Exploring Dataframes (Part 1 of 2)

Chapter 5.

This chapter delves into an in-depth examination of dataframes in R.

  • The mtcars dataset is a readily available set in R, originally sourced from the 1974 Motor Trend US magazine. It includes data related to fuel consumption and 10 other factors pertaining to car design and performance, recorded for 32 vehicles from the 1973-74 model years. [1]

Next, we will understand R code to explore a dataframe, step-by-step. We review eight basic functions to get started exploring dataframes [2] [7]

  1. To load the mtcars dataset in R, use this command:
data(mtcars)

Reviewing a dataframe

  1. View(): This function opens the dataset in a spreadsheet-style data viewer.
View(mtcars)
  1. head(): This function prints the first six 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(): This function prints the last six 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. dim(): This function retrieves the dimensions of a dataframe, i.e., the number of rows and columns.
dim(mtcars)
[1] 32 11
  1. nrow(): This function retrieves the number of rows in the dataframe.
nrow(mtcars)
[1] 32
  1. ncol(): This function retrieves the number of columns in the dataframe.
ncol(mtcars)
[1] 11
  1. names(): This function retrieves the column names of a dataframe.

colnames(): This function also retrieves the column names of a dataframe.

names(mtcars)
 [1] "mpg"  "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"   "gear"
[11] "carb"
colnames(mtcars)
 [1] "mpg"  "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"   "gear"
[11] "carb"

Accessing data within a dataframe

  1. $
  • In R, the dollar sign $ is a unique operator that lets us retrieve specific columns from a dataframe or elements from a list. [2]
  • For instance, consider the dataframe mtcars. If we wish to fetch the data from the data column mpg (miles per gallon), we would use the code mtcars$mpg. This will yield a vector containing the data from the mpg column. [2] [7]
# Extract the mpg column in mtcars dataframe as a vector
mpg_vector <- mtcars$mpg
# Print the mpg vector
print(mpg_vector)
 [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
[16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
[31] 15.0 21.4
  1. [[ or [

  • The usage of $ is limited since it doesn’t support character substitution for dynamic column access inside functions. In such cases, we can use the double square brackets [[ or single square brackets [.

  • As an example, suppose we have a character string stored in a variable var as var <- "mpg".

  • Here, the code mtcars$var will not return the mpg column.

  • However, if we instead use the code mtcars[[var]] or mtcars[var], we will get the mpg column.

# Let's say we have a variable var
var <- "mpg"
# Now we can access the mpg column in mtcars dataframe using [[
mpg_data1 <- mtcars[[var]]
print(mpg_data1)
 [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
[16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
[31] 15.0 21.4
# Alternatively, we can use [
mpg_data2 <- mtcars[, var]
print(mpg_data2)
 [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
[16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
[31] 15.0 21.4

Data Structures

In R, str() and class() functions are essential for understanding data structures. str() reveals the detailed structure of objects, such as the mtcars dataset, providing a clear view of data composition. The class() function identifies an object’s data type, crucial for applying correct methods in R. It efficiently categorizes objects, like numeric vectors, character vectors, and data frames, facilitating appropriate data manipulation and analysis.

  1. str(): This function displays the internal structure of an R object. [2] [7]
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. class(): This function is used to determine the class or data type of an object. It returns a character vector specifying the class or classes of the object.
x <- c(1, 2, 3)  # Create a numeric vector
class(x)        # Output: "numeric"
[1] "numeric"
y <- "Hello, My name is Sameer Mathur!"  # Create a character vector
class(y)             # Output: "character"
[1] "character"
  • class(x) returns “numeric” because x is a numeric vector. Similarly, class(y) returns “character” because y is a character vector.
z <- data.frame(a = 1:5, b = letters[1:5])  # Create a data frame
class(z) # Output: "data.frame"
[1] "data.frame"
  • class(z) returns “data.frame” because z is a data frame.
sapply(mtcars, class)
      mpg       cyl      disp        hp      drat        wt      qsec        vs 
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
       am      gear      carb 
"numeric" "numeric" "numeric"

Factors

  1. In R, factors are a specific data type used for representing categorical variables or data with discrete levels or categories. They are employed to store data that has a limited number of distinct values, such as “male” or “female,” “red,” “green,” or “blue,” or “low,” “medium,” or “high.” [3]

  2. Factors in R consist of both values and levels. The values represent the actual data, while the levels correspond to the distinct categories or levels within the factor. Factors are particularly useful for statistical analysis as they facilitate the representation and analysis of categorical data efficiently.

