Visual Glossary of Terms

Datetime:

  • stores a calendar date and time
time <- as.POSIXct(
  "2026-09-23 14:30:00"
)

time
## [1] "2026-09-23 14:30:00 EDT"
class(time)
## [1] "POSIXct" "POSIXt"

Character:

  • stores text in R
cormorant <- "Phalacrocoracidae"
cormorant 
## [1] "Phalacrocoracidae"
class(cormorant)
## [1] "character"

Numeric:

  • store numbers to be used in mathematical calculations
age <- 23

age
## [1] 23
class(age)
## [1] "numeric"

Boolean

  • In R, logical. This gives a true/false output.
gene_count <- 150

gene_count > 100
## [1] TRUE
high_expression <- gene_count > 100

high_expression
## [1] TRUE
class(high_expression)
## [1] "logical"

Array:

  • Stores data of the same type in multiple dimensions, with rows and columns, etc.
gene_array <- array(
  1:12,
  dim = c(3, 2, 2)
)

gene_array
## , , 1
## 
##      [,1] [,2]
## [1,]    1    4
## [2,]    2    5
## [3,]    3    6
## 
## , , 2
## 
##      [,1] [,2]
## [1,]    7   10
## [2,]    8   11
## [3,]    9   12

Vector:

  • Stores multiple values og the same type in a sequence
gene_counts <- c(100, 150, 125, 175)

gene_counts
## [1] 100 150 125 175
class(gene_counts)
## [1] "numeric"
length(gene_counts)
## [1] 4

Dataframe:

  • Organizes data with observations in rows and variables in columns. Can include different types of data.
gene_data <- data.frame(
  gene = c("GeneA", "GeneB", "GeneC"),
  expression = c(100, 150, 125),
  expressed = c(TRUE, TRUE, FALSE)
)

gene_data
##    gene expression expressed
## 1 GeneA        100      TRUE
## 2 GeneB        150      TRUE
## 3 GeneC        125     FALSE
class(gene_data)
## [1] "data.frame"

List:

  • A flexible type od R data structure where elements dont need to have the same type of structure.
experiment <- list(
  organism = "Yeast",
  counts = c(100, 150, 125),
  successful = TRUE
)

experiment
## $organism
## [1] "Yeast"
## 
## $counts
## [1] 100 150 125
## 
## $successful
## [1] TRUE
class(experiment)
## [1] "list"

Tibble:

  • A type of dataframe used by tidyverse with less interference and stricter rules to avoid altering data and allow one to catch errors early.
library(tibble)

gene_tibble <- tibble(
  gene = c("GeneA", "GeneB", "GeneC"),
  expression = c(100, 150, 125),
  expressed = c(TRUE, TRUE, FALSE)
)

gene_tibble
## # A tibble: 3 × 3
##   gene  expression expressed
##   <chr>      <dbl> <lgl>    
## 1 GeneA        100 TRUE     
## 2 GeneB        150 TRUE     
## 3 GeneC        125 FALSE