10 Importing data

(AST230) R for Data Science

Md Rasel Biswas

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.

library(tidyverse)

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)

• Download the data folder for this session.

1 Importing CSV Files

• Using read.csv():

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

• Using readr::read_csv():

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:

install.packages("readxl")

• 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:

install.packages("haven")

• 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:

data()

• 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:

• CSV, Excel, Text: readr::read_csv(), readxl::read_excel(), read.table()
• SPSS, Stata, SAS: haven::read_sav(), haven::read_dta(), haven::read_sas()
• RData: load()
• R’s built-in data: data()
• Data from any R Package: Install the package, use data()

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()