The document discusses exploratory data analysis techniques in R, including various plotting systems and graph types. It provides code examples for creating boxplots, histograms, bar plots, and scatter plots in Base, Lattice, and ggplot2. It also covers downloading data, transforming data, adding scales and themes, and creating faceted plots. The final challenge involves creating a boxplot with rectangles to represent regions and jittered points to show trends over years.
Slide show for the webinar on "Spatial Data Science with R" organized for the GeoDevelopers.org community. The video of the webinar and all the related materials including source code and sample data can be downloaded from this link: http://amsantac.co/blog/en/2016/08/07/spatial-data-science-r.html
In this webinar I talked about Data Science in the context of its application to spatial data and explained how we can use the R language for the analysis of geographic information within the different stages of a data science workflow, from the import and processing of spatial data to visualization and publication of results.
A survey of data visualization functions and packages in R. In particular, I discuss three approaches for data visualization in R: (i) the built-in base graphics functions, (ii) the ggplot2 package, and (iii) the lattice package. I also discuss some methods for visualizing large data sets.
R visualization: ggplot2, googlevis, plotly, igraph OverviewOlga Scrivner
In this workshop you will learn about 4 R packages to perform data visualization: ggplot2, googlevis, plotly and igraph. You will learn about their strengths and weaknesses. Code snippets are provides.
Slide show for the webinar on "Spatial Data Science with R" organized for the GeoDevelopers.org community. The video of the webinar and all the related materials including source code and sample data can be downloaded from this link: http://amsantac.co/blog/en/2016/08/07/spatial-data-science-r.html
In this webinar I talked about Data Science in the context of its application to spatial data and explained how we can use the R language for the analysis of geographic information within the different stages of a data science workflow, from the import and processing of spatial data to visualization and publication of results.
A survey of data visualization functions and packages in R. In particular, I discuss three approaches for data visualization in R: (i) the built-in base graphics functions, (ii) the ggplot2 package, and (iii) the lattice package. I also discuss some methods for visualizing large data sets.
R visualization: ggplot2, googlevis, plotly, igraph OverviewOlga Scrivner
In this workshop you will learn about 4 R packages to perform data visualization: ggplot2, googlevis, plotly and igraph. You will learn about their strengths and weaknesses. Code snippets are provides.
A chapter describing the use and application of exploratory factor analysis using principal axis factoring with oblique rotation.
Provides a step by step guide to exploratory factor analysis using SPSS.
3 Things Every Sales Team Needs to Be Thinking About in 2017Drift
Thinking about your sales team's goals for 2017? Drift's VP of Sales shares 3 things you can do to improve conversion rates and drive more revenue.
Read the full story on the Drift blog here: http://blog.drift.com/sales-team-tips
Attached here is a presentation that I made covering some bits and pieces of what I got to discover about Data Science and Machine Learning using R Programming Language.
Query optimizers and people have one thing in common: the better they understand their data, the better they can do their jobs. Optimizing queries is hard if you don't have good estimates for the sizes of the intermediate join and aggregate results. Data profiling is a technique that scans data, looking for patterns within the data such as keys, functional dependencies, and correlated columns. These richer statistics can be used in Apache Calcite's query optimizer, and the projects that use it, such as Apache Hive, Phoenix and Drill. We describe how we built a data profiler as a table function in Apache Calcite, review the recent research and algorithms that made it possible, and show how you can use the profiler to improve the quality of your data.
Query optimizers and people have one thing in common: the better they understand their data, the better they can do their jobs. Optimizing queries is hard if you don't have good estimates for the sizes of the intermediate join and aggregate results. Data profiling is a technique that scans data, looking for patterns within the data such as keys, functional dependencies, and correlated columns. These richer statistics can be used in Apache Calcite's query optimizer, and the projects that use it, such as Apache Hive, Phoenix and Drill. We describe how we built a data profiler as a table function in Apache Calcite, review the recent research and algorithms that made it possible, and show how you can use the profiler to improve the quality of your data.
A talk given by Julian Hyde at DataWorks Summit, San Jose, on June 14th 2017.
A chapter describing the use and application of exploratory factor analysis using principal axis factoring with oblique rotation.
Provides a step by step guide to exploratory factor analysis using SPSS.
3 Things Every Sales Team Needs to Be Thinking About in 2017Drift
Thinking about your sales team's goals for 2017? Drift's VP of Sales shares 3 things you can do to improve conversion rates and drive more revenue.
Read the full story on the Drift blog here: http://blog.drift.com/sales-team-tips
Attached here is a presentation that I made covering some bits and pieces of what I got to discover about Data Science and Machine Learning using R Programming Language.
Query optimizers and people have one thing in common: the better they understand their data, the better they can do their jobs. Optimizing queries is hard if you don't have good estimates for the sizes of the intermediate join and aggregate results. Data profiling is a technique that scans data, looking for patterns within the data such as keys, functional dependencies, and correlated columns. These richer statistics can be used in Apache Calcite's query optimizer, and the projects that use it, such as Apache Hive, Phoenix and Drill. We describe how we built a data profiler as a table function in Apache Calcite, review the recent research and algorithms that made it possible, and show how you can use the profiler to improve the quality of your data.
