Geospatial Analysis with R

Today

• The next unit
• Code from last class
• File paths, reading and writing data
• Dates
• dplyr
• Integrative exercise

Exercise from last class

Create the following:

• dat, a data.frame built from V1, V2, V3, and V4, where:
  • V1 = 1:20
  • V2 is a random sample between 1:100
  • V3 is drawn from a random uniform distribution between 0 and 50
    
  • V4 is a random selection of the letters A-E
  • Use set.seed(50)
• Do this all at once (i.e. wrap the creation of V1-V4 in the data.frame call, precede it with set.seed())

Exercises

• Use a for to iterate over each row of dat and calculate it’s sum
• Do the same with lapply and sapply
• Do the same using rowSums
• Select rows from dat containing the letter “E” in V4, and take the mean of values from the result in column V3
• Create a function called myfun (just in your script, not as a package function). Have it add 20% of x (input value) to x. Apply it to all eligible values in dat

Spot the bug

set.seed(10)
dat <= dat.frame(
  V1 = 1;20, 
  V2 = sample(1:100, size = 20, replace = True),
  V3 = runif(n = 20, min = 0, max = 50), 
  V4 = sample(letters(1:5), size = 20)
)

for(i in 1:dat) {
  print(sum(dat[i]))
}

lapply(i in 1:nrow(dat) function(x) sum(dat[i, ]))

sapply(1:nrow(dat) function(x) sum(dat[, x]))

rowSums(dat)

mean(dat[dat`V4` == "E", "V3"])

Reading and writing data

File paths

Let’s read in a csv a few different ways.

Full path - clear for you, bad for code sharing.

data_tib <- read.csv(
  "/home/rstudio/geospaar/inst/extdata/cdf_corn.csv"
)
str(data_tib)

System file

data_tib <- readr::read_csv(
  system.file("extdata", "cdf_corn.csv", package = "geospaar")
)
str(data_tib)

Working directory "."

• Working directory. Use getwd() (from console)
  
• Usually set to project folder.

getwd() ## if in an RMD, this will show the folder of the RMD

Use . to start a file path from the working directory

list.files(".") ## 

data_tib <- readr::read_csv("./inst/extdata/cdf_corn.csv")

• Use “..” to go up one folder level

list.files(".") ## files in working directory
list.files("..") ## files in folder one level up

User directory "~"

• Set by environment variable
• Use command below to see value

path.expand("~")

[1] "/Users/LEstes"

data_tib <- readr::read_csv("~/geospaar/inst/extdata/cdf_corn.csv")

Writing files

• Use write.csv or readr::write_csv to write

readr::write_csv(data_tib, file = "temp.csv") 

• But use tempdir() to have write to temporary directory

readr::write_csv(data_tib, file = file.path(tempdir(), "temp.csv")) 

Saving/loading files

• If you want to save an R object, like a data.frame, tibble etc.
• Use save, and .rda extension

save(data_tib, file = file.path(tempdir(), "temp.rda")) 

• Load file back to environment

data_tib <- NULL
load(file = file.path(tempdir(), "temp.rda")) 

Dates with lubridate

• The main function you want to use is as_date, which can convert a character to date format.

library(lubridate)
date1 <- as_date("2022-03-01") ## date in standard YYYY-MM-DD format
date1

[1] "2022-03-01"

Dates with lubridate

• More challenging with unclear date formats.

date2 <- as_date("3/1/22") ## is month or date first?
date2

[1] NA

Include format as shown below. See list of formats at ?strptime

date2 <- as_date("3/1/22", format = "%m/%d/%y" )
date2

[1] "2022-03-01"

We can also write dates in desired format

date2_char <- as.character(date2, format = "%A %B %d, %Y")
date2_char

[1] "2022-03-01"

Date formats

How can we read in this date?

date3 <- as_date("Apr 3, 1999", format = "...")
date3

[1] NA

dplyr

library(dplyr)
dat %>% slice(1)

  V1 V2      V3 V4
1  1  9 20.2925  a

dat %>% filter(V4 == "e")

  V1 V2       V3 V4
1  3 76 41.91438  e
2 14 74 47.73268  e
3 18 95 11.44520  e

Exercise: lapply + read + write data

• Let’s use lapply to create a list l of 3 data.frames in which the first data.frame has it’s V2 column multiplied by 5, the second data.frame has V2 multiplied by 10, and the third has V2 multipled by 20. Use dat (see earlier slides) as the starting data.frame.
  
• Within the lapply, write each data.frame out to tempdir, naming each dataX.csv, where X is replaced by the multiplier value applied to V2
• Use list.dir() to read the file names and form a file path vector from them. Name the vector temp_files
• Use lapply with temp_files to read the files back into a list l2. Compare l and l2 for equivalency.

Buggy answer

l <- lappl(c(5, 10, 20), function[x] {
  d >= datt[dat$V2 * "x"]
  readr:write.csv(d, file = filepath(tempdir(), paste0("data", X, ".csv")))
  return(d)
}})

temp-files <- list.files(tempdir(), full.names = True, row.names = FALSE)

l2 <- lapply(temp-files, function(x) readr:::read.csv(x))

for(i in 1:length(l)) print(l[[i]] == l2[[i]])