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Centered the images in the README
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README.md
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README.md
@ -32,7 +32,9 @@ curl -sL https://git.io/ISLANDScsv \
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| uplot bar -d, -t "Areas of the World's Major Landmasses"
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```
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<p align="center">
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<img alt="barplot" src="https://user-images.githubusercontent.com/5798442/101999903-d36a2d00-3d24-11eb-9361-b89116f44122.png">
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</p>
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### histogram
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@ -43,7 +45,10 @@ echo -e "from numpy import random;" \
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| python \
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| uplot hist --nbins 20
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```
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<p align="center">
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<img alt="histogram" src="https://user-images.githubusercontent.com/5798442/101999820-21cafc00-3d24-11eb-86db-e410d19b07df.png">
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</p>
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### lineplot
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@ -53,7 +58,9 @@ curl -sL https://git.io/AirPassengers \
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| uplot line -d, -w 50 -h 15 -t AirPassengers --xlim 1950,1960 --ylim 0,600
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```
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<p align="center">
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<img alt="lineplot" src="https://user-images.githubusercontent.com/5798442/101999825-24c5ec80-3d24-11eb-99f4-c642e8d221bc.png">
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</p>
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### scatter
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@ -63,7 +70,9 @@ curl -sL https://git.io/IRIStsv \
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| uplot scatter -H -t IRIS
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```
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<p align="center">
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<img alt="scatter" src="https://user-images.githubusercontent.com/5798442/101999827-27284680-3d24-11eb-9903-551857eaa69c.png">
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</p>
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### density
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@ -73,7 +82,9 @@ curl -sL https://git.io/IRIStsv \
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| uplot density -H -t IRIS
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```
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<p align="center">
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<img alt="density" src="https://user-images.githubusercontent.com/5798442/101999828-2abbcd80-3d24-11eb-902c-2f44266fa6ae.png">
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</p>
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### boxplot
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@ -83,7 +94,9 @@ curl -sL https://git.io/IRIStsv \
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| uplot boxplot -H -t IRIS
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```
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<p align="center">
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<img alt="boxplot" src="https://user-images.githubusercontent.com/5798442/101999830-2e4f5480-3d24-11eb-8891-728c18bf5b35.png">
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</p>
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### count
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@ -97,7 +110,9 @@ cat gencode.v35.annotation.gff3 \
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uplot count -t "The number of human gene annotations per chromosome" -c blue
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```
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<p align="center">
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<img alt="count" src="https://user-images.githubusercontent.com/5798442/101999832-30b1ae80-3d24-11eb-96fe-e5000bed1f5c.png">
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</p>
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Note: `count` is not very fast because it runs in a Ruby script.
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This is fine in most cases, as long as the data size is small. If you want to visualize huge data, it is faster to use a combination of common Unix commands as shown below.
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