Feb 9, 2026

Today

  • read.table() and subfunctions
  • write.table() and write.csv()
  • readLines()
  • writeLines()
  • more about save()

read.table()

A lot of options… 
read.table(file, header = FALSE, sep = "", quote = "\"'",
           dec = ".", numerals = c("allow.loss", "warn.loss", "no.loss"),
           row.names, col.names, as.is = !stringsAsFactors,
           na.strings = "NA", colClasses = NA, nrows = -1,
           skip = 0, check.names = TRUE, fill = !blank.lines.skip,
           strip.white = FALSE, blank.lines.skip = TRUE,
           comment.char = "#",
           allowEscapes = FALSE, flush = FALSE,
           stringsAsFactors = FALSE,
           fileEncoding = "", encoding = "unknown", text, skipNul = FALSE)
  • file = file name you want to read in as a character (in quotes)
  • header = does it have a header? (T or F)
  • sep = what symbol is separating the values in each column

read.table()

…more options… 
read.table(file, header = FALSE, sep = "", quote = "\"'",
           dec = ".", numerals = c("allow.loss", "warn.loss", "no.loss"),
           row.names, col.names, as.is = !stringsAsFactors,
           na.strings = "NA", colClasses = NA, nrows = -1,
           skip = 0, check.names = TRUE, fill = !blank.lines.skip,
           strip.white = FALSE, blank.lines.skip = TRUE,
           comment.char = "#",
           allowEscapes = FALSE, flush = FALSE,
           stringsAsFactors = FALSE,
           fileEncoding = "", encoding = "unknown", text, skipNul = FALSE)
  • quote = What characters are used to denote quotes?
  • dec = What’s used to denote decimals? Usually “.” or “,”.
  • row.names = which column are the rownames in?
  • col.names = specify column names directly

read.table()

…more options… 
read.table(file, header = FALSE, sep = "", quote = "\"'",
           dec = ".", numerals = c("allow.loss", "warn.loss", "no.loss"),
           row.names, col.names, as.is = !stringsAsFactors,
           na.strings = "NA", colClasses = NA, nrows = -1,
           skip = 0, check.names = TRUE, fill = !blank.lines.skip,
           strip.white = FALSE, blank.lines.skip = TRUE,
           comment.char = "#",
           allowEscapes = FALSE, flush = FALSE,
           stringsAsFactors = FALSE,
           fileEncoding = "", encoding = "unknown", text, skipNul = FALSE)
  • as.is = Which columns to keep strings as factors (prevent conversion)?
  • na.strings = What character symbols should be interpreted as NAs?
  • colClasses = A vector of classes to be assumed for columns

read.table()

…more options… 
read.table(file, header = FALSE, sep = "", quote = "\"'",
           dec = ".", numerals = c("allow.loss", "warn.loss", "no.loss"),
           row.names, col.names, as.is = !stringsAsFactors,
           na.strings = "NA", colClasses = NA, nrows = -1,
           skip = 0, check.names = TRUE, fill = !blank.lines.skip,
           strip.white = FALSE, blank.lines.skip = TRUE,
           comment.char = "#",
           allowEscapes = FALSE, flush = FALSE,
           stringsAsFactors = FALSE,
           fileEncoding = "", encoding = "unknown", text, skipNul = FALSE)
  • nrows = How many total rows to read in (after the header, if applicable)
  • skip = How many rows to skip before start reading. This includes skipping the header.
  • check.names = Makes sure names are valid column names. No special characters, etc.

read.table()

…more options… 
read.table(file, header = FALSE, sep = "", quote = "\"'",
           dec = ".", numerals = c("allow.loss", "warn.loss", "no.loss"),
           row.names, col.names, as.is = !stringsAsFactors,
           na.strings = "NA", colClasses = NA, nrows = -1,
           skip = 0, check.names = TRUE, fill = !blank.lines.skip,
           strip.white = FALSE, blank.lines.skip = TRUE,
           comment.char = "#",
           allowEscapes = FALSE, flush = FALSE,
           stringsAsFactors = FALSE,
           fileEncoding = "", encoding = "unknown", text, skipNul = FALSE)
  • fill = Should missing values be filled in?
  • strip.white = Should spaces or tabs before or after values be removed?
  • blank.lines.skip = Should blank rows be ignored?

Know the options for read.table()

If you know how they work, you will save time by avoiding typing, find-and-replacing, etc.

read.csv() and read.delim()

“.csv” = comma-separated values

read.csv(file, header = TRUE, sep = ",", quote = "\"",
         dec = ".", fill = TRUE, comment.char = "", ...)

