README.md
In wangjiaxuan666/xbox: author's packages for himself
xbox:wrench: 
Author: Jiaxuan Wang :pencil:
The goal of xbox is created with some script writed in my spare time. on
the other side, the R packages is created just for myself. My names is
Wang Jiaxuan, a chinese name. the packages named “xbox” because the tail
name is xuan. like xuan’s tool box(Why not xtoolbox, or xtbox, No! these
are so complex).
with the time flewed, the R packages function will be more and more.
update log
update 20201212
- [x] change the function
get_df fix the bug for select the column
names
- [x] add the function
get_mat for transfer data.frame or tibble to
matrix
- [x]
get_tb add the var for change the “rownames” to “id”
update 20201227
- [x] add the fucntion ‘need_colors’ to choose the chinese
traditional color palette that disigned by 大东设计. who is a bolger
in tiktok(抖音). When I saw his painting, I feel shock for those
color’s power. So I decide make a R packages to put those color into
a color schemes that everyone can use easily. and you can use
disco to display the color you choose. It is convenient to choose
the one which you like most.
update 20210123
- [x] add the
vennplot function for draw venn plot, It’s not a
simple venn plot , it’s a venn plot can change the intersect area
represent the intersect number size. also somebody call it
area-proportional Venn diagrams.
Installation
The R packages only can install the development version from
GitHub with:
# install.packages("devtools")
devtools::install_github("wangjiaxuan666/xbox")
Notice, If install failed with the messages
ERROR: dependencies 'KEGGREST', 'qvalue' are not available for package 'xbox'
* removing 'C:/R/library/xbox'
You should install R packages KEGGREST and qvalue firstly with code:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("qvalue")
BiocManager::install("KEGGREST")
Update
It’s best to update before you start use the package. just because it is
constantly updated and add new fucntion within.
devtools::update_packages("xbox")
Function
Firstlrmarkdown::render(“README.Rmd”) y, you should library the xbox R
packages.
library(xbox)
need_colors and disco
add the fucntion ‘need_colors’ to choose the chinese traditional color
palette that disigned by 大东设计. who is a bolger in tiktok(抖音). When
I saw his painting, I feel shock for those color’s power. So I decide
make a R packages to put those color into a color schemes that everyone
can use easily. and you can use disco to display the color you choose.
It is convenient to choose the one which you like most.
need_colors("two2")
#> [1] "#8CADA6" "#D19848"
disco("two2")
need_colors("nine1",5)
#> [1] "#9d2c32" "#eb5934" "#c32a2c" "#8c4356" "#494367"
disco("nine1",5)
In my disign, for ease of use. I use the ‘the number which you want’ +
‘id’ to name the color palette, such as
“two1”,“two2”,“three1”,“four1”,“nine1”. Even you forget so simple name,
it’s doesn’t matter. Beacuse you can use the number directly. Now, in a
word, it’s very convenient!!
need_colors(10)
#> Randomly select 10 colors from all the colors.
#> [1] "#494367" "#F1ABAD" "#D19848" "#EFB08A" "#8CADA6" "#F1ABAD"
#> [7] "#eb5934" "#cb6922" "#8c4356" "#c32a2c"
disco(10)
#> Randomly select 10 colors from all the colors.
#> Randomly select 10 colors from all the colors.
#> Randomly select 10 colors from all the colors.
#> Randomly select 10 colors from all the colors.
For more details, please see the bolg
“R包xbox-增加中国风配色方案”,
which publiced on WeChat official
account.Abego
col_ttest
the first function is col_ttest, the function is creat for the
t.test in the two column group to test. the output including the
pvalue,qvalue. Notice,the qvalue is the result of the R packages
qvalue.the
parameter is defult. I think is adjust pvalue method is
bonferroni.
