Data Types & Structures

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# Data Types & Structures
### Thierry Warin, PhD
### quantum simulations<a style="color:#6f97d0">*</a>

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# A tidyverse approach

"The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures." (https://www.tidyverse.org/)

.center[![](images/hex-tidyverse.png)]

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# Outline

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### outline

1. Tidy Data

2. Data Types

3. Data Structures

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# Tidy Data

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# Tidy Data

Your data will be easier to work with in R if it follows the three tidy conditions:

.panelset[
.panel[.panel-name[Variable]

Each variable is saved in its own column

<img src="./images/variables.png" width="200px" />
]
.panel[.panel-name[Observation]
Each observation is saved in its own row

<img src="./images/observations.png" width="200px" />
]

.panel[.panel-name[Value]
Each value is placed in its own cell

]
]

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# Data Types

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# Data Types

R has 5 basic data types:

- character
- numeric
- integer
- logical
- complex

*A sixth type of data exists (raw), but we will not discuss it here*

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# Data Types

.panelset[
.panel[.panel-name[character]
.pull-left[

```r
x <- "hello"
z <- c("hello","sir")
```

]

.pull-right[

```r
typeof(x)
```

```
## [1] "character"
```

```r
typeof(z)
```

```
## [1] "character"
```

```r
length(x)
```

```
## [1] 1
```

```r
length(z)
```

```
## [1] 2
```
]

]
.panel[.panel-name[numeric]
.pull-left[

```r
y <- c(1.2,4.6,3.5)
```
]
.pull-right[

```r
typeof(y)
```

```
## [1] "double"
```

```r
str(y)
```

```
##  num [1:3] 1.2 4.6 3.5
```
]
]

.panel[.panel-name[integer]
.pull-left[

```r
y <- 1:10
```
]
.pull-right[

```r
typeof(y)
```

```
## [1] "integer"
```

```r
str(y)
```

```
##  int [1:10] 1 2 3 4 5 6 7 8 9 10
```
]

]
.panel[.panel-name[logical]
.pull-left[

```r
answer <- c(TRUE,FALSE,NA)
```
]
.pull-right[

```r
typeof(answer)
```

```
## [1] "logical"
```
]

]
.panel[.panel-name[complex]
.pull-left[

```r
z <- 1 +2i
```
]
.pull-right[

```r
typeof(z)
```

```
## [1] "complex"
```

```r
class(z)
```

```
## [1] "complex"
```
]

]
]

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# Data Structures

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# Data Structures

R has many data structures. These include

- atomic vector
- list 
- matrix
- data frame
- factors

In the next slides we will talk about the atomic vector and data frame

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# Data Structures

.panelset[
.panel[.panel-name[Vector]

A vector is a collection of elements that are most commonly of mode character, logical, integer or numeric. You can create vectors by directly specifying their content.

.pull-left[

```r
x <- c(3.14,1.5,2.66)
x1 <- c(1L,2L,3L)
y <- c(TRUE,FALSE,FALSE,TRUE)
z <- c("tech","simulation","quantum")
```
]

.pull-right[

```r
class(x)
```

```
## [1] "numeric"
```

```r
length(x1)
```

```
## [1] 3
```

```r
str(y)
```

```
##  logi [1:4] TRUE FALSE FALSE TRUE
```
]

]
.panel[.panel-name[Data frame]
A data frame is a very important data type in R. It's pretty much the de facto data structure for most tabular data and what we use for statistics.

.pull-left[
Useful functions :

- head() - see first 6 rows
- tail() - see last 6 rows
- dim() - see dimensions
- nrow() - number of rows
- ncol() - number of columns
- str() - structure of each column
- names() - give the column names
]
.pull-right[

```r
str(iris)
```

```
## 'data.frame':	150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
```

```r
head(iris,4)
```

```
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
```

]
]
]

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# Data structures for analysis

.panelset[
.panel[.panel-name[Time series data]

It is a collection of observations (behavior) for a single subject (entity) at different time intervals (generally equally spaced)

Example: Max Temperature, Humidity and Wind (all three behaviors) in New York City (single entity) collected on First day of every year (multiple intervals of time)

<img src="./images/time.png" width="500px" />
]
.panel[.panel-name[Cross-sectional data]

Its is a collection of observations(behavior) for multiple subjects(entities) at single point in time.

Example: Max Temperature, Humidity and Wind( all three behaviors) in New York City, SFO, Boston, Chicago(multiple entities) on 1/1/2015(single instance)

<img src="./images/cross.png" width="500px" />
]

.panel[.panel-name[Panel Data (Longitudinal Data)]
It is usually called as Cross-sectional Time-series data as it a combination of above mentioned types, i.e., collection of observations for multiple subjects at multiple instances.

Example: Max Temperature, Humidity and Wind( all three behaviors) in New York City, SFO, Boston, Chicago(multiple entities) on First day of every year(multiple intervals of time)

]
]