Lesson 12: Data management & transformation in R
Modesto
2022-12-14 (17:11 h)
Wellcome & Disclaimer
This site contains the materials for the Coding tools for Biochemistry & Molecular Biology (Herramientas de Programación para Bioquímica y Biología Molecular) course of fall 2022 in the Bachelor’s Degree in Biochemistry @UAM. This materials are the basis for GitHub-pages-based website that can be accessed here. Detailed academic information about the course contents, dates and assessment only can be found at the UAM Moodle site.
All this material is open access and it is shared under CC BY-NC license.
Transform dataframes
Sorting and subsetting data
When you want to present or analyze your data in R you often need to
order a vector or a dataframe by the value of a variable. This order
might be increasing or decreasing, numeric or alphabetic. There are two
main functions in R to arrange your data, sort() and
order().
Let’s see an example using the dataset coli_genomes_renamed.csv that we saved in the Lesson 9.
# load the dataset
coli_genomes <- read.csv2(file = "data/coli_genomes_renamed.csv")
# test sort() & order() in a vector
order(coli_genomes$Year)## [1] 15 7 8 6 3 2 5 14 16 17 18 20 22 9 10 11 12 13 19 21 1 4sort(coli_genomes$Year)## [1] 2005 2006 2006 2007 2008 2010 2011 2011 2012 2012 2012 2012 2012 2013 2013
## [16] 2013 2013 2013 2015 2016sort(coli_genomes$Year, decreasing = TRUE)## [1] 2016 2015 2013 2013 2013 2013 2013 2012 2012 2012 2012 2012 2011 2011 2010
## [16] 2008 2007 2006 2006 2005order(coli_genomes$Phylogroup)## [1] 4 5 6 7 8 12 13 18 20 21 3 14 22 2 15 16 17 19 1 9 10 11sort(coli_genomes$Phylogroup)## [1] "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "B1" "B1" "B1" "C" "C"
## [16] "C" "C" "C" "D" "D" "D" "D"As you noticed, the R order() function returns a
permutation of the order of the elements of a vector. The output is an
index vector, not the vector itself. Note also that if the vector
contains any NA values there will be at the end of the
index vector by default.
On the other hand, the sort() function returns sorted,
in ascending order by default, the vector you pass as input.
Both function will arrange the data increasingly, and can be used for
numeric or string variables. If you set the decreasing
argument to TRUE in a sort() or
order(), you will have the vector of indices in descending
order.
You can use these functions also to arrange a dataframe by one vector. Alternatively, you may want to reverse order or shuffle a dataframe.
# order the dataframe by one column, sort or order?
coli_genomes[order(coli_genomes$Year), ]coli_genomes[sort(coli_genomes$Year), ] #what happened?# Custom order of rows (random)
coli_genomes[sample(nrow(coli_genomes), replace = FALSE), ]# Reverse order of rows
coli_genomes[nrow(coli_genomes):1, ]Now, imagine you have a large dataset and you want to subset only
some cases (=rows). You know already some ways to do that, but in the
following example we are going to test the function
subset() that is very convenient when you want to extract
the cases that that fulfill several conditions.
Imagine that for a new project we want to select the strains with better genome draft quality or from some subgroup. We will keep strains from phylogrupo A, with an assembly N50>150,000 bp and less than 100 contigs >= 1 kb.
# option 1: multi-step
selection <- coli_genomes[coli_genomes$contigs1kb < 100, ]
selection <- selection[selection$N50 > 150000, ]
selection <- selection[selection$Phylogroup == "A", ]
# option 2: several which statements
selection2 <- coli_genomes[which(coli_genomes$contigs1kb < 100 &
coli_genomes$N50 > 150000 & coli_genomes$Phylogroup == "A"),
]
# option 2b:with attach(dataframe) you avoid repeat the
# name of the dataframe
attach(coli_genomes)
selection2 <- coli_genomes[which(contigs1kb < 100 & N50 > 150000 &
Phylogroup == "A"), ]
detach(coli_genomes) #detach dataframe
# option 3: subset() does the same
selection3 <- subset(coli_genomes, subset = contigs1kb < 100 &
N50 > 150000 & Phylogroup == "A")
# Are they the same?
all.equal(selection, selection2)## [1] TRUEall.equal(selection, selection3)## [1] TRUEIt you want to verify that the three methods will give rise to the
same output you can review the dataframe, or check their dimensions or
structure with dim() or str(), but that is
difficult for large datasets with hundreds or thousands of cases (see
below). The function all.equal() will do that for you.
Combining dataframes
When you work with a lot of data, you may have several tables and
sometimes you want to merge the tables in different ways. An easy way
can be the use of cbind() or rbind() if you
have the same number of rows and columns. A more handy way is using the
function merge(). We will see some alternatives using the
coli_genomes dataframe and a new table, named
colis3.csv.
colis <- read.csv2(file = "data/colis3.csv")
# rbind() and cbind()
rbind(coli_genomes, colis) #did it work? why??## Error in rbind(deparse.level, ...): numbers of columns of arguments do not matchcbind(coli_genomes, colis)## Error in data.frame(..., check.names = FALSE): arguments imply differing number of rows: 22, 27# merge()
merge <- merge(coli_genomes, colis) #inner join
str(merge)## 'data.frame': 22 obs. of 24 variables:
## $ Strain : chr "LREC237" "LREC239" "LREC240" "LREC241" ...
## $ Year : int NA 2010 2008 NA 2011 2007 2006 2006 2013 2013 ...
## $ Biosample : chr "SAMN14278613" "SAMN14278614" "SAMN14278615" "SAMN14278616" ...
## $ Source : chr "Human" "Human" "Human" "Human" ...
## $ Phylogroup : chr "D" "C" "B1" "A" ...
## $ Serotype : chr "ONT:H28" "O153:H19" "O76:H30" "O78:H11" ...
## $ Clonotype : chr "CH23-331" "CH4-25" "CH29-38" "CH11-41" ...
## $ Sequence.Type : chr "ST524" "ST88" "ST156" "ST48" ...
## $ VF : int 18 14 10 5 5 7 4 2 22 22 ...
## $ Plasmids : int 3 3 2 3 9 3 7 7 1 3 ...
## $ kmer : int 117 117 89 117 89 93 115 115 115 111 ...
## $ Contigs : int 223 159 114 212 320 158 277 203 376 205 ...
## $ N50 : int 272287 323172 270767 112160 45936 106897 89185 94368 200150 281589 ...
## $ longest.contig..bp.: int 662555 760527 738861 285056 128053 369508 281444 280268 424527 617142 ...
## $ Assembly_length : int 5341632 5415613 4875343 5167401 4858138 4638334 5406295 4796593 5389075 5340478 ...
## $ contigs1kb : int 74 57 47 101 212 93 155 114 131 78 ...
## $ average_contig : num 23954 34060 42766 24375 15182 ...
## $ X : int 1 2 3 4 5 6 7 8 12 13 ...
## $ CRISPR : int 9 12 5 9 9 10 7 10 7 7 ...
## $ Cas : chr "[CAS-TypeIE (n=1),]" "[CAS-TypeIE (n=1),]" "[CAS-TypeIE (n=1),]" "[CAS (n=1),, CAS (n=1),, CAS (n=1),, CAS-TypeIE (n=1),]" ...
## $ AMR : int 2 2 5 2 4 2 2 4 2 2 ...
## $ Vir : int 20 12 10 3 4 6 7 4 26 21 ...
## $ Integron : int 0 0 0 0 0 0 0 0 0 0 ...
## $ seqs : chr "2020-07-24" "2019-02-27" "2018-03-08" "2020-10-12" ...merge1 <- merge(coli_genomes, colis, by = "Strain")
str(merge1)## 'data.frame': 22 obs. of 25 variables:
## $ Strain : chr "LREC237" "LREC239" "LREC240" "LREC241" ...
## $ Biosample : chr "SAMN14278613" "SAMN14278614" "SAMN14278615" "SAMN14278616" ...
## $ Year.x : int NA 2010 2008 NA 2011 2007 2006 2006 2013 2013 ...
## $ Source : chr "Human" "Human" "Human" "Human" ...
## $ Phylogroup : chr "D" "C" "B1" "A" ...
## $ Serotype : chr "ONT:H28" "O153:H19" "O76:H30" "O78:H11" ...
## $ Clonotype : chr "CH23-331" "CH4-25" "CH29-38" "CH11-41" ...
## $ Sequence.Type : chr "ST524" "ST88" "ST156" "ST48" ...
## $ VF : int 18 14 10 5 5 7 4 2 22 22 ...
## $ Plasmids : int 3 3 2 3 9 3 7 7 1 3 ...
## $ kmer : int 117 117 89 117 89 93 115 115 115 111 ...
## $ Contigs : int 223 159 114 212 320 158 277 203 376 205 ...
## $ N50 : int 272287 323172 270767 112160 45936 106897 89185 94368 200150 281589 ...
## $ longest.contig..bp.: int 662555 760527 738861 285056 128053 369508 281444 280268 424527 617142 ...
## $ Assembly_length : int 5341632 5415613 4875343 5167401 4858138 4638334 5406295 4796593 5389075 5340478 ...
## $ contigs1kb : int 74 57 47 101 212 93 155 114 131 78 ...
## $ average_contig : num 23954 34060 42766 24375 15182 ...
## $ X : int 1 2 3 4 5 6 7 8 12 13 ...
## $ Year.y : int NA 2010 2008 NA 2011 2007 2006 2006 2013 2013 ...
## $ CRISPR : int 9 12 5 9 9 10 7 10 7 7 ...
## $ Cas : chr "[CAS-TypeIE (n=1),]" "[CAS-TypeIE (n=1),]" "[CAS-TypeIE (n=1),]" "[CAS (n=1),, CAS (n=1),, CAS (n=1),, CAS-TypeIE (n=1),]" ...
## $ AMR : int 2 2 5 2 4 2 2 4 2 2 ...
## $ Vir : int 20 12 10 3 4 6 7 4 26 21 ...
## $ Integron : int 0 0 0 0 0 0 0 0 0 0 ...
## $ seqs : chr "2020-07-24" "2019-02-27" "2018-03-08" "2020-10-12" ...merge2 <- merge(coli_genomes, colis, by = "Year")
str(merge2)## 'data.frame': 81 obs. of 25 variables:
## $ Year : int 2005 2006 2006 2006 2006 2007 2008 2010 2010 2011 ...
## $ Strain.x : chr "LREC255" "LREC244" "LREC244" "LREC245" ...
## $ Biosample : chr "SAMN14278630" "SAMN14278619" "SAMN14278619" "SAMN14278620" ...
## $ Source : chr "Avian" "Porcine" "Porcine" "Porcine" ...
## $ Phylogroup : chr "C" "A" "A" "A" ...
## $ Serotype : chr "O78:H19" "O2:H32" "O2:H32" "ONT:H45" ...
## $ Clonotype : chr "CH4-27" "CH11-23" "CH11-23" "C11-398" ...
## $ Sequence.Type : chr "ST88" "ST10" "ST10" "ST10888" ...
## $ VF : int 11 4 4 2 2 7 10 14 14 5 ...
## $ Plasmids : int 4 7 7 7 7 3 2 3 3 9 ...
## $ kmer : int 113 115 115 115 115 93 89 117 117 89 ...
## $ Contigs : int 108 277 277 203 203 158 114 159 159 320 ...
## $ N50 : int 405376 89185 89185 94368 94368 106897 270767 323172 323172 45936 ...
## $ longest.contig..bp.: int 1190696 281444 281444 280268 280268 369508 738861 760527 760527 128053 ...
## $ Assembly_length : int 5196698 5406295 5406295 4796593 4796593 4638334 4875343 5415613 5415613 4858138 ...
## $ contigs1kb : int 38 155 155 114 114 93 47 57 57 212 ...
## $ average_contig : num 48118 19517 19517 23629 23629 ...
## $ X : int 18 8 7 8 7 6 3 9 2 5 ...
## $ Strain.y : chr "LREC255" "LREC245" "LREC244" "LREC245" ...
## $ CRISPR : int 10 10 7 10 7 10 5 6 12 9 ...
## $ Cas : chr "[CAS-TypeIE (n=1),, CAS-TypeID (n=1),]" "[CAS (n=1),, CAS (n=1),, CAS-TypeIE (n=1),, CAS (n=1),, CAS (n=1),]" "[CAS (n=1),, CAS (n=1),, CAS (n=1),]" "[CAS (n=1),, CAS (n=1),, CAS-TypeIE (n=1),, CAS (n=1),, CAS (n=1),]" ...
