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R wisdom


NOTE

I attempted to keep optional additions in the Label additions section. Required additions are included w/the command. Keep in mind the + needs to be appended to the line you are adding the command to.


COLOR PALETTES

colors <- c(
    "#000000","#004949","#009292","#ff6db6","#ffb6db",
    "#490092","#006ddb","#b66dff","#6db6ff","#b6dbff",
    "#920000","#924900","#db6d00","#24ff24","#ffff6d"
    )

colors <- c(
    "#F0A3FF", "#0075DC", "#993F00", "#4C005C", "#191919",
    "#005C31", "#2BCE48", "#FFCC99", "#808080", "#94FFB5",
    "#8F7C00", "#9DCC00", "#C20088", "#003380", "#FFA405",
    "#FFA8BB", "#426600", "#FF0010", "#5EF1F2", "#00998F",
    "#E0FF66", "#740AFF", "#990000", "#FFFF80", "#FFFF00",
    "#FF5005"
    )

colors <- c(
    '#000000', '#010067', '#d5ff00', '#ff0056', '#9e008e',
    '#0e4ca1', '#ffe502', '#005f39', '#00ff00', '#95003a',
    '#ff937e', '#a42400', '#001544', '#91d0cb', '#620e00',
    '#6b6882', '#0000ff', '#007db5', '#6a826c', '#00ae7e',
    '#c28c9f', '#be9970', '#008f9c', '#5fad4e', '#ff0000',
    '#ff00f6', '#ff029d', '#683d3b', '#ff74a3', '#968ae8',
    '#98ff52', '#a75740', '#01fffe', '#ffeee8', '#fe8900',
    '#bdc6ff', '#01d0ff', '#bb8800', '#7544b1', '#a5ffd2',
    '#ffa6fe', '#774d00', '#7a4782', '#263400', '#004754',
    '#43002c', '#b500ff', '#ffb167', '#ffdb66', '#90fb92',
    '#7e2dd2', '#bdd393', '#e56ffe', '#deff74', '#00ff78',
    '#009bff', '#006401', '#0076ff', '#85a900', '#00b917',
    '#788231', '#00ffc6', '#ff6e41', '#e85ebe'
    )




INSTALLING PACKAGES

ggplot2 example

install.packages('ggplot2')

gg3d (github) example - allows 3D ggplot2 extension

install.packages('devtools')
library('devtools')
devtools::install_github('AckerDWM/gg3D')



TRANSFORMATIONS

melt - reshape2 package

melted = melt($COLUMN, id.vars = "$COLUMN" )

subset melted df

submelted = melted[melted$COLUMn %in% c('$LABEL1', '$LABELn', ...), ]

matrix conversion

m_com = as.matrix(com)



LABEL ADDITIONS

label points as text

+ geom_text( aes( label = ifelse( index == '$SUBPHYLUM', as.character( $NAME ), '')), hjust=0, vjust=0 )

vertical axis label

+ theme( axis.text.x = element_text( angle = 90, hjust = 1, vjust = 0.5 ))

trendlines

+ geom_smooth( method = lm, se = FALSE, fullrange = TRUE )



SCATTER PLOTS

basic scatter plot

ggplot($DATA, aes( x = $X, y = $Y, color = $COLOR )) + geom_point()

trendlines

+ geom_smooth( method = lm, se = FALSE, fullrange = TRUE )

trendlines w/confidence intervals

+ geom_smooth( method = lm, se = TRUE, fullrange = TRUE )

plotly

plot_ly(x = data$V6, y = data$V5, text=data$V1, marker=list(opacity=0.2), type='scatter', mode='marker')

remove the marker options to not have opacity. This opacity setting makes overlapping points darker



BAR PLOTS

Bar plots often have to use a "melted" dataset. This requires reshape2 package installed and loaded.

basic bar plot

ggplot( melted, aes( $COLUMN, value, fill = variable )) + geom_bar( position = "fill", stat = "identity")

percent bar plot

+ scale_y_continuous( labels = percent )

stacked bar plot

+ geom_bar(position = "stack", stat = "identity" )

dodge bar plot

+ geom_bar(position = "dodge", stat = "identity" )



VIOLIN PLOTS

ggplot( df, aes( x = $X, y = $Y )) + geom_violin( width = $WIDTH )




HEATMAP

There is some finessing involved with specifying column names etc. The dataframe here has row names in the first column and column names as the column names in the dataframe.

preparing data and plotting

data = tsv[,-1]
rownames(data) = tsv[,1]
heatmap(as.matrix(data), scale="none")

no dendogram or reordering

heatmap(as.matrix(data), Colv = NA, Rowv = NA, scale="none") # can specify column in scale to make relative to column

colors

heatmap(as.matrix(data1), scale="none", Colv=NA, Rowv=NA, col= colorRampPalette(brewer.pal(9, "Reds"))(256))




ORDINATIONS

NMDS

preparing data (2D)

library(vegan)
com = metabolicCounts[,2:20] # range of columns that have values
m_com = as.matrix(com)
nmds = metaMDS(m_com, distance="euclidean", k=2) # distance can be changed to your use case, k = dimensions
data.scores = as.data.frame(scores(nmds))
data.scores$INDEX = metabolicCounts$INDEX

optional: adding the fit

if it is a categorical variable, plot as points and substitute 'vectors' for 'factors'
mds.fit = envfit( nmds, com, permutations = 999, na.rm = TRUE )
mds_coord_cont = as.data.frame(scores(mds.fit, "vectors") * ordiArrowMul(mds.fit))

plotting the data

library(ggplot2)
colors = c(brewer.pal(name="Dark2", n = 8), brewer.pal(name="Paired", n = 6))
xx = ggplot(data.scores, aes(x = NMDS1, y = NMDS2)) + geom_text( aes( label = as.character( ome ), colour = sphy)) + scale_color_manual(values = colors)
with the fit
xx = ggplot(data.scores, aes(x = NMDS1, y = NMDS2)) + geom_text( aes( label = as.character( ome ), colour = sphy)) + scale_color_manual(values = colors) + geom_segment(aes(x = 0, y= 0, xend = NMDS1, yend = NMDS2), data = mds_coord_cat, size =1, alpha = 0.5, colour = "grey30") + geom_text(data = mds_coord_cat, aes( x = NMDS1, y = NMDS2), label = row.names(mds_coord_cat), colour = "grey10", fontface = "bold")

preparing (3D)

library('vegan3d')
com = metabolicCounts[,2:20]
m_com = as.matrix(com)
nmds3 = metaMDS(m_com, distance='euclidean', k=3)
data.scores = as.data.frame(scores(nmds3))
data.scores$INDEX = metabolicCounts$INDEX

optional: prepping the fit
mds.fit = envfit(nmds3, com, permutations = 9999, na.rm = TRUE)
mds_coords = as.data.frame(scores(mds.fit, 'vectors') * ordiArrowMul(mds.fit))

plotting (3D)

plot <- plot_ly( x = data.scores$NMDS1, y = data.scores$NMDS2, z = data.scores$NMDS3, color = data.scores$sphy, colors = colors )

with the fit (3D)

for ( row in 1:nrow(mds_coords) ) {
    t_row <- mds_coords[row,]
    t_row[2,] <- 0
    plot <- plot %>% add_trace(
        t_row, x = t_row$NMDS1, y = t_row$NMDS2, z = t_row$NMDS3,
        opacity = 1, type = 'scatter3d', mode = 'lines', line = list(
            width = 3, color = 'black'
            ),
        showlegend = FALSE
    )
}