15 48 0.3125
## 9 3445 New Castle… 2825 New Ca… 94 42 40 176 0.2272…
## 10 3500 Kokomo Sch… 3013 Kokomo… 195 72 56 323 0.1733…
## # … with 15 more rows, 1 more variable: Diversity <dbl>, and abbreviated
## # variable names ¹`School ID`, ²`School Name`, ³`Math\r\nBelow Benchmark`,
## # ⁴`Math \r\nApproaching Benchmark`, ⁵`Math \r\nAt\r\nBenchmark`,
## # ⁶`Math\r\nTotal\r\nTested`, ⁷`Math\r\nBenchmark \r\n%`Combined%>%
right_join(SATMath)%>%
filter(Diversity > .46 & Diversity < .50)## Joining, by = "School Name"## # A tibble: 25 × 10
## `School Name` Diver…¹ Corp …² Corp …³ Schoo…⁴ Math\…⁵ Math …⁶ Math …⁷ Math\…⁸
## <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 R Nelson Sni… 0.481 0235 Fort W… 0102 218 99 99 416
## 2 Pioneer Jr-S… 0.494 0775 Pionee… 0645 39 19 18 76
## 3 Clarksville … 0.478 1000 Clarks… 0833 61 18 13 92
## 4 Greensburg C… 0.475 1730 Greens… 1268 75 34 40 149
## 5 Elkhart High… 0.473 2305 Elkhar… 1754 429 135 123 687
## 6 Goshen High … 0.497 2315 Goshen… 1821 222 110 111 443
## 7 NAFC Virtual… 0.475 2400 New Al… 2408 11 6 4 21
## 8 Attica High … 0.463 2435 Attica… 2053 22 11 15 48
## 9 New Castle H… 0.479 3445 New Ca… 2825 94 42 40 176
## 10 Kokomo High … 0.491 3500 Kokomo… 3013 195 72 56 323
## # … with 15 more rows, 1 more variable: `Math\r\nBenchmark \r\n%` <chr>, and
## # abbreviated variable names ¹Diversity, ²`Corp ID`, ³`Corp Name`,
## # ⁴`School ID`, ⁵`Math\r\nBelow Benchmark`,
## # ⁶`Math \r\nApproaching Benchmark`, ⁷`Math \r\nAt\r\nBenchmark`,
## # ⁸`Math\r\nTotal\r\nTested`Combined%>%
right_join(SATEnglish)%>%
filter(Diversity > .46 & Diversity < .50)## Joining, by = "School Name"## # A tibble: 25 × 10
## `School Name` Diver…¹ Corp …² Corp …³ Schoo…⁴ EBRW\…⁵ EBRW …⁶ EBRW …⁷ EBRW\…⁸
## <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 R Nelson Sni… 0.481 0235 Fort W… 0102 174 62 180 416
## 2 Pioneer Jr-S… 0.494 0775 Pionee… 0645 35 9 32 76
## 3 Clarksville … 0.478 1000 Clarks… 0833 49 10 33 92
## 4 Greensburg C… 0.475 1730 Greens… 1268 55 23 71 149
## 5 Elkhart High… 0.473 2305 Elkhar… 1754 357 93 237 687
## 6 Goshen High … 0.497 2315 Goshen… 1821 211 57 175 443
## 7 NAFC Virtual… 0.475 2400 New Al… 2408 12 1 8 21
## 8 Attica High … 0.463 2435 Attica… 2053 22 4 22 48
## 9 New Castle H… 0.479 3445 New Ca… 2825 78 33 65 176
## 10 Kokomo High … 0.491 3500 Kokomo… 3013 173 33 117 323
## # … with 15 more rows, 1 more variable: `EBRW\r\nBenchmark \r\n%` <chr>, and
## # abbreviated variable names ¹Diversity, ²`Corp ID`, ³`Corp Name`,
## # ⁴`School ID`, ⁵`EBRW\r\nBelow Benchmark`,
## # ⁶`EBRW \r\nApproaching Benchmark`, ⁷`EBRW \r\nAt\r\nBenchmark`,
## # ⁸`EBRW\r\nTotal\r\nTested`NorthSide <-
Enrollment %>%
mutate(Diversity = `Free/Reduced Price Meals`/`TOTAL ENROLLMENT`)%>%
select(`School Name`, Diversity) %>%
filter(Diversity > .65 & Diversity < .70)
NorthSide## # A tibble: 96 × 2
## `School Name` Diversity
## <chr> <dbl>
## 1 North Side High School 0.679
## 2 Wayne High School 0.655
## 3 Mabel K Holland Elementary School 0.651
## 4 Indian Village Elementary School 0.668
## 5 John S Irwin Elementary School 0.653
## 6 Lindley Elementary School 0.671
## 7 Shawnee Middle School 0.677
## 8 Fort Wayne Virtual Academy 0.696
## 9 Lincoln Elementary School 0.681
## 10 Clifty Creek Elementary School 0.667
## # … with 86 more rowsNSSAT <- SATMath%>%
left_join(NorthSide)%>%