## $ AMR : int NA 4 2 4 2 2 5 4 2 4 ...
## $ Vir : int 7 4 7 4 7 6 10 8 12 4 ...
## $ Integron : int 0 0 0 0 0 0 0 0 0 0 ...
## $ seqs : chr "2020-08-03" "2019-12-30" "2018-08-14" "2019-12-30" ...merge3 <- merge(coli_genomes, colis, by = c("Strain", "Year"))
str(merge3)## 'data.frame': 22 obs. of 24 variables:
## $ Strain : chr "LREC237" "LREC239" "LREC240" "LREC241" ...
## $ Year : int NA 2010 2008 NA 2011 2007 2006 2006 2013 2013 ...
## $ Biosample : chr "SAMN14278613" "SAMN14278614" "SAMN14278615" "SAMN14278616" ...
## $ Source : chr "Human" "Human" "Human" "Human" ...
## $ Phylogroup : chr "D" "C" "B1" "A" ...
## $ Serotype : chr "ONT:H28" "O153:H19" "O76:H30" "O78:H11" ...
## $ Clonotype : chr "CH23-331" "CH4-25" "CH29-38" "CH11-41" ...
## $ Sequence.Type : chr "ST524" "ST88" "ST156" "ST48" ...
## $ VF : int 18 14 10 5 5 7 4 2 22 22 ...
## $ Plasmids : int 3 3 2 3 9 3 7 7 1 3 ...
## $ kmer : int 117 117 89 117 89 93 115 115 115 111 ...
## $ Contigs : int 223 159 114 212 320 158 277 203 376 205 ...
## $ N50 : int 272287 323172 270767 112160 45936 106897 89185 94368 200150 281589 ...
## $ longest.contig..bp.: int 662555 760527 738861 285056 128053 369508 281444 280268 424527 617142 ...
## $ Assembly_length : int 5341632 5415613 4875343 5167401 4858138 4638334 5406295 4796593 5389075 5340478 ...
## $ contigs1kb : int 74 57 47 101 212 93 155 114 131 78 ...
## $ average_contig : num 23954 34060 42766 24375 15182 ...
## $ X : int 1 2 3 4 5 6 7 8 12 13 ...
## $ CRISPR : int 9 12 5 9 9 10 7 10 7 7 ...
## $ Cas : chr "[CAS-TypeIE (n=1),]" "[CAS-TypeIE (n=1),]" "[CAS-TypeIE (n=1),]" "[CAS (n=1),, CAS (n=1),, CAS (n=1),, CAS-TypeIE (n=1),]" ...
## $ AMR : int 2 2 5 2 4 2 2 4 2 2 ...
## $ Vir : int 20 12 10 3 4 6 7 4 26 21 ...
## $ Integron : int 0 0 0 0 0 0 0 0 0 0 ...
## $ seqs : chr "2020-07-24" "2019-02-27" "2018-03-08" "2020-10-12" ...merge4 <- merge(coli_genomes, colis, all = TRUE) #outer join
str(merge4)## 'data.frame': 27 obs. of 24 variables:
## $ Strain : chr "LREC237" "LREC239" "LREC240" "LREC241" ...
## $ Year : int NA 2010 2008 NA 2011 2007 2006 2006 2010 2013 ...
## $ Biosample : chr "SAMN14278613" "SAMN14278614" "SAMN14278615" "SAMN14278616" ...
## $ Source : chr "Human" "Human" "Human" "Human" ...
## $ Phylogroup : chr "D" "C" "B1" "A" ...
## $ Serotype : chr "ONT:H28" "O153:H19" "O76:H30" "O78:H11" ...
## $ Clonotype : chr "CH23-331" "CH4-25" "CH29-38" "CH11-41" ...
## $ Sequence.Type : chr "ST524" "ST88" "ST156" "ST48" ...
## $ VF : int 18 14 10 5 5 7 4 2 NA NA ...
## $ Plasmids : int 3 3 2 3 9 3 7 7 NA NA ...
## $ kmer : int 117 117 89 117 89 93 115 115 NA NA ...
## $ Contigs : int 223 159 114 212 320 158 277 203 NA NA ...
## $ N50 : int 272287 323172 270767 112160 45936 106897 89185 94368 NA NA ...
## $ longest.contig..bp.: int 662555 760527 738861 285056 128053 369508 281444 280268 NA NA ...
## $ Assembly_length : int 5341632 5415613 4875343 5167401 4858138 4638334 5406295 4796593 NA NA ...
## $ contigs1kb : int 74 57 47 101 212 93 155 114 NA NA ...
## $ average_contig : num 23954 34060 42766 24375 15182 ...
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ CRISPR : int 9 12 5 9 9 10 7 10 6 8 ...
## $ Cas : chr "[CAS-TypeIE (n=1),]" "[CAS-TypeIE (n=1),]" "[CAS-TypeIE (n=1),]" "[CAS (n=1),, CAS (n=1),, CAS (n=1),, CAS-TypeIE (n=1),]" ...
## $ AMR : int 2 2 5 2 4 2 2 4 4 2 ...
## $ Vir : int 20 12 10 3 4 6 7 4 8 23 ...
## $ Integron : int 0 0 0 0 0 0 0 0 0 0 ...
## $ seqs : chr "2020-07-24" "2019-02-27" "2018-03-08" "2020-10-12" ...merge5 <- merge(coli_genomes, colis, all.x = TRUE)
str(merge5)## 'data.frame': 22 obs. of 24 variables:
## $ Strain : chr "LREC237" "LREC239" "LREC240" "LREC241" ...
## $ Year : int NA 2010 2008 NA 2011 2007 2006 2006 2013 2013 ...
## $ Biosample : chr "SAMN14278613" "SAMN14278614" "SAMN14278615" "SAMN14278616" ...
## $ Source : chr "Human" "Human" "Human" "Human" ...
## $ Phylogroup : chr "D" "C" "B1" "A" ...
## $ Serotype : chr "ONT:H28" "O153:H19" "O76:H30" "O78:H11" ...
## $ Clonotype : chr "CH23-331" "CH4-25" "CH29-38" "CH11-41" ...
## $ Sequence.Type : chr "ST524" "ST88" "ST156" "ST48" ...
## $ VF : int 18 14 10 5 5 7 4 2 22 22 ...
## $ Plasmids : int 3 3 2 3 9 3 7 7 1 3 ...
## $ kmer : int 117 117 89 117 89 93 115 115 115 111 ...
## $ Contigs : int 223 159 114 212 320 158 277 203 376 205 ...
## $ N50 : int 272287 323172 270767 112160 45936 106897 89185 94368 200150 281589 ...
## $ longest.contig..bp.: int 662555 760527 738861 285056 128053 369508 281444 280268 424527 617142 ...
## $ Assembly_length : int 5341632 5415613 4875343 5167401 4858138 4638334 5406295 4796593 5389075 5340478 ...
## $ contigs1kb : int 74 57 47 101 212 93 155 114 131 78 ...
## $ average_contig : num 23954 34060 42766 24375 15182 ...
## $ X : int 1 2 3 4 5 6 7 8 12 13 ...
## $ CRISPR : int 9 12 5 9 9 10 7 10 7 7 ...
## $ Cas : chr "[CAS-TypeIE (n=1),]" "[CAS-TypeIE (n=1),]" "[CAS-TypeIE (n=1),]" "[CAS (n=1),, CAS (n=1),, CAS (n=1),, CAS-TypeIE (n=1),]" ...
## $ AMR : int 2 2 5 2 4 2 2 4 2 2 ...
## $ Vir : int 20 12 10 3 4 6 7 4 26 21 ...
## $ Integron : int 0 0 0 0 0 0 0 0 0 0 ...
## $ seqs : chr "2020-07-24" "2019-02-27" "2018-03-08" "2020-10-12" ...merge6 <- merge(coli_genomes, colis, all.y = TRUE)
str(merge6)## 'data.frame': 27 obs. of 24 variables:
## $ Strain : chr "LREC237" "LREC239" "LREC240" "LREC241" ...
## $ Year : int NA 2010 2008 NA 2011 2007 2006 2006 2010 2013 ...
## $ Biosample : chr "SAMN14278613" "SAMN14278614" "SAMN14278615" "SAMN14278616" ...
## $ Source : chr "Human" "Human" "Human" "Human" ...
## $ Phylogroup : chr "D" "C" "B1" "A" ...
## $ Serotype : chr "ONT:H28" "O153:H19" "O76:H30" "O78:H11" ...
## $ Clonotype : chr "CH23-331" "CH4-25" "CH29-38" "CH11-41" ...
## $ Sequence.Type : chr "ST524" "ST88" "ST156" "ST48" ...
## $ VF : int 18 14 10 5 5 7 4 2 NA NA ...
## $ Plasmids : int 3 3 2 3 9 3 7 7 NA NA ...
## $ kmer : int 117 117 89 117 89 93 115 115 NA NA ...
## $ Contigs : int 223 159 114 212 320 158 277 203 NA NA ...
## $ N50 : int 272287 323172 270767 112160 45936 106897 89185 94368 NA NA ...
## $ longest.contig..bp.: int 662555 760527 738861 285056 128053 369508 281444 280268 NA NA ...
## $ Assembly_length : int 5341632 5415613 4875343 5167401 4858138 4638334 5406295 4796593 NA NA ...
## $ contigs1kb : int 74 57 47 101 212 93 155 114 NA NA ...
## $ average_contig : num 23954 34060 42766 24375 15182 ...
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ CRISPR : int 9 12 5 9 9 10 7 10 6 8 ...
## $ Cas : chr "[CAS-TypeIE (n=1),]" "[CAS-TypeIE (n=1),]" "[CAS-TypeIE (n=1),]" "[CAS (n=1),, CAS (n=1),, CAS (n=1),, CAS-TypeIE (n=1),]" ...