  3. For example, in order to change the data type of the am, cyl, vs, and gear variables in the mtcars dataset to factors, we can utilize the factor() function. Here’s an example demonstrating how to achieve this:

# Convert variables to factors
mtcars$am <- factor(mtcars$am)
mtcars$cyl <- factor(mtcars$cyl)
mtcars$vs <- factor(mtcars$vs)
mtcars$gear <- factor(mtcars$gear)

  • The code above applies the factor() function to each variable, thereby converting them to factors. By assigning the result back to the respective variables, we effectively change their data type to factors. This conversion retains the original values while establishing levels based on the distinct values present in each variable.

  • After executing this code, the am, cyl, vs, and gear data variables in the mtcars dataset will be of the factor data type. And we can verify this by re-running the str() function

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  : Factor w/ 2 levels "0","1": 1 1 2 2 1 2 1 2 2 2 ...
 $ am  : Factor w/ 2 levels "0","1": 2 2 2 1 1 1 1 1 1 1 ...
 $ gear: Factor w/ 3 levels "3","4","5": 2 2 2 1 1 1 1 2 2 2 ...
 $ carb: num  4 4 1 1 2 1 4 2 2 4 ...
  1. Levels of a factor variable:

  • The levels() function can be used to extract the distinct levels or categories of a factor variable. [3]

  • For example, after the cyl variable is converted to a factor, the levels() function can be used to extract the distinct levels or categories of that factor. By executing levels(mtcars\$cyl), we see the levels present in the cyl variable. For example, if the cyl variable has been transformed into a factor with levels “4”, “6”, and “8”, the result of levels(mtcars$cyl) will be a character vector displaying these three levels:

levels(mtcars$cyl)
[1] "4" "6" "8"

  • It is important to note that the order of the levels in the output corresponds to their appearance in the original data.

  • To change the base level of a factor variable in R, we can use the relevel() function. This function allows us to reassign a new base level by rearranging the order of the levels in the factor variable.

# Assuming 'cyl' is a factor variable with levels "4", "6", and "8"
mtcars$cyl <- relevel(mtcars$cyl, ref = "6")

  • In the code above, we apply the relevel() function to the cyl variable, specifying ref = "6" to set “6” as the new base level.

  • After executing this code, the levels of the mtcars$cyl factor variable will be reordered, with “6” becoming the new base level. The order of the levels will be “6”, “4”, and “8” instead of the original order.

  • For convenience, we will change the base level back to “4”.

# Assuming `cyl` is a factor variable with levels "4", "6", and "8"
mtcars$cyl <- relevel(mtcars$cyl, ref = "4")
  • droplevels(): This function is helpful for removing unused factor levels. It removes levels from a factor variable that do not appear in the data, reducing unnecessary levels and ensuring that the factor only includes relevant levels.
# Remove unused levels from `cyl`
mtcars$cyl <- droplevels(mtcars$cyl)

# Check the levels of `cyl` after removing unused levels
levels(mtcars$cyl)
[1] "4" "6" "8"

  • We can apply droplevels() to mtcars$cyl to remove any unused levels from the factor variable. This function removes factor levels that are not present in the data. In this case all three levels were present in the data and therefore nothing was removed.

  • cut(): This function allows us to convert a continuous variable into a factor variable by dividing it into intervals or bins. This is useful when we want to group numeric data into categories or levels. [3]

# Create a new factor variable `mpg_category` by cutting `mpg` into intervals
mtcars$mpg_category <- cut(mtcars$mpg, 
                           breaks = c(0, 20, 30, Inf), 
                           labels = c("Low", "Medium", "High"))
# Summarize  the resulting `mpg_category` variable
summary(mtcars$mpg_category)
   Low Medium   High 
    18     10      4

  • In the provided code, a new factor variable called mpg_category is generated based on the mpg (miles per gallon) variable from the mtcars dataset. This is achieved using the cut() function, which segments the mpg values into distinct intervals and assigns appropriate factor labels.