filter(Diversity > .65 & Diversity < .70)## Joining, by = "School Name"NSSAT## # A tibble: 19 × 10
## `Corp ID` `Corp Name` Schoo…¹ Schoo…² Math\…³ Math …⁴ Math …⁵ Math\…⁶ Math\…⁷
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr>
## 1 0235 Fort Wayne… 0101 North … 245 58 45 348 0.1293…
## 2 0235 Fort Wayne… 0177 Wayne … 200 64 36 300 0.12
## 3 0235 Fort Wayne… 0248 Fort W… 15 4 1 20 0.05
## 4 1170 Community … 0997 Frankf… 129 50 22 201 0.1094…
## 5 4600 Merrillvil… 3809 Merril… 291 94 67 452 0.1482…
## 6 5275 Anderson C… 4945 Anders… 249 69 46 364 0.1263…
## 7 5300 MSD Decatu… 5177 Decatu… 227 111 54 392 0.1377…
## 8 5380 Beech Grov… 5449 Beech … 116 47 26 189 0.1375…
## 9 5385 Indianapol… 5462 Arsena… 308 31 8 347 2.3054…
## 10 5385 Indianapol… 5482 Emmeri… 70 12 2 84 2.3809…
## 11 6340 Cannelton … 6733 Cannel… 19 1 0 20 0
## 12 6795 Union Scho… 7131 Indian… 255 58 24 337 7.1216…
## 13 7205 South Bend… 7534 Rise U… 24 0 1 25 0.04
## 14 8385 Richmond C… 8993 Richmo… 160 58 33 251 0.1314…
## 15 8385 Richmond C… 9056 Commun… *** *** *** 3 ***
## 16 9015 Purdue Pol… 4271 Purdue… 50 25 12 87 0.1379…
## 17 9610 Indiana Sc… C695 Indian… 17 1 0 18 0
## 18 9885 Gary Middl… 4027 Gary M… 41 2 0 43 0
## 19 9950 Dugger Uni… 7952 Dugger… 39 3 3 45 6.6666…
## # … with 1 more variable: Diversity <dbl>, and abbreviated variable names
## # ¹`School ID`, ²`School Name`, ³`Math\r\nBelow Benchmark`,
## # ⁴`Math \r\nApproaching Benchmark`, ⁵`Math \r\nAt\r\nBenchmark`,
## # ⁶`Math\r\nTotal\r\nTested`, ⁷`Math\r\nBenchmark \r\n%`datatable(NSSAT)NorthSide%>%
right_join(SATMath)%>%
filter(Diversity > .65 & Diversity < .70)## Joining, by = "School Name"## # A tibble: 19 × 10
## `School Name` Diver…¹ Corp …² Corp …³ Schoo…⁴ Math\…⁵ Math …⁶ Math …⁷ Math\…⁸
## <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 North Side H… 0.679 0235 Fort W… 0101 245 58 45 348
## 2 Wayne High S… 0.655 0235 Fort W… 0177 200 64 36 300
## 3 Fort Wayne V… 0.696 0235 Fort W… 0248 15 4 1 20
## 4 Frankfort Hi… 0.677 1170 Commun… 0997 129 50 22 201
## 5 Merrillville… 0.674 4600 Merril… 3809 291 94 67 452
## 6 Anderson Hig… 0.657 5275 Anders… 4945 249 69 46 364
## 7 Decatur Cent… 0.663 5300 MSD De… 5177 227 111 54 392
## 8 Beech Grove … 0.661 5380 Beech … 5449 116 47 26 189
## 9 Arsenal Tech… 0.661 5385 Indian… 5462 308 31 8 347
## 10 Emmerich Man… 0.68 5385 Indian… 5482 70 12 2 84
## 11 Cannelton El… 0.657 6340 Cannel… 6733 19 1 0 20
## 12 Indiana Digi… 0.656 6795 Union … 7131 255 58 24 337
## 13 Rise Up Acad… 0.667 7205 South … 7534 24 0 1 25
## 14 Richmond Hig… 0.656 8385 Richmo… 8993 160 58 33 251
## 15 Community Yo… 0.670 8385 Richmo… 9056 *** *** *** 3
## 16 Purdue Polyt… 0.659 9015 Purdue… 4271 50 25 12 87
## 17 Indiana Scho… 0.696 9610 Indian… C695 17 1 0 18
## 18 Gary Middle … 0.697 9885 Gary M… 4027 41 2 0 43
## 19 Dugger Union… 0.651 9950 Dugger… 7952 39 3 3 45
## # … with 1 more variable: `Math\r\nBenchmark \r\n%` <chr>, and abbreviated
## # variable names ¹Diversity, ²`Corp ID`, ³`Corp Name`, ⁴`School ID`,
## # ⁵`Math\r\nBelow Benchmark`, ⁶`Math \r\nApproaching Benchmark`,
## # ⁷`Math \r\nAt\r\nBenchmark`, ⁸`Math\r\nTotal\r\nTested`NorthSide%>%
right_join(SATEnglish)%>%
