Section 1.6 Data Frames
Vectors are an essential way to store information, but specifically, one type of information. When we tried to mix different types of data in one vector, we saw that R did not like that, and considered everything as part of a character string. So, what if we want to be able to have different types of data all in one place?
This is exactly where data frames come into play. Data frames are the most common way to view data, with maybe the most prominent examples being Microsoft Excel.
Subsection 1.6.1 Creating Your Own Data Frame
Imagine you have three vectors and you want to turn them into a data frame, with each of the three vectors representing a different column in your data frame. In R, once the vectors have been created, you are able to do exactly this using
data.frame().
# Build a tiny class roster data frame
names_vec <- c("John", "Bob", "Carmen", "Sarah")
ages_vec <- c(20, 22, 21, 23)
major_vec <- c("Psych", "Econ", "Psych", "CS")
roster <- data.frame(
name = names_vec,
age = ages_vec,
major = major_vec,
stringsAsFactors = FALSE)
roster
class(roster) # Class is now data.frame
name age major 1 John 20 Psych 2 Bob 22 Econ 3 Carmen 21 Psych 4 Sarah 23 CS [1] "data.frame"
The data frame
roster was created using three different vectors: names_vec, ages_vec, and major_vec. Now, we have an example of both numeric and character values together.
Subsection 1.6.2 Functions to Explore Datasets
When exploring datasets, there are some great functions to do this:
-
head(): Returns the first part of data. -
tail(): Returns the last part of data. -
str(): Displays the structure of the data. -
summary(): Provides a summary of summary of your data. -
View(): Opens your dataset as a new window in R. -
colnames(): Returns the names of all of your columns. -
table(): Returns a frequency table. -
nrow(): Returns the total number of rows. -
ncol(): Returns the total number of columns.
Thankfully, R comes preinstalled with datasets to explore and practice R with. Below we will test these functions out using the dataset iris.
# iris
head(iris) # By default returns 6 rows
tail(iris) # By default returns 6 rows
str(iris) # Provides the structure of each column
summary(iris) # Provides statistics for each column
colnames(iris) # Provides the column names of your data
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
145 6.7 3.3 5.7 2.5 virginica
146 6.7 3.0 5.2 2.3 virginica
147 6.3 2.5 5.0 1.9 virginica
148 6.5 3.0 5.2 2.0 virginica
149 6.2 3.4 5.4 2.3 virginica
150 5.9 3.0 5.1 1.8 virginica
'data.frame': 150 obs. of 5 variables:
$ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
$ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
$ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
$ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
$ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
Sepal.Length Sepal.Width Petal.Length Petal.Width
Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
Median :5.800 Median :3.000 Median :4.350 Median :1.300
Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
Species
setosa :50
versicolor:50
virginica :50
[1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
These are by far not the only ones that can or should be used. Of course, experiment with other commands to see what works best for your style and data.
Subsection 1.6.3 Working With Columns Within Data Frames
When we created the roster data frame, it was three columns: name, age, and major. Let’s say we wanted to find the median of age in our roster data frame. How can we find the median of just the age column?
In R, to select specific columns in data frames, we can use $. The formula to follow is:
dataset_name$column_name
Below is an example of finding the median of the age column in the roster dataframe.
median(roster$age)
[1] 21.5
Just like in real life, the dollar sign ($) is very powerful. Now only can we use it to call columns from a dataset, but we can also use it to make columns in a dataset that do not exist yet. The formula to follow is:
dataset_name$new_column_name <- new_data
Below are examples of creating a new column in a data frame using the $.
# Having one value for each row of data
roster$year <- 2025
# Having a different value for each row of data
roster$minor <- c("Chemistry", "Biology", "History", "Art")
roster
name age major year minor 1 John 20 Psych 2025 Chemistry 2 Bob 22 Econ 2025 Biology 3 Carmen 21 Psych 2025 History 4 Sarah 23 CS 2025 Art
