These are examples related to the Interactive Data Visualization with Plotly course on DataCamp

if (!require("dplyr")) install.packages("dplyr")
library(dplyr)
library(plotly)
library(rattle)

1 Static Graph 1

head(wine)

library(ggplot2)
g <- wine %>% ggplot(aes(Flavanoids, Proline, color = Type, size = Hue)) +
  geom_point()
g

2 Interactive Graph 1

ggplotly(g)

3 Static Graph 2

ggplot(wine, aes(Phenols, fill = "red")) +
  geom_bar(binwidth = 0.15)

## Warning: `geom_bar()` no longer has a `binwidth` parameter. Please use
## `geom_histogram()` instead.

4 Interactive Graph 2

wine %>%
  plot_ly(x = ~Phenols, hoverinfo = "y") %>%
  add_histogram(opacity = 0.7, color = I("pink"), nbinsx = 25)

5 Data Wrangling

# Data Citation:
# P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. 
# Accessed from UCI website.
# Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.
winequality <- read.csv("winequality-red.csv",header = TRUE,sep = ";")
#str(winequality)

winequality$type <- "red" 


winequality2 <- read.csv("winequality-white.csv", header = TRUE, sep = ";")
#str(winequality2)

winequality2$type <- "white"

totalwine <- rbind(winequality, winequality2)

totalwine$type <- factor(totalwine$type)

str(totalwine)

## 'data.frame':    6497 obs. of  13 variables:
##  $ fixed.acidity       : num  7.4 7.8 7.8 11.2 7.4 7.4 7.9 7.3 7.8 7.5 ...
##  $ volatile.acidity    : num  0.7 0.88 0.76 0.28 0.7 0.66 0.6 0.65 0.58 0.5 ...
##  $ citric.acid         : num  0 0 0.04 0.56 0 0 0.06 0 0.02 0.36 ...
##  $ residual.sugar      : num  1.9 2.6 2.3 1.9 1.9 1.8 1.6 1.2 2 6.1 ...
##  $ chlorides           : num  0.076 0.098 0.092 0.075 0.076 0.075 0.069 0.065 0.073 0.071 ...
##  $ free.sulfur.dioxide : num  11 25 15 17 11 13 15 15 9 17 ...
##  $ total.sulfur.dioxide: num  34 67 54 60 34 40 59 21 18 102 ...
##  $ density             : num  0.998 0.997 0.997 0.998 0.998 ...
##  $ pH                  : num  3.51 3.2 3.26 3.16 3.51 3.51 3.3 3.39 3.36 3.35 ...
##  $ sulphates           : num  0.56 0.68 0.65 0.58 0.56 0.56 0.46 0.47 0.57 0.8 ...
##  $ alcohol             : num  9.4 9.8 9.8 9.8 9.4 9.4 9.4 10 9.5 10.5 ...
##  $ quality             : int  5 5 5 6 5 5 5 7 7 5 ...
##  $ type                : Factor w/ 2 levels "red","white": 1 1 1 1 1 1 1 1 1 1 ...

totalwine <- totalwine[c(1:11,13,12)]

totalwine$quality <- as.character(totalwine$quality)

6 Interactive Graph Bars

totalwine %>%
  count(type, quality) %>%
  plot_ly(x = ~type, y = ~n, color = ~quality) %>%
  add_bars() %>%
  layout(barmode = "stack")

for (x in 1:nrow(totalwine)){
  if (totalwine$quality[x] == "9" | totalwine$quality[x] == "8" | totalwine$quality[x]== "7"){
    totalwine$quality[x] <- "High"
  }else if (totalwine$quality[x] == "3" | totalwine$quality[x] == "4" | totalwine$quality[x] == "5"){
    totalwine$quality[x] <- "Low"
  }else{
    totalwine$quality[x] <- "Medium"
  }
}

totalwine$quality <- factor(totalwine$quality, ordered = TRUE,levels = c("Low", "Medium", "High"))

7 Interactive Graph 3 Proportions

totalwine %>%
  count(type, quality) %>%
  group_by(type) %>%
  mutate(prop = n / sum(n)) %>%
  plot_ly(x = ~type, y = ~prop, color = ~quality) %>%
  add_bars(opacity = 0.5) %>%
  layout(barmode = "stack")

8 Interactive Scatter Plot

totalwine %>%
  filter(residual.sugar > 15 & residual.sugar < 30 & fixed.acidity > 5.5 & fixed.acidity < 9) %>%
  plot_ly(x = ~residual.sugar, y = ~fixed.acidity, symbol = ~quality ) %>%
  add_markers(marker = list(opacity=0.5, size = 10))

9 Interactive Bar Chart

totalwine %>%
  plot_ly(x = ~quality, y = ~alcohol) %>%
  add_boxplot()

