California Education Data
Vid Chan
10/15/2017
Let’s explore some of the data sets that exist in the package.
Cohorts Dataset:
library(rCAEDDATA)
data(cohorts)
head(cohorts)## CDS Name AggLevel DFC Subgroup Subgrouptype
## 1 30664643030574 Aliso Niguel High S N All 4
## 2 30664643030574 Aliso Niguel High S N All 5
## 3 30664643030574 Aliso Niguel High S N All 1
## 4 30664643030574 Aliso Niguel High S N All 3
## 5 30664643030574 Aliso Niguel High S N All 9
## 6 30664643030574 Aliso Niguel High S N All 7
## NumCohort NumGraduates Cohort Graduation Rate NumDropouts
## 1 23 22 95.65 *
## 2 83 83 100.00 *
## 3 * * 100.00 *
## 4 * * 100.00 *
## 5 25 25 100.00 *
## 6 531 520 97.93 *
## Cohort Dropout Rate NumSpecialEducation Special Ed Completers Rate
## 1 0.0 * 4.3
## 2 0.0 * 0.0
## 3 0.0 * 0.0
## 4 0.0 * 0.0
## 5 0.0 * 0.0
## 6 1.1 * 0.8
## NumStillEnrolled Still Enrolled Rate NumGED GED Rate Year
## 1 * 0.0 * 0 0910
## 2 * 0.0 * 0 0910
## 3 * 0.0 * 0 0910
## 4 * 0.0 * 0 0910
## 5 * 0.0 * 0 0910
## 6 * 0.2 * 0 0910colnames(cohorts)## [1] "CDS" "Name"
## [3] "AggLevel" "DFC"
## [5] "Subgroup" "Subgrouptype"
## [7] "NumCohort" "NumGraduates"
## [9] "Cohort Graduation Rate" "NumDropouts"
## [11] "Cohort Dropout Rate" "NumSpecialEducation"
## [13] "Special Ed Completers Rate" "NumStillEnrolled"
## [15] "Still Enrolled Rate" "NumGED"
## [17] "GED Rate" "Year"ncol(cohorts)## [1] 18nrow(cohorts)## [1] 512812# Let's find out how many unique cases there are in the Cohorts dataset.
length(unique(cohorts$CDS)) ## [1] 3759# Let's find out the min and max number of cases. First, we need to compute the total number
# of frequency of each case in the dataset.
cohorts1 <- data.frame(table(cohorts$Name))
summary(cohorts1$Freq) # this way, you can see the max and min. ## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.0 74.0 137.0 144.8 197.0 12842.0# One way to find out which program has the max and min.
cohorts1 <- cohorts1[order(cohorts1$Freq),]
# After sorting, you can use the following to see which program has the lowest # of cases
# and which one has the highest # of cases.
head(cohorts1) ## Var1 Freq
## 291 Bear Valley High 4
## 806 Del Norte County Community Day (Secondary) 4
## 1264 Gridley Unified Community Day (7-12) 4
## 2426 Pioneer/Quincy Elementary 4
## 2870 SBC - Pacific Technology 4
## 58 Alabama Hills Community Day 5tail(cohorts1)## Var1 Freq
## 1686 Liberty High 839
## 1422 Independence High 920
## 1495 John F. Kennedy High 1036
## 860 District Office 1085
## 1112 Foothill High 1176
## 2142 Non-public, non-sectarian schools 12842# Another way to find out which program has the max and min after knowing the max and min values.