  • The cut() function takes several arguments: mtcars$mpg represents the variable to be divided; breaks specifies the cutoff points for interval creation. Here, we define three intervals: values up to 20, values between 20 and 30 (inclusive), and values greater than 30. Here, the breaks argument is defined as c(0, 20, 30, Inf) to indicate these intervals; labels assigns labels to the resulting factor levels. In this instance, the labels “Low”, “Medium”, and “High” are provided to correspond with the respective intervals.

  • Having demonstrated how to create the new colums mpg_category, we will now drop this column from the dataframe.

# drop the column `mpg_category`
mtcars$mpg_category = NULL

Logical operations

Here are some logical operations functions in R. [4] [7]

  • subset(): This function returns a subset of a data frame according to condition(s).
# Find cars that have cyl = 4 and mpg < 28
subset(mtcars, cyl == 4 & mpg < 22)
               mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Toyota Corona 21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Volvo 142E    21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
# Find cars that have wt > 5 or mpg < 15
subset(mtcars, wt > 5 | mpg < 15)
                     mpg cyl disp  hp drat    wt  qsec vs am gear carb
Duster 360          14.3   8  360 245 3.21 3.570 15.84  0  0    3    4
Cadillac Fleetwood  10.4   8  472 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8  460 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial   14.7   8  440 230 3.23 5.345 17.42  0  0    3    4
Camaro Z28          13.3   8  350 245 3.73 3.840 15.41  0  0    3    4
  • which(): This function returns the indexes of a vector’s members that satisfy a condition.
# Find the indices of rows where mpg > 20
indices <- which(mtcars$mpg > 20)
indices
 [1]  1  2  3  4  8  9 18 19 20 21 26 27 28 32
  • ifelse(): This function applies a logical condition to a vector and returns a new vector with values depending on whether the condition is TRUE or FALSE.
# Create a new column "high_mpg" based on mpg > 20
mtcars$high_mpg <- ifelse(mtcars$mpg > 20, "Yes", "No")
  • Dropping a column: We can drop a column by setting it to NULL. [7]
# Drop the column "high_mpg"
mtcars$high_mpg <- NULL
  • all(): If every element in a vector satisfies a logical criterion, this function returns TRUE; otherwise, it returns FALSE.
# Check if all values in mpg column are greater than 20
all(mtcars$mpg > 20)
[1] FALSE
  • any(): If at least one element in a vector satisfies a logical criterion, this function returns TRUE; otherwise, it returns FALSE.
# Check if any of the values in the mpg column are greater than 20
any(mtcars$mpg > 20)
[1] TRUE
  • Subsetting based on a condition:

The logical expression [] and square bracket notation can be used to subset the mtcars dataset according to one or more conditions. [4] [7]

# Subset mtcars based on mpg > 20
mtcars_subset <- mtcars[mtcars$mpg > 20, ]
mtcars_subset
                mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710     22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Merc 240D      24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230       22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Fiat 128       32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic    30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Toyota Corona  21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Fiat X1-9      27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
  • sort(): This function arranges a vector in an increasing or decreasing sequence.
sort(mtcars$mpg) # increasing order
 [1] 10.4 10.4 13.3 14.3 14.7 15.0 15.2 15.2 15.5 15.8 16.4 17.3 17.8 18.1 18.7
[16] 19.2 19.2 19.7 21.0 21.0 21.4 21.4 21.5 22.8 22.8 24.4 26.0 27.3 30.4 30.4
[31] 32.4 33.9
sort(mtcars$mpg, decreasing = TRUE) # decreasing order
 [1] 33.9 32.4 30.4 30.4 27.3 26.0 24.4 22.8 22.8 21.5 21.4 21.4 21.0 21.0 19.7
[16] 19.2 19.2 18.7 18.1 17.8 17.3 16.4 15.8 15.5 15.2 15.2 15.0 14.7 14.3 13.3
[31] 10.4 10.4
  • order(): This function provides an arrangement which sorts its initial argument into ascending or descending order.
mtcars[order(mtcars$mpg), ] # ascending order
                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
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
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    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 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
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
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  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 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1

