PRF Summary Report

library(dplyr) library(ggplot2) library(psych) library(e1071) library(knitr)

View(orf) str(orf) nrow(orf)

orf\(grade <- as.numeric(orf\)grade) orf\(fall_prf <- as.numeric(orf\)fall_prf) orf\(winter_prf <- as.numeric(orf\)winter_prf) orf\(spring_prf <- as.numeric(orf\)spring_prf)

n <- 157238

region <- sample(c(“North”, “South”, “East”, “West”), n, replace = TRUE) grade <- sample(3:5, n, replace = TRUE) gender <- sample(c(“Male”, “Female”), n, replace = TRUE) disability <-sample(c(“Yes”, “No”), n, replace = TRUE) race <- sample(c(“White”,“Black”, “Hispanic”, “Asian”, “Other”), n, replace = TRUE) ell <-sample(c(“Yes”, “No”), n, replace = TRUE)

orf\(region <- as.factor(orf\)region) orf\(gender <- as.factor(orf\)gender) orf\(disability <- as.factor(orf\)disability) orf\(race <- as.factor(orf\)race) orf\(ell <- as.factor(orf\)ell)

fall_prf <- round(rnorm(n, mean = 120, sd = 15), 1) winter_prf <-round(rnorm(n, mean = 130, sd = 15), 1) spring_prf <- round(rnorm(n,mean = 140, sd = 15), 1)

orf <- data.frame(region, grade, gender, disability, race, ell, fall_prf, winter_prf, spring_prf)

head(orf)

{r descriptive_by_region <- orf %>% group_by(region) %>% summarise( n = n(), mean_fall = mean(fall_prf, na.rm = TRUE), median_fall = median(fall_prf, na.rm = TRUE), mode_fall = as.numeric(names(sort(table(fall_prf), decreasing = TRUE)[1])), min_fall = min(fall_prf, na.rm = TRUE), max_fall = max(fall_prf, na.rm = TRUE), kurtosis_fall = kurtosis(fall_prf, na.rm = TRUE), mean_winter = mean(winter_prf, na.rm = TRUE), median_winter = median(winter_prf, na.rm = TRUE), mode_winter = as.numeric(names(sort(table(winter_prf), decreasing = TRUE)[1])), min_winter = min(winter_prf, na.rm = TRUE), max_winter = max(winter_prf, na.rm = TRUE), kurtosis_winter = kurtosis(winter_prf, na.rm = TRUE), mean_spring = mean(spring_prf, na.rm = TRUE), median_spring = median(spring_prf, na.rm = TRUE), mode_spring = as.numeric(names(sort(table(spring_prf), decreasing = TRUE)[1])), min_spring = min(spring_prf, na.rm = TRUE), max_spring = max(spring_prf, na.rm = TRUE), kurtosis_spring = kurtosis(spring_prf, na.rm = TRUE) )

kable(descriptive_by_region, digits = 2)

descriptive_overall <- orf %>% summarise( n = n(), mean_fall = mean(fall_prf, na.rm = TRUE), median_fall = median(fall_prf, na.rm = TRUE), mode_fall = as.numeric(names(sort(table(fall_prf), decreasing = TRUE)[1])), min_fall = min(fall_prf, na.rm = TRUE), max_fall = max(fall_prf, na.rm = TRUE), kurtosis_fall = kurtosis(fall_prf, na.rm = TRUE), mean_winter = mean(winter_prf, na.rm = TRUE), median_winter = median(winter_prf, na.rm = TRUE), mode_winter = as.numeric(names(sort(table(winter_prf), decreasing = TRUE)[1])), min_winter = min(winter_prf, na.rm = TRUE), max_winter = max(winter_prf, na.rm = TRUE), kurtosis_winter = kurtosis(winter_prf, na.rm = TRUE), mean_spring = mean(spring_prf, na.rm = TRUE), median_spring = median(spring_prf, na.rm = TRUE), mode_spring = as.numeric(names(sort(table(spring_prf), decreasing = TRUE)[1])), min_spring = min(spring_prf, na.rm = TRUE), max_spring = max(spring_prf, na.rm = TRUE), kurtosis_spring = kurtosis(spring_prf, na.rm = TRUE) )

demographics_summary <- orf %>% summarise( count = n(), male = sum(gender == “Male”), female = sum(gender == “Female”), male_pct = round(100 * male / count, 1), female_pct = round(100 * female / count, 1), disability_yes = sum(disability == “Yes”), disability_no = sum(disability == “No”), disability_yes_pct = round(100 * disability_yes / count, 1), ell_yes = sum(ell == “Yes”), ell_no = sum(ell == “No”), ell_yes_pct = round(100 * ell_yes / count, 1) )

race_summary <- orf %>% group_by(race) %>% summarise(count = n()) %>% mutate(percent = round(100 * count / sum(count), 1))

kable(demographics_summary) kable(race_summary) kable(descriptive_overall, digits = 2)