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# geom_smooth in ggplot2

How to use the abline geom in ggplot2 online to add a line with specified slope and intercept to the plot.

New to Plotly?

Plotly is a free and open-source graphing library for R. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

### Gaussian

library(plotly)

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

fig <- ggplotly(p)

fig


Inspired by Stack Overflow

### Horizontal Line & Fit

library(plotly)

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)

fig <- ggplotly(p)

fig


Inspired by Stack Overflow

### Facets

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()

fig <- ggplotly(p)

fig


Inspired by Steven Buechler.

Dash for R is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

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)
)
)
)