## $ AMR : int 2 2 5 2 4 2 2 4 4 2 ...
## $ Vir : int 20 12 10 3 4 6 7 4 8 23 ...
## $ Integron : int 0 0 0 0 0 0 0 0 0 0 ...
## $ seqs : chr "2020-07-24" "2019-02-27" "2018-03-08" "2020-10-12" ...merge7 <- merge(coli_genomes, colis, by = NULL)
str(merge7)## 'data.frame': 594 obs. of 26 variables:
## $ Strain.x : chr "LREC237" "LREC239" "LREC240" "LREC241" ...
## $ Biosample : chr "SAMN14278613" "SAMN14278614" "SAMN14278615" "SAMN14278616" ...
## $ Year.x : int NA 2010 2008 NA 2011 2007 2006 2006 2013 2013 ...
## $ Source : chr "Human" "Human" "Human" "Human" ...
## $ Phylogroup : chr "D" "C" "B1" "A" ...
## $ Serotype : chr "ONT:H28" "O153:H19" "O76:H30" "O78:H11" ...
## $ Clonotype : chr "CH23-331" "CH4-25" "CH29-38" "CH11-41" ...
## $ Sequence.Type : chr "ST524" "ST88" "ST156" "ST48" ...
## $ VF : int 18 14 10 5 5 7 4 2 22 22 ...
## $ Plasmids : int 3 3 2 3 9 3 7 7 1 3 ...
## $ kmer : int 117 117 89 117 89 93 115 115 115 111 ...
## $ Contigs : int 223 159 114 212 320 158 277 203 376 205 ...
## $ N50 : int 272287 323172 270767 112160 45936 106897 89185 94368 200150 281589 ...
## $ longest.contig..bp.: int 662555 760527 738861 285056 128053 369508 281444 280268 424527 617142 ...
## $ Assembly_length : int 5341632 5415613 4875343 5167401 4858138 4638334 5406295 4796593 5389075 5340478 ...
## $ contigs1kb : int 74 57 47 101 212 93 155 114 131 78 ...
## $ average_contig : num 23954 34060 42766 24375 15182 ...
## $ X : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Strain.y : chr "LREC237" "LREC237" "LREC237" "LREC237" ...
## $ Year.y : int NA NA NA NA NA NA NA NA NA NA ...
## $ CRISPR : int 9 9 9 9 9 9 9 9 9 9 ...
## $ Cas : chr "[CAS-TypeIE (n=1),]" "[CAS-TypeIE (n=1),]" "[CAS-TypeIE (n=1),]" "[CAS-TypeIE (n=1),]" ...
## $ AMR : int 2 2 2 2 2 2 2 2 2 2 ...
## $ Vir : int 20 20 20 20 20 20 20 20 20 20 ...
## $ Integron : int 0 0 0 0 0 0 0 0 0 0 ...
## $ seqs : chr "2020-07-24" "2020-07-24" "2020-07-24" "2020-07-24" ...As you noticed, the function merge() combines
dataframes, however it is a generic function that can be also used with
other objects (like vectors or matrices), but they will be coerced to
data.frame class.
By default, merge() will do a natural join or
inner join, merging two dataframes in one that contains only
once the common elements of both. The arguments all=TRUE,
all.x=TRUE and all.y=TRUE will force an
outer join in which all elements of both, the first or the
second dataframes are selected. In these cases, if not all rows in the
first data frame match all the rows in the second, the output is filled
with NA values in those cases.
Finally, the Cartesian product of both dataframes can be obtained in
R setting as NULL the argument by of the
merge() function.
Data matrix format (wide vs. long table)
A common mistake in data analysis is the wrong identification of conditions and variables. In the table Zebrafish_data.csv we have the results of an experiment in which a collaborator scored the number of metastatic cancer cells upon the expression of different transcripts of the EFNA3 gene. Each transcript is cloned into a pLoC plasmid, and we have negative (empty plasmid) and positive controls (wt transcript). Let’s import and check the data.
ZFdata <- read.csv("data/Zebrafish_data.csv")
str(ZFdata)## 'data.frame': 41 obs. of 6 variables:
## $ pLoC : int 36 32 10 26 15 23 17 14 44 12 ...
## $ EFNA3: int 35 33 17 25 89 36 40 36 35 37 ...
## $ NC1 : int 58 26 26 18 20 24 10 31 28 26 ...
## $ NC1s : int 11 11 19 12 12 20 7 104 116 11 ...
## $ NC2 : int 53 37 56 48 27 29 22 79 22 18 ...
## $ NC2s : int 40 43 19 18 33 29 29 25 28 47 ...head(ZFdata)How many columns has the table? How many variables are there?
Most likely you realized that this table format does not agree with the standard way to introduce the data in a table, the data-matrix (rows for cases and columns for variables).
Having the data, we transform it in a datamatrix (aka long
table) using the function stack(). You can also do it the
other way around with the function unstack().
ZF_stacked <- stack(ZFdata)
str(ZF_stacked)## 'data.frame': 246 obs. of 2 variables:
## $ values: int 36 32 10 26 15 23 17 14 44 12 ...
## $ ind : Factor w/ 6 levels "pLoC","EFNA3",..: 1 1 1 1 1 1 1 1 1 1 ...head(ZF_stacked)ZF_old <- unstack(ZF_stacked)
str(ZF_old)## 'data.frame': 41 obs. of 6 variables:
## $ pLoC : int 36 32 10 26 15 23 17 14 44 12 ...
## $ EFNA3: int 35 33 17 25 89 36 40 36 35 37 ...
## $ NC1 : int 58 26 26 18 20 24 10 31 28 26 ...
## $ NC1s : int 11 11 19 12 12 20 7 104 116 11 ...
## $ NC2 : int 53 37 56 48 27 29 22 79 22 18 ...
## $ NC2s : int 40 43 19 18 33 29 29 25 28 47 ...head(ZF_old)However, some times the dataset is more complex and there are other variables that change the table structure, as in the table Zebrafish_full.csv. In this case, you can stack only the desired variables and then reconstruct the table.
ZF_full <- read.csv("data/Zebrafish_full.csv")
head(ZF_full)# stack
ZF_full_some_stack <- stack(ZF_full[, 1:6])
# reconstruct the table
ZF_full_stack <- cbind(ZF_full[, 1], ZF_full_some_stack)
# short way
ZF_full_stack2 <- cbind(ZF_full[, 1], stack(ZF_full[, 1:6]))
all.equal(ZF_full_stack, ZF_full_stack2)## [1] TRUERemember that datamatrix is not an R
structure, but a general concept in data analysis. Also, for more
complex dataframes, I suggest the function melt(). However,
this function name refers to two different functions in two alternative
packages: reshape2 and data.table, with slightly
different behavior.
Working efficiently with large datasets
Fast loading of huge datasets and creation of contingency tables
We are going to make some examples with a huge dataset of Covid19 Vaccination data in EU. The data is publicly available at https://www.ecdc.europa.eu/en/publications-data/data-covid-19-vaccination-eu-eea. While you can download and open a file from internet in R, in order to use the same dataset, we have a file vaccines_EU_22oct2022.csv in our data folder.
NOTE: From this point on, we will show only the first 20-25 lines of the output of some code chunks (you will notice that the output starts and ends with …). Otherwise, this document is very long and difficult to follow. Run the code yourself to see the full returned output.
# open directly from the ECDC vaccines <- read.csv(file =
# 'https://opendata.ecdc.europa.eu/covid19/vaccine_tracker/csv/data.csv',
# header=TRUE) we will use all the same dataset that I
# already downloaded
vaccines <- read.csv(file = "data/vaccines_EU_22oct2022.csv",
header = TRUE)
# explore the data
str(vaccines)...
## 'data.frame': 529241 obs. of 16 variables:
## $ YearWeekISO : chr "2020-W53" "2020-W53" "2020-W53" "2020-W53" ...
## $ ReportingCountry : chr "AT" "AT" "AT" "AT" ...
## $ Denominator : int 7388778 7388778 7388778 7388778 7388778 7388778 7388778 7388778 7388778 7388778 ...
## $ NumberDosesReceived: int 0 0 0 0 0 0 61425 0 0 0 ...
## $ NumberDosesExported: int 0 0 0 0 0 0 0 0 0 0 ...
## $ FirstDose : int 0 0 0 0 0 0 5339 0 0 0 ...
## $ FirstDoseRefused : int NA NA NA NA NA NA NA NA NA NA ...
## $ SecondDose : int 0 0 0 0 0 0 0 0 0 0 ...
## $ DoseAdditional1 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ DoseAdditional2 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ DoseAdditional3 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ UnknownDose : int 0 0 0 0 0 8 0 0 0 0 ...
## $ Region : chr "AT" "AT" "AT" "AT" ...
## $ TargetGroup : chr "ALL" "ALL" "ALL" "ALL" ...
## $ Vaccine : chr "VLA" "AZ" "COMBA.4-5" "NVXD" ...
## $ Population : int 8901064 8901064 8901064 8901064 8901064 8901064 8901064 8901064 8901064 8901064 ...
NA
NA
NA
...head(vaccines)table(vaccines$Region)...
##
## AT BE BG CY CZ DE DK EE EL ES
## 11520 4510 3977 4237 3967 594 3922 3741 10752 5820
## FI FI19 FI193 FI194 FI195 FI197 FI1B FI1B1 FI1C FI1C1
## 11232 11232 11232 11232 11232 11232 11232 11232 11232 11232
## FI1C2 FI1C3 FI1C4 FI1C5 FI1D FI1D1 FI1D2 FI1D3 FI1D5 FI1D7
## 11232 11232 11232 11232 11232 11232 11232 11232 11232 11232
## FI1D8 FI1D9 FI20 FI200 FR FR1 FRB FRC FRD FRE
## 11232 11232 11232 11232 1316 752 752 752 752 752
## FRF FRG FRH FRI FRJ FRK FRL FRM FRY1 FRY2
## 752 752 752 752 752 752 752 752 752 752
## FRY3 FRY4 FRY5 HR HU IE IS IT ITC1 ITC2
## 752 752 752 7005 4939 3001 3850 3776 331 270
## ITC3 ITC4 ITF1 ITF2 ITF3 ITF4 ITF5 ITF6 ITG1 ITG2
## 301 315 302 269 316 307 283 298 304 269
## ITH1 ITH2 ITH3 ITH4 ITH5 ITI1 ITI2 ITI3 ITI4 LI
## 315 297 317 319 313 316 294 315 321 3030
## LT LT01 LT02 LU LV MT NL NO PL PL21
## 5252 808 808 5244 5705 4028 5940 2616 6240 6240
## PL22 PL41 PL42 PL43 PL51 PL52 PL61 PL62 PL63 PL71
...head(table(vaccines$Region, vaccines$NumberDosesReceived))...
##
## 0 1 2 3 4 5 6 7 8 9 10 11
## AT 11320 0 0 0 0 0 0 0 0 0 0 0
## BE 129 0 0 0 0 0 0 0 0 0 0 0
## BG 243 0 0 0 0 0 0 0 0 0 0 0
## CY 4102 0 0 0 0 0 0 0 0 0 0 0
## CZ 206 0 0 0 0 0 0 0 0 0 0 0
## DE 80 0 0 0 0 0 0 0 0 0 0 0
##
## 12 13 14 15 16 17 18 19 20 21 22 23
## AT 0 0 0 0 0 0 0 0 0 0 0 0
## BE 0 0 0 0 0 0 0 0 0 0 0 0
## BG 0 0 0 0 0 0 0 0 0 0 0 0
## CY 0 0 0 0 0 0 0 0 0 0 0 0
## CZ 0 0 0 0 0 0 0 0 0 0 0 0
## DE 0 0 0 0 0 0 0 0 0 0 0 0
##
## 24 25 26 27 28 29 30 31 32 33 34 35
## AT 0 0 0 0 0 0 0 0 0 0 0 0
## BE 0 0 0 0 0 0 0 0 0 0 0 0
...summary(vaccines[which(vaccines$Region == "ES"), ][, 6]) #see the data from Spain## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 1 14641 511 1512313table(vaccines)## Error in table(vaccines): attempt to make a table with >= 2^31 elementsWhen exploring (very) large datasets as in the example above you can find that some functions don’t work because they can’t handle all that data (Error in table(vaccines) : attempt to make a table with >= 2^31 elements). Here, we are going to use alternative methods, more efficient and convenient for this datasets.
