Advanced quantitative data analysis

class: center, middle, inverse, title-slide

.title[
# Advanced quantitative data analysis
]
.subtitle[
## R basics I
]
.author[
### Mengni Chen
]
.institute[
### Department of Sociology, University of Copenhagen
]

---

#Vector and Data Frame
- Vector
  - numeric vector
  - factor (categorical) vector
  - other types of vector
- Use packages in R
- Code missing in R
- Dataframe: build a dataset from vectors
- Import data: dta format, csv format, xls format
- Create and access to a list in R

---
#Vector 
- The most fundamental data type in R is a  vector: a sequence/chain/series of information. You can "concatenate" information into a vector by using **c()**

``` r
# Concatenate the sequence 5 4 3 2 1 to a vector, assign it to object x1, and print it.
(x1 <- c(5, 4, 3, 2, 4))
```

```
## [1] 5 4 3 2 4
```

- You can access a specific element of a vector via it's vector[index]:

``` r
# Print the second element of x1.
x1[2]
```

```
## [1] 4
```
- If you want to access several elements, you need to supply another vector to the vector's index:

``` r
# Print the second and fourth element of x1.
x1[c(2, 4)]
```

```
## [1] 4 2
```
---
#Types of common-used vectors
- Numeric vector
- Factor vector*
- Character vector
- Logical vector
- Date vectors

---
#Numeric vectors
- Numeric vectors are sequences of numbers.

``` r
x1
```

```
## [1] 5 4 3 2 4
```

``` r
class(x1) # what class of this object is?
```

```
## [1] "numeric"
```
---
#Transform numeric vectors
  - arithmetic operators **"+ - * / ^"**
  - a myriad of different functions
  - combine arithmetic operators and several functions

``` r
#arithmetic operators
(x1 <- x1 + 5) # Assign the object X1 plus 5 added to each of its elements to x1.
```

```
## [1] 10  9  8  7  9
```

``` r
(x1 <- log2(x1)) # Assign the logarithm with base 2 of x1 to x1.
```

```
## [1] 3.321928 3.169925 3.000000 2.807355 3.169925
```

``` r
(x1 <- (x1 - mean(x1)) / sd(x1)) # z-standardize x1.
```

```
## [1]  1.1606991  0.3872282 -0.4774386 -1.4577170  0.3872282
```
---
#Factor vectors
Factors are for categorical variables that make a distinction but whose values cannot be compared on a common scale. They are composed of a sequence of categorical values (i.e., argument x), and a (ideally comprehensive) list of potential levels (i.e., theoretically-possible values).

``` r
# Concatenate argument "x" to a factor and list "levels" of all potential categories.

intimate <- factor(x = c("Yes", "Yes", "No", "No", "No"),
 levels = c("Yes", "No", "Don't know","No answer"))

# Have a look at our new factor vector.
intimate
```

```
## [1] Yes Yes No  No  No 
## Levels: Yes No Don't know No answer
```

``` r
# Print a frequency table of our new factor vector.
table(intimate)
```

```
## intimate
##        Yes         No Don't know  No answer 
##          2          3          0          0
```

---
#Factor
R forces you to decide whether a variable is continuous (numeric) or categorical (factor)

- Numeric variables have a scale, such as cm, years, or DKK. Hence there is no need for labels.
- Categorical variables, by contrast, have no actual representation in numbers.
- Because factors are categorical, they cannot be numerically transformed.
- If you nevertheless try to numerically transform a factor,
the result is **NA** (i.e., "not available").

``` r
intimate * 2
```

```
## Warning in Ops.factor(intimate, 2): '*' not meaningful for factors
```

```
## [1] NA NA NA NA NA
```
Furthermore, we learn to use recode functions from the **forcats** package, which is part of the **tidyverse** package. 
---
#A package in R
.left-column[
<img src="http://cdn.osxdaily.com/wp-content/uploads/2016/03/package-file-check.jpg" width="60%" style="display: block; margin: auto;" >
]

.right-column[- A package is a collection of functions and their documentation (sometimes also data). 
- Some packages are pre-installed as sub-packages of R's Base package. In addition, there are, currently, +17,000 (user-written) packages on the Comprehensive R Archive Network (CRAN). The tidyverse and its forcats package are such user-written packages.

