For the Developing Data Products Course Project on Coursera I developed a Shiny App that predicts median house sale for a given in month in Texas through the txhousing data set from ggplot2.

A user can manipulate factors like the house’s city, and the number of sales and listing in a month to determine the most cost effective time to buy a house for a given city and selection of cities.

The txhousing data set used in the applicable is shown below. It consits of 9 variables and 8,602 records. The 1,434 records with missing listings or median sales price were dropped from the Shiny App.

library(ggplot2)
head(txhousing)

## # A tibble: 6 x 9
##   city     year month sales   volume median listings inventory  date
##   <chr>   <int> <int> <dbl>    <dbl>  <dbl>    <dbl>     <dbl> <dbl>
## 1 Abilene  2000     1    72  5380000  71400      701       6.3 2000 
## 2 Abilene  2000     2    98  6505000  58700      746       6.6 2000.
## 3 Abilene  2000     3   130  9285000  58100      784       6.8 2000.
## 4 Abilene  2000     4    98  9730000  68600      785       6.9 2000.
## 5 Abilene  2000     5   141 10590000  67300      794       6.8 2000.
## 6 Abilene  2000     6   156 13910000  66900      780       6.6 2000.

A screen shot of the Texas housing Shiny App is displayed below. Users can manipulate the variables ‘city’, ‘year’, ‘month’, ‘sales’, ‘median’ and ‘listings’ in application.

The Shiny App can be access through the following link: https://cooper-carpenter.shinyapps.io/TXHousingPricePrediction/

The full GitHub repo can be accessed at: https://github.com/coopercarpenter/Data-Products-Course-Project