# Max = 12842
# Min = 4
cohorts1[which.max(cohorts1$Freq),]## Var1 Freq
## 2142 Non-public, non-sectarian schools 12842cohorts1[which.min(cohorts1$Freq),]## Var1 Freq
## 291 Bear Valley High 4# These two commands would not give you all the rows that have max and min frequency.
# They only give us the first and last max and min frequencies.
cohorts1[cohorts1$Freq==min(cohorts1$Freq),]## Var1 Freq
## 291 Bear Valley High 4
## 806 Del Norte County Community Day (Secondary) 4
## 1264 Gridley Unified Community Day (7-12) 4
## 2426 Pioneer/Quincy Elementary 4
## 2870 SBC - Pacific Technology 4cohorts1[cohorts1$Freq==max(cohorts1$Freq),]## Var1 Freq
## 2142 Non-public, non-sectarian schools 12842## cohorts' variables.
unique(cohorts$AggLevel)## [1] "S" "O" "T" "D"unique(cohorts$DFC)## [1] "N" "Y"unique(cohorts$Subgroup)## [1] "All" "SE" "MIG" "EL" "SD" "FEM" "MAL" "FOS"unique(cohorts$Subgrouptype)## [1] "4" "5" "1" "3" "9" "7" "2" "6" "0" "All"library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union# Average graduation rate by each program
cohorts %>% group_by(Name) %>% summarize(GradRateAvg=round(mean(`Cohort Graduation Rate`))) %>% arrange(desc(GradRateAvg))## # A tibble: 3,542 x 2
## Name GradRateAvg
## <chr> <dbl>
## 1 Alabama Hills Community Day 100
## 2 Aspire Vanguard College Preparatory Academy 100
## 3 Baypoint Preparatory Academy 100
## 4 Bear Valley High 100
## 5 Big Sur Unified 100
## 6 Burbank Unified Independent Learning Academy (BUILA) 100
## 7 California Virtual Academy @ Maricopa 100
## 8 Casa Blanca Community Day 100
## 9 Central Coast New Tech High 100
## 10 Charles J. Carver Elementary 100
## # ... with 3,532 more rows# Average "still enrolled" rate by each program
cohorts %>% group_by(Name) %>% summarize(StillEnrolledRateAvg=round(mean(`Still Enrolled Rate`))) %>% arrange(desc(StillEnrolledRateAvg))## # A tibble: 3,542 x 2
## Name StillEnrolledRateAvg
## <chr> <dbl>
## 1 Alfonso B. Perez Special Education Center 100
## 2 Brier Elementary 100
## 3 Career & Technical High 100
## 4 Dunsmuir Joint Union High Community Day 100
## 5 Elite Academy 100
## 6 Everest 100
## 7 Highland 100
## 8 Loma Vista 100
## 9 Los Alisos Middle 100
## 10 Mt. Burney Special Education Center 100
## # ... with 3,532 more rows# Average "special ed completion" rate by each program
cohorts %>% group_by(Name) %>% summarize(SpecialEdRateAvg=round(mean(`Special Ed Completers Rate`))) %>% arrange(desc(SpecialEdRateAvg))## # A tibble: 3,542 x 2
## Name SpecialEdRateAvg
## <chr> <dbl>
## 1 La Contenta Middle 100
## 2 Colusa County Special Education 85
## 3 East County Elementary Special Education 77
## 4 West Sonoma County Consortium 71
## 5 Monarch Academy 70
## 6 Floyd I. Marchus 67
## 7 Florence E. Rata 49
## 8 Heritage CCCOE Special Education Programs 41
## 9 Irvine Adult Transition Programs 40
## 10 Sequoia Academy 40
## # ... with 3,532 more rows# Average drop out rate by each program
cohorts %>% group_by(Name) %>% summarize(DropOutRateAvg=round(mean(`Cohort Dropout Rate`))) %>% arrange(desc(DropOutRateAvg))## # A tibble: 3,542 x 2
## Name DropOutRateAvg
## <chr> <dbl>
## 1 Adelanto Charter Academy 100
## 2 Adelanto Elementary 100
## 3 Advent Community Day 100
## 4 BUSD School of Opportunity 100
## 5 Carr Lake Community Day 100
## 6 Clearlake Creativity 100
## 7 Corcoran Academy 100
## 8 Cutler-Orosi Community Day 100
## 9 Del Norte County Community Day (Secondary) 100
## 10 Dixon Community Day 100
## # ... with 3,532 more rows## What if we want to combine all four rates into one table.
fouravgcohorts <- cohorts %>% group_by(Name) %>% summarise_each(funs(mean(.,na.rm=TRUE)),c(`Cohort Graduation Rate`,`Still Enrolled Rate`,`Special Ed Completers Rate`,`Cohort Dropout Rate`))## `summarise_each()` is deprecated.
