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11 Importing data

Importing data

  • Importing data is the process of loading data from external files into R for analysis.

  • Most real-world data is stored outside of R (e.g., spreadsheets, databases, web).

  • Effective data importing is crucial for data cleaning, analysis, and visualization.

Common Data Formats

  1. CSV (comma-separated values) (.csv)
  2. Excel (.xlsx, .xls)
  3. Text files (.txt)
  4. SPSS (.sav)
  5. Stata (.dta)
  6. SAS (.sas7bdat)
  7. R’s native data format (.RData)

1 Importing CSV Files

data1 <- read.csv("data/prawnGR.csv")
head(data1)
#>     GRate    diet
#> 1  9.7741 Natural
#> 2 10.2931 Natural
#> 3 10.0474 Natural
#> 4 10.0808 Natural
#> 5  9.3106 Natural
#> 6 10.4414 Natural
library(readr)
data1 <- read_csv("data/prawnGR.csv")
head(data1)
#> # A tibble: 6 × 2
#>   GRate diet   
#>   <dbl> <chr>  
#> 1  9.77 Natural
#> 2 10.3  Natural
#> 3 10.0  Natural
#> 4 10.1  Natural
#> 5  9.31 Natural
#> 6 10.4  Natural

2 Importing Excel Files

  • Install the readxl package:
  • Load the package and read data:
library(readxl)
data2 <- read_excel("data/whaledata.xls")
head(data2)
#> # A tibble: 6 × 8
#>   month time.at.station water.noise number.whales latitude longitude depth
#>   <chr>           <dbl> <chr>       <chr>            <dbl>     <dbl> <dbl>
#> 1 May              1344 low         7                 60.4     -4.18   520
#> 2 May              1633 medium      13                60.4     -4.19   559
#> 3 May               743 medium      12                60.5     -4.62  1006
#> 4 May              1050 medium      10                60.3     -4.35   540
#> 5 May              1764 medium      12                60.4     -5.2   1000
#> 6 May               580 high        10                60.4     -5.22  1000
#> # ℹ 1 more variable: gradient <dbl>

3 Importing Text Files

Using read.table():

data3 <- read.table("data/atmosphere.txt", header = TRUE)
head(data3)
#>   moisture treatment
#> 1    300.6    seeded
#> 2    302.4    seeded
#> 3    298.6    seeded
#> 4    315.9    seeded
#> 5    306.9    seeded
#> 6    300.1    seeded

4 Importing SPSS Files

  • Install the haven package:
  • Load the package and read data:
library(haven)
data4 <- read_sav("data/hw_dat.sav")
head(data4)
#> # A tibble: 6 × 13
#>   year_birth   age division residence religion edu      wealth_index total_birth
#>        <dbl> <dbl> <chr>    <chr>     <chr>    <chr>    <chr>              <dbl>
#> 1       1988    26 Barisal  Rural     Islam    Primary  Poorest                2
#> 2       1973    41 Barisal  Rural     Islam    Primary  Middle                 4
#> 3       1976    38 Barisal  Rural     Islam    Primary  Poorest                2
#> 4       1996    18 Barisal  Rural     Islam    Seconda… Poorest                0
#> 5       1986    28 Barisal  Rural     Islam    Primary  Poorest                2
#> 6       1980    34 Barisal  Rural     Islam    Primary  Poorer                 3
#> # ℹ 5 more variables: current_pregnant <chr>, current_breast_feed <chr>,
#> #   edu_husband <chr>, bmi <dbl>, overweight <dbl>

5 Importing Stata Files

  • Using haven Package:
data5 <- haven::read_dta("data/hw_dat.dta")
head(data5)
#> # A tibble: 6 × 13
#>   year_birth   age division residence religion edu      wealth_index total_birth
#>        <dbl> <dbl> <chr>    <chr>     <chr>    <chr>    <chr>              <dbl>
#> 1       1988    26 Barisal  Rural     Islam    Primary  Poorest                2
#> 2       1973    41 Barisal  Rural     Islam    Primary  Middle                 4
#> 3       1976    38 Barisal  Rural     Islam    Primary  Poorest                2
#> 4       1996    18 Barisal  Rural     Islam    Seconda… Poorest                0
#> 5       1986    28 Barisal  Rural     Islam    Primary  Poorest                2
#> 6       1980    34 Barisal  Rural     Islam    Primary  Poorer                 3
#> # ℹ 5 more variables: current_pregnant <chr>, current_breast_feed <chr>,
#> #   edu_husband <chr>, bmi <dbl>, overweight <dbl>

