Enrollment
Sam Rupp
2022-11-01
library(readxl)
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## 'rlang::check_dots_empty' when loading 'pillar'## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.4 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.0## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()SAT_2022_MATH <- read_excel("SAT_2022_MATH.xlsx") %>%
mutate(Math_Benchmark_PCT = as.numeric(Math_Benchmark_PCT))## Warning: Problem with `mutate()` input `Math_Benchmark_PCT`.
## i NAs introduced by coercion
## i Input `Math_Benchmark_PCT` is `as.numeric(Math_Benchmark_PCT)`.## Warning in mask$eval_all_mutate(dots[[i]]): NAs introduced by coercionSAT_2022_EBRW <- read_excel("SAT_2022_EBRW.xlsx") %>%
mutate(EBRW_Benchmark_PCT = as.numeric(EBRW_Benchmark_PCT))## Warning: Problem with `mutate()` input `EBRW_Benchmark_PCT`.
## i NAs introduced by coercion
## i Input `EBRW_Benchmark_PCT` is `as.numeric(EBRW_Benchmark_PCT)`.
## Warning: NAs introduced by coercionschool_enrollment <- read_excel("school_enrollment.xlsx")
str(SAT_2022_EBRW)## tibble [527 × 9] (S3: tbl_df/tbl/data.frame)
## $ Corp_ID : chr [1:527] "0015" "0025" "0035" "0125" ...
## $ Corp_Name : chr [1:527] "Adams Central Community Schools" "North Adams Community Schools" "South Adams Schools" "MSD Southwest Allen County Schls" ...
## $ Schl_ID : chr [1:527] "0021" "0029" "0023" "0047" ...
## $ Schl_Name : chr [1:527] "Adams Central High School" "Bellmont Senior High School" "South Adams High School" "Homestead High School" ...
## $ EBRW_Below_Benchmark : chr [1:527] "22.0" "43.0" "31.0" "116.0" ...
## $ EBRW_Approaching_Benchmark: chr [1:527] "15.0" "28.0" "9.0" "60.0" ...
## $ EBRW_At_Benchmark : chr [1:527] "43.0" "86.0" "35.0" "411.0" ...
## $ EBRW_Total_Tested : num [1:527] 80 157 75 587 13 598 348 416 336 300 ...
## $ EBRW_Benchmark_PCT : num [1:527] 0.537 0.548 0.467 0.7 0.615 ...Snider High School
Of all 25 schools in this socioeconomic range, Snider ranks 13th in EBRW Benchmark Percentage and 13th in Math Benchmark Percentage. The Benchmark Percentage denotes the percent of students at or above the benchmark.
#school_enrollment
Snider_Range <- SAT_2022_EBRW %>%
left_join(SAT_2022_MATH, by = "Schl_Name") %>%
left_join(school_enrollment, by = "Schl_Name") %>%
select(Schl_Name, FreeReduced_Price_Meals, Paid_Meals, TOTAL_ENROLLMENT, Percent_FRL, EBRW_Benchmark_PCT, Math_Benchmark_PCT) %>%
filter(Percent_FRL <= 0.50, Percent_FRL >= 0.46) %>%
arrange(desc(EBRW_Benchmark_PCT))
Snider_Range## # A tibble: 25 x 7
## Schl_Name FreeReduced_Pri… Paid_Meals TOTAL_ENROLLMENT Percent_FRL
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Springs … 211 238 449 0.470
## 2 Greencas… 231 244 475 0.486
## 3 Central … 516 584 1100 0.469
## 4 Mitchell… 219 234 453 0.483
## 5 Greensbu… 340 376 716 0.475
## 6 Argos Co… 156 179 335 0.466
## 7 Plymouth… 508 572 1080 0.470
## 8 Attica H… 142 165 307 0.463
## 9 Seymour … 771 834 1605 0.480
## 10 Portage … 1065 1213 2278 0.468
## # … with 15 more rows, and 2 more variables: EBRW_Benchmark_PCT <dbl>,
## # Math_Benchmark_PCT <dbl>Snider_Range %>%
arrange(desc(Math_Benchmark_PCT))## # A tibble: 25 x 7
## Schl_Name FreeReduced_Pri… Paid_Meals TOTAL_ENROLLMENT Percent_FRL
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Springs … 211 238 449 0.470
## 2 Greencas… 231 244 475 0.486
## 3 Central … 516 584 1100 0.469
## 4 Attica H… 142 165 307 0.463
## 5 Argos Co… 156 179 335 0.466
## 6 Plymouth… 508 572 1080 0.470
## 7 Alexandr… 358 394 752 0.476
## 8 Greensbu… 340 376 716 0.475
## 9 Achieve … 234 272 506 0.462
## 10 Seymour … 771 834 1605 0.480
## # … with 15 more rows, and 2 more variables: EBRW_Benchmark_PCT <dbl>,
## # Math_Benchmark_PCT <dbl>Northrop High School
Of all 28 schools in this socioeconomic range, Northrop ranks 18th in EBRW Benchmark Percentage and 19th in Math Benchmark Percentage. The Benchmark Percentage denotes the percent of students at or above the benchmark.
