Data manipulation
Oumo David
2025-02-14
This is a tutorial on data manipulation in R using the mtcars dataset. This example covers common data manipulation tasks such as filtering, sorting, grouping, summarizing, and creating new variables.
Load the Dataset
The mtcars dataset is included in R by default. You can load it using the data() function.
View the first few rows of the dataset
data("mtcars")
head(mtcars)## 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 1Basic Data Manipulations
1. Filter Rows
Filter the dataset to include only cars with more than 6 cylinders.
library(dplyr)## Warning: package 'dplyr' was built under R version 4.4.2##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionfiltered_data <- mtcars %>%
filter(cyl > 6)
head(filtered_data)## mpg cyl disp hp drat wt qsec vs am gear carb
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.44 17.02 0 0 3 2
## Duster 360 14.3 8 360.0 245 3.21 3.57 15.84 0 0 3 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.73 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.78 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.25 17.98 0 0 3 42. Select Columns
Select specific columns (e.g., mpg, cyl, hp).
selected_data <- mtcars %>%
select(mpg, cyl, hp)
head(selected_data)## mpg cyl hp
## Mazda RX4 21.0 6 110
## Mazda RX4 Wag 21.0 6 110
## Datsun 710 22.8 4 93
## Hornet 4 Drive 21.4 6 110
## Hornet Sportabout 18.7 8 175
## Valiant 18.1 6 1053. Sort Data
Sort the dataset by mpg (miles per gallon) in descending order.
sorted_data <- mtcars %>%
arrange(desc(mpg))
head(sorted_data)## mpg cyl disp hp drat wt qsec vs am gear carb
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## 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
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 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 24. Create New Variables
Add a new column kmpl (kilometers per liter) by converting mpg to kilometers per liter (1 mpg ≈ 0.425 kmpl).
mutated_data <- mtcars %>%
mutate(kmpl = mpg * 0.425)
head(mutated_data)## mpg cyl disp hp drat wt qsec vs am gear carb kmpl
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 8.9250
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 8.9250
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 9.6900
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 9.0950
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 7.9475
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 7.69255. Group and Summarize
Group the data by cyl (number of cylinders) and calculate the mean mpg for each group.
summary_data <- mtcars %>%
group_by(cyl) %>%
summarize(mean_mpg = mean(mpg),
max_hp = max(hp))
print(summary_data)## # A tibble: 3 × 3
## cyl mean_mpg max_hp
## <dbl> <dbl> <dbl>
## 1 4 26.7 113
## 2 6 19.7 175
## 3 8 15.1 3356. Rename Columns
Rename the mpg column to miles_per_gallon.
renamed_data <- mtcars %>%
rename(miles_per_gallon = mpg)
head(renamed_data)## miles_per_gallon cyl disp hp drat wt qsec vs am gear
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3
## carb
## Mazda RX4 4
## Mazda RX4 Wag 4
## Datsun 710 1
## Hornet 4 Drive 1
## Hornet Sportabout 2
## Valiant 17. Add Row Numbers
Add a new column with row numbers.
row_numbered_data <- mtcars %>%
mutate(row_num = row_number())
head(row_numbered_data)## mpg cyl disp hp drat wt qsec vs am gear carb row_num
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 1
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 2
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 3
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 4
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 5
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 6Advanced Data Manipulations
1. Merge with Another Dataset
Create a small dataset and merge it with mtcars.
Create a small dataset
car_names <- data.frame(
car = rownames(mtcars),
brand = c("Toyota", "Ford", "Mazda", "Honda", "Datsun", "Chrysler", "AMC", "Pontiac", "Fiat", "Porsche", "Lotus", "Ford", "Chevrolet", "Cadillac", "Lincoln", "Dodge", "Merc", "Valiant", "Duster", "Merc", "Merc", "Merc", "Merc", "Merc", "Merc", "Merc", "Merc", "Merc", "Merc", "Merc", "Merc", "Merc")
)Merge with mtcars
merged_data <- mtcars %>%
mutate(car = rownames(mtcars)) %>%
left_join(car_names, by = "car")
head(merged_data)## mpg cyl disp hp drat wt qsec vs am gear carb car brand
## 1 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Toyota
## 2 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 Mazda RX4 Wag Ford
## 3 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 Datsun 710 Mazda
## 4 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet 4 Drive Honda
## 5 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 Hornet Sportabout Datsun
## 6 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 Valiant Chrysler2. Pivot Data
Reshape the data from wide to long format using pivot_longer.
library(tidyr)
long_data <- mtcars %>%
pivot_longer(cols = c(mpg, hp, wt),
names_to = "metric",
values_to = "value")
head(long_data)## # A tibble: 6 × 10
## cyl disp drat qsec vs am gear carb metric value
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 6 160 3.9 16.5 0 1 4 4 mpg 21
## 2 6 160 3.9 16.5 0 1 4 4 hp 110
## 3 6 160 3.9 16.5 0 1 4 4 wt 2.62
## 4 6 160 3.9 17.0 0 1 4 4 mpg 21
## 5 6 160 3.9 17.0 0 1 4 4 hp 110
## 6 6 160 3.9 17.0 0 1 4 4 wt 2.883. Handle Missing Data
Add some missing values and handle them.