## Use `summarise_all()`, `summarise_at()` or `summarise_if()` instead.
## To map `funs` over a selection of variables, use `summarise_at()`head(fouravgcohorts)## # A tibble: 6 x 5
## Name `Cohort Graduation Rate`
## <chr> <dbl>
## 1 21st Century Learning Institute 67.38432
## 2 ABC Secondary (Alternative) 41.55227
## 3 ABC Unified 91.51634
## 4 Abraham Lincoln Alternative 43.43574
## 5 Abraham Lincoln Continuation 61.79528
## 6 Abraham Lincoln High 92.59166
## # ... with 3 more variables: `Still Enrolled Rate` <dbl>, `Special Ed
## # Completers Rate` <dbl>, `Cohort Dropout Rate` <dbl>Dropouts Dataset:
head(dropouts)## # A tibble: 6 x 20
## CDS_CODE ETHNIC GENDER E7 E8 E9 E10 E11 E12 EUS
## <chr> <int> <chr> <int> <int> <int> <int> <int> <int> <int>
## 1 01100170130401 1 M 0 0 1 0 0 0 0
## 2 01100170130401 2 M 0 0 3 3 3 2 0
## 3 01100170130401 3 M 0 0 0 1 1 2 0
## 4 01100170130401 4 M 0 0 0 2 0 1 0
## 5 01100170130401 5 M 0 6 4 13 12 13 0
## 6 01100170130401 5 F 0 1 1 0 3 0 0
## # ... with 10 more variables: ETOT <int>, D7 <int>, D8 <int>, D9 <int>,
## # D10 <int>, D11 <int>, D12 <int>, DUS <int>, DTOT <int>, YEAR <fctr>data.frame(table(dropouts$GENDER))## Var1 Freq
## 1 F 556012
## 2 M 576533# Ethinic = 0: Not reported
# Ethinic = 1: American Indian
# Ethinic = 2: Asian
# Ethinic = 3: Pacific Islander
# Ethinic = 4: Filipino
# Ethinic = 5: Hispanic
# Ethinic = 6: African-American
# Ethinic = 7: White
# Ethinic = 8|9: Two or More
## Let's graph proportion of student droupouts by genders grade 7-12
library(reshape2)
library(dplyr)
dropouts1 <- dropouts %>%
group_by(GENDER, add=TRUE) %>%
summarise_at(.vars=colnames(dropouts[12:17]),.funs=c(sum="sum")) %>%
mutate(D7 = D7_sum/sum(D7_sum),
D8 = D8_sum/sum(D8_sum),
D9 = D9_sum/sum(D9_sum),
D10 = D10_sum/sum(D10_sum),
D11 = D11_sum/sum(D11_sum),
D12 = D12_sum/sum(D12_sum)) %>% as.data.frame()
dropouts2 <- melt(dropouts1,id.vars="GENDER",
measure.vars=c("D7","D8","D9","D10","D11","D12"))
library(ggplot2)
dropouts2 %>% ggplot(aes(variable,value,fill=GENDER)) +
geom_bar(stat="identity",position="fill") +
labs(x="Grade",
y="Percentage",
title="Proportion of Student Dropouts by Gender, Grades 7-12",
fill= "Gender")
Dropouts Dataset: A different approach to achieve the same plot as above:
library(tidyr) # have to load this library for "gather" function to work.##
## Attaching package: 'tidyr'## The following object is masked from 'package:reshape2':
##
## smithsdropouts %>%
select(GENDER, matches("D[0-9]")) %>%
gather(GRADE, DROPOUTS, -GENDER) %>%
mutate(GRADE = as.numeric(stringr::str_replace(GRADE, "D", ""))) %>%
group_by(GENDER, GRADE) %>%
summarize(DROPOUTS = sum(DROPOUTS)) %>%
ggplot(aes(GRADE, DROPOUTS, fill = GENDER)) +
geom_bar(stat = "identity", position = "fill") +
scale_x_continuous(breaks = c(7:12)) +
labs(x = "Grade",
y = "",
title = "Proportion of Student Dropouts by Gender, Grades 7-12",
fill = "Gender") +
theme_minimal()
## Let's graph proportion of student enrollments by genders grade 7-12
dropouts %>%
select(GENDER,matches("E[0-9]")) %>%
gather(GRADE, ENROLLMENTS, -GENDER) %>%
group_by(GENDER,GRADE) %>%
summarize(ENROLLMENTS = sum(ENROLLMENTS)) %>%
ggplot(aes(GRADE,ENROLLMENTS,fill=GENDER)) +
geom_bar(stat="identity",position="fill")+
labs(x="Grade",
y="Percentage",
title = "Proportion of Student Enrollments by Gender, Grades 7-12",
fill="Gender")
## Let's graph proportion of student enrollments by ethinicity grade 7-12
library(scales)
dropouts %>%
select(ETHNIC, matches("E[0-9]")) %>%
gather(GRADE, ENROLLMENTS,-ETHNIC) %>%
mutate(GRADE = as.numeric(stringr::str_replace(GRADE,"E",""))) %>%
group_by(ETHNIC,GRADE) %>%
summarize(ENROLLMENTS=sum(ENROLLMENTS)) %>%
ggplot(aes(GRADE,ENROLLMENTS,fill=ETHNIC)) +
geom_bar(stat="identity",position="fill")+
scale_x_continuous(breaks=c(7:12))+
scale_y_continuous(labels=percent_format())+
scale_fill_gradient(high="blue",low="green") +
#scale_color_discrete()+
#scale_color_gradientn(colours = terrain.colors(10))+
labs(x="Grade",
y="Percentage",
title = "Proportion of Student Enrollments by Ethinicity, Grades 7-12",
fill="Ethnicity")
## English Learner Dataset:
data(english_learners)
unique(english_learners$LANGUAGE)## [1] "Vietnamese"