Northrop_Range <- SAT_2022_EBRW %>%
left_join(SAT_2022_MATH, by = "Schl_Name") %>%
left_join(school_enrollment, by = "Schl_Name") %>%
select(Schl_Name, FreeReduced_Price_Meals, Paid_Meals, TOTAL_ENROLLMENT, Percent_FRL, EBRW_Benchmark_PCT, Math_Benchmark_PCT) %>%
filter(Percent_FRL <= 0.56, Percent_FRL >= 0.52) %>%
arrange(desc(EBRW_Benchmark_PCT))
Northrop_Range## # A tibble: 28 x 7
## Schl_Name FreeReduced_Pri… Paid_Meals TOTAL_ENROLLMENT Percent_FRL
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Terre Ha… 848 727 1575 0.538
## 2 Parke He… 205 163 368 0.557
## 3 LaPorte … 944 860 1804 0.523
## 4 Paoli Jr… 335 266 601 0.557
## 5 South Ri… 191 171 362 0.528
## 6 Twin Lak… 343 312 655 0.524
## 7 Jay Coun… 676 622 1298 0.521
## 8 Terre Ha… 880 771 1651 0.533
## 9 Blackfor… 390 349 739 0.528
## 10 Rushvill… 344 316 660 0.521
## # … with 18 more rows, and 2 more variables: EBRW_Benchmark_PCT <dbl>,
## # Math_Benchmark_PCT <dbl>Northrop_Range %>%
arrange(desc(Math_Benchmark_PCT))## # A tibble: 28 x 7
## Schl_Name FreeReduced_Pri… Paid_Meals TOTAL_ENROLLMENT Percent_FRL
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Jay Coun… 676 622 1298 0.521
## 2 Twin Lak… 343 312 655 0.524
## 3 South Ri… 191 171 362 0.528
## 4 Paoli Jr… 335 266 601 0.557
## 5 Terre Ha… 848 727 1575 0.538
## 6 Wabash H… 256 215 471 0.544
## 7 Crawford… 378 318 696 0.543
## 8 LaPorte … 944 860 1804 0.523
## 9 Terre Ha… 880 771 1651 0.533
## 10 Parke He… 205 163 368 0.557
## # … with 18 more rows, and 2 more variables: EBRW_Benchmark_PCT <dbl>,
## # Math_Benchmark_PCT <dbl>North Side High School
Of all 16 schools in this socioeconomic range, North Side ranks 5th in EBRW Benchmark Percentage and 6th in Math Benchmark Percentage. The Benchmark Percentage denotes the percent of students at or above the benchmark.
NorthSide_Range <- SAT_2022_EBRW %>%
left_join(SAT_2022_MATH, by = "Schl_Name") %>%
left_join(school_enrollment, by = "Schl_Name") %>%
select(Schl_Name, FreeReduced_Price_Meals, Paid_Meals, TOTAL_ENROLLMENT, Percent_FRL, EBRW_Benchmark_PCT, Math_Benchmark_PCT) %>%
filter(Percent_FRL <= 0.69, Percent_FRL >= 0.65) %>%
arrange(desc(EBRW_Benchmark_PCT))
NorthSide_Range## # A tibble: 16 x 7
## Schl_Name FreeReduced_Pri… Paid_Meals TOTAL_ENROLLMENT Percent_FRL
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Richmond… 871 456 1327 0.656
## 2 Purdue P… 371 192 563 0.659
## 3 Decatur … 1333 678 2011 0.663
## 4 Beech Gr… 640 328 968 0.661
## 5 North Si… 1027 486 1513 0.679
## 6 Wayne Hi… 903 476 1379 0.655
## 7 Indiana … 2004 1052 3056 0.656
## 8 Merrillv… 1351 653 2004 0.674
## 9 Dugger U… 347 186 533 0.651
## 10 Anderson… 1207 630 1837 0.657
## 11 Frankfor… 605 288 893 0.677
## 12 Cannelto… 157 82 239 0.657
## 13 Emmerich… 153 72 225 0.68
## 14 Arsenal … 1402 720 2122 0.661
## 15 Rise Up … 112 56 168 0.667
## 16 Communit… 65 32 97 0.670
## # … with 2 more variables: EBRW_Benchmark_PCT <dbl>, Math_Benchmark_PCT <dbl>NorthSide_Range %>%
arrange(desc(Math_Benchmark_PCT))## # A tibble: 16 x 7
## Schl_Name FreeReduced_Pri… Paid_Meals TOTAL_ENROLLMENT Percent_FRL
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Merrillv… 1351 653 2004 0.674
## 2 Purdue P… 371 192 563 0.659
## 3 Decatur … 1333 678 2011 0.663
## 4 Beech Gr… 640 328 968 0.661
## 5 Richmond… 871 456 1327 0.656
## 6 North Si… 1027 486 1513 0.679
## 7 Anderson… 1207 630 1837 0.657
## 8 Wayne Hi… 903 476 1379 0.655
## 9 Frankfor… 605 288 893 0.677
## 10 Indiana … 2004 1052 3056 0.656
## 11 Dugger U… 347 186 533 0.651
## 12 Rise Up … 112 56 168 0.667
## 13 Emmerich… 153 72 225 0.68
## 14 Arsenal … 1402 720 2122 0.661
## 15 Cannelto… 157 82 239 0.657
## 16 Communit… 65 32 97 0.670
## # … with 2 more variables: EBRW_Benchmark_PCT <dbl>, Math_Benchmark_PCT <dbl>Wayne High School
Of all 36 schools in this socioeconomic range, Wayne ranks 14th in EBRW Benchmark Percentage and 19th in Math Benchmark Percentage. The Benchmark Percentage denotes the percent of students at or above the benchmark.
