knitr::opts_chunk$set(echo = TRUE)
# R Notebook: Exploring and Understanding
## Example 1: Loading and Summarizing Data
``` r
# Load the necessary library
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union# Load the dataset
mtcars_data <- mtcars
# Display the first few rows
head(mtcars_data)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1# Summary statistics
summary(mtcars_data)## 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. :8.000Question: What does the summary function tell us about
the dataset?
Answer:
The summary function provides descriptive statistics for
each column in the dataset, including the minimum, first quartile,
median, mean, third quartile, and maximum for numeric columns.
Example 2: Filtering Data
# Filter cars with mpg greater than 20
high_mpg_cars <- mtcars_data %>% filter(mpg > 20)
# View filtered data
head(high_mpg_cars)## 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
## 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 2Question: How many cars have an MPG greater than 20?
Answer:
To find the count of cars with MPG greater than 20, we can use the
nrow function:
nrow(high_mpg_cars)## [1] 14Example 3: Creating New Variables
# Add a new column: Power-to-Weight Ratio
mtcars_data <- mtcars_data %>%
mutate(power_to_weight = hp / wt)
# Display the updated data
head(mtcars_data)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
## power_to_weight
## Mazda RX4 41.98473
## Mazda RX4 Wag 38.26087
## Datsun 710 40.08621
## Hornet 4 Drive 34.21462
## Hornet Sportabout 50.87209
## Valiant 30.34682Question: What does the new column represent, and why might it be useful?
Answer:
The power_to_weight column represents the ratio of
horsepower to the weight of each car. It can help in understanding the
performance potential of a car relative to its weight.
Example 4: Visualization
# Load ggplot2 library
library(ggplot2)
# Create a scatter plot: MPG vs Horsepower
scatter_plot <- ggplot(mtcars_data, aes(x = hp, y = mpg)) +
geom_point() +
labs(title = "Scatter Plot of MPG vs Horsepower",
x = "Horsepower",
y = "Miles Per Gallon (MPG)")
# Display the plot
scatter_plot
Question: What insights can you gain from the scatter plot?
Answer:
The scatter plot shows a negative relationship between horsepower and MPG. Cars with higher horsepower tend to have lower MPG, indicating less fuel efficiency.
Example 5: Grouping and Summarizing Data
# Group data by the number of cylinders and summarize average MPG
cyl_mpg_summary <- mtcars_data %>%
group_by(cyl) %>%
summarize(avg_mpg = mean(mpg, na.rm = TRUE))
# View the summary
cyl_mpg_summary## # A tibble: 3 × 2
## cyl avg_mpg
## <dbl> <dbl>
## 1 4 26.7
## 2 6 19.7
## 3 8 15.1Question: Which cylinder group has the highest average MPG?
Answer:
The cylinder group with the highest average MPG can be identified by
inspecting the cyl_mpg_summary table.