Terraclimate Map visualization using ClimateR Tutorial

This Tutorial below is on How to create climateR maps using Terraclimate dataset this will Include Yearly,Monthly and Finally Mean As illustrated ,The Main Country/Area of Interest would be Kingdom of Saudi Arabia and Before I continue Much Special Thanks to Creator of ClimateR Mike Johnson -[@mikejohnson51] and also Monika Anna Tomaszewska for helping me out, you can check it out and ClimateR official Repository link below

https://github.com/mikejohnson51/climateR

How to Install ClimateR Package

ClimateR can be Installed using the below codes you need to install ClimateR codes

remotes::install_github("mikejohnson51/AOI") # suggested!
remotes::install_github("mikejohnson51/climateR")

Other Packages to Install which might be required

Other Packages needed might be terra,tidyterra,ggplot2,sf,shapefiles ;- which can be installed in Rstudio by going to Package ,Install then writing name of one of 5 packages listed above

Tutorial for Yearly Temperature Map of Area of Interest

So first thing is to load/write up the packages to be used

library(climateR)
library(terra)
library(tidyterra)
library(ggplot2)
library(sf)
library(shapefiles)

Then you write a code to fetch area of Interest using 3-Country code of The country of Interest

SAU = AOI::aoi_get(country = "SAU")

The you write the codes to fetch the raster files containing climate(Take note on Parameter or varname) and the years can change wherever year you want let say 2000 to 2010 ,but for this example Only two years were used 2011 and 2012

test_data = getTerraClim(
  AOI = SAU,
  varname = "tmax",
  startDate = "2011-01-01",
  endDate   = "2012-12-01"
)

Then You write code for Indexing and masking the Raster

data = tapp(test_data[[1]],
            rep(1:(nlyr(test_data[[1]]) / 12), 12),
            mean) |>
  mask(project(vect(SAU), crs(test_data[[1]])))

names(data) = c("2011", "2012")

Then finally You write code for adding annotation and coloring the Raster

ggplot() +
  geom_spatraster(data = data) +
  geom_spatvector(data = SAU, fill = NA, lwd = 1) +
  facet_wrap( ~ lyr) +
  scale_fill_whitebox_c(
    palette  = "muted",
    n.breaks = 12,
    guide    = guide_legend(reverse = TRUE)
  ) +
  labs(title = "Yearly temperature of Saudi Arabia of the years 2011 and 2012",
       fill = "Temperature (°C)") + # Add title
  theme_minimal()

The full Code would be like this

library(climateR)
library(terra)
library(tidyterra)
library(ggplot2)
library(sf)
library(shapefiles)

SAU = AOI::aoi_get(country = "SAU")

test_data = getTerraClim(
  AOI = SAU,
  varname = "tmax",
  startDate = "2011-01-01",
  endDate   = "2012-12-01"
)

data = tapp(test_data[[1]],
            rep(1:(nlyr(test_data[[1]]) / 12), 12),
            mean) |>
  mask(project(vect(SAU), crs(test_data[[1]])))

names(data) = c("2011", "2012")

ggplot() +
  geom_spatraster(data = data) +
  geom_spatvector(data = SAU, fill = NA, lwd = 1) +
  facet_wrap( ~ lyr) +
  scale_fill_whitebox_c(
    palette  = "muted",
    n.breaks = 12,
    guide    = guide_legend(reverse = TRUE)
  ) +
  labs(title = "Yearly temperature of Saudi Arabia of the years 2011 and 2012",
       fill = "Temperature (°C)") + # Add title
  theme_minimal()

The final Output on This section would look like This

Tutorial for Monthly Temperature Map of Area of Interest

The Monthly Temperature slight things changes on Indexing and Annotation

library(climateR)
library(terra)
library(tidyterra)
library(ggplot2)
library(sf)
library(shapefiles)

