Experimenting with data over a span of time is something I've done with ggplot2 for quite some time; one such example was during the previous year when I wrote a series of functions to create visualizations of the price of certain videogames over the course of a decade. For this assignment, I opted to go for something more simple: a line graph showing the rate of employment from the late 1960's up to the mid-2010's using data from the economics dataset. ggplot ( economics , aes ( x = date , y = unemploy ) ) + geom_line ( ) + geom_smooth ( ) + labs ( title = "Time Series Plot of Unemployment with Smooth Trend Line" , x = "Date" , y = "Unemployment" ) + theme_minimal ( ) The addition of a trend line makes the data presented here more easily understandable to onlookers, with the main message of the data being made clear in the presentation, that being that unemployment in recent times has been steadily incre...
For the R package to make at the end of this course, I've decided to make a package called DescribeR. This package (hopefully) will be able to instantaneously provide a detailed summary of any data type given to it, from a typical variable held within RStudio's memory to descriptions of the columns in a given data frame. I'd also like to have the package utilize ggplot2 to make example visualizations of data frame information so as to better have the user understand the variables in said data frame and how they relate to one another. No two graph outputs should be alike when coming from the same data frame. GitHub link to description file: https://github.com/Retrolovania/R_Programming/blob/main/DescribeR/DESCRIPTION.txt
Despite nearly all the other functions leading to differing results between both sets of values, Variance, Interquartile and Standard Deviations actually returned equal values for both.
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