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Data visualization is key to effective communication across all organizations. In this blog, we briefly introduce 33 tools to visualize data. 

 

Data-driven enterprises are evidently the new normal. Not only does this require companies to wrestle with data for internal and external decision-making challenges, but also requires effective communication. This is where data visualization comes in. 

 

Without visualization results found via rigorous data analytics procedures, key analyses could be forgone. Here’s where data visualization methods such as charts, graphs, scatter plots, 3D visualization, and so on, simplify the task. Visual data is far easier to absorb, retain, and recall.  

 

And so, we describe a total of 33 data visualization tools that offer a plethora of possibilities.  

 

Recommended data visualization tools you must know about  

 

Using these along with data visualization tips ensures healthy communication of results across organizations. 

 

1. Visual.ly 

 

Popular for its incredible distribution network which allows data import and export to third parties, Visual.ly is a great data visualization tool in the market.  

 

2. Sisense 

 

Known for its agility, Sisense provides immediate data analytics by means of effective data visualization. This tool identifies key patterns and summarizes data statistics, assisting data-driven strategies. 

 

3. Data wrapper 

 

Data Wrapper, a popular and free data visualization tool, produces quick charts and other graphical presentations of the statistics of big data.  

 

4. Zoho reports 

 

Zoho Reports is a straightforward data visualization tool that provides online reporting services on business intelligence. 

 

5. Highcharts 

 

The Highcharts visualization tool is used by many global top companies and works seamlessly in visualizing big data analytics.  

 

6. Qlikview 

 

Providing solutions to around 40,000 clients across a hundred countries, Qlickview’s data visualization tools provide features such as customized visualization and enterprise reporting for business intelligence. 

 

7. Sigma.js 

  

A JavaScript library for creating graphs, Sigma uplifts developers by making it easier to publish networks on websites.  

 

8. JupyteR 

 

A strongly rated, web-based application, JupyteR allows users to share and create documents with equations, code, text, and other visualizations.  

 

9. Google charts 

 

Another major data visualization tool, Google charts is popular for its ability to create graphical and pictorial data visualizations. 

 

10. Fusioncharts 

 

Fusioncharts is a Javascript-based data visualization tool that provides up to ninety chart-building packages that seamlessly integrate with significant platforms and frameworks.  

 

11. Infogram 

 

Infogram is a popular web-based tool used for creating infographics and visualizing data.  

 

12. Polymaps 

 

A free Javascript-based library, Polymaps allows users to create interactive maps in web browsers such as real-time display of datasets. 

 

13. Tableau 

 

Tableau allows its users to connect with various data sources, enabling them to create data visualization by means of maps, dashboards, stories, and charts, via a simple drag-and-drop interface. Its applications are far-reaching such as exploring healthcare data.  

 

14. Klipfolio 

 

Klipfolio provides immediate data from hundreds of services by means of pre-built instant metrics. It’s ideal for businesses that require custom dashboards 

 

15. Domo 

 

Domo is especially great for small businesses thanks to its accessible interface allowing users to create advanced charts, custom apps, and other data visualizations that assist them in making data-driven decisions.  

 

16. Looker 

 

A versatile data visualization tool, Looker provides a directory of various visualization types from bar gauges to calendar heat maps.  

 

17. Qlik sense 

 

Qlik Sense uses artificial intelligence to make data more understandable and usable. It provides greater interactivity, quick calculations, and the option to integrate data from hundreds of sources. 

 

18. Grafana 

 

Allowing users to create dynamic dashboards and offering other visualizations, Grafana is a great open-source visualization software.  

 

19. Chartist.js 

 

This free, open-source Javascript library allows users to create basic responsive charts that offer both customizability and compatibility across multiple browsers.  

 

20. Chart.js 

 

A versatile Javascript library, Chart.js is open source and provides a variety of 8 chart types while allowing animation and interaction.  

 

21. D3.js 

 

Another Javascript library, D3.js requires some Javascript knowledge and is used to manipulate documents via data.  

 

22. ChartBlocks 

 

ChartBlocks allows data import from nearly any source. It further provides detailed customization of visualizations created. 

 

23. Microsoft Power BI 

 

Used by nearly 200K+ organizations, Microsoft Power BI is a data visualization tool used for business intelligence datatypes. However, it can be used for educational data exploration as well.  

 

24. Plotly 

 

Used for interactive charts, maps, and graphs, Plotly is a great data visualization tool whose visualization products can be shared further on social media platforms. 

