diff --git a/exercises/Philip Final R Exercise.R b/exercises/Philip Final R Exercise.R
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+# Importing packages
+
+library(dplyr)
+library(ggplot2)
+
+# Importing data sets
+
+nys_acs <- read.csv("D:/Northwestern MLDS/Bootcamp/R-bootcamp-2024/data/nys_acs.csv")
+
+
+nys_schools <- read.csv("D:/Northwestern MLDS/Bootcamp/R-bootcamp-2024/data/nys_schools.csv")
+
+
+# Checking for missing values and cleanign the DF
+
+nys_schools <- nys_schools[!apply(nys_schools == -99, 1, any), ]
+
+
+#  Adding new column for poverty categories
+
+nys_acs <- nys_acs %>% 
+  mutate(poverty_level = case_when(
+    county_per_poverty < quantile(nys_acs$county_per_poverty)[2]  ~"Low",
+    county_per_poverty >= quantile(nys_acs$county_per_poverty)[2]  & county_per_poverty <= quantile(nys_acs$county_per_poverty)[3]  ~"Medium",
+    county_per_poverty >= quantile(nys_acs$county_per_poverty)[3]  ~"High",
+  ))
+
+
+# GRouping by year 
+
+
+# Function for calc z score:
+
+calc_z <- function(values){
+  
+  m = mean(values)
+  sd = sd(values)
+  
+  z = (values - mean) / sd
+  
+  return(z)
+  
+}
+
+nys_schools <- nys_schools %>%
+  group_by(year) %>%
+  mutate(z_score = scale(mean_ela_score))
+
+
+
+
+
+
+#  PLotting
+
+
+ggplot(data = nys_schools) +
+  geom_point(mapping = aes(x=per_free_lunch, y = mean_ela_score)) + 
+  labs(title = 'Free Lunch vs ELA Score', x = 'per_free_lunch', y = 'mean_ela_score')
+
+