Tab-delimited values

read.delim(file, header = TRUE, sep = "\t", quote = "\"",
           dec = ".", fill = TRUE, comment.char = "", ...)

Wrappers for read.table()

Access the object read.csv.

read.csv
## function (file, header = TRUE, sep = ",", quote = "\"", dec = ".", 
##     fill = TRUE, comment.char = "", ...) 
## read.table(file = file, header = header, sep = sep, quote = quote, 
##     dec = dec, fill = fill, comment.char = comment.char, ...)
## <bytecode: 0x140683ba0>
## <environment: namespace:utils>

Read Puromycin data

List all files in the directory with the extension “.csv”

list.files(pattern=".csv")
## [1] "Puromycin_data.csv"

“The Puromycin data frame has 23 rows and 3 columns of the reaction velocity versus substrate concentration in an enzymatic reaction involving untreated cells or cells treated with Puromycin.”—R help

Written as a “.csv” file

"","conc","rate","state"
"1",0.02,76,"treated"
"2",0.02,47,"treated"
"3",0.06,97,"treated"
"4",0.06,107,"treated"
"5",0.11,123,"treated"
"6",0.11,139,"treated"
"7",0.22,159,"treated"
"8",0.22,152,"treated"
"9",0.56,191,"treated"
"10",0.56,201,"treated"
"11",1.1,207,"treated"
"12",1.1,200,"treated"
"13",0.02,67,"untreated"
"14",0.02,51,"untreated"
"15",0.06,84,"untreated"
"16",0.06,86,"untreated"
"17",0.11,98,"untreated"
"18",0.11,115,"untreated"
"19",0.22,131,"untreated"
"20",0.22,124,"untreated"
"21",0.56,144,"untreated"
"22",0.56,158,"untreated"
"23",1.1,160,"untreated"

Read Puromycin data

pur<-read.csv("Puromycin_data.csv", row.names=1)
head(pur)
##   conc rate   state
## 1 0.02   76 treated
## 2 0.02   47 treated
## 3 0.06   97 treated
## 4 0.06  107 treated
## 5 0.11  123 treated
## 6 0.11  139 treated

Write Puromycin data

Change to tab-delimted text using write.table()

write.table(pur, file = "Puromycin_data.delim", row.names=F, sep= "\t")
  • There is no write.delim() function in base R.
"conc"  "rate"  "state"
0.02    76  "treated"
0.02    47  "treated"
0.06    97  "treated"
0.06    107 "treated"
0.11    123 "treated"
0.11    139 "treated"
0.22    159 "treated"
0.22    152 "treated"
0.56    191 "treated"
0.56    201 "treated"
1.1 207 "treated"
1.1 200 "treated"
0.02    67  "untreated"
0.02    51  "untreated"
0.06    84  "untreated"
0.06    86  "untreated"
0.11    98  "untreated"
0.11    115 "untreated"
0.22    131 "untreated"
0.22    124 "untreated"
0.56    144 "untreated"
0.56    158 "untreated"
1.1 160 "untreated"

readLines()

What if we just downloaded a massive file with an unknown extension, but we think it’s text.

file.info("usearch.otus.97.tax")
##                         size isdir mode               mtime               ctime
## usearch.otus.97.tax 16315973 FALSE  755 2026-01-09 14:30:06 2026-01-09 14:30:06
##                                   atime uid gid  uname grname
## usearch.otus.97.tax 2026-01-09 14:43:28 502  20 hgreen  staff

readLines()

tax<-readLines("usearch.otus.97.tax", n=4)
head(tax)
## [1] "Cluster_ID\tTaxonomy\tRank\tCluster_Size\tMin_GDist\tAvg_GDist\tVote\tMin_rank\tTaxa_counts\tMax_pcts\tNa_pcts"                                                                                           
## [2] "Cluster0\tBacteria;Proteobacteria;Betaproteobacteria;Burkholderiales;Alcaligenaceae;Alcaligenes\tgenus\t120779\t0\t0\t100\tgenus\t1;1;1;1;1;1;0;0\t100;100;100;100;100;100;0;0\t0;0;0;0;0;0;100;100"      
## [3] "Cluster1\tBacteria;Firmicutes;Clostridia;Clostridiales;Lachnospiraceae;Roseburia\tgenus\t61062\t0\t0\t100\tgenus\t1;1;1;1;1;1;0;0\t100;100;100;100;100;100;0;0\t0;0;0;0;0;0;100;100"                      
## [4] "Cluster10\tBacteria;Actinobacteria;Actinobacteria;Bifidobacteriales;Bifidobacteriaceae;Bifidobacterium\tgenus\t26131\t0\t0\t100\tgenus\t1;1;1;1;1;1;0;0\t100;100;100;100;100;100;0;0\t0;0;0;0;0;0;100;100"
  • readLines() can be used to quickly sample and explore unfamiliar data

cat() and writeLines()

