Tutorials – R survival manual

R tutorial

Description	A step by step guide to start on R
Authors	christine Tranchant-Dubreuil (christine.tranchant@ird.fr)
Creation Date	21/09/2018
Last Modified Date	22/09/2018

Summary

• Starting slowly on R

  • Installing new packages install.packages(packages)
  • Loading librarylibrary
  • Setting a working directory
  • Getting the current working directory getwd

• Importing Data From csv File Into a Dataframe

  • Storing the content of a file allGenomeVersion.csv into a dataframe myGenome - read.csv2() (french format)
  • Displaying the whole dataframe
  • Getting the type - class(dataframe)
  • Getting the structure of the dataframe (data type, number of levels) - str(dataframe)
  • Getting basic stats about te content of a dataframe - summary(dataframe)

• Displaying basic informations about the dataframe structure

  • Column names - names(dataframe) or colnames(dataframe)
  • Lines and columns number - dim(dataframe)
  • Lines number - nrow(dataframe)
  • Columns number - ncol(dataframe)

• Displaying the dataframe content

  • Printing the first lines - head(dataframe)
  • Printing the last lines - last(dataframe)
  • Printing a column - dataframe[colNum|colName] or dataframe$colName
  • Printing one whole line - dataframe[lineNum,]
  • Printing one line, one column - dataframe[lineNum,colNum]
  • Printing some lines - dataframe[LineStart:LineEnd,]
  • Printing one column of some lines - dataframe[c(line1,line2,line3),colNum]

• Manipulating the content of a dataframe

  • Adding a new column
  • Extracting unique values of a column - unique(dataframe$colName)
  • Extracting one part of a dataframe - subset(dataframe)
  • Calculating a sum - sum(dataframe) with filtering on an other column
  • Getting the number of elements in a dataframe - length(dataframe) with filtering on an other column
  • Ordering dataframe on one column
  • Getting summaries of values of a column - summary

• Plotting data from a dataframe with ggplot library

  • Creating a barplot with geom_bar(stat = "identity")
  • Distinct plot - position=position_dodge() and flip plot with coord_flip()
  • Save into a file`
  • Creating a barplot with geom_bar(stat = "count")
  • Plotting a violin plot - geom_violin()

• License

---

Starting slowly on R

Installing New Packages/libraries - install.packages(package)

>install.packages('ggplot2')

Loading libraries - library(package)

>library(ggplot2) # plot
>library(scales)  # plot scales
>library(stringr) # str_detect
>library(gridExtra) # grid`

Setting a Working Directory setwd(path)

>setwd(/users/tranchan/riceAnalysis)

Getting the current Working Directory getwd()

>getwd()
[1] "/Users/tranchan/Documents/Bioanalyse/panGenome"

---

Importing Data From csv File Into a Dataframe

Storing a file allGenomeVersion.csv into the dataframe myGenome - read.csv2() (french format)

#File with 6 columns and 2010 lines
Name    Type    Length  %GC Organism    Type
Chr01   N   32613412    43.53   dna:chromosome  v1
Chr01   N   39656875    43.05   dna:chromosome  v2
Chr02   N   29142869    43.19   dna:chromosome  v1
Chr02   N   34451706    42.81   dna:chromosome  v2
Chr03   N   33053699    43.20   dna:chromosome  v1
Chr03   N   35526804    43.23   dna:chromosome  v2
Chr04   N   26299011    43.54   dna:chromosome  v1
Chr04   N   31572834    43.19   dna:chromosome  v2
Chr05   N   23192814    42.91   dna:chromosome  v1
Chr05   N   26274045    42.82   dna:chromosome  v2
Chr06   N   24174485    42.90   dna:chromosome  v1
Chr06   N   29275208    42.90   dna:chromosome  v2
Chr07   N   21799424    43.09   dna:chromosome  v1
Chr07   N   26599614    42.82   dna:chromosome  v2
Chr08   N   20292731    42.97   dna:chromosome  v1
Chr08   N   25472747    42.64   dna:chromosome  v2
Chr09   N   17607432    43.51   dna:chromosome  v1
Chr09   N   21796211    42.91   dna:chromosome  v2
Chr10   N   16910673    43.10   dna:chromosome  v1
Chr10   N   22418184    42.96   dna:chromosome  v2
Chr11   N   20796451    42.59   dna:chromosome  v1
Chr11   N   26393634    42.28   dna:chromosome  v2
Chr12   N   19154523    42.47   dna:chromosome  v1
Chr12   N   25020143    42.38   dna:chromosome  v2
ADWL01002872.1  N   9871    37.93   dna:scaffold    v1
ADWL01002880.1  N   2202    52.91   dna:scaffold    v1
ADWL01002881.1  N   2284    63.09   dna:scaffold    v1
....

