SAT
Grace Buchanan
2022-11-01
Observations!!!!
Snider
Everyone in this data set had an average EBRW percentage of 0.3-0.5 percent. Except for an outliar Excel Center for Adult Learners, with an EBRW percentage of 7.14. This is an academy of adults getting their diploma. It would be hard to compare us to them because there is a lot of variables there that do not correlate to us. For example they might have taken the SAT more seriously since they have more on the line.
Northside
Something I find interesting about this is that the number of students doesn’t change the Free Reduced Lunch percentage. Some schools have 2,000 while others have 200.
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
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## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
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math <- read_excel("math.xlsx", sheet = "Math Demographics")## New names:
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%...76`library(readxl)
ebrw <- read_excel("math.xlsx", sheet = "EBRW")library(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")
#View(school_enrollment_ethnicity_and_free_reduced_price_meal_status_2006_22_2_)
enrollment3 <- school_enrollment_ethnicity_and_free_reduced_price_meal_status_2006_22_2_library(readxl)
Copy_of_SAT_2022_Grade11_Final_School_v2_1_ <- read_excel("Copy of SAT-2022-Grade11-Final-School-v2 (1).xlsx")
#View(Copy_of_SAT_2022_Grade11_Final_School_v2_1_)
math2 <- Copy_of_SAT_2022_Grade11_Final_School_v2_1_edit_enroll <- enrollment3 %>%
mutate(FRLpercentage = `Free/Reduced Price Meals` / `TOTAL ENROLLMENT`)
Sat_both <-
math2 %>%
right_join(ebrw)## Joining, by = c("Corp ID", "Corp Name", "School ID", "School Name")Sat_both## # A tibble: 527 × 14
## `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 0015 Adams Cent… 0021 Adams … 23 24 33 80 0.4124…
## 2 0025 North Adam… 0029 Bellmo… 52 32 73 157 0.4649…
## 3 0035 South Adam… 0023 South … 32 18 25 75 0.3333…
## 4 0125 MSD Southw… 0047 Homest… 130 153 304 587 0.5178…
## 5 0125 MSD Southw… 0188 eSACS … 5 4 4 13 0.3076…
## 6 0225 Northwest … 0091 Carrol… 131 156 311 598 0.5200…
## 7 0235 Fort Wayne… 0101 North … 245 58 45 348 0.1293…
## 8 0235 Fort Wayne… 0102 R Nels… 218 99 99 416 0.2379…
## 9 0235 Fort Wayne… 0105 South … 259 52 25 336 7.4404…
## 10 0235 Fort Wayne… 0177 Wayne … 200 64 36 300 0.12
## # … with 517 more rows, 5 more variables: `EBRW\r\nBelow Benchmark` <chr>,
## # `EBRW \r\nApproaching Benchmark` <chr>, `EBRW \r\nAt\r\nBenchmark` <chr>,
## # `EBRW\r\nTotal\r\nTested` <dbl>, `EBRW\r\nBenchmark \r\n%` <chr>, 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%`Sat_both_FRL <-
Sat_both %>%
left_join(edit_enroll)## Joining, by = c("Corp ID", "Corp Name", "School Name")Sat_both_FRL## # A tibble: 527 × 26
## `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 0015 Adams Cent… 0021 Adams … 23 24 33 80 0.4124…
## 2 0025 North Adam… 0029 Bellmo… 52 32 73 157 0.4649…
## 3 0035 South Adam… 0023 South … 32 18 25 75 0.3333…
## 4 0125 MSD Southw… 0047 Homest… 130 153 304 587 0.5178…
## 5 0125 MSD Southw… 0188 eSACS … 5 4 4 13 0.3076…
## 6 0225 Northwest … 0091 Carrol… 131 156 311 598 0.5200…
## 7 0235 Fort Wayne… 0101 North … 245 58 45 348 0.1293…
## 8 0235 Fort Wayne… 0102 R Nels… 218 99 99 416 0.2379…
## 9 0235 Fort Wayne… 0105 South … 259 52 25 336 7.4404…
## 10 0235 Fort Wayne… 0177 Wayne … 200 64 36 300 0.12
## # … with 517 more rows, 17 more variables: `EBRW\r\nBelow Benchmark` <chr>,
## # `EBRW \r\nApproaching Benchmark` <chr>, `EBRW \r\nAt\r\nBenchmark` <chr>,
## # `EBRW\r\nTotal\r\nTested` <dbl>, `EBRW\r\nBenchmark \r\n%` <chr>,
## # `Schl ID` <chr>, `American Indian` <dbl>, Asian <dbl>, Black <dbl>,
## # Hispanic <dbl>, Multiracial <dbl>,
## # `Native Hawaiian or Other Pacific Islander` <dbl>, White <dbl>,
## # `Free/Reduced Price Meals` <dbl>, `Paid Meals` <dbl>, …sniderinfo <-
enrollment3 %>%
mutate(FRLpercentage = `Free/Reduced Price Meals` / `TOTAL ENROLLMENT`) %>%
select(`School Name`, FRLpercentage) %>%
filter(FRLpercentage > .46 & FRLpercentage < .56)
sniderinfo## # A tibble: 295 × 2
