forked from WorldHealthOrganization/epi50-vaccine-impact
-
Notifications
You must be signed in to change notification settings - Fork 0
/
dependencies.R
130 lines (102 loc) · 4.07 KB
/
dependencies.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
###########################################################
# DEPENDENCIES
#
# Deal with all package dependencies in one place.
#
###########################################################
# ---- R version check ----
# R versions for which this project has been tested and is stable
stable_versions = c("4.3.0", "4.3.2")
# R versions for which this project is stable (as a string)
stable_str = paste(stable_versions, collapse = ", ")
# Get details of R version currently running
version_info = R.Version()
# Construct version number from list details
version_num = paste0(version_info$major, ".", version_info$minor)
# Throw an error if this R version is unsuitable
if (!version_num %in% stable_versions)
stop("This software is stable with R version(s): ", stable_str,
" (currently running ", version_num, ")")
# Clear global environment
rm(list = ls())
# ---- Source files ----
# Scripts that should bot be sourced
no_src = c("launch.R", "dependencies.R")
# All R files, and those to source
all_files = list.files(pattern = ".+\\.R$")
src_files = setdiff(all_files, no_src)
# Source each of these files
for (file in src_files)
source(file)
# ---- Define packages ----
# Complete list of all R packages required for this project
packages = c(
"tidyverse", # Includes ggplot2, dplyr, tidyr (www.tidyverse.org/packages/)
"data.table", # Next generation dataframes
"dtplyr", # Syntax of dplyr with datatable speed
"gsubfn", # Output multiple variables from functions
"magrittr", # Additional pipe operators, such as %<>%
"wrapr", # Convenience functions (eg qc)
"matrixStats", # Matrix row and column operations
"stats", # Statistical calculations and random number generation
"stats4", # MLE algorithm
"MCMCpack", # MCMC functionality
"forecast", # Time series forecasting
"fpp3", # Time series forecasting
"tsibble", # Time series data format
"imputeTS", # Linear interpolation in dplyr pipes
"Hmisc", # Correlation functions
"tgp", # Latin hypercube sampler
"rsample", # Resampling
"splines", # Spline fitting functions
"countrycode", # Country name <-> code transformation
"yaml", # Data loading functionality
"parallel", # Local parallelisation
"lubridate", # Data formatting functionality
"naniar", # Data formatting functionality
"progress", # Progress bar
"GGally", # Plotting correlation
"ggpubr", # Plotting functionality
"ggnewscale", # Plotting functionality
"ggtext", # Plotting functionality
"ggh4x", # Plotting functionality
"ggridges", # Plotting functionality
"patchwork", # Plotting functionality
"scales", # Plotting functionality
"lemon", # Plotting functionality
"dotwhisker", # Plotting functionality
"pals", # Colour palettes
"colorspace", # Colour palettes
"svglite") # Figure saving
# List of all packages only available from github
gh_packages = c(
"PPgp/wpp2022") # Demographic data
# ---- Install and/or load R packages with pacman ----
message("* Installing required packages")
# Allow extra time for downloading big packages
options(timeout = 600) # Timeout in seconds
# Check whether pacman itself has been installed
pacman_installed = "pacman" %in% rownames(installed.packages())
# If not, install it
if (!pacman_installed)
install.packages("pacman")
# Load pacman
library(pacman)
# Load all required packages, installing them if required
pacman::p_load(char = packages)
# Same for github packages
pacman::p_load_gh(gh_packages)
# ---- Redefine or unmask particular functions ----
# Unmask certain commonly used functions
select = dplyr::select
filter = dplyr::filter
rename = dplyr::rename
recode = dplyr::recode
count = dplyr::count
predict = stats::predict
# ---- Clean up ----
# Close any open connections
closeAllConnections()
# Tidy up console after package loading
if (interactive()) clf() # Close figures
if (interactive()) clc() # Clear console