# 6.Matching Data and Graph Types: Interpreting Data Visualizations

Chart selection depends on the structure of the data available and  existing data visualization techniques have multiple functions. For example, comparison, proportioning, distribution, correlation, hierarchy, relationship. Following two links can help for data visualization techniques: [https://datavizcatalogue.com/](https://datavizcatalogue.com/) [http://datavizproject.com/#](http://datavizproject.com)  \
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Data Viz Catalog’s function section heps you to show what you want when you do chart. For example, here you can find a list of charts categorised by their data visualization functions or by what you want a chart to communicate to an audience. While the allocation of each chart into specific functions isn't a perfect system, it still works as a useful guide for selecting chart based on your analysis or communication needs.\
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Can be visiting here: [https://datavizcatalogue.com/search.html\
](https://datavizcatalogue.com/search.html)

![](https://lh5.googleusercontent.com/MNZB0yJ2_Rri-Q9fPrlU_q_HbgE61VXkaSPKTi6nfy7qAMVjyQ_7aOUOfIFSnOjqKSowzoWVRSDKjQmrkXaXUAaQFrmiFXQQbhVAZ_PNRjbpy82MGQgkVEElAj_5yF6rTKKSMhg)

**Basic graphics**\


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Column & Bar Chart: Used to compare categorical data\
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![](https://lh4.googleusercontent.com/5qQqtkdxwUFXc6d_ozNV9HajgbbYIqrR6UqaKMeQudUIQ0j1ra9_SkRghgdS2RuBT-KiHhS5WOZ0JcM88x1lTFVvoTQu_YeEj7dnnLLlPVuFHp_SqIdAwfSUPSp-s0Qkjycy9IU)

Multiple & Grouped Bar Graph: In these graphs, one axis represents the category and the other represents the value of that category.

![](https://lh3.googleusercontent.com/CSJIJGxK8iO8elnH3A6nv1FuI17ezfmLfW6fAgUadmAg1EYU1joug4RllhTUjRImNe6aiPU4TzgLmbHYT8FKRT3g26eoSgb2LKEYDPfLQ_WItsdkPtoH2w7R1JxzdhzyF-BEFI4)

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Stacked Bar Graph: Unlike the other two, the graph shows the ratio of different categorical values to each other. It is an alternative to pie chart.\


![](https://lh4.googleusercontent.com/L4lZawO4zavAZHML1oEiVmjPtOrINbKj7TFkAavvDE8vxmDmHakukPBJnINnUVw-FBQwL9DAZeWdtVSi1-WLRW0sNOxd2tu6c9GHBHeqQUUx_wo3Uyeflr4mvpeCyCjA-s6ZhnI)

Line Chart: It is used to indicate numerical changes over a specific time interval. The alternative is the area graph.\


![](https://lh6.googleusercontent.com/iyhuxTeHN9xxnwtRrzxwQH9RPWMHkjMVbi3wLZgzSkXVBdA6ZmRN0rlkAahlaFvg3Egi0E5Y4wiiSTzjzgzAQEO8BCKTIpxr6EUFWUr0yUU5uTni0pIiA95JGaz2xtOjN6iab6k)

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Pie Chart: The pie chart that divides a circle proportionally into categories calculates the categories in percent. Full circle equals 100

![](https://lh5.googleusercontent.com/zr5YfvC_1MvWEePWC9jnhH8_oZYsElcbEfgkjGs4EsRji4lN-16WSKCtkGvseUopNW0Dp-Vw_Q6QloU77u9zqdshFp9Q9ztijeWUT4iEwtiPvZwb9-atDgtRpqz-8gYcFfVWWbI)

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Scatter Plot: It is used to show the correlation between two variables placed on the horizontal and vertical axis.



![](https://lh5.googleusercontent.com/KREcGwoAwoJ2F-Vnwpx4GZgg_IwiSSee87l6P0rtl1UGFgHdf-tKS2ENiXEg9SulbVF-_egi5pPeSTddWswVkKXl8dNXfP9PETuJI7fHx_320beFmz4xw1QoeoLppnH5QKO6QY4)

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**Color** \
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Another important issue in data visualization is color selection. Color palettes are as important as statistics and data in data visualization in the delivery of the desired message. Practical pallets are also helpful, especially in the final cycle. The following examples are a few of them.

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http://colorbrewer2.org = Color Brewer\
https://htmlcolorcodes.com/  = HTML color codes\
http://colorhunt.co/popular  = Color hunt\
https://color.adobe.com/create/color-wheel/ = Adobe color\


If you need to proceed by giving test examples, we found the following red round circle faster in the section with turquoise rounds.

![](https://lh4.googleusercontent.com/ABp6MDsLhfQI8otzT8Hcudc1lKjyNv-fJoKEAP3rCAiJcWC2prKSNc37gVvC5CuPHOPitDwxHNujwlXswIDhNxWq9bRdUw3eEebTNTD6dtJxNh-BSo2AxT_YNkSm9m91sARzXK4)

We can see the boundary between the two groups more easily on the left.\


![](https://lh4.googleusercontent.com/uScpt1PSSTC1Qi3yQbX4qeKdGjUuefFj6iecoXaRKDsqS97bk1r33bH08RwgHm-xai8CdYkpZX0L0AMD-jhsRmEYnEUL_EdGSDvpp6sgWdgcppU5IBJ7GBRQxEEc4E0GJ8fsdpc)

In summary, good data visualization can improve the quality of insights. When so much effort is spent pursuing a data-driven culture, good data visualization becomes invaluable.\
