Zara  Bryant

Zara Bryant


Importing SAS / SPSS data into R when the variables are defined in another file

I’m trying to import this data into R.

I know I need the survey package, but these files are odd.

Anyone know what to do?


What is GEEK

Buddha Community

Will SAS Language Continue To Hold Ground In Data Science?

Technology has a shelf life of a banana’: these are the famous words by Scott McNealy, the co-founder of Sun Microsystems. This is specifically true for SAS programming language, which has been an important software for data scientists around the world for quite some time now. However, with new inventions, many believe that SAS seems to be trailing behind. How true this is and what lies ahead for the future of SAS language in data science?

#opinions #python for data science #r for data science #relevance in data science #sas #sas analytics #sas business analytics leader #sas data science #sas training

Siphiwe  Nair

Siphiwe Nair


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink


Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.


As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Angela  Dickens

Angela Dickens


Data Cleaning in R for Data Science

A data scientist/analyst in the making needs to format and clean data before being able to perform any kind of exploratory data analysis. Because when you have raw data, it has numerous problems that need fixing.

So when we say we are cleaning data into a tidy data set to be used for analysis later, we are actually (among many other things):

1. Removing duplicate values

2. Removing null values

3. Changing column names to readable, understandable, formatted names

4. Removing commas from numeric values i.e. (1,000,657 to 1000657)

5. Converting data types into their appropriate types for analysis

This article is based upon a brief course project I have recently completed in my Data Science Specialization, focused on retrieving raw data, combining it into one dataset and getting it ready for later analysis (not covered in this article). The language opted is R using Rstudio.

The Experiment:

The experiment conducted here is retrieved from UCI Machine Learning Repository where a group of 30 volunteers (age bracket of 19–48 years) performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a Samsung Galaxy S smartphone. The data collected from the embedded accelerometers was divided into testing and trained data. More information regarding the experiment can be found at this link.

Step 1: Retrieving Data from URL

The first step required is to obtain the data. Often, to avoid the headache of manually downloading thousands of files, they are downloaded using small code snippets. Since this was a zipped folder, I used the following commands to get started.

download.file(“", destfile = “files”, method = “curl”, mode = “wb”)

The download.file functions takes the URL as the first argument and saves it on your local PC in the name you assign to destfile.


This function just unzips the zipped folder.

Step 2: Reading the files into R

features <- read.table(“UCI HAR Dataset/features.txt”, col.names = c(“serial”, “Functions”))

activities <- read.table(“UCI HAR Dataset/activity_labels.txt”, col.names = c(“serial”, “Activity”))
x_test <- read.table(“UCI HAR Dataset/test/X_test.txt”, col.names = features$Functions)
y_test <- read.table(“UCI HAR Dataset/test/y_test.txt”, col.names = “serial”)
subject_test <- read.table(“UCI HAR Dataset/test/subject_test.txt”, col.names = “subject”)
subject_train <- read.table(“UCI HAR Dataset/train/subject_train.txt”, col.names = “subject”)
x_train <- read.table(“UCI HAR Dataset/train/X_train.txt”, col.names = features$Functions)
y_train <- read.table(“UCI HAR Dataset/train/y_train.txt”, col.names = “serial”)

Note: It might be difficult to understand at first what the data means and what column names to use, but after a while you’ll start making sense. For example, it is important to note that the x_test and x_train files are values that refer to the columns in features.txt (hence I’ve linked them up using features$functions)

Making sense of the Data:

After being able to actually look at the files, I found out they were a mess of several files with hundreds of just column names in one .txt file, others having the row values and one having the activity labels. After spending hours of trying to understand the logical representation of data, I was able to visualize it something as follows:

This clearly implies two things:

  1. I had to merge the training and test sets by row binding them

  2. I had to merge the different attributes of the subjects by column binding them.

This is where step 3 comes into play.

Step 3: Merging the tables intelligently

First, I performed the rbind() function to make one huge dataset.

binded_x <- rbind(x_test, x_train)

binded_y <- rbind(y_test, y_train)
subject <- rbind(subject_test, subject_train)
Next, I used the cbind() function to complete attaching the columns as well.
raw_data_combined <- cbind(subject, binded_x, binded_y)

#r #data-science-tools #data-analytics #data-science #data-cleaning #data analysis

Tia  Gottlieb

Tia Gottlieb


A Beginner’s Guide To Cleaning Data In R — Part 1

My journey into the vast world of data has been a fun and enthralling ride. I have been glued to my courses, waiting to finish one so I can proceed to the next. After completing introductory courses, I made my way over to data cleaning. It is no secret that most of the effort in any data science project goes into cleaning the data set and tidying it up for analysis. Therefore, it is crucial to have substantial knowledge about this topic.

Firstly, to understand the need for clean data, we need to look at the workflow for a typical data science project. Data is first accessed, followed by manipulation and analysis of the data. Afterward, insights are extracted, and finally, visualized and reported.

Accessing data, followed by analysis, insight generation and visualisation.

Typical Project Workflow

Errors and mistakes in data, if present, could end up generating errors throughout the entire workflow. Ultimately, the insights generated that are used to make critical business decisions are incorrect, which may lead to monetary and business losses. Thus, if untidy data is not tackled and corrected in the first step, the compounding effect can be immense.

This guide will serve as a quick onboarding tool for data cleaning by compiling all the necessary functions and actions that should be taken. I will briefly describe three types of common data errors and then explain how these can be identified in data sets and corrected. I will also be introducing some powerful cleaning and manipulation libraries including dplyr, stringr, and assertive. These can be installed by simply writing the following code in RStudio:


1. Incorrect Data Type

When data is imported, a possibility exists that RStudio incorrectly interprets a data column type, or the data column was wrongly labeled during extraction. For example, a common error is when numeric data containing numbers are improperly identified and labeled as a character type.

a) Identification

Firstly, to identify incorrect data type errors, the glimpse function is used to check the data types of all columns. The glimpse function is part of the **dplyr **package which needs to be installed before glimpse can be used. Glimpse will return all the columns with their respective data types.


Another form of logical checks includes the is function. The is function can be used for each data type and will return with a logical output (true/false). I have only mentioned the common is functions, but it can be used for all data types. If a numeric column is an argument for the is.numeric function, the output will be true, while if a character column is an argument for the is.numeric function the output will be false.


b) Correction

After all the incorrect data type columns have been identified, they can simply be converted to the correct data type by using the as functions. For example, if a numeric data type has been incorrectly imported as a character data type, the as.numeric function will convert it to numeric data type.


#data-analysis #data-scientist #data #data-cleaning #r #data analysis