Training Workshop on the basics of SEM using R

# Training Workshop on the basics of<br> SEM using R
## Intro to R and basic data wrangling

---

---

## Overview

+ R objects

+ R packages

+ Reading data into R

+ Basic data wrangling with tidyverse

+ `select()`
  + `filter()`
  + `mutate()`
  + `rename()`
  + `arrange()`
  + `group_by()` and `summarize()`
  + `%>%` pipe operator 
  
---

### It's normal to struggle but it gets better and exciting!

---

## R Objects

+ e.g., text, number, matrix, vector, dataframe.

+ In other words everything in R is an object.
]

---

## R Objects

+ Objects are assigned a value using **`<-`**

```r
a1 <- 10
print(a1)
```

```
[1] 10
```
]

```r
a2 <- 20
a2
```

```
[1] 20
```
]

```r
a3 <- c(10, 20, 30)
a3
```

```
[1] 10 20 30
```
]
]

```r
a1 * a2
```

```
[1] 200
```
]

```r
st_name <- "christopher"
st_age <- 23
st_sex <- "male"

student <- c(st_name, st_age, st_sex)
student
```

```
[1] "christopher" "23"          "male"       
```
]
]

---

## R Objects

+ I personally use lower-case style.

+ Check-out the recommended good practices in R (e.g., naming objects, writing codes)by the [tidyverse style guide](https://style.tidyverse.org/syntax.html)
]

---

## R packages

+ It contain code that other R users have prepared for the community.

+ Installing packages

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

+ Loading packages

```r
library(tidyverse)
```

]

<img src="img/package.gif" width="70%" style="display: block; margin: auto 0 auto auto;" />
]

---

## Importing data

+ SPSS, Stata, SAS files: [haven package](https://haven.tidyverse.org/)

+ Excel files: [readxl package](https://readxl.tidyverse.org/)

+ CSV files: [readr package](https://readr.tidyverse.org/)

---

## Reading data into R

#### SPSS, Stata & SAS using [haven package](https://haven.tidyverse.org/)

```r
library(haven)
```

```r
# SPSS
read_sav("path/data.sav")
```

```r
# Stata
read_dta("path/data.dta")
```

```r
# SAS
read_sas("path/data.sas7bdat")
```
]

---

## Reading data into R

#### Excel files using [readxl package](https://readxl.tidyverse.org/)

```r
library(readxl)
read_excel("path/dataset.xls")
```

```
# A tibble: 150 x 5
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
          <dbl>       <dbl>        <dbl>       <dbl> <chr>  
 1          5.1         3.5          1.4         0.2 setosa 
 2          4.9         3            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 
 5          5           3.6          1.4         0.2 setosa 
 6          5.4         3.9          1.7         0.4 setosa 
 7          4.6         3.4          1.4         0.3 setosa 
 8          5           3.4          1.5         0.2 setosa 
 9          4.4         2.9          1.4         0.2 setosa 
10          4.9         3.1          1.5         0.1 setosa 
# ... with 140 more rows
```
]

---

## Reading data into R

#### CSV files using [readr package](https://readr.tidyverse.org/)

```r
install.packages("readr")
library(readr)
```

```r
# comma separated (CSV) files
read_csv("path/data.csv")
```

```r
# tab separated files
read_tsv("path/data.tsv")
```

```r
# general delimited files
read_delim("path/data.delim")
```
]

---

## Reading data into R

#### Tips on naming (file) names

+ human readable
  + easy to search for files later
  + easy narrow files lists based on names
  + easy to extract into from file names
  
+ J. Bryan "[How to name files](https://speakerdeck.com/jennybc/how-to-name-files)"
]

.rightcol[
<img src="img/namine_files.jfif" width="80%" style="display: block; margin: auto 0 auto auto;" />
.right[.fifty[Illustration adapted from [Jennifer Bryan](https://speakerdeck.com/jennybc/how-to-name-files)]]
]

---
class: middle center

# Tidyverse

---

## What is a tidyverse?

A collection of R packages designed for data science.

All packages share an underlying philosophy, grammar, and data structure.

