Xenia's submission

submissions/Final_exercise_R_bootcamp.R

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+#Load packages 
+library(dplyr)
+library(ggplot2)
+
+#Set your working dirextory 
+setwd("~/Documents/Northwestern/R_bootcamp/bootcamp-2023-main")
+
+#load the data 
+nyc_schools <- read.csv("data/nys_schools.csv")
+nyc_acs <- read.csv("data/nys_acs.csv")
+
+# Explore the data frames 
+str(nyc_schools)
+nrow(nyc_schools)
+
+str(nyc_acs)
+nrow(nyc_acs)
+
+sum(is.na(nyc_schools))
+sum(is.na(nyc_acs))
+
+summary(nyc_schools)
+summary(nyc_acs)
+
+# Replace missing data -99 with NA
+nyc_schools_2 <- replace(nyc_schools, nyc_schools ==-99, NA)
+sum(is.na(nyc_schools_2))
+summary(nyc_schools_2)
+
+# create new categorical variable for counties 
+nyc_acs <- nyc_acs %>% 
+    mutate(poverty_level = case_when(county_per_poverty <= 0.1090 ~ "low", 
+                                     county_per_poverty > 0.1090 & county_per_poverty < 0.1493 ~ "median",
+                                     county_per_poverty >= 0.1493 ~ "high"))
+table(nyc_acs$poverty_level)
+
+# Run a z-test for the math and ELA 
+scale_scores <- nyc_schools_2 %>% 
+    group_by("year") %>% 
+    mutate(z1 = scale(mean_ela_score, center = TRUE, scale = TRUE), z2 = scale(mean_math_score, center = TRUE, scale = TRUE))
+
+hist(scale_scores$z1)
+hist(scale_scores$z2)
+
+# merge the 2 datasets 
+merged_data <- merge(scale_scores,nyc_acs)
+
+# Analyze data 
+merged_data %>% 
+
+# visualization
+plot(merged_data$total_enroll, )
+
+merged_data %>% 
+ggplot() + 
+    geom_line(aes(x = year, y = povertylevel)) + 
+    labs(title="Powerty level over time ", x="Year", y="Poverty") 
+