Wayne_Range <- SAT_2022_EBRW %>%
left_join(SAT_2022_MATH, by = "Schl_Name") %>%
left_join(school_enrollment, by = "Schl_Name") %>%
select(Schl_Name, FreeReduced_Price_Meals, Paid_Meals, TOTAL_ENROLLMENT, Percent_FRL, EBRW_Benchmark_PCT, Math_Benchmark_PCT) %>%
filter(Percent_FRL <= 0.67, Percent_FRL >= 0.63) %>%
arrange(desc(EBRW_Benchmark_PCT))
Wayne_Range## # A tibble: 36 x 7
## Schl_Name FreeReduced_Pri… Paid_Meals TOTAL_ENROLLMENT Percent_FRL
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Greater … 143 82 225 0.636
## 2 Edinburg… 145 83 228 0.636
## 3 Perry Me… 1516 869 2385 0.636
## 4 Richmond… 871 456 1327 0.656
## 5 Washingt… 483 263 746 0.647
## 6 Washingt… 510 291 801 0.637
## 7 Washingt… 483 263 746 0.647
## 8 Washingt… 510 291 801 0.637
## 9 Lawrence… 1807 1037 2844 0.635
## 10 Purdue P… 371 192 563 0.659
## # … with 26 more rows, and 2 more variables: EBRW_Benchmark_PCT <dbl>,
## # Math_Benchmark_PCT <dbl>Wayne_Range %>%
arrange(desc(Math_Benchmark_PCT))## # A tibble: 36 x 7
## Schl_Name FreeReduced_Pri… Paid_Meals TOTAL_ENROLLMENT Percent_FRL
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Perry Me… 1516 869 2385 0.636
## 2 Washingt… 483 263 746 0.647
## 3 Washingt… 510 291 801 0.637
## 4 Washingt… 483 263 746 0.647
## 5 Washingt… 510 291 801 0.637
## 6 Lawrence… 1807 1037 2844 0.635
## 7 Shortrid… 665 360 1025 0.649
## 8 Edinburg… 145 83 228 0.636
## 9 Riversid… 263 146 409 0.643
## 10 Purdue P… 371 192 563 0.659
## # … with 26 more rows, and 2 more variables: EBRW_Benchmark_PCT <dbl>,
## # Math_Benchmark_PCT <dbl>Carroll High School
Of all 10 schools in this socioeconomic range, Carroll ranks 7th in EBRW Benchmark Percentage and 4th in Math Benchmark Percentage. The Benchmark Percentage denotes the percent of students at or above the benchmark.