>myGenome <- read.csv2("allGenomeVersion.csv",header = TRUE)

Displaying the whole dataframe

>myGenome

Name  Type  Length  X.GC  Organism  Type.1
Chr10   N   16910673    43.10   dna:chromosome  v1
Chr09   N   17607432    43.51   dna:chromosome  v1
Chr12   N   19154523    42.47   dna:chromosome  v1
Chr08   N   20292731    42.97   dna:chromosome  v1
Chr11   N   20796451    42.59   dna:chromosome  v1
Chr07   N   21799424    43.09   dna:chromosome  v1
....

Get the type - class(dataframe)

>class(myGenome)
[1] "data.frame"

Get the structure of the dataframe (data type, number of levels) - str(dataframe)

>str(myGenome) 
'data.frame': 2010 obs. of  6 variables:
 $ Name    : Factor w/ 1998 levels "ADWL01002872.1",..: 1210 1209 1212 1208 1211 1207 1205 1206 1204 1202 ...
 $ Type    : Factor w/ 1 level "N": 1 1 1 1 1 1 1 1 1 1 ...
 $ Length  : int  16910673 17607432 19154523 20292731 20796451 21799424 23192814 24174485 26299011 29142869 ...
 $ X.GC    : Factor w/ 1191 levels "26.08","30.70",..: 512 547 454 499 466 511 493 492 550 520 ...
 $ Organism: Factor w/ 2 levels "dna:chromosome",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Type.1  : Factor w/ 2 levels "v1","v2": 1 1 1 1 1 1 1 1 1 1 ...

Get basics stats about dataframe content - summary(dataframe)

>summary(myGenome)
      Name      Type         Length              X.GC                Organism    Type.1   
 Chr01  :   2   N:2010   Min.   :    1026   44.90  :   7   dna:chromosome:  24   v1:1951  
 Chr02  :   2            1st Qu.:    3229   41.27  :   6   dna:scaffold  :1986   v2:  59  
 Chr03  :   2            Median :    5742   41.73  :   6                                  
 Chr04  :   2            Mean   :  330219   42.82  :   6                                  
 Chr05  :   2            3rd Qu.:   13154   42.91  :   6                                  
 Chr06  :   2            Max.   :39656875   43.00  :   6                                  
 (Other):1998                               (Other):1973  

---

Displaying basic informations about the dataframe structure

Column names - names(dataframe) or colnames(dataframe)

> names(myGenome)
[1] "Name"     "Type"     "Length"   "X.GC"     "Organism" "Type.1"  

> colnames(myGenome)
[1] "Name"     "Type"     "Length"   "X.GC"     "Organism" "Type.1"  

Lines and columns number - dim(dataframe)

> dim(myGenome) 
[1] 2010    6

Lines number - nrow(dataframe)

> nrow(myGenome)
[1] 2010

Columns number - ncol(dataframe)]

> ncol(myGenome)
[1] 6

---

Displaying and manipulating the dataframe content

Printing the first lines - head(dataframe)

> head(myGenome)
   Name Type   Length  X.GC       Organism Type.1
1 Chr10    N 16910673 43.10 dna:chromosome     v1
2 Chr09    N 17607432 43.51 dna:chromosome     v1
3 Chr12    N 19154523 42.47 dna:chromosome     v1
4 Chr08    N 20292731 42.97 dna:chromosome     v1
5 Chr11    N 20796451 42.59 dna:chromosome     v1
6 Chr07    N 21799424 43.09 dna:chromosome     v1

> head(myGenome,n=2)
   Name Type   Length  X.GC       Organism Type.1
1 Chr10    N 16910673 43.10 dna:chromosome     v1
2 Chr09    N 17607432 43.51 dna:chromosome     v1

Printing the last lines - last(dataframe)

> tail(myGenome)
            Name Type Length  X.GC     Organism Type.1
2005 ChrUN-Ctg78    N  21053 43.05 dna:scaffold     v2
2006 ChrUN-Ctg79    N  19363 40.63 dna:scaffold     v2
2007 ChrUN-Ctg80    N  15054 41.18 dna:scaffold     v2
2008 ChrUN-Ctg81    N  13998 42.98 dna:scaffold     v2
2009 ChrUN-Ctg82    N  13471 42.29 dna:scaffold     v2
2010 ChrUN-Ctg83    N  10534 42.37 dna:scaffold     v2

