Hello
ENroll.sch[ENroll.sch==-8]<-0
ENroll.sch[ENroll.sch==-9]<-0
ENroll.sch[ENroll.sch==-8]<-0
ENroll.sch[ENroll.sch==-6]<-0
ENroll.sch[ENroll.sch==-5]<-0
ENroll.sch[ENroll.sch==-3]<-0
charter.sch[charter.sch==-8]<-0
charter.sch[charter.sch==-9]<-0
charter.sch[charter.sch==-8]<-0
charter.sch[charter.sch==-6]<-0
charter.sch[charter.sch==-5]<-0
charter.sch[charter.sch==-3]<-0
Charter Schools
## [1] 3091232
## [1] 11.13042
b <- a %>%
group_by(LEA_STATE_NAME.y) %>%
summarize(TOT_ENR_F = sum(TOT_ENR_F))
c <- a %>%
group_by(LEA_STATE_NAME.y) %>%
summarize(TOT_ENR_M = sum(TOT_ENR_M))
d <- a %>%
group_by(LEA_STATE_NAME.y) %>%
summarize(SCH_ENR_LEP_F = sum(SCH_ENR_LEP_F))
e <- a %>%
group_by(LEA_STATE_NAME.y) %>%
summarize(SCH_ENR_LEP_M = sum(SCH_ENR_LEP_M))
combo <- left_join(b,c, by= "LEA_STATE_NAME.y")
combo$TOT_ENR_CHARTER <- combo$TOT_ENR_F + combo$TOT_ENR_M
combo
## # A tibble: 46 x 4
## LEA_STATE_NAME.y TOT_ENR_F TOT_ENR_M TOT_ENR_CHARTER
## <chr> <dbl> <dbl> <dbl>
## 1 ALABAMA 218 290 508
## 2 ALASKA 3369 3378 6747
## 3 ARIZONA 102285 98859 201144
## 4 ARKANSAS 16018 15729 31747
## 5 CALIFORNIA 302366 300700 603066
## 6 COLORADO 60526 60185 120711
## 7 CONNECTICUT 5285 5121 10406
## 8 DELAWARE 7797 7588 15385
## 9 DISTRICT OF COLUMBIA 18545 19165 37710
## 10 FLORIDA 147846 147979 295825
## # ... with 36 more rows
combolep <- left_join(d,e, by = "LEA_STATE_NAME.y")
combolep$TOT_ENR_CHARTER_LEP <- combolep$SCH_ENR_LEP_F + combolep$SCH_ENR_LEP_M
combolep
## # A tibble: 46 x 4
## LEA_STATE_NAME.y SCH_ENR_LEP_F SCH_ENR_LEP_M TOT_ENR_CHARTER_LEP
## <chr> <dbl> <dbl> <dbl>
## 1 ALABAMA 6 7 13
## 2 ALASKA 109 127 236
## 3 ARIZONA 4633 5598 10231
## 4 ARKANSAS 874 981 1855
## 5 CALIFORNIA 44428 50023 94451
## 6 COLORADO 9196 9813 19009
## 7 CONNECTICUT 307 336 643
## 8 DELAWARE 407 462 869
## 9 DISTRICT OF COLUMBIA 1278 1522 2800
## 10 FLORIDA 13783 15225 29008
## # ... with 36 more rows
tbl <- left_join(combo,combolep, by = "LEA_STATE_NAME.y")
tbl$LEPPERC <- round((tbl$TOT_ENR_CHARTER_LEP/tbl$TOT_ENR_CHARTER)*100,2)
tbl
## # A tibble: 46 x 8
## LEA_STATE_NAME.y TOT_ENR_F TOT_ENR_M TOT_ENR_CHARTER SCH_ENR_LEP_F
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 ALABAMA 218 290 508 6
## 2 ALASKA 3369 3378 6747 109
## 3 ARIZONA 102285 98859 201144 4633
## 4 ARKANSAS 16018 15729 31747 874
## 5 CALIFORNIA 302366 300700 603066 44428
## 6 COLORADO 60526 60185 120711 9196
## 7 CONNECTICUT 5285 5121 10406 307
## 8 DELAWARE 7797 7588 15385 407
## 9 DISTRICT OF COL~ 18545 19165 37710 1278
## 10 FLORIDA 147846 147979 295825 13783
## # ... with 36 more rows, and 3 more variables: SCH_ENR_LEP_M <dbl>,
## # TOT_ENR_CHARTER_LEP <dbl>, LEPPERC <dbl>
smalltbl <- data.frame(cbind(tbl$LEA_STATE_NAME.y, tbl$TOT_ENR_CHARTER_LEP, tbl$TOT_ENR_CHARTER, tbl$LEPPERC))
smalltbl
## X1 X2 X3 X4
## 1 ALABAMA 13 508 2.56
## 2 ALASKA 236 6747 3.5
## 3 ARIZONA 10231 201144 5.09
## 4 ARKANSAS 1855 31747 5.84
## 5 CALIFORNIA 94451 603066 15.66
## 6 COLORADO 19009 120711 15.75
## 7 CONNECTICUT 643 10406 6.18
## 8 DELAWARE 869 15385 5.65
## 9 DISTRICT OF COLUMBIA 2800 37710 7.43
## 10 FLORIDA 29008 295825 9.81
## 11 GEORGIA 2380 72659 3.28
## 12 HAWAII 1182 11145 10.61
## 13 IDAHO 439 21756 2.02
## 14 ILLINOIS 8720 65132 13.39
## 15 INDIANA 3204 43245 7.41
## 16 IOWA 34 428 7.94
## 17 KANSAS 58 3031 1.91
## 18 LOUISIANA 3895 78880 4.94
## 19 MAINE 15 2293 0.65
## 20 MARYLAND 586 22641 2.59
## 21 MASSACHUSETTS 6255 45500 13.75
## 22 MICHIGAN 16053 143972 11.15
## 23 MINNESOTA 12180 56717 21.48
## 24 MISSISSIPPI 0 948 0
## 25 MISSOURI 3927 24242 16.2
## 26 NEBRASKA 5 16 31.25
## 27 NEVADA 3795 44810 8.47
## 28 NEW HAMPSHIRE 38 3735 1.02
## 29 NEW JERSEY 2004 49677 4.03
## 30 NEW MEXICO 2676 25567 10.47
## 31 NEW YORK 9386 134831 6.96
## 32 NORTH CAROLINA 2714 100469 2.7
## 33 OHIO 5427 96898 5.6
## 34 OKLAHOMA 2774 29262 9.48
## 35 OREGON 951 34829 2.73
## 36 PENNSYLVANIA 5314 137874 3.85
## 37 RHODE ISLAND 1227 9009 13.62
## 38 SOUTH CAROLINA 1639 33607 4.88
## 39 TENNESSEE 2630 27836 9.45
## 40 TEXAS 78236 323418 24.19
## 41 UTAH 3888 75678 5.14
## 42 VIRGINIA 14 1183 1.18
## 43 WASHINGTON 215 2465 8.72
## 44 WEST VIRGINIA 1 848 0.12
## 45 WISCONSIN 3060 42814 7.15
## 46 WYOMING 30 568 5.28