Carroll_Range <- SAT_2022_EBRW %>%
left_join(SAT_2022_MATH, by = "Schl_Name") %>%
left_join(school_enrollment, by = "Schl_Name") %>%
select(Schl_Name, FreeReduced_Price_Meals, Paid_Meals, TOTAL_ENROLLMENT, Percent_FRL, EBRW_Benchmark_PCT, Math_Benchmark_PCT) %>%
filter(Percent_FRL <= 0.19, Percent_FRL >= 0.15) %>%
arrange(desc(EBRW_Benchmark_PCT))
Carroll_Range## # A tibble: 10 x 7
## Schl_Name FreeReduced_Pri… Paid_Meals TOTAL_ENROLLMENT Percent_FRL
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Signatur… 62 321 383 0.162
## 2 Hamilton… 521 2909 3430 0.152
## 3 Tri-West… 118 507 625 0.189
## 4 Westfiel… 447 2180 2627 0.170
## 5 Leo Juni… 237 1154 1391 0.170
## 6 Hanover … 143 620 763 0.187
## 7 Carroll … 425 2050 2475 0.172
## 8 East Cen… 212 1057 1269 0.167
## 9 Adams Ce… 66 302 368 0.179
## 10 Prairie … 78 354 432 0.181
## # … with 2 more variables: EBRW_Benchmark_PCT <dbl>, Math_Benchmark_PCT <dbl>Carroll_Range %>%
arrange(desc(Math_Benchmark_PCT))## # A tibble: 10 x 7
## Schl_Name FreeReduced_Pri… Paid_Meals TOTAL_ENROLLMENT Percent_FRL
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Signatur… 62 321 383 0.162
## 2 Hamilton… 521 2909 3430 0.152
## 3 Westfiel… 447 2180 2627 0.170
## 4 Carroll … 425 2050 2475 0.172
## 5 Hanover … 143 620 763 0.187
## 6 Leo Juni… 237 1154 1391 0.170
## 7 Adams Ce… 66 302 368 0.179
## 8 Tri-West… 118 507 625 0.189
## 9 East Cen… 212 1057 1269 0.167
## 10 Prairie … 78 354 432 0.181
## # … with 2 more variables: EBRW_Benchmark_PCT <dbl>, Math_Benchmark_PCT <dbl>South Side High School
Of all 13 schools in this socioeconomic range, South Side ranks 6th in EBRW Benchmark Percentage and 4th in Math Benchmark Percentage. The Benchmark Percentage denotes the percent of students at or above the benchmark.
SouthSide_Range <- SAT_2022_EBRW %>%
left_join(SAT_2022_MATH, by = "Schl_Name") %>%
left_join(school_enrollment, by = "Schl_Name") %>%
select(Schl_Name, FreeReduced_Price_Meals, Paid_Meals, TOTAL_ENROLLMENT, Percent_FRL, EBRW_Benchmark_PCT, Math_Benchmark_PCT, EBRW_Total_Tested) %>%
filter(Percent_FRL <= 0.77, Percent_FRL >= 0.73, EBRW_Total_Tested > 8) %>%
arrange(desc(EBRW_Benchmark_PCT))
##Roberts Trying with Zubovic
SouthSide_Range$EBRW_Benchmark_PCT <- as.numeric(SouthSide_Range$EBRW_Benchmark_PCT)
ggplot(SouthSide_Range, aes(x = Schl_Name, y = EBRW_Benchmark_PCT)) +
geom_line()+
labs(
x = "School Name",
y = "English Benchmark Percentage",
#fill = "School",
title = "Math and English Benchmark % of this socioeconomic range") + theme(axis.text.x = element_text(angle = 60, hjust = 1))## geom_path: Each group consists of only one observation. Do you need to adjust
## the group aesthetic?
ggplot(SouthSide_Range, aes(x = Math_Benchmark_PCT, y = EBRW_Benchmark_PCT, fill = Schl_Name)) +
geom_col()+
labs(
x = "Math Benchmark Percentage",
y = "English Benchmark Percentage",
fill = "School",
title = "Math and English Benchmark % of this socioeconomic range") + theme(axis.text.x = element_text(angle = 60, hjust = 1))
#Im not gonna lie I dont know what this graph means, but I have a better one below that I've been pulling my hair out overIn this block of code, I combined the 5 schools that we want to look at into one data set and made a graph of the Math and EBRW benchmark percentage.
TheBigFive <- SAT_2022_EBRW %>%
left_join(SAT_2022_MATH, by = "Schl_Name") %>%
left_join(school_enrollment, by = "Schl_Name") %>%
filter(Schl_Name == "R Nelson Snider High School" | Schl_Name == "Wayne High School" | Schl_Name == "South Side High School" | Schl_Name == "North Side High School" | Schl_Name == "Northrop High School") %>%
select(Schl_Name, EBRW_Benchmark_PCT, Math_Benchmark_PCT, Percent_FRL)
TheBigFive## # A tibble: 5 x 4
## Schl_Name EBRW_Benchmark_PCT Math_Benchmark_PCT Percent_FRL
## <chr> <dbl> <dbl> <dbl>
## 1 North Side High School 0.345 0.129 0.679
## 2 R Nelson Snider High School 0.433 0.238 0.481
## 3 South Side High School 0.211 0.0744 0.756
## 4 Wayne High School 0.34 0.12 0.655
## 5 Northrop High School 0.411 0.185 0.541ggplot(TheBigFive, aes(x = EBRW_Benchmark_PCT, y = Math_Benchmark_PCT, color = Schl_Name)) +
geom_point() +
labs(
x = "Math Benchmark Percentage",
y = "English Benchmark Percentage",
fill = "School",
title = "Math and English Benchmark % of these 5 schools") + theme(axis.text.x = element_text(angle = 60, hjust = 1))