## Data Explorration

This should include summary statistics, means, medians, quartiles, or any other relevant information about the data set. Please include some conclusions in the R Markdown text.

summary(housing) mean(housing[[“listings”]]) median(housing[[“listings”]]) mean(housing[[“sales”]]) median(housing[[“sales”]])

## Data Wrangling

Please perform some basic transformations. They will need to make sense but could include column renaming, creating a subset of the data, replacingvalues, or creating new columns with derived data

colnames(housing)[2]<- “Location” housingsubset <- housing[sample(1:nrow(housing),100,replace = FALSE),] housingsubset

## Graphics

Please make sure to display at least one scatter plot, box plot and histogram.

sp<-ggplot(housing, aes(x=year, y=sales)) + geom_point() sp

bp<-ggplot(housingsubset, aes(x=listings, y=inventory))+geom_boxplot() bp

his<-ggplot(housingsubset, aes(housingsubset$year))+geom_histogram() his

## Meaningful Question

Please state a meaningful question for analysis. Use the first three steps and anything else that would be helpful to answer the question you are posing from the data set you chose.Please write a brief conclusion paragraphin R markdown at the end.

1.What month has the most sales? 2.What year has the most sales? 3. What city has the largest inventory available?

Based on scatterplot it appears that July has the most sales.Based on scatterplot it appears that 2015 has the most sales.Based on scatter plot it appears that South Padre Island had the most inventory.