1. Crash course in R – plotting maps
Dag Hjermann & Susi Schneider, NIVA
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2. Installing R Hopefully already done Otherwise:
cran.r-project.org
find ….
You may in addition install Rstudio
Nice window layout
Many practical features
Is also based on coding
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3. Starting R 3 types of windows
Console window - Write commands, run each line by ENTER button - Shows results of commands (analyses, tables)
Script window(s) - Write commands, run one/several lines by Ctrl-R - Can be saved for later
Plot window(s) - For plots
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4. Run your first command (in two ways)
Run a single command In the command window, write 2+2 and push enter - To change/correct command, push «up arrow» key
Run command from script window In the menu: File > Open script In the script window, write 2+2 and push Control-R (or use Edit menu) - Save the script (Menu: File > Save)
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5. Storing variables and data
In the script window, write x <- 1 y <- 2 x+y Mark all lines and push Control-R
R uses the arrow sign <- for storing variables and data sets
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6. Installing packages
R has built-in commands for simple statistics and graphs
Additional functions are in packages
Each package contains (usually) several functions (and often example data)
Ca packages can be found on cran.r-project.org
Each package only has to be installed once (are stored on your computer)
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7. Packages we will use here
readxl – for reading Excel files ggplot2 – for nice plots ggmap – for plotting on maps
2 ways to install:
In the menu: Packages > Install package(s)
In the console or script window: install.packages(c("readxl", "ggplot2", "ggmap")) and run it using Control-R
In both cases, you will be asked to select the country of the server
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8. Make Excel data ready Look at the data set in Excel Make sure of this:
First row has names of columns
Data starts in row number 2
No empty columns to the left of the data
Column names can not contain hyphen (-) or start with a number
Column names can contain spaces and ‘foreign’ letters (such as ä, ñ, æ, ø, å, …) - but makes things more difficult
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9. Set the working directory
Make sure that R “is” in the folder as the data:
Either using the menu: File > Change dir…
Or using the command: setwd(“C:/Work/R course”)
Note that we must use “forward slash” (/)
Using backslashes (“C:\Work\R course”) will not work
Check - this command should show the data file: dir()
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10. Read Excel data
First, load the library of the read_excel command into memory… library(readxl)
Then, we read the data and store it as “mydata”: mydata <- read_excel("Macrophyte index.xls")
Loads the read_excel command into memory
What you want to call your data in R (choose freely!)
Command
The name of your file (if you have set the working directory, you don’t have to say in which folder it is)
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11. Read Excel data
Note that «mydata» (the «R version» of your data) has now only been loaded into the memory, not saved in an R file
The next time you open R, «mydata» will have disappeared
…but if you have saved the script, reading the file again is very fast
And then you need only one copy of the data
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12. Read Excel data – possible errors
Error in read_fun(path = path, sheet = sheet, limits = limits, shim = shim, :
path[1]="Macrophyte index.xls": The system cannot find the file specified
- R often gives confusing error messages, with a lot of extra information you don’t need
- In this case, either 1) The name of the file is spelled incorrectly, or 2) You have not set the work directory (R is in the wrong folder)
This is the useful information
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13. Look at the data, in four ways
str(mydata)
head(mydata)
View(mydata)
mydata
List of variables, the type of each variable
The first rows of the data
“Excel-like” window (may not show all columns!)
Shows all the data on the screen
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14. How to get help in R ? + the command name (with no space between)
?read_excel ?str ?head
Often not very well written
Alternatives on the web
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15. Plot a kind of ‘map’ of the data
Loads commands we need (ggplot, geom_point, etc.)
library(ggplot2)
ggplot(mydata, aes(Elongitude, Nlatitude)) + geom_point()
Name of data
X and Y variables
How to show the data
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16. Plot a kind of ‘map’ of the data
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17. Kind of ‘map’, with names
ggplot(mydata, aes(Elongitude, Nlatitude)) + geom_point() + geom_text(aes(x = Elongitude , label=Station), hjust = "left")
Add text to the plot
Put the text a bit to the East of the points
This is the variable we want as tee
text
Make the text left-adjusted (so it starts to the East of the point)
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18. Plot a kind of ‘map’ of the data
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19. Plot a real map of the data
Loads the qmap command (and others)
library(ggmap)
map_ohrid <- qmap(c(20.733, ), zoom=11)
map_ohrid
Center of map (longitude, latitude)
Zoom level (3-21)
Show the map
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20. Plot a real map of the data
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21. Satellite map without names
map_ohrid <- qmap(location = c(20.733, ), zoom=11, maptype = "satellite")
map_ohrid
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22. Different map variants
Location can also be a place name (or even address)
qmap(location = "Bay of Kotor") qmap(location = "Boka Kotorska") qmap(location = "Downing Street 10, London", zoom = 17)
Can select different map variants:
qmap(location = "Downing Street 10, London", zoom = 17, source = “stamen”)
qmap(location = "Lake Ohrid", maptype = "toner-lite", source = "stamen", zoom = 11)
map_ohrid
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23. Show stations on the map
map_ohrid +
geom_point(data = mydata, aes(Elongitude, Nlatitude), color = "red") +
geom_text(data = mydata, aes(x = Elongitude , y = Nlatitude, label=Station), hjust = "left", color = "red")
Add points to the map
Add station names to the map
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24. Show index data on the map
map_ohrid +
geom_point(data = mydata, aes(Elongitude, Nlatitude, color = Index), size = 4) +
geom_text(data = mydata, aes(x = Elongitude , y = Nlatitude, label=Station), hjust = "left", color = "red")
Show values of the variable ‘Index’ as colours
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25. Show index data, better colours
map_ohrid +
geom_point(data = mydata, aes(Elongitude, Nlatitude, color = Index), size = 4) +
geom_text(data = mydata, aes(x = Elongitude , y = Nlatitude, label=Station), hjust = "left", color = "red") +
scale_color_distiller(palette = "Spectral")
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26. Show index data, better colours
map_ohrid +
geom_point(data = mydata, aes(Elongitude, Nlatitude, color = Index), size = 4) +
geom_text(data = mydata, aes(x = Elongitude , y = Nlatitude, label=Station), hjust = "left", color = "red") +
scale_color_distiller(palette = "Spectral“, direction = 1)
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27. Turn color scale around (red = bad)
map_ohrid +
geom_point(data = mydata, aes(Elongitude, Nlatitude, color = Index), size = 4) +
geom_text(data = mydata, aes(x = Elongitude , y = Nlatitude, label=Station), hjust = "left", color = "red") +
scale_color_distiller(palette = "Spectral“, direction = 1)
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28. Scale following eutrophy categories
Define categories and their colours
Add variable for categories to the data
Plot the new variable
Macrophyte index
Category
Oligotrophic
2.40 – 2.70
Oligo-mesotrophic
2.70 – 2.95
Mesotrophic 1
2.95 – 3.30
Mesotrophic 2
3.30 – 3.55
Eutrophic 1
3.55 – 3.90
Eutrophic 2
3.90 – 5.00
Eutrophic 3
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29. Scale following eutrophy categories
Define categories and their colours
categories <- c("oligotrophic", "oligo-mesotrophic", "mesotrophic 1", "mesotrophic 2", "eutrophic 1", "eutrophic 2", "eutrophic 3")
colors <- c("darkblue","blue","darkgreen","green", "yellow","orange","red")
names(colors) <- categories # the 'link' between categories and colors
Test: write “colors” and run (Ctrl + R)
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30. Scale following eutrophy categories
2. Add variable for categories to the data
mydata$Index_cat <- cut(mydata$Index, c(1, 2.4, 2.7, 2.95, 3.3, 3.55, 3.9, 5),
labels = categories), include.lowest = TRUE)
1. In our data set ‘mydata’ we will make a new variable called ‘Index_cat’
2. This variable is based on the existing variable called ‘Index’
3. We will make categories (function cut) based on ‘Index’
4. This is the 8 limits we will use for making the categories
5. This is the 7 names we will give to the categories (saved in the previous slide)
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31. Scale following eutrophy categories
3. Make the plot – almost as before
map_ohrid +
geom_point(data = mydata, aes(Elongitude, Nlatitude, color = Index_cat), size = 4) +
geom_text(data = mydata, aes(x = Elongitude , y = Nlatitude, label=Station), color = "yellow", hjust = "left") +
scale_color_manual(values = colors)
1. We will use our new variable ‘Index_cat’ for colors
2. We will use the colours we saved under the name ‘colors’
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32. Scale following eutrophy categories
We are almost happy now, but…
…we want all the categories in the legend

