Some R Examples[R table and Graphics] -Advanced Data Visualization in R (Some Examples)
1 like974 views
The document includes R code examples for advanced data visualization using libraries such as ggplot2 and ggmap. It showcases various plotting techniques, including mapping, creating histograms, and customizing plot aesthetics, along with how to apply color coding for data representation. Additionally, it discusses the use of data frames and handling datasets in R for effective data analysis.
![Reference: http://zevross.com/blog/2014/08/04/beautiful-plotting-in-r-a-ggplot2-cheatsheet-3/
http://www.cookbook-r.com/
http://moderndata.plot.ly/trisurf-plots-in-r-using-plotly/
Prepared by Volkan OBAN
Some R Examples
[R table and Graphics]
Advanced Data Visualization in R (Some Examples)](https://image.slidesharecdn.com/exampless-160922204223/85/Some-R-Examples-R-table-and-Graphics-Advanced-Data-Visualization-in-R-Some-Examples-1-320.jpg)
![row.colours <‐ rep(c("white", "#C0C0C0"), nrow(data)/2+1)
hwrite(data[1:nrow(data), 1:6],
paste(fdir, "AlternatingRowsTable.html", sep="/"),
center=TRUE,
row.bgcolor=row.colours)](https://image.slidesharecdn.com/exampless-160922204223/85/Some-R-Examples-R-table-and-Graphics-Advanced-Data-Visualization-in-R-Some-Examples-7-320.jpg)
![Ahmet Muhip Diranas,337,258,21,62,90
Faruk Nafiz Camlibel,294,255,28,68,74
Necip Fazil Kisakurek,336,250,24,39,84
Nazim Hikmet, 349,246,30,73,113
Can Yucel,361,224,19,35,99")
data <‐ read.csv(data, h=T)
data$Name_cell_color <‐ "white"
for(col in names(data[,c(‐1, ‐7)])) {
ntiles <‐ quantile(data[,c(col)], probs=c(.25, .75))
color_col <‐ paste(col, "cell_color", sep="_")
columnData <‐ data[,c(col)]
data[,c(color_col)] <‐ ifelse(columnData<=ntiles[1], "red",
ifelse(columnData>=ntiles[2], "green",
"white"))
}
# transpose the color columns of the data frame and convert it to a list
# goes from dataframe to matrix to dataframe to list
# see http://stackoverflow.com/questions/3492379/data‐frame‐rows‐to‐a‐list
row.colours <‐ as.list(as.data.frame(t(data[1:nrow(data),7:12])))
hwrite(data[1:nrow(data), 1:6],
paste(fdir, "QuartilesTable.html", sep="/"),
center=TRUE,
row.bgcolor=row.colours)](https://image.slidesharecdn.com/exampless-160922204223/85/Some-R-Examples-R-table-and-Graphics-Advanced-Data-Visualization-in-R-Some-Examples-10-320.jpg)
![+
+ # If layout is NULL, then use 'cols' to determine layout
+ if (is.null(layout)) {
+ # Make the panel
+ # ncol: Number of columns of plots
+ # nrow: Number of rows needed, calculated from # of cols
+ layout <‐ matrix(seq(1, cols * ceiling(numPlots/cols)),
+ ncol = cols, nrow = ceiling(numPlots/cols))
+ }
+
+ if (numPlots==1) {
+ print(plots[[1]])
+
+ } else {
+ # Set up the page
+ grid.newpage()
+ pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))
+
+ # Make each plot, in the correct location
+ for (i in 1:numPlots) {
+ # Get the i,j matrix positions of the regions that contain this subp
lot
+ matchidx <‐ as.data.frame(which(layout == i, arr.ind = TRUE))
+
+ print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
+ layout.pos.col = matchidx$col))
+ }
+ }
+ }
> multiplot(a1,a2,a3)](https://image.slidesharecdn.com/exampless-160922204223/85/Some-R-Examples-R-table-and-Graphics-Advanced-Data-Visualization-in-R-Some-Examples-16-320.jpg)
