Useful R code

 

Date: 2024-04-23
R version: 3.5.0
*Corresponding author: matthew.malishev [at] gmail.com
This document can be found at https://github.com/darwinanddavis/UsefulCode

Overview

This document outlines some useful R code for plotting, cool functions, and other random tidbits.

Install dependencies

Attributes

Access structural attributes of unique classes, such as raster and ggmap (bbox).

# Normal example
df <- data.frame(X = c(1:5), Y = c(6:10))
str(df)
df$X

# `attr` method
require(ggmap)
map <- get_map("Atlanta", zoom = 12, source = "stamen", 
    maptype = "toner-lines")
str(map)
attr(map, "bb")$ll.lat

Classes

Convert character to factor to numeric without conversion error

read.table(f, header = T, sep = ",", row.names = NULL, 
    stringsAsFactors = FALSE, strip.white = TRUE)
f$V2 <- as.numeric(f$V2)

See call options for class

methods(class = "estUDm")

Set dynamic input for variable / assign variable to char vector

shadedens <- function(shadedens) {
    # set shade density to clumped (to match food) or
    # sparse
    if (shadedens == "Random") {
        NLCommand("set Shade-density \"Random\" ")
    } else {
        NLCommand("set Shade-density \"Clumped\" ")
    }
}
shadedens("Clumped")  # set clumped resources

D3 apps

Interactive network plots using d3

# Load package
install.packages("networkD3")
library(networkD3)

# Load energy projection data
URL <- "https://cdn.rawgit.com/christophergandrud/networkD3/master/JSONdata/energy.json"
Energy <- jsonlite::fromJSON(URL)

# Now we have 2 data frames: a 'links' data frame
# with 3 columns (from, to, value), and a 'nodes'
# data frame that gives the name of each node.
head(Energy$links)
head(Energy$nodes)

# Thus we can plot it
sankeyNetwork(Links = Energy$links, Nodes = Energy$nodes, 
    Source = "source", Target = "target", Value = "value", 
    NodeID = "name", units = "TWh", fontSize = 12, 
    nodeWidth = 30)

`?`(sankeyNetwork)

Dataframes

Optimal empty data frame

df <- data.frame(Date = as.Date(character()), X = numeric(), 
    Y = integer(), stringsAsFactors = FALSE)

Add df cols with mutate

require(dplyr)
df <- data.frame(a = rnorm(10), b = (1:20))
df %>% mutate(c = rnorm(20), b = b * 67)

Change df column names

colnames(data)[c(1,2,3)] <- c("TimeStamp","Lat","Long")

# tidyr
df %>% rename("Updated_col" = "Current_col")
df %>% rename(.cols = 1:2, # specify columns 
              "Updated_col" = "Current_col",
              "Updated_col2" = 2)

Remove multiple columns from df

### Remove multiple NA columns
rm_cols <- grep("NA", names(tt), ignore.case = F)
df[, colnames(df[, rm_cols])] <- list(NULL)

Check number of characters in each column

sapply(meso1, function(x) sum(nchar(x)))

Create empty df with N cols

df <- matrix(ncol = 1:500, nrow = 0) %>% data.frame()
names(df) <- LETTERS[1:500]

Generic functions

Generic useful functions that I can’t place under any other headings here

# dput() for converting outputs such as copied text
# or data tables into vectors
xx <- "Some copied text or table from the internet"
dput(xx)

Round up integers to optimal rounded value

nn <- c(46, 11, 23)
round_any(nn, 10)
round_any(nn, 10, ceiling)
round_any(nn, 10, floor)

Get summary stats for dataset (means)

means = aggregate(Cumulative_cercs ~ r * hb, data = df, 
    FUN = mean)

Lists

Find maximum value in entire list

master <- list(1:10, 100, rnorm(12))
do.call(max, master)

Plot all elements in a list

xx <- list(sample(5, 1000, replace = T), rnorm(1000), 
    sample(50, 1000, replace = T))
plot(unlist(xx), type = "l")

Apply each row of df or vector to individual elements of a list

df = data.frame(events = LETTERS[1:10], outs = 1:10)
sapply(df$outs, list)

Append extra element onto existing list

rv <- sample(1000, 15)  # random vector
listvec <- sapply(rep(NA, 7), list)  # list with 7 empty elements
listvec_final <- c(listvec, list(rv))  # append rv
listvec_final <- c(listvec, rv)  # to append rv contents as separate elements, remove internal list 

Loops

Save loop output in master list

pars <- seq(0, 1, 0.5)
master <- list()
t_list <- list()
for (p in 1:length(pars)) {
    for (t in 5) {
        tt <- rnorm(1000 * t)
        t_list[t] <- tt
    }
    master[[length(master) + 1]] <- t_list  # store in master list
}

