Exploring a Dataframe

This chapter is incomplete, work-in-progress.

Here’s how you can explore the mtcars data frame:

View the data: Use the head() or tail() function to view the first or last few rows of the data frame, respectively. For example:

  1. Find the dimensions (rows and columns) of the dataframe
data(mtcars)
dim(mtcars)
[1] 32 11
  1. Retrieve the names of the columns in the dataframe
names(mtcars)
 [1] "mpg"  "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"   "gear"
[11] "carb"
  1. str()

to display the structure of the dataframe, including the data type and the first few rows.

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 ...
  1. head()

to view the first few rows of the dataframe.

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    1
  1. tail()

to view the last few rows of the dataframe.

tail(mtcars)
                mpg cyl  disp  hp drat    wt qsec vs am gear carb
Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.7  0  1    5    2
Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.9  1  1    5    2
Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.5  0  1    5    4
Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.5  0  1    5    6
Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.6  0  1    5    8
Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.6  1  1    4    2
  1. summary()

to generate summary statistics for each column in the dataframe.

summary(mtcars)
      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.   :8.000
  1. table()

to generate a frequency table for a categorical variable.

table(mtcars$cyl)

 4  6  8 
11  7 14
  1. unique()

to find unique values in a column of the dataframe.

unique(mtcars$cyl)
[1] 6 4 8

Logical operations

Here are some examples of logical operations functions in R using the mtcars dataset:

  1. Subsetting based on a condition:

The logical expression [] and square bracket notation can be used to subset the mtcars dataset according to a criterion. For instance, to only choose the rows where the mpg is higher than 20:

# Subset mtcars based on mpg > 20
mtcars_subset <- mtcars[mtcars$mpg > 20, ]
mtcars_subset
                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    2
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
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
Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
  1. The which() function:

The which() function returns the indexes of the vector’s members that adhere to a predicate. To determine the indices of the rows where mpg is larger than 20 for instance:

# Find the indices of rows where mpg > 20
indices <- which(mtcars$mpg > 20)
indices
 [1]  1  2  3  4  8  9 18 19 20 21 26 27 28 32
  1. The ifelse() function:

The ifelse() function applies a logical condition to a vector and returns a new vector with values depending on whether the condition is TRUE or FALSE. It is a vectorized version of the if-else statement. For instance, to add a new column called high mpg that shows whether or not the mpg value is more than 20:

# Create a new column "high_mpg" based on mpg > 20
mtcars$high_mpg <- ifelse(mtcars$mpg > 20, "Yes", "No")
  1. The all() and any() functions:

If every element in a vector satisfies a logical criterion, the all() function returns TRUE; otherwise, it returns FALSE. If at least one element in a vector satisfies a logical criterion, the any() method returns TRUE; otherwise, it returns FALSE. To determine whether every value in the mpg column is larger than 20, for instance:

# Check if all values in mpg column are greater than 20
all(mtcars$mpg > 20)
[1] FALSE

And to check if at least one value in the mpg column is greater than 20:

Check if any value in mpg column is greater than 20

any(mtcars$mpg > 20)
[1] TRUE

Creating new functions in R

  1. Function to calculate average mileage:
avg_mileage <- function(data) {
  mean(data$mpg)
}

# Usage
avg_mileage(mtcars) # Returns the average mileage of all cars in the dataset
[1] 20.09062
  1. Function to plot a scatter plot of horsepower vs. miles per gallon:
plot_horsepower_vs_mpg <- function(data) {
  plot(data$hp, data$mpg, xlab = "Horsepower", ylab = "Miles per gallon")
}

# Usage
plot_horsepower_vs_mpg(mtcars) # Plots a scatter plot of horsepower vs. miles per gallon

  1. Function to calculate average mileage for cars with a specific number of cylinders:
avg_mileage_by_cyl <- function(data, cyl) {
  mean(data$mpg[data$cyl == cyl])
}

# Usage

# Returns the average mileage of cars with 4 cylinders
avg_mileage_by_cyl(mtcars, 4) 
[1] 26.66364
# Returns the average mileage of cars with 6 cylinders
avg_mileage_by_cyl(mtcars, 6) 
[1] 19.74286
  1. Function to calculate average horsepower for cars with a specific number of gears:
avg_hp_by_gear <- function(data, gear) {
  mean(data$hp[data$gear == gear])
}

# Returns the average horsepower of cars with 3 gears
avg_hp_by_gear(mtcars, 3) 
[1] 176.1333
# Returns the average horsepower of cars with 4 gears
avg_hp_by_gear(mtcars, 4) 
[1] 89.5