nyc_middle_schools

# selected variables (columns)
columns <-c("district","name","borough","latitude","longitude",
            "coursepassrate","elaprof","mathprof","surveysafety",
            "totalstudents","gradespan","tophs1","tophs2","tophs3",
            "acceleratedclasses","electiveclasses","languageclasses",
            "diversityinadmissions")
mydata <- data |> 
  select(all_of(columns))

Warning in

read.socrata(“https://data.cityofnewyork.us/resource/f6s7-vytj.csv”): Dates and

currency fields will be converted to character

# selected variables (columns)
columns <-c("district","name","borough","latitude","longitude",
            "coursepassrate","elaprof","mathprof","surveysafety",
            "totalstudents","gradespan","tophs1","tophs2","tophs3",
            "acceleratedclasses","electiveclasses","languageclasses",
            "diversityinadmissions")
mydata <- data |> 
  select(all_of(columns))

mydata |>
  select(1:3) |>
  slice(1:7) |> kable()

district	name	borough
1	P.S. 034 Franklin D. Roosevelt	MANHATTAN
1	P.S. 140 Nathan Straus	MANHATTAN
1	P.S. 184m Shuang Wen	MANHATTAN
1	P.S. 188 The Island School	MANHATTAN
1	University Neighborhood Middle School	MANHATTAN
1	School for Global Leaders	MANHATTAN
1	East Side Community School	MANHATTAN

mydata |> 
  filter(borough == "MANHATTAN") |> 
  select(c(name,coursepassrate,mathprof,elaprof)) |> 
  arrange(desc(mathprof)) |> 
  slice(1:10) |> 
  kable()

name	coursepassrate	mathprof	elaprof
Special Music School	100	98	93
East Side Middle School	100	97	94
The Anderson School	100	97	97
Tag Young Scholars	99	96	92
New Explorations into Science, Technology & Math	99	95	94
New York City Lab Middle School for Collaborative Studies	100	94	91
The Clinton School	100	93	92
M.S. 255 Salk School of Science	100	92	94
M.S. 243 Center School	99	90	94
Columbia Secondary School	95	88	87

mydata |> 
  filter(district == 13 | district == 2) |> 
  select(c(district,name,mathprof)) |> 
  arrange(desc(mathprof)) |> 
  datatable() # for html only

#kable() # for pdf and html

district_stats<-mydata |> 
  group_by(district) |> 
  summarize(med_mathprof = median(mathprof, na.rm=TRUE),
            avg_mathprof = mean(mathprof, na.rm=TRUE)) 

district_stats |> 
  arrange(desc(med_mathprof)) |> 
  #kable()
  datatable()

bar_chart(district_stats, x=district, y=med_mathprof)

mydata <- mydata |> 
  mutate(borough = replace(borough, 
                           name == "P.S. 046 Arthur Tappan", "MANHATTAN")) |> 
  mutate(borough = replace(borough, 
                           name == "M.S. 935", "BROOKLYN"))

borough_stats<-mydata |> 
  group_by(borough) |> 
  summarize(med_mathprof = median(mathprof, na.rm=TRUE),
            avg_mathprof = mean(mathprof, na.rm=TRUE))

borough_stats |> 
  arrange(desc(med_mathprof)) |> 
  #kable()
  datatable()

bar_chart(borough_stats, x=borough, y=med_mathprof)

mydata |> 
  group_by(district) |> 
  summarize(nschools = n()) |> 
  mutate(district=reorder(district,nschools)) |> 
  ggplot(aes(x=district,y=nschools,fill=district)) +
  geom_col(show.legend = FALSE) +
  coord_flip()

mathprof_zscore <- mydata |>
  select(district,borough,name,mathprof) |> 
  mutate(zscore = scale(mathprof)) |> 
  filter(!is.na(zscore))

Warning: Using one column matrices in `filter()` was deprecated in dplyr 1.1.0.
Please use one dimensional logical vectors instead.

mathprof_zscore |> 
  filter(borough %in% c("QUEENS","MANHATTAN","BROOKLYN","BRONX")) |>
  gg_histogram(x=zscore,facet=borough, bins=12)