 

25. Excel 

 

The old-school Microsoft Exel is a data visualization tool that provides an easy interface and offers visualizations such as scatter plots, which establish relationships between datasets. 

 

26. IBM watson analytics 

 

IBM’s cloud-based investigation administration, Watson Analytics allows users to discover trends in information quickly and is among their top free tools. 

 

27. FushionCharts 

 

A product of InfoSoft Global, FusionCharts is used by nearly 80% of Fortune 500 companies across the globe. It provides over ninety diagrams and outlines that are both simple and sophisticated.  

 

28. Dundas BI 

 

This data visualization tool offers highly customizable visualization with interactive maps, charts, scorecards. Dundas BI provides a simplified way to clean, inspect, and transform large datasets by giving users full control over the visual elements.  

 

29. RAW 

 

RAW, or RawGraphs, works as a link between spreadsheets and data visualization. Providing a variety of both conventional and non-conventional layouts, RAW offers quality data security. 

 

30. Redash 

 

An open-source web application, Redas is used for database cleaning and visualizing results.  

 

31. Dygraphs 

 

A fast, open-source, Javascript-based charting library, Dygraphs allows users to interpret and explore dense data sets.  

 

32. RapidMiner 

 

A data science platform for companies, RapidMiner allows analyses of the overall impact of organizations’ employees, data, and expertise. This platform supports many analytics users.  

 

33. Gephi 

 

Among the top open-source and free visualizations and exploration softwares, Gephi provides users with all kinds of charts and graphs. It’s great for users working with graphs for simple data analysis.  

 

  

 

The dplyr package in R is a powerful tool to do data munging and data manipulation, perhaps more so than many people would initially realize, making it extremely useful in data science.
Shortly after I embarked on the data science journey earlier this year, I came to increasingly appreciate the handy utilities of dplyr, particularly the mighty combo functions of group_by() and summarize (). Below, I will go through the first project I completed as a budding data scientist using the package along with ggplot. I will demonstrate some convenient features of both.

I obtained my dataset from Kaggle. It has 150,930 observations containing wine ratings from across the world. The data had been scraped from Wine Enthusiast during the week of June 15th, 2017. Right off the bat, we should recognize one caveat when deriving any insight from this data: the magazine only posted reviews on wines receiving a grade of 80 or more (out of 100).

As a best practice, any data analysis should be done with limitations and constraints of the data in mind. The analyst should bear in mind the conclusions he or she draws from the data will be impacted by the inherent limitations in breadth and depth of the data at hand.

After reading the dataset in RStudio and naming it “wine,” we’ll get started by installing and loading the packages.

Install and load packages (dplyr, ggplot)

# Please do install.packages() for these two libraries if you don't have them
library(dplyr)

library(ggplot2)

Data preparation

First, we want to clean the data. As I will leave textual data out of this analysis and not touch on NLP techniques in this post, I will drop the “description” column using the select () function from dplyr that lets us select columns by name. As you would’ve probably guessed, the minus sign in front of it indicates we want to exclude this column.

As select() is a non-mutating function, don’t forget to reassign the data frame to overwrite it (or you could create a new name for the new data frame if you want to keep the original one for reference). A convenient way to pass functions with dplyr is the pipe operator, %>%, which allows us to call multiple functions on an object sequentially and will take the immediately preceding output as the object of each function.

wine = wine %>% select(-c(description))

There is quite a range of producer countries in the list, and I want to find out which countries are most represented in the dataset. This is the first instance where we encounter one of my favorites uses in R: the group-by aggregation using “group_by” followed by “summarize”:

wine %>% group_by(country) %>% summarize(count=n()) %>% arrange(desc(count))

## # A tibble: 49 x 2

## country count

##

## 1 US 62397

## 2 Italy 23478

## 3 France 21098

## 4 Spain 8268

## 5 Chile 5816

## 6 Argentina 5631

## 7 Portugal 5322

## 8 Australia 4957

## 9 New Zealand 3320

## 10 Austria 3057

## # ... with 39 more rows

We want to only focus our attention on the top producers; say we want to select only the top ten countries. We’ll again turn to the powerful group_by()
and summarize() functions for group-by aggregation, followed by another select() command to choose the column we want from the newly created data frame.

Note* that after the group-by aggregation, we only retain the relevant portion of the original data frame. In this case, since we grouped by country and summarized the count per country, the result will only be a two-column data frame consisting of “country” and the newly named variable “count.” All other variables in the original set, such as “designation” and “points” were removed.