SAU = AOI::aoi_get(country = "SAU")

test_data = getTerraClim(
  AOI = SAU,
  varname = "tmax",
  startDate = "2011-01-01",
  endDate   = "2012-12-01"
)

data = tapp(test_data[[1]],
            rep(1:12, (nlyr(test_data[[1]]) / 12)),
            mean) |>
  mask(project(vect(SAU), crs(test_data[[1]])))

names(data) = c("January", "February","March","April","May","June","July","August","September","October","November","December")

ggplot() +
  geom_spatraster(data = data) +
  geom_spatvector(data = SAU, fill = NA, lwd = 1) +
  facet_wrap( ~ lyr) +
  scale_fill_whitebox_c(
    palette  = "muted",
    n.breaks = 12,
    guide    = guide_legend(reverse = TRUE)
  ) +
  labs(title = "Monthly Day temperature of Saudi Arabia for years 2011 and 2012",fill = "Temperature (°C)") +
  theme_minimal()

Which would result into This Output

Mean Temperature Map of Area of Interest

So it also differs from the other as full code is as follows

library(climateR)
library(terra)
library(tidyterra)
library(ggplot2)
library(sf)
library(shapefiles)

SAU = AOI::aoi_get(country = "SAU")

test_data = getTerraClim(
  AOI = SAU,
  varname = "tmax",
  startDate = "2011-01-01",
  endDate   = "2012-12-01"
)

data = mask(mean(test_data[[1]]), project(vect(SAU), crs(test_data[[1]])))

ggplot() +
  geom_spatraster(data = data) +
  geom_spatvector(data = SAU, fill = NA, lwd = 1) +
  scale_fill_whitebox_c(
    palette  = "muted",
    n.breaks = 12,
    guide    = guide_legend(reverse = TRUE)
  ) +
  labs(title = "Mean Daily Max Temperature of Saudi Arabia for the years 2011 and 2012 ",fill = "Temperature (°C)") + 
  theme_minimal()

Concerning Other Visual Climatic Maps for Example Rainfall

So for this case we have visualized Temperature map using code

varname = "tmax"

Concerning other visual all you need to do is to change varname if you want for Rainfall

varname = "ppt"

And Min Temperature

varname = "tmin"

An One Example on Monthly Rainfall Data for Area of Interest

So for Rainfall Visualization for Rainfall we write code as follows

library(climateR)
library(terra)
library(tidyterra)
library(ggplot2)
library(sf)
library(shapefile)

SAU = AOI::aoi_get(country = "SAU")

test_data = getTerraClim(
  AOI = SAU,
  varname = "ppt",
  startDate = "2011-01-01",
  endDate   = "2012-12-01"
)

data = tapp(test_data[[1]],
            rep(1:12, (nlyr(test_data[[1]]) / 12)),
            mean) |>
  mask(project(vect(SAU), crs(test_data[[1]])))

names(data) = c("January", "February","March","April","May","June","July","August","September","October","November","December")

ggplot() +
  geom_spatraster(data = data) +
  geom_spatvector(data = SAU, fill = NA, lwd = 1) +
  facet_wrap( ~ lyr) +
  scale_fill_whitebox_c(
    palette  = "deep",
    n.breaks = 12,
    guide    = guide_legend(reverse = TRUE)
  ) +
  labs(title = "Monthly Precipitation of Saudi Arabia for years 2011 and 2012",fill = "Precipitation (mm)") +
  theme_minimal()

The result would look like this below

Area of Interest using shapefile

For instance you have an area of interest that is not country wise let say its sub-country or it can be a basin not to worry it can still be used in terraclimate using Shapefile and sf packages,You need to prepare it in QGIS or Arc-GIS pro then export it to place you want to save (My preference is normally local disk ) then you can easily fetch shapefile through Pathway and continue as normal for Instance in this example I'm going to illustrate for Makkah region/province which is sub-region of Kingdom of Saudi Arabia ,Prepared shapefile from QGIS Through GADM then saved it and made the code below ;-

library(climateR)
library(terra)
library(tidyterra)
library(ggplot2)
library(sf)
library(shapefiles)

