Module # 10 assignment

 For this week I worked with ggplot2 and messed around with time series data to see how it actually looks over time. The hot dog contest example was kinda cool because you can clearly see which years had record performances instead of just looking at numbers. Then with the economics dataset, the line graphs made it way easier to see how things like unemployment change over time and how different variables relate to each other. I feel like visualization makes a big difference with time series data because you can instantly spot trends, patterns, or anything weird going on. Without graphs it would honestly just be a bunch of numbers that are harder to understand. Using ggplot2 also makes everything look cleaner and more organized, so it just makes the data way easier to read and interpret.

Comments

Post a Comment

Popular posts from this blog

Module 10

 lm(cystfibr$spemax ~ age + weight + bmp + fev1, data=cystfibr) anova(lm(cystfibr$spemax ~ age + weight + bmp + fev1, data=cystfibr))   I used the cystfibr dataset from the ISwR package In completing this assignment. I ran a regression on spemax Based on the predictor variables age, weight, BMI an FEV 1 with spemex is being predicted. My results indicated that my overall model model was statistically significant. Therefore, at least one of the predictor variables is significantly associated with spemax .   Based upon the coefficients produced in this analysis, I examined age. Since the coefficient for age was negative, it can be concluded that the age increases spemex We'll decrease while controlling for the effects of the other predictor variables. This indicates that older patients generally exhibit lower maximum specific power.  
Read more

basic graph

Image
 For this assignment I created a histogram in R using the built in mtcars dataset to show the distribution of miles per gallon (mpg). The visualization makes it easy to see that most cars fall between about 15 and 25 mpg, with only a few vehicles above 30 mpg. This fits with Few’s and Yau’s discussion on basic descriptive visualization because it focuses on clarity and simplicity rather than decoration. The chart communicates one clear message about the spread of the data and avoids unnecessary visual elements. Overall, it works well as a basic example of descriptive analysis in R.
Read more

Module 3 assignemnt

  1. Central Tendency Set #1: 10, 2, 3, 2, 4, 2, 5 Mean = 4.0 Median = 3.0 Mode = 2 Set #2: 20, 12, 13, 12, 14, 12, 15 Mean = 14.0 Median = 13.0 Mode = 12 2. Variation Set #1 Range = 8 IQR = 2.5 Variance = 8.33 Standard Deviation = 2.89 Set #2 Range = 8 IQR = 2.5 Variance = 8.33 Standard Deviation = 2.89 3. Comparison Set #1 stays pretty low overall with an average of 4, a median of 3, and a mode of 2. Set #2 is higher across the board with an average around 14, a median of 13, and a mode of 12. Basically, Set #2 looks like Set #1 just shifted up by about 10 points. The spread is the same in both sets though. They have the exact same range, IQR, variance, and standard deviation. That means the consistency of the data is identical, even though one set has smaller values and the other has larger ones. So overall, the difference is just where the numbers sit . Set #1 clusters around lower values, while Set #2 clusters higher...
Read more