filter(Diversity > .65 & Diversity < .70)## Joining, by = "School Name"## # A tibble: 19 × 10
## `School Name` Diver…¹ Corp …² Corp …³ Schoo…⁴ EBRW\…⁵ EBRW …⁶ EBRW …⁷ EBRW\…⁸
## <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 North Side H… 0.679 0235 Fort W… 0101 196 32 120 348
## 2 Wayne High S… 0.655 0235 Fort W… 0177 170 28 102 300
## 3 Fort Wayne V… 0.696 0235 Fort W… 0248 11 4 5 20
## 4 Frankfort Hi… 0.677 1170 Commun… 0997 129 27 45 201
## 5 Merrillville… 0.674 4600 Merril… 3809 255 52 145 452
## 6 Anderson Hig… 0.657 5275 Anders… 4945 232 30 102 364
## 7 Decatur Cent… 0.663 5300 MSD De… 5177 202 47 143 392
## 8 Beech Grove … 0.661 5380 Beech … 5449 98 25 66 189
## 9 Arsenal Tech… 0.661 5385 Indian… 5462 279 31 37 347
## 10 Emmerich Man… 0.68 5385 Indian… 5482 66 9 9 84
## 11 Cannelton El… 0.657 6340 Cannel… 6733 16 1 3 20
## 12 Indiana Digi… 0.656 6795 Union … 7131 178 48 111 337
## 13 Rise Up Acad… 0.667 7205 South … 7534 18 5 2 25
## 14 Richmond Hig… 0.656 8385 Richmo… 8993 125 32 94 251
## 15 Community Yo… 0.670 8385 Richmo… 9056 *** *** *** 3
## 16 Purdue Polyt… 0.659 9015 Purdue… 4271 44 11 32 87
## 17 Indiana Scho… 0.696 9610 Indian… C695 17 0 1 18
## 18 Gary Middle … 0.697 9885 Gary M… 4027 39 2 2 43
## 19 Dugger Union… 0.651 9950 Dugger… 7952 26 5 14 45
## # … with 1 more variable: `EBRW\r\nBenchmark \r\n%` <chr>, and abbreviated
## # variable names ¹Diversity, ²`Corp ID`, ³`Corp Name`, ⁴`School ID`,
## # ⁵`EBRW\r\nBelow Benchmark`, ⁶`EBRW \r\nApproaching Benchmark`,
## # ⁷`EBRW \r\nAt\r\nBenchmark`, ⁸`EBRW\r\nTotal\r\nTested`Snider <-
Enrollment %>%
mutate(Diversity = `Free/Reduced Price Meals`/`TOTAL ENROLLMENT`)%>%
select(`School Name`, Diversity) %>%
filter(Diversity > .46 & Diversity < .50)
Snider## # A tibble: 109 × 2
## `School Name` Diversity
## <chr> <dbl>
## 1 Bellmont Middle School 0.475
## 2 R Nelson Snider High School 0.481
## 3 Saint Joseph Central School 0.496
## 4 L F Smith Elementary 0.497
## 5 Hope Elementary School 0.470
## 6 Lebanon Middle School 0.471
## 7 Pioneer Jr-Sr High School 0.494
## 8 Clarksville Senior High School 0.478
## 9 Jonathan Jennings Elementary Sch 0.492
## 10 North Clay Middle School 0.483
## # … with 99 more rowsSSAT <- SATMath%>%
left_join(Snider)%>%
filter(Diversity > .46 & Diversity < .50)## Joining, by = "School Name"SSAT## # A tibble: 25 × 10
## `Corp ID` `Corp Name` Schoo…¹ Schoo…² Math\…³ Math …⁴ Math …⁵ Math\…⁶ Math\…⁷
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr>
