Dataframe
In R, a dataframe is a fundamental data structure used for storing and manipulating tabular data. It is similar to a table in a relational database or a spreadsheet in other software. Dataframes are commonly used in data analysis, data manipulation, and statistical modelling tasks.
A dataframe is a collection of vectors of equal length, where each vector represents a column of the dataframe. The vectors can be of different data types, such as numeric, character, factor, or logical. This flexibility allows dataframes to handle diverse types of data.
Creating a Dataframe
- Create vectors that contain the information you want inside your dataframe. Bear in mind that the name of the vector will be the name of the variables in your dataframe and all the information inside each vector will be the observations.
- Use the
data.frame()function and use the vectors as arguments of the function. - Assign a name to the dataframe.
# Definition of vectors
progestins <- c("Drospirenone", "Cyproteronacetate",
"Chlormadinonacetate", "Levonorgestrel", "Gestodene", "Dienogest",
"Norgestimate", "Desogestrel", "Danozol")
derivative <- c("progesterone", "progesterone",
"progesterone", "nortestosterone", "nortestosterone", "nortestosterone",
"nortestosterone", "nortestosterone", "testosterone")
pro_gesteronic <- c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE)
pro_androgenic <- c(FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, TRUE)
anti_mineralocorticoidic <- c(TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE)
half_life_hours <- c(32.0, 45.0, 34.0, 26.0, 17.0, 10.0, 27.0, 1.5, 10.0)
# Create a dataframe from the vectors
progestins_df <- data.frame (progestins, derivative,
pro_gesteronic, pro_androgenic, anti_mineralocorticoidic, half_life_hours)
progestins_df
The dataframe will look like this:
progestins derivative pro_gesteronic pro_androgenic anti_mineralocorticoidic half_life_hours
1 Drospirenone progesterone TRUE FALSE TRUE 32.0
2 Cyproteronacetate progesterone TRUE FALSE FALSE 45.0
3 Chlormadinonacetate progesterone TRUE FALSE FALSE 34.0
4 Levonorgestrel nortestosterone TRUE TRUE FALSE 26.0
5 Gestodene nortestosterone TRUE TRUE TRUE 17.0
6 Dienogest nortestosterone TRUE FALSE FALSE 10.0
7 Norgestimate nortestosterone TRUE TRUE FALSE 27.0
8 Desogestrel nortestosterone TRUE TRUE FALSE 1.5
9 Danozol testosterone TRUE TRUE FALSE 10.0
Getting an Overview of your Data
Dataframes are often used to hold extremely large datasets.
To make it easier to develop an understanding of the data presented, you can choose to only show the first few observations of the dataframe.
You can do this using the head() function:
# use the head function to display the upper part of the dataframe
head(progestins_df)
progestins derivative pro_gesteronic pro_androgenic anti_mineralocorticoidic half_life_hours
1 Drospirenone progesterone TRUE FALSE TRUE 32.0
2 Cyproteronacetate progesterone TRUE FALSE FALSE 45.0
3 Chlormadinonacetate progesterone TRUE FALSE FALSE 34.0
4 Levonorgestrel nortestosterone TRUE TRUE FALSE 26.0
5 Gestodene nortestosterone TRUE TRUE TRUE 17.0
6 Dienogest nortestosterone TRUE FALSE FALSE 10.0
On the other hand, if you only want to see the last data sets in a dataframe, use the tail() function:
tail(progestins_df)
progestins derivative pro_gesteronic pro_androgenic anti_mineralocorticoidic half_life_hours
4 Levonorgestrel nortestosterone TRUE TRUE FALSE 26.0
5 Gestodene nortestosterone TRUE TRUE TRUE 17.0
6 Dienogest nortestosterone TRUE FALSE FALSE 10.0
7 Norgestimate nortestosterone TRUE TRUE FALSE 27.0
8 Desogestrel nortestosterone TRUE TRUE FALSE 1.5
9 Danozol testosterone TRUE TRUE FALSE 10.0
Both functions will also print out the column names of the dataframe.
