Day 02

Pablo Gomez

How can we find help with R?


Using the ? operator:

How can we find help with R?


How can we find help with R?


How can we find help with R?


How can we find help with R?


ChatGPT:

How can we find help with R?


ChatGPT:

! WARNING: CHAT GPT CAN GIVE INCORRECT INFORMATION !

  • If chat GPT does not knows something, sometimes will make up information (i.e. made up references, name of packages, libraries etc…)
  • Make sure to verify the information provided by Chat GPT

How can we find help with R?


Recap from Day 01

R syntax

x <- seq(from = 5, to = 23, length.out = 10) # create a sequence of numbers
y <- seq(from = 0.1, to = 0.78, length.out = 10) # Create another sequence

mean(x*y) # Get the mean of the multiplication
[1] 7.406667

Objects? Operators? Functions? Arguments?

Objects:
- x
- y

Operators:
- *
- <-

Functions:
- seq()
- mean()

Arguments:
- from
- to
- lengt.out

Recap: functions for reduction


Some of the function we reviewed:

  • select()
  • slice()
  • filter()

Recap: functions for reduction


Some of the function we reviewed:

  • select() to select specific columns
  • slice()
  • filter()

Recap: functions for reduction


Some of the function we reviewed:

  • select() to select specific columns
  • slice() to select specific rows based on position
  • filter()

Recap: functions for reduction


Some of the function we reviewed:

  • select() to select specific columns
  • slice() to select specific rows based on position
  • filter() to select specific rows based on a condition

Review: functions


# Creating a new variable
PRRS %>% 
  filter(Sex == 'M') %>% 
  mutate( 
    SowFarm = ifelse(farm_type == 'sow farm', 1, 0) 
  ) %>% 
  select(Result, name, SowFarm)

Review: functions


# Creating a new variable
PRRS %>% # name of the data set
  filter(Sex == 'M') %>% 
  mutate( 
    SowFarm = ifelse(farm_type == 'sow farm', 1, 0) 
  ) %>% 
  select(Result, name, SowFarm)

Review: functions


# Creating a new variable
PRRS %>% # name of the data set
  filter(Sex == 'M') %>% # Selecting rows where only males
  mutate( 
    SowFarm = ifelse(farm_type == 'sow farm', 1, 0) 
  ) %>% 
  select(Result, name, SowFarm)

Review: functions


# Creating a new variable
PRRS %>% # name of the data set
  filter(Sex == 'M') %>% # Selecting rows where only males
  mutate( # Function to create a new variable
    SowFarm = ifelse(farm_type == 'sow farm', 1, 0) 
  ) %>% 
  select(Result, name, SowFarm)

Review: functions


# Creating a new variable
PRRS %>% # name of the data set
  filter(Sex == 'M') %>% # Selecting rows where only males
  mutate( # Function to create a new variable
    SowFarm = ifelse(farm_type == 'sow farm', 1, 0) # Create a variable based on farm type
  ) %>% 
  select(Result, name, SowFarm)

Review: functions


# Creating a new variable
PRRS %>% # name of the data set
  filter(Sex == 'M') %>% # Selecting rows where only males
  mutate( # Function to create a new variable
    SowFarm = ifelse(farm_type == 'sow farm', 1, 0) # Create a variable based on farm type
  ) %>% 
  select(Result, name, SowFarm) # select columns

Review: functions


Other function we reviewed:

  • count() Count rows by one or more groups
  • group_by() aggregate the data by one or more groups
  • summarise() applies functions to the grouped variables
  • left_join() join tables based on one or more index variables

Data visualization

ggplot2


  • We build our figures based on layers
  • Similar syntax as dplyr
  • We can combine data wrangling and visualization into a single code chunk

Lectures

Instead of the %>%, in ggplot we connect pieces of code with +

ggplot2


The basic components that we need to define for a plot are the following:

  • data, the data set we will use to generate the figure
  • geometry, or type of graphic we will generate (i.e. histogram, bar, scatter, etc..)
  • aesthetic, variables or arguments that will be used for the figure for example: location, color, size, etc..