---

## Tidyverse :: tidy data

---

## Tidyverse :: tidy data

---

## Tidyverse :: tidy data

**Tidy data makes it easier for reproducibility and reuse**

---

## Tidyverse :: tidy data

**Yehey! Tidy Data for the win!**

---

## Data wrangling using `dplyr`

---

## `dplyr`
.leftcol[
**Overview**

+ `select()` picks variables based on their names

+ `mutate()` adds new variables

+ `filter()` picks cases based on their values

+ `summarise()` reduces multiple values down to a single summary

+ `arrange()` change the ordering of the rows

see `dplyr` [cheatsheets](https://github.com/rstudio/cheatsheets/blob/master/data-transformation.pdf)
]

---

## `select()`

![](https://favstats.shinyapps.io/r_intro/_w_bfa1a45e/images/select.png)

```r
data
```

```
# A tibble: 1,704 x 6
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.
 2 Afghanistan Asia       1957    30.3  9240934      821.
 3 Afghanistan Asia       1962    32.0 10267083      853.
 4 Afghanistan Asia       1967    34.0 11537966      836.
 5 Afghanistan Asia       1972    36.1 13079460      740.
 6 Afghanistan Asia       1977    38.4 14880372      786.
 7 Afghanistan Asia       1982    39.9 12881816      978.
 8 Afghanistan Asia       1987    40.8 13867957      852.
 9 Afghanistan Asia       1992    41.7 16317921      649.
10 Afghanistan Asia       1997    41.8 22227415      635.
# ... with 1,694 more rows
```
]

```r
select(data, continent, country, pop)
```

```
# A tibble: 1,704 x 3
   continent country          pop
   <fct>     <fct>          <int>
 1 Asia      Afghanistan  8425333
 2 Asia      Afghanistan  9240934
 3 Asia      Afghanistan 10267083
 4 Asia      Afghanistan 11537966
 5 Asia      Afghanistan 13079460
 6 Asia      Afghanistan 14880372
 7 Asia      Afghanistan 12881816
 8 Asia      Afghanistan 13867957
 9 Asia      Afghanistan 16317921
10 Asia      Afghanistan 22227415
# ... with 1,694 more rows
```

]

---

## `select()`

We can also **remove** variables with a **`-`** (minus)

```r
data
```

```r
select(data, -year, -pop)
```

```
# A tibble: 1,704 x 4
   country     continent lifeExp gdpPercap
   <fct>       <fct>       <dbl>     <dbl>
 1 Afghanistan Asia         28.8      779.
 2 Afghanistan Asia         30.3      821.
 3 Afghanistan Asia         32.0      853.
 4 Afghanistan Asia         34.0      836.
 5 Afghanistan Asia         36.1      740.
 6 Afghanistan Asia         38.4      786.
 7 Afghanistan Asia         39.9      978.
 8 Afghanistan Asia         40.8      852.
 9 Afghanistan Asia         41.7      649.
10 Afghanistan Asia         41.8      635.
# ... with 1,694 more rows
```
]

---

## `select()`

**Selection helpers**

These *selection helpers* match variables according to a given pattern.

+ `starts_with()` starts with a prefix

+ `ends_with()` ends with a suffix

+ `contains()` contains a literal string

+ `matches()` matches regular expression

---

## `filter()`

![](https://favstats.shinyapps.io/r_intro/_w_bfa1a45e/images/filter.png)

```r
data
```

```r
filter(data, country == "Philippines")
```

```
# A tibble: 12 x 6
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Philippines Asia       1952    47.8 22438691     1273.
 2 Philippines Asia       1957    51.3 26072194     1548.
 3 Philippines Asia       1962    54.8 30325264     1650.
 4 Philippines Asia       1967    56.4 35356600     1814.
 5 Philippines Asia       1972    58.1 40850141     1989.
 6 Philippines Asia       1977    60.1 46850962     2373.
 7 Philippines Asia       1982    62.1 53456774     2603.
 8 Philippines Asia       1987    64.2 60017788     2190.
 9 Philippines Asia       1992    66.5 67185766     2279.
10 Philippines Asia       1997    68.6 75012988     2537.
11 Philippines Asia       2002    70.3 82995088     2651.
12 Philippines Asia       2007    71.7 91077287     3190.
```
]

---

## `mutate()`

![](https://favstats.shinyapps.io/r_intro/_w_dfe6b732/images/mutate.png)

The `mutate` function will take a statement similar to this:

+ `variable_name` = `do_some_calculation`

+ `variable_name` will be attached at the end of the dataset.