- Use the
str()function to inspect the structure of a dataframe. - This function displays a number of important basic information about the dataframe in question:
- the number of observations: how many rows are there
- the number of variables: how many columns are there.
- a list of all the variable names: the names of the columns.
- the data type of each column
- The first few observations of each variable.
# Investigate the structure of progestins_df
str(progestins_df)
'data.frame': 9 obs. of 6 variables:
$ progestins : chr "Drospirenone" "Cyproteronacetate" "Chlormadinonacetate" "Levonorgestrel" ...
$ derivative : chr "progesterone" "progesterone" "progesterone" "nortestosterone" ...
$ pro_gesteronic : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
$ pro_androgenic : logi FALSE FALSE FALSE TRUE TRUE FALSE ...
$ anti_mineralocorticoidic: logi TRUE FALSE FALSE FALSE TRUE FALSE ...
$ half_life_hours : num 32 45 34 26 17 10 27 1.5 10
Subsetting Dataframes
To select elements from a dataframe, use square brackets [ ]. Just like with matrixes, you can use a comma to indicate the rows and columns you want to select from. Number before the comma will select from rows and numbers after the comma will select from columns.
For example:
the_dataframe[1,2]selects the value of the first row and second column inthe_dataframe.the_dataframe[1:3,2:4]selects rows 1, 2, 3 and columns 2, 3, 4 inthe_dataframe.- If you want to select all elements of a row or a column:
the_dataframe[,1]selects all elements of the first column.the_dataframe[1,]selects all elements of the first row.
- Sometimes you may not know the index of the column; instead you can also select elements by using the name of the variable:
progestins_df[1:3,"half_life_hours"]
If you want to select an entire column (so the data of one entire variable), you can also use $, e.g. progestins_df$half_life_hours.
It will yield the same result:
> progestins_df$half_life_hours
[1] 32.0 45.0 34.0 26.0 17.0 10.0 27.0 1.5 10.0
Subset Function
The subset() function is used to select out your data set based on certain conditions.
For instance, let's assume we want to access all information about pro-androgenic compounds from the previously generated dataframe.
You could do this by using the following code:
subset(progestins_df, subset = pro_androgenic)
You would get the following result:
progestins derivative pro_gesteronic pro_androgenic anti_mineralocorticoidic half_life_hours
4 Levonorgestrel nortestosterone TRUE TRUE FALSE 26.0
5 Gestodene nortestosterone TRUE TRUE TRUE 17.0
7 Norgestimate nortestosterone TRUE TRUE FALSE 27.0
8 Desogestrel nortestosterone TRUE TRUE FALSE 1.5
9 Danozol testosterone TRUE TRUE FALSE
order() Function
order() is a function that gives you the ranked position of each element when it is applied on a variable, such as a vector for example:
number_vector <- c(19, 3, 84)
order(number_vector)
[1] 2 1 3
The order() function can also be used to rank elements in data structures from smallest to largest:
number_vector[order(number_vector)]
[1] 3 19 84
We can apply this to the dataframe created earlier. For instance, we can sort the compounds according to their half-life using the code below:
# Use order() to create the ranking order and assign it to the ranking vector
ranking <- order(progestins_df$half_life_hours)
# Use the ranking vector to sort progestins_df
progestins_df[ranking,]
progestins derivative pro_gesteronic pro_androgenic anti_mineralocorticoidic half_life_hours
8 Desogestrel nortestosterone TRUE TRUE FALSE 1.5
6 Dienogest nortestosterone TRUE FALSE FALSE 10.0
9 Danozol testosterone TRUE TRUE FALSE 10.0
5 Gestodene nortestosterone TRUE TRUE TRUE 17.0
4 Levonorgestrel nortestosterone TRUE TRUE FALSE 26.0
7 Norgestimate nortestosterone TRUE TRUE FALSE 27.0
1 Drospirenone progesterone TRUE FALSE TRUE 32.0
3 Chlormadinonacetate progesterone TRUE FALSE FALSE 34.0
2 Cyproteronacetate progesterone TRUE FALSE FALSE 45.0