Problem

• Which data structure is created and stored in the object a?

a <- log(as.matrix(data.frame(id = 1:4,
                              b = c(5, 4, 2, 3),
                              c = c(7, 7, 6, 7))))

Answer

• a matrix

log(as.matrix(data.frame(id = 1:4,
                         b = c(5, 4, 2, 3),
                         c = c(7, 7, 6, 7))))

##             id         b        c
## [1,] 0.0000000 1.6094379 1.945910
## [2,] 0.6931472 1.3862944 1.945910
## [3,] 1.0986123 0.6931472 1.791759
## [4,] 1.3862944 1.0986123 1.945910

Problem

• Find the errors!

data.frame(id = 1:3
           b = c("a", "b", "c"),
           c = (9, 4, 5))

Answer

data.frame(id = 1:3
           b = c("a", "b", "c"),
           c = (9, 4, 5))

• comma is missing in the first line
• c[oncatenate] function is missing in the last line

data.frame(id = 1:3,
           b = c("a", "b", "c"),
           c = c(9, 4, 5))

Problem

• Find the errors!
• Which data structure is created and stored in the object d?

a <- log(as.matrix(data.frame(id = 1:4,
                              b = c(5, 4, 2, 3),
                              c = c(7, 7, 6, 7))))
d <- meen(a[2:4, 2:4]))

Answer

a <- log(as.matrix(data.frame(id = 1:4,
                              b = c(5, 4, 2, 3),
                              c = c(7, 7, 6, 7))))
d <- meen(a[2:4, 2:4]))

• meen instead of mean
• 2:4 o 1:3, because there are only three columns
• one round bracket to much
• object d is a numeric vector

a <- log(as.matrix(data.frame(id = 1:4,
                              b = c(5, 4, 2, 3),
                              c = c(7, 7, 6, 7))))
d <- mean(a[2:4, 1:3])
d

## [1] 1.337743

Create data objects

• combine simple objects to more complex ones
• combine dataframes from existing vectors.
• column names can be provided as parameter names

a <- c(1, 2, 3)
b <- c(6, 3, 6)
df <- data.frame(a,
                 col_b = b)
df

a <- c(1, 2, 3)
b <- c(6, 3, 6)
df2 <- cbind(a,
             col_b = b)
df2

##      a col_b
## [1,] 1     6
## [2,] 2     3
## [3,] 3     6

Create data objects

str(df)

## 'data.frame':    3 obs. of  2 variables:
##  $ a    : num  1 2 3
##  $ col_b: num  6 3 6

str(df2)

##  num [1:3, 1:2] 1 2 3 6 3 6
##  - attr(*, "dimnames")=List of 2
##   ..$ : NULL
##   ..$ : chr [1:2] "a" "col_b"

str(as.data.frame(df2))

## 'data.frame':    3 obs. of  2 variables:
##  $ a    : num  1 2 3
##  $ col_b: num  6 3 6

Create data objects

explain the Code!

a <- c(1, 6)
b <- c(2, 3)
c <- c(3, 6)
df3 <- as.data.frame(rbind(a, b, c))
df3

… and update of column names:

colnames(df3) <- c("a", "b")
df3

Create data externally

Note

• R can access a data base directly.

Why don’t we recommend direct database access in most cases?

Answer

• accessing the original database might not be reproducible
• use a database snapshot or exported data instead
  • you can use R for this purpose
  • and you can use R to feed back results into a database

Working directory

• The working directory is the main directory of the project.
• RStudio projects automatically manage working directories.
• The Markdown working directories can be different from the general working directories.

getwd()

## [1] "/home/fon/Dropbox/daten/lehre/lv/LV100_uebEinfR/6praes"

wd <- "C:\\Folder\\Subfolder\\SubSubFolder"    # Windows
wd <- "/home/xxx/subFolder/SubSubFolder"       # Linux
setwd(wd)

Read files

tdaten_name <- "../2data/22archdata/testdaten1.csv"
test_df <- read.csv(tdaten_name)
head(test_df)

Is the result all right?

Read files

• We prefer text not to be a factor.

test_df$c <- as.character(test_df$c)
head(test_df)

Since R Version 4.0 text is loaded as text and not as factor.

Read files

• Numbers should be numeric and not factors.

as.double(test_df$b)

## [1] 4 3 2 1 5

Is this correct? Why not?

Read files

• The decimal point is the problem. There are different solutions:
  • don’t use commas but ; as separator and . as as decimal point
  • use the parameter dec = “,” for reading the file (next slide)
  • convert decimal point

as.numeric(sub(",",
               ".",
               test_df$b,
               fixed = TRUE))

## [1] 8.3 7.5 5.2 4.8 8.9

tdaten_name <- "../2data/22archdata/testdaten1.csv"
test_df <- read.csv(tdaten_name,
                    dec = ",",
                    stringsAsFactors = FALSE)   # since R 4.0 default
head(test_df)

Hinweis

• There are some packages for reading data, for instance:
  • tibble::readr, Wickham/Hester (2021) (part of tidyverse)
  • data.table::data.table, Dowle/Srinivasan (2019) (particularly for large data sets)

Write files

tdaten_name <- "../2data/22archdata/testdaten2.csv"
write.csv(test_df,
          tdaten_name,
          row.names = FALSE)

• check with a text editor if the results are correct (e.g. RStudio)
• explore export parameters (use help)

functions with underscores such as write_csv() or read_csv() are part of the tidyverse

write.csv2() uses ; as separator by default

Databases

library("RSQLite")
con = RSQLite::dbConnect(SQLite(),
                dbname = "../2data/22archdata/neckRings.sqlite")
RSQLite::dbListTables(con)
rings = RSQLite::dbGetQuery(con,
                   'select * from rings')
RSQLite::dbDisconnect(con)
rings[1:7, 1:3]

## [1] "rings"

RSQLite: Müller et al. (2020)

SQL on dataframes

library(sqldf)
sqldf::sqldf('select type from rings where id is 3')
sqldf::sqldf("select * from rings where type LIKE 'M2.1.1.1.3.1%'")

sqldf: Grothendieck (2017)

Links:

• SQL Syntax on Wikipedia
• SQL Joins on Stackoverflow

5 min. Break

Visualisation

plot(test_df[, 2:3])

Visualisation

plot(test_df[, 2:3],
     main  = "Testplot")
lines(test_df[, 2:3])
points(test_df[1:3, 2:3],
       pch = 17,
       col = "red",
       cex = 3)
text(test_df[, 2:3],
       labels = test_df[, 1],
       pos    = 4,
       cex    = 1)

Visualisation

boxplot(test_df[, 2:3],
        main = "Boxplot")

barplot(test_df[, 2],
        main = "Barplot")

Visualisation

hist(test_df[, 2],
     main = "Histogramm",
     col = "grey")

table(test_df[, 2])

## 
## 2 4 5 6 
## 1 1 2 1

Export plots

png(filename  = "../4figures/histogr.png",
    width     = 480,
    height    = 480,
    units     = "px",
    pointsize = 12,
    bg        = "white"
    )
        hist(test_df[, 2],
             main = "Histogramm",
             col  = "grey"
             )
dev.off()

Explore parameter for .pdf, .svg, .jpeg and .tiff.

Note

A tutorial for data wrangling in base R is here.

Archaeological data

The package archdata (Carlson/Roth 2021) provides some archaeological data.

library(archdata)
data("Fibulae")
head(Fibulae)

Exercise

• insert the this table into a spreadsheet and save it to .csv:

• Read the file and create some basic plots with the columns a and b.
• Explore archdata

Break