WA1_Lindner
Alexander Lindner
2025-11-17
1. Dataset overview
For this weekly challenge I used the built-in mtcars dataset. A short overview to describe the dataset’s structure and basic statistics in the dataset is shown here.
dim(mt) # show dimension of mtcars## [1] 32 11names(mt) # show names from variables## [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear"
## [11] "carb"head(mt, 10) # show first 10 rows## 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 4str(mt) # show roles (structure) of variables## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...summary(mt) # general overview## mpg cyl disp hp
## Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
## 1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5
## Median :19.20 Median :6.000 Median :196.3 Median :123.0
## Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7
## 3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
## Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
## drat wt qsec vs
## Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
## 1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
## Median :3.695 Median :3.325 Median :17.71 Median :0.0000
## Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
## 3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
## Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
## am gear carb
## Min. :0.0000 Min. :3.000 Min. :1.000
## 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000
## Median :0.0000 Median :4.000 Median :2.000
## Mean :0.4062 Mean :3.688 Mean :2.812
## 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :1.0000 Max. :5.000 Max. :8.0002. Two visualizations
For the visualisation of the dateset mtcars I used a scatter and a box plot:
2.1 Scatter plot
In the scatter plot I want to visualize the correlation between the weight and the usage of fuel (fuel efficiency in MPG) of the cars.
# Scatter plot: weight vs. miles per gallon (MPG) (fuel efficiency)
ggplot(mt, aes(x = wt, y = mpg)) + # define data and axes
geom_point(color = "black", size = 2) + # plot values with specific color and size
geom_smooth(method = "lm", se = TRUE, color = "red") + # linear regression: linear model with confidence interval with specific color
labs(
title = "Correlation between car weight and fuel efficiency", # define title
x = "Weight [1000 lbs]", # define x axis
y = "Miles per gallon [MPG]" # define y axis
) +
theme(
plot.title = element_text(size = 18, face = "bold", hjust = 0.4), # define font, style and position of title
axis.title = element_text(size = 14), # define axes font size
axis.line = element_line(linewidth = 0.8), # define axis thickness
panel.background = element_blank(), # remove grid and grey background
) +
scale_x_continuous(limits = c(0, 6), expand = c(0, 0)) + # define x axis range, starting at 0
scale_y_continuous(limits = c(0, 40), expand = c(0, 0)) # define y range, starting at 0 ## `geom_smooth()` using formula = 'y ~ x'
Figure 1. Correlation between weight (in 1000 lbs) and
the usage miles per gallon (MPG) of cars. A linear regression was
performed.
2.2 Box plot
In the box plot I want to visualize the correlation between the transmission type (0 = automatic cars, 1 = manual cars) and the usage of fuel (fuel efficiency in MPG) of the cars.
# Box plot: transmission vs. miles per gallon (MPG) (fuel efficiency)
ggplot(mt, aes(
x = factor(am, labels = c("Automatic", "Manual")), # define values for x axis, define 0 = automatic cars, 1 = manual cars
y = mpg, # define values y axis
fill = factor(am))) + # use colors in plot
geom_boxplot() + # plot values
geom_jitter(width = 0.35, size = 1.5, color = "black") + # define positioning, size and color of values
labs(
title = "Correlation between transmission type and fuel efficiency", # define title
x = "Transmission type", # define x axis
y = "Miles per gallon [MPG]" # define y axis
) +
theme(
plot.title = element_text(size = 18, face = "bold", hjust = 1.0), # define size, style and position of title
axis.title = element_text(size = 14), # define axes font size
axis.text.x = element_text(size = 12, color=("black")), # increase font size of labels, change color
axis.line = element_line(linewidth = 0.8), # define axes thickness
panel.background = element_blank(), # remove grey background
legend.position = "none" # remove legend
) +
scale_y_continuous(limits = c(0, 40), expand = c(0, 0)) + # define y axis range, starting at 0
scale_fill_manual(values = c("lightblue", "orange")) # define color of box plot
Figure 2. Correlation between transmission type (manual
and automatic) and the usage miles per gallon (MPG) of cars.
3. First hypothesis
Regression: The fuel efficiency tend to decrease as the weight of the car increases. Lighter cars can drive further on the same amount of fuel compared to heavier cars.
Box plot: Manual cars tend to have a higher fuel efficiency than automatic cars. Manual cars can drive further on the same amount of fuel compared to automatic cars.
4. Reflection on AI assistance
With the use of AI (ChatGPT) I generated a first idea of the code for the linear regression and the box plot. With using the code in R and the following knitting, I developed ideas for possible changes and then modified the code. I changed e.g. the color, font or size of objects. Further, I used AI to add specific items (e.g. color of box plots). In general, AI helped me a lot with coding, but I did the last modifications. I could not identify mistakes made by AI. All suggestions were helpful. The code was clear and because of my explicit question: “show me the easiest and cleanest way to do it” straightforward without any fancy coding. But, I often changed the title or axes titles to a more scientific context.
Prompt 1:
I want to create a scatter plot with linear regression in R with ggplot2. I will use the data set mtcars (defined as mt). I want to use the weight against the miles per gallon. Show me the basic code, which is well structured and straight forward, without fancy coding. Explain the prompts with # … .
