Reading files

• Errors while reading in files!
• Demo of things that could go wrong when reading in files into R
• Demo on using reserved variables like T, F, character and many others ...
• How can you check if something is a reserved variable?

Special operator

• %>%
  • from the dplyr package
  • works like a pipe

read.table("data/counts_raw.txt", header = T, row.names = 1, sep = "\t") %>%
  head(6)

##                 Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7
## ENSG00000000003      321      303      204      492      455      359      376
## ENSG00000000005        0        0        0        0        0        0        0
## ENSG00000000419      696      660      472      951      963      689      706
## ENSG00000000457       59       54       44      109       73       66       60
## ENSG00000000460      399      405      236      445      454      374      316
## ENSG00000000938        0        0        0        0        0        1        0
##                 Sample_8 Sample_9 Sample_10 Sample_11 Sample_12
## ENSG00000000003      523      450       950       760      1436
## ENSG00000000005        0        0         0         0         0
## ENSG00000000419     1041      796      1036       789      1413
## ENSG00000000457      125       74       108       115       174
## ENSG00000000460      505      398       141       168       259
## ENSG00000000938        0        0         1         0         0

Special operator

• %>%
  • from the dplyr package
  • works like a pipe

read.table("data/counts_raw.txt", header = T, row.names = 1, sep = "\t") %>%
  head(6) %>%
  rownames_to_column(var = "Gene")

##              Gene Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6
## 1 ENSG00000000003      321      303      204      492      455      359
## 2 ENSG00000000005        0        0        0        0        0        0
## 3 ENSG00000000419      696      660      472      951      963      689
## 4 ENSG00000000457       59       54       44      109       73       66
## 5 ENSG00000000460      399      405      236      445      454      374
## 6 ENSG00000000938        0        0        0        0        0        1
##   Sample_7 Sample_8 Sample_9 Sample_10 Sample_11 Sample_12
## 1      376      523      450       950       760      1436
## 2        0        0        0         0         0         0
## 3      706     1041      796      1036       789      1413
## 4       60      125       74       108       115       174
## 5      316      505      398       141       168       259
## 6        0        0        0         1         0         0

Special operator

• %>%
  • from the dplyr package
  • works like a pipe

read.table("data/counts_raw.txt", header = T, row.names = 1, sep = "\t") %>%
  head(1) %>%
  rownames_to_column(var = "Gene") %>%
  gather(Sample_ID, count, -Gene)

##               Gene Sample_ID count
## 1  ENSG00000000003  Sample_1   321
## 2  ENSG00000000003  Sample_2   303
## 3  ENSG00000000003  Sample_3   204
## 4  ENSG00000000003  Sample_4   492
## 5  ENSG00000000003  Sample_5   455
## 6  ENSG00000000003  Sample_6   359
## 7  ENSG00000000003  Sample_7   376
## 8  ENSG00000000003  Sample_8   523
## 9  ENSG00000000003  Sample_9   450
## 10 ENSG00000000003 Sample_10   950
## 11 ENSG00000000003 Sample_11   760
## 12 ENSG00000000003 Sample_12  1436

Tidyr or dplyr functions

• gather()

  • converts wide to long format
  • key is usally what you measure: -Gene

• select()

  • you can choose which columns you want,