Author

Tony Duan

Published

July 10, 2023

Code

library(tidyverse)
library(plotly)
library(gapminder)

Code

data001=gapminder
head(data001)

# A tibble: 6 × 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.

Code

data002= data001 %>% filter(year==1997)

1 dot chart

plot_ly(data = data002, x = ~xxx, y = ~xxx, color =~xxx)

Code

plot_ly(data = data002, x = ~lifeExp, y = ~gdpPercap, color =~continent)

2 line chart

Code

data002= data001 %>% group_by(continent,year) %>% summarise(pop=sum(pop))

Code

plot_ly(data = data002, x = ~year, y = ~pop, color =~continent,mode='lines')

3 bar chart

Code

data002= data001 %>% filter(year==1997) %>% group_by(continent) %>% summarise(pop=sum(pop))

Code

plot_ly(data002, x = ~continent, y = ~pop, type = 'bar')

4 histogram

Code

data002= data001 %>% filter(year==1997)

Code

plot_ly(data002,x = ~gdpPercap, type = "histogram",color = ~continent)

5 box plot

Code

data002= data001 %>% filter(year==1997)

Code

plot_ly(data002,x=~continent,y = ~gdpPercap, type = "box")

Code

data002 %>% filter(continent=='Americas') %>% arrange(desc(gdpPercap)) %>% head()

# A tibble: 6 × 6
  country       continent  year lifeExp       pop gdpPercap
  <fct>         <fct>     <int>   <dbl>     <int>     <dbl>
1 United States Americas   1997    76.8 272911760    35767.
2 Canada        Americas   1997    78.6  30305843    28955.
3 Puerto Rico   Americas   1997    74.9   3759430    16999.
4 Argentina     Americas   1997    73.3  36203463    10967.
5 Venezuela     Americas   1997    72.1  22374398    10165.
6 Chile         Americas   1997    75.8  14599929    10118.

6 Reference

https://plotly.com/r/

---
title: "R package:[plotly]"
subtitle: "using plotly to draw point,line,histogram ect"
author: "Tony Duan"
date: "2023-07-10"
categories: [packages]
execute:
  warning: false
  error: false
format:
  html:
    toc: true
    code-fold: show
    code-tools: true
    number-sections: true
    code-block-bg: true
    code-block-border-left: "#31BAE9"
---

![](images/download.png)

```{r}
library(tidyverse)
library(plotly)
library(gapminder)
```

```{r}
data001=gapminder
head(data001)
```

```{r}
data002= data001 %>% filter(year==1997)
```

# dot chart

plot_ly(data = data002, x = ~xxx, y = ~xxx, color =~xxx)
```{r}
plot_ly(data = data002, x = ~lifeExp, y = ~gdpPercap, color =~continent)
```

# line chart

## one data set and one variable 
```{r}
data002= data001 %>% group_by(continent,year) %>% summarise(pop=sum(pop)) 
```

```{r}
plot_ly(data = data002, x = ~year, y = ~pop, color =~continent,mode='lines')
```


## one data set and two variable 

```{r}
data002= data001 %>% group_by(year) %>% summarise(lifeExp=mean(lifeExp),gdpPercap=mean(gdpPercap)) %>% mutate(lifeExp2=lifeExp+1)
```

```{r}
plot_ly(data = data002, x = ~year, y = ~lifeExp,type = 'scatter', mode = 'lines') %>% add_trace(y = ~lifeExp2, name = 'lifeExp2', mode = 'lines+markers') 
```







```{r}
library(plotly)

trace_0 <- rnorm(100, mean = 5)
trace_1 <- rnorm(100, mean = 0)
trace_2 <- rnorm(100, mean = -5)
x <- c(1:100)

data <- data.frame(x, trace_0, trace_1, trace_2)

fig <- plot_ly(data, x = ~x, y = ~trace_0, name = 'trace 0', type = 'scatter', mode = 'lines') 
fig <- fig %>% add_trace(y = ~trace_1, name = 'trace 1', mode = 'lines+markers') 
fig <- fig %>% add_trace(y = ~trace_2, name = 'trace 2', mode = 'markers')

fig
```

# bar chart

```{r}
data002= data001 %>% filter(year==1997) %>% group_by(continent) %>% summarise(pop=sum(pop))
```

```{r}
plot_ly(data002, x = ~continent, y = ~pop, type = 'bar')
```

# histogram

```{r}
data002= data001 %>% filter(year==1997)
```

```{r}
plot_ly(data002,x = ~gdpPercap, type = "histogram",color = ~continent)
```

# box plot

```{r}
data002= data001 %>% filter(year==1997)
```

```{r}
plot_ly(data002,x=~continent,y = ~gdpPercap, type = "box")
```

```{r}
data002 %>% filter(continent=='Americas') %>% arrange(desc(gdpPercap)) %>% head()
```



# Reference

https://plotly.com/r/