One of those tricks is the use of the package data.table,
very handy for large datasets. In the following lines, we will compare
reading/writting data with the function fread() and
fwrite().
# load the package may require installing it before
if (!require(data.table)) {
install.packages("data.table")
}## Loading required package: data.table# if that doesn't work try: install.packages('data.table',
# type = 'source', repos =
# 'https://Rdatatable.gitlab.io/data.table')
system.time(vaccines <- read.csv(file = "data/vaccines_EU_22oct2022.csv",
header = TRUE))## user system elapsed
## 1.692 0.060 1.755system.time(vaccines <- fread("data/vaccines_EU_22oct2022.csv"))## user system elapsed
## 0.280 0.014 0.296# we can also prevent loading of some columns to save time
system.time(vaccines2 <- fread("data/vaccines_EU_22oct2022.csv",
drop = c(3, 5, 7, 9, 10)))## user system elapsed
## 0.155 0.006 0.160system.time(vaccines3 <- fread("data/vaccines_EU_22oct2022.csv",
colClasses = "character")) #we can also select the variables by type## user system elapsed
## 0.308 0.008 0.316Resume data
Tabulating data is very common and quick way to obtain information
from a dataset. As you already now, the table() function in
R can be used to quickly create frequency tables. The output of both
xtabs() and table() is nearly the same,
although xtabs() is more convenient for complex cross
tabulations. Still some users prefer the use of derivative
table() uses, like including it within the
with() function. I believe that xtabs()
provides a more straightforward alternative. Moreover,
xtabs() has interesting advantages: 1) row and column
labels are included automatically, set to the variable names and 2)
there is a data= argument, which means you just have to reference the
variable names. With xtabs(), you do not list out the
variables of interest separated by commas. Instead you use formula
notation, which is ~variable1+variable2+… where variable1 and variable2
are the names of the variables of interest.
xtabs(vaccines) #works## < table of extent 0 x 0 x 0 x 0 x 0 x 0 x 0 x 0 x 0 x 0 x 0 x 0 x 0 x 0 x 0 ># library(stats) if xtabs() does not work compare table()
# and xtabs()
table(vaccines$ReportingCountry)##
## AT BE BG CY CZ DE DK EE EL ES FI
## 11520 4510 3977 4237 3967 594 3922 3741 10752 5820 269568
## FR HR HU IE IS IT LI LT LU LV MT
## 15604 7005 4939 3001 3850 10148 3030 6868 5244 5705 4028
## NL NO PL PT RO SE SI SK
## 5940 2616 106080 7882 3245 6113 2371 2964str(table(vaccines$ReportingCountry))## 'table' int [1:30(1d)] 11520 4510 3977 4237 3967 594 3922 3741 10752 5820 ...
## - attr(*, "dimnames")=List of 1
## ..$ : chr [1:30] "AT" "BE" "BG" "CY" ...xtabs(formula = ~ReportingCountry, data = vaccines)## ReportingCountry
## AT BE BG CY CZ DE DK EE EL ES FI
## 11520 4510 3977 4237 3967 594 3922 3741 10752 5820 269568
## FR HR HU IE IS IT LI LT LU LV MT
## 15604 7005 4939 3001 3850 10148 3030 6868 5244 5705 4028
## NL NO PL PT RO SE SI SK
## 5940 2616 106080 7882 3245 6113 2371 2964str(xtabs(formula = ~ReportingCountry, data = vaccines))## 'xtabs' int [1:30(1d)] 11520 4510 3977 4237 3967 594 3922 3741 10752 5820 ...
## - attr(*, "dimnames")=List of 1
## ..$ ReportingCountry: chr [1:30] "AT" "BE" "BG" "CY" ...
## - attr(*, "call")= language xtabs(formula = ~ReportingCountry, data = vaccines)# with 2 or more variables?
table(vaccines[, c(ReportingCountry, as.factor(Vaccine))]) #not really...
##
## 1 10 11 12 13 14 15 16 17 2 3
## 75003 1744 49 6 61004 976 683 49997 31109 1847 183
## 4 5 6 7 8 9 AT BE BG CY CZ
## 83781 33897 33422 6 74417 81117 11520 4510 3977 4237 3967
## DE DK EE EL ES FI FR HR HU IE IS
## 594 3922 3741 10752 5820 269568 15604 7005 4939 3001 3850
## IT LI LT LU LV MT NL NO PL PT RO
## 10148 3030 6868 5244 5705 4028 5940 2616 106080 7882 3245
## SE SI SK
## 6113 2371 2964
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
...with(vaccines, table(ReportingCountry, as.factor(Vaccine))) ## note 'with'...
##
## ReportingCountry AZ BECNBG BHACOV COM COMBA.1 COMBA.4-5 COMBIV JANSS MOD
## AT 1152 0 0 1152 1152 1152 0 1152 1152
## BE 795 0 0 1035 155 115 0 865 1015
## BG 994 0 0 1045 0 0 0 894 1044
## CY 932 0 0 1052 48 48 0 799 910
## CZ 542 0 0 1177 72 52 0 686 1091
## DE 88 0 0 95 0 0 6 78 92
## DK 588 0 0 1179 116 90 0 590 987
## EE 824 0 0 901 54 27 0 719 856
## EL 1520 0 0 1602 1520 1522 0 1548 1520
## ES 1455 0 0 1455 0 0 0 1455 1455
## FI 29952 0 0 29952 29952 29952 0 29952 29952
## FR 94 0 0 94 0 0 0 94 94
## HR 1245 0 0 1440 0 0 0 1335 1425
## HU 701 905 0 985 54 0 0 825 955
## IE 372 0 0 1178 0 0 0 410 878
## IS 646 0 0 1316 72 17 0 847 766
## IT 1306 0 0 3032 258 220 0 1606 2750
## LI 0 0 0 960 30 0 0 960 960
## LT 1496 0 0 1615 0 0 0 1326 1564
## LU 1116 0 0 1188 120 96 0 996 1152
## LV 1295 0 0 1373 0 0 0 1178 1334
## MT 1040 0 0 1065 0 0 0 912 1011
## NL 990 0 0 990 0 0 0 990 990
...# alternative
vaccines_split <- xtabs(formula = ~ReportingCountry + as.factor(Vaccine),
data = vaccines)
# now create a standard dataframe
vaccines_split <- as.data.frame(xtabs(~ReportingCountry + as.factor(Vaccine),
vaccines))As discussed above, xtabs() can be used with more than
two variables, although the resulting cross-tabulated table may be too
long. Optionally, on the left hand side, one may give a vector or a
matrix of counts; in the latter case, the columns are interpreted as
corresponding to the levels of a variable.
# also with several variables, although it takes some time
vaccines_split2 <- xtabs(~ReportingCountry + NumberDosesReceived +
as.factor(Vaccine) + as.factor(TargetGroup), vaccines)
vaccines_split2b <- xtabs(NumberDosesReceived ~ ReportingCountry +
as.factor(Vaccine) + as.factor(TargetGroup), vaccines)Sometimes, it is better to use a shorter table as an example…
Using coli_genomes_renamed.csv, could you make a table with genome frequencies by isolation source (Source), phylogenetic group (Phylogroup) and multilocus sequence type (Sequence.Type)?
coli_genomes <- read.csv2(file = "data/coli_genomes_renamed.csv",
strip.white = TRUE) #load the table
xgenomes <- xtabs(~Source + Phylogroup + Sequence.Type, coli_genomes)This is quite a long cross-table, maybe you could find more
convenient transforming it to a flat contingency table with
ftable().
xgenomesf <- ftable(xtabs(~Source + Phylogroup + Sequence.Type,
coli_genomes))
# Oh wait, what I want the STs on the left?
xgenomesf2 <- ftable(xtabs(~Sequence.Type + Source + Phylogroup,
coli_genomes))
# No, only the STs on the left
xgenomesf3 <- ftable(xtabs(~Sequence.Type + Source + Phylogroup,
coli_genomes), row.vars = 1)
xgenomesf3b <- ftable(xtabs(~Sequence.Type + Source + Phylogroup,
coli_genomes), col.vars = 2)
xgenomesf3c <- ftable(xtabs(~Sequence.Type + Source + Phylogroup,
coli_genomes), col.vars = 2:3)As you notice, the format of the table can be defined by the order of
the variables and also using the arguments row.vars and
col.vars to define which variables will be summarized as
column or row.
Again, rather than concepts from R or RStudio, cross-tabulation and flat contingency tables are general concepts in data analysis.
Data aggregation and transformation
By() & aggregate()
While frequency tables can be very quickly generated with
table() and xtable(), sometimes you may want
other calculations than frequency, like basic statistics per group or
other, even custom, calculations.
You have already tried the function by() as a very
useful trick to make group-calculations. However, this function has some
limitations when we have large datasets and when we try to use multiple
factors that can be solved with aggregate(). In both cases,
the function can be any function that suits your data, either from R
packages or a custom function.
In the following example, we are using a smaller dataset that we
extracted by sample().
vaccines2 <- vaccines[sample(x = 1:nrow(vaccines), size = 1000,
replace = FALSE), ]
# using by()
by(vaccines2$NumberDosesReceived, INDICES = vaccines2$Region,
FUN = mean, na.rm = TRUE)...
## vaccines2$Region: AT
## [1] 0
## ------------------------------------------------------------
## vaccines2$Region: BE
## [1] NaN
## ------------------------------------------------------------
## vaccines2$Region: BG
## [1] NaN
## ------------------------------------------------------------
## vaccines2$Region: CY
## [1] 0
## ------------------------------------------------------------
## vaccines2$Region: CZ
## [1] NaN
## ------------------------------------------------------------
## vaccines2$Region: DE
## [1] 1339936
## ------------------------------------------------------------
## vaccines2$Region: DK
## [1] 0
## ------------------------------------------------------------
## vaccines2$Region: EE
## [1] 0
## ------------------------------------------------------------
## vaccines2$Region: EL
...# more than one factor? how?
by(vaccines2$NumberDosesReceived, INDICES = vaccines2$Region +
vaccines2$Vaccine, FUN = mean, na.rm = TRUE)## Error in vaccines2$Region + vaccines2$Vaccine: argumento no-numérico para operador binarioby(vaccines2$NumberDosesReceived, INDICES = c(vaccines2$Region,
vaccines2$Vaccine), FUN = mean, na.rm = TRUE)## Error in tapply(seq_len(1000L), list(`c(vaccines2$Region, vaccines2$Vaccine)` = c("FI1B", : arguments must have same lengthby(vaccines2$NumberDosesReceived, INDICES = list(vaccines2$Region,
vaccines2$Vaccine), FUN = mean, na.rm = TRUE)...