set.seed("20201225")
test = matrix(rnorm(20), 2, 10)
colnames(test) = paste("Test", 1:10, sep = "")
rownames(test) = paste("Gene", 1:2, sep = "")
str(col_ttest(data = test,g1 = 1:5,g2 = 6:10))
#> The number is less than 5000, and adjust pvalue is not recommended
#> 'data.frame': 2 obs. of 11 variables:
#> $ Test1 : num 1.342 0.182
#> $ Test2 : num 0.524 -0.16
#> $ Test3 : num 0.6 1.27
#> $ Test4 : num 1.169 -0.359
#> $ Test5 : num -1.42 -0.24
#> $ Test6 : num 1.237 0.145
#> $ Test7 : num 0.0264 -2.3262
#> $ Test8 : num -0.306 0.251
#> $ Test9 : num -0.386 -0.864
#> $ Test10: num 1.079 0.726
#> $ pv : num 0.855 0.406
t_dat
the t_dat function is uesed for transpose the data. Notice,it’s not
the function transpose in tidyverse or tidyr. t_dat can exchange
the column and row. Make the column names become the row names and the
row names become the column names. the data is tranpose with the names.
it is complex for me to explain it clearly with the English. let’s see
how does it work.
df = mtcars[1:6,1:6]
print(df)
#> mpg cyl disp hp drat wt
#> Mazda RX4 21.0 6 160 110 3.90 2.620
#> Mazda RX4 Wag 21.0 6 160 110 3.90 2.875
#> Datsun 710 22.8 4 108 93 3.85 2.320
#> Hornet 4 Drive 21.4 6 258 110 3.08 3.215
#> Hornet Sportabout 18.7 8 360 175 3.15 3.440
#> Valiant 18.1 6 225 105 2.76 3.460
print(t_dat(df))
#> ... Notice: Use`retype` function in `hablar` packages
#> Should be better to check it again# A tibble: 6 x 6
#> `Mazda RX4` `Mazda RX4 Wag` `Datsun 710` `Hornet 4 Drive`
#> * <dbl> <dbl> <dbl> <dbl>
#> 1 21 21 22.8 21.4
#> 2 6 6 4 6
#> 3 160 160 108 258
#> 4 110 110 93 110
#> 5 3.9 3.9 3.85 3.08
#> 6 2.62 2.88 2.32 3.22
#> # ... with 2 more variables: `Hornet Sportabout` <dbl>,
#> # Valiant <dbl>
Maybe you have see the NOTICE that mentioned the R packages hablar.
The hablar is creat by David Sjoberg. a excellent R packages. You can
see its introdution in Github.
i use the funtion retype to help the change and ensure the column
type. But I still think we should chect the result again by str().
koid_to_pathway
the function is useful the biology. If you have a list of ko number id.
But don’t know the the name or descrption of the ko ID . You should try
the function. I use the kegggetfuntion in R packages KEEGREST which
R packages in
bioconda.
the native function can scrawl the all information form KEGG Database.
But I only need the KEGG A Anoation,KEGG B Anoation and the
KEGG Pathway Anoation. So the koid_to_pathway only extract the the
information of the pathway description. like thie:
koid_to_pathway("ko04075")
#> koid
#> 1 ko04075
#> kegg_AB
#> 1 Environmental Information Processing; Signal transduction
#> desc
#> 1 Plant hormone signal transduction
#Success depends on the network.
#koid <-c("ko00010", "ko00020", "ko00030")
#koid_to_pathway(koid)
get_tb, get_df and get_mat
the simple function! the thing only can be done is change the data.frame
to a tibble. I creat it just because of the funtion rownams_to_column
which’s names is too long!
The simple example is :
head(cars)
#> speed dist
#> 1 4 2
#> 2 4 10
#> 3 7 4
#> 4 7 22
#> 5 8 16
#> 6 9 10
head(get_tb(cars))
#> ...Finished:the input data is a data frame not a tibble
#> # A tibble: 6 x 3
#> id speed dist
#> <chr> <dbl> <dbl>
#> 1 1 4 2
#> 2 2 4 10
#> 3 3 7 4
#> 4 4 7 22
#> 5 5 8 16
#> 6 6 9 10
Not end the sister function get_df, Which change the tibble to
data.frame .
