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Community Contributions for EDAV Fall 2022 Mon/Wed

32 Introduction to Plotly in R

Jingqi Huang, Yi Lu

32.1 Overview

As we learned exploratory data analysis throughout this semester, we have discovered various R packages and techniques that could help us perform analysis and conduct research upon large dataset. For example, histogram and boxplot for unidimensional continuous variables, scatterplot and heatmap for two continuous variables. However, in addition to static graphs, interactive ones also play an important part in modern standard of data analysis and research purposes. To help the Columbia Data Science community holistically equipped, our team decided to bring it to the table. One of the package that implements interactive plots is plotly. It is an R package for creating interactive publication-quality graphs. https://plotly.com/r/ provides very rich examples of the package in R under various tags. We want to extract the most basic logic to use plotly and combine most relevant examples on the same page.

32.2 Install and Packages 

# install.packages("ggplot2")
# install.packages("plotly")
library(ggplot2)
library(plotly)
library(dplyr)
library(gapminder)

32.3 Basic Grammar

Basic grammar for plotly is simple. If type or more are not specified, by default things will set to what makes most sense.

p <- plot_ly(dataframe, x=~column, y=~column2, type="graph type such as scatter, bar, box, heatmap, etc.", mode="mode type such as markers, lines, and etc.")
p <-p %>% add_trace()
p <-p %>% add_notations()
p <-p %>% layout()
p

Plotly’s graph are described in two categories: traces and layout. Multiple parts on the same graph can be added with add_trace() and add changed configurations. Multiple texts can be set by add_notations() by specifying locations with xref and yref. Axis and title can be set by layout(). Plotly can convert ggplot graph to interactive mode by wrap ggplot p with ggplotly(p).

With these in mind, let’s go through the package together.

32.4 Basic examples

32.4.1 Histogram 

df <- data.frame(type=rep(c("A", "B"), each=500), subtype=rep(c("1", "2"), each=250), value=rnorm(1000))
hist <- ggplot(df, aes(x=value, fill=subtype))+
  geom_histogram(position="identity", alpha=0.5, binwidth=0.2)+
  facet_grid(~type)
ggplotly(hist)

32.4.2 2D Histogram 

p <- plot_ly(x = filter(df, type=="A")$value, y = filter(df, type=="B")$value)
hist_2d <- subplot(add_markers(p, alpha=0.4), add_trace(p, type='histogram2dcontour'), add_histogram2d(p))
hist_2d

32.4.3 Boxplot 

boxplt <- plot_ly(diamonds, x = ~price/carat, y = ~clarity, color = ~clarity, type = "box") %>%
  layout(title="Interactive BoxPlot with Plotly")

# Second method
p <- ggplot(diamonds, aes(x=clarity, y=price/carat, color=clarity)) +
  geom_boxplot() + 
  coord_flip() +
  ggtitle("BoxPlot with ggplot2")
# the following line generates the same interactive graph
# ggplotly(p)

boxplt

32.4.4 2D Scatter Plot 

scatter2d <- plot_ly(filter(diamonds, color=='D'), x=~carat, y=~price, color=~clarity, marker=list(size=4, opacity=0.5), 
        # hover text
        text = ~paste("Price: ", price, "$<br>Cut: ", cut, "<br>Clarity: ", clarity)) %>%
  # set title and axis
  layout(title="Interactive Scatter Plot with Plotly")

scatter2d

32.4.5 3D Scatter Plot 

scatter3d <- plot_ly(diamonds[sample(nrow(diamonds), 1000), ], x=~price/carat, y=~table, z=~depth, color=~cut, 
        marker = list(size=4, opacity=0.5))%>%
  layout(title="Interactive 3D Scatter Plot with Plotly")

scatter3d
mtcars <- mutate(mtcars, type = case_when(mtcars$am == 0 ~ "Auto", mtcars$am == 1 ~ "Manual"))
plot_ly(mtcars, x=~mpg, y=~wt, z=~hp, color=~type) %>%
  layout(title="Interactive 3D Scatter Plot with Plotly", 
         scene = list(xaxis = list(title = "mpg"), yaxis = list(title = "weight"), zaxis = list(title = "horsepower")))