MAK <- st_read("C:/Makkah.shp")
test_data = getTerraClim(
  AOI = MAK,
  varname = "tmax",
  startDate = "2011-01-01",
  endDate   = "2012-12-01"
)

data = tapp(test_data[[1]],
            rep(1:12, (nlyr(test_data[[1]]) / 12)),
            mean) |>
  mask(project(vect(MAK), crs(test_data[[1]])))

names(data) = c("January", "February","March","April","May","June","July","August","September","October","November","December")

ggplot() +
  geom_spatraster(data = data) +
  geom_spatvector(data = MAK, fill = NA, lwd = 1) +
  facet_wrap( ~ lyr) +
  scale_fill_whitebox_c(
    palette  = "muted",
    n.breaks = 12,
    guide    = guide_legend(reverse = TRUE)
  ) +
  labs(title = "Monthly Temperature of Makkah Province for years 2011 and 2012",fill = "Temperature (°C)") +
  theme_minimal()

Which would bring This Output

Adjusting Longitude and Latitude

Sometimes The default coordinates doesn't appear nicely (For example above coordinate) so all you need to do is to Adjust using coord_sf function as illustrated below ,xlim represent Longitude while ylim represent latitude which you can adjust manually

library(climateR)
library(terra)
library(tidyterra)
library(ggplot2)
library(sf)
library(shapefiles)

MAK <- st_read("C:/Makkah.shp")
test_data = getTerraClim(
  AOI = MAK,
  varname = "tmax",
  startDate = "2011-01-01",
  endDate   = "2012-12-01"
)

data = tapp(test_data[[1]],
            rep(1:12, (nlyr(test_data[[1]]) / 12)),
            mean) |>
  mask(project(vect(MAK), crs(test_data[[1]])))

names(data) = c("January", "February","March","April","May","June","July","August","September","October","November","December")

ggplot() +
  geom_spatraster(data = data) +
  geom_spatvector(data = MAK, fill = NA, lwd = 1) +
  facet_wrap( ~ lyr) +
  scale_fill_whitebox_c(
    palette  = "muted",
    n.breaks = 12,
    guide    = guide_legend(reverse = TRUE)
  ) +
  labs(title = "Monthly Temperature of Makkah Province for years 2011 and 2012",fill = "Temperature (°C)") +
  theme_minimal() +
  coord_sf(xlim = c(36, 45), ylim = c(21, 29)) # Adjust longitude values as needed

It would result to This Map/Output I also want to try another example for a southern hemisphere region ,An Example is From Morogoro Region,Tanzania So How does it appear when you adjust coordinates

library(climateR)
library(terra)
library(tidyterra)
library(ggplot2)
library(sf)
library(shapefiles)

MOR <- st_read("C:/Moro.shp")
test_data = getTerraClim(
  AOI = MOR,
  varname = "tmax",
  startDate = "2011-01-01",
  endDate   = "2012-12-01"
)

data = tapp(test_data[[1]],
            rep(1:12, (nlyr(test_data[[1]]) / 12)),
            mean) |>
  mask(project(vect(MOR), crs(test_data[[1]])))

names(data) = c("January", "February","March","April","May","June","July","August","September","October","November","December")

ggplot() +
  geom_spatraster(data = data) +
  geom_spatvector(data = MOR, fill = NA, lwd = 1) +
  facet_wrap( ~ lyr) +
  scale_fill_whitebox_c(
    palette  = "muted",
    n.breaks = 12,
    guide    = guide_legend(reverse = TRUE)
  ) +
  labs(title = "Monthly Temperature of Morogoro Region for years 2011 and 2012",fill = "Temperature (°C)") +
  theme_minimal()+ 
  coord_sf(xlim = c(35.5, 40), ylim = c(-5.5, -10.5)) # Adjust longitude values as needed

It would result into this Output

Visualizing with Different colors

So the other thing I wanted to share with you is visualizing with more colors with pallete package called colorspace which can be downloaded as follows

install.package(colorspace)

which could be utilized as following code

library(climateR)
library(terra)
library(tidyterra)
library(ggplot2)
library(sf)
library(shapefiles)
library(colorspace)