## : AT
## : AZ
## [1] NA
## ------------------------------------------------------------
## : BE
## : AZ
## [1] NaN
## ------------------------------------------------------------
## : BG
## : AZ
## [1] NaN
## ------------------------------------------------------------
## : CY
## : AZ
## [1] 0
## ------------------------------------------------------------
## : CZ
## : AZ
## [1] NA
## ------------------------------------------------------------
## : DE
## : AZ
## [1] NA
## ------------------------------------------------------------
## : DK
...# aggregate is more convenient sometimes
aggregate(vaccines2$NumberDosesReceived ~ vaccines2$Region, FUN = mean)aggregate(NumberDosesReceived ~ Region + Vaccine, data = vaccines2,
FUN = mean)aggregate(FirstDoseRefused ~ TargetGroup, data = vaccines2, FUN = median)# by default NAs are disregarded
aggregate(FirstDoseRefused ~ TargetGroup, data = vaccines2, na.action = NULL,
FUN = median)# we can make it for several numeric variables at the same
# time
aggregate(cbind(NumberDosesReceived, FirstDose, SecondDose) ~
as.factor(TargetGroup), data = vaccines2, FUN = mean)We can also add a custom function, using the short, inline notation:
# we can also use custom functions here
aggregate(cbind(VF, Plasmids) ~ as.factor(Source), data = coli_genomes,
FUN = function(x) mean(x) * 100/mean(coli_genomes$Contigs))Apply family of functions
As you discussed previously, one of the issues with for loop can be
its memory consumption and its slowness in executing a repetitive task
at hand. Often dealing with large data and iterating it, for loop is not
advised. R provides many few alternatives to be applied on vectors for
looping operations. In this section, we deal with apply
function and its variants. The functions apply(),
lapply(), sapply(), tapply(),
rapply() & mapply(). As a general
definition, Apply is a very efficient tool to perform
repetitive calculations. In the following examples we test some of the
powers of the applies.
apply(vaccines[, 4:12], 1, mean, na.rm = TRUE)...
## [1] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [6] 1.000000e+00 8.345500e+03 0.000000e+00 0.000000e+00 0.000000e+00
## [11] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [16] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.250000e-01
## [21] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [26] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [31] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 6.250000e-01
## [36] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [41] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.250000e-01
## [46] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.137500e+01
## [51] 0.000000e+00 0.000000e+00 0.000000e+00 1.662500e+02 0.000000e+00
## [56] 0.000000e+00 0.000000e+00 0.000000e+00 6.250000e-01 0.000000e+00
## [61] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [66] 0.000000e+00 1.313750e+02 2.500000e-01 0.000000e+00 0.000000e+00
## [71] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [76] 0.000000e+00 0.000000e+00 1.250000e-01 0.000000e+00 0.000000e+00
## [81] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [86] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 7.512500e+01
## [91] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [96] 0.000000e+00 0.000000e+00 7.050000e+01 0.000000e+00 0.000000e+00
## [101] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 2.127500e+02
## [106] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [111] 0.000000e+00 8.750000e-01 0.000000e+00 0.000000e+00 0.000000e+00
## [116] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [121] 8.750000e-01 0.000000e+00 0.000000e+00 0.000000e+00 2.500000e-01
...apply(vaccines[, 4:12], 2, mean, na.rm = TRUE) # 1 for row-wise operation, 2 for column-wise...
## NumberDosesReceived NumberDosesExported FirstDose FirstDoseRefused
## 2.021725e+04 1.326050e+03 1.749675e+03 4.015204e-01
## SecondDose DoseAdditional1 DoseAdditional2 DoseAdditional3
## 1.582874e+03 1.251688e+03 1.962433e+02 4.458589e+00
## UnknownDose
## 1.395121e+01
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
...lapply(by(vaccines$Population, vaccines$ReportingCountry, mean),
log10)## $AT
## [1] 6.950965
##
## $BE
## [1] 7.063178
##
## $BG
## [1] 6.839901
##
## $CY
## [1] 5.952294
##
## $CZ
## [1] 7.029454
##
## $DE
## [1] 7.919889
##
## $DK
## [1] 6.766401
##
## $EE
## [1] 6.123872
##
## $EL
## [1] 7.028549
##
## $ES
## [1] 7.67576
##
## $FI
## [1] 6.743009
##
## $FR
## [1] 7.830288
##
## $HR
## [1] 6.606014
##
## $HU
## [1] 6.988154
##
## $IE
## [1] 6.699501
##
## $IS
## [1] 5.566762
##
## $IT
## [1] 7.772605
##
## $LI
## [1] 4.591677
##
## $LT
## [1] 6.446486
##
## $LU
## [1] 5.802562
##
## $LV
## [1] 6.277217
##
## $MT
## [1] 5.712724
##
## $NL
## [1] 7.24239
##
## $NO
## [1] 6.731687
##
## $PL
## [1] 7.577979
##
## $PT
## [1] 7.012763
##
## $RO
## [1] 7.283339
##
## $SE
## [1] 7.016122
##
## $SI
## [1] 6.324052
##
## $SK
## [1] 6.737174sapply(by(vaccines$Population, vaccines$ReportingCountry, mean),
log10)## AT BE BG CY CZ DE DK EE
## 6.950965 7.063178 6.839901 5.952294 7.029454 7.919889 6.766401 6.123872
## EL ES FI FR HR HU IE IS
## 7.028549 7.675760 6.743009 7.830288 6.606014 6.988154 6.699501 5.566762
## IT LI LT LU LV MT NL NO
## 7.772605 4.591677 6.446486 5.802562 6.277217 5.712724 7.242390 6.731687
## PL PT RO SE SI SK
## 7.577979 7.012763 7.283339 7.016122 6.324052 6.737174class(lapply(by(vaccines$Population, vaccines$ReportingCountry,
mean), log10)) #lapply take a list (or object that can be coerced as one) and gives a list## [1] "list"class(sapply(by(vaccines$Population, vaccines$ReportingCountry,
mean), log10)) #a wrapper of lapply that returns a vector or matrix instead of a list## [1] "numeric"sapply(list(vaccines2$Population, vaccines2$FirstDose, vaccines2$SecondDose,
vaccines2$NumberDosesReceived), median) # we can do it with several variables## [1] 5533793 0 0 NAsapply(vaccines2, class) #calculations is a broad concept here## YearWeekISO ReportingCountry Denominator NumberDosesReceived
## "character" "character" "integer" "integer"
## NumberDosesExported FirstDose FirstDoseRefused SecondDose
## "integer" "integer" "integer" "integer"
## DoseAdditional1 DoseAdditional2 DoseAdditional3 UnknownDose
## "integer" "integer" "integer" "integer"
## Region TargetGroup Vaccine Population
## "character" "character" "character" "integer"# mapply is a matrix (or multivariable) version
mapply(sub, vaccines2[, 4], vaccines2[, 6], vaccines2[, 7])## Warning in (function (pattern, replacement, x, ignore.case = FALSE, perl =
## FALSE, : el argumento 'pattern' tiene tiene longitud > 1 y sólo el primer
## elemento será usado## Warning in (function (pattern, replacement, x, ignore.case = FALSE, perl =
## FALSE, : el argumento 'replacement' tiene tiene longitud > 1 y sólo el primer
## elemento será usado## NumberDosesReceived
## [1,] NA
## [2,] NA
## [3,] NA
## [4,] NA
## [5,] NA
## [6,] NA
## [7,] NA
## [8,] NA
## [9,] NA
## [10,] NA
## [11,] NA
## [12,] NA
## [13,] NA
## [14,] NA
## [15,] NA
## [16,] NA
## [17,] NA
## [18,] NA
## [19,] NA
## [20,] NA
## [21,] NA
## [22,] NA
## [23,] NA
## [24,] NA
## [25,] NA
## [26,] NA
## [27,] NA
## [28,] NA
## [29,] NA
## [30,] NA
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## [1000,] NAmapply(rep, letters[1:4], 4:1)## $a
## [1] "a" "a" "a" "a"
##
## $b
## [1] "b" "b" "b"
##
## $c
## [1] "c" "c"
##
## $d
## [1] "d"# tapply breaks a vector into pieces, can be used instead
# of aggregate
tapply(vaccines$NumberDosesReceived, vaccines$ReportingCountry,
median, na.rm = TRUE)## AT BE BG CY CZ DE DK EE EL ES FI FR HR
## 0 0 0 0 0 53900 0 0 0 0 87750 0 32400
## HU IE IS IT LI LT LU LV MT NL NO PL PT
## 0 0 1300 62 0 0 0 0 NA 0 0 0 0
## RO SE SI SK
## 0 0 0 0tapply(coli_genomes$VF, coli_genomes$Source, mean, na.rm = TRUE)## Avian Human Porcine
## 7.333333 11.266667 4.500000# vapply is similar to sapply, but has a pre-specified type
# of return value
vapply(vaccines[, 8:11], mean, numeric(1))## SecondDose DoseAdditional1 DoseAdditional2 DoseAdditional3