6 Importing SAS Files

  • Using haven Package:
data6 <- haven::read_sas("data/airline.sas7bdat")
head(data6)
#> # A tibble: 6 × 6
#>    YEAR     Y     W     R     L     K
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1  1948  1.21 0.243 0.145  1.41 0.612
#> 2  1949  1.35 0.260 0.218  1.38 0.559
#> 3  1950  1.57 0.278 0.316  1.39 0.573
#> 4  1951  1.95 0.297 0.394  1.55 0.564
#> 5  1952  2.27 0.310 0.356  1.80 0.574
#> 6  1953  2.73 0.322 0.359  1.93 0.711

7 Importing R’s native data format

  • Simply load() the .Rdata File
load("data/TemoraBR.RData")
  • This loads all objects stored in the file into the R environment.
head(TemoraBR)
#> # A tibble: 6 × 3
#>    temp beat_rate acclimitisation_temp
#>   <dbl>     <dbl>                <dbl>
#> 1     5      3.76                    5
#> 2     6      5.4                     5
#> 3     7      8                       5
#> 4    10      9.4                     5
#> 5    11     16.6                     5
#> 6    12     18.5                     5

Data entry

  • Sometimes you’ll need to assemble a tibble “by hand” doing a little data entry in your R script.

  • There are two useful functions to help you do this which differ in whether you layout the tibble by columns or by rows. tibble() works by column.

tibble(
  x = c(1, 2, 5), 
  y = c("h", "m", "g"),
  z = c(0.08, 0.83, 0.60)
)
#> # A tibble: 3 × 3
#>       x y         z
#>   <dbl> <chr> <dbl>
#> 1     1 h      0.08
#> 2     2 m      0.83
#> 3     5 g      0.6

Using R’s Built-in Datasets

  • R comes with several built-in datasets, and we can access them using the data() function.

  • List all available datasets:

  • Load a dataset into the environment:
data(mtcars)
head(mtcars)
#>                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
#> Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
#> Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
#> Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
#> Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
#> Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
#> Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

Using dataset from R Packages

  • Steps to Import Dataset from external R Packages:

    1. Install and Load the Package
    2. Load the Dataset

  • For example: Import the gapminder dataset from the gapminder package

install.packages("gapminder")
library(gapminder)
data(gapminder)
head(gapminder)
#> # A tibble: 6 × 6
#>   country     continent  year lifeExp      pop gdpPercap
#>   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
#> 1 Afghanistan Asia       1952    28.8  8425333      779.
#> 2 Afghanistan Asia       1957    30.3  9240934      821.
#> 3 Afghanistan Asia       1962    32.0 10267083      853.
#> 4 Afghanistan Asia       1967    34.0 11537966      836.
#> 5 Afghanistan Asia       1972    36.1 13079460      740.
#> 6 Afghanistan Asia       1977    38.4 14880372      786.

Summary:

Exporting data

Exporting to R’s data formats

  • Simply save() one or more R objects to an .Rdata file:
save(mtcars, TemoraBR, file = "two_data_sets.RData")

Exporting to CSV files

  • To export tibbles and data frames, we can use the write.csv() or readr::write_excel_csv() function

  • This creates CSV file that can opened by spreadsheet software such as Excel

write.csv(mtcars, "data/mtcars3.csv")
readr::write_excel_csv(mtcars, "data/mtcars4.csv")

Exporting to Text Files

Use write.table() when you need to export a data frame or tibble as a plain text file.

  • sep controls the column separator
  • row.names = FALSE removes row names
  • quote = FALSE removes quotes around character data
# Export as space-separated text
write.table(mtcars, "mtcars.txt")

# Export as tab-separated text
write.table(mtcars, "mtcars_tab.txt", sep = "\t")

# Export as CSV using write.table
write.table(mtcars, "mtcars_custom.csv",
            sep = ",", row.names = FALSE)