title: “Practice: Cleaning and Summarizing Data with dplyr”
author: “Student Name”
output: html_document
In this activity, you will practice using tidyverse and dplyr to clean a dataset and prepare summary tables that could later be used to make figures. The focus is on understanding how data structure affects visualization.
Work through each section in order. You may work quietly with classmates nearby, but everyone should write and submit their own work.
Load the library of tidyverse, install it if necessary.
# YOUR CODE HERE
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.2.1 ✔ readr 2.2.0
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.3 ✔ tibble 3.3.1
## ✔ lubridate 1.9.5 ✔ tidyr 1.3.2
## ✔ purrr 1.2.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsFor today’s activity, we will use a built-in dataset so that everyone is working with the same data. Load the mtcars dataset
# YOUR CODE HERE
data(mtcars)Take a moment to look at the dataset.
# YOUR CODE HERE
mtcars## 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 2Before cleaning data, it is important to understand its structure. View the structure of your variables.
# YOUR CODE HERE
str(mtcars)## '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 ...Answer the following questions in text below (not as code):
What does each row represent? one car model
Name two variables that are numeric. mpg & hp
Name one variable that represents a category, even if it is currently stored as a number. cyl
Some variables in this dataset are stored as numbers even though they represent categories.
Convert the following variables to factors:
cyl (number of cylinders)
am (transmission type)
# YOUR CODE HERE
mtcars$cyl <- as.factor(mtcars$cyl)
mtcars$am <- as.factor(mtcars$am)Check that the conversion worked by looking at the structure again.
# YOUR CODE HERE
str(mtcars)## '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 : Factor w/ 3 levels "4","6","8": 2 2 1 2 3 2 3 1 1 2 ...
## $ 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 : Factor w/ 2 levels "0","1": 2 2 2 1 1 1 1 1 1 1 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...For visualization, it is often helpful to work with only the variables you need.
Create a new object called cars_clean that contains
only:
mpg
hp
wt
cyl
am
# YOUR CODE HERE
cars_clean <- mtcars %>%
select(mpg, hp, wt, cyl, am)Now filter the dataset to include only cars with:
Save the result as a new object called cars_hp.
# YOUR CODE HERE
cars_hp <- cars_clean %>%
filter(hp > 100)Check how many rows remain.
# YOUR CODE HERE
nrow(cars_hp)## [1] 23Create a new variable called power_to_weight defined
as:
horsepower / weight
Add this variable to cars_hp.
# YOUR CODE HERE
cars_hp <- cars_hp %>%
mutate(power_to_weight = hp / wt)To prepare data for figures, we often summarize values by group.
Create a summary table that shows:
Mean miles per gallon (mpg)
Mean horsepower (hp)
Number of observations
Grouped by:
cylSave this as summary_cyl.
# YOUR CODE HERE
summary_cyl <- cars_hp %>%
group_by(cyl) %>%
summarise(
mean_mpg = mean(mpg),
mean_hp = mean(hp),
n = n()
)Display the table.
summary_cyl## # A tibble: 3 × 4
## cyl mean_mpg mean_hp n
## <fct> <dbl> <dbl> <int>
## 1 4 25.9 111 2
## 2 6 19.7 122. 7
## 3 8 15.1 209. 14Now create a second summary table grouped by:
amInclude:
Mean miles per gallon
Mean power-to-weight ratio
Save this as summary_transmission.
# YOUR CODE HERE
summary_transmission <- cars_hp %>%
group_by(am) %>%
summarise(
mean_mpg = mean(mpg),
mean_power_to_weight = mean(power_to_weight)
)
summary_transmission## # A tibble: 2 × 3
## am mean_mpg mean_power_to_weight
## <fct> <dbl> <dbl>
## 1 0 16.1 44.6
## 2 1 20.6 62.1Answer the following questions in text:
Which group appears to have higher fuel efficiency? am1 has higher fuel efficiency
Which summary table would be useful for making a bar plot? summary_transmission
Which would work better for a boxplot later? cars_hp since boxplots need distribution
In a short paragraph, describe:
One thing that was confusing
One thing that makes more sense now
Why cleaning and summarizing data before plotting is important
I didn’t really know how to filter, but now it makes much more sense. Cleaning and summarizing data first is important because raw data is often too messy or detailed to visualize directly.
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