How do I count treated and untreated in R - r

Question

I'm trying to learn R again and am trying to count the number total number of genes that are "treated" and "untreated" with dex in the bioconductor airway dataset. (https://bioconductor.org/packages/release/data/experiment/html/airway.html).
I'm trying:
airway$dex=='trted'
#[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
and it's not working.

Answer 1

After installing that package I performed the following actions at my console ( and including all output):
> library(airway)
Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats
Attaching package: ‘matrixStats’
The following object is masked from ‘package:dplyr’:
count
Attaching package: ‘MatrixGenerics’
The following objects are masked from ‘package:matrixStats’:
colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, colCounts, colCummaxs, colCummins,
colCumprods, colCumsums, colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, colMads, colMaxs,
colMeans2, colMedians, colMins, colOrderStats, colProds, colQuantiles, colRanges, colRanks, colSdDiffs,
colSds, colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads, colWeightedMeans,
colWeightedMedians, colWeightedSds, colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, rowCumsums, rowDiffs, rowIQRDiffs,
rowIQRs, rowLogSumExps, rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins, rowOrderStats,
rowProds, rowQuantiles, rowRanges, rowRanks, rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs,
rowVars, rowWeightedMads, rowWeightedMeans, rowWeightedMedians, rowWeightedSds, rowWeightedVars
Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: ‘BiocGenerics’
The following objects are masked from ‘package:parallel’:
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, parApply,
parCapply, parLapply, parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from ‘package:bit64’:
match, order, rank
The following objects are masked from ‘package:dplyr’:
combine, intersect, setdiff, union
The following objects are masked from ‘package:stats’:
IQR, mad, sd, var, xtabs
The following objects are masked from ‘package:base’:
anyDuplicated, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval,
evalq, Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget, order,
paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,
table, tapply, union, unique, unsplit, which.max, which.min
Loading required package: S4Vectors
Attaching package: ‘S4Vectors’
The following object is masked from ‘package:Matrix’:
expand
The following objects are masked from ‘package:data.table’:
first, second
The following objects are masked from ‘package:tidygraph’:
active, rename
The following object is masked from ‘package:tidyr’:
expand
The following objects are masked from ‘package:dplyr’:
first, rename
The following object is masked from ‘package:base’:
expand.grid
Loading required package: IRanges
Attaching package: ‘IRanges’
The following object is masked from ‘package:data.table’:
shift
The following object is masked from ‘package:nlme’:
collapse
The following object is masked from ‘package:tidygraph’:
slice
The following object is masked from ‘package:purrr’:
reduce
The following objects are masked from ‘package:dplyr’:
collapse, desc, slice
Loading required package: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Attaching package: ‘Biobase’
The following object is masked from ‘package:MatrixGenerics’:
rowMedians
The following objects are masked from ‘package:matrixStats’:
anyMissing, rowMedians
The following object is masked from ‘package:bit64’:
cache
Attaching package: ‘SummarizedExperiment’
The following object is masked from ‘package:SeuratObject’:
Assays
The following object is masked from ‘package:Seurat’:
Assays
I looked at the help page
> help(pac=airway)
So after reading that I thought the airway dataset might be accessible, but no:
> str(airway)
Error in str(airway) : object 'airway' not found
So I tried loading it with the data function (and no error was reported) so I looked at its structure:
> data(airway)
> str(airway)
Formal class 'RangedSummarizedExperiment' [package "SummarizedExperiment"] with 6 slots
..# rowRanges :Formal class 'GRangesList' [package "GenomicRanges"] with 3 slots
.. .. ..# elementMetadata:Formal class 'DataFrame' [package "IRanges"] with 6 slots
.. .. .. .. ..# rownames : NULL
.. .. .. .. ..# nrows : int 64102
.. .. .. .. ..# listData : Named list()
.. .. .. .. ..# elementType : chr "ANY"
.. .. .. .. ..# elementMetadata: NULL
.. .. .. .. ..# metadata : list()
.. .. ..# elementType : chr "GRanges"