---

## `mutate()`

Let's calculate the `gdp`

```r
data
```

```r
mutate(data, GDP = gdpPercap * pop)
```

```
# A tibble: 1,704 x 7
   country     continent  year lifeExp      pop gdpPercap          GDP
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>        <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.  6567086330.
 2 Afghanistan Asia       1957    30.3  9240934      821.  7585448670.
 3 Afghanistan Asia       1962    32.0 10267083      853.  8758855797.
 4 Afghanistan Asia       1967    34.0 11537966      836.  9648014150.
 5 Afghanistan Asia       1972    36.1 13079460      740.  9678553274.
 6 Afghanistan Asia       1977    38.4 14880372      786. 11697659231.
 7 Afghanistan Asia       1982    39.9 12881816      978. 12598563401.
 8 Afghanistan Asia       1987    40.8 13867957      852. 11820990309.
 9 Afghanistan Asia       1992    41.7 16317921      649. 10595901589.
10 Afghanistan Asia       1997    41.8 22227415      635. 14121995875.
# ... with 1,694 more rows
```
]

---

## `rename()`

Changes the variable name while keeping all else intact.

+ `new_variable_name` = `old_variable_name`

```r
data
```

```r
rename(data, population = pop)
```

```
# A tibble: 1,704 x 6
   country     continent  year lifeExp population gdpPercap
   <fct>       <fct>     <int>   <dbl>      <int>     <dbl>
 1 Afghanistan Asia       1952    28.8    8425333      779.
 2 Afghanistan Asia       1957    30.3    9240934      821.
 3 Afghanistan Asia       1962    32.0   10267083      853.
 4 Afghanistan Asia       1967    34.0   11537966      836.
 5 Afghanistan Asia       1972    36.1   13079460      740.
 6 Afghanistan Asia       1977    38.4   14880372      786.
 7 Afghanistan Asia       1982    39.9   12881816      978.
 8 Afghanistan Asia       1987    40.8   13867957      852.
 9 Afghanistan Asia       1992    41.7   16317921      649.
10 Afghanistan Asia       1997    41.8   22227415      635.
# ... with 1,694 more rows
```
]

---

## `arrange()`

You can order data by variable to show the highest or lowest values first.

```r
data
```

```r
arrange(data, desc(lifeExp))
```

```
# A tibble: 1,704 x 6
   country          continent  year lifeExp       pop gdpPercap
   <fct>            <fct>     <int>   <dbl>     <int>     <dbl>
 1 Japan            Asia       2007    82.6 127467972    31656.
 2 Hong Kong, China Asia       2007    82.2   6980412    39725.
 3 Japan            Asia       2002    82   127065841    28605.
 4 Iceland          Europe     2007    81.8    301931    36181.
 5 Switzerland      Europe     2007    81.7   7554661    37506.
 6 Hong Kong, China Asia       2002    81.5   6762476    30209.
 7 Australia        Oceania    2007    81.2  20434176    34435.
 8 Spain            Europe     2007    80.9  40448191    28821.
 9 Sweden           Europe     2007    80.9   9031088    33860.
10 Israel           Asia       2007    80.7   6426679    25523.
# ... with 1,694 more rows
```
]

---

## `group_by` and `summarise()`

+ Use when you want to aggregate your data (by groups).

+ Sometimes we want to calculate group statistics.