head(mtcars)
#> mpg cyl disp hp drat wt qsec vs am gear
#> Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4
#> Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4
#> Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4
#> Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3
#> Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3
#> Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3
#> carb
#> Mazda RX4 4
#> Mazda RX4 Wag 4
#> Datsun 710 1
#> Hornet 4 Drive 1
#> Hornet Sportabout 2
#> Valiant 1
head(get_df(mtcars))
#> ...Finished:the input data is a data.frame
#> have no change
#> mpg cyl disp hp drat wt qsec vs am gear
#> Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4
#> Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4
#> Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4
#> Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3
#> Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3
#> Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3
#> carb
#> Mazda RX4 4
#> Mazda RX4 Wag 4
#> Datsun 710 1
#> Hornet 4 Drive 1
#> Hornet Sportabout 2
#> Valiant 1
The get_mat function is used for tansfer data whatever which type it
is to a matrix.
head(get_mat(cars))
#> speed dist
#> 1 4 2
#> 2 4 10
#> 3 7 4
#> 4 7 22
#> 5 8 16
#> 6 9 10
the heat density point plot
The function heatpoint is created for the desity plot, It can caculate
the point density in 2-dimension coordinate or 1D coordinate by the
parameter only. Of course, the Main purpose is the plot a density
heatpoint.
points = 5000
x = c(rnorm(points/2))
y = x + rnorm(points/2,sd=0.8)
x = sign(x)*abs(x)^1.3
heatpoint(x,y) -> dat_result
str(dat_result)
#> List of 3
#> $ plot.data :'data.frame': 2500 obs. of 3 variables:
#> ..$ x : num [1:2500] -0.25 -1.664 -0.192 -0.124 2.003 ...
#> ..$ y : num [1:2500] -0.7618 -0.906 0.0769 0.5704 1.5979 ...
#> ..$ Density: num [1:2500] 0.1649 0.0324 0.1933 0.1825 0.0314 ...
#> $ cor.result:List of 3
#> ..$ cor : num 0.777
#> ..$ pvalue: num 0
#> ..$ method:List of 1
#> .. ..$ method: chr "Pearson's product-moment correlation"
#> $ lm.result :List of 3
#> ..$ formula: Named num [1:2] -0.00628 0.87033
#> .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "x"
#> ..$ method : symbol lm
#> ..$ exp :Class 'formula' language y ~ x
#> .. .. ..- attr(*, ".Environment")=<environment: 0x0000019ed274a390>
#> - attr(*, "class")= chr "heatpoint"
#including the three list the plot data
head(dat_result$plot.data)
#> x y Density
#> 1 -0.2496371 -0.76182440 0.16489377
#> 2 -1.6637152 -0.90595769 0.03244137
#> 3 -0.1917298 0.07688888 0.19327535
#> 4 -0.1238316 0.57038733 0.18248076
#> 5 2.0029295 1.59785190 0.03138423
#> 6 0.3509336 -0.74778409 0.08520389
# and the result of cor
data.frame(dat_result$cor.result)
#> cor pvalue method
#> 1 0.7765534 0 Pearson's product-moment correlation
# and the result of lm
str(dat_result$lm.result)
#> List of 3
#> $ formula: Named num [1:2] -0.00628 0.87033
#> ..- attr(*, "names")= chr [1:2] "(Intercept)" "x"
#> $ method : symbol lm
#> $ exp :Class 'formula' language y ~ x
#> .. ..- attr(*, ".Environment")=<environment: 0x0000019ed274a390>
You can use the result of heatpointto plot by yourself, also can use
the the R scription in R packages namesplot
xplot(dat_result)
#> Warning in is.na(x): is.na()不适用于类别为'language'的非串列或非
#> 矢量
vennplot
Draw an area-proportional Venn diagram of 2 or 3 circles change by
wangjiaxuan The original function from bioVenn R
packages(https://cran.r-project.org/web/packages/BioVenn/index.html),
I improve the data output and delete the output parameter. on the next
step , I plan to add the ggplot2 on the function which draw venn plot.
or change the postion of group title name whcih is on the centre of
circle now, very ugly.
This function creates an area-proportional Venn diagram of 2 or 3
circles, based on lists of (biological) identifiers. It requires three
parameters: input lists X, Y and Z. For a 2-circle Venn diagram, one of
these lists should be left empty. Duplicate identifiers are removed
automatically.