SAU = AOI::aoi_get(country = "SAU")

test_data = getTerraClim(
  AOI = SAU,
  varname = "tmax",
  startDate = "2011-01-01",
  endDate   = "2012-12-01"
)

data = tapp(test_data[[1]],
            rep(1:(nlyr(test_data[[1]]) / 12), 12),
            mean) |>
  mask(project(vect(SAU), crs(test_data[[1]])))

names(data) = c("2011", "2012")

ggplot() +
  geom_spatraster(data = data) +
  geom_spatvector(data = SAU, fill = NA, lwd = 1) + # Set color to "black"
  facet_wrap( ~ lyr) +
  scale_fill_continuous_sequential(palette = "YlOrRd",na.value = "transparent") +
  labs(title = "Yearly temperature of Saudi Arabia of the years 2011 and 2012",
       fill = "Temperature (°C)") + # Add title
  theme_minimal()

Which would result into following output

Also Another example is Monthly Visualization which I would illustrate as follows

library(climateR)
library(terra)
library(tidyterra)
library(ggplot2)
library(colorspace)

SAU = AOI::aoi_get(country = "SAU")

test_data = getTerraClim(
  AOI = SAU,
  varname = "tmax",
  startDate = "2011-01-01",
  endDate   = "2012-12-01"
)

data = tapp(test_data[[1]],
            rep(1:12, (nlyr(test_data[[1]]) / 12)),
            mean) |>
  mask(project(vect(SAU), crs(test_data[[1]])))

names(data) = c("January", "February","March","April","May","June","July","August","September","October","November","December")
ggplot() +
  geom_spatraster(data = data) +
  geom_spatvector(data = SAU, fill = NA, lwd = 1) + # Set color to "black"
  facet_wrap( ~ lyr) +
  scale_fill_continuous_sequential(palette = "YlOrRd",na.value = "transparent") +
  labs(title = "Monthly temperature of Saudi Arabia of the years 2011 and 2012",
       fill = "Temperature (°C)") + # Add title
  theme_minimal()

which would result into following output

Another final example on color use im going to represent Morogoro Region,Tanzania Rainfall in between 2011 and 2012 as illustrated in code below

library(climateR)
library(terra)
library(tidyterra)
library(ggplot2)
library(sf)
library(shapefiles)

MOR <- st_read("C:/Moro.shp")
test_data = getTerraClim(
  AOI = MOR,
  varname = "ppt",
  startDate = "2011-01-01",
  endDate   = "2012-12-01"
)

data = tapp(test_data[[1]],
            rep(1:12, (nlyr(test_data[[1]]) / 12)),
            mean) |>
  mask(project(vect(MOR), crs(test_data[[1]])))

names(data) = c("January", "February","March","April","May","June","July","August","September","October","November","December")

ggplot() +
  geom_spatraster(data = data) +
  geom_spatvector(data = MOR, fill = NA, lwd = 1) +
  facet_wrap( ~ lyr) +
  scale_fill_continuous_sequential(palette = "Blues 3",na.value = "transparent") +
  labs(title = "Monthly Precipitation of Morogoro Region for years 2011 and 2012",fill = "Precipitation (mm)") +
  theme_minimal()+ 
  coord_sf(xlim = c(35.5, 40), ylim = c(-5.5, -10.5)) # Adjust longitude values as needed

Which would result into following Output

Colorspace has a lot of color choices which you could use in Visualizing Temperature and Precipitation ,In which i would illustrate further

Final Touches - Pallete explanation

So I wanted to share you the pallete is used found in Tidyterra The words "muted" and "deep" are color palette used ,normally for Temperature and Rainfall Representation Below is photo of more color pallete

But when colorspace mostly "YlOrRd","Heat2" and "OrYel" are good for Temperature while for Rainfall/Precipitation "Mako" and "PuBu" are preferable but choice is yours below is photo for more color pallete for colorspace package

Citation and reference

Special thanks to Mike Johnson and Citation is as follows

@Manual{,
  title = {climateR: climateR},
  author = {Mike Johnson},
  year = {2023},
  note = {R package version 0.3.2},
  url = {https://github.com/mikejohnson51/climateR},
}