26/6/2020
In this presentation I am going to show my little shiny aplication, specially how it works, and the fundament of each variable that I used.
To see my shiny presentation you can click on this link https://ougousteren.shinyapps.io/FinalProject_DevelopmentProducts/ and you can share with otherpeople.
I used the gapminder data from the dataset package that have the following structure:
library(dslabs);library(dplyr) library(ggplot2);library(plotly) str(gapminder)
'data.frame': 10545 obs. of 9 variables: $ country : Factor w/ 185 levels "Albania","Algeria",..: 1 2 3 4 5 6 7 8 9 10 ... $ year : int 1960 1960 1960 1960 1960 1960 1960 1960 1960 1960 ... $ infant_mortality: num 115.4 148.2 208 NA 59.9 ... $ life_expectancy : num 62.9 47.5 36 63 65.4 ... $ fertility : num 6.19 7.65 7.32 4.43 3.11 4.55 4.82 3.45 2.7 5.57 ... $ population : num 1636054 11124892 5270844 54681 20619075 ... $ gdp : num NA 1.38e+10 NA NA 1.08e+11 ... $ continent : Factor w/ 5 levels "Africa","Americas",..: 4 1 1 2 2 3 2 5 4 3 ... $ region : Factor w/ 22 levels "Australia and New Zealand",..: 19 11 10 2 15 21 2 1 22 21 ...
This app only contain two version of tools:
At first the app starts with a histogram graph that show us the way that a variable has.
hist(gapminder$infant_mortality)
In the second plot I show you the same histogram but in logs because the scale of some variables are so different to other and can not see them well.
hist(log(gapminder$gdp))
For part 3 I designed a ggplot usin plotly package that show us the relationship between imputs but filtered but some factor.
gg <- gapminder %>% ggplot(aes(infant_mortality,life_expectancy, color = region)) + geom_point(color = "black") +geom_smooth(method ="loess") + facet_wrap(.~ region) ggplotly(gg)
Finally I designed a time seris that show us how in mean the variable selected was changing trough time.
ggg <- gapminder %>% ggplot(aes(year,life_expectancy, color = region)) + geom_line(color = "black") +geom_smooth(method ="loess") + facet_wrap(.~ region) ggplotly(ggg)