3. Dataframes.Rmd

---
title: "Dataframes"
author: Daphne Janelyn L. Go^[De La Salle University, Manila, Philippines, daphne_janelyn_go@dlsu.edu.ph]
output: html_notebook
---

In data analysis, information is often structured in organized in tables that have a certain number of rows and columns like an Excel spreadsheet or relational database table.

R data frames are a type of data structure designed to hold such tabular data. A data frame consists of a number of rows and columns, with each column representing some variable or feature of the data and each row representing a record.

A data frame is similar to a matrix in that it is a 2-dimensional data structure but unlike a matrix, different columns can hold data of different types. It is composed of many lists.

You can create a new data frame by passing vectors of the same length to the data.frame() function.

```{r}
a <- c(1, 2, 3, 4, 5)
b <- c("R", "Workshop", "SCOMB", "bioinfom", "biology")
c <- c(TRUE, FALSE, TRUE, TRUE, FALSE)

# Create a new data frame
my_frame <- data.frame(a, b, c)

my_frame
```

You can also change your column and row names.

```{r}
# Change column names
colnames(my_frame) <- c("c1", "c2", "c3")

# Add row names
rownames(my_frame) <- c("r1", "r2", "r3", "r4", "r5")
my_frame
```

For reference, we will be loading a dataset on phage-host interaction to illustrate the use of dataframes in reading dataset files.

```{r}
dataset <- read.delim("phages.tsv")
```

**Summarizing Data Frames**

Data frames support many of the summary functions that apply to matrices and lists.

The str() function gives an overview of the structure of the Data Frame. It is useful to check the structure first, since running a full summary can take a while if you are working with a lot of data.

The summary() function gives summary statistics for each variable in the data frame.

```{r}
str(dataset) # get a sense of the range of values in your dataset
summary(dataset)
```

Data frames support a few other basic summary operations.

```{r}
dim(dataset)
nrow(dataset)
ncol(dataset)
```

If a data frame is large, you won't want to try to print the entire frame to the screen. You can look at a few rows at the beginning or end of a data frame using the head() and tail() functions respectively:

```{r}
head(dataset, 5)

tail(dataset, 5)
```

**Dataframe Indexing**

Since data frame are lists under the hood, where each list object is a column, they support the indexing operations that apply to lists.

Data frames also support matrix-like indexing by using a single square bracket with a comma separating the index value for the row and column. Matrix indexing allows you get values by row or specific values within the data frame.

****You can also delete columns in a data frame by assigning them a value of NULL.***

```{r}
head(dataset$Molecule)
head(dataset[["Molecule"]])
head(dataset[[7]])

# Get the value at row 2 column 6
dataset[2, 6]

# Get the second row
dataset[2, ]

# Get the 6th column
dataset[, 6]

# Get a column by using its name
dataset[, "Description"]
```

**Filtering of Dataframes**

You can also use the subset() function to create data frame subsets based on logical statements. subset() takes the data frame as the first argument and then a logical statement as the second argument create a subset.

```{r}
subset(dataset, (dataset$molGC.... > 50))
```