Optimal way to save results to data frame in loop

require(dplyr)

fun <- sum  # sum # choose mean or sum  
out_first <- list()  # create first empty list
out_second <- list()  # create second empty list

for (me in 1:10) {
    global_output_fh = paste0(getwd(), "/", me, ".R")  # get file handle
    output <- readRDS(global_output_fh)  # read in file 
    cercs <- output[[1]]  # get data 
    # define function
    SEM = function(x) {
        sd(x)/sqrt(length(x))
    }
    # create col name to pass to aggregate function
    cerc_outs = list(Outs = cercs)
    outs = aggregate(Outs ~ ., data = cerc_outs, FUN = fun)
    cerc_se = list(SEs = cercs)
    se = aggregate(SEs ~ ., data = cerc_se, FUN = SEM)
    
    # save to df by creating new df cols
    outs$me <- me  # create new col with iteration
    out_first[[me]] <- outs  # add first output to list
    out_second[[me]] <- se  # add second output to list
}  # end file read  

# option 1
out_final = do.call(rbind, out_first)
# option 2
out_final <- bind_rows(out_first)  # make fresh df 
out_final$Second <- bind_rows(out_second)  # add second col

Maps

High res maps

# https://hecate.hakai.org/rguide/mapping-in-r.html
require(maptools)
d <- map_data("worldHires", c("Colombia", "Ecuador", 
    "Peru", "Panama"))

# plot
ggplot() + geom_polygon(data = d, aes(x = long, y = lat, 
    group = group), fill = "black", col = "pink") + 
    # theme_tufte(ticks=F) +
theme_nothing() + coord_map("mercator", xlim = c(-75, 
    -81), ylim = c(-2, 8))

Read in KMZ/KML data (Google Maps data)

require(sf)
zp <- sf::st_read("ziggy_test.kml")

Messages

Display status message of progress

for (i in 1:10) {
    Sys.sleep(0.2)
    # comment to use appendLF=FALSE.
    message(i, "\r", appendLF = FALSE)  # appendLF = new line 
    flush.console()
}

Display popup progress bar

require(tcltk)
pb <- tkProgressBar("test progress bar", "Some information in %", 
    0, 100, 50)
Sys.sleep(0.5)
u <- c(0, sort(runif(20, 0, 100)), 100)
for (i in u) {
    Sys.sleep(0.1)
    info <- sprintf("%d%% done", round(i))
    setTkProgressBar(pb, i, sprintf("test (%s)", info), 
        info)
}
Sys.sleep(5)
close(pb)

NAs and NaNs

Replace NAs and NaNs with 0’s

df[is.na(df)] <- 0
df[is.nan(df)] <- 0  # good for matrices 

Replace X values less than given value (V) with 0

df$X[df$X < V] <- 0

Check for NAs

df %>% sapply(is.na) %>% unique  # check where NAs exist
sapply(df, function(x) sum(is.na(x)))
df %>% sapply(is.na) %>% sum  # get total NAs

Replace NaN and Inf values with NA

df$col1[which(!is.finite(df$col1))] <- NA

Fill in missing data values in sequence with NA

# /Users/malishev/Documents/Manuscripts/Chapter4/Sims/Chapter4_figs.R
library(zoo)
data <- data.frame(index = c(1:4, 6:10), data = c(1.5, 
    4.3, 5.6, 6.7, 7.1, 12.5, 14.5, 16.8, 3.4))
# you can create a series
z <- zoo(data$data, data$index)
# end extend it to the grid 1:10
z <- merge(zoo(, 1:10), z)

# worked example fill in missing Tb values
minTb.d <- zoo(minTb$Tick, minTb$Days)
minTb.d <- merge(zoo(NULL, 1:days), minTb.d)  # make the minTb series match the temp series (117 days)
minTb.d <- as.numeric(minTb.d)  # = time individuals reached VTMIN in ticks
minTb <- minTb.d - temp$Tick  # get diff between starting time and time to reach VTMIN
minTb <- minTb/2  # convert ticks to minutes
minTb <- minTb/60  #convert to hours
minTb <- data.frame(Days = 1:days, Time = minTb)

# then fill in missing values
approx(minTb$Time, method = "linear")

Remove rows with NA

data <- data[!is.na(data$X), ]

Turn NULLs in list into NAs to get numeric values (fix for ‘cannot coerce double’ error)

hl_list <- lapply(hl_list, function(x) ifelse(x == 
    "NULL", NA, x))