.. .. ..# metadata :List of 1
.. .. .. ..$ genomeInfo:List of 20
.. .. .. .. ..$ Db type : chr "TranscriptDb"
.. .. .. .. ..$ Supporting package : chr "GenomicFeatures"
.. .. .. .. ..$ Data source : chr "BioMart"
.. .. .. .. ..$ Organism : chr "Homo sapiens"
.. .. .. .. ..$ Resource URL : chr "www.biomart.org:80"
.. .. .. .. ..$ BioMart database : chr "ensembl"
.. .. .. .. ..$ BioMart database version : chr "ENSEMBL GENES 75 (SANGER UK)"
.. .. .. .. ..$ BioMart dataset : chr "hsapiens_gene_ensembl"
.. .. .. .. ..$ BioMart dataset description : chr "Homo sapiens genes (GRCh37.p13)"
.. .. .. .. ..$ BioMart dataset version : chr "GRCh37.p13"
.. .. .. .. ..$ Full dataset : chr "yes"
.. .. .. .. ..$ miRBase build ID : chr NA
.. .. .. .. ..$ transcript_nrow : chr "215647"
.. .. .. .. ..$ exon_nrow : chr "745593"
.. .. .. .. ..$ cds_nrow : chr "537555"
.. .. .. .. ..$ Db created by : chr "GenomicFeatures package from Bioconductor"
.. .. .. .. ..$ Creation time : chr "2014-07-10 14:55:55 -0400 (Thu, 10 Jul 2014)"
.. .. .. .. ..$ GenomicFeatures version at creation time: chr "1.17.9"
.. .. .. .. ..$ RSQLite version at creation time : chr "0.11.4"
.. .. .. .. ..$ DBSCHEMAVERSION : chr "1.0"
..# colData :Formal class 'DataFrame' [package "IRanges"] with 6 slots
.. .. ..# rownames : chr [1:8] "SRR1039508" "SRR1039509" "SRR1039512" "SRR1039513" ...
.. .. ..# nrows : int 8
.. .. ..# listData :List of 9
.. .. .. ..$ SampleName: Factor w/ 8 levels "GSM1275862","GSM1275863",..: 1 2 3 4 5 6 7 8
.. .. .. ..$ cell : Factor w/ 4 levels "N052611","N061011",..: 4 4 1 1 3 3 2 2
.. .. .. ..$ dex : Factor w/ 2 levels "trt","untrt": 2 1 2 1 2 1 2 1
.. .. .. ..$ albut : Factor w/ 1 level "untrt": 1 1 1 1 1 1 1 1
.. .. .. ..$ Run : Factor w/ 8 levels "SRR1039508","SRR1039509",..: 1 2 3 4 5 6 7 8
.. .. .. ..$ avgLength : int [1:8] 126 126 126 87 120 126 101 98
.. .. .. ..$ Experiment: Factor w/ 8 levels "SRX384345","SRX384346",..: 1 2 3 4 5 6 7 8
.. .. .. ..$ Sample : Factor w/ 8 levels "SRS508567","SRS508568",..: 2 1 3 4 5 6 7 8
.. .. .. ..$ BioSample : Factor w/ 8 levels "SAMN02422669",..: 1 4 6 2 7 3 8 5
.. .. ..# elementType : chr "ANY"
.. .. ..# elementMetadata: NULL
.. .. ..# metadata : list()
..# assays :Reference class 'ShallowSimpleListAssays' [package "GenomicRanges"] with 1 field
.. ..$ data:Formal class 'SimpleList' [package "IRanges"] with 4 slots
.. .. .. ..# listData :List of 1
.. .. .. .. ..$ counts: int [1:64102, 1:8] 679 0 467 260 60 0 3251 1433 519 394 ...
.. .. .. ..# elementType : chr "ANY"
.. .. .. ..# elementMetadata: NULL
.. .. .. ..# metadata : list()
.. ..and 12 methods.
..# NAMES : NULL
..# elementMetadata:Formal class 'DataFrame' [package "S4Vectors"] with 6 slots
.. .. ..# rownames : NULL
.. .. ..# nrows : int 64102
.. .. ..# listData : Named list()
.. .. ..# elementType : chr "ANY"
.. .. ..# elementMetadata: NULL
.. .. ..# metadata : list()
..# metadata :List of 1
.. ..$ :Formal class 'MIAME' [package "Biobase"] with 13 slots
.. .. .. ..# name : chr "Himes BE"
.. .. .. ..# lab : chr NA
.. .. .. ..# contact : chr ""
.. .. .. ..# title : chr "RNA-Seq transcriptome profiling identifies CRISPLD2 as a glucocorticoid responsive gene that modulates cytokine"| __truncated__
.. .. .. ..# abstract : chr "Asthma is a chronic inflammatory respiratory disease that affects over 300 million people worldwide. Glucocorti"| __truncated__
.. .. .. ..# url : chr "http://www.ncbi.nlm.nih.gov/pubmed/24926665"
.. .. .. ..# pubMedIds : chr "24926665"
.. .. .. ..# samples : list()
.. .. .. ..# hybridizations : list()
.. .. .. ..# normControls : list()
.. .. .. ..# preprocessing : list()
.. .. .. ..# other : list()
.. .. .. ..# .__classVersion__:Formal class 'Versions' [package "Biobase"] with 1 slot
.. .. .. .. .. ..# .Data:List of 2
.. .. .. .. .. .. ..$ : int [1:3] 1 0 0
.. .. .. .. .. .. ..$ : int [1:3] 1 1 0
Scanning through that list of S4 structured data I saw this line:
.. .. .. ..$ dex : Factor w/ 2 levels "trt","untrt": 2 1 2 1 2 1 2 1
So the dex items do have "trt" and "untrt" as values but that "column" is located somewhat deeper in the entire DesignedExperiment structure. There might be a specific function, that I do not know the name of, to pull out values from such structures, but we now have enough information to answer (or hack together) the question. Follow the names and operators in that nested list backward to its origin and use the S4 extraction operator: "#" where it appropriate and $ when not:
sum( airway# colData # listData $ dex == "trt")
#[1] 4

Answer 2

Use sum() function to count True values:
sum(airway$dex=='trted')