## `School Name` FRLpercentage
## <chr> <dbl>
## 1 Bellmont Middle School 0.475
## 2 R Nelson Snider High School 0.481
## 3 Jefferson Middle School 0.546
## 4 Weisser Park Elementary School 0.558
## 5 Willard Shambaugh Elementary Sch 0.533
## 6 Northrop High School 0.541
## 7 Saint Joseph Central School 0.496
## 8 Heritage Jr/Sr High School 0.554
## 9 L F Smith Elementary 0.497
## 10 Hope Elementary School 0.470
## # … with 285 more rowsSnider <-
Sat_both_FRL %>%
left_join(sniderinfo) %>%
filter(FRLpercentage > .46 & FRLpercentage < .56) ## Joining, by = c("School Name", "FRLpercentage")Snider## # A tibble: 71 × 26
## `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 0235 Fort Wayne… 0219 Northr… 286 107 89 482 0.1846…
## 3 0255 East Allen… 0081 Herita… 78 39 29 146 0.1986…
## 4 0515 Blackford … 0489 Blackf… 61 25 22 108 0.2037…
## 5 0670 Brown Coun… 0573 Brown … 58 21 35 114 0.3070…
## 6 0775 Pioneer Re… 0645 Pionee… 39 19 18 76 0.2368…
## 7 0875 Logansport… 0701 Logans… 172 53 61 286 0.2132…
## 8 1000 Clarksvill… 0833 Clarks… 61 18 13 92 0.1413…
## 9 1010 Greater Cl… 0849 Jeffer… 269 90 90 449 0.2004…
## 10 1730 Greensburg… 1268 Greens… 75 34 40 149 0.2684…
## # … with 61 more rows, 17 more variables: `EBRW\r\nBelow Benchmark` <chr>,
## # `EBRW \r\nApproaching Benchmark` <chr>, `EBRW \r\nAt\r\nBenchmark` <chr>,
## # `EBRW\r\nTotal\r\nTested` <dbl>, `EBRW\r\nBenchmark \r\n%` <chr>,
## # `Schl ID` <chr>, `American Indian` <dbl>, Asian <dbl>, Black <dbl>,
## # Hispanic <dbl>, Multiracial <dbl>,
## # `Native Hawaiian or Other Pacific Islander` <dbl>, White <dbl>,
## # `Free/Reduced Price Meals` <dbl>, `Paid Meals` <dbl>, …ggplot(sniderinfo, aes(x = FRLpercentage)) +
geom_histogram(binwidth = .005) +
labs(
x = "FRLpercentage",
y = "School Name",
title = "Free Reduced Lunch Precentage/ Schools"
)
ggplot(sniderinfo, aes(x = FRLpercentage)) +
geom_density(adjust = 2) +
labs(
x = "FRLpercentage",
y = "School Name",
title = "Free Reduced Lunch Precentage/ Schools"
)
northsideinfo <-
enrollment3 %>%
mutate(FRLpercentage = `Free/Reduced Price Meals` / `TOTAL ENROLLMENT`) %>%
select(`School Name`, FRLpercentage) %>%
filter(FRLpercentage > .66 & FRLpercentage < .70)
northsideinfo## # A tibble: 71 × 2
## `School Name` FRLpercentage
## <chr> <dbl>
## 1 North Side High School 0.679
## 2 Indian Village Elementary School 0.668
## 3 Lindley Elementary School 0.671
## 4 Shawnee Middle School 0.677
## 5 Fort Wayne Virtual Academy 0.696
## 6 Lincoln Elementary School 0.681
## 7 Clifty Creek Elementary School 0.667
## 8 Columbia Elementary School 0.671
## 9 Northaven Elementary School 0.669
## 10 East Side Elementary School 0.695
## # … with 61 more rowsNorthside <-
Sat_both_FRL %>%
left_join(northsideinfo) %>%
filter(FRLpercentage > .46 & FRLpercentage < .56) ## Joining, by = c("School Name", "FRLpercentage")Northside## # A tibble: 71 × 26
## `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 0235 Fort Wayne… 0219 Northr… 286 107 89 482 0.1846…
## 3 0255 East Allen… 0081 Herita… 78 39 29 146 0.1986…
## 4 0515 Blackford … 0489 Blackf… 61 25 22 108 0.2037…
## 5 0670 Brown Coun… 0573 Brown … 58 21 35 114 0.3070…
## 6 0775 Pioneer Re… 0645 Pionee… 39 19 18 76 0.2368…
## 7 0875 Logansport… 0701 Logans… 172 53 61 286 0.2132…
## 8 1000 Clarksvill… 0833 Clarks… 61 18 13 92 0.1413…
## 9 1010 Greater Cl… 0849 Jeffer… 269 90 90 449 0.2004…
## 10 1730 Greensburg… 1268 Greens… 75 34 40 149 0.2684…
## # … with 61 more rows, 17 more variables: `EBRW\r\nBelow Benchmark` <chr>,
## # `EBRW \r\nApproaching Benchmark` <chr>, `EBRW \r\nAt\r\nBenchmark` <chr>,
## # `EBRW\r\nTotal\r\nTested` <dbl>, `EBRW\r\nBenchmark \r\n%` <chr>,
## # `Schl ID` <chr>, `American Indian` <dbl>, Asian <dbl>, Black <dbl>,
## # Hispanic <dbl>, Multiracial <dbl>,
## # `Native Hawaiian or Other Pacific Islander` <dbl>, White <dbl>,
## # `Free/Reduced Price Meals` <dbl>, `Paid Meals` <dbl>, …ggplot(northsideinfo, aes(x = FRLpercentage)) +
geom_histogram(binwidth = .005) +
labs(
x = "FRLpercentage",
y = "School Name",
title = "Free Reduced Lunch Precentage/ Schools"
)
ggplot(northsideinfo, aes(x = FRLpercentage)) +
geom_density(adjust = 2) +
labs(
x = "FRLpercentage",
y = "School Name",
title = "Free Reduced Lunch Precentage/ Schools"
)