## [2] "Cantonese"
## [3] "Japanese"
## [4] "All other non-English lan"
## [5] "Russian"
## [6] "Spanish"
## [7] "Portuguese"
## [8] "German"
## [9] "Turkish"
## [10] "Arabic"
## [11] "Farsi (Persian)"
## [12] "Hindi"
## [13] "Urdu"
## [14] "Pashto"
## [15] "Bengali"
## [16] "Gujarati"
## [17] "Korean"
## [18] "Punjabi"
## [19] "Albanian"
## [20] "Khmer (Cambodian)"
## [21] "Mandarin (Putonghua)"
## [22] "Italian"
## [23] "Tigrinya"
## [24] "Filipino (Pilipino or Tag"
## [25] "Taiwanese"
## [26] "Thai"
## [27] "Tongan"
## [28] "Cebuano (Visayan)"
## [29] "Ilocano"
## [30] "Samoan"
## [31] "Mien (Yao)"
## [32] "Serbo-Croatian (Bosnian,"
## [33] "Indonesian"
## [34] "French"
## [35] "Hungarian"
## [36] "Hmong"
## [37] "Lao"
## [38] "Chaozhou (Chaochow)"
## [39] "Dutch"
## [40] "Greek"
## [41] "Chamorro (Guamanian)"
## [42] "Hebrew"
## [43] "Burmese"
## [44] "Rumanian"
## [45] "Polish"
## [46] "Armenian"
## [47] "Kurdish (Kurdi, Kurmanji)"
## [48] "Ukrainian"
## [49] "Toishanese"
## [50] "Assyrian"
## [51] "Marshallese"
## [52] "Somali"
## [53] "Lahu"
## [54] "Khmu"
## [55] "Mixteco"
## [56] "Chaldean"
## [57] "Other non-English languages"
## [58] "Filipino (Pilipino or Tagalog)"
## [59] "Serbo-Croatian (Bosnian, Croatian, Serbian)"
## [60] "Chaozhou (Chiuchow)"
## [61] "Unknown"
## [62] "Tamil"
## [63] "Telugu"
## [64] "Kannada"
## [65] "Marathi"
## [66] "Swedish"
## [67] "Amharic"
## [68] "Bulgarian"
## [69] "Zapoteco"
## [70] "Uzbek"
## [71] "Kashmiri"
## [72] "Kikuyu (Gikuyu)"# which schools that have Khmer speakers?
english_learners %>% select(COUNTY,DISTRICT,YEAR,SCHOOL,LANGUAGE,TOTAL_EL, matches("Gr_[0-9]")) %>%
filter(LANGUAGE=="Khmer (Cambodian)") %>% top_n(10)## Selecting by GR_12## # A tibble: 10 x 18
## COUNTY DISTRICT YEAR SCHOOL
## <chr> <chr> <chr> <chr>
## 1 Los Angeles Long Beach Unified 2007-08 Polytechnic High
## 2 San Joaquin Lodi Unified 2007-08 Ronald E. McNair High
## 3 Los Angeles Long Beach Unified 2008-09 Polytechnic High
## 4 San Joaquin Lodi Unified 2008-09 Ronald E. McNair High
## 5 