- How to install package
  - with code
  - with click]

---
#Install package

``` r
install.packages("name_of_the_package")

#for example install package called "tidyverse"
install.packages("tidyverse") ##don't forget the quotation mark""
```

**Do NOT add the install.packages() to your R Scripts every time! You will need to install a package only once, and not every time you run your script. If you want to update, use update.packages().**
 
---
#How to use package
- library() to use a package
 - Because there are so many user-written packages, they oftentimes contain functions with *conflicting names*. To avoid conflicts, you need to specify for each R session, which packages you want to work with. You do that, by adding the packages to your current R session's **library**.

- It is good practice to add all packages to the library on the very top of an R script. Please all add the tidyverse to your library by writing the following as the very first line in your R script.

``` r
# use library() to use the tidyverse package.
library(tidyverse)

#if you want to use several packages at the same time
# install.packages("pacman")
pacman::p_load(tidyverse,ggplot2)
# p_load():checks if a package is installed;
#if not installed → it installs it; then it loads it (like library()).
```
Some tidyverse functions conflict with Base R's Stats package. You can always address a function from a specific package by initiating it with `package::function()`.
For example, `pacman::p_load()`

---
#Back to recoding factors

Factors can best be **recoded** with `forcats::fct_recode()`.

``` r
# Recode intimate to make Y for Yes and N for N. Watch out: first the new, then the old value...
table(intimate)
```

```
## intimate
##        Yes         No Don't know  No answer 
##          2          3          0          0
```

``` r
intimate1 <- fct_recode(intimate, 
 "Y" = "Yes", "N" = "No") # new coding=old coding

# Frequency table of intimate.
table(intimate1) 
```

```
## intimate1
##          Y          N Don't know  No answer 
##          2          3          0          0
```

---
#Back to recoding factors
forcats contains many more useful functions to handle factors!
Also check out [Chapter 15 Factors](https://r4ds.had.co.nz/factors.html)  of Grolemund and Wickham (2017).

``` r
intimate2 <- fct_drop(intimate) #drop unsed levels
table(intimate2)
```

```
## intimate2
## Yes  No 
##   2   3
```

``` r
intimate3 <- fct_relevel(intimate, "Don't know","No answer", "No","Yes",) #re-order levels 
levels(intimate3)
```

```
## [1] "Don't know" "No answer"  "No"         "Yes"
```

``` r
table(intimate3)
```

```
## intimate3
## Don't know  No answer         No        Yes 
##          0          0          3          2
```

---
#Other types of vectors
- Character/string vectors are sequences of text. (an extended example)

``` r
# Concatenate these four strings as one vector 
# and assign it to object "x2".
x2 <- c("I","love", "this", "course", "!")
```

- Logical vectors
Logical vectors are sequences of TRUE and FALSE statements:

``` r
x3 <- c(TRUE, TRUE, FALSE, TRUE,TRUE)
```

---
#No mixed vectors!!!

``` r
# Concatenate the sequence 1-4 to a numeric vector
(x4 <- seq(1, 4)) 
```

```
## [1] 1 2 3 4
```

``` r
class(x4)
```

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

``` r
# Replace the third element with the word "test"
x4[3] <- "test"
x4
```

```
## [1] "1"    "2"    "test" "4"
```

``` r
class(x4)
```

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

---
#No mixed vectors!!!

``` r
# What type of object is x4?
class(x4)
```

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

``` r
# Make x4 a numeric vector again
as.numeric(x4)
```

```
## Warning: NAs introduced by coercion
```

```
## [1]  1  2 NA  4
```

---
#NA: Not Available
In general, missing values in R are NA.

``` r
x5 <- c(1, 2, 3, NA, 5, NA, 7)
```
--
Many functions will not ignore NA by default and thus return NA.

``` r
# Estimate mean of x5
mean(x5)
```

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

``` r
# Estimate mean of x5 ignoring the NA (i.e., casewise deletion)
mean(x5, na.rm = TRUE) #na.rm is a function to tell R to remove NA when doing the calculation
```

```
## [1] 3.6
```
na.rm of `mean(x5, na.rm = TRUE)` is whether to remove NA when doing analysis
---
#NA: Not Available
is.na() generates logical vectors that identify missing values.

``` r
x5 <- c(1, 2, 3, NA, 5, NA, 7)
# Which elements are not missing?
!is.na(x5)
```

```
## [1]  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE
```
---
#NA: Not Available

``` r
# How many are missing
table(is.na(x5))
```

```
## 
## FALSE  TRUE 
##     5     2
```

``` r
# Print only non-missing values of x5
x5[!is.na(x5)] 
```

```
## [1] 1 2 3 5 7
```