Query optimizers and people have one thing in common: the better they understand their data, the better they can do their jobs. Optimizing queries is hard if you don't have good estimates for the sizes of the intermediate join and aggregate results. Data profiling is a technique that scans data, looking for patterns within the data such as keys, functional dependencies, and correlated columns. These richer statistics can be used in Apache Calcite's query optimizer, and the projects that use it, such as Apache Hive, Phoenix and Drill. We describe how we built a data profiler as a table function in Apache Calcite, review the recent research and algorithms that made it possible, and show how you can use the profiler to improve the quality of your data.
A talk given by Julian Hyde at DataWorks Summit, San Jose, on June 14th 2017.
Query optimizers and people have one thing in common: the better they understand their data, the better they can do their jobs. Optimizing queries is hard if you don't have good estimates for the sizes of the intermediate join and aggregate results. Data profiling is a technique that scans data, looking for patterns within the data such as keys, functional dependencies, and correlated columns. These richer statistics can be used in Apache Calcite's query optimizer, and the projects that use it, such as Apache Hive, Phoenix and Drill. We describe how we built a data profiler as a table function in Apache Calcite, review the recent research and algorithms that made it possible, and show how you can use the profiler to improve the quality of your data.
A talk given by Julian Hyde at Apache: Big Data, Miami, on May 16th 2017.
“Practical Data Science”. R programming language and Jupiter notebooks are used in this tutorial. However, the concepts are generic and can be applied for Python or other programming language users as well.
ggplot2: An Extensible Platform for Publication-quality GraphicsClaus Wilke
Talk given at the Symposium on Data Science and Statistics in Bellevue, Washington, May 29 - June 1, 2019, organized by the American Statistical Association and Interface Foundation of North America.
Week-3 – System RSupplemental material1Recap •.docxhelzerpatrina
Week-3 – System R
Supplemental material
1
Recap
• R - workhorse data structures
• Data frame
• List
• Matrix / Array
• Vector
• System-R – Input and output
• read() function
• read.table and read.csv
• scan() function
• typeof() function
• Setwd() function
• print()
• Factor variables
• Used in category analysis and statistical modelling
• Contains predefined set value called levels
• Descriptive statistics
• ls() – list of named objects
• str() – structure of the data and not the data itself
• summary() – provides a summary of data
• Plot() – Simple plot
2
Descriptive statistics - continued
• Summary of commands with single-value result. These commands will work on variables
containing numeric value.
• max() ---- It shows the maximum value in the vector
• min() ----- It shows the minimum value in the vector
• sum() ----- It shows the sum of all the vector elements.
• mean() ---- It shows the arithmetic mean for the entire vector
• median() – It shows the median value of the vector
• sd() – It shows the standard deviation
• var() – It show the variance
3
Descriptive statistics - single value results -
example
temp is the name of the vector
containing all numeric values
4
• log(dataset) – Shows log value for each
element.
• summary(dataset) –shows the summary
of values
• quantile() - Shows the quantiles by
default—the 0%, 25%, 50%, 75%, and
100% quantiles. It is possible to select
other quantiles also.
Descriptive statistics - multiple value results -
example
5
Descriptive Statistics in R for Data Frames
• Max(frame) – Returns the largest value in the entire data frame.
• Min(frame) – Returns the smallest value in the entire data frame.
• Sum(frame) – Returns the sum of the entire data frame.
• Fivenum(frame) – Returns the Tukey summary values for the entire
data frame.
• Length(frame)- Returns the number of columns in the data frame.
• Summary(frame) – Returns the summary for each column.
6
Descriptive Statistics in R for Data Frames -
Example
7
Descriptive Statistics in R for Data Frames –
RowMeans example
8
Descriptive Statistics in R for Data Frames –
ColMeans example
9
Graphical analysis - simple linear regression model
in R
• Logistic regression is implemented to understand if the dependent
variable is a linear function of the independent variable.
• Logistic regression is used for fitting the regression curve.
• Pre-requisite for implementing linear regression:
• Dependent variable should conform to normal distribution
• Cars dataset that is part of the R-Studio will be used as an example to
explain linear regression model.
10
Creating a simple linear model
• cars is a dataset preloaded into
System-R studio.
• head() function prints the first
few rows of the list/df
• cars dataset contains two major
columns
• X = speed (cars$speed)
• Y = dist (cars$dist)
• data() function is used to list all
the active datasets in the
environment.
• ...
Exploratory data analysis of 2017 US Employment data using RChetan Khanzode
Data Science- Exploratory data analysis of year 2017 US Employment data using R – Use Case.Use of R library's for visualization of Employment data by state, county and industry sector - simple Geo spatial data visualization of employment data
CuRious about R in Power BI? End to end R in Power BI for beginners Jen Stirrup
In this session, we will start R right from the beginning, from installing R through to datatransformation and integration, through to visualizing data by using R in PowerBI. Then, we will move towards powerful but simple to use datatypes in R such as data frames. We will also upgrade our data analysis skills by looking at Rdata transformation using a powerful set of tools to make things simple: the tidyverse. Then, we will look at integrating our R work into Power BI, and visualizing our data using beautiful visualizations with R and Power BI. Finally, we will share our work by publishing our Power BI project, with our R code, to the Power BI service. We will also look at refreshing our dataset so that our new dashboard has refreshed data.
