Introduce gganimate for Psychometric

R

Tutorial

Author

Jihong

Published

February 22, 2019

A new R packge (gganimate ) provides some new features for animation in R. Its big advantage is it could make use of ggplot API and embeded into ggplot. Next, I will use a sample data to show the example. Then I will use some real educational data to explore a little bit what we can do in psychometric area.

1 A Simple Example

I want to introduce this package.

1.1 1.0 Load the packages requried

1.2 1.1 prepare the data

⌘+C

data("austres")
dt <- data.frame(x=as.matrix(austres), date=time(austres))
dt$y <- rnorm(nrow(dt))
dt$date  <- as.numeric(dt$date)

⌘+C

p <- ggplot(dt,
            aes(y = y, x =x)) +
  geom_line(alpha = 0.7) +
  geom_point()
p

1.2.1 `transition_reveal`: gradually reveal the data

⌘+C

p + transition_reveal(date)

⌘+C

dt$g <- rep( c("red", "green", "blue"), length.out = nrow(dt))

⌘+C

p_bygroup <- ggplot(dt,
            aes(y = y, x =x, col = g)) +
  geom_line(alpha = 0.7) +
  geom_point()
p_bygroup

⌘+C

p_bygroup + transition_reveal(date)

1.2.2

---
title: Introduce gganimate for Psychometric
author: Jihong
date: '2019-02-22'
categories:
  - R
  - Tutorial
tags:
  - gganimate
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(gganimate)
library(datasets)
library(gifski)
theme_set(theme_bw())
```

> A new R packge (`gganimate` ) provides some new features for animation in R. Its big advantage is it could make use of ggplot API and embeded into ggplot. Next, I will use a sample data to show the example. Then I will use some real educational data to explore a little bit what we can do in psychometric area.





# A Simple Example

I want to introduce this package.

## 1.0 Load the packages requried
```{r libraies, message=FALSE, warning=FALSE, include=FALSE, paged.print=FALSE}
library(gganimate)
library(ggplot2)
library(datasets)
library(gifski)
theme_set(theme_bw())
```

## 1.1 prepare the data
```{r}
data("austres")
dt <- data.frame(x=as.matrix(austres), date=time(austres))
dt$y <- rnorm(nrow(dt))
dt$date  <- as.numeric(dt$date)
```

```{r}
p <- ggplot(dt,
            aes(y = y, x =x)) +
  geom_line(alpha = 0.7) +
  geom_point()
p
```

### `transition_reveal`: gradually reveal the data
```{r}
p + transition_reveal(date)
```

```{r}
dt$g <- rep( c("red", "green", "blue"), length.out = nrow(dt))
```

```{r}
p_bygroup <- ggplot(dt,
            aes(y = y, x =x, col = g)) +
  geom_line(alpha = 0.7) +
  geom_point()
p_bygroup
```

```{r}
p_bygroup + transition_reveal(date)
```

###