Motivation
Introduction to Plotly
Ann Arbor R User Group
Rami Krispin
September 14, 2017
How many are familiar with:
Data visualization is the art of exploring and explaining data using different graphical formats including interactive charts, statistical graphs, explanatory plots, etc. It can be divided into two main categories:
Exploratory - the use of data visualization to explore the data for patterns, outliers and insights, in order to define hypothesis tests, research questions and to choose the appropriate models to apply.
Explanatory - “story-telling” using data, in an efficient and concise manner.
R base plot
plot(d$carat, d$price)
With plotly
plot_ly(x = d$carat, y = d$price)And with some additional effort
plot_ly(d, x = ~carat, y = ~price, color = ~carat, name = "Diamonds Price", size = ~carat, text = ~paste("Clarity: ", clarity)) %>%
layout(title = "Diamonds Dataset - Price vs Carat", xaxis = list(title = "Carat"), yaxis = list(title = "Price"))Use the lasso option on the map!!!
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3D Surface Plot
What is so great about plotly:
What I wish was avilable:
Will demo the uses of plotly for exploratory analysis and to communicate the results utilizing key function of the package.
Data:
US monthly vehicle sales (collected by the U.S. Bureau of Economic Analysis and sourced from FRED)
Civilian unemployment rate (collected by the U.S. Bureau of Labor Statistics and sourced from FRED)
Key functions:
Data prep:
# Loading the data from FRED
df.sales <- Quandl("FRED/TOTALNSA", type = "raw") #Total Vehicle Sales
df.unemp <- Quandl("FRED/UNRATENSA", type = "raw") #Unemployment Rate
names(df.unemp) <- c("Date", "UnRate")
# Flaging years where the sales droped compared to the previuos year
df <- df.sales %>% arrange(Date) %>%
mutate(year = lubridate::year(Date))
df.y <- group_by(df,year) %>% # Calculate the yearly Sales
summarise(sales.y = sum(Value))
df.y$delta.flag <- ifelse((df.y$sales.y -
c(NA, df.y$sales.y[-nrow(df.y)])) < 0,
1,0 )
df.y$delta.flag <- ifelse(is.na(df.y$delta.flag), 0,
df.y$delta.flag) # Modifing NA's
df1 <- df %>% left_join(df.y) %>% # Merging vehical sales
left_join(df.unemp) # and unemployment ratep <- plot_ly(data = df1)%>%
add_lines(x = ~ Date, y = ~ Value, mode = "line", name = "Vehical Sales")
pp <- plot_ly(data = df1)%>%
add_lines(x = ~ Date, y = ~ Value, mode = "line", name = "Vehical Sales") %>%
layout(
title = "Total Vehicle Sales",
xaxis = list(title = "Source: U.S. Bureau of Economic Analysis"),
yaxis = list(title = "Millions of Units"))Shapes function:
s <- e <- NULL
s.flag <- 0
e.flag <- 0
rect.list <- NULL
for(i in 2:nrow(df1)){
if(df1$delta.flag[i] == 1 & df1$delta.flag[i - 1] == 0){
s <- df1$Date[i]
s.flag <- 1 }
if(df1$delta.flag[i] == 0 & df1$delta.flag[i - 1] == 1){
e <- df1$Date[i - 1]
e.flag <- 1}
if(s.flag == 1 & e.flag == 1){
if(is.null(rect.list)){
rect.list[[1]] <- list(type = "rect",
fillcolor = "blue", line = list(color = "blue"), opacity = 0.3,
x0 = s, x1 = e, xref = "x",
y0 = 0, y1 = max(df1$Value)*1.05, yref = "y"
)
} else {
rect.list[[length(rect.list) + 1]] <- list(type = "rect",
fillcolor = "blue", line = list(color = "blue"), opacity = 0.3,
x0 = s, x1 = e, xref = "x",
y0 = 0, y1 = max(df1$Value)*1.05, yref = "y")
}
s <- e <- NULL
s.flag <- e.flag <- 0
}
}p <- p %>% layout(shapes = list(
rect.list[[1]],
rect.list[[2]],
rect.list[[3]],
rect.list[[4]],
rect.list[[5]],
rect.list[[6]]))Using multiple axis:
p1 <- plot_ly(data = df1)%>%
add_lines(x = ~ Date, y = ~ Value, mode = "line", name = "Vehical Sales") %>%
add_trace(x = ~ Date, y = ~UnRate, name = "Unemployment Rate", yaxis = "y2",
mode = 'dash', line = list(color = "red")) %>%
layout(
title = "Total Vehicle Sales",
xaxis = list(title = "Source: U.S. Bureau of Economic Analysis"),
yaxis = list(title = "Millions of Units",
range = c(min(df1$Value) * 0.95, max(df1$Value) * 1.05)),# Set the y axis range
# add rectangle shapes
shapes = list(