## 1582.874050 1251.687770 196.243314 4.458589vapply(vaccines[, 8:11], function(x) x^2, numeric(nrow(vaccines)))## SecondDose DoseAdditional1 DoseAdditional2 DoseAdditional3
## [1,] 0.000000e+00 0.000000e+00 0 0
## [2,] 0.000000e+00 0.000000e+00 0 0
## [3,] 0.000000e+00 0.000000e+00 0 0
## [4,] 0.000000e+00 0.000000e+00 0 0
## [5,] 0.000000e+00 0.000000e+00 0 0
## [6,] 0.000000e+00 0.000000e+00 0 0
## [7,] 0.000000e+00 0.000000e+00 0 0
## [8,] 0.000000e+00 0.000000e+00 0 0
## [9,] 0.000000e+00 0.000000e+00 0 0
## [10,] 0.000000e+00 0.000000e+00 0 0
## [11,] 0.000000e+00 0.000000e+00 0 0
## [12,] 0.000000e+00 0.000000e+00 0 0
## [13,] 0.000000e+00 0.000000e+00 0 0
## [14,] 0.000000e+00 0.000000e+00 0 0
## [15,] 0.000000e+00 0.000000e+00 0 0
## [16,] 0.000000e+00 0.000000e+00 0 0
## [17,] 0.000000e+00 0.000000e+00 0 0
## [18,] 0.000000e+00 0.000000e+00 0 0
## [19,] 0.000000e+00 0.000000e+00 0 0
## [20,] 0.000000e+00 0.000000e+00 0 0
## [21,] 0.000000e+00 0.000000e+00 0 0
## [22,] 0.000000e+00 0.000000e+00 0 0
## [23,] 0.000000e+00 0.000000e+00 0 0
## [24,] 0.000000e+00 0.000000e+00 0 0
## [25,] 0.000000e+00 0.000000e+00 0 0
## [26,] 0.000000e+00 0.000000e+00 0 0
## [27,] 0.000000e+00 0.000000e+00 0 0
## [28,] 0.000000e+00 0.000000e+00 0 0
## [29,] 0.000000e+00 0.000000e+00 0 0
## [30,] 0.000000e+00 0.000000e+00 0 0
## [31,] 0.000000e+00 0.000000e+00 0 0
## [32,] 0.000000e+00 0.000000e+00 0 0
## [33,] 0.000000e+00 0.000000e+00 0 0
## [34,] 0.000000e+00 0.000000e+00 0 0
## [35,] 0.000000e+00 0.000000e+00 0 0
## [36,] 0.000000e+00 0.000000e+00 0 0
## [37,] 0.000000e+00 0.000000e+00 0 0
## [38,] 0.000000e+00 0.000000e+00 0 0
## [39,] 0.000000e+00 0.000000e+00 0 0
## [40,] 0.000000e+00 0.000000e+00 0 0
## [41,] 0.000000e+00 0.000000e+00 0 0
## [42,] 0.000000e+00 0.000000e+00 0 0
## [43,] 0.000000e+00 0.000000e+00 0 0
## [44,] 0.000000e+00 0.000000e+00 0 0
## [45,] 0.000000e+00 0.000000e+00 0 0
## [46,] 0.000000e+00 0.000000e+00 0 0
## [47,] 0.000000e+00 0.000000e+00 0 0
## [48,] 0.000000e+00 0.000000e+00 0 0
## [49,] 0.000000e+00 0.000000e+00 0 0
## [50,] 0.000000e+00 0.000000e+00 0 0
## [51,] 0.000000e+00 0.000000e+00 0 0
## [52,] 0.000000e+00 0.000000e+00 0 0
## [53,] 0.000000e+00 0.000000e+00 0 0
## [54,] 0.000000e+00 0.000000e+00 0 0
## [55,] 0.000000e+00 0.000000e+00 0 0
## [56,] 0.000000e+00 0.000000e+00 0 0
## [57,] 0.000000e+00 0.000000e+00 0 0
## [58,] 0.000000e+00 0.000000e+00 0 0
## [59,] 0.000000e+00 0.000000e+00 0 0
## [60,] 0.000000e+00 0.000000e+00 0 0
## [61,] 0.000000e+00 0.000000e+00 0 0
## [62,] 0.000000e+00 0.000000e+00 0 0
## [63,] 0.000000e+00 0.000000e+00 0 0
## [64,] 0.000000e+00 0.000000e+00 0 0
## [65,] 0.000000e+00 0.000000e+00 0 0
## [66,] 0.000000e+00 0.000000e+00 0 0
## [67,] 0.000000e+00 0.000000e+00 0 0
## [68,] 0.000000e+00 0.000000e+00 0 0
## [69,] 0.000000e+00 0.000000e+00 0 0
## [70,] 0.000000e+00 0.000000e+00 0 0
## [71,] 0.000000e+00 0.000000e+00 0 0
## [72,] 0.000000e+00 0.000000e+00 0 0
## [73,] 0.000000e+00 0.000000e+00 0 0
## [74,] 0.000000e+00 0.000000e+00 0 0
## [75,] 0.000000e+00 0.000000e+00 0 0
## [76,] 0.000000e+00 0.000000e+00 0 0
## [77,] 0.000000e+00 0.000000e+00 0 0
## [78,] 0.000000e+00 0.000000e+00 0 0
## [79,] 0.000000e+00 0.000000e+00 0 0
## [80,] 0.000000e+00 0.000000e+00 0 0
## [81,] 0.000000e+00 0.000000e+00 0 0
## [82,] 0.000000e+00 0.000000e+00 0 0
## [83,] 0.000000e+00 0.000000e+00 0 0
## [84,] 0.000000e+00 0.000000e+00 0 0
## [85,] 0.000000e+00 0.000000e+00 0 0
## [86,] 0.000000e+00 0.000000e+00 0 0
## [87,] 0.000000e+00 0.000000e+00 0 0
## [88,] 0.000000e+00 0.000000e+00 0 0
## [89,] 0.000000e+00 0.000000e+00 0 0
## [90,] 0.000000e+00 0.000000e+00 0 0
## [91,] 0.000000e+00 0.000000e+00 0 0
## [92,] 0.000000e+00 0.000000e+00 0 0
## [93,] 0.000000e+00 0.000000e+00 0 0
## [94,] 0.000000e+00 0.000000e+00 0 0
## [95,] 0.000000e+00 0.000000e+00 0 0
## [96,] 0.000000e+00 0.000000e+00 0 0
## [97,] 0.000000e+00 0.000000e+00 0 0
## [98,] 0.000000e+00 0.000000e+00 0 0
## [99,] 0.000000e+00 0.000000e+00 0 0
## [100,] 0.000000e+00 0.000000e+00 0 0
## [101,] 0.000000e+00 0.000000e+00 0 0
## [102,] 0.000000e+00 0.000000e+00 0 0
## [103,] 0.000000e+00 0.000000e+00 0 0
## [104,] 0.000000e+00 0.000000e+00 0 0
## [105,] 0.000000e+00 0.000000e+00 0 0
## [106,] 0.000000e+00 0.000000e+00 0 0
## [107,] 0.000000e+00 0.000000e+00 0 0
## [108,] 0.000000e+00 0.000000e+00 0 0
## [109,] 0.000000e+00 0.000000e+00 0 0
## [110,] 0.000000e+00 0.000000e+00 0 0
## [111,] 0.000000e+00 0.000000e+00 0 0
## [112,] 0.000000e+00 0.000000e+00 0 0
## [113,] 0.000000e+00 0.000000e+00 0 0
## [114,] 0.000000e+00 0.000000e+00 0 0
## [115,] 0.000000e+00 0.000000e+00 0 0
## [116,] 0.000000e+00 0.000000e+00 0 0
## [117,] 0.000000e+00 0.000000e+00 0 0
## [118,] 0.000000e+00 0.000000e+00 0 0
## [119,] 0.000000e+00 0.000000e+00 0 0
## [120,] 0.000000e+00 0.000000e+00 0 0
## [121,] 0.000000e+00 0.000000e+00 0 0
## [122,] 0.000000e+00 0.000000e+00 0 0
## [123,] 0.000000e+00 0.000000e+00 0 0
## [124,] 0.000000e+00 0.000000e+00 0 0
## [125,] 0.000000e+00 0.000000e+00 0 0
## [126,] 0.000000e+00 0.000000e+00 0 0
## [127,] 9.000000e+00 0.000000e+00 0 0
## [128,] 0.000000e+00 0.000000e+00 0 0
## [129,] 0.000000e+00 0.000000e+00 0 0
## [130,] 0.000000e+00 0.000000e+00 0 0
## [131,] 0.000000e+00 0.000000e+00 0 0
## [132,] 0.000000e+00 0.000000e+00 0 0
## [133,] 0.000000e+00 0.000000e+00 0 0
## [134,] 0.000000e+00 0.000000e+00 0 0
## [135,] 0.000000e+00 0.000000e+00 0 0
## [136,] 0.000000e+00 0.000000e+00 0 0
## [137,] 0.000000e+00 0.000000e+00 0 0
## [138,] 0.000000e+00 0.000000e+00 0 0
## [139,] 0.000000e+00 0.000000e+00 0 0
## [140,] 0.000000e+00 0.000000e+00 0 0
## [141,] 0.000000e+00 0.000000e+00 0 0
## [142,] 0.000000e+00 0.000000e+00 0 0
## [143,] 0.000000e+00 0.000000e+00 0 0
## [144,] 0.000000e+00 0.000000e+00 0 0
## [145,] 0.000000e+00 0.000000e+00 0 0
## [146,] 0.000000e+00 0.000000e+00 0 0
## [147,] 0.000000e+00 0.000000e+00 0 0
## [148,] 0.000000e+00 0.000000e+00 0 0
## [149,] 0.000000e+00 0.000000e+00 0 0
## [150,] 0.000000e+00 0.000000e+00 0 0
## [151,] 0.000000e+00 0.000000e+00 0 0
## [152,] 0.000000e+00 0.000000e+00 0 0
## [153,] 0.000000e+00 0.000000e+00 0 0
## [154,] 0.000000e+00 0.000000e+00 0 0
## [155,] 0.000000e+00 0.000000e+00 0 0
## [156,] 0.000000e+00 0.000000e+00 0 0
## [157,] 0.000000e+00 0.000000e+00 0 0
## [158,] 0.000000e+00 0.000000e+00 0 0
## [159,] 0.000000e+00 0.000000e+00 0 0
## [160,] 0.000000e+00 0.000000e+00 0 0
## [161,] 0.000000e+00 0.000000e+00 0 0
## [162,] 0.000000e+00 0.000000e+00 0 0
## [163,] 0.000000e+00 0.000000e+00 0 0
## [164,] 0.000000e+00 0.000000e+00 0 0
## [165,] 0.000000e+00 0.000000e+00 0 0
## [166,] 0.000000e+00 0.000000e+00 0 0
## [167,] 0.000000e+00 0.000000e+00 0 0
## [168,] 0.000000e+00 0.000000e+00 0 0
## [169,] 0.000000e+00 0.000000e+00 0 0
## [170,] 0.000000e+00 0.000000e+00 0 0
## [171,] 0.000000e+00 0.000000e+00 0 0
## [172,] 0.000000e+00 0.000000e+00 0 0
## [173,] 0.000000e+00 0.000000e+00 0 0
## [174,] 1.000000e+00 0.000000e+00 0 0
## [175,] 0.000000e+00 0.000000e+00 0 0
## [176,] 0.000000e+00 0.000000e+00 0 0
## [177,] 0.000000e+00 0.000000e+00 0 0
## [178,] 0.000000e+00 0.000000e+00 0 0
## [179,] 0.000000e+00 0.000000e+00 0 0
## [180,] 0.000000e+00 0.000000e+00 0 0
## [181,] 0.000000e+00 0.000000e+00 0 0
## [182,] 0.000000e+00 0.000000e+00 0 0
## [183,] 1.000000e+00 0.000000e+00 0 0
## [184,] 0.000000e+00 0.000000e+00 0 0
## [185,] 0.000000e+00 0.000000e+00 0 0
## [186,] 0.000000e+00 0.000000e+00 0 0
## [187,] 0.000000e+00 0.000000e+00 0 0
## [188,] 0.000000e+00 0.000000e+00 0 0
## [189,] 0.000000e+00 0.000000e+00 0 0
## [190,] 0.000000e+00 0.000000e+00 0 0
## [191,] 0.000000e+00 0.000000e+00 0 0
## [192,] 0.000000e+00 0.000000e+00 0 0
## [193,] 0.000000e+00 0.000000e+00 0 0
## [194,] 0.000000e+00 0.000000e+00 0 0
## [195,] 0.000000e+00 0.000000e+00 0 0
## [196,] 0.000000e+00 0.000000e+00 0 0
## [197,] 0.000000e+00 0.000000e+00 0 0
## [198,] 0.000000e+00 0.000000e+00 0 0
## [199,] 0.000000e+00 0.000000e+00 0 0
## [200,] 0.000000e+00 0.000000e+00 0 0
## [201,] 1.000000e+00 0.000000e+00 0 0
## [202,] 0.000000e+00 0.000000e+00 0 0
## [203,] 0.000000e+00 0.000000e+00 0 0
## [204,] 0.000000e+00 0.000000e+00 0 0
## [205,] 0.000000e+00 0.000000e+00 0 0
## [206,] 0.000000e+00 0.000000e+00 0 0
## [207,] 0.000000e+00 0.000000e+00 0 0
## [208,] 0.000000e+00 0.000000e+00 0 0
## [209,] 0.000000e+00 0.000000e+00 0 0
## [210,] 0.000000e+00 0.000000e+00 0 0
## [211,] 0.000000e+00 0.000000e+00 0 0
## [212,] 0.000000e+00 0.000000e+00 0 0
## [213,] 0.000000e+00 0.000000e+00 0 0
## [214,] 0.000000e+00 0.000000e+00 0 0
## [215,] 0.000000e+00 0.000000e+00 0 0
## [216,] 0.000000e+00 0.000000e+00 0 0
## [217,] 0.000000e+00 0.000000e+00 0 0
## [218,] 0.000000e+00 0.000000e+00 0 0
## [219,] 0.000000e+00 0.000000e+00 0 0
## [220,] 0.000000e+00 0.000000e+00 0 0
## [221,] 0.000000e+00 0.000000e+00 0 0
## [222,] 0.000000e+00 0.000000e+00 0 0
## [223,] 0.000000e+00 0.000000e+00 0 0
## [224,] 0.000000e+00 0.000000e+00 0 