Module 10 Assignment

 For Module 10, the class was instructed to create a plot using ggplot2. I created a scatter plot using Covid data. The code is below:

#install packages

install.packages("ggplot2")

library(ggplot2)


#create the dataset

read.csv("/Users/kelsey/Desktop/LIS 5318 Visual Analytics/LIS 5318 Module 10/LIS5318-Visual-Analysis-2024/synthetic_covid_impact_on_work.csv")

covid <- read.csv("/Users/kelsey/Desktop/LIS 5318 Visual Analytics/LIS 5318 Module 10/LIS5318-Visual-Analysis-2024/synthetic_covid_impact_on_work.csv")

View(covid)


#create dataframe

coviddata <- data.frame(covid)


#create the plot using ggplot2

covidplot <- ggplot(data = coviddata, aes(x=Hours_Worked_Per_Day, y=Meetings_Per_Day, color=Productivity_Change)) + geom_point()


help(geom_point)


print(covidplot)


The plot can be seen below:



This plot is not correlated.     

This reflects the erratic work patterns of covid data.

Employee outliers include a data point at 7 hours per day and nearly 10 hours per day. Employees productivity was impacted by covid and created erratic work patterns.

Comments

Post a Comment

Popular posts from this blog

Module 2 - Geographic Visualizations in Tableau

Image
The following visualization (which can be interacted with here: https://tinyurl.com/publichousinginflorida) was developed based on Housing and Urban Development data for public housing developments in the United States, updated in December of 2023. The visualization represents the average household income (size of the marks) and average rent (shade of the marks) for each development in the state of Florida.  The dataset used for this visualization contains many variables, including number of occupied dwellings and vacancies, percentage of demographic groups in each development (Native American, Hispanic, female head of household with and without children), among many others.  Initially, I found it difficult to organize the visualization, because my impulse was to create pie chart markings based on occupancy. However, this proved difficult. This visualization is the result of a compromise I made with myself to represent some aspect of the data without thinking too hard about ho...
Read more

Final Project

Image
 For the final project, I created a correlated graph a selection of data from the Orlando Police Department. View here. Between 2009 and 2024, suspect fatalities were fewer than suspects shot. Suspect fatalities were alarmingly high.
Read more

Module 7 - Distribution Analysis

Image
 For this module, we were assigned to create a visualization based on distribution analysis. I created a bar graph based on the mpg table of the mtcars dataset in R. Below is my graph: This graph shows the miles per gallon of each vehicle in the mtcars dataset. A drawback of this graph is that the vehicles are not identified on the x axis, therefore, the graph does not demonstrate the best information on distribution for this dataset.  Here is the code:  data("mtcars") head(mtcars) counts <- table(mtcars$mgp) counts2 <- table(mtcars$wt) counts View(mtcars) barplot(mtcars$mpg, main = "Miles per Gallon", xlab = "MPG", ylab = "count", col = "orange")
Read more