Basic things you should know to use s-plot

This lists the basic information for using s-plot.

Currently supported plots

Please type s-plot.sh in command line directly to have the latest list.

#### Bars
./s-plot.sh barPlot
./s-plot.sh horizontalBar
./s-plot.sh multiBarNew

#### Lines
./s-plot.sh lines
./s-plot.sh lines.2

#### Dots
./s-plot.sh scatterplot
./s-plot.sh scatterplotColor
./s-plot.sh scatterplotContour
./s-plot.sh scatterplotLotsData
./s-plot.sh scatterplotMatrix
./s-plot.sh contourPlot (unfinished)

#### Distribution
./s-plot.sh areaplot
./s-plot.sh boxplot
./s-plot.sh densityPlot
./s-plot.sh densityHistPlot
./s-plot.sh histogram
./s-plot.sh histogram.2

#### Cluster
./s-plot.sh hcluster
./s-plot.sh hclust

#### Heatmap
./s-plot.sh heatmapS
./s-plot.sh heatmapM
./s-plot.sh heatmap.2

#### Others
./s-plot.sh volcano
./s-plot.sh vennDiagram

Basic test data set

Here lists the general information of diamonds dataset that comes packaged with ggplot2.

This dataset contains ~50,000 entries. Each row is an individual diamond, and some of the variables of interest include the weight of the diamond in carats, color, clarity, and its price.

One can get, save and view the dataset using below commands. Or download data set from

> library(ggplot2)
> data(diamonds)

> head(diamonds)
carat cut color clarity depth table price x y z
1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48

> summary(diamonds)
carat cut color clarity depth
Min. :0.2000 Fair : 1610 D: 6775 SI1 :13065 Min. :43.00
1st Qu.:0.4000 Good : 4906 E: 9797 VS2 :12258 1st Qu.:61.00
Median :0.7000 Very Good:12082 F: 9542 SI2 : 9194 Median :61.80
Mean :0.7979 Premium :13791 G:11292 VS1 : 8171 Mean :61.75
3rd Qu.:1.0400 Ideal :21551 H: 8304 VVS2 : 5066 3rd Qu.:62.50
Max. :5.0100 I: 5422 VVS1 : 3655 Max. :79.00
J: 2808 (Other): 2531
table price x y
Min. :43.00 Min. : 326 Min. : 0.000 Min. : 0.000
1st Qu.:56.00 1st Qu.: 950 1st Qu.: 4.710 1st Qu.: 4.720
Median :57.00 Median : 2401 Median : 5.700 Median : 5.710
Mean :57.46 Mean : 3933 Mean : 5.731 Mean : 5.735
3rd Qu.:59.00 3rd Qu.: 5324 3rd Qu.: 6.540 3rd Qu.: 6.540
Max. :95.00 Max. :18823 Max. :10.740 Max. :58.900

z
Min. : 0.000
1st Qu.: 2.910
Median : 3.530
Mean : 3.539
3rd Qu.: 4.040
Max. :31.800

#col.names=NA can generate an empty string to represent the name of first column
> write.table(diamonds, file="diamond.matrix",sep="\t", col.names=NA, row.names=T, quote=F)

#melt data set
> library(reshape2)

#Default all non-numerical column will be used as id variables
#melt will group each data value into combinations of factor variable or categorial variable.
> data_m <- melt(diamonds)
Using cut, color, clarity as id variables

> head(data_m)
cut color clarity variable value
1 Ideal E SI2 carat 0.23
2 Premium E SI1 carat 0.21
3 Good E VS1 carat 0.23
4 Premium I VS2 carat 0.29
5 Good J SI2 carat 0.31
6 Very Good J VVS2 carat 0.24

# Extract four columns from full data sets
> diamonds_extract <- diamonds[c(1,2,3,7)]
> diamonds_extract$price <- diamonds_extract$price / 1000
> head(diamonds_extract)
carat cut color price
1 0.23 Ideal E 0.326
2 0.21 Premium E 0.326
3 0.23 Good E 0.327
4 0.29 Premium I 0.334
5 0.31 Good J 0.335
6 0.24 Very Good J 0.336
> write.table(diamonds_extract, file="diamond.extract.matrix",sep="\t", col.names=NA, row.names=T, quote=F)

> diamonds_extract_melt <- melt(diamonds_extract)
Using cut, color as id variables
> head(diamonds_extract_melt)
cut color variable value
1 Ideal E carat 0.23
2 Premium E carat 0.21
3 Good E carat 0.23
4 Premium I carat 0.29
5 Good J carat 0.31
6 Very Good J carat 0.24

> tail(diamonds_extract_melt)
cut color variable value
107875 Premium D price 2.757
107876 Ideal D price 2.757
107877 Good D price 2.757
107878 Very Good D price 2.757
107879 Premium H price 2.757
107880 Ideal D price 2.757

> write.table(diamonds_extract_melt, file="diamond.extract.matrix.melt",sep="\t", row.names=F, quote=F)

Basic layouts and themes

  • Legend position

    Defult, the legend is posited at the right of pictures. One can give top to -p to put the legend above pictures. Other accepted strings to -p is bottom,left,right, or c(0.008,0.8). The two element numerical vactor indicats the reltaive position of legend in pictures. 0.008 means position relative y-axis and 0.8 means position relative to x-axis. Specially, c(1,1) put legends at top-right.

  • Width, Height, Resolution, type of output pictures

    Default, width is 20 cm, one can give number to -w to change it. Height is 12 cm, give number to -u to change height. Give number to -r to alter resolution instead of using 300 as default.

    8 picture formats are supported, eps/ps, tex (pictex), pdf, jpeg, tiff, bmp, svg and wmf, with png as default. Give any mentioned string to -E to change output format.

  • Title, xlab, ylab of picture

    One can set title, xlab, ylab with -t, -x, -y.

  • Install modules

    Give TRUE to -i to install required modules for the first time. (i is shorted for install)

    Give FALSE to -e if you only want to get the R scripts instead of running them. (e is shorted for execute)

CHENTONG
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CHENTONG

CHENTONG
积微,月不胜日,时不胜月,岁不胜时。凡人好敖慢小事,大事至,然后兴之务之。如是,则常不胜夫敦比于小事者矣!何也?小事之至也数,其悬日也博,其为积也大。大事之至也希,其悬日也浅,其为积也小。故善日者王,善时者霸,补漏者危,大荒者亡!故,王者敬日,霸者敬时,仅存之国危而后戚之。亡国至亡而后知亡,至死而后知死,亡国之祸败,不可胜悔也。霸者之善著也,可以时托也。王者之功名,不可胜日志也。财物货宝以大为重,政教功名者反是,能积微者速成。诗曰:德如毛,民鲜能克举之。此之谓也。

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