This session is aimed at getting beginners up to speed as gently and quickly as possible. Join this session if you are curious about R and want to know more. If you are already a Power BI expert, join this session to open up a whole new world of Power BI to add toyour skill set. If you are new to Power BI, you will still get value from this session since you'll be able to see a Power BI dashboard being built in an end-to-end solution.
This introduction to the popular ggplot2 R graphics package will show you how to create a wide variety of graphical displays in R. Data sets and additional workshop materials available at http://projects.iq.harvard.edu/rtc/event/r-graphics
Similar to Exploratory Analysis Part1 Coursera DataScience Specialisation (20)
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
ANAMOLOUS SECONDARY GROWTH IN DICOT ROOTS.pptxRASHMI M G
Abnormal or anomalous secondary growth in plants. It defines secondary growth as an increase in plant girth due to vascular cambium or cork cambium. Anomalous secondary growth does not follow the normal pattern of a single vascular cambium producing xylem internally and phloem externally.
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
2. In today’s session
• Principles behind exploratory analyses
• Plotting data out on to popular exploratory graphs
• Plotting Systems in R
• Base (Week1)
• Lattice (Week2)
• GGPLOT2 (Week2)
• Choosing and using Graphic Devices aka the output formats
Scripts can be downloaded at:
https://www.dropbox.com/s/ii1yj8f650d4l1q/lesson1.r?dl=0
https://www.dropbox.com/s/eme44h6lrhn775l/final.r?dl=0
3. Principles behind exploratory analyses
• Show comparisons
• Show causality, mechanism, explanation
• Show multivariate data
• Integrate multiple modes of evidence
• Describe and document the evidence
• Content is king
• SPEED
14. Base plots: Scatterplot
R code
data1 = read.table("scatter1.txt", h=T)
data2 = read.table("scatter2.txt", h=T)
#Color
with(data1, plot(xv, ys, col="red"))
with(data1, abline(lm(ys~xv)))
#shape
with(data2,
points(xv2, ys2, col="blue",
pch =11))
Symbol shape
15. Base plots: Scatterplot
R code
data1 = read.table("scatter1.txt", h=T)
data2 = read.table("scatter2.txt", h=T)
#Color
with(data1, plot(xv, ys, col="red"))
with(data1, abline(lm(ys~xv)))
#shape
with(data2,
points(xv2, ys2, col="blue",
pch =11))
Symbol shape
16. Base plots: Using par for multiple plots
R code
par(mfrow=c(1,2))
with(data1, plot(xv, ys, col="red"))
with(data1, abline(lm(ys~xv)))
#Plot2
with(data2,
plot(xv2, ys2, col="blue",
pch =11))
title(“My Title", outer=TRUE)
17. Par: To set global settings
R code
mfrow(
mar=c(5.1,4.1,4.1,2.1),
oma=c(2,2,2,2)
)
18. Lattice
productivity = read.table("productivity.txt",h=T)
# of species in forest against differing productivity
library(lattice)
#plotting
xyplot( x~y, productivity,
xlab=list(label="Productivity"),
ylab=list(label="Mammal Species"))
R code
Formular
Data frame
19.
20. Lattice
productivity = read.table("productivity.txt",h=T)
# of species in forest against differing productivity
library(lattice)
#plotting
xyplot( x~y, productivity,
xlab=list(label="Productivity"),
ylab=list(label="Mammal Species"))
xyplot( x~y | f, productivity,
xlab=list(label="Productivity"),
ylab=list(label="Mammal Species"))
R code
Formular
Data frame
given
21.
22. ggplot2
• Grammar of graphics (gg)
• Based on GRID plotting system, cannot be
mixed with base
ggplot2.org
23. ggplot
Components
• Data & relationship
• GEOMetric Object
• Statistical transformation
• Scales
• Coordinate system
• Facetting
28. ggplot
Components
• Data & relationship ✔
• GEOMetric Object
• Statistical transformation
• Scales
• Coordinate system
• Facetting
R code
Rmbr to change
month into a
factor
data.frame
Aesthetics function which maps the relationships
ggplot(weather, aes(x=month, y=upper))+
geom_boxplot()
34. qplot
A separate function which wraps ggplot, for simpler syntax
R code
qplot(month, upper, fill=month, data=weather, facets = ~yr, geom="bar",
stat="identity")
37. Final Challenge
R code
library(ggplot2)
#Reads in data
data = read.csv("final.csv")
#Preparing for the rectangle background
areas=unique(subset(data, select=c(Planning_Area,Planning_Region)))
areas=areas[order(areas$Planning_Region),]
areas$rectid=1:nrow(areas)
rectdata = areas %>% group_by(Planning_Region) %>% summarise(xstart=min(rectid)-
0.5,xend= max(rectid)+0.5)
#Order the levels
data$Planning_Area=factor(data$Planning_Area,
levels=as.character(areas[order(areas$Planning_Region),]$Planning_Area))