![Example:
library(ggplot2)
> nmmaps<-read.csv("chicago-nmmaps.csv", as.is=T)
> nmmaps$date<-as.Date(nmmaps$date)
> nmmaps<-nmmaps[nmmaps$date>as.Date("1996-12-31"),]
> nmmaps$year<-substring(nmmaps$date,1,4)
> head(nmmaps)
city date death temp dewpoint pm10 o3 time season ye
ar
3654 chic 1997-01-01 137 36.0 37.50 13.052268 5.659256 3654 winter 19
97
3655 chic 1997-01-02 123 45.0 47.25 41.948600 5.525417 3655 winter 19
97
3656 chic 1997-01-03 127 40.0 38.00 27.041751 6.288548 3656 winter 19
97
3657 chic 1997-01-04 146 51.5 45.50 25.072573 7.537758 3657 winter 19
97](https://image.slidesharecdn.com/exampless-160922204223/85/Some-R-Examples-R-table-and-Graphics-Advanced-Data-Visualization-in-R-Some-Examples-17-320.jpg)
![> td <- t(d)
> # but using m for plotting rather than the 2d g
>
> # define layout options
> axs <- list(
+ backgroundcolor="rgb(230,230,230)",
+ gridcolor="rgb(255,255,255)",
+ showbackground=TRUE,
+ zerolinecolor="rgb(255,255,255"
+ )
>
> # now figure out the colormap
> # start by determining the mean of z for each row
> # of the Delaunay vertices
> zmean <- apply(d, MARGIN=1, function(row){mean(m[row,3])})
>
> library(scales)
> # result will be slighlty different
> # since colour_ramp uses CIELAB instead of RGB
> # could use colorRamp for exact replication
> facecolor = colour_ramp(
+ brewer_pal(palette="RdBu")(9)
+ )(rescale(x=zmean))
>
>
> plot_ly(
+ x = m[, 1], y = m[, 2], z = m[, 3],
+ # JavaScript is 0 based index so subtract 1
+ i = d[, 1]-1, j = d[, 2]-1, k = d[, 3]-1,
+ facecolor = facecolor,
+ type = "mesh3d"
+ ) %>%
+ layout(
+ title="Moebius band triangulation",
+ scene=list(xaxis=axs,yaxis=axs,zaxis=axs),
+ aspectratio=list(x=1,y=1,z=0.5)
+ )](https://image.slidesharecdn.com/exampless-160922204223/85/Some-R-Examples-R-table-and-Graphics-Advanced-Data-Visualization-in-R-Some-Examples-38-320.jpg)
![ncol = 3,
dimnames = list(NULL, c("x", "y", "z"))
)
tri <- delaunayn(m[,1:2])
# now figure out the colormap
zmean <- apply(tri,MARGIN=1,function(row){mean(m[row,3])})
library(scales)
library(rje)
facecolor = colour_ramp(
cubeHelix(12)
)(rescale(x=zmean))
plot_ly(
x=x, y=y, z=z,
i=tri[,1]-1, j=tri[,2]-1, k=tri[,3]-1,
facecolor=facecolor,
type="mesh3d"
) %>%
layout(
title="Triangulated surface",
scene=list(
xaxis=axs,
yaxis=axs,
zaxis=axs,
camera=list(
eye=list(x=1.75,y=-0.7,z=0.75)
)
),
aspectratio=list(x=1,y=1,z=0.5)
)](https://image.slidesharecdn.com/exampless-160922204223/85/Some-R-Examples-R-table-and-Graphics-Advanced-Data-Visualization-in-R-Some-Examples-40-320.jpg)
![Code:
> library(geomorph)
> plyFile <- 'http://people.sc.fsu.edu/~jburkardt/data/ply/chopper.ply'
> dest <- basename(plyFile)
> if (!file.exists(dest)) {
+ download.file(plyFile, dest)
+ }
>
> mesh <- read.ply(dest)
> # see getS3method("shade3d", "mesh3d") for details on how to plot
>
> # plot point cloud
> x <- mesh$vb["xpts",]
> y <- mesh$vb["ypts",]
> z <- mesh$vb["zpts",]
> m <- matrix(c(x,y,z), ncol=3, dimnames=list(NULL,c("x","y","z")))
>
> # now figure out the colormap
> zmean <- apply(t(mesh$it),MARGIN=1,function(row){mean(m[row,3])})
>
> library(scales)
> facecolor = colour_ramp(