Turn NaN or NAs in list into 0s

# NaN
global_output <- rapply(global_output, f = function(x) ifelse(is.nan(x), 
    0, x), how = "replace")

# NA
global_output <- rapply(global_output, f = function(x) ifelse(is.na(x), 
    0, x), how = "replace")

Replace NaN with 0 using dplyr

site$layer %>% head
site <- "day9_raster.Rda" %>% readRDS %>% mutate_at("layer", 
    ~replace(., is.nan(.), 0))

Good NA exploration

sapply(df, function(x) sum(is.na(x)))
df %>% sapply(is.na) %>% sum
df$col1[which(!is.finite(df$col1))] <- NA  # Replace `NaN` and `Inf` values with `NA` 
data <- data[!is.na(data$X), ]  # rm all rows with nas
global_output <- rapply(global_output, f = function(x) ifelse(is.na(x), 
    0, x), how = "replace")  # Turn NaN or NAs in list into 0s  

Remove NA’s based on column value

require(tidyr)

df %>% drop_na(c("var1", "var2"))

Packages

rLandsat
Sourcing, requesting, and downloading NASA Landsat 8 satellite data.

Radix
Improved RMarkdown output and interaction.

rpanel
Reference guide
Create interactive GUI control toggles from R. Like an early Shiny.

Plotting

Plot one plot window above and two below

layout(matrix(c(1, 1, 2, 3), 2, 2, byrow = TRUE))

Bookend axis ticks for plot E.g. at 0 and 100 when data is 1:99

axis(1, at = c(0, length(loco$X)), labels = c("", ""))  # bookending axis tick marks

Optimal legend formatting for base

legend("right", legend = c("Small", "Intermediate", 
    "Large"), col = c(colfunc[colvec[1:3]]), bty = "n", 
    pch = 20, pt.cex = 1.5, cex = 0.7, y.intersp = 0.5, 
    xjust = 0.5, title = "Size class", title.adj = 0.3, 
    text.font = 2, trace = T, inset = 0.1)

Plot inset plot in current plot (https://stackoverflow.com/questions/17041246/how-to-add-an-inset-subplot-to-topright-of-an-r-plot)

# calculate position of inset
plotdim <- par("plt")  # get plot window dims as fraction of current plot dims 
xleft = plotdim[2] - (plotdim[2] - plotdim[1]) * 0.5
xright = plotdim[2]  #
ybottom = plotdim[4] - (plotdim[4] - plotdim[3]) * 
    0.5  #
ytop = plotdim[4]  #

# set position for plot inset
par(fig = c(xleft, xright, ybottom, ytop), mar = c(0, 
    0, 0, 0), new = TRUE)

boxplot(Eggs ~ Size, data = meso2, col = adjustcolor(colfunc[colvec[1:3]], 
    alpha = 0.5), notch = T, xlab = "Week", ylab = "Diameter (mm)", 
    xaxs = "i", yaxs = "i")

Interactive plots with rCharts (javascript and d3 viz)
http://ramnathv.github.io/rCharts/

require(devtools)
install_github("rCharts", "ramnathv")

Cluster plot
https://rpubs.com/dgrtwo/technology-clusters

library(readr)
library(dplyr)
library(igraph)
library(ggraph)
library(ggforce)

# This shared file contains the number of question
# that have each pair of tags This counts only
# questions that are not deleted and have a
# positive score
tag_pair_data <- read_csv("http://varianceexplained.org/files/tag_pairs.csv.gz")

relationships <- tag_pair_data %>% mutate(Fraction = Cooccur/Tag1Total) %>% 
    filter(Fraction >= 0.35) %>% distinct(Tag1)

v <- tag_pair_data %>% select(Tag1, Tag1Total) %>% 
    distinct(Tag1) %>% filter(Tag1 %in% relationships$Tag1 | 
    Tag1 %in% relationships$Tag2) %>% arrange(desc(Tag1Total))

a <- grid::arrow(length = grid::unit(0.08, "inches"), 
    ends = "first", type = "closed")

set.seed(2016)

relationships %>% graph_from_data_frame(vertices = v) %>% 
    ggraph(layout = "fr") + geom_edge_link(aes(alpha = Fraction), 
    arrow = a) + geom_node_point(aes(size = Tag1Total), 
    color = "lightblue") + geom_node_text(aes(size = Tag1Total, 
    label = name), check_overlap = TRUE) + scale_size_continuous(range = c(2, 
    9)) + ggforce::theme_no_axes() + theme(legend.position = "none")

Define global plotting graphics function.