0
## [225,] 0.000000e+00 0.000000e+00 0 0
## [226,] 0.000000e+00 0.000000e+00 0 0
## [227,] 0.000000e+00 0.000000e+00 0 0
## [228,] 0.000000e+00 0.000000e+00 0 0
## [229,] 0.000000e+00 0.000000e+00 0 0
## [230,] 0.000000e+00 0.000000e+00 0 0
## [231,] 0.000000e+00 0.000000e+00 0 0
## [232,] 0.000000e+00 0.000000e+00 0 0
## [233,] 0.000000e+00 0.000000e+00 0 0
## [234,] 0.000000e+00 0.000000e+00 0 0
## [235,] 0.000000e+00 0.000000e+00 0 0
## [236,] 0.000000e+00 0.000000e+00 0 0
## [237,] 0.000000e+00 0.000000e+00 0 0
## [238,] 0.000000e+00 0.000000e+00 0 0
## [239,] 0.000000e+00 0.000000e+00 0 0
## [240,] 0.000000e+00 0.000000e+00 0 0
## [241,] 1.648360e+05 0.000000e+00 0 0
## [242,] 0.000000e+00 0.000000e+00 0 0
## [243,] 0.000000e+00 0.000000e+00 0 0
## [244,] 0.000000e+00 0.000000e+00 0 0
## [245,] 0.000000e+00 0.000000e+00 0 0
## [246,] 0.000000e+00 0.000000e+00 0 0
## [247,] 0.000000e+00 0.000000e+00 0 0
## [248,] 0.000000e+00 0.000000e+00 0 0
## [249,] 0.000000e+00 0.000000e+00 0 0
## [250,] 0.000000e+00 0.000000e+00 0 0
## [251,] 0.000000e+00 0.000000e+00 0 0
## [252,] 0.000000e+00 0.000000e+00 0 0
## [253,] 0.000000e+00 0.000000e+00 0 0
## [254,] 0.000000e+00 0.000000e+00 0 0
## [255,] 0.000000e+00 0.000000e+00 0 0
## [256,] 0.000000e+00 0.000000e+00 0 0
## [257,] 0.000000e+00 0.000000e+00 0 0
## [258,] 0.000000e+00 0.000000e+00 0 0
## [259,] 0.000000e+00 0.000000e+00 0 0
## [260,] 0.000000e+00 0.000000e+00 0 0
## [261,] 0.000000e+00 0.000000e+00 0 0
## [262,] 0.000000e+00 0.000000e+00 0 0
## [263,] 0.000000e+00 0.000000e+00 0 0
## [264,] 0.000000e+00 0.000000e+00 0 0
## [265,] 0.000000e+00 0.000000e+00 0 0
## [266,] 0.000000e+00 0.000000e+00 0 0
## [267,] 0.000000e+00 0.000000e+00 0 0
## [268,] 0.000000e+00 0.000000e+00 0 0
## [269,] 0.000000e+00 0.000000e+00 0 0
## [270,] 0.000000e+00 0.000000e+00 0 0
## [271,] 0.000000e+00 0.000000e+00 0 0
## [272,] 0.000000e+00 0.000000e+00 0 0
## [273,] 0.000000e+00 0.000000e+00 0 0
## [274,] 0.000000e+00 0.000000e+00 0 0
## [275,] 1.000000e+00 0.000000e+00 0 0
## [276,] 0.000000e+00 0.000000e+00 0 0
## [277,] 0.000000e+00 0.000000e+00 0 0
## [278,] 0.000000e+00 0.000000e+00 0 0
## [279,] 0.000000e+00 0.000000e+00 0 0
## [280,] 0.000000e+00 0.000000e+00 0 0
## [281,] 8.100000e+01 0.000000e+00 0 0
## [282,] 0.000000e+00 0.000000e+00 0 0
## [283,] 0.000000e+00 0.000000e+00 0 0
## [284,] 0.000000e+00 0.000000e+00 0 0
## [285,] 0.000000e+00 0.000000e+00 0 0
## [286,] 0.000000e+00 0.000000e+00 0 0
## [287,] 0.000000e+00 0.000000e+00 0 0
## [288,] 0.000000e+00 0.000000e+00 0 0
## [289,] 0.000000e+00 0.000000e+00 0 0
## [290,] 0.000000e+00 0.000000e+00 0 0
## [291,] 0.000000e+00 0.000000e+00 0 0
## [292,] 0.000000e+00 0.000000e+00 0 0
## [293,] 0.000000e+00 0.000000e+00 0 0
## [294,] 0.000000e+00 0.000000e+00 0 0
## [295,] 0.000000e+00 0.000000e+00 0 0
## [296,] 0.000000e+00 0.000000e+00 0 0
## [297,] 0.000000e+00 0.000000e+00 0 0
## [298,] 0.000000e+00 0.000000e+00 0 0
## [299,] 9.801000e+03 0.000000e+00 0 0
## [300,] 0.000000e+00 0.000000e+00 0 0
## [301,] 0.000000e+00 0.000000e+00 0 0
## [302,] 0.000000e+00 0.000000e+00 0 0
## [303,] 0.000000e+00 0.000000e+00 0 0
## [304,] 0.000000e+00 0.000000e+00 0 0
## [305,] 0.000000e+00 0.000000e+00 0 0
## [306,] 0.000000e+00 0.000000e+00 0 0
## [307,] 0.000000e+00 0.000000e+00 0 0
## [308,] 0.000000e+00 0.000000e+00 0 0
## [309,] 1.232100e+04 0.000000e+00 0 0
## [310,] 0.000000e+00 0.000000e+00 0 0
## [311,] 0.000000e+00 0.000000e+00 0 0
## [312,] 0.000000e+00 0.000000e+00 0 0
## [313,] 0.000000e+00 0.000000e+00 0 0
## [314,] 0.000000e+00 0.000000e+00 0 0
## [315,] 0.000000e+00 0.000000e+00 0 0
## [316,] 0.000000e+00 0.000000e+00 0 0
## [317,] 0.000000e+00 0.000000e+00 0 0
## [318,] 0.000000e+00 0.000000e+00 0 0
## [319,] 0.000000e+00 0.000000e+00 0 0
## [320,] 0.000000e+00 0.000000e+00 0 0
## [321,] 0.000000e+00 0.000000e+00 0 0
## [322,] 0.000000e+00 0.000000e+00 0 0
## [323,] 0.000000e+00 0.000000e+00 0 0
## [324,] 2.500000e+03 0.000000e+00 0 0
## [325,] 0.000000e+00 0.000000e+00 0 0
## [326,] 0.000000e+00 0.000000e+00 0 0
## [327,] 0.000000e+00 0.000000e+00 0 0
## [328,] 0.000000e+00 0.000000e+00 0 0
## [329,] 0.000000e+00 0.000000e+00 0 0
## [330,] 0.000000e+00 0.000000e+00 0 0
## [331,] 0.000000e+00 0.000000e+00 0 0
## [332,] 0.000000e+00 0.000000e+00 0 0
## [333,] 0.000000e+00 0.000000e+00 0 0
## [334,] 0.000000e+00 0.000000e+00 0 0
## [335,] 2.401000e+03 0.000000e+00 0 0
## [336,] 0.000000e+00 0.000000e+00 0 0
## [337,] 0.000000e+00 0.000000e+00 0 0
## [338,] 0.000000e+00 0.000000e+00 0 0
## [339,] 0.000000e+00 0.000000e+00 0 0
## [340,] 0.000000e+00 0.000000e+00 0 0
## [341,] 0.000000e+00 0.000000e+00 0 0
## [342,] 0.000000e+00 0.000000e+00 0 0
## [343,] 0.000000e+00 0.000000e+00 0 0
## [344,] 0.000000e+00 0.000000e+00 0 0
## [345,] 0.000000e+00 0.000000e+00 0 0
## [346,] 0.000000e+00 0.000000e+00 0 0
## [347,] 0.000000e+00 0.000000e+00 0 0
## [348,] 7.744000e+03 0.000000e+00 0 0
## [349,] 0.000000e+00 0.000000e+00 0 0
## [350,] 0.000000e+00 0.000000e+00 0 0
## [351,] 0.000000e+00 0.000000e+00 0 0
## [352,] 0.000000e+00 0.000000e+00 0 0
## [353,] 0.000000e+00 0.000000e+00 0 0
## [354,] 0.000000e+00 0.000000e+00 0 0
## [355,] 1.000000e+00 0.000000e+00 0 0
## [356,] 0.000000e+00 0.000000e+00 0 0
## [357,] 0.000000e+00 0.000000e+00 0 0
## [358,] 0.000000e+00 0.000000e+00 0 0
## [359,] 0.000000e+00 0.000000e+00 0 0
## [360,] 0.000000e+00 0.000000e+00 0 0
## [361,] 2.167834e+07 0.000000e+00 0 0
## [362,] 0.000000e+00 0.000000e+00 0 0
## [363,] 0.000000e+00 0.000000e+00 0 0
## [364,] 0.000000e+00 0.000000e+00 0 0
## [365,] 0.000000e+00 0.000000e+00 0 0
## [366,] 0.000000e+00 0.000000e+00 0 0
## [367,] 0.000000e+00 0.000000e+00 0 0
## [368,] 0.000000e+00 0.000000e+00 0 0
## [369,] 0.000000e+00 0.000000e+00 0 0
## [370,] 0.000000e+00 0.000000e+00 0 0
## [371,] 0.000000e+00 0.000000e+00 0 0
## [372,] 0.000000e+00 0.000000e+00 0 0
## [373,] 0.000000e+00 0.000000e+00 0 0
## [374,] 0.000000e+00 0.000000e+00 0 0
## [375,] 1.000000e+00 0.000000e+00 0 0
## [376,] 0.000000e+00 0.000000e+00 0 0
## [377,] 0.000000e+00 0.000000e+00 0 0
## [378,] 0.000000e+00 0.000000e+00 0 0
## [379,] 0.000000e+00 0.000000e+00 0 0
## [380,] 0.000000e+00 0.000000e+00 0 0
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## [24981,] 6.885680e+07 0.000000e+00 0 0
## [24982,] 0.000000e+00 0.000000e+00 0 0
## [24983,] 1.500625e+06 0.000000e+00 0 0
## [24984,] 1.076821e+08 0.000000e+00 0 0
## [24985,] 0.000000e+00 0.000000e+00 0 0
## [24986,] 1.584040e+07 0.000000e+00 0 0
## [24987,] 4.000000e+00 0.000000e+00 0 0
## [24988,] 1.000000e+00 0.000000e+00 0 0
## [24989,] 6.400000e+03 0.000000e+00 0 0
## [24990,] 0.000000e+00 0.000000e+00 0 0
## [24991,] 6.889000e+05 0.000000e+00 0 0
## [24992,] 4.137156e+06 0.000000e+00 0 0
## [24993,] 6.250484e+07 0.000000e+00 0 0
## [24994,] 0.000000e+00 0.000000e+00 0 0
## [24995,] 2.391210e+05 0.000000e+00 0 0
## [24996,] 7.279204e+06 0.000000e+00 0 0
## [24997,] 1.174433e+07 0.000000e+00 0 0
## [24998,] 3.046819e+09 0.000000e+00 0 0
## [24999,] 1.977961e+08 0.000000e+00 0 0
## [ reached getOption("max.print") -- omitted 504242 rows ]# rapply for recursive calculations
rapply(vaccines, mean, class = "integer")## Denominator NumberDosesReceived NumberDosesExported FirstDose
## NA NA NA 1.749675e+03
## FirstDoseRefused SecondDose DoseAdditional1 DoseAdditional2
## NA 1.582874e+03 1.251688e+03 1.962433e+02
## DoseAdditional3 UnknownDose Population
## 4.458589e+00 1.395121e+01 1.574692e+07rapply(vaccines, table, class = "factor")## NULLvaccines$ReportingCountry <- as.factor(vaccines$ReportingCountry)
vaccines$Vaccine <- as.factor(vaccines$Vaccine)
vaccines$TargetGroup <- as.factor(vaccines$TargetGroup)
vaccines$Region <- as.factor(vaccines$Region)
rapply(vaccines, table, class = "factor") #factors need to be defined and now it gives the counts for each group of each factor## ReportingCountry.AT ReportingCountry.BE ReportingCountry.BG