+ brewer_pal(palette="RdBu")(9)
+ )(rescale(x=zmean))
>
> plot_ly(
+ x = x, y = y, z = z,
+ i = mesh$it[1,]-1, j = mesh$it[2,]-1, k = mesh$it[3,]-1,
+ facecolor = facecolor,
+ type = "mesh3d"
+ )](https://image.slidesharecdn.com/exampless-160922204223/85/Some-R-Examples-R-table-and-Graphics-Advanced-Data-Visualization-in-R-Some-Examples-42-320.jpg)
![Some R Examples[R table and Graphics] -Advanced Data Visualization in R (Some Examples)](https://image.slidesharecdn.com/exampless-160922204223/85/Some-R-Examples-R-table-and-Graphics-Advanced-Data-Visualization-in-R-Some-Examples-44-320.jpg)
![Example:
> library(plotly)
> p <- plot_ly(plotly::mic, r = r, t = t, color = nms, mode =
"lines")
> layout(p, title = "Mic Patterns", orientation = -90)
Example: Waterfall Chart
> library(ggplot2)
> library(reshape2)
>
> data <- textConnection("Step,Value,Change
+ Start,100
+ Step 1,80,20
+ Step 2,70,10
+ Step 3,62,8
+ Step 4,52,10
+ Final result,52
+ ")
> data <- read.csv(data, h=T)
>
> data$id <- 1:nrow(data)
>
> naChange <- is.na(data$Change)
> data[naChange,]$Change <- data[is.na(data$Change),]$Value
> data[naChange,]$Value <- 0
>
> data$start <- data$Value
> data$end <- data$start+data$Change
>
> p <- ggplot(data, aes(Step, fill = "blue"))](https://image.slidesharecdn.com/exampless-160922204223/85/Some-R-Examples-R-table-and-Graphics-Advanced-Data-Visualization-in-R-Some-Examples-47-320.jpg)
Some R Examples[R table and Graphics] -Advanced Data Visualization in R (Some Examples)
- 1. Reference: http://zevross.com/blog/2014/08/04/beautiful-plotting-in-r-a-ggplot2-cheatsheet-3/ http://www.cookbook-r.com/ http://moderndata.plot.ly/trisurf-plots-in-r-using-plotly/ Prepared by Volkan OBAN Some R Examples [R table and Graphics] Advanced Data Visualization in R (Some Examples)
- 2. Example: > library(ggmap) > map1 = suppressMessages(get_map( + location = 'Istanbul', zoom = 14, #zoom-in level + maptype="satellite")) #map type > ggmap(map1) > Example: > library(ggmap) > map = suppressMessages(get_map(location = 'Turkey', zoom = 4)) > ggmap(map)
- 3. Example: trk = suppressMessages(geocode(c( + "Erzurum, Turkey", + "Istanbul,Turkey", + "Izmir,Turkey",
- 4. + "Bursa,Turkey"), source="google")) > ggmap(map) + geom_point(aes(x=trk$lon, y = trk$lat), + lwd = 4, colour = "red") + ggtitle("Ziyaret ettiğ im iller") Example: Table
- 11. Example: >library(reshape2) > head(tips) total_bill tip sex smoker day time size 1 16.99 1.01 Female No Sun Dinner 2 2 10.34 1.66 Male No Sun Dinner 3 3 21.01 3.50 Male No Sun Dinner 3 4 23.68 3.31 Male No Sun Dinner 2 5 24.59 3.61 Female No Sun Dinner 4 6 25.29 4.71 Male No Sun Dinner 4 > # A histogram of bill sizes > hp <- ggplot(tips, aes(x=total_bill)) + geom_histogram(binwidth=2,colour= "white") > > # Histogram of total_bill, divided by sex and smoker > hp + facet_grid(sex ~ smoker) > > # Same as above, with scales="free_y" > hp + facet_grid(sex ~ smoker, scales="free_y") > > # With panels that have the same scaling, but different range (and theref ore different physical sizes) > hp + facet_grid(sex ~ smoker, scales="free", space="free")