#Response (excerpt):
#Load ggplot2
library(ggplot2)
#Use mtcars data set and assign it to mt
mt <- mtcars
#Create scatter plot with regression line
ggplot(mt, aes(x = wt, y = mpg)) +
geom_point(color = "blue") + # Draw scatter points
geom_smooth(method = "lm", # Add linear regression line
color = "red", # Line color
se = FALSE) + # Do not show confidence interval
labs(title = "MPG vs Car Weight", # Plot title
x = "Weight (1000 lbs)", # X-axis label
y = "Miles per Gallon") # Y-axis label
Adjustment:
I then changed some promts (e.g. load ggplot unnecessary because I already loaded it in the beginning) and modified the code (with confidence interval, other color, point size) and specified the axes titles and the plot title.
Prompt 2:
I want to modify the theme. Show me the promts for: adjusting the plot title in size and position; adjust the size of axes titles; increase the linewidth of the x and y axes; and remove the grey background. Use the easiest way to do
# Theme adjustments
theme(
plot.title = element_text(size = 18, hjust = 0.5), # Increase title size & center it
axis.title = element_text(size = 14), # Increase axes title sizes
axis.line = element_line(linewidth = 1), # Thicken x and y axis lines
panel.background = element_blank() # Remove grey background
)
Adjustment:
Modification of explanation and change of color of labels. I then modified the prompts that I like the appearance of the plot (I added face = “bold”)
Prompt 3:
I want that the x axis is starting at 0 to 6 and y axis from 0 to 40 and that both axes are located at 0 and not at -0.5.
# Set axis limits and remove padding
scale_x_continuous(limits = c(0, 6), expand = c(0, 0)) + # X axis from 0 to 6
scale_y_continuous(limits = c(0, 40), expand = c(0, 0)) + # Y axis from 0 to 40
Adjustment:
I just changed the explanation.
Prompt 4:
I want to do the same but with a box plot and the correlation between automatic and manual cars with the MPG. Show me the simpliest code for this.
#Box plot with clear labels
ggplot(mt, aes(x = factor(am, labels = c("Automatic", "Manual")),
y = mpg)) +
geom_boxplot() +
labs(title = "MPG by Transmission Type",
x = "Transmission",
y = "Miles per Gallon") +
theme_minimal()
Adjustment:
Clearer explanation and adjustment of axes titles.
Prompt 5:
I want to modify the theme: define title; size of axes font, add labels to x axis; remove legend
#Theme adjustments
theme(
plot.title = element_text(size = 18, hjust = 0.5), # Increase title size & center it
axis.title = element_text(size = 14), # Increase axes title sizes
axis.text.x = element_text(size = 12), # Increase label size
)Adjustment:
I changed the explanations. I then added some modifications from the previous plot (remove grey background, increase thickness of axes, define y axis and range).
Prompt 6:
I want to use colors in the box plot. What is the easiest way to do it?
#include in aes
fill = factor(am))) + # map fill to transmission
scale_fill_manual(values = c("blue", "orange")) + # 0 = blue, 1 = orange
Adjustment:
Change of blue to lightblue.
Prompt 7:
I want to plot the actual values. Easiest way?
geom_boxplot(alpha = 0.6) + # Box plot with semi-transparent fill
geom_jitter(width = 0.15, size = 2, color = "black") + # Add points with slight horizontal jitter
Adjustment:
I removed the alpha and changed the value for the width.
5. Session info
sessionInfo()## R version 4.3.3 (2024-02-29 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 11 x64 (build 26100)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=German_Germany.utf8 LC_CTYPE=German_Germany.utf8
## [3] LC_MONETARY=German_Germany.utf8 LC_NUMERIC=C
## [5] LC_TIME=German_Germany.utf8
##
## time zone: Europe/Berlin
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
## [5] purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
## [9] ggplot2_4.0.0 tidyverse_2.0.0
##
## loaded via a namespace (and not attached):
## [1] sass_0.4.9 utf8_1.2.4 generics_0.1.3 stringi_1.8.3
## [5] lattice_0.22-5 hms_1.1.3 digest_0.6.35 magrittr_2.0.3
## [9] evaluate_0.23 grid_4.3.3 timechange_0.3.0 RColorBrewer_1.1-3
## [13] fastmap_1.1.1 jsonlite_1.8.8 Matrix_1.6-5 mgcv_1.9-1
## [17] fansi_1.0.6 scales_1.4.0 jquerylib_0.1.4 cli_3.6.2
## [21] rlang_1.1.3 splines_4.3.3 withr_3.0.0 cachem_1.0.8
## [25] yaml_2.3.8 tools_4.3.3 tzdb_0.4.0 vctrs_0.6.5
## [29] R6_2.5.1 lifecycle_1.0.4 pkgconfig_2.0.3 pillar_1.9.0
## [33] bslib_0.7.0 gtable_0.3.6 glue_1.7.0 xfun_0.43
## [37] tidyselect_1.2.1 highr_0.10 rstudioapi_0.16.0 knitr_1.46
## [41] farver_2.1.1 htmltools_0.5.8.1 nlme_3.1-164 rmarkdown_2.26
## [45] labeling_0.4.3 compiler_4.3.3 S7_0.2.0