When using a BioVenn diagram for a publication, please cite: BioVenn - a
web application for the comparison and visualization of biological lists
using area-proportional Venn diagrams T. Hulsen, J. de Vlieg and W.
Alkema, BMC Genomics 2008, 9 (1): 488
list_x <- c("1007_s_at","1053_at","117_at","121_at","1255_g_at","1294_at")
list_y <- c("1255_g_at","1294_at","1316_at","1320_at","1405_i_at")
list_z <- c("1007_s_at","1405_i_at","1255_g_at","1431_at","1438_at","1487_at","1494_f_at")
biovenn <- vennplot(list_x, list_y, list_z)
#> -----------------------------------------------------------------
#> Venn Data:
#> x.total y.total z.total x.only y_only z_only xuy xuz yuz xny
#> 6 5 7 3 2 4 2 2 2 1
#> xnz ynz xnynz xuyuz
#> 1 1 1 3
#> -----------------------------------------------------------------
## count_range
This function is to be able to calculate counts in various ranges. It
comes with a drawing function. At present, this function is s3 general
function, which can automatically identify the input data type and draw.
data = range_count(x = rnorm(100,5)*100,gap = 100,min_num = 0,max_num = 1000)
print(data)
#> Range Freq
#> 1 0-100 0
#> 2 100-200 0
#> 3 200-300 2
#> 4 300-400 20
#> 5 400-500 29
#> 6 500-600 34
#> 7 600-700 14
#> 8 700-800 1
#> 9 800-900 0
#> 10 900-1000 0
#> 11 >1000 0
xplot(data)
END
devtools::session_info()[["platform"]]
#> setting value
#> version R version 4.0.3 (2020-10-10)
#> os Windows 10 x64
#> system x86_64, mingw32
#> ui RStudio
#> language (EN)
#> collate Chinese (Simplified)_China.936
#> ctype Chinese (Simplified)_China.936
#> tz Asia/Taipei
#> date 2021-03-12
Sys.Date()
#> [1] "2021-03-12"
wangjiaxuan666/xbox documentation built on Sept. 4, 2024, 5:03 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
xbox:wrench:
Author: Jiaxuan Wang :pencil:
The goal of xbox is created with some script writed in my spare time. on the other side, the R packages is created just for myself. My names is Wang Jiaxuan, a chinese name. the packages named “xbox” because the tail name is xuan. like xuan’s tool box(Why not xtoolbox, or xtbox, No! these are so complex).
with the time flewed, the R packages function will be more and more.
update log
update 20201212
- [x] change the function
get_dffix the bug for select the column names - [x] add the function
get_matfor transfer data.frame or tibble to matrix - [x]
get_tbadd thevarfor change the “rownames” to “id”
update 20201227
- [x] add the fucntion ‘need_colors’ to choose the chinese
traditional color palette that disigned by 大东设计. who is a bolger
in tiktok(抖音). When I saw his painting, I feel shock for those
color’s power. So I decide make a R packages to put those color into
a color schemes that everyone can use easily. and you can use
discoto display the color you choose. It is convenient to choose the one which you like most.
update 20210123
- [x] add the
vennplotfunction for draw venn plot, It’s not a simple venn plot , it’s a venn plot can change the intersect area represent the intersect number size. also somebody call it area-proportional Venn diagrams.
Installation
The R packages only can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("wangjiaxuan666/xbox")
Notice, If install failed with the messages
ERROR: dependencies 'KEGGREST', 'qvalue' are not available for package 'xbox'
* removing 'C:/R/library/xbox'
You should install R packages KEGGREST and qvalue firstly with code:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("qvalue")
BiocManager::install("KEGGREST")
Update
It’s best to update before you start use the package. just because it is constantly updated and add new fucntion within.
devtools::update_packages("xbox")
Function
Firstlrmarkdown::render(“README.Rmd”) y, you should library the xbox R
packages.
library(xbox)
need_colors and disco
add the fucntion ‘need_colors’ to choose the chinese traditional color
palette that disigned by 大东设计. who is a bolger in tiktok(抖音). When
I saw his painting, I feel shock for those color’s power. So I decide
make a R packages to put those color into a color schemes that everyone
can use easily. and you can use disco to display the color you choose.