- 17. Example: library(ggplot2) > nmmaps<-read.csv("chicago-nmmaps.csv", as.is=T) > nmmaps$date<-as.Date(nmmaps$date) > nmmaps<-nmmaps[nmmaps$date>as.Date("1996-12-31"),] > nmmaps$year<-substring(nmmaps$date,1,4) > head(nmmaps) city date death temp dewpoint pm10 o3 time season ye ar 3654 chic 1997-01-01 137 36.0 37.50 13.052268 5.659256 3654 winter 19 97 3655 chic 1997-01-02 123 45.0 47.25 41.948600 5.525417 3655 winter 19 97 3656 chic 1997-01-03 127 40.0 38.00 27.041751 6.288548 3656 winter 19 97 3657 chic 1997-01-04 146 51.5 45.50 25.072573 7.537758 3657 winter 19 97
- 18. 3658 chic 1997-01-05 102 27.0 11.25 15.343121 20.760798 3658 winter 19 97 3659 chic 1997-01-06 127 17.0 5.75 9.364655 14.940874 3659 winter 19 97 >g<-ggplot(nmmaps, aes(date, temp))+geom_point(color="firebrick") > g >g<-ggplot(nmmaps, aes(date, temp))+geom_point(color="firebrick") > g + ylim(c(0,60))
- 19. > g<-ggplot(nmmaps, aes(date, temp, color=factor(season)))+geom_point() > g
- 20. g+theme(legend.title = element_text(colour="chocolate", size=16, face="bold "))+ + scale_color_discrete(name="This color isncalled chocolate!?")
- 21. > ggplot(nmmaps, aes(x=date, y=o3))+geom_line(color="grey")+geom_point(colo r="red")
- 22. > ggplot(nmmaps, aes(date,temp))+geom_point(color="chartreuse4")+ + facet_wrap(~year, ncol=2)
- 23. > ggplot(nmmaps, aes(date,temp))+geom_point(color="chartreuse4")+ + facet_wrap(~year, ncol=2, scales="free")
- 24. > library(grid) > pushViewport(viewport(layout = grid.layout(1, 2))) > print(myplot1, vp = viewport(layout.pos.row = 1, layout.pos.col = 1)) > print(myplot2, vp = viewport(layout.pos.row = 1, layout.pos.col = 2)) > > # alternative, a little easier > library(gridExtra) > grid.arrange(myplot1, myplot2, ncol=2)
- 25. > library(ggthemes) > ggplot(nmmaps, aes(date, temp, color=factor(season)))+ + geom_point()+ggtitle("This plot looks a lot different from the defaul t")+ + theme_economist()+scale_colour_economist()
- 26. > ggplot(nmmaps, aes(date, temp, color=factor(season)))+ + geom_point() + + scale_color_manual(values=c("dodgerblue4", "darkolivegreen4", + "darkorchid3", "goldenrod1"))
- 27. > library(ggthemes) > ggplot(nmmaps, aes(date, temp, color=factor(season)))+ + geom_point() + + scale_colour_tableau()
- 28. > ggplot(nmmaps, aes(date, temp, color=o3))+geom_point()+ + scale_color_gradient(low="darkkhaki", high="darkgreen")
- 29. > mid<-mean(nmmaps$o3) > ggplot(nmmaps, aes(date, temp, color=o3))+geom_point()+ + scale_color_gradient2(midpoint=mid, + low="blue", mid="white", high="red" )
- 30. > library(grid) > > my_grob = grobTree(textGrob("This text stays in place!", x=0.1, y=0.95, hjust=0, + gp=gpar(col="blue", fontsize=12, fontface="it alic"))) > > ggplot(nmmaps, aes(temp, o3))+geom_point(color="firebrick")+facet_wrap(~s eason, scales="free")+ + annotation_custom(my_grob)
- 31. > g<-ggplot(nmmaps, aes(x=season, y=o3)) > g+geom_jitter(alpha=0.5, aes(color=season),position = position_jitter(wid th = .2))
- 33. > g+geom_violin(alpha=0.5, color="gray")+geom_jitter(alpha=0.5, aes(color=s eason), + position = position_ji tter(width = 0.1))+coord_flip()
- 34. > nmmaps$o3run<-as.numeric(filter(nmmaps$o3, rep(1/30,30), sides=2)) > ggplot(nmmaps, aes(date, o3run))+geom_line(color="lightpink4", lwd=1)