For descending order, we can instead write the following code: mtcars[order(-mtcars$mpg), ]

Statistical functions

Statistical functions in R, such as mean(), median(), sd(), var(), cor(), and unique(), provide fundamental tools for data analysis. mean() calculates the arithmetic mean, offering an average value. median() determines the middle value in a dataset, providing a measure of central tendency. sd() calculates the standard deviation, indicating data variability. var() computes variance, measuring data spread. cor() assesses the correlation between variables, essential for understanding relationships in data. Lastly, unique() extracts distinct elements from a vector, useful for identifying variety within datasets. These functions, demonstrated using the mtcars dataset, are key in statistical analysis and data exploration. [5] [7]

mean(mtcars$mpg)
[1] 20.09062
median(mtcars$mpg)
[1] 19.2
sd(mtcars$mpg)
[1] 6.026948
var(mtcars$mpg)
[1] 36.3241
cor(mtcars$mpg, mtcars$wt)
[1] -0.8676594
unique(mtcars$mpg)
 [1] 21.0 22.8 21.4 18.7 18.1 14.3 24.4 19.2 17.8 16.4 17.3 15.2 10.4 14.7 32.4
[16] 30.4 33.9 21.5 15.5 13.3 27.3 26.0 15.8 19.7 15.0

Summarizing a dataframe

Summarizing a continuous data column

  1. summary(): This function is a convenient tool to generate basic descriptive statistics for our dataset. It provides a succinct snapshot of the distribution characteristics of our data. [5] [7]
summary(mtcars$mpg)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  10.40   15.43   19.20   20.09   22.80   33.90
  1. When applied to a vector or a specific column in a dataframe, it generates the following:

  • Min: This represents the smallest recorded value in the mpg column.

  • 1st Qu: This indicates the first quartile or the 25th percentile of the mpg column. It implies that 25% of all mpg values fall below this threshold.

  • Median: This value signifies the median or the middle value of the mpg column, also known as the 50th percentile. Half of the mpg values are less than this value.

  • Mean: This denotes the average value of the mpg column.

  • 3rd Qu: This represents the third quartile or the 75th percentile of the mpg column. It shows that 75% of all mpg values are less than this value.

  • Max: This indicates the highest value observed in the mpg column.

  • When we use summary(mtcars$mpg), it returns these six statistics for the mpg (miles per gallon) column in the mtcars dataset.

  • When used with an entire dataframe, it applies to each column individually and provides a quick overview of the data.

Summarizing a categorical data column

summary(mtcars$cyl)
 4  6  8 
11  7 14
  • The output of summary(mtcars$cyl) displays the frequency distribution of the levels within the cyl factor variable. It shows the count or frequency of each level, which in this case are “4”, “6”, and “8”. The summary will provide a concise overview of the distribution of these levels within the dataset.
summary(mtcars)
      mpg        cyl         disp             hp             drat      
 Min.   :10.40   4:11   Min.   : 71.1   Min.   : 52.0   Min.   :2.760  
 1st Qu.:15.43   6: 7   1st Qu.:120.8   1st Qu.: 96.5   1st Qu.:3.080  
 Median :19.20   8:14   Median :196.3   Median :123.0   Median :3.695  
 Mean   :20.09          Mean   :230.7   Mean   :146.7   Mean   :3.597  
 3rd Qu.:22.80          3rd Qu.:326.0   3rd Qu.:180.0   3rd Qu.:3.920  
 Max.   :33.90          Max.   :472.0   Max.   :335.0   Max.   :4.930  
       wt             qsec       vs     am     gear        carb      
 Min.   :1.513   Min.   :14.50   0:18   0:19   3:15   Min.   :1.000  
 1st Qu.:2.581   1st Qu.:16.89   1:14   1:13   4:12   1st Qu.:2.000  
 Median :3.325   Median :17.71                 5: 5   Median :2.000  
 Mean   :3.217   Mean   :17.85                        Mean   :2.812  
 3rd Qu.:3.610   3rd Qu.:18.90                        3rd Qu.:4.000  
 Max.   :5.424   Max.   :22.90                        Max.   :8.000

