2_Dataframes.Rmd

---
title: "Introduction to Dataframes in R"
author: "Dhiraj Saharia"
date: "06/02/2020"
output:
  html_document:
    df_print: paged
    theme: flatly
    toc: yes
    number_sections: true
  pdf_document: default
---

* Dataframes Basics -
Generic data objects of R, used to store tabular data.
```{r}
name = c("dhiraj", "Steve", "Dave")
age = c(21, 25, 28)
subjects = c("CAO", "DS", "DAA")
df = data.frame(name, age, subjects)
print(df)
```
* Accessing Rows and Columns -
  * df[v1, v2] refers to row v1 and column v2, Number or String. Can also be arrays.
  * df[v2] -  Just refers to column v2.
  * Index can be `c(1,3) or 1:3` also.
  
* Subset -
`subset()` extracts subset of data based on conditions.
```{r}
name = c("dhiraj", "Steve", "Dave")
age = c(21, 25, 28)
subjects = c("CAO", "DS", "DAA")
df = data.frame(name, age, subjects)
df2 = subset(df, name=="dhiraj" | age > 25)
print(df2)
```
* Edit Dataframes -
```{r}
df[[2]][2] = 30
print(df)
```
Can also use `edit(df)` command.

* Adding extra rows and columns -
Use `rbin()` to add extra rows and `cbind()` for extra columns.
```{r}
df = cbind(df, degree = c("BTECH", "CA", "MA"))
df = rbind(df, data.frame(name="James", age = 32, subjects="OOP", degree="MSc"))
print(df)
```

* Deleting rows and columns -
  * Access the row or column and then place a `-` sign to delete it.
  * Conditional Deletion - 

* Manipulating Rows - The factor issue
  * When character columns are created in the data.frame, they become `factors`.
  * Factor variables are those where the character column is split into categories or factor levels.
```{r}
df[2,1] = "Kirk" # This produces <NA> value
```
  * To solve the factor issue, use `stringAsFactors=F` when creating the dataframe.