- 35. > ggplot(nmmaps, aes(date, o3run))+geom_ribbon(aes(ymin=0, ymax=o3run), fil l="lightpink3", color="lightpink3")+ + geom_line(color="lightpink4", lwd=1)
- 36. > ggplot(nmmaps, aes(date, temp))+ + geom_point(color="grey")+ + stat_smooth(method="gam", formula=y~s(x,k=10), col="darkolivegreen2", se=FALSE, size=1)+ + stat_smooth(method="gam", formula=y~s(x,k=30), col="red", se=FALSE, s ize=1)+ + stat_smooth(method="gam", formula=y~s(x,k=500), col="dodgerblue4", se =FALSE, size=1)
- 37. Examples: > library(plotly) > library(geometry) > > g <- expand.grid( + u = seq(0, 2 * pi, length.out = 24), + v = seq(-1, 1, length.out = 8) + ) > tp <- 1 + 0.5 * g$v * cos(g$u / 2) > m <- matrix( + c(tp * cos(g$u), tp * sin(g$u), 0.5 * g$v * sin(g$u / 2)), + ncol = 3, dimnames = list(NULL, c("x", "y", "z")) + ) > > # the key though is running delaunayn on g rather than m > d <- delaunayn(g)
- 38. > td <- t(d) > # but using m for plotting rather than the 2d g > > # define layout options > axs <- list( + backgroundcolor="rgb(230,230,230)", + gridcolor="rgb(255,255,255)", + showbackground=TRUE, + zerolinecolor="rgb(255,255,255" + ) > > # now figure out the colormap > # start by determining the mean of z for each row > # of the Delaunay vertices > zmean <- apply(d, MARGIN=1, function(row){mean(m[row,3])}) > > library(scales) > # result will be slighlty different > # since colour_ramp uses CIELAB instead of RGB > # could use colorRamp for exact replication > facecolor = colour_ramp( + brewer_pal(palette="RdBu")(9) + )(rescale(x=zmean)) > > > plot_ly( + x = m[, 1], y = m[, 2], z = m[, 3], + # JavaScript is 0 based index so subtract 1 + i = d[, 1]-1, j = d[, 2]-1, k = d[, 3]-1, + facecolor = facecolor, + type = "mesh3d" + ) %>% + layout( + title="Moebius band triangulation", + scene=list(xaxis=axs,yaxis=axs,zaxis=axs), + aspectratio=list(x=1,y=1,z=0.5) + )
- 39. Example: n <- 12 h <- 1/(n-1) r = seq(h, 1, length.out=n) theta = seq(0, 2*pi, length.out=36) g <- expand.grid(r=r, theta=theta) x <- c(g$r * cos(g$theta),0) y <- c(g$r * sin(g$theta),0) z <- sin(x*y) m <- matrix( c(x,y,z),
- 40. ncol = 3, dimnames = list(NULL, c("x", "y", "z")) ) tri <- delaunayn(m[,1:2]) # now figure out the colormap zmean <- apply(tri,MARGIN=1,function(row){mean(m[row,3])}) library(scales) library(rje) facecolor = colour_ramp( cubeHelix(12) )(rescale(x=zmean)) plot_ly( x=x, y=y, z=z, i=tri[,1]-1, j=tri[,2]-1, k=tri[,3]-1, facecolor=facecolor, type="mesh3d" ) %>% layout( title="Triangulated surface", scene=list( xaxis=axs, yaxis=axs, zaxis=axs, camera=list( eye=list(x=1.75,y=-0.7,z=0.75) ) ), aspectratio=list(x=1,y=1,z=0.5) )
- 41. Example:
- 42. Code: > library(geomorph) > plyFile <- 'http://people.sc.fsu.edu/~jburkardt/data/ply/chopper.ply' > dest <- basename(plyFile) > if (!file.exists(dest)) { + download.file(plyFile, dest) + } > > mesh <- read.ply(dest) > # see getS3method("shade3d", "mesh3d") for details on how to plot > > # plot point cloud > x <- mesh$vb["xpts",] > y <- mesh$vb["ypts",] > z <- mesh$vb["zpts",] > m <- matrix(c(x,y,z), ncol=3, dimnames=list(NULL,c("x","y","z"))) > > # now figure out the colormap > zmean <- apply(t(mesh$it),MARGIN=1,function(row){mean(m[row,3])}) > > library(scales) > facecolor = colour_ramp( + brewer_pal(palette="RdBu")(9) + )(rescale(x=zmean)) > > plot_ly( + x = x, y = y, z = z, + i = mesh$it[1,]-1, j = mesh$it[2,]-1, k = mesh$it[3,]-1, + facecolor = facecolor, + type = "mesh3d" + )