## 1 0235 Fort Wayne… 0102 R Nels… 218 99 99 416 0.2379…
## 2 0775 Pioneer Re… 0645 Pionee… 39 19 18 76 0.2368…
## 3 1000 Clarksvill… 0833 Clarks… 61 18 13 92 0.1413…
## 4 1730 Greensburg… 1268 Greens… 75 34 40 149 0.2684…
## 5 2305 Elkhart Co… 1754 Elkhar… 429 135 123 687 0.1790…
## 6 2315 Goshen Com… 1821 Goshen… 222 110 111 443 0.2505…
## 7 2400 New Albany… 2408 NAFC V… 11 6 4 21 0.1904…
## 8 2435 Attica Con… 2053 Attica… 22 11 15 48 0.3125
## 9 3445 New Castle… 2825 New Ca… 94 42 40 176 0.2272…
## 10 3500 Kokomo Sch… 3013 Kokomo… 195 72 56 323 0.1733…
## # … with 15 more rows, 1 more variable: Diversity <dbl>, and abbreviated
## # variable names ¹`School ID`, ²`School Name`, ³`Math\r\nBelow Benchmark`,
## # ⁴`Math \r\nApproaching Benchmark`, ⁵`Math \r\nAt\r\nBenchmark`,
## # ⁶`Math\r\nTotal\r\nTested`, ⁷`Math\r\nBenchmark \r\n%`datatable(SSAT)Snider%>%
right_join(SATMath)%>%
filter(Diversity > .46 & Diversity < .50)## Joining, by = "School Name"## # A tibble: 25 × 10
## `School Name` Diver…¹ Corp …² Corp …³ Schoo…⁴ Math\…⁵ Math …⁶ Math …⁷ Math\…⁸
## <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 R Nelson Sni… 0.481 0235 Fort W… 0102 218 99 99 416
## 2 Pioneer Jr-S… 0.494 0775 Pionee… 0645 39 19 18 76
## 3 Clarksville … 0.478 1000 Clarks… 0833 61 18 13 92
## 4 Greensburg C… 0.475 1730 Greens… 1268 75 34 40 149
## 5 Elkhart High… 0.473 2305 Elkhar… 1754 429 135 123 687
## 6 Goshen High … 0.497 2315 Goshen… 1821 222 110 111 443
## 7 NAFC Virtual… 0.475 2400 New Al… 2408 11 6 4 21
## 8 Attica High … 0.463 2435 Attica… 2053 22 11 15 48
## 9 New Castle H… 0.479 3445 New Ca… 2825 94 42 40 176
## 10 Kokomo High … 0.491 3500 Kokomo… 3013 195 72 56 323
## # … with 15 more rows, 1 more variable: `Math\r\nBenchmark \r\n%` <chr>, and
## # abbreviated variable names ¹Diversity, ²`Corp ID`, ³`Corp Name`,
## # ⁴`School ID`, ⁵`Math\r\nBelow Benchmark`,
## # ⁶`Math \r\nApproaching Benchmark`, ⁷`Math \r\nAt\r\nBenchmark`,
## # ⁸`Math\r\nTotal\r\nTested`Snider%>%
right_join(SATEnglish)%>%
filter(Diversity > .46 & Diversity < .50)## Joining, by = "School Name"## # A tibble: 25 × 10
## `School Name` Diver…¹ Corp …² Corp …³ Schoo…⁴ EBRW\…⁵ EBRW …⁶ EBRW …⁷ EBRW\…⁸
## <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
## 1 R Nelson Sni… 0.481 0235 Fort W… 0102 174 62 180 416
## 2 Pioneer Jr-S… 0.494 0775 Pionee… 0645 35 9 32 76
## 3 Clarksville … 0.478 1000 Clarks… 0833 49 10 33 92
## 4 Greensburg C… 0.475 1730 Greens… 1268 55 23 71 149
## 5 Elkhart High… 0.473 2305 Elkhar… 1754 357 93 237 687
## 6 Goshen High … 0.497 2315 Goshen… 1821 211 57 175 443
## 7 NAFC Virtual… 0.475 2400 New Al… 2408 12 1 8 21
## 8 Attica High … 0.463 2435 Attica… 2053 22 4 22 48
## 9 New Castle H… 0.479 3445 New Ca… 2825 78 33 65 176
## 10 Kokomo High … 0.491 3500 Kokomo… 3013 173 33 117 323
## # … with 15 more rows, 1 more variable: `EBRW\r\nBenchmark \r\n%` <chr>, and
## # abbreviated variable names ¹Diversity, ²`Corp ID`, ³`Corp Name`,
## # ⁴`School ID`, ⁵`EBRW\r\nBelow Benchmark`,
## # ⁶`EBRW \r\nApproaching Benchmark`, ⁷`EBRW \r\nAt\r\nBenchmark`,
## # ⁸`EBRW\r\nTotal\r\nTested` ggplot(data = NSSAT, mapping = aes(x = `School Name`, y = 'Math Total Tested')) +
geom_col() +
theme(axis.text.x = element_text(angle=45, vjust=1, hjust=1))
ggplot(data = NSSAT, mapping = aes(x = `School Name`, y = 'Math Total Tested')) +
geom_col() +
theme(axis.text.x = element_text(angle=45, vjust=1, hjust=1))