> tail(myGenome,n=1)
            Name Type Length  X.GC     Organism Type.1
2010 ChrUN-Ctg83    N  10534 42.37 dna:scaffold     v2

Printing a column - dataframe[colNum|colName] or dataframe$colName

• output result (line)

> head(myGenome[,"Length"])
[1] 16910673 17607432 19154523 20292731 20796451 21799424

> head(myGenome$Length) 
[1] 16910673 17607432 19154523 20292731 20796451 21799424

• output result (column)

> head(myGenome[1])
   Name
1 Chr10
2 Chr09
3 Chr12
4 Chr08
5 Chr11
6 Chr07

> head(myGenome['Length'])
    Length
1 16910673
2 17607432
3 19154523
4 20292731
5 20796451
6 21799424

Printing one whole line - dataframe[lineNum,]

Line 2

> myGenome[2,]
   Name Type   Length  X.GC       Organism Type.1
2 Chr09    N 17607432 43.51 dna:chromosome     v1

Printing one line, one column - dataframe[lineNum,colNum]

Line 2, column 6 then line 2, column 3

> myGenome[2,6]
[1] v1
Levels: v1 v2

> myGenome[2,3]
[1] 17607432

Printing some lines - dataframe[LineStart:LineEnd,]

> myGenome[1:3,]
   Name Type   Length  X.GC       Organism Type.1
1 Chr10    N 16910673 43.10 dna:chromosome     v1
2 Chr09    N 17607432 43.51 dna:chromosome     v1
3 Chr12    N 19154523 42.47 dna:chromosome     v1

> myGenome[10:15,]
    Name Type   Length  X.GC       Organism Type.1
10 Chr02    N 29142869 43.19 dna:chromosome     v1
11 Chr01    N 32613412 43.53 dna:chromosome     v1
12 Chr03    N 33053699 43.20 dna:chromosome     v1
13 Chr01    N 39656875 43.05 dna:chromosome     v2
14 Chr02    N 34451706 42.81 dna:chromosome     v2
15 Chr03    N 35526804 43.23 dna:chromosome     v2

Printing one column of some lines - dataframe[c(line1,line2,line3),colNum]

Print the complete lines 1, 3, 7, 6 then just the column 3 of the same lines

> myGenome[c(1,3,7,6),]
   Name Type   Length  X.GC       Organism Type.1
1 Chr10    N 16910673 43.10 dna:chromosome     v1
3 Chr12    N 19154523 42.47 dna:chromosome     v1
7 Chr05    N 23192814 42.91 dna:chromosome     v1
6 Chr07    N 21799424 43.09 dna:chromosome     v1

> myGenome[c(1,3,7,6),3]
[1] 16910673 19154523 23192814 21799424

---

Manipulating the content of a dataframe

Adding a new column

• A new collumn firstly filled with "NA"

  
  > myGenome["mb"] <- NA 

head(myGenome)
Name Type Length X.GC Organism Type.1 mb
1 Chr10 N 16910673 43.10 dna:chromosome v1 NA
2 Chr09 N 17607432 43.51 dna:chromosome v1 NA
3 Chr12 N 19154523 42.47 dna:chromosome v1 NA
4 Chr08 N 20292731 42.97 dna:chromosome v1 NA
5 Chr11 N 20796451 42.59 dna:chromosome v1 NA
6 Chr07 N 21799424 43.09 dna:chromosome v1 NA

• The new column receives the result of an operation

  
  > myGenome$mb <- as.integer(myGenome$Length/1000000)

head(myGenome)
Name Type Length X.GC Organism Type.1 mb
1 Chr10 N 16910673 43.10 dna:chromosome v1 16
2 Chr09 N 17607432 43.51 dna:chromosome v1 17
3 Chr12 N 19154523 42.47 dna:chromosome v1 19
4 Chr08 N 20292731 42.97 dna:chromosome v1 20
5 Chr11 N 20796451 42.59 dna:chromosome v1 20
6 Chr07 N 21799424 43.09 dna:chromosome v1 21

Extracting unique values of a column - unique(dataframe$colName)