The plot_it.R function is updated on the plot_it Github page.

require(ggplot2)
require(ggthemes)
### set plotting params plotting function (plot for
### MS or not, set bg color, set color palette from
### RColorBrewer, set alpha value for transperancy)
plot_it <- function(manuscript, bg, cp1, cp2, alpha, 
    family) {
    graphics.off()
    if (manuscript == 0) {
        if (bg == "black") {
            colvec <<- magma(200, 1)  # plot window bg # USES <<- OPERATOR
            par(bg = colvec[1], col.axis = "white", 
                col.lab = "white", col.main = "white", 
                fg = "white", bty = "n", las = 1, mar = c(5, 
                  6, 4, 2), family = family)  #mono
            border = adjustcolor("purple", alpha = 0.5)
        } else {
            colvec <<- bpy.colors(200)  # plot window bg # USES <<- OPERATOR
            par(bg = colvec[1], col.axis = "white", 
                col.lab = "white", col.main = "white", 
                fg = "white", bty = "n", las = 1, mar = c(5, 
                  6, 4, 2), family = family)
            border = adjustcolor("blue", alpha = 0.5)
        }
    } else {
        # graphics.off()
        par(bty = "n", las = 1, family = family)
        colv <- "white"
    }
    # color palettes
    # ifelse(manuscript==1,colvec<-adjustcolor(brewer.pal(9,cp1)[9],
    # alpha = alpha),colvec <-
    # adjustcolor(brewer.pal(9,cp1)[5], alpha = alpha))
    # # fine tune plotting colors for plotting bg
    # colfunc <<-
    # colorRampPalette(brewer.pal(9,cp1),alpha=alpha)
    cp1_info <- brewer.pal.info[cp1, ]$maxcolors
    cp2_info <- brewer.pal.info[cp2, ]$maxcolors
    colv <<- brewer.pal(cp1_info, cp1)  # USES <<- OPERATOR
    colv2 <<- brewer.pal(cp2_info, cp2)  # USES <<- OPERATOR
}

# Setting ggplot theme graphics bg = colour to plot
# bg, family = font family
plot_it_gg <- function(bg) {
    if (bg == "white") {
        bg <- "white"
        fg <- "black"
        theme_tufte(base_family = "HersheySans") + 
            theme(panel.border = element_blank(), panel.grid.major = element_blank(), 
                panel.grid.minor = element_blank(), 
                panel.background = element_rect(fill = bg, 
                  colour = bg), plot.background = element_rect(fill = bg)) + 
            theme(axis.line = element_line(color = fg)) + 
            theme(axis.ticks = element_line(color = fg)) + 
            theme(plot.title = element_text(colour = fg)) + 
            theme(axis.title.x = element_text(colour = fg), 
                axis.title.y = element_text(colour = fg)) + 
            theme(axis.text.x = element_text(color = fg), 
                axis.text.y = element_text(color = fg)) + 
            theme(legend.key = element_rect(fill = bg)) + 
            theme(legend.title = element_text(colour = fg)) + 
            theme(legend.text = element_text(colour = fg))
    }
}  # end gg

### Set plotting function

require("RCurl")
script <- getURL("https://raw.githubusercontent.com/darwinanddavis/plot_it/master/plot_it.R", 
    ssl.verifypeer = FALSE)
eval(parse(text = script))

cat("plot_it( \n0 for presentation, 1 for manuscript, \nset colour for background, \nset colour palette. use 'display.brewer.all()', \nset alpha for colour transperancy, \nset font style \n)")
plot_it(0, "blue", "Spectral", "Greens", 1, "mono")  # set col function params
plot_it_gg("white")  # same as above 

Make plot cycle on one page

plot(m_abundance$gam, pages = 1)

Get plot summaries and values from plot

plot.gam(m_abundance$gam, shade = T, pages = 1, seWithMean = T)[1]  # everything
plot.gam(m_abundance$gam, shade = T, pages = 1, seWithMean = T)[1][[1]]$x  #subset x
plot.gam(m_abundance$gam, shade = T, pages = 1, seWithMean = T)[1][[1]]$fit  #get values to produce fit curve 

Package for stock world maps

# worldmap
library(choroplethrMaps)

Circle packing, tree, dendogram, network plots

# dendogram tree nested bubble circle packing
# network
# https://www.r-graph-gallery.com/313-basic-circle-packing-with-several-levels/

# circle packing plot Libraries
p <- c("ggraph", "igraph", "tidyverse", "DeducerSpatial", 
    "Rcpp", "car")
install.packages(p, dependencies = T)
lapply(p, library, character.only = T)

# We need a data frame giving a hierarchical
# structure. Let's consider the flare dataset:
edges = flare$edges
# edges cols = character

# Usually we associate another dataset that give
# information about each node of the dataset:
vertices = flare$vertices
# vertices cols = character, numeric, character