33. Scale following eutrophy categories
3. Make the plot – almost as before
map_ohrid +
geom_point(data = mydata, aes(Elongitude, Nlatitude, color = Index_cat), size = 4) +
geom_text(data = mydata, aes(x = Elongitude , y = Nlatitude, label=Station), color = "yellow", hjust = "left") +
scale_color_manual(values = colors, drop = FALSE)
drop = FALSE means that we will show all categories in the legend – not only those we have in the map
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34. Scale following eutrophy categories
Result from our hard work

35. Show index data on the map
map_ohrid +
geom_point(data = mydata, aes(Elongitude, Nlatitude, color = Index), size = 4) +
geom_text(data = mydata, aes(x = Elongitude , y = Nlatitude, label=Station), hjust = "left", color = "red")
map_ohrid
Show values of the variable ‘Index’ as colours
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36. For your own data Change the things in red
categories <- c("oligotrophic", "oligo-mesotrophic", "mesotrophic 1", "mesotrophic 2", "eutrophic 1", "eutrophic 2", "eutrophic 3")
colors <- c("darkblue","blue","darkgreen","green", "yellow","orange","red")
names(colors) <- categories mydata$Index_cat <- cut(mydata$Index, c(1, 2.4, 2.7, 2.95, 3.3, 3.55, 3.9, 5),
labels = categories))
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37. Show species abundance data on map
Read data (and look at the data in R)
species_data <- read_excel("Species data.xls")
str(species_data)
Variable names without spaces (e.g. Chara_aspera)
One line with variable names
Also includes variables Elongitude, Nlatitude

38. Pick selection of rows in data
Example: pick depth = II
subset(species_data, depth %in% "II")

39. Plotting data subset(species_data, depth == "II")
map_both <- qmap(location = c(20.85, 40.97), zoom=10, maptype = "satellite")
map_both +
geom_point(data = subset(species_data, depth %in% "II"),
aes(Elongitude, Nlatitude, size = Cladophora_spec., color = Cladophora_spec.)) +
scale_color_distiller(palette = "Spectral")
subset(species_data, depth == "II")
subset(species_data, depth %in% "II")
Select which rows to pick from the data
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40. Plotting species data
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