In JBLecomte/JBUtilities:

```r library('JBUtilities') library('ggplot2') knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )

# GGplot2 related function

## Extract legend from ggplot2 object

```r
# Create a plot with legend
iris_plot <- ggplot(data = iris, aes(x = Species, y = Sepal.Width, colour = Species)) +
  geom_boxplot()
# print(iris_plot)

# Extract the legend plot
iris_plot_legend <- extract_plot_legend(iris_plot,
                                        Save = TRUE,
                                        device = 'png',
                                        width = 5,
                                        height = 5)

The png file saved:

library(knitr)
knitr::include_graphics('legend.png', dpi = 300)

Save plot

# Create a plot with legend
iris_plot <- ggplot(data = iris, aes(x = Species, y = Sepal.Width, colour = Species)) +
  geom_boxplot()

# Extract the legend plot
save_plot(iris_plot,
          output = 'Beamer',
          width = 5,
          height = 5)

Print plot

# Create a plot with legend
iris_plot <- ggplot(data = iris, aes(x = Species, y = Sepal.Width, colour = Species)) +
  geom_boxplot()

# Print the plot
show_ggplot2(iris_plot, ShowPlot = TRUE)

Color blind palette

Available color blind palette:

l_cb_palette <- JBUtilities:::l_cb_palette

JBUtilities:::list_plotter(l_cb_palette,
                           names = names(l_cb_palette),
                           package_name = 'JBUtilities')

How to use discrete palette with ggplot2

# Create a plot with legend
iris_plot <- ggplot(data = iris, aes(x = Species, y = Sepal.Width, color = Species)) +
  scale_color_manual(values = cb_pal(n = 3, name = "cb.grey.pal")) +
  geom_boxplot()

# Print the plot
show_ggplot2(iris_plot, ShowPlot = TRUE)

How to use continuois palette with ggplot2

# Create a plot with legend
iris_plot2 <- ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width, color = Sepal.Length)) +
  scale_color_gradientn(colours = cb_pal(name = "cb.map.blue.red.pal",  type = "continuous")) +
  geom_point()

# Print the plot
show_ggplot2(iris_plot2, ShowPlot = TRUE)

ggplot2 theme

For one plot :

# Create a plot with legend
iris_plot <- ggplot(data = iris, aes(x = Species, y = Sepal.Width, color = Species)) +
  simple_ggplot2_theme() +
  geom_boxplot()

# Print the plot
show_ggplot2(iris_plot, ShowPlot = TRUE)

For all plots in a R session:

theme_set(simple_ggplot2_theme())

# Create a plot with legend
iris_plot <- ggplot(data = iris, aes(x = Species, y = Sepal.Width, color = Species)) +
  geom_boxplot()
show_ggplot2(iris_plot, ShowPlot = TRUE)

# Create a plot with legend
iris_plot2 <- ggplot(data = iris, aes(x = Species, y = Sepal.Length, color = Species)) +
  geom_boxplot()

# Print the plot
show_ggplot2(iris_plot2, ShowPlot = TRUE)

Data related function

Standardisation of a numeric variable:

## simulation of a covariate
covariate <- runif(100, 1, 10)
## standardisation
covariate_std <- cov_std(covariate)
head(covariate_std)

## the same covariate sampled in a different place but we want the same standardisation
covariate_grid <- runif(100, 5, 10)
## use the fonction covariate_grid_std
covariate_grid_std <- cov_std_grid(covariate,
                                   mean_x_obs = mean(covariate),
                                   sd_x_obs = sd(covariate))
head(covariate_grid_std)

Create a vector of month in letter from a numeric vector

## simulation of a vector of month
month_numeric <- sample(1:12, size = 10)
head(month_numeric)

## transform to letters
month_f <- month_number2letter(month_numeric)
head(month_f)

In order to fill NA gaps in a vector with the previous value:

## simulation of a vector
vec <- c(1, NA, 5, 6, 1, NA, 1)
fill_NA_gaps(vec)

## simulation of a vector
vec <- c(1, NA, NA, 6, 1, NA, 1)
fill_NA_gaps(vec)

vec <- c(NA, 2, NA, 6, 1, NA, 1)
fill_NA_gaps(vec, firstBack = TRUE)

Compute summarizing statistics of a vector:

## simulation of a vector
vec <- runif(100,1,1000)

## compute quantiles
compute_q025_q25_q50_q75_q975(vec)

## compute min, max, sd and mean
compute_min_mean_sd_max(vec)

Spaital data related function

R session related function

Which Operating System is in use.

## function to get the os of the computer where R is executed
get_os()

##  ## Makes it so you can write this:
X <- 2
Y <- 3
cat0("X = ", X, " and Y = ", Y)
## instead of this:
cat(paste("X = ", X, " and Y = ", Y, "\n", sep=""))

## install package if not already installed
InstallPackages('devtools')

JBLecomte/JBUtilities documentation built on March 31, 2021, 7:25 p.m.