  • cat() = raw flexible output to file or console
month.abb[1:4]
## [1] "Jan" "Feb" "Mar" "Apr"
cat(month.abb[1:4])
## Jan Feb Mar Apr

cat() and writeLines()

  • writeLines() = similar to cat(), but default sep = "\n"
month.abb[1:4]
## [1] "Jan" "Feb" "Mar" "Apr"
writeLines(month.abb[1:4])
## Jan
## Feb
## Mar
## Apr
  • cat() is probably not what you want unless you specify sep.

Saving big files

Read in “tax” file:

system.time(tax<-read.delim("usearch.otus.97.tax"))
dim(tax)
object.size(tax)
##    user  system elapsed 
##   0.153   0.006   0.160 
## [1] 93488    11
## 13883864 bytes

Save as R object with compression

save(tax, file = "TAX_data_GZIP.Rdata", compress="gzip")
list.files()
##  [1] "custom.css"                       "Importing and Exporting data.pdf"
##  [3] "Importing-Exporting-Data.html"    "Importing-Exporting-Data.Rmd"    
##  [5] "Puromycin_data.csv"               "Puromycin_data.delim"            
##  [7] "qPCR-data.Rdata"                  "qPCR-data.txt"                   
##  [9] "TAX_data_GZIP.Rdata"              "usearch.otus.97.tax"

Compressed vs Original

Compressed R object 10X smaller than original text file

file.info("usearch.otus.97.tax")[1] / 10^6 #Original text file
file.info("TAX_data_GZIP.Rdata")[1]/ 10^6 # Compressed R object
##                         size
## usearch.otus.97.tax 16.31597
##                         size
## TAX_data_GZIP.Rdata 1.223305
  • Multiple compression methods
  • Compression adds time for packing and unpacking

Summary

  • Ensure columns have a single class of data only, i.e., either numeric, factor, or character. Get rid of values like “<5”.
  • Know the options of read.table() and related functions.
  • Windows users: check out packages RExcel, xlsx, XLConnect, or gdata.

Try it Yourself

  1. Import the qPCR-data.txt file into R removing the metadata at the top of the file.
  2. Find the median “CT” of all wells.
  3. Save the data frame as a compressed R object.

Try it Yourself

  1. Import the qPCR-data.txt file into R removing the metadata at the top of the file.

I did it this way:

dat<-read.delim("qPCR-data.txt", skip=41, na.strings="Undetermined")

Try it Yourself

2.Find the median “CT” of all wells.

head(dat[, 1:10])
##   Well Well.Position  Omit Sample.Name Target.Name     Task Reporter Quencher
## 1    1            A1 FALSE    Sample 1       HF183 STANDARD      FAM  NFQ-MGB
## 2    2            A2 FALSE    Sample 1       HF183 STANDARD      FAM  NFQ-MGB
## 3    3            A3 FALSE    Sample 1       HF183 STANDARD      FAM  NFQ-MGB
## 4    4            A4 FALSE    Sample 1       HF183 STANDARD      FAM  NFQ-MGB
## 5    5            A5 FALSE    Sample 1       HF183 STANDARD      FAM  NFQ-MGB
## 6    6            A6 FALSE    Sample 1       HF183 STANDARD      FAM  NFQ-MGB
##       CT Quantity
## 1     NA        1
## 2     NA        1
## 3     NA        1
## 4 19.215        1
## 5 18.648        1
## 6 18.506        1
median(dat$CT, na.rm=T)
## [1] 29.234

3.Save the data frame as a compressed R object.

save(dat, file="qPCR-data.Rdata", compress = "gzip")
list.files()
##  [1] "custom.css"                       "Importing and Exporting data.pdf"
##  [3] "Importing-Exporting-Data.html"    "Importing-Exporting-Data.Rmd"    
##  [5] "Puromycin_data.csv"               "Puromycin_data.delim"            
##  [7] "qPCR-data.Rdata"                  "qPCR-data.txt"                   
##  [9] "TAX_data_GZIP.Rdata"              "usearch.otus.97.tax"