- 43. > Subplot example: > library(plotly) > p <- subplot( + plot_ly(economics, x = date, y = uempmed), + plot_ly(economics, x = date, y = unemploy), + margin = 0.05, + nrows=2 + ) %>% layout(showlegend = FALSE) > p
- 45. Example: > data(iris) > iris$id <- as.integer(iris$Species) > p <- plot_ly(iris, x = Petal.Length, y = Petal.Width, group = Species, + xaxis = paste0("x", id), mode = "markers") > p <- plotly_build(subplot(p)) > p$filename <- "overwrite_subplot" > p Example: > library(plotly) > df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/ 1962_2006_walmart_store_openings.csv') > > # common map properties > g <- list(scope = 'usa', showland = T, landcolor = toRGB("gray90"), showc ountries = F, subunitcolor = toRGB("white")) > > # year text labels > yrs <- unique(df$YEAR) > id <- seq_along(yrs) > df2 <- data.frame( + YEAR = yrs, + id = id + )
- 46. > > # id for anchoring traces on different plots > df$id <- as.integer(factor(df$YEAR)) > > p <- plot_ly(df, type = 'scattergeo', lon = LON, lat = LAT, group = YEAR, + geo = paste0("geo", id), + showlegend = F, marker = list(color = toRGB("blue"), opacity = 0.5)) %>% + add_trace(lon = -78, lat = 47, mode = 'text', group = YEAR, + geo = paste0("geo", id), + text = YEAR, data = df2) %>% + layout(title = 'New Walmart Stores per year 1962-2006<br> Source: <a href="http://www.econ.umn.edu/~holmes/data/WalMart/index.html">University o f Minnesota</a>', + geo = g, + autosize = F, + width = 1000, + height = 900, + hovermode = F) > > p <- subplot(p, nrows = 9) > p
- 47. Example: > library(plotly) > p <- plot_ly(plotly::mic, r = r, t = t, color = nms, mode = "lines") > layout(p, title = "Mic Patterns", orientation = -90) Example: Waterfall Chart > library(ggplot2) > library(reshape2) > > data <- textConnection("Step,Value,Change + Start,100 + Step 1,80,20 + Step 2,70,10 + Step 3,62,8 + Step 4,52,10 + Final result,52 + ") > data <- read.csv(data, h=T) > > data$id <- 1:nrow(data) > > naChange <- is.na(data$Change) > data[naChange,]$Change <- data[is.na(data$Change),]$Value > data[naChange,]$Value <- 0 > > data$start <- data$Value > data$end <- data$start+data$Change > > p <- ggplot(data, aes(Step, fill = "blue"))
- 48. > p + geom_rect(aes(x = Step, xmin = id - 0.45, xmax = id + 0.45, + ymin = start, ymax = end)) + + scale_fill_identity("Metric Name") + + labs(x="X Label", y="Y Label", title="Waterfall Chart Example")
- 49. Example: Basic Table. library(xtable) fdir <- "C:/Users/lenovo/Documents/Calisma/Yeni klasör" data <- textConnection("Name,A,B,C,D,E Volkan OBAN,0,2,4,6,8 Volkan OBAN,3,5,7,9,11 Volkan OBAN,11,13,15,17,19 Volkan OBAN,20,22,24,26,28 Volkan OBAN,1,2,3,4,5 VOLKAN OBAN,1,5,25,125,625 VOLKAN OBAN,0,-1,-2,-3,0 VOLKAN OBAN,2,4,6,8,10") data <- read.csv(data, h=T) table_as_html <- print(xtable(data), type="html", print.results = FALSE) table_as_latex <- print(xtable(data), print.results = FALSE) cat(table_as_html, file=paste(fdir, "BasicTable.html", sep="/")) cat(table_as_latex, file=paste(fdir, "BasicTable.tex", sep="/"))