# Create a subset of the dataset (I remove 1 level)
edges = flare$edges %>% filter(to %in% from) %>% droplevels()
vertices = flare$vertices %>% filter(name %in% c(edges$from, 
    edges$to)) %>% droplevels()
vertices$size = runif(nrow(vertices))

# Then we have to make a 'graph' object using the
# igraph library:
mygraph <- graph_from_data_frame(edges, vertices = vertices)

# circle packing
ggraph(mygraph, layout = "circlepack", weight = "size", 
    sort.by = NULL, direction = "out") + geom_node_circle(aes(fill = depth)) + 
    geom_node_text(aes(label = shortName, filter = leaf, 
        fill = depth, size = size)) + theme_void() + 
    # theme(legend.position='F') + #show legend
scale_fill_viridis(alpha = 0.5, direction = -1, option = "magma")

# scale_fill_distiller(palette = 'Blues')

# geom_node_label(aes(label=shortName, filter=leaf,
# size=size)) + # add text boxes

# circular dendo
str(mygraph)
ggraph(mygraph, layout = "dendrogram", circular = T) + 
    geom_edge_diagonal(flipped = F, label_colour = "black", 
        label_alpha = 1, angle_calc = "rot", force_flip = TRUE, 
        label_dodge = NULL, label_push = NULL, show.legend = NA) + 
    theme_void() + # theme(legend.position='none') +
scale_fill_distiller(palette = "Blues")

# tree map
ggraph(mygraph, "treemap", weight = "size") + geom_node_tile(aes(fill = depth), 
    size = 0.25) + theme_void() + theme(legend.position = "none")

# circular partition
ggraph(mygraph, "partition", circular = TRUE) + geom_node_arc_bar(aes(fill = depth), 
    size = 0.25) + theme_void() + theme(legend.position = "none")

# node
ggraph(mygraph) + geom_edge_link() + geom_node_point() + 
    theme_void() + theme(legend.position = "none")

Insert an animal silhouette into a plot

# 1. Get image from http://www.phylopic.org
library(png)
ima <- readPNG("thething.png")
plot(1:3, 1:3)
rasterImage(image = ima, xleft = 2, ybottom = 1.8, 
    xright = 2.7, ytop = 2.7)

Create an empty plot window

# 1
plot(0, type = "n", axes = FALSE, ann = FALSE)
# 2
plot(1, type = "n", xlab = "", ylab = "", xlim = c(0, 
    10), ylim = c(0, 10))
# 3
plot.new()

Set color gradient, palette for smoothing data points

require(RColorBrewer)

alpha <- 0.8  # transparency (0 to 1 value)
set.seed(5000)
rr <- rnorm(5000)

# user defined gradient
col <- colorRampPalette(c("steelblue", "lightblue", 
    "orange", "red"))  # set your own col gradient with as many colours as you want
colfunc <- col(length(rr))[as.numeric(cut(rr, breaks = length(rr)))]  # define breaks in col gradient
plot(rr, col = colfunc, pch = 20)

# gradient from palette
display.brewer.all()
col <- "Greens"
col <- colorRampPalette(brewer.pal(brewer.pal.info[col, 
    ]$maxcolors, col))  # col gradient
colfunc <- col(length(rr))[as.numeric(cut(rr, breaks = length(rr)))]  # define breaks in col gradient
plot(rr, col = colfunc, pch = 20)

Add plot point every nth element

n <- 3
plot(runif(10, 0, 1), type = "o", pch = c(20, rep(NA, 
    n)))

Create function to make line as default type in plot

lplot <- function(...) plot(..., type = "l")
lplot(runif(200))

Stack dataframe columns automatically in plot

head(outplot)
# time N P S I 1 0.00 200.000000 200.0000 20.00000
# 2.000000 2 0.01 78.245140 177.1952 20.58217
# 2.067159 3 0.02 34.785145 168.9650 21.12174
# 2.136073
dats <- zoo(outplot)
plot(dats)

Make 3D scatterplot

require(scatterplot3d)
xx <- rnorm(1000)
yy <- runif(1000)
dens <- c(rep(1e-04, 500), rep(1, 500))
controls <- runif(3)
add.control <- 1
dens_val <- 1 * 10^-10  # 0 or 1*10^-10. value to knock out blanket of colour on plot surface 
# linear model of r/ship between coords
dens_lm <- lm(dens ~ xx + yy)

xlim <- c(min(xx), max(xx))
ylim <- c(min(yy), max(yy))
zlim = c(min(dens), max(dens))  # set lims
colv <- "Blues"
colvv <- colorRampPalette(brewer.pal(brewer.pal.info[colv, 
    ]$maxcolors, colv))  # col gradient
colvv <- colorRampPalette(c("steelblue", "lightblue", 
    "orange", "red"))  # set your own col gradient with as many colours as you want
# colvv<-colorRampPalette(magma(length(dens))) #
# set your own col gradient with as many colours as
# you want