## 11520 4510 3977
## ReportingCountry.CY ReportingCountry.CZ ReportingCountry.DE
## 4237 3967 594
## ReportingCountry.DK ReportingCountry.EE ReportingCountry.EL
## 3922 3741 10752
## ReportingCountry.ES ReportingCountry.FI ReportingCountry.FR
## 5820 269568 15604
## ReportingCountry.HR ReportingCountry.HU ReportingCountry.IE
## 7005 4939 3001
## ReportingCountry.IS ReportingCountry.IT ReportingCountry.LI
## 3850 10148 3030
## ReportingCountry.LT ReportingCountry.LU ReportingCountry.LV
## 6868 5244 5705
## ReportingCountry.MT ReportingCountry.NL ReportingCountry.NO
## 4028 5940 2616
## ReportingCountry.PL ReportingCountry.PT ReportingCountry.RO
## 106080 7882 3245
## ReportingCountry.SE ReportingCountry.SI ReportingCountry.SK
## 6113 2371 2964
## Region.AT Region.BE Region.BG
## 11520 4510 3977
## Region.CY Region.CZ Region.DE
## 4237 3967 594
## Region.DK Region.EE Region.EL
## 3922 3741 10752
## Region.ES Region.FI Region.FI19
## 5820 11232 11232
## Region.FI193 Region.FI194 Region.FI195
## 11232 11232 11232
## Region.FI197 Region.FI1B Region.FI1B1
## 11232 11232 11232
## Region.FI1C Region.FI1C1 Region.FI1C2
## 11232 11232 11232
## Region.FI1C3 Region.FI1C4 Region.FI1C5
## 11232 11232 11232
## Region.FI1D Region.FI1D1 Region.FI1D2
## 11232 11232 11232
## Region.FI1D3 Region.FI1D5 Region.FI1D7
## 11232 11232 11232
## Region.FI1D8 Region.FI1D9 Region.FI20
## 11232 11232 11232
## Region.FI200 Region.FR Region.FR1
## 11232 1316 752
## Region.FRB Region.FRC Region.FRD
## 752 752 752
## Region.FRE Region.FRF Region.FRG
## 752 752 752
## Region.FRH Region.FRI Region.FRJ
## 752 752 752
## Region.FRK Region.FRL Region.FRM
## 752 752 752
## Region.FRY1 Region.FRY2 Region.FRY3
## 752 752 752
## Region.FRY4 Region.FRY5 Region.HR
## 752 752 7005
## Region.HU Region.IE Region.IS
## 4939 3001 3850
## Region.IT Region.ITC1 Region.ITC2
## 3776 331 270
## Region.ITC3 Region.ITC4 Region.ITF1
## 301 315 302
## Region.ITF2 Region.ITF3 Region.ITF4
## 269 316 307
## Region.ITF5 Region.ITF6 Region.ITG1
## 283 298 304
## Region.ITG2 Region.ITH1 Region.ITH2
## 269 315 297
## Region.ITH3 Region.ITH4 Region.ITH5
## 317 319 313
## Region.ITI1 Region.ITI2 Region.ITI3
## 316 294 315
## Region.ITI4 Region.LI Region.LT
## 321 3030 5252
## Region.LT01 Region.LT02 Region.LU
## 808 808 5244
## Region.LV Region.MT Region.NL
## 5705 4028 5940
## Region.NO Region.PL Region.PL21
## 2616 6240 6240
## Region.PL22 Region.PL41 Region.PL42
## 6240 6240 6240
## Region.PL43 Region.PL51 Region.PL52
## 6240 6240 6240
## Region.PL61 Region.PL62 Region.PL63
## 6240 6240 6240
## Region.PL71 Region.PL72 Region.PL81
## 6240 6240 6240
## Region.PL82 Region.PL84 Region.PL92X
## 6240 6240 6240
## Region.PT Region.PTCSR01 Region.PTCSR02
## 5022 492 489
## Region.PTCSR04 Region.PTCSR05 Region.PTCSR07
## 586 666 627
## Region.RO Region.SE Region.SE11
## 3245 3073 380
## Region.SE12 Region.SE21 Region.SE22
## 380 380 380
## Region.SE23 Region.SE31 Region.SE32
## 380 380 380
## Region.SE33 Region.SI Region.SK
## 380 2371 2964
## Region.UNK TargetGroup.1_Age<60 TargetGroup.1_Age60+
## 752 1073 1073
## TargetGroup.Age<18 TargetGroup.Age0_4 TargetGroup.Age10_14
## 29811 32496 34880
## TargetGroup.Age15_17 TargetGroup.Age18_24 TargetGroup.Age25_49
## 35572 41427 41807
## TargetGroup.Age5_9 TargetGroup.Age50_59 TargetGroup.Age60_69
## 34093 41501 41465
## TargetGroup.Age70_79 TargetGroup.Age80+ TargetGroup.AgeUNK
## 41303 41013 34528
## TargetGroup.ALL TargetGroup.HCW TargetGroup.LTCF
## 56499 14998 5702
## Vaccine.AZ Vaccine.BECNBG Vaccine.BHACOV
## 75003 1847 183
## Vaccine.COM Vaccine.COMBA.1 Vaccine.COMBA.4-5
## 83781 33897 33422
## Vaccine.COMBIV Vaccine.JANSS Vaccine.MOD
## 6 74417 81117
## Vaccine.MODBA.1 Vaccine.MODBA.4-5 Vaccine.MODBIV
## 1744 49 6
## Vaccine.NVXD Vaccine.SIN Vaccine.SPU
## 61004 976 683
## Vaccine.UNK Vaccine.VLA
## 49997 31109rapply(vaccines, median, how = "list", class = "integer", na.rm = TRUE) #returns a list## $YearWeekISO
## NULL
##
## $ReportingCountry
## NULL
##
## $Denominator
## [1] 258230
##
## $NumberDosesReceived
## [1] 0
##
## $NumberDosesExported
## [1] 0
##
## $FirstDose
## [1] 0
##
## $FirstDoseRefused
## [1] 0
##
## $SecondDose
## [1] 0
##
## $DoseAdditional1
## [1] 0
##
## $DoseAdditional2
## [1] 0
##
## $DoseAdditional3
## [1] 0
##
## $UnknownDose
## [1] 0
##
## $Region
## NULL
##
## $TargetGroup
## NULL
##
## $Vaccine
## NULL
##
## $Population
## [1] 5533793rapply(vaccines, median, how = "unlist", class = "integer", na.rm = TRUE) #gives a vector## Denominator NumberDosesReceived NumberDosesExported FirstDose
## 258230 0 0 0
## FirstDoseRefused SecondDose DoseAdditional1 DoseAdditional2
## 0 0 0 0
## DoseAdditional3 UnknownDose Population
## 0 0 5533793# what do you mean by RECURSIVELY?
x <- list("a", list(24, 443), 434, list(54, list(6443, 7234))) #this is a complex list with sublists
str(x)## List of 4
## $ : chr "a"
## $ :List of 2
## ..$ : num 24
## ..$ : num 443
## $ : num 434
## $ :List of 2
## ..$ : num 54
## ..$ :List of 2
## .. ..$ : num 6443
## .. ..$ : num 7234rapply(x, log2, class = c("numeric"))## [1] 4.584963 8.791163 8.761551 5.754888 12.653517 12.820578(r <- rapply(x, log2, class = c("numeric"), how = "unlist"))## [1] 4.584963 8.791163 8.761551 5.754888 12.653517 12.820578str(r)## num [1:6] 4.58 8.79 8.76 5.75 12.65 ...Note that tapply() and by() are very
similar. Indeed, by() is a wrapper of
tapply(). On the other hand, vapply() is
similar to sapply, but has a pre-specified type of return
value, so it can be safer (or faster) to use sometimes. Finally,
rapply() is not very often used, but it can be the best
option for complex datasets, like those including nested lists or
complex lists with several dataframes and/or vectors.
The applications of apply functions are very wide. You can use them to parse data with many different input and output structures, even for plots (see Lesson 11).
References
Manipulación de datos en R: https://r-coder.com/manipulacion-datos-r/ [ES] & https://r-coder.com/r-data-manipulation/ [EN]
R in action. Robert I. Kabacoff. March 2022 ISBN 9781617296055
Working with tables in R: https://bookdown.org/kdonovan125/ibis_data_analysis_r4/working-with-tables-in-r.html
About flat tables in R: https://cran.r-project.org/web/packages/memisc/vignettes/ftable-matrix.html
data.table cheatsheet: https://raw.githubusercontent.com/rstudio/cheatsheets/main/datatable.pdf
Data aggregation: https://r-coder.com/aggregate-r/
About Applies: https://www.r-bloggers.com/2016/03/apply-lapply-rapply-sapply-functions-in-r-2/ & https://gist.github.com/lyndametref/4d137fcba1ec4d9af80ad53245b358ff
Exercises
1. Using the merge3 dataset, create a data matrix table containing the strain name and the columns Plasmids, CRISPR, VR and AMR are transformed as a single variable.
2. Two students enrolled in different University courses: Rafa in A,B,D, E and G and Roger in B, C, D and F. Rafa got the following marks 8, 9 ,9.5, 8.75 and 9 and Roger obtained 10, 9.7, 9 and 10. Write a R program to create merged dataframes with all the marks (outer join), all the Rafa’s marks (left outer), all the Roger’s marks (right outer), and the marks in the common courses (inner join).
3. We are using the Covid19 test data for exercises 3-6. You can download the updated csv file from https://opendata.ecdc.europa.eu/covid19/testing/csv/data.csv (see also https://www.ecdc.europa.eu/en/publications-data/covid-19-testing). Read the data and write the table in your computer as covid_tests.csv.
4. Generate two contingency tables showing the number of tests done (1) by country per week and (2) by country per month.
Hint. For the second table, use the function ISOweek2date() from the package “ISOweek” to transform the ISO date_week to standard date format.
3. Remove all the character columns to create a subset dataframe
named minitests. You can use subset() and
sapply() here.
4. Use apply() to obtain the min, max, median and mean of each column.
5. Use tapply() to obtain the mean, median and min of
new_cases per country and construct a dataframe with the result.
Hint. Consider how to handle NA’s.
6. Create a function that calculates log10() of the mean, apply it to new_cases per country and append it to the dataframe from the previous exercise. Then, write the results in a csv file.
Short exercises
The topics in this lesson can be dense and bewildering the first time. I selected some sheets of exercises from great websites that can give you an extra practice.
Session Info
sessionInfo()## R version 4.2.2 (2022-10-31)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] es_ES.UTF-8/es_ES.UTF-8/es_ES.UTF-8/C/es_ES.UTF-8/es_ES.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] data.table_1.14.6 formatR_1.12 knitr_1.41
##
## loaded via a namespace (and not attached):
## [1] digest_0.6.30 R6_2.5.1 jsonlite_1.8.3 magrittr_2.0.3
## [5] evaluate_0.18 stringi_1.7.8 cachem_1.0.6 rlang_1.0.6
## [9] cli_3.4.1 rstudioapi_0.14 jquerylib_0.1.4 bslib_0.4.1
## [13] vctrs_0.5.1 rmarkdown_2.18 tools_4.2.2 stringr_1.4.1
## [17] xfun_0.35 yaml_2.3.6 fastmap_1.1.0 compiler_4.2.2
## [21] htmltools_0.5.3 sass_0.4.4