> myGenome$Organism
   [1] dna:chromosome dna:chromosome dna:chromosome dna:chromosome dna:chromosome dna:chromosome dna:chromosome
   [8] dna:chromosome dna:chromosome dna:chromosome dna:chromosome dna:chromosome dna:chromosome dna:chromosome
  [15] dna:chromosome dna:chromosome dna:chromosome dna:chromosome dna:chromosome dna:chromosome dna:chromosome
  [22] dna:chromosome dna:chromosome dna:chromosome dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold  
  [29] dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold  
  [36] dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold  
  [43] dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold  
  [50] dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold  
  [57] dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold  
  [64] dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold  
 ... 
 [981] dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold  
 [988] dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold  
 [995] dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold   dna:scaffold  
 [ reached getOption("max.print") -- omitted 1010 entries ]
Levels: dna:chromosome dna:scaffold

> unique(myRef$Organism)
[1] dna:chromosome dna:scaffold  
Levels: dna:chromosome dna:scaffold

> unique(myGenomef$Type.1)
[1] v1 v2
Levels: v1 v2

Extracting one part of a dataframe into a new dataframe - subset(dataframe)

• Extract only lines about chromosome (Organism column equals to "dna:chromosome") - ==

> myGenomeSubset <- subset(myGenome, Organism=="dna:chromosome")
> head(myrefGenome)

> myrefSubset <- subset(myGenome, Organism=="dna:chromosome")
> head(myrefGenome)
   Name Type   Length  X.GC       Organism Type.1 mb
1 Chr10    N 16910673 43.10 dna:chromosome     v1 16
2 Chr09    N 17607432 43.51 dna:chromosome     v1 17
3 Chr12    N 19154523 42.47 dna:chromosome     v1 19
4 Chr08    N 20292731 42.97 dna:chromosome     v1 20
5 Chr11    N 20796451 42.59 dna:chromosome     v1 20
6 Chr07    N 21799424 43.09 dna:chromosome     v1 21

> dim(myRGenome)
[1] 2010    7

> dim(myGenomeSubset)
[1] 24  7

• Extract only lines about scaffold (Organism column not equals to "dna:chromosome") - !=

> myScaffSubset <- subset(myRef, Organism!="dna:chromosome")

> head(myScaffSubset)
                  Name Type Length  X.GC     Organism Type.1 mb
25 Oglab05_unplaced019    N   1026 54.29 dna:scaffold     v1  0
26      ADWL01015058.1    N   1067 47.14 dna:scaffold     v1  0
27 Oglab04_unplaced014    N   1099 43.68 dna:scaffold     v1  0
28      ADWL01009716.1    N   1200 50.08 dna:scaffold     v1  0
29 Oglab04_unplaced051    N   1834 48.39 dna:scaffold     v1  0
30      ADWL01025219.1    N   2000 54.35 dna:scaffold     v1  0

Calculating a sum - sum(dataframe) with filtering on an other column

> sum(myGenomeSubset$Length)
[1] 629495529

> sum(myGenomeSubset$Length[myGenomeSubset$Type.1=="v1"])
[1] 285037524

> sum(myGenomeSubset$Length[myGenomeSubset$Type.1=="v2"])
[1] 344458005

Getting the number of elements in a dataframe - length(dataframe) with filtering on an other column

> length(myGenomeSubset$Length[myGenomeSubset$Type.1=="v1"])
[1] 1939

> sum(myGenomeSubset$Length[myGenomeSubset$Type.1=="v1"])
[1] 31382050

> length(myGenomeSubset$Length[myGenomeSubset$Type.1=="v2"])
[1] 47

> sum(myGenomeSubset$Length[myGenomeSubset$Type.1=="v2"])
[1] 2863041

Ordering dataframe on one column

myGenomeOrdered <- myGenomeSubset[order(myGenomeSubset$Name),]

> head(myGenomeOrdered)
    Name Type   Length  X.GC       Organism Type.1 mb
11 Chr01    N 32613412 43.53 dna:chromosome     v1 32
13 Chr01    N 39656875 43.05 dna:chromosome     v2 39
10 Chr02    N 29142869 43.19 dna:chromosome     v1 29
14 Chr02    N 34451706 42.81 dna:chromosome     v2 34
12 Chr03    N 33053699 43.20 dna:chromosome     v1 33
15 Chr03    N 35526804 43.23 dna:chromosome     v2 35

Getting summaries of values of a column - summary

> summary(mysSaffSubset$Length)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1026    3214    5613   17243   12601 1478587 