10 Interactive Graph

wine %>%
  plot_ly(x = ~Flavanoids, y = ~Alcohol, symbol = ~Type, hoverinfo = "text",
          text = ~paste("Flavanoids: ", Flavanoids, "<br>", "Alcohol: ", Alcohol, "<br>", "Phenols: ", Phenols)) %>%
  add_markers(colors = c("orange", "green", "blue"), marker = list(size = 12, opacity = 0.5)) %>%
  layout(xaxis = list(title = "Flavanoids Conc.", type = "log"),
         yaxis = list(title = "Alcohol Percentage"),
         title = "Title Here")

11 Interactive Graph Customize Grid

rands <- sample_n(totalwine, 1000)

rands %>%
  plot_ly(x = ~free.sulfur.dioxide, y = ~total.sulfur.dioxide) %>%
  add_markers(marker = list(opacity = 0.5)) %>%
  layout(xaxis = list(title = "Free SO2 (ppm)", showgrid = FALSE),
         yaxis = list(title = "Total SO2 (ppm)"),
         paper_bgcolor = toRGB("grey90"),
         plot_bgcolor = toRGB("pink"))

12 Adding Layers (Smoother)

m <- loess(total.sulfur.dioxide ~ free.sulfur.dioxide, data = rands, span = 1.5)
pol <- lm(total.sulfur.dioxide ~ poly(free.sulfur.dioxide, 2), data = rands)

rands %>%
  plot_ly(x = ~free.sulfur.dioxide, y = ~total.sulfur.dioxide) %>%
  add_markers(showlegend = FALSE) %>%
  add_lines(y = ~fitted(pol), name = "Polynomial") %>%
  add_lines(y = ~fitted(m), name = "LOESS")

13 Density Layering

d1 <- filter(rands, type == "white")
d2 <- filter(rands, type == "red")

density1 <- density(d1$citric.acid)
density2 <- density(d2$citric.acid)

plot_ly(opacity = 0.5) %>%
  add_lines(x = ~density1$x, y = ~density1$y, name = "white", fill = 'tozeroy') %>%
  add_lines(x = ~density2$x, y = ~density2$y, name = "red", fill = 'tozeroy') %>%
  layout(xaxis = list(title = 'Citric.acid', showgrid = FALSE),
         yaxis = list(title = 'Density'))

14 Subplots

p1 <- wine %>%
  filter(Type == c(3)) %>%
  plot_ly(x = ~Alcohol, y = ~Malic) %>%
  add_markers(name = ~Type)

p2 <- wine %>%
  filter(Type == c(1,2)) %>%
  plot_ly(x = ~Alcohol, y = ~Malic) %>%
  add_markers(name = ~Type)

subplot(p1, p2, nrows = 1, shareY = TRUE, shareX = TRUE)

15 Subplots Correct

cardata <- mtcars
cardata$cyl <- factor(cardata$cyl)

cardata %>%
  group_by(cyl) %>%
  do(
    plot = plot_ly(data = ., x = ~mpg, y = ~hp) %>%
       add_markers(name = ~carb) %>%
      subplot(nrows = 2)
    )

16 Binned Scatterplot

totalwine %>%
  plot_ly(x = ~residual.sugar, y = ~alcohol) %>%
  add_histogram2d(nbinsx = 100, nbinsy = 50)

17 Election Data

turnout <- read.csv("turnout.txt", header = TRUE, sep = ",")

str(turnout)

## 'data.frame':    51 obs. of  8 variables:
##  $ X          : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ state      : Factor w/ 51 levels "Alabama","Alaska",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ state.abbr : Factor w/ 51 levels "AK","AL","AR",..: 2 1 4 3 5 6 7 9 8 10 ...
##  $ turnout2018: num  0.474 0.537 0.486 0.412 0.478 0.619 0.526 0.511 0.416 0.548 ...
##  $ turnout2014: num  0.332 0.548 0.341 0.403 0.307 0.547 0.425 0.349 0.357 0.433 ...
##  $ ballots    : int  1725000 280000 2385000 895000 12250000 2540000 1375000 363000 220000 8302000 ...
##  $ vep        : int  3641209 521777 4910625 2171940 25635139 4103903 2614176 710434 529198 15140654 ...
##  $ vap        : int  3802714 554426 5519036 2319740 30836229 4445013 2856023 769716 577553 17168712 ...

turnout %>%
plot_geo(locationmode = 'USA-states') %>%
  add_trace(
    z = ~turnout,
    locations = ~state.abbr
  ) %>%
  layout(geo = list(scope = 'usa'))

18 Interactive 3D graph

# Interactive Bar Graph

Interactive Graphs in R

1 Static Graph 1

2 Interactive Graph 1

3 Static Graph 2

4 Interactive Graph 2

5 Data Wrangling

6 Interactive Graph Bars

7 Interactive Graph 3 Proportions

8 Interactive Scatter Plot

9 Interactive Bar Chart

10 Interactive Graph

11 Interactive Graph Customize Grid

12 Adding Layers (Smoother)

13 Density Layering

14 Subplots

15 Subplots Correct

16 Binned Scatterplot

17 Election Data

18 Interactive 3D graph