<br>

.center[
<img src="https://learn.r-journalism.com/wrangling/dplyr/images/groupby.png" style="width: 60%" />
]

---

## `group_by` and `summarise()`

Suppose we want to know the average population by continent.
.leftcol40[

```r
data
```

```r
grouped_by_continent <- group_by(data, continent)
summarise(grouped_by_continent, avg_pop = mean(pop))
```

```
# A tibble: 5 x 2
  continent   avg_pop
  <fct>         <dbl>
1 Africa     9916003.
2 Americas  24504795.
3 Asia      77038722.
4 Europe    17169765.
5 Oceania    8874672.
```
]

---

## `group_by` and `summarise()`

Suppose we want to know the average population by continent.
.leftcol40[

```r
data
```

```r
grouped_by_continent <- group_by(data, continent)
summarised_data <- summarise(grouped_by_continent, avg_pop = mean(pop))
arrange(summarised_data, desc(avg_pop))
```

```
# A tibble: 5 x 2
  continent   avg_pop
  <fct>         <dbl>
1 Asia      77038722.
2 Americas  24504795.
3 Europe    17169765.
4 Africa     9916003.
5 Oceania    8874672.
```
]

---

### It's hard to follow!

### It's hard to keep track of the codes!
]

---

## `%>%` pipe operator

---

## The %>% operator

The **`%>%`** helps your write code in a way that is easier to read and understand.

```r
grouped_by_continent <- group_by(data, continent)
summarised_data <- summarise(grouped_by_continent, avg_pop = mean(pop))
arrange(summarised_data, desc(avg_pop))
```

```
# A tibble: 5 x 2
  continent   avg_pop
  <fct>         <dbl>
1 Asia      77038722.
2 Americas  24504795.
3 Europe    17169765.
4 Africa     9916003.
5 Oceania    8874672.
```
]

```r
data %>% 
  group_by(continent) %>% 
  summarise(avg_pop = mean(pop)) %>% 
  arrange(desc(avg_pop))
```

```
# A tibble: 5 x 2
  continent   avg_pop
  <fct>         <dbl>
1 Asia      77038722.
2 Americas  24504795.
3 Europe    17169765.
4 Africa     9916003.
5 Oceania    8874672.
```
]

---

## The %>% operator

Suppose we want to know the evarage life expectancy of Asian countries per year.

```r
filtered_by_asia <- filter(data, continent == "Asia")
grouped_by_country_year <- group_by(filtered_by_asia, country, year)
summarise(grouped_by_country_year, avg_lifeExp = mean(lifeExp))
```

```
# A tibble: 396 x 3
# Groups:   country [33]
   country      year avg_lifeExp
   <fct>       <int>       <dbl>
 1 Afghanistan  1952        28.8
 2 Afghanistan  1957        30.3
 3 Afghanistan  1962        32.0
 4 Afghanistan  1967        34.0
 5 Afghanistan  1972        36.1
 6 Afghanistan  1977        38.4
 7 Afghanistan  1982        39.9
 8 Afghanistan  1987        40.8
 9 Afghanistan  1992        41.7
10 Afghanistan  1997        41.8
# ... with 386 more rows
```
]
]

```r
data %>% 
  filter(continent == "Asia") %>% 
  group_by(country, year) %>% 
  summarise(avg_lifeExp = mean(lifeExp))
```

---

## The %>% operator

```r
filtered_by_asia <- filter(data, continent == "Asia")
grouped_by_country <- group_by(filtered_by_asia, country)
summarised_by_country <- summarise(grouped_by_country, avg_lifeExp = mean(lifeExp))
arrange(summarised_by_country, desc(avg_lifeExp))
```

```
# A tibble: 33 x 2
   country          avg_lifeExp
   <fct>                  <dbl>
 1 Japan                   74.8
 2 Israel                  73.6
 3 Hong Kong, China        73.5
 4 Singapore               71.2
 5 Taiwan                  70.3
 6 Kuwait                  68.9
 7 Sri Lanka               66.5
 8 Lebanon                 65.9
 9 Bahrain                 65.6
10 Korea, Rep.             65.0
# ... with 23 more rows
```
]
]

```r
data %>% 
  filter(continent == "Asia") %>% 
  group_by(country) %>% 
  summarise(avg_lifeExp = mean(lifeExp)) %>% 
  arrange(desc(avg_lifeExp))
```

---

# Let's practice!

---

# Thank you!

#### Some slide content were heavily adapted from [Fabio Votta](https://github.com/favstats)
#### Slides created via the R packages:

### xaringan by Yihui
]

### xaringanthemer and xaringanExtra by Garrick
]