32.4.6 Line Plot 

a <- rnorm(100, 5, 1)
b <- rnorm(100, 0, 1)
c <- rnorm(100, -5, 1)
df <- data.frame(x=c(1:100), a, b, c)

lineplt <- plot_ly(df, x = ~x) %>%
  add_trace(y = ~a, name="line", type="scatter", mode = "lines", line=list(color='rgb(23, 54, 211)', width=2)) %>% 
  add_trace(y=~b, name="dot line with markers", mode = "lines+markers", line=list(dash='dot')) %>% 
  add_trace(y=~c, name="scatter markers only", mode = "markers") %>%   #same as scatter plot
  layout(title="Interactive Line Plot with Plotly", yaxis=list(title="value"))

lineplt

32.4.7 Bar Plot 

barplt <- plot_ly(count(diamonds, cut, clarity), x=~cut, y=~n, color=~clarity, type="bar", text=~n, marker=list(opacity=0.4, line=list(color='rgba(8,48,148, 1)', width=1.5))) %>% 
  layout(barmode = 'group')

barplt

32.4.8 Some intersesting examples 

# Initialization
question <- c('The course was effectively<br>organized',
       'The course developed my<br>abilities and skills for<br>the subject',
       'I would recommend this<br>course to a friend',
       'Any other questions')
df <- data.frame(question, sa=c(21, 24, 27, 29), a=c(30, 31, 26, 24), ne=c(21, 19, 23, 15), ds=c(16, 15, 11, 18), sds=c(12, 11, 13, 14))
answer_label <- c('Strongly<br>agree', 'Agree', 'Neutral', 'Disagree', 'Strongly<br>disagree')

# Interactive plot
p <- plot_ly(df, x=~sa, y=~question, type="bar", orientation="h", marker = list(color = 'rgba(38, 24, 74, 0.8)',
                      line = list(color = 'rgb(248, 248, 249)', width = 1))) %>% 
  add_trace(x=~a, marker = list(color = 'rgba(71, 58, 131, 0.8)')) %>% 
  add_trace(x=~ne, marker = list(color = 'rgba(122, 120, 168, 0.8)')) %>%
  add_trace(x=~ds, marker = list(color = 'rgba(164, 163, 204, 0.85)')) %>% 
  add_trace(x=~sds, marker = list(color = 'rgba(190, 192, 213, 1)')) %>% 
  layout(barmode='stack', 
                  xaxis = list(title = "", showgrid = FALSE,showticklabels = FALSE, zeroline = FALSE), 
                  yaxis = list(title=""), 
                  paper_bgcolor = 'rgb(248, 248, 255)',
                  plot_bgcolor = 'rgb(248, 248, 255)',
                  margin = list(l = 120, r = 10, t = 40, b = 10),
                  showlegend = FALSE) %>% 
  add_annotations(xref='x', yref='y', x=~sa/2, y=~question, text=paste(df[,"sa"], '%'), font=list(size=12, color="white"), showarrow=FALSE) %>% 
  add_annotations(x=~sa+a/2, text=paste(df[,"a"], '%'), font=list(size=12, color="white"), showarrow=FALSE) %>% 
  add_annotations(x=~sa+a+ne/2, text=paste(df[,"ne"], '%'), font=list(size=12, color="white"), showarrow=FALSE) %>% 
  add_annotations(x=~sa+a+ne+ds/2, text=paste(df[,"ds"], '%'), font=list(size=12, color="white"), showarrow=FALSE) %>%
  add_annotations(x=~sa+a+ne+ds+sds/2, text=paste(df[,"sds"], '%'), font=list(size=12, color="white"), showarrow=FALSE) %>%
  add_annotations(xref = 'x', yref = 'paper',
                  x = c(21 / 2, 21 + 30 / 2, 21 + 30 + 21 / 2, 21 + 30 + 21 + 16 / 2,
                        21 + 30 + 21 + 16 + 12 / 2), y = 1.05,
                  text = answer_label,
                  font = list(size = 12, color = 'rgb(67, 67, 67)'), showarrow = FALSE)

p

32.5 Animations with Plotly 

df <- data.frame(x = c(1:10), y = rnorm(10), time = c(1:10))
plot_ly(df, x = ~x, y = ~y, frame = ~time, type = 'scatter', mode = 'markers', showlegend = F)
df <- gapminder 
plot_ly(df, x=~gdpPercap, y=~lifeExp, color=~continent, size=~pop, frame=~year, type="scatter", mode="markers", text=~country, hoverinfo = "text") %>%
  layout(xaxis = list(type = "log"))

32.6 Self-evaluation

Our project generally meets the expectation derived from the motivation described at the beginning of the project. We have explained the basic language format and provided code example of each graph. We learned how to use the plotly package to build various types of graphs. We combined the rich but separate examples on plotly website into a more concentrated form.

However, we are missing with detailed explanation of them but rely on the codes and graphs to explain themselves correspondingly. We are also using simple datasets built-in R, which create similar examples as the website. We will make it more detailed and well-explained, and use newer data next time.

32.7 Reference:

https://plotly.com/r/