Los Angeles Long Beach Unified 2009-10 Polytechnic High
## 6 San Joaquin Lodi Unified 2009-10 Ronald E. McNair High
## 7 Los Angeles Long Beach Unified 2010-11 Polytechnic High
## 8 Los Angeles Long Beach Unified 2011-12 Polytechnic High
## 9 Los Angeles Long Beach Unified 2012-13 Polytechnic High
## 10 Los Angeles Long Beach Unified 2013-14 Polytechnic High
## # ... with 14 more variables: LANGUAGE <chr>, TOTAL_EL <int>, GR_1 <int>,
## # GR_2 <int>, GR_3 <int>, GR_4 <int>, GR_5 <int>, GR_6 <int>,
## # GR_7 <int>, GR_8 <int>, GR_9 <int>, GR_10 <int>, GR_11 <int>,
## # GR_12 <int># which "language" school that has the highest number of english learners.
maxlearner = max(english_learners$TOTAL_EL)
english_learners %>% select(COUNTY,SCHOOL,LANGUAGE,TOTAL_EL) %>%
filter(TOTAL_EL==maxlearner)## # A tibble: 1 x 4
## COUNTY SCHOOL LANGUAGE TOTAL_EL
## <chr> <chr> <chr> <int>
## 1 Orange Access County Community Spanish 2131# Find top and bottom 10 schools ranked by the number of english learners.
english_learners %>% group_by(SCHOOL) %>% tally(TOTAL_EL) %>% top_n(10)## Selecting by n## # A tibble: 10 x 2
## SCHOOL n
## <chr> <int>
## 1 Cesar Chavez Elementary 29353
## 2 Franklin Elementary 30307
## 3 Fremont Elementary 31732
## 4 Garfield Elementary 29058
## 5 Jefferson Elementary 61990
## 6 Lincoln Elementary 66301
## 7 McKinley Elementary 36650
## 8 Roosevelt Elementary 51068
## 9 Washington Elementary 60127
## 10 Wilson Elementary 34473english_learners %>% group_by(SCHOOL) %>% tally(TOTAL_EL) %>% top_n(-10)## Selecting by n## # A tibble: 35 x 2
## SCHOOL n
## <chr> <int>
## 1 Alameda County Special Education 0
## 2 Archway Academy 0
## 3 Bass Lake Elementary 0
## 4 Benton Community Day 0
## 5 Berkeley Special Education Preschool 0
## 6 Bridgeville Elementary 0
## 7 Cambrian Community 0
## 8 Charter Transitional Reporting Educational Center (TREC I) 0
## 9 Chrysalis Charter 0
## 10 Credence High 0
## # ... with 25 more rowsEnrollment Dataset:
# Let's graph diversity in FIVE districts of LA, 2007-2017
data(enrollments)