# set col palette
colfunc <- colvv(length(dens))[as.numeric(cut(dens, 
    breaks = length(dens)))]  # define breaks in col gradient
bg <- bpy.colors(1)
alpha <- 0.8

# pdf(paste0(plot.dir,strat,'_',density,'_',stage,'_kudspdf.pdf'),width=8.27,height=11.69,paper='a4r')

# color=ifelse(col_heat==1, adjustcolor(colfunc,
# alpha=1),adjustcolor('lightgreen',alpha=0.2)),
scatterplot3d(x = xx, y = yy, z = dens, color = ifelse(dens <= 
    dens_val, adjustcolor(ifelse(bg == bpy.colors(1), 
    bpy.colors(1), "white"), alpha = 0.1), adjustcolor(colfunc, 
    alpha = alpha)), las = 1, pch = 15, type = "p", 
    lty.hplot = 1, xlim = xlim, ylim = ylim, zlim = zlim, 
    xlab = "X", ylab = "Y", zlab = "Density", main = "Main", 
    box = F, lty.axis = par(1), grid = F, col.grid = adjustcolor("gray", 
        1), lty.grid = par(3), axis = T)

# other plot options cex.symbols=dens*3,
# cex.symbols = ifelse(z<=0,0,0.5), highlight.3d=T,
# angle=70,


# append the below section starting at the '$' to
# the above closing bracket

# $plane3d(dens_lm, # add 3d linear model plane. #
# ??plane3d(Intercept, x.coef = NULL, y.coef =
# NULL, lty = 'dashed', lty.box = NULL, draw_lines
# = TRUE, draw_polygon = FALSE, polygon_args =
# list(border = NA, col = rgb(0,0,0,0.2))
# lty='dashed', lty.box = NULL, draw_lines = F,
# draw_polygon = T, polygon_args = list(border =
# NA, col = adjustcolor('light green',alpha=0.4)))

# add control dates
if (add.control == 1) {
    par(new = T)
    scatterplot3d(x = rep(0, length(controls)), y = controls, 
        z = rep(max(dens), length(controls)), color = "gray", 
        las = 1, pch = "", lty.hplot = 1, xlim = xlim, 
        ylim = ylim, zlim = zlim, xlab = "", ylab = "", 
        zlab = "", box = F, grid = F, cex.symbols = 2, 
        axis = F, type = "h")
}

Adding title from separate list to plot in loop (ggplot)

# plot all sim results in one window
gspl <- list()
ttl_list <- c("cerc", "food", "juv", "adult", "infec", 
    "infec (shed)", "host L", "parasite mass")

# choose sim to plot
global_sim_plot <- global_detritus

for (g in 1:10) {
    gspl[[g]] <- ggplot() + geom_line(data = y_m, aes(x = rep.int(1:n.ticks, 
        max(L1)), y = value, group = L1, colour = factor(L1)), 
        ) + # scale_color_manual(values = viridis(length(mm)))
    # + linetype=y_m$L1) +
    theme_tufte() + labs(title = ttl_list[g], x = "", 
        y = "") + if (g == length(global_sim_plot)) {
        theme(legend.title = element_text(size = 0.2), 
            legend.text = element_text(size = 0.2)) + 
            theme(legend.position = "top")
        labs(x = "Time")
    } else {
        theme(legend.position = "none")
    }
}
# + geom_text(x=,y=,label =
# max(value),check_overlap = TUE)
do.call(grid.arrange, gspl)  # plot in one window 

Using math expressions in plot labels

plot(rnorm(1000), xlab = expression(paste("X values"^2)), 
    ylab = expression(paste("Y values"^3, hat(beta))))

Adding faint gridlines to plot

# add gridlines
grid(nx = NA, ny = NULL)

Storing current par variables for plotting

og_pars <- par(no.readonly = T)  # store current par values

Clear graphics memory

dput(par(no.readonly = TRUE))  # reset graphical params 
par()

Reading in files/data

Read in file manually

get.file.vol <- read.table(file.choose())  #read file manually
v.file <- get.file.vol[1:100, 1]  #get the volume

Loop through files from dir and append to list

# option 1 reading in spdf (hrpath) files from
# drive
setwd("/Users/camel/Desktop/Matt2016/Manuscripts/MalishevBullKearney/Resubmission/2016/barcoo sims/barcooresults/hrpath_75")
file.list <- list.files()
hrs75 <- as.list(rep(1, 100))  # empty list
for (f in 1:100) {
    load(file.list[f])
    hrs75[f] <- hrpath
}