It is convenient to choose the one which you like most.
need_colors("two2")
#> [1] "#8CADA6" "#D19848"
disco("two2")
need_colors("nine1",5)
#> [1] "#9d2c32" "#eb5934" "#c32a2c" "#8c4356" "#494367"
disco("nine1",5)
In my disign, for ease of use. I use the ‘the number which you want’ + ‘id’ to name the color palette, such as “two1”,“two2”,“three1”,“four1”,“nine1”. Even you forget so simple name, it’s doesn’t matter. Beacuse you can use the number directly. Now, in a word, it’s very convenient!!
need_colors(10)
#> Randomly select 10 colors from all the colors.
#> [1] "#494367" "#F1ABAD" "#D19848" "#EFB08A" "#8CADA6" "#F1ABAD"
#> [7] "#eb5934" "#cb6922" "#8c4356" "#c32a2c"
disco(10)
#> Randomly select 10 colors from all the colors.
#> Randomly select 10 colors from all the colors.
#> Randomly select 10 colors from all the colors.
#> Randomly select 10 colors from all the colors.
For more details, please see the bolg “R包xbox-增加中国风配色方案”, which publiced on WeChat official account.Abego
col_ttest
the first function is col_ttest, the function is creat for the
t.test in the two column group to test. the output including the
pvalue,qvalue. Notice,the qvalue is the result of the R packages
qvalue.the
parameter is defult. I think is adjust pvalue method is
bonferroni.
set.seed("20201225")
test = matrix(rnorm(20), 2, 10)
colnames(test) = paste("Test", 1:10, sep = "")
rownames(test) = paste("Gene", 1:2, sep = "")
str(col_ttest(data = test,g1 = 1:5,g2 = 6:10))
#> The number is less than 5000, and adjust pvalue is not recommended
#> 'data.frame': 2 obs. of 11 variables:
#> $ Test1 : num 1.342 0.182
#> $ Test2 : num 0.524 -0.16
#> $ Test3 : num 0.6 1.27
#> $ Test4 : num 1.169 -0.359
#> $ Test5 : num -1.42 -0.24
#> $ Test6 : num 1.237 0.145
#> $ Test7 : num 0.0264 -2.3262
#> $ Test8 : num -0.306 0.251
#> $ Test9 : num -0.386 -0.864
#> $ Test10: num 1.079 0.726
#> $ pv : num 0.855 0.406
t_dat
the t_dat function is uesed for transpose the data. Notice,it’s not
the function transpose in tidyverse or tidyr. t_dat can exchange
the column and row. Make the column names become the row names and the
row names become the column names. the data is tranpose with the names.
it is complex for me to explain it clearly with the English. let’s see
how does it work.
df = mtcars[1:6,1:6]
print(df)
#> mpg cyl disp hp drat wt
#> Mazda RX4 21.0 6 160 110 3.90 2.620
#> Mazda RX4 Wag 21.0 6 160 110 3.90 2.875
#> Datsun 710 22.8 4 108 93 3.85 2.320
#> Hornet 4 Drive 21.4 6 258 110 3.08 3.215
#> Hornet Sportabout 18.7 8 360 175 3.15 3.440
#> Valiant 18.1 6 225 105 2.76 3.460
print(t_dat(df))
#> ... Notice: Use`retype` function in `hablar` packages
#> Should be better to check it again# A tibble: 6 x 6
#> `Mazda RX4` `Mazda RX4 Wag` `Datsun 710` `Hornet 4 Drive`
#> * <dbl> <dbl> <dbl> <dbl>
#> 1 21 21 22.8 21.4
#> 2 6 6 4 6
#> 3 160 160 108 258
#> 4 110 110 93 110
#> 5 3.9 3.9 3.85 3.08
#> 6 2.62 2.88 2.32 3.22
#> # ... with 2 more variables: `Hornet Sportabout` <dbl>,
#> # Valiant <dbl>
Maybe you have see the NOTICE that mentioned the R packages hablar.