rect.list[[1]], rect.list[[2]], rect.list[[3]],
rect.list[[4]], rect.list[[5]], rect.list[[6]]
),
# Defind the second y axis parameters
yaxis2 = list(
tickfont = list(color = "red"),
overlaying = "y",
side = "right",
title = "Percentage"
)
)Prepare the data for time series analysis:
Subset the data from 2010 – present
Transform the data into time series format
Reshape the data for wide format (by year)
sales.ts <- window(ts(df1$Value,
start = c(lubridate::year(min(df1$Date)),
lubridate::month(min(df1$Date))),
frequency = 12
), start = c(2010,1)
)
ts.df <- data.frame(dec_left = floor(time(sales.ts)),
dec_right = round((time(sales.ts ) - floor(time(sales.ts))) *
frequency(sales.ts ) + 1),
value = as.numeric(sales.ts))
ts.df_wide <- reshape2::dcast(ts.df, dec_right ~ dec_left )Creating seasonal plot:
p <- plot_ly()
for(f in 2:ncol(ts.df_wide)){
p <- p %>% add_trace(x = ts.df_wide[,1], y = ts.df_wide[,f],
name = names(ts.df_wide)[f],
mode = "line")}
p <- p %>% layout(
title = "Total Vehicle Sales by Year",
xaxis = list(title = "Source: U.S. Bureau of Economic Analysis"),
yaxis = list(title = "Millions of Units"))Seasonal plot:
Lags plot:
lag <- NULL
lag_plots <- NULL
max.lags <- 12
for(g in 1:max.lags){
if(g == 1){
lag <- c(NA, ts.df$value[- nrow(ts.df)])
} else {
lag <- c(NA,lag[-nrow(ts.df)])
}
lag_plots[[g]] <- plot_ly(x = lag, y = ts.df$value,
name = paste("Lag", g, sep = " ")) %>%
layout(xaxis = list(title = paste("Lag", g, sep = " "),
range = c( min(na.omit(as.numeric(lag))),
max(na.omit(as.numeric(lag))))),
yaxis = list(title = paste("Series", sep = ""),
range = c( min(na.omit(as.numeric(ts.df$value))),
max(na.omit(as.numeric(ts.df$value))))),
title = paste("Series vs Lags", sep = " "),
annotations = list(
showarrow = FALSE,
xref = paste("x", g, sep = ""),
yref = paste("y", g, sep = ""),
text = paste("Lag", g, sep = " "))
)
}
p2 <- subplot(lag_plots,
titleX = FALSE, titleY = TRUE,
shareX = FALSE, shareY = FALSE,
margin = 0.05,
nrows = ceiling(length(lag_plots) / 3))%>%
hide_legend()Can you identify which lag has the highest correlation?
Plotting the forecast object:
Split the data into training and testing
# Set the forecasting horizon
h <- 12
# Split to training and testing
train <- window(sales.ts,start = time(sales.ts)[1],
end = time(sales.ts)[length(time(sales.ts)) - h])
test <- window(sales.ts,start = time(sales.ts)[length(time(sales.ts)) - h + 1],
end = time(sales.ts)[length(time(sales.ts))])Set ARIMA model
fc_arima <- forecast(auto.arima(train, test = "kpss", lambda = 0.01), h = h)Evaluate the model performance
accuracy(fc_arima$mean, test)## ME RMSE MAE MPE MAPE ACF1
## Test set -57.14542 73.01442 64.18531 -4.07079 4.484585 -0.09016593
## Theil's U
## Test set 0.3403804attributes(fc_arima)## $names
## [1] "method" "model" "level" "mean" "lower"
## [6] "upper" "x" "series" "fitted" "residuals"
##
## $class
## [1] "forecast"fc_arima$fitted## Jan Feb Mar Apr May Jun Jul
## 2010 707.9844 762.5600 929.6726 933.1246 1011.0814 976.0613 1015.2704
## 2011 809.3701 916.0455 1243.8609 1164.0966 1283.6648 1120.5892 1172.5497
## 2012 941.7641 1108.3731 1382.3587 1292.7437 1185.9707 1210.5116 1244.8426
## 2013 1070.0174 1304.4099 1532.6698 1305.0498 1463.3377 1409.2534 1280.1891
## 2014 1158.0538 1275.6566 1501.2458 1362.9241 1517.6689 1498.2466 1405.6951
## 2015 1149.3428 1327.3974 1638.7879 1484.1523 1700.4510 1517.8283 1538.4475
## 2016 1286.9860 1367.8525 1642.2536 1544.6261 1712.9681 1546.0057 1571.7896
## Aug Sep Oct Nov Dec
## 2010 997.8500 980.8246 974.7105 930.7550 1063.4490
## 2011 1102.1543 1064.5933 1058.3719 981.5960 1256.7312
## 2012 1238.2894 1232.7363 1199.5195 1163.6191 1400.8906
## 2013 1419.8720 1343.9127 1220.1126 1271.8710 1466.5594
## 2014 1597.1404 1247.8401 1331.4890 1361.5798 1458.9034
## 2015 1662.2671 1341.8956 1405.5665 1432.5994 1618.0502
## 2016fc_arima$mean## Jan Feb Mar Apr May Jun Jul
## 2016
## 2017 1242.231 1411.636 1626.736 1546.477 1572.040 1568.647 1566.771
## Aug Sep Oct Nov Dec
## 2016 1612.685 1502.369 1512.694 1397.589 1677.472
## 2017p <- plot_ly() %>%
add_trace(x = time(sales.ts),
