Smoothing in ggplot2

library(plotly)

p <- qplot(speed, dist, data=cars)
p <- p + geom_smooth(method = "glm", formula = y~x, family = gaussian(link = 'log'))

ggplotly(p)

library(plotly)

the.data <- read.table( header=TRUE, sep=",",
                        text="source,year,value
    S1,1976,56.98
    S1,1977,55.26
    S1,1978,68.83
    S1,1979,59.70
    S1,1980,57.58
    S1,1981,61.54
    S1,1982,48.65
    S1,1983,53.45
    S1,1984,45.95
    S1,1985,51.95
    S1,1986,51.85
    S1,1987,54.55
    S1,1988,51.61
    S1,1989,52.24
    S1,1990,49.28
    S1,1991,57.33
    S1,1992,51.28
    S1,1993,55.07
    S1,1994,50.88
    S2,1993,54.90
    S2,1994,51.20
    S2,1995,52.10
    S2,1996,51.40
    S3,2002,57.95
    S3,2003,47.95
    S3,2004,48.15
    S3,2005,37.80
    S3,2006,56.96
    S3,2007,48.91
    S3,2008,44.00
    S3,2009,45.35
    S3,2010,49.40
    S3,2011,51.19")

cutoff <- data.frame( x = c(-Inf, Inf), y = 50, cutoff = factor(50) )

p <- ggplot(the.data, aes( year, value ) ) +
    geom_point(aes( colour = source )) +
    geom_smooth(aes( group = 1 )) +
    geom_hline(yintercept = 50)

ggplotly(p)

library(plyr)
library(plotly)
library(Lahman)

hr_stats_df <- ddply(Batting, .(playerID), function(df) c(mean(df$HR, na.rm = T),
                                                          max(df$HR, na.rm = T), sum(df$HR, na.rm = T), nrow(df)))
names(hr_stats_df)[c(2, 3, 4, 5)] <- c("HR.mean", "HR.max", "HR.total", "career.length")
hr_stats_long_df <- subset(hr_stats_df, career.length >= 10)
Batting_hr <- merge(Batting, hr_stats_long_df)
Batting_hr_cy <- ddply(Batting_hr, .(playerID), function(df) transform(df, career.year = yearID -
                                                                           min(yearID) + 1))
start_year_df <- ddply(Batting_hr_cy, .(playerID), function(df) min(df$yearID))
names(start_year_df)[2] <- "start.year"

# Merge this with other data.
Batting_hr_cy2 <- merge(Batting_hr_cy, start_year_df)
Batting_early <- subset(Batting_hr_cy2, start.year < 1940)
Batting_late <- subset(Batting_hr_cy2, start.year > 1950)
tot_HR_early <- subset(Batting_early, select = c(playerID, HR.total))

# Remove the duplicate rows:
tot_HR_early <- unique(tot_HR_early)
tot_HR_early_srt <- arrange(tot_HR_early, desc(HR.total))
top10_HR_hitters_early <- tot_HR_early_srt[1:10, "playerID"]
tot_HR_late <- subset(Batting_late, select = c(playerID, HR.total))

# Remove the duplicate rows:
tot_HR_late <- unique(tot_HR_late)
tot_HR_late_srt <- arrange(tot_HR_late, desc(HR.total))
top10_HR_hitters_late <- tot_HR_late_srt[1:10, "playerID"]
Batting_early_top10 <- subset(Batting_early, playerID %in% top10_HR_hitters_early)

p <- ggplot(data = Batting_early_top10, aes(x = career.year, y = HR/AB)) +
  geom_point() +
  facet_wrap(~playerID, ncol = 3) + 
  geom_smooth()

ggplotly(p)

library(plotly)

fig <- plot_ly() 
# fig <- fig %>% add_trace( ... )
# fig <- fig %>% layout( ... ) 

library(dash)
library(dashCoreComponents)
library(dashHtmlComponents)

app <- Dash$new()
app$layout(
    htmlDiv(
        list(
            dccGraph(figure=fig) 
        )
     )
)

app$run_server(debug=TRUE, dev_tools_hot_reload=FALSE)