---

Plotting data from a dataframe with ggplot library

To build a plot, the same following basic template can be used for several plot types :

ggplot(data = <DATA>, aes = (<MAPPINGS>)) + <GEOM_FUNCTION>()

• use the ggplot() function and bind the plot to a specific data frame using the data argument
• use the aes function to select the variables to be plotted and to define how tu present them in the graph (e.g. x/y positions, size, shape, color)
• add ‘geoms’ – graphical representations of the data in the plot (points, lines, bars) such as geom_point(), geom_boxplot(),geom_line(), geom-bar()

with ggplot() + geom_bar(stat = "identity")]

ggplot is a plotting package that makes simple to create complex plots from data stored in a data frame. It provides a programmatic interface for specifying what variables to plot, how they are displayed, and general visual properties.

https://datacarpentry.org/r-socialsci/04-ggplot2/

Creating a barplot with geom_bar(stat = "identity")]

Barplots are useful for visualizing categorical data. By default, geom_bar accepts a variable for x, and plots the number of instances each value of x (in this case, chromosome) appears in the dataset.

Here, we will :

• plot the size (mb) of each chromosome from each genome version with geom_bar
• use the fill aesthetic for the geom_bar() geom to color bars by the size of each chromosome for each genome version
• By default, geom_bar uses stat="bin" to make the height of each bar equal to the number of cases in each group (it is incompatible with mapping values to the y aesthetic). Here we use stat="identity" because we want the heights of the bars to represent values in the data (with a y aesthetic).

http://www.sthda.com/french/wiki/ggplot2-barplots-guide-de-demarrage-rapide-logiciel-r-et-visualisation-de-donnees#barplots-basiques

#basic barplot
p <- ggplot(data = myGenomeSubset, aes(x = Name, y=mb, fill=Type.1)) + 
      geom_bar(stat = "identity")
p 

distinct plot - position=position_dodge() and flip plot with coord_flip()

• We can separate the portions of the stacked bar that correspond to each genome version and put them side-by-side by using the position argument for geom_bar() and setting it to “dodge”.

• coord_flip(): Flip cartesian coordinates so that horizontal becomes vertical, and vertical, horizontal

#basic barplot
q <- ggplot(data = myGenomeSubset, aes(x = Name, y=mb, fill=Type.1)) + 
      geom_bar(stat = "identity", position=position_dodge())
#horizontal
q + coord_flip()

save into a file

jpeg(file="pseudomolOMAP.jpg");
#basic barplot
q <- ggplot(data = myGenomeSubset, aes(x = Name, y=mb, fill=Type.1)) + 
      geom_bar(stat = "identity", position=position_dodge())
#horizontal
q + coord_flip()
dev.off;

---

Creating a barplot with geom_bar(stat = "count")]

• we use stat="count" to make the height of each bar equal to the count of lines
• Adding Labels and Titles with ylab(<Y-LABEL>), xlab(<X-LABEL>) , ggtitle(<TITLE-LABEL>)
  

p <- ggplot(myscaffSubset, aes(x=Type.1)) + geom_bar(stat="count", width=0.4, fill="steelblue") +
    xlab("Genome version") +
    ylab("Scaffold number") +
    ggtitle("Number of scaffold per genome version")
p

---

Plotting a violin plot - geom_violin()

• Create a violin plot

p <- ggplot(myScaffSubset, aes(x=Type.1, y=Length, color=Type.1)) + 
+   geom_violin(trim=TRUE) + stat_summary(fun.y=median, geom="point", size=2, color="red")
> p

• Save plot into a file

  
  > jpeg(file="scaffoldOMAP.jpg");
  > p <- ggplot(myScaffSubset, aes(x=Type.1, y=Length, color=Type.1)) + 

• geom_violin(trim=TRUE) + stat_summary(fun.y=median, geom="point", size=2, color="red")
  

  p
  dev.off;

---

Arranging plots in a grid - grid.arrange()

myplot1 <- ggplot(data = myGenomeSubset, aes(x = Name, y=mb, fill=Type.1)) + 
      geom_bar(stat = "identity", position=position_dodge()) + coord_flip()

myplot2 <- ggplot(myScaffSubset, aes(x=Type.1, y=Length, color=Type.1)) + 
  geom_violin(trim=TRUE) + stat_summary(fun.y=median, geom="point", size=2, color="red")

grid.arrange(myplot1, myplot2, ncol=2)

---

Displaying and manipulating the dataframe content

Printing the first lines - head(dataframe)

---

License