Furthermore, the new data frame only has as many rows as there were unique values in the variable grouped by – in our case, “country.” There were 49 unique countries in this column when we started out, so this new data frame has 49 rows and 2 columns. From there, we use arrange () to sort the entries by count. Passing desc(count) as an argument ensures we’re sorting from the largest to the smallest value, as the default is the opposite.

The next step top_n(10) selects the top ten producers. Finally, select () retains only the “country” column and our final object “selected_countries” becomes a one-column data frame. We transform it into a character vector using as.character() as it will become handy later on.

selected_countries = wine %>% group_by(country) %>% summarize(count=n ()) %>% arrange(desc(count)) %>% top_n(10) %>% select(country)
selected_countries = as.character(selected_countries$country)

So far we’ve already learned one of the most powerful tools from dplyr, group-by aggregation, and a method to select columns. Now we’ll see how we can select rows.

# creating a country and points data frame containing only the 10 selected countries' data select_points=wine %>% filter (country %in% selected_countries) %>% select(country, points) %>% arrange(country)

In the above code, filter(country %in% selected_countries) ensures we’re only selecting rows where the “country” variable has a value that’s in the “selected_countries” vector we created just a moment ago. After subsetting these rows, we use select() them to select the two columns we want to keep and arrange to sort the values. Not that the argument passed into the latter ensures we’re sorting by the “country” variable, as the function by default sorts by the last column in the data frame – which would be “points” in our case since we selected that column after “country.”

Data exploration and visualization

At a high level, we want to know if higher-priced wines are really better, or at least as judged by Wine Enthusiast. To achieve this goal we create a scatterplot of “points” and “price” and add a smoothed line to see the general trajectory.

ggplot(wine, aes(points,price)) + geom_point() + geom_smooth()

It seems overall expensive wines tend to be rated higher, and the most expensive wines tend to be among the highest-rated as well.

Let’s further explore possible visualizations with ggplot, and create a panel of boxplots sorted by the national median point received. Passing x=reorder(country,points,median) creates a reordered vector for the x-axis, ranked by the median “points” value by country. aes(fill=country) fills each boxplot with a distinct color for the country represented. xlab() and ylab() give labels to the axes, and ggtitle()gives the whole plot a title.

Finally, passing element_text(hjust = 0.5) to the theme() function essentially moves the plot title to horizontally centered, as “hjust”controls horizontal justification of the text’s positioning on the graph.

gplot(select_points, aes(x=reorder(country,points,median),y=points)) + geom_boxplot(aes(fill=country)) + xlab("Country") +

ylab(“Points”) + ggtitle(“Distribution of Top 10 Wine Producing Countries”) + theme(plot.title = element_text(hjust = 0.5))

When we ask the question “which countries may be hidden dream destinations for an oenophile?” we can subset rows of countries that aren’t in the top ten producer list. When we pass a new parameter into summarize() and assign it a new value based on a function of another variable, we create a new feature – “median” in our case. Using arrange(desc()) ensures we’re sorting by descending order of this new feature.

As we grouped by country and created one new variable, we end up with a new data frame containing two columns and however many rows there were that had values for “country” not listed in “selected_countries.”

wine %>% filter(!(country %in% selected_countries)) %>% group_by(country) %>% summarize(median=median(points))
%>% arrange(desc(median))

## # A tibble: 39 x 2
## country median
##
## 1 England 94.0
## 2 India 89.5
## 3 Germany 89.0
## 4 Slovenia 89.0
## 5 Canada 88.5
## 6 Morocco 88.5
## 7 Albania 88.0
## 8 Serbia 88.0
## 9 Switzerland 88.0
## 10 Turkey 88.0
## # ... with 29 more rows

We find England, India, Germany, Slovenia, and Canada as top-quality producers, despite not being the most prolific ones. If you’re an oenophile like me, this may shed light on some ideas for hidden treasures when we think about where to find our next favorite wines. Beyond the usual suspects like France and Italy, maybe our next bottle will come from Slovenia or even India.