The hablar is creat by David Sjoberg. a excellent R packages. You can
see its introdution in Github.
i use the funtion retype to help the change and ensure the column
type. But I still think we should chect the result again by str().
koid_to_pathway
the function is useful the biology. If you have a list of ko number id.
But don’t know the the name or descrption of the ko ID . You should try
the function. I use the kegggetfuntion in R packages KEEGREST which
R packages in
bioconda.
the native function can scrawl the all information form KEGG Database.
But I only need the KEGG A Anoation,KEGG B Anoation and the
KEGG Pathway Anoation. So the koid_to_pathway only extract the the
information of the pathway description. like thie:
koid_to_pathway("ko04075")
#> koid
#> 1 ko04075
#> kegg_AB
#> 1 Environmental Information Processing; Signal transduction
#> desc
#> 1 Plant hormone signal transduction
#Success depends on the network.
#koid <-c("ko00010", "ko00020", "ko00030")
#koid_to_pathway(koid)
get_tb, get_df and get_mat
the simple function! the thing only can be done is change the data.frame
to a tibble. I creat it just because of the funtion rownams_to_column
which’s names is too long!
The simple example is :
head(cars)
#> speed dist
#> 1 4 2
#> 2 4 10
#> 3 7 4
#> 4 7 22
#> 5 8 16
#> 6 9 10
head(get_tb(cars))
#> ...Finished:the input data is a data frame not a tibble
#> # A tibble: 6 x 3
#> id speed dist
#> <chr> <dbl> <dbl>
#> 1 1 4 2
#> 2 2 4 10
#> 3 3 7 4
#> 4 4 7 22
#> 5 5 8 16
#> 6 6 9 10
Not end the sister function get_df, Which change the tibble to
data.frame .
head(mtcars)
#> mpg cyl disp hp drat wt qsec vs am gear
#> Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4
#> Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4
#> Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4
#> Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3
#> Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3
#> Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3
#> carb
#> Mazda RX4 4
#> Mazda RX4 Wag 4
#> Datsun 710 1
#> Hornet 4 Drive 1
#> Hornet Sportabout 2
#> Valiant 1
head(get_df(mtcars))
#> ...Finished:the input data is a data.frame
#> have no change
#> mpg cyl disp hp drat wt qsec vs am gear
#> Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4
#> Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4
#> Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4
#> Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3
#> Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3
#> Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3
#> carb
#> Mazda RX4 4
#> Mazda RX4 Wag 4
#> Datsun 710 1
#> Hornet 4 Drive 1
#> Hornet Sportabout 2
#> Valiant 1
The get_mat function is used for tansfer data whatever which type it
is to a matrix.
head(get_mat(cars))
#> speed dist
#> 1 4 2
#> 2 4 10
#> 3 7 4
#> 4 7 22
#> 5 8 16
#> 6 9 10
the heat density point plot
The function heatpoint is created for the desity plot, It can caculate
the point density in 2-dimension coordinate or 1D coordinate by the
parameter only. Of course, the Main purpose is the plot a density
heatpoint.
points = 5000
x = c(rnorm(points/2))
y = x + rnorm(points/2,sd=0.8)
x = sign(x)*abs(x)^1.3
heatpoint(x,y) -> dat_result
str(dat_result)
#> List of 3
#> $ plot.data :'data.frame': 2500 obs. of 3 variables:
#> ..$ x : num [1:2500] -0.25 -1.664 -0.192 -0.124 2.003 ...
#> ..$ y : num [1:2500] -0.7618 -0.906 0.0769 0.5704 1.5979 ...
#> ..$ Density: num [1:2500] 0.1649 0.0324 0.1933 0.1825 0.0314 ...
