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Programming languages

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Programming languages are important tools in data visualization since they can be used for data manipulation. The programming languages are known to have visualization capabilities. The languages are powerful tools used to generate graphics. At this point, data visualization goes beyond charts graphs and Microsoft excel sheet. These tools come with more functionality when compared to the excel sheets. The tools help the creator to perform advanced analysis to data as well as keeping track of the data source. The most used programming languages are Python and R. python is known to have two libraries for data visualization that is Seaborn and Matplotlib while R contains four graphic systems that are lattice graphics, ggplot2 and base graphics (Ozgur, Colliau, Rogers, Hughes & Myer-Tyson, 2017). Both of them are directed toward data analytics. Python is a general-purpose language that can be read with a syntax while R is developed by data analytics and it involves their specific language. Python and R can perform the same task. The python codes are more robust and easier to understand compared to R.

Accessibility and replicability are easier when one is utilizing the Python language. Data analytics and statistics are operations that can be performed by R while Python is involved in production and deployment. The primary users for R language are R&D and Scholar while the primary users of python are programmers and developers (Rivers & Koedinger, 2017). Both Python and R languages are open-source thus used in various data analysis areas.

R language coding

f <- read.csv(“~/Programs/difui/American States.GRCh38.85.gff3.gz”,

header = TRUE,

sep = “\t”

col.names = c(‘seqid’, ‘source’, ‘type’, ‘start’, ‘end’, ‘score’, ‘strand’, ‘phase’, ‘attributes’),

comment.char = “#”)

head(df)

 

Python language coding

df = pd.read_csv(American States.GRCh38.85.gff3.gz’,

compression = ‘gzip’,

sep = ‘\t’,

comment = ‘#’,

Texas = True,

header = None,

names = [‘seqid’, ‘source’, ‘type’, ‘start’, ‘end’, ‘score’, ‘strand’, ‘phase’, ‘attributes’])

df.head()

I can use python language since it is a general-purpose language. I will be able to analyze data using python. Can read and learn using python. I have been able to predict business success and business failure due to disruptions. Python can describe what happened in case of a disaster. I can evaluate metrics and understand trends hence giving my director the possible outcomes as well as helping in decision making. I can use Matplotlib to create visualizations.

 

References

Ozgur, C., Colliau, T., Rogers, G., Hughes, Z., & Myer-Tyson, B. (2017). MatLab vs. Python vs. R. Journal of Data Science15(3), 355-372.

Rivers, K., & Koedinger, K. R. (2017). Data-driven hint generation in vast solution spaces: a self-improving python programming tutor. International Journal of Artificial Intelligence in Education27(1), 37-64.

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