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+# #########################
+#
+#Name: Naomi Zhang
+#Bootcamp 2021
+#Final bootcamp exercise
+#
+##########################
+
+
+
+
+
+###1 : libraries
+
+library(tidyverse)
+library(dplyr)
+library(lubridate)
+library(reshape2)
+library(ggplot2)
+
+###import data
+schools <- read.csv("data/nys_schools.csv")
+counties <- read.csv("data/nys_acs.csv")
+
+
+
+
+
+###2 : review data
+head(schools)
+str(schools)
+#12 variables, 35663 rows
+
+head(counties)
+str(counties)
+#5 variables, 496 rows
+
+#check for blank rows
+sum(is.na(schools))
+sum(is.na(counties))
+#0
+#0
+#this makes sense, NA are noted to be -99
+
+
+
+
+
+###3 : Recoding and variable manipulation
+#Recoding and variable manipulation
+#Deal with missing values, which are currently coded as -99.
+#Create a categorical variable that groups counties into "high", "medium", and "low" poverty groups. 
+#Decide how you want to split up the groups and briefly explain your decision.
+#The tests that the NYS Department of Education administers changes from time to time, 
+#so scale scores are not directly comparable year-to-year. 
+#Create a new variable that is the standardized z-score for math and English Language Arts (ELA) for each year 
+#(hint: group by year and use the scale() function)
+
+summary(schools)
+summary(counties)
+#counties is good
+#only issue is schools
+
+filter(schools, total_enroll == -99 | per_free_lunch == -99 | per_lep == -99 | mean_ela_score == -99 | mean_math_score == -99)
+#2136 records
+
+#decide to drop rows with -99's, no mercy
+schools_cleaned <- schools[schools$district_name != -99 &
+                              schools$county_name != -99 &
+                              schools$total_enroll != -99 & 
+                              schools$per_free_lunch != -99 &
+                              schools$per_lep != -99 &
+                              schools$mean_ela_score != -99 &
+                              schools$mean_math_score != -99
+                              ,]
+
+#double check
+summary(schools_cleaned)
+#it clean!
+
+#categorical column now
+#doing it by household income
+summary(counties)
+#1st quart 46347
+#3rd quart 56448
+
+low_thresh <- 46347
+high_thresh <- 56448
+
+#add column to counties
+mutate(counties, poverty_group=NA)
+
+for (i in 1:nrow(counties)) {
+    if (counties[i,]$median_household_income < low_thresh ) {
+        counties[i,"poverty_group"] = "low"
+    }
+    else if (counties[i,]$median_household_income >= low_thresh & counties[i,]$median_household_income < high_thresh ) {
+        counties[i,"poverty_group"] = "medium"
+    }
+    else if (counties[i,]$median_household_income >= high_thresh ) {
+        counties[i,"poverty_group"] = "high"
+    }
+    
+}
+
+#double check
+summary(counties)
+head(counties)
+#looks good!
+
+
+#scale now
+#take a backup first
+schools_cleaned_backup_01 <- schools_cleaned
+
+#add new columns
+schools_cleaned <- schools_cleaned %>%
+    group_by(year) %>%
+    mutate(mean_ela_score_scaled = scale(mean_ela_score, scale=TRUE)) %>%
+    mutate(mean_math_score_scaled = scale(mean_math_score, scale=TRUE))
+
+
+
+
+
+###4 : Merge datasets
+
+merged_schools_countries <- merge(schools_cleaned, counties, by = c("year","county_name"))
+
+head(merged_schools_countries)
+summary(merged_schools_countries)
+#looks good
+
+
+
+
+
+###5 : Create summary tables
+
+head(merged_schools_countries)
+
+#take backup first
+merged_schools_countries_backup <- merged_schools_countries
+
+#For each county: total enrollment, percent of students qualifying for free 
+#or reduced price lunch, and percent of population in poverty.
