practice.qmd

---
title: "Data import & descriptives"
title-block-banner: "#870052"
title-block-banner-color: "#FFFFFF"
author: 
  name: Magnus Johansson
  affiliation: RISE Research Institutes of Sweden
  affiliation-url: https://ri.se/shic
  orcid: 0000-0003-1669-592X
date: 2023-06-12
date-format: YYYY-MM-DD
format: 
  html:
    
    toc: true
    embed-resources: true
    standalone: true
    mainfont: 'Arial'
    code-overflow: wrap
    code-fold: true
    code-link: true
    number-sections: true
    fig-dpi: 96
    layout-align: left
    linestretch: 1.6
    theme: materia
  pdf:
    papersize: a4
    documentclass: article #article, report or book
    #classoption: [twocolumn, portrait]
  docx:
    
    toc: false
    number-sections: false
    highlight-style: github
always_allow_html: true
execute:
  echo: true
  warning: false
  message: false
  cache: false
css: style.css
editor_options: 
  chunk_output_type: console
---

## Setting up

```{r}
#| code-fold: show
# these are mostly for data management/wrangling and visualization
library(tidyverse) # for most things
library(foreign) # for reading SPSS files
library(readxl) # read MS Excel files
library(showtext) # get fonts
library(glue) # simplifies mixing text and code in figures and tables
library(arrow) # support for efficient file formats
library(grateful) # create table+references for packages used in a project
library(styler) # only a one-time installation (it is an Rstudio plugin)
library(car) # for car::recode only
library(skimr) # data skimming
library(lubridate) # for handling dates in data
library(janitor) # for many things in data cleaning

# these are mostly for data analysis and visualization
library(gtsummary)
library(scales)
library(visdat)
library(psych)
library(lme4)
library(nlme)
library(broom.mixed)
library(patchwork)
library(easystats)
library(mice)
library(modelsummary)
library(ggrain)
library(ggdist)
library(kableExtra)
library(formattable)
library(ggrepel)
library(GGally)

source("ki.R") # this reads an external file and loads whatever is in it

# define preferred functions
select <- dplyr::select
count <- dplyr::count
recode <- car::recode
rename <- dplyr::rename
filter <- dplyr::filter
clean_names <- janitor::clean_names
```

## Importing data

```{r}

```

### Inspecting data

try glimpse() and skim(), maybe the latter with group_by()?

```{r}

```

### Renaming variables?

Also remember that clean_names() can be useful.

```{r}

```

## Demographics

Make a table or two, and a figure or two, based on demographic information.


Then render the whole document, try all three formats. Since we specified options for html, pdf, and docx in the YAML code in the beginning of this file, the Render button at the top of this window has a small triangle next to it that allows you to choose output format.