# working version converting spdf into
# mcp(spdf,100,unout='m2)
ghr <- list()
for (i in hrs75[1:10]) {
    m <- mcp(i, 100, unout = "m2")
    ghr <- c(ghr, m)
}
ghr

# option 2
wd <- getwd()
me_list <- list()  # create list 
for (me_day in c("A", "B", "C")) {
    for (me_im in 1:5) {
        mes <- readRDS(paste0(wd, resource_type, "_meday_", 
            me_day, "_meim", me_im, ".R"))  # read .R files from dir  
        cat("\n", paste0(wd, resource_type, "_meday_", 
            me_day, "_meim", me_im, ".R"))
        names(mes) <- c("cerc", "food", "juv", "adult", 
            "infected", "infected shedding", "mean host length", 
            "mean parasite mass", "summed host biomass", 
            "summed host eggs", "mean host eggs", "infected host length")  # name all original R file list elements
        mes <- mes$cerc  # get cercs (as list) use mes$'cerc'[[1]] for numeric 
        names(mes) <- paste0(me_day, "_", me_im)  # name list elements according to loop iterations 
        me_list <- c(me_list, mes)  # bind to master list 
    }
}

Read in PDF files from online source in R and save to drive.

# from https://github.com/ropensci/pdftools

require(pdftools)
url <- "https://raw.githubusercontent.com/darwinanddavis/499R/master/exp_pop_growth.pdf"
dir <- "FOLDER ON YOUR COMPUTER WHERE YOU WANT THE FILE SAVED"
f <- "NAME OF THE FILE"
f <- paste0(f, ".pdf")

# run all this
download.file(url, paste0(dir, "/", f), mode = "wb")
txt <- pdf_text(paste0(dir, "/", f))

# first page text
page <- 1  # enter the page number
cat(txt[page])

toc <- pdf_toc(paste0(dir, "/", f))

require(jsonlite)
# Show as JSON
jsonlite::toJSON(toc, auto_unbox = TRUE, pretty = TRUE)

# show author, version, etc
info <- pdf_info(f)

# renders pdf to bitmap array
bitmap <- pdf_render_page(f, page = 1)

# save bitmap image
png::writePNG(bitmap, "page.png")
jpeg::writeJPEG(bitmap, "page.jpeg")
webp::write_webp(bitmap, "page.webp")

Read .txt files

readLines("search_terms.txt")  # must have a blank line at end of file to avoid line read error  

Load in data to avoid ‘magic number error’

# avoid load()
readRDS("path to file .R")  # can use .R and .Rdata
source("path to file .R")

Access files anywhere without changing working dir

# https://github.com/jennybc/here_here
require(here)
getwd()
# '/Users/malishev/Documents/Data/gggmap'
here_loc <- here("here_test", "here_test.txt")
here_loc
# '/Users/malishev/Documents/Data/gggmap/here_test/here_test.txt'
readLines(here_loc)  # access the file even though your working dir is up N levels from the file in your dir 

Regular expressions (regex)

Get just numbers or characters

vec <- "16-Feb-2018 20:08:04 PM"
vecN <- gsub("[^[:digit:]]", "", vec)
vec
print(paste0("Just numbers: ", vecN))
vecC <- gsub("[[:digit:]]", "", vec)
vec
print(paste0("Just characters: ", vecC))

# with tidyr. requires data frame
require(tidyr)
df <- data.frame(N1 = c("APPLE348744", "BANANA77845", 
    "OATS2647892", "EGG98586456"))
print("tidyr doesn't work with strings separated by spaces")
df %>% separate(N1, into = c("text", "num"), sep = "(?<=[A-Za-z])(?=[0-9])")

Insert or replace a character in a string at a specific location

require(stringi)
vec <- "ABCEF"
stri_sub(vec, 4, 2) <- "d"
print(paste0("Original: ABCEF"))

## [1] "Original: ABCEF"

print(paste0("New: ", vec))

## [1] "New: ABCdEF"

Testing regex expressions and their output

# Testing regex expressions and their output

# https://regex101.com/r/ksY7HU/2

Removing multiple cols from df using grep

packages <- c("dplyr", "purrr")

fh <- "LEC100testrecords.txt"
tt <- read.delim(paste0(wd, "/", fh), header = T, sep = "\t")

# Enter data column you want to search
col2search <- "Title"
keyterms <- c("evidence", "human", "africa")

# 1. find key terms
final <- tt[grep(keyterms, tt[, col2search], ignore.case = T), 
    ]  #
length(final[, col2search])  # get number of results
tt[final[, col2search], col2search]  # show raw outputs 