head(enrollments)## # A tibble: 6 x 24
## CDS_CODE ETHNIC GENDER KDGN GR_1 GR_2 GR_3 GR_4 GR_5 GR_6
## <chr> <int> <chr> <int> <int> <int> <int> <int> <int> <int>
## 1 01100170130401 1 M 0 0 0 0 0 0 0
## 2 01100170130401 2 F 0 0 0 0 0 0 0
## 3 01100170130401 2 M 0 0 0 0 0 0 0
## 4 01100170130401 3 M 0 0 0 0 0 0 0
## 5 01100170130401 4 M 0 0 0 0 0 0 0
## 6 01100170130401 5 F 0 0 0 0 0 0 0
## # ... with 14 more variables: GR_7 <int>, GR_8 <int>, UNGR_ELM <int>,
## # GR_9 <int>, GR_10 <int>, GR_11 <int>, GR_12 <int>, UNGR_SEC <int>,
## # ENR_TOTAL <int>, ADULT <int>, YEAR <fctr>, COUNTY <chr>,
## # DISTRICT <chr>, SCHOOL <chr># Let's randomly pick the first five different districts in LA.
enrollments %>% select(DISTRICT,COUNTY) %>% distinct(DISTRICT,COUNTY) %>% filter(COUNTY=="Los Angeles")## # A tibble: 104 x 2
## DISTRICT COUNTY
## <chr> <chr>
## 1 Los Angeles Unified Los Angeles
## 2 Lowell Joint Los Angeles
## 3 Lynwood Unified Los Angeles
## 4 Los Nietos Los Angeles
## 5 Little Lake City Elementary Los Angeles
## 6 Long Beach Unified Los Angeles
## 7 Hermosa Beach City Elementary Los Angeles
## 8 Inglewood Unified Los Angeles
## 9 Las Virgenes Unified Los Angeles
## 10 Lawndale Elementary Los Angeles
## # ... with 94 more rows# Los Angeles Unified
# Lowell Joint
# Lynwood Unified
# Los Nietos
# Long Beach Unified
# Ethinic = 0: Not reported
# Ethinic = 1: American Indian
# Ethinic = 2: Asian
# Ethinic = 3: Pacific Islander
# Ethinic = 4: Filipino
# Ethinic = 5: Hispanic
# Ethinic = 6: African-American
# Ethinic = 7: White
# Ethinic = 8|9: Two or More
enrollments %>%
mutate(ETHNIC= case_when(ETHNIC==0 ~"Not Reported",
ETHNIC==1 ~"American Indian",
ETHNIC==2 ~"Asian",
ETHNIC==3 ~"Pacific Islander",
ETHNIC==4 ~"Filipino",
ETHNIC==5 ~"Hispanic",
ETHNIC==6 ~"African-American",
ETHNIC==7 ~"White",
ETHNIC==8 | ETHNIC==9 ~"Tow or More")) %>%
filter(DISTRICT==c("Los Angeles Unified","Lowell Joint",
"Lynwood Unified","Los Nietos",
"Long Beach Unified")) %>%
select(ETHNIC,DISTRICT,ENR_TOTAL,YEAR) %>%
group_by(ETHNIC,DISTRICT,YEAR) %>% summarise(`Total Enrollments` = sum(ENR_TOTAL)) %>%
ggplot(aes(YEAR,`Total Enrollments`,fill=ETHNIC)) +
geom_bar(stat="identity",position="fill") +
facet_wrap(~DISTRICT, nrow = 5) +
labs(title="Enrollment Diversity in 5 Districts of Los Angeles, 2007-2017",fill="Ethinicity")
## Suspensions Dataset:
data(suspensions)
head(suspensions)## # A tibble: 6 x 15
## AGGEGATELEVEL CDS NAME DISCIPLINETYPE ETHNICITY WEAPONS
## <chr> <chr> <chr> <chr> <int> <int>
## 1 O 01000000000000 Alameda E 2 1
## 2 O 01000000000000 Alameda E 6 5
## 3 O 01000000000000 Alameda E 4 0
## 4 O 01000000000000 Alameda E 5 12
## 5 O 01000000000000 Alameda E 1 1
## 6 O 01000000000000 Alameda E 3 2
## # ... with 9 more variables: DRUGS <int>, VIOLENCEWITHINJURY <int>,
## # VIOLENCEWITHOUTINJURY <int>, OTHERNONDEFIANCE <int>,
## # OTHERDEFIANCE <int>, TOTAL <int>, YEAR <fctr>, DATECREATED <chr>,
## # DATEUPDATED <chr>library(maps)
library(ggmap)
library(mapdata)
states <- map_data("state")
ca_df <- subset(states, region == "california")
counties <- map_data("county")
ca_county <- subset(counties, region == "california")
drugtotal <- suspensions %>% select(YEAR,DRUGS,TOTAL,AGGEGATELEVEL,NAME) %>%
group_by(NAME,YEAR) %>%
filter(YEAR=="2014-15",AGGEGATELEVEL=="O") %>%
summarise(DRUG=sum(DRUGS,na.rm=TRUE),TOTAL=sum(TOTAL,na.rm=TRUE), Drug_prop = round(DRUG/TOTAL,2)) %>%
arrange(desc(Drug_prop))
drugmapdata <- left_join(ca_county,
drugtotal %>% mutate(subregion = stringr::str_to_lower(NAME)),
by="subregion")
ggplot(data = ca_df, mapping = aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
geom_polygon(color = "black", fill = "gray") +
geom_polygon(data = drugmapdata, aes(fill = Drug_prop), color = "white") +
geom_polygon(color = "black", fill = NA) +
labs(title = "Proportion of Drugs-Related Suspensions by County, 2014-2015",
fill = "Proportion") +
theme_void() +
viridis::scale_fill_viridis()
## Acknowledgement: #https://github.com/daranzolin/rCAEDDATA