---
#Some vectors

``` r
age <- c(34, 22, 42, 12, 76)
conti <- factor(x = c("Europe", "Africa", "Africa", "Asia", "S. America"),
 levels = c("Africa", "Asia", "Australia", "Europe", "N. America", "S. America"))
employed <- c(FALSE, TRUE, TRUE, TRUE, TRUE)
name <- c("Agnes", "Martin", "Hakan", "Tu", "Thais")
nr_kids <- c(1, 0, 3, 0, 4)
```

---
#Data frames
Data frames organize vectors of **equal length** along their indices.
.pull-left[

``` r
# Bind our 4 vectors along their index into a data frame.
# Assign that data frame to object "Dat".
(Dat <- data.frame(name, age, conti, employed, nr_kids))
```

```
##     name age      conti employed nr_kids
## 1  Agnes  34     Europe    FALSE       1
## 2 Martin  22     Africa     TRUE       0
## 3  Hakan  42     Africa     TRUE       3
## 4     Tu  12       Asia     TRUE       0
## 5  Thais  76 S. America     TRUE       4
```
]

.pull-right[

``` r
age <- c(34, 22, 42, 12, NA)
name <- c("Agnes", "Martin", "Hakan", "Tu", "Thais")
(Dat_wNA <- data.frame(name, age))
```

```
##     name age
## 1  Agnes  34
## 2 Martin  22
## 3  Hakan  42
## 4     Tu  12
## 5  Thais  NA
```
]
---
#Data frames

.center[Data frames are the typical "rectangular" way to organize data:] 
<img src="https://d33wubrfki0l68.cloudfront.net/6f1ddb544fc5c69a2478e444ab8112fb0eea23f8/91adc/images/tidy-1.png" width="80%" style="display: block; margin: auto;" >

---
#import "pairfam" data
- Import Stata, SPSS, SAS files: ["haven" package](https://haven.tidyverse.org/)
- Import csv, tsv, fwf files: ["readr" package](https://readr.tidyverse.org/)
- Import Excel's xlsx files: ["readxl" package](https://readxl.tidyverse.org/)
---
#import "pairfam" data

``` r
# Create an object pairfam and assign the imported anchor1_50percent.dta to it
# if you have downloaded it, please put it into your current working directory
# If you are not sure what is current working directory
getwd()
```

```
## [1] "C:/Users/rxv320/OneDrive - University of Copenhagen/Documents/My docs/My teaching/2026/Advanced quant/2 R basics-I/R basics-1"
```

``` r
#I put the data in the file: 
#"C:/Users/rxv320/Documents/My docs/My teaching/2025/Advanced quant/2 R basics-I/R basics-1"

# library(haven) #make sure that you call out the "haven" package
library(haven)
```

```
## Warning: package 'haven' was built under R version 4.5.2
```

``` r
pairfam <- read_dta("anchor1_50percent_Eng.dta")
pairfam
```

```
## # A tibble: 6,201 × 1,458
## id demodiff wave sample pid parentidk1 parentidk2 parentidk3
## <dbl> <dbl+lbl> <dbl+l> <dbl+l> <dbl> <dbl+lbl> <dbl+lbl> <dbl+lbl> 
## 1 267206000 0 [0 non-… 1 [1 2… 1 [1 p… NA NA NA NA 
## 2 112963000 0 [0 non-… 1 [1 2… 1 [1 p… NA NA NA NA 
## 3 327937000 0 [0 non-… 1 [1 2… 1 [1 p… 3.28e8 NA NA NA 
## 4 318656000 0 [0 non-… 1 [1 2… 1 [1 p… 3.19e8 318656101 NA NA 
## 5 717889000 0 [0 non-… 1 [1 2… 1 [1 p… 7.18e8 717889101 717889101 NA 
## 6 222517000 0 [0 non-… 1 [1 2… 1 [1 p… NA NA NA NA 
## 7 144712000 0 [0 non-… 1 [1 2… 1 [1 p… NA NA NA NA 
## 8 659357000 0 [0 non-… 1 [1 2… 1 [1 p… 6.59e8 NA NA NA 
## 9 506367000 0 [0 non-… 1 [1 2… 1 [1 p… 5.06e8 506367101 NA NA 
## 10 64044000 0 [0 non-… 1 [1 2… 1 [1 p… NA NA NA NA 
## # ℹ 6,191 more rows
## # ℹ 1,450 more variables: parentidk4 <dbl+lbl>, parentidk5 <dbl+lbl>,
## # parentidk6 <dbl+lbl>, parentidk7 <dbl+lbl>, parentidk8 <dbl+lbl>,
## # parentidk9 <dbl+lbl>, parentidk10 <dbl+lbl>, parentidk11 <dbl+lbl>,
## # parentidk12 <dbl+lbl>, parentidk13 <dbl+lbl>, parentidk14 <dbl+lbl>,
## # parentidk15 <dbl+lbl>, sex_gen <dbl+lbl>, psex_gen <dbl+lbl>,
## # k1sex_gen <dbl+lbl>, k2sex_gen <dbl+lbl>, k3sex_gen <dbl+lbl>, …
```