Which countries produce a large quantity of wine but also offer high-quality wines? We’ll create a new data frame called “top” that contains the countries with the highest median “points” values. Using the intersect() function and subsetting the observations that appear in both the “selected_countries” and “top” data frames, we can find out the answer to that question.

top=wine %>% group_by(country) %>% summarize(median=median(points)) %>% arrange(desc(median))
top=as.character(top$country)
both=intersect(top,selected_countries)
both
##  [1] "Austria"     "France"      "Australia"   "Italy"       "Portugal"
## [6] "US" "New Zealand" "Spain" "Argentina" "Chile"

We see there are ten countries that appear in both lists. These are the real deals not highly represented just because of their mass production. Note that we transformed “top” from a data frame structure to a vector one, just like we had done for “selected_countries,” prior to intersecting the two.

Next, let’s turn from the country to the grape, and find the top ten most represented grape varietals in this set:

topwine = wine %>% group_by(variety) %>% summarize(number=n()) %>% arrange(desc(number)) %>% top_n(10)
topwine=as.character(topwine$variety)
topwine
##  [1] "Chardonnay"               "Pinot Noir"
## [3] "Cabernet Sauvignon" "Red Blend"
## [5] "Bordeaux-style Red Blend" "Sauvignon Blanc"
## [7] "Syrah" "Riesling"
## [9] "Merlot" "Zinfandel"

The pipe operator doesn’t work just with dplyr functions. Below we’ll examine graphs with ggplot functions that work seamlessly with dplyr syntax.

wine %>% filter(variety %in% topwine) %>% group_by(variety)%>% summarize(median=median(points)) %>% ggplot(aes(reorder(variety,median),median))
+ geom_col(aes(fill=variety)) + xlab('Variety') + ylab('Median Point') + scale_x_discrete(labels=abbreviate)

Finally, we’d be interested in learning which wines provide the best value, meaning priced toward the bottom rung but ranked in the top rung:

top15percent=wine %>% arrange(desc(points)) %>% filter(points > quantile(points, prob = 0.85))
cheapest15percent=wine %>% arrange(price) %>% head(nrow(top15percent))
goodvalue = intersect(top15percent,cheapest15percent)
goodvalue

## 2  Portugal Picos do Couto Reserva     92    11     Dão
## 3        US                            92    11       Washington
## 4        US                            92    11       Washington
## 5    France                            92    12         Bordeaux
## 6        US                            92    12           Oregon
## 7    France        Aydie l'Origine     93    12 Southwest France
## 8        US       Moscato d'Andrea     92    12       California
## 9        US                            92    12       California
## 10       US                            93    12       Washington
## 11    Italy             Villachigi     92    13          Tuscany
## 12 Portugal            Dona Sophia     92    13             Tejo
## 13   France       Château Labrande     92    13 Southwest France
## 14 Portugal              Alvarinho     92    13            Minho
## 15  Austria                  Andau     92    13       Burgenland
## 16 Portugal             Grand'Arte     92    13           Lisboa
##                region_1          region_2                  variety
## 1                                                   Portuguese Red
## 2                                                   Portuguese Red
## 3  Columbia Valley (WA)   Columbia Valley                 Riesling
## 4  Columbia Valley (WA)   Columbia Valley                 Riesling
## 5            Haut-Médoc                   Bordeaux-style Red Blend
## 6     Willamette Valley Willamette Valley               Pinot Gris
## 7               Madiran                      Tannat-Cabernet Franc
## 8           Napa Valley              Napa           Muscat Canelli
## 9           Napa Valley              Napa          Sauvignon Blanc
## 10 Columbia Valley (WA)   Columbia Valley    Johannisberg Riesling
## 11              Chianti                                 Sangiovese
## 12                                                  Portuguese Red
## 13               Cahors                                     Malbec
## 14                                                       Alvarinho
## 15                                                        Zweigelt
## 16                                                Touriga Nacional
##                       winery
## 1              Pedra Cancela
## 2          Quinta do Serrado
## 3                Pacific Rim
## 4                   Bridgman
## 5  Château Devise d'Ardilley
## 6                      Lujon
## 7            Château d'Aydie
## 8              Robert Pecota
## 9               Honker Blanc
## 10             J. Bookwalter
## 11            Chigi Saracini
## 12    Quinta do Casal Branco
## 13           Jean-Luc Baldès
## 14                   Aveleda
## 15              Scheiblhofer
## 16                DFJ Vinhos

Now that you’ve learned some handy tools you can use with dplyr, I hope you can go off into the world and explore something of interest to you. Feel free to make a comment below and share what other dplyr features you find helpful or interesting.

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Contributor: Ningxi Xu

Ningxi holds a MS in Finance with honors from Georgetown McDonough School of Business, and graduated magna cum laude with a BA from the George Washington University.