Example

captures # Data
municipality location Loc date year captures treated lat lon trap_type
Temascaltepec San Pedro Tenayac Cueva el Uno 11/06/14 2014 6 6 18.03546 -100.2095 1
Tlatlaya Nuevo Copaltepec La alcantarilla 12/05/05 2005 3 2 18.40417 -100.2688 1
Tlatlaya Nuevo Copaltepec La alcantarilla 12/05/07 2007 30 29 18.40417 -100.2688 4
Tlatlaya Nuevo Copaltepec La alcantarilla 12/03/09 2009 0 0 18.40417 -100.2688 3
Tlatlaya Nuevo Copaltepec La alcantarilla 10/08/10 2010 4 3 18.40417 -100.2688 1

Example

captures %>% # Data used
  count(year, wt = treated)  # Some data transformation
year n
2005 167
2006 103
2007 249
2008 143
2009 125

Example

captures %>% # Data used
  count(year, wt = treated) %>%   # Some data wrangling
  ggplot() # Add an empty canvas

Example

captures %>% # Data used
  count(year, wt = treated) %>%   # Some data wrangling
  ggplot() + # Add an empty canvas
  geom_bar() # This is the geometry type

Example

captures %>% # Data used
  count(year, wt = treated) %>%   # Some data wrangling
  ggplot() + # Add an empty canvas
  geom_bar( # This is the geometry type
    aes( # Aesthetics or mapping
      x = year, # X axis
      y = n # Y axis
      ), 
    stat = 'identity' # extra arguments
  )

Example

captures %>% # Data used
  count(year, wt = treated) %>%   # Some data wrangling
  ggplot() + # Add an empty canvas
  geom_bar(aes(x = year, y = n), stat = 'identity') +
  labs(# extra arguments
    title = 'Bar plot',
    x = 'Year', y = 'Frequency'
  )

ggplot2

Link to the cheasheet

Other cheatsheets

It’s Lab time!

Graphics in R

How can we find help with R?


Data to Viz:

Spatial Data

Spatial data formats


Vectors

Rasters

Spatial resolution


Vectors


Point

Lines

Polygon

Spatial data in R


library(sf)

# Loading the spatial data from the package
MxSp <- st_read(system.file("data/MxShp.shp", package = "STNet")) 
Reading layer `MxShp' from data source 
  `/Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library/STNet/data/MxShp.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 2471 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 1058748 ymin: 319149.1 xmax: 4082958 ymax: 2349605
Projected CRS: MEXICO_ITRF_2008_LCC

Spatial data in R


# Filter to study area
Area <- MxSp %>% # This is the data we will filter
  filter(CVE_ENT %in% c('15', '12', '16')) %>%  # Filter the data to use the states with codes: 15, 12 y 16
  st_transform(crs = st_crs(4326)) # transform to lat/long

Spatial data in R


head(Area)
Simple feature collection with 6 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -100.5848 ymin: 16.88094 xmax: -98.218 ymax: 18.35471
Geodetic CRS:  WGS 84
  CVEGEO CVE_ENT CVE_MUN                  NOMGEO AREA_LCC    ID
1  12067      12     067              Tlapehuala  284.696 12067
2  12043      12     043              Metlatónoc  584.023 12043
3  12081      12     081               Iliatenco  235.682 12081
4  12066      12     066      Tlapa de Comonfort  609.030 12066
5  12078      12     078      Cochoapa el Grande  638.160 12078
6  12079      12     079 José Joaquín de Herrera  131.977 12079
                        geometry
1 MULTIPOLYGON (((-100.3237 1...
2 MULTIPOLYGON (((-98.26956 1...
3 MULTIPOLYGON (((-98.57511 1...
4 MULTIPOLYGON (((-98.5618 17...
5 MULTIPOLYGON (((-98.28944 1...
6 MULTIPOLYGON (((-98.95271 1...

Spatial data in R


The output shows:

  • geometry type: The type of shapefile (either point data, lines or polygons).
  • dimension Dimensions used in the data.
  • Bounding box: The extent of our data.
  • CRS: The coordinate reference system.
  • And the first 10 features.

Maps


ggplot() + # create the empty canvas
  geom_stars(data = Mxst) + # add raster layer
  geom_sf(data = Area, fill = NA, col = 'grey60') + # add polygon layer
  geom_sf(data = capturesSp, cex = 0.3, col = 'skyblue') + # add point layer
  theme_void() + # theme for the figure
  scale_fill_gradient(low = 'black', high = 'red', na.value = NA) + # color for the gradient
  labs(title = 'Map of the study area', fill = 'Altitude') # labels for the figure

Workshop homepage