charanhu

1
The R language is a dialect of which of the following programming languages?
S

2
The definition of free software consists of four freedoms (freedoms 0 through 3). Which of the following is NOT one of the freedoms that are part of the definition?
The freedom to prevent users from using the software for undesirable purposes.

3
In R the following are all atomic data types EXCEPT 

charanhu

1
Take a look at the 'iris' dataset that comes with R. The data can be loaded with the code:

library(datasets)
data(iris)

A description of the dataset can be found by running

?iris

charanhu

Selection: 6


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| In this lesson, we'll see how to extract elements from a vector based on some
| conditions that we specify.

...

charanhu

"%p%" <- function(left, right){
  paste(left, right)
}

charanhu

R Programming Project 1

github repo for rest of specialization: Data Science Coursera

For this first programming assignment you will write three functions that are meant to interact with dataset that accompanies this assignment. The dataset is contained in a zip file specdata.zip that you can download from the Coursera web site.

Although this is a programming assignment, you will be assessed using a separate quiz.

The zip file containing the data can be downloaded here: specdata.zip [2.4MB]

charanhu

election: 12


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| Whenever you're working with a new dataset, the first thing you should do is look at it! What is the format of the data? What are the dimensions? What are the
| variable names? How are the variables stored? Are there missing data? Are there any flaws in the data?

...

charanhu

Selection: 15


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| One of the greatest strengths of R, relative to other programming languages, is the ease with which we can create publication-quality graphics. In this lesson,
| you'll learn about base graphics in R.

charanhu

Getting and Cleaning Data Quiz 3 (JHU) Coursera

Question 1

The American Community Survey distributes downloadable data about United States communities. Download the 2006 microdata survey about housing for the state of Idaho using download.file() from here:

https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2Fss06hid.csv

and load the data into R. The code book, describing the variable names is here:

charanhu

# credits : https://www.kaggle.com/julian3833/1-quick-start-read-csv-and-flatten-json-fields

def load_df(csv_path, nrows=None):
    '''Just loads the data and flattens the json fields. returns the data frames by converting json files into data frames'''
    
    JSON_COLUMNS = ['device', 'geoNetwork', 'totals', 'trafficSource'] # we are definig a list of json column names

    df = pd.read_csv(csv_path,
                     converters={column: json.loads for column in JSON_COLUMNS}, # It will create JSON object for every json column
                     dtype={'fullVisitorId': 'str'},  # we are considering 'fullvisitor id as string'

charanhu

#include <iostream>
using namespace std;

int main() {
  cout << "Hello, I'm Charan H U!";
  return 0;
}