R Markdown

Hide unwanted code output, such as inherent examples for functions

# ```{r, cache = TRUE, tidy = TRUE, lazy = TRUE,
# results='markup'}

Math notation in R Markdown

x=y \(x = y\)
x<y \(x < y\)
x>y \(x > y\)
x≤y \(x \le y\)
x≥y \(x \ge y\)
xn \(x^{n}\)
xn \(x_{n}\)
x⎯⎯⎯ \(\overline{x}\)
x̂ \(\hat{x}\)
x̃ \(\tilde{x}\)
ab \(\frac{a}{b}\)
∂f∂x \(\frac{a}{b}\)
∂f∂x \(\displaystyle \frac{a}{b}\)
(nk) \(\binom{n}{k}\)
x1+x2+⋯+xn \(x_{1} + x_{2} + \cdots + x_{n}\)
x1,x2,…,xn \(x_{1}, x_{2}, \dots, x_{n}\)
x=⟨x1,x2,…,xn \(\mathbf{x} = \langle x_{1}, x_{2}, \dots, x_{n}\rangle\)
x∈A \(x \in A\)
|A| \(|A|\)
A⊂B \(x \subset B\)
A⊆B \(x \subseteq B\)
A∪B \(A \cup B\)
A∩B \(A \cap B\)
X∼𝖡𝗂𝗇𝗈𝗆(n,π) \(X \sim {\sf Binom}(n, \pi)\)

P(X≤x)=𝚙𝚋𝚒𝚗𝚘𝚖(x,n,π) \(\mathrm{P}(X \le x) = {\tt pbinom}(x, n, \pi)\)
P(A∣B) \(P(A \mid B)\)
P(A∣B) \(\mathrm{P}(A \mid B)\)
{1,2,3} \(\{1, 2, 3\}\)
sin(x) \(\sin(x)\)
log(x) \(\log(x)\)
∫ba \(\int_{a}^{b}\)
(∫baf(x)dx) \(\left(\int_{a}^{b} f(x) \; dx\right)\)
[∫∞−∞f(x)dx] \(\left[\int_{\-infty}^{\infty} f(x) \; dx\right]\)
F(x)|ba \(\left. F(x) \right|_{a}^{b}\)
∑bx=af(x) \(\sum_{x = a}^{b} f(x)\)
∏bx=af(x) \(\prod_{x = a}^{b} f(x)\)
limx→∞f(x) \(\lim_{x \to \infty} f(x)\)
limx→∞f(x) \(\displaystyle \lim_{x \to \infty} f(x)\)

Greek Letters

αA \(\alpha A\)
νN $N $
βB \(\beta B\)
ξΞ \(\xi\Xi\)
γΓ \(\gamma \Gamma\)
oO \(o O\) (omicron)
δΔ \(\delta \Delta\)
πΠ \(\pi \Pi\)
ϵεE \(\epsilon \varepsilon E\)
ρϱP \(\rho\varrho P\)
ζZ \(\zeta Z \sigma \,\!\)
Σ \(\sigma \Sigma\)
ηH \(\eta H\)
τT \(\tau T\)
θϑΘ \(\theta \vartheta \Theta\)
υΥ \(\upsilon \Upsilon\)
ιI \(\iota I\)
ϕφΦ \(\phi \varphi \Phi\)
κK \(\kappa K\)
χX \(\chi X\)
λΛ \(\lambda \Lambda\)
ψΨ \(\psi \Psi\)
μM \(\mu M\)
ω Ω\(\omega \Omega\)

Subsetting

Select specific rows E.g. select rows of sfeed_move not in foodh

library(sqldf)
a1NotIna2_h <- sqldf("SELECT * FROM sfeed_move EXCEPT SELECT * FROM foodh")
a1NotIna2_l <- sqldf("SELECT * FROM sfeed_move EXCEPT SELECT * FROM foodl")
# select rows from sfeed_move that also appear in
# foodh
a1Ina2_h <- sqldf("SELECT * FROM sfeed_move INTERSECT SELECT * FROM foodh")
a1Ina2_l <- sqldf("SELECT * FROM sfeed_move INTERSECT SELECT * FROM foodl")

Count occurrences of values in data frame

table(unlist(df$X))

Remove a specific column from a data frame

within(df, rm("Col1"))

Web scraping

Scraping web tables
http://web.mit.edu/~r/current/arch/i386_linux26/lib/R/library/XML/html/readHTMLTable.html[http://web.mit.edu/~r/current/arch/i386_linux26/lib/R/library/XML/html/readHTMLTable.html]

library(XML)
readHTMLTable()

Scraping Twitter timelines
See complete example at http://varianceexplained.org/r/trump-tweets/

# https://cran.r-project.org/web/packages/twitteR/
library(dplyr)
library(purrr)
library(twitteR)