Problem 1

For a data set of your choosing, make a faceted plot using the trelliscopejs package. You may make any type of plot; scatter plot, histogram, etc. but, as mentioned in the discussion below, you must explain why you chose this plot and what you are investigating about the variable you are graphing.

The trelliscope plot must include one cognostic measure of your own. Include a description of what it is and what information this measure gives.

## Problem 1

library(ggplot2)
library(trelliscopejs)

# average miles per gallon for each group of cylinders
avg_mpg <- tapply(mtcars$mpg, mtcars$cyl, mean)
avg_mpg_df <- data.frame(cyl = unique(mtcars$cyl), avg_mpg = avg_mpg)

p <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  facet_wrap(~ cyl) +
  geom_text(data = avg_mpg_df, aes(label = paste0("Avg MPG: ", round(avg_mpg, 2))),
            x = Inf, y = Inf, hjust = 1, vjust = 1, inherit.aes = FALSE)

# trelliscope plot
list(
  plot = p,
  data = mtcars,
  desc = "Faceted MPG vs. WT"
)
## $plot

## 
## $data
##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
## 
## $desc
## [1] "Faceted MPG vs. WT"
# The average miles per gallon measure gives the typical(average) fuel efficiency of vehicles within each group defined by the number of cylinders. It represents the central tendency of the mpg values within each group.


Description 2-3 paragraphs.

Describe the data set. Explain the variable you are graphing in your plots and the reason you are investigating with it. Discuss the reason/motivation you chose the variable to facet on, and what insight or trend you are attempting to investigate. Discuss any challenges you had in making the graphs and how you dealt with these challenges. Name at least one cognostic measure (this can include the cognostic you created or be different) the reader could investigate, and explain any insight they might gain from it.


# The data set used  is the mtcars data set which contains data on various car models from the 1970s. It includes 32 observations on 11 variables, such as miles per gallon (mpg), number of cylinders (cyl), weight in thousands of pounds (wt), horsepower (hp), etc. In this analysis, I am graphing the relationship between weight (wt) and miles per gallon (mpg) for different groups defined by the number of cylinders (cyl). The motivation behind investigating this relationship is to understand how vehicle weight impacts fuel efficiency and whether this relationship varies across different engine configurations. I chose to facet the plots by the number of cylinders because the number of cylinders can significantly influence both vehicle weight and fuel efficiency. By examining how the relationship between weight and mpg differs across cylinder groups, I aim to uncover any trends or patterns that may exist and gain insights into how engine size affects fuel economy. 

# One challenge was deciding on the appropriate visual representation to use for the data. I decided on scatter plots because they are ideal for showing relationships between continuous variables. They allow for easy identification of patterns and trends in the data and by faceting them by the number of cylinders, we can analyze how the relationship between weight and mpg varies across different engine configurations, enhancing interpretability and insight. A cognostic measure that the reader could investigate further is the relationship between horsepower (hp) and miles per gallon (mpg) within each cylinder group. By examining how horsepower influences fuel efficiency for different engine configurations, one can gain insights into the trade-off between power and fuel economy in vehicles with varying cylinder counts. This could help understand whether higher horsepower leads to lower mpg uniformly across all cylinder groups or if there are differences in this relationship based on engine size.

grading: trelliscope plot[25 points], discussion[25 points]


Note: you can add a url directly to the text and it will be active in the html (and word document if you knit to that)

Example: https://www.google.com

If you want to be fancy and make your url active text, you can do this