#> $ cor.result:List of 3
#> ..$ cor : num 0.777
#> ..$ pvalue: num 0
#> ..$ method:List of 1
#> .. ..$ method: chr "Pearson's product-moment correlation"
#> $ lm.result :List of 3
#> ..$ formula: Named num [1:2] -0.00628 0.87033
#> .. ..- attr(*, "names")= chr [1:2] "(Intercept)" "x"
#> ..$ method : symbol lm
#> ..$ exp :Class 'formula' language y ~ x
#> .. .. ..- attr(*, ".Environment")=<environment: 0x0000019ed274a390>
#> - attr(*, "class")= chr "heatpoint"
#including the three list the plot data
head(dat_result$plot.data)
#> x y Density
#> 1 -0.2496371 -0.76182440 0.16489377
#> 2 -1.6637152 -0.90595769 0.03244137
#> 3 -0.1917298 0.07688888 0.19327535
#> 4 -0.1238316 0.57038733 0.18248076
#> 5 2.0029295 1.59785190 0.03138423
#> 6 0.3509336 -0.74778409 0.08520389
# and the result of cor
data.frame(dat_result$cor.result)
#> cor pvalue method
#> 1 0.7765534 0 Pearson's product-moment correlation
# and the result of lm
str(dat_result$lm.result)
#> List of 3
#> $ formula: Named num [1:2] -0.00628 0.87033
#> ..- attr(*, "names")= chr [1:2] "(Intercept)" "x"
#> $ method : symbol lm
#> $ exp :Class 'formula' language y ~ x
#> .. ..- attr(*, ".Environment")=<environment: 0x0000019ed274a390>
You can use the result of heatpointto plot by yourself, also can use
the the R scription in R packages namesplot
xplot(dat_result)
#> Warning in is.na(x): is.na()不适用于类别为'language'的非串列或非
#> 矢量
vennplot
Draw an area-proportional Venn diagram of 2 or 3 circles change by
wangjiaxuan The original function from bioVenn R
packages(https://cran.r-project.org/web/packages/BioVenn/index.html),
I improve the data output and delete the output parameter. on the next
step , I plan to add the ggplot2 on the function which draw venn plot.
or change the postion of group title name whcih is on the centre of
circle now, very ugly.
This function creates an area-proportional Venn diagram of 2 or 3 circles, based on lists of (biological) identifiers. It requires three parameters: input lists X, Y and Z. For a 2-circle Venn diagram, one of these lists should be left empty. Duplicate identifiers are removed automatically.
When using a BioVenn diagram for a publication, please cite: BioVenn - a web application for the comparison and visualization of biological lists using area-proportional Venn diagrams T. Hulsen, J. de Vlieg and W. Alkema, BMC Genomics 2008, 9 (1): 488
list_x <- c("1007_s_at","1053_at","117_at","121_at","1255_g_at","1294_at")
list_y <- c("1255_g_at","1294_at","1316_at","1320_at","1405_i_at")
list_z <- c("1007_s_at","1405_i_at","1255_g_at","1431_at","1438_at","1487_at","1494_f_at")
biovenn <- vennplot(list_x, list_y, list_z)
#> -----------------------------------------------------------------
#> Venn Data:
#> x.total y.total z.total x.only y_only z_only xuy xuz yuz xny
#> 6 5 7 3 2 4 2 2 2 1
#> xnz ynz xnynz xuyuz
#> 1 1 1 3
#> -----------------------------------------------------------------
## count_range
This function is to be able to calculate counts in various ranges. It comes with a drawing function. At present, this function is s3 general function, which can automatically identify the input data type and draw.
data = range_count(x = rnorm(100,5)*100,gap = 100,min_num = 0,max_num = 1000)
print(data)
#> Range Freq
#> 1 0-100 0
#> 2 100-200 0
#> 3 200-300 2
#> 4 300-400 20
#> 5 400-500 29
#> 6 500-600 34
#> 7 600-700 14
#> 8 700-800 1
#> 9 800-900 0
#> 10 900-1000 0
#> 11 >1000 0
xplot(data)
END
devtools::session_info()[["platform"]]
#> setting value
#> version R version 4.0.3 (2020-10-10)
#> os Windows 10 x64
#> system x86_64, mingw32
#> ui RStudio
#> language (EN)
#> collate Chinese (Simplified)_China.936
#> ctype Chinese (Simplified)_China.936
#> tz Asia/Taipei
#> date 2021-03-12
Sys.Date()
#> [1] "2021-03-12"
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