For the final course project of the Coursera course Developing Data Products, a simple Shniy application was built and published.

This project includes

• Shiny app
• Presentation
• Github repo

The app is very user friendly and easy to use.

The dataset that is used for this applicated is the airquality dataset from the R dataset package. It contains airquality measuremenst of the city New York.

data <- airquality
head(data)

##   Ozone Solar.R Wind Temp Month Day
## 1    41     190  7.4   67     5   1
## 2    36     118  8.0   72     5   2
## 3    12     149 12.6   74     5   3
## 4    18     313 11.5   62     5   4
## 5    NA      NA 14.3   56     5   5
## 6    28      NA 14.9   66     5   6

The app estimates a linear model between two variables of the airquality dataset. You can choose your preferred variables for analysis in two drop down menus on the left side of the app. The app will then calculate a linear model between the two chosen variables.

The results will be displayed by a summary with statistics and with a plot. The plot shows a scatterplot between the two variables as well as the fitted line from the linear model.

AS said before, the output contains of a plot and summary statistics. The summary looks like this

model_lm <- lm(Ozone ~Wind, airquality)
summary(model_lm)

## 
## Call:
## lm(formula = Ozone ~ Wind, data = airquality)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -51.572 -18.854  -4.868  15.234  90.000 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  96.8729     7.2387   13.38  < 2e-16 ***
## Wind         -5.5509     0.6904   -8.04 9.27e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 26.47 on 114 degrees of freedom
##   (37 observations deleted due to missingness)
## Multiple R-squared:  0.3619, Adjusted R-squared:  0.3563 
## F-statistic: 64.64 on 1 and 114 DF,  p-value: 9.272e-13