---
#List in R
- In R, a **list** is a  data structure that can hold elements of different types, including numbers, strings, vectors, dataframe, and even other lists. Unlike vectors that can only store elements of the same type, lists offer flexibility in organizing heterogeneous data.

- You will see that many regression results are stored in the form of "list"

<img src="https://github.com/fancycmn/26-Session2/blob/main/S2_F1(2026).png?raw=true" width="70%" style="display: block; margin: auto;" >
---
#Create a list in R

``` r
weekdays <- c("Monday","Tuesday", "Wednesday", "Thursday", "Friday")
dailysleep <- c(7, 6, 5, NA, 5, NA, 9)
pairfam <- read_dta("anchor1_50percent_Eng.dta")

class(weekdays)
```

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

``` r
class(dailysleep)
```

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

``` r
class(pairfam)
```

```
## [1] "tbl_df"     "tbl"        "data.frame"
```

``` r
example_list <- list(
 text_obj=weekdays,
 num_obj=dailysleep,
 data_obj=pairfam
 )
```

---
#Create a list in R
<img src="https://github.com/fancycmn/26-Session2/blob/main/S2_F2(2026).png?raw=true" width="100%" style="display: block; margin: auto;" >

``` r
class(example_list)
```

```
## [1] "list"
```

---
#Access elements in a list

``` r
example_list$text_obj #gives the content of the element called text_ojb
```

```
## [1] "Monday"    "Tuesday"   "Wednesday" "Thursday"  "Friday"
```

``` r
example_list[[1]] #gives the content of fist element
```

```
## [1] "Monday"    "Tuesday"   "Wednesday" "Thursday"  "Friday"
```

``` r
example_list[1] #gives fist element, including the name and content of first element
```

```
## $text_obj
## [1] "Monday"    "Tuesday"   "Wednesday" "Thursday"  "Friday"
```

``` r
my_pairfam<- example_list$data_obj #you can get the dataset included in the list and assign it as a dataset called my_pairfam
```

---
#Take home
1. Vector: a sequence/chain/series of information. Elements of a vector can be addressed via it's index [i].
2. Classes of vectors: numeric, factor, date, character, and logical vectors
3. Numerical vs categorical variables: numeric variables have a scale (e.g., cm, years, DKK), while categorical variables have no true representation in numbers
4. Packages: bundles of functions along with their documentation, you need to install and use `library` to call it out
5. `NA`: is "Not Available" and thus the code for missing values in R.
6. Data frames organize vectors of equal length along their indices.
7. Create and access to a list in R

---
#Important codes
- `install.packages()`: Installs packages from CRAN.
- `library()`: adds a package to the library for the current session.
- `c()`: concatenate a sequence to a vector.
- `factor()`: Make a vector categorical.
- `fct_recode`: Recode values of a factor.
- `fct_relevel`: change the level of a factor
- `as.numeric()`: Make a vector numeric.
- `table()`: simple frequency or cross table.
- `is.na()`: generate logical vector that identifies missing values.
- `data.frame()`: combine vectors to make a dataset
- `read_dta()`: read a data of stata format (dta)
- `getwd()`: get the current workding directory 
- `list()`

---
class: center, middle
#[Exercise](https://rpubs.com/fancycmn/1452985)
you can use= chatgpt for help