y = sales.ts,
mode = 'lines+markers', name = "Actual") %>%
add_trace(x = time(sales.ts),
y = c(fc_arima$fitted, rep(NA,length(sales.ts) - length(train))),
mode = 'lines+markers',name = "Fitted", line = list(color = "red")) %>%
add_trace(x = time(sales.ts),
y = c(rep(NA,length(sales.ts) - length(test)), fc_arima$mean),
mode = 'lines+markers',name = "Forecast") %>%
layout(
title = "Forecast and Fitted vs Actual",
xaxis = list(title = ""),
yaxis = list(title = "Millions of Units")
)Forecasting and ploting the reasults:
# Forecasting the next 12 months
fc_arima <- forecast(auto.arima(sales.ts, test = "kpss", lambda = 0.01), h = h)
p <- plot_ly() %>%
add_lines(x = time(sales.ts), y = sales.ts,
color = I("blue"), name = "Actual") %>%
add_ribbons(x = time(fc_arima$mean), ymin = fc_arima$lower[, 2], ymax = fc_arima$upper[, 2],
color = I("gray95"), name = "95% confidence") %>%
add_ribbons(x = time(fc_arima$mean), ymin = fc_arima$lower[, 1], ymax = fc_arima$upper[, 1],
color = I("gray80"), name = "80% confidence") %>%
add_lines(x = time(fc_arima$mean), y = fc_arima$mean, color = I("orange"), name = "Forecast") %>%
layout(
title = "US Vehicle Sales - 12 Months Forecast",
xaxis = list(title = ""),
yaxis = list(title = "Millions of Units")
)What if we want to present multiple forecast outputs in the same plot frame?
mmm… that’s too messy
The use of the button function could address this issue:
p <- plot_ly() %>%
add_lines(x = time(sales.ts), y = sales.ts,
color = I("blue"), name = "Actual", visible = TRUE) %>%
add_ribbons(x = time(fc_arima$mean), ymin = fc_arima$lower[, 2], ymax = fc_arima$upper[, 2],
color = I("gray95"), name = "95% Confidence", visible = TRUE) %>%
add_ribbons(x = time(fc_arima$mean), ymin = fc_arima$lower[, 1], ymax = fc_arima$upper[, 1],
color = I("gray80"), name = "80% Confidence", visible = TRUE) %>%
add_lines(x = time(fc_arima$mean), y = fc_arima$mean, color = I("orange"), name = "ARIMA", visible = TRUE) %>%
add_ribbons(x = time(fc_ets$mean), ymin = fc_ets$lower[, 2], ymax = fc_ets$upper[, 2],
color = I("gray95"), name = "95% Confidence", visible = FALSE) %>%
add_ribbons(x = time(fc_ets$mean), ymin = fc_ets$lower[, 1], ymax = fc_ets$upper[, 1],
color = I("gray80"), name = "80% Confidence", visible = FALSE) %>%
add_lines(x = time(fc_ets$mean), y = fc_ets$mean, color = I("green"), name = "ETS", visible = FALSE) %>%
add_ribbons(x = time(fc_hw$mean), ymin = fc_hw$lower[, 2], ymax = fc_hw$upper[, 2],
color = I("gray95"), name = "95% Confidence", visible = FALSE) %>%
add_ribbons(x = time(fc_hw$mean), ymin = fc_hw$lower[, 1], ymax = fc_hw$upper[, 1],
color = I("gray80"), name = "80% Confidence", visible = FALSE) %>%
add_lines(x = time(fc_hw$mean), y = fc_hw$mean, color = I("red"), name = "HW", visible = FALSE) %>%
layout(
title = "US Vehicle Sales - 12 Months Forecast",
xaxis = list(title = ""),
yaxis = list(title = "Millions of Units"),
updatemenus = list(
list(
y = 0.95, x= - 0.1,
buttons = list(
# ARIMA Button
list(method = "restyle",
args = list("visible", list(TRUE, # Actual line
TRUE, TRUE, TRUE, # ARIMA model
FALSE, FALSE, FALSE, # ETS
FALSE, FALSE, FALSE # HW
)),
label = "ARIMA"),
# ETS Button
list(method = "restyle",
args = list("visible", list(TRUE, # Actual line
FALSE, FALSE, FALSE, # ARIMA model
TRUE, TRUE, TRUE, # ETS
FALSE, FALSE, FALSE # HW
)),
label = "ETS"),
# HW Button
list(method = "restyle",
args = list("visible", list(TRUE, # Actual line
FALSE, FALSE, FALSE, # ARIMA model
FALSE, FALSE, FALSE, # ETS
TRUE, TRUE, TRUE # HW
)),
label = "HW")
)
)
)
)The use of the button function could address this issue:
My shiny based package with data visualization application utilizing plotly (draft version)
# source("https://raw.githubusercontent.com/RamiKrispin/Shiny-App/master/Shiny%20Modeling%20Git.R")
#runApp(list(ui = ui, server = server), launch.browser = TRUE)