library(plotly) p <- qplot(speed, dist, data=cars) p <- p + geom_smooth(method = "glm", formula = y~x, family = gaussian(link = 'log')) ggplotly(p)
Inspired by Stack Overflow
Horizontal Line & Fit
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)
Inspired by Stack Overflow
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) <- "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)
Inspired by Steven Buechler.
What About Dash?
Learn about how to install Dash for R at https://dashr.plot.ly/installation.
Everywhere in this page that you see
fig, you can display the same figure in a Dash for R application by passing it to the
figure argument of the
Graph component from the built-in
dashCoreComponents package like this:
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)