Introduce missing values
mtcars_with_na <- mtcars
mtcars_with_na[1:5, "mpg"] <- NARemove rows with missing values
cleaned_data <- mtcars_with_na %>%
drop_na(mpg)
head(cleaned_data)## mpg cyl disp hp drat wt qsec vs am gear carb
## Valiant 18.1 6 225.0 105 2.76 3.46 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.57 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.19 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.15 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.44 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.44 18.90 1 0 4 4Data visualization
Data visualization is a crucial part of data analysis, and R provides powerful tools like ggplot2 and base R plotting functions to create insightful visualizations. We will demonstrate various types of visualizations using the mtcars dataset.
Load necessary libraries
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.4.21. Scatter Plot
Visualize the relationship between two numeric variables, such as mpg (miles per gallon) and wt (weight). We shall make a Scatter plot using ggplot2
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point(color = "blue", size = 3) +
labs(title = "Scatter Plot of MPG vs Weight",
x = "Weight (1000 lbs)",
y = "Miles per Gallon") +
theme_minimal()
Figure 1: The relationship between the weight of the motor vehicle and the miles per gallon
2. Box Plot
Compare the distribution of mpg across different numbers of cylinders (cyl). Let us make a Box plot using ggplot2
ggplot(mtcars, aes(x = factor(cyl), y = mpg, fill = factor(cyl))) +
geom_boxplot() +
labs(title = "Box Plot of MPG by Cylinders",
x = "Number of Cylinders",
y = "Miles per Gallon") +
theme_minimal()
3. Bar Plot
Visualize the average mpg for each number of cylinders. Here, we shall create a Bar plot using ggplot2
avg_mpg <- mtcars %>%
group_by(cyl) %>%
summarize(mean_mpg = mean(mpg))
ggplot(avg_mpg, aes(x = factor(cyl), y = mean_mpg, fill = factor(cyl))) +
geom_bar(stat = "identity") +
labs(title = "Average MPG by Cylinders",
x = "Number of Cylinders",
y = "Average Miles per Gallon") +
theme_minimal()
4. Histogram
Visualize the distribution of mpg. Let us create a Histogram using ggplot2
ggplot(mtcars, aes(x = mpg)) +
geom_histogram(binwidth = 2, fill = "orange", color = "black") +
labs(title = "Histogram of MPG",
x = "Miles per Gallon",
y = "Frequency") +
theme_minimal()
5. Density Plot
Visualize the density distribution of mpg. Let us make a Density plot using ggplot2
ggplot(mtcars, aes(x = mpg)) +
geom_density(fill = "lightblue", alpha = 0.5) +
labs(title = "Density Plot of MPG",
x = "Miles per Gallon",
y = "Density") +
theme_minimal()
6. Line Plot
Visualize the relationship between mpg and hp (horsepower) using a line plot. Here, we shall make a Line plot using ggplot2
ggplot(mtcars, aes(x = hp, y = mpg)) +
geom_line(color = "red") +
labs(title = "Line Plot of MPG vs Horsepower",
x = "Horsepower",
y = "Miles per Gallon") +
theme_minimal()
7. Heatmap
Visualize correlations between numeric variables in the dataset. Let us make a Heatmap using base R
correlation_matrix <- cor(mtcars)
heatmap(correlation_matrix,
col = colorRampPalette(c("blue", "white", "red"))(100),
main = "Correlation Heatmap of mtcars Dataset")
8. Faceted Plot
Compare mpg vs wt for different numbers of cylinders using facets. Making a Faceted plot using ggplot2
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point(color = "purple") +
facet_wrap(~ cyl) +
labs(title = "Faceted Scatter Plot of MPG vs Weight by Cylinders",
x = "Weight (1000 lbs)",
y = "Miles per Gallon") +
theme_minimal()
9. Violin Plot
Visualize the distribution of mpg across different numbers of cylinders. Let us make a Violin plot using ggplot2
ggplot(mtcars, aes(x = factor(cyl), y = mpg, fill = factor(cyl))) +
geom_violin() +
labs(title = "Violin Plot of MPG by Cylinders",
x = "Number of Cylinders",
y = "Miles per Gallon") +
theme_minimal()
10. Pair Plot
Visualize pairwise relationships between numeric variables. Making a Pair plot using base R
pairs(mtcars[, 1:5],
main = "Pair Plot of mtcars Dataset",
pch = 19,
col = "darkblue")
11. Custom Theme
Customize the theme of your plots for better aesthetics.
Custom theme
custom_theme <- theme_minimal() +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
axis.title = element_text(size = 12),
axis.text = element_text(size = 10),
legend.position = "bottom"
)Example plot with custom theme
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point(color = "green", size = 3) +
labs(title = "Custom Themed Scatter Plot",
x = "Weight (1000 lbs)",
y = "Miles per Gallon") +
custom_theme