diff --git a/submissions/omar solution.R b/submissions/omar solution.R
new file mode 100644
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+++ b/submissions/omar solution.R	
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+
+
+library(tidyverse)
+library(here)
+library(reshape2)
+
+ny_schools <- read_csv(here("Documents/data/nys_schools.csv"))
+ny_survey <- read_csv(here("Documents/data/nys_acs.csv"))
+
+summary(ny_schools)
+
+joined_data <- inner_join(ny_schools, ny_survey, by = c('county_name', 'year'))
+
+summary(joined_data)
+
+num_cols <- sapply(joined_data, is.numeric)
+numerics <- names(joined_data)[num_cols]
+
+joined_data %>%
+  ggplot() +
+  geom_density(aes(x = total_enroll)) +
+  facet_wrap(~year)
+
+summary(joined_data)
+cleaned_cols = c('total_enroll', 'per_free_lunch', 'per_reduced_lunch',
+                 'per_lep', 'mean_ela_score', 'mean_math_score') 
+
+
+filtered_df <- joined_data %>%
+  filter(across(all_of(numerics), ~ . > 0))
+
+filtered_df %>%
+  ggplot() +
+  geom_histogram(aes(x = county_per_poverty)) 
+  #+ facet_wrap(~year)
+
+#ctrl shift m gets auto pipe %>% %>% %>% 
+
+# Calculate the percentiles for the 'Value' column
+percentile_25 <- quantile(filtered_df$county_per_poverty, probs = 0.25)
+percentile_75 <- quantile(filtered_df$county_per_poverty, probs = 0.75)
+
+# Create a new column 'Category' based on percentiles
+filtered_df$poverty_class <- cut(filtered_df$county_per_poverty,
+                   breaks = c(-Inf, percentile_25, percentile_75, Inf),
+                   labels = c("Low", "Medium", "High"),
+                   include.lowest = TRUE)
+
+'Generate a few summary tables to help answer the questions you were originally asked. For example:
+For each county: total enrollment, percent of students qualifying for free or reduced price lunch,
+and percent of population in poverty. 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.'
+
+filtered_df %>% 
+  group_by(county_name) %>% 
+  summarise(output = mean(county_per_poverty)) %>% 
+  ggplot() +
+  geom_density(aes(x = output))
+
+
+filtered_df %>% 
+  ggplot() +
+  geom_density(aes(x = county_per_poverty), fill = 'black') +
+  facet_wrap(~ county_name)
+