Creating new functions in R

  • We illustrate how to create a custom function in R that computes the mean of any given numeric column in the mtcars dataframe [6] [7]
# Function creation
compute_average <- function(df, column) {
  # Compute the average of the specified column
  average_val <- mean(df[[column]], na.rm = TRUE)
  
  # Return the computed average
  return(average_val)
}
# Utilize the created function
average_mpg <- compute_average(mtcars, "mpg")
print(average_mpg)
[1] 20.09062
average_hp <- compute_average(mtcars, "hp")
print(average_hp)
[1] 146.6875

  • In the above code, compute_average is a custom function which takes two arguments: a dataframe (df) and a column name as a string. The function computes the mean of the specified column in the provided dataframe, with na.rm = TRUE ensuring that NA values (if any) are removed before the mean calculation.

  • After defining the function, we utilize it to calculate the average values of the mpg and hp columns in the mtcars dataframe. These computed averages are then printed.

Summary of Chapter 5 – Exploring Dataframes

Chapter 5 offers an in-depth exploration of dataframes in R, emphasizing the mtcars dataset. It begins by introducing essential functions for examining dataframes like View(), head(), tail(), and dim(), progressing to more complex data accessing methods using $ and square brackets. The chapter also covers data structures, emphasizing factors in R and their relevance in statistical modeling. Logical operations in R are explored, highlighting functions like subset(), which(), and ifelse(). Statistical analysis is addressed through functions like mean(), median(), and cor(). The chapter culminates with a focus on custom function creation, enhancing R’s functionality for specific tasks.

References

[1] Krasser, R. (2023, October 11). Explore mtcars. The Comprehensive R Archive Network. Retrieved from https://cran.r-project.org/web/packages/explore/vignettes/explore_mtcars.html

RDocumentation. (n.d.). mtcars: Motor Trend Car Road Tests. Retrieved from https://www.rdocumentation.org/packages/datasets/versions/3.6.2/topics/mtcars

[2] W3Schools. (n.d.). R Data Frames. Retrieved from https://www.w3schools.com/r/r_data_frames.asp

RDocumentation. (n.d.). data.frame function. Retrieved from https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/data.frame

Programiz. (n.d.). R Data Frame (with Examples). Retrieved from https://www.programiz.com/r-programming/data-frame

Dataquest. (2023). How to Create a Dataframe in R with 30 Code Examples. Retrieved from https://www.dataquest.io/blog/tutorial-dataframe-in-r

[3] University of California, Berkeley. (n.d.). Factors in R. Retrieved from https://www.stat.berkeley.edu/~s133/factors.html

[4] R Core Team. (n.d.). subset: Subsetting Vectors, Matrices, and Data Frames. Retrieved from https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/subset

R Core Team. (n.d.). ifelse: Conditional Element Selection. Retrieved from https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/ifelse

[5] R Core Team. (n.d.). mean: Arithmetic Mean. Retrieved from https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/mean

R Core Team. (n.d.). median: Median Value. Retrieved from https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/median

R Core Team. (n.d.). sd: Standard Deviation. Retrieved from https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/sd

R Core Team. (n.d.). var: Variance. Retrieved from https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/var

R Core Team. (n.d.). cor: Correlation. Retrieved from https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/cor

R Core Team. (n.d.). summary: Object Summaries. Retrieved from https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/summary

[6] R Core Team. (n.d.). function: Function Definition. Retrieved from https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/function

Basic R Programming

[7] Chambers, J. M. (2008). Software for Data Analysis: Programming with R (Vol. 2, No. 1). New York: Springer.

Crawley, M. J. (2012). The R Book. John Wiley & Sons.

Gardener, M. (2012). Beginning R: The Statistical Programming Language. John Wiley & Sons.

Grolemund, G. (2014). Hands-On Programming with R: Write Your Own Functions and Simulations. O’Reilly Media, Inc.

Kabacoff, R. (2022). R in Action: Data Analysis and Graphics with R and Tidyverse. Simon and Schuster.

Peng, R. D. (2016). R Programming for Data Science (pp. 86-181). Victoria, BC, Canada: Leanpub.

R Core Team. (2020). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from https://www.R-project.org/.

Tippmann, S. (2015). Programming Tools: Adventures with R. Nature, 517(7532), 109-110.

Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for Data Science. O’Reilly Media, Inc.