library(DT)
library(tidyverse)
library(readxl)
library(dplyr)
crime_stats <- read_csv("http://jsuleiman.com/datasets/Crime_2015.csv")
score_card_dict <-read_excel("CollegeScorecardDataDictionary.xlsx")
score_card <- read.csv("CollegeScorecard.csv")
# Select only institutions that are Accredited by Southern Association of Colleges and Schools Commission on Colleges
score_card_2 <- score_card %>%
filter(AccredAgency=='Southern Association of Colleges and Schools Commission on Colleges')
# Select only institutions where the midpoint of SAT scores at the institution (math) is above the median of the dataset.
score_card_3 <- score_card_2 %>% drop_na(SATMTMID)
score_card_3_median <- median(score_card_3$SATMTMID)
score_card_4 <- score_card_3 %>% filter(SATMTMID > score_card_3_median)
# Select only Institutions where the violent crime rate is below the median of the dataset.
data <- left_join(score_card_4,crime_stats, by=c("CITY"="City","STABBR"="State"))
data_2 <- data %>% drop_na(ViolentCrime)
median_violent_crime_rate <- median(data_2$ViolentCrime)
data_3 <- data_2 %>% filter(ViolentCrime < median_violent_crime_rate)
#Select final report for Cornelia
data_final <- data_3 %>% select(INSTNM,CITY,ViolentCrime,AccredAgency,SATMTMID)
data_final_2 <- data_final[with(data_final, order(data_final$SATMTMID)), ]
row.names(data_final_2) <- NULL
length(data_final_2$SATMTMID)
## [1] 47
This table displays the name, city, violent crime rate, accrediting agency, and midpoint SAT math score, and is sorted by the midpoint SAT math score, from highest to lowest.
datatable(data_final_2)