+summary_merged_schools_countries <- merged_schools_countries %>%
+    mutate(sum_lunch = per_reduced_lunch + per_free_lunch) %>%
+    group_by(county_name) %>%
+    summarize(total_enroll_county = sum(total_enroll), 
+              county_per_poverty = sum(total_enroll * county_per_poverty) / sum(total_enroll), #we must recalculate the percentage per total enrollment
+              sum_lunch = sum(total_enroll * sum_lunch) / sum(total_enroll))
+    
+#For the counties with the top 5 and bottom 5 poverty rate: percent of population in poverty, 
+#percent of students qualifying for free or reduced price lunch, mean reading score, and mean math score.
+    
+top_bottom_five <- merged_schools_countries %>%
+    mutate(sum_lunch = per_reduced_lunch + per_free_lunch) %>%
+    group_by(county_name) %>%
+    summarize(county_per_poverty = sum(total_enroll * county_per_poverty) / sum(total_enroll), 
+              sum_lunch = sum(total_enroll * sum_lunch) / sum(total_enroll), 
+              avg_mean_ela = mean(mean_ela_score_scaled), 
+              avg_mean_math = mean(mean_math_score_scaled)) %>%
+    arrange(county_per_poverty)
+
+summary(top_bottom_five)
+#there are 62 rows
+
+#now take the top 5 and bottom 5
+top_bottom_five[c(1:5,58:62), ]
+
+
+
+
+
+###6 : Data visualization
+#The relationship between access to free/reduced price lunch and test performance, at the school level.
+
+#add sum_lunch as a permanent column
+merged_schools_countries <- merged_schools_countries %>%
+    mutate(sum_lunch = per_reduced_lunch + per_free_lunch)
+
+merged_schools_countries %>%
+    ggplot() + 
+    geom_point(aes(x=sum_lunch, y=mean_ela_score_scaled)) +
+    labs(title = "Relationship between Free/Reduced Lunch vs ELA Test Performance",
+        x = "% Free or Reduced Lunch", 
+        y = "Scaled ELA Score")
+
+merged_schools_countries %>%
+    ggplot() + 
+    geom_point(aes(x=sum_lunch, y=mean_math_score_scaled)) +
+    labs(title = "Relationship between Free/Reduced Lunch vs Math Test Performance",
+         x = "% Free or Reduced Lunch", 
+         y = "Scaled Math Score")
+#general downward trend for both test scores
+
+
+#Average test performance across counties with high, low, and medium poverty.
+
+merged_schools_countries %>%
+    group_by(county_name, poverty_group) %>%
+    summarize(avg_ela = mean(mean_ela_score_scaled), avg_math = mean(mean_math_score_scaled)) %>%
+    ggplot() +
+    geom_boxplot(aes(x= factor(poverty_group, levels=c("low","medium","high")), y=avg_ela)) +
+    labs(title = "ELA Test Performance Across Poverty Levels",
+         x = "Poverty Level", 
+         y = "Scaled ELA Score")
+
+merged_schools_countries %>%
+    group_by(county_name, poverty_group) %>%
+    summarize(avg_ela = mean(mean_ela_score_scaled), avg_math = mean(mean_math_score_scaled)) %>%
+    ggplot() +
+    geom_boxplot(aes(x= factor(poverty_group, levels=c("low","medium","high")), y=avg_math)) +
+    labs(title = "Math Test Performance Across Poverty Levels",
+         x = "Poverty Level", 
+         y = "Scaled Math Score")
+
+#general upward trend with higher test scores for richer counties
+
+
+
+
+
+###7 : Answering questions
+
+#What can the data tell us about the relationship between poverty and test performance in New York public schools? 
+#Has this relationship changed over time? Is this relationship at all moderated by access to free/reduced price lunch?
+
+The poverty level was previously determined using the household income. 
+From Step 6 above, we can conclude that there is a general trend where higher test scores correlated with lower poverty (i.e. a negative correlation).
+This is especially true for Math. However, for ELA tests, lower and medium poverty level does not appear to have a significant difference.
+