Reading and Writing Data

readr and haven

2025-08-09

readr

Function Reads
read_csv() Comma separated values
read_csv2() Semi-colon separate values
read_delim() General delimited files
read_fwf() Fixed width files
read_log() Apache log files
read_table() Space separated files
read_tsv() Tab delimited values

Importing Data

dataset <- read_csv("file_name.csv")
dataset

R functions

R functions

R functions

Your Turn 1

Find diabetes.csv on your computer. Then read it into an object. Then view the results.

Your Turn 1

Find diabetes.csv on your computer. Then read it into an object. Then view the results.

diabetes <- read_csv("diabetes.csv")

diabetes
# A tibble: 403 × 19
      id  chol stab.glu   hdl ratio glyhb location     age
   <dbl> <dbl>    <dbl> <dbl> <dbl> <dbl> <chr>      <dbl>
 1  1000   203       82    56  3.60  4.31 Buckingham    46
 2  1001   165       97    24  6.90  4.44 Buckingham    29
 3  1002   228       92    37  6.20  4.64 Buckingham    58
 4  1003    78       93    12  6.5   4.63 Buckingham    67
 5  1005   249       90    28  8.90  7.72 Buckingham    64
 6  1008   248       94    69  3.60  4.81 Buckingham    34
 7  1011   195       92    41  4.80  4.84 Buckingham    30
 8  1015   227       75    44  5.20  3.94 Buckingham    37
 9  1016   177       87    49  3.60  4.84 Buckingham    45
10  1022   263       89    40  6.60  5.78 Buckingham    55
# ℹ 393 more rows
# ℹ 11 more variables: gender <chr>, height <dbl>,
#   weight <dbl>, frame <chr>, bp.1s <dbl>, bp.1d <dbl>, …

Tibbles

data.frames are the basic form of rectangular data in R (columns of variables, rows of observations)

read_csv() reads the data into a tibble, a modern version of the data frame.

a tibble is a data frame

Missing values

It’s common to use codes for missing values (-99, 9999)

The na option can change these values to NA

read_csv(
  "a,b,c,d
  1,-99,3,4
  5,6,-99,8", 
  na = "-99" 
)
# A tibble: 2 × 4
      a     b     c     d
  <dbl> <dbl> <dbl> <dbl>
1     1    NA     3     4
2     5     6    NA     8

Parsing data types

The read functions in readr try to guess each data type, but sometimes it’s wrong

To tell readr how to parse the columns, add the argument col_types to read_csv()

diabetes <- read_csv(
  "diabetes.csv",
  col_types = list(id = col_character()) 
)

Parsing data types

Or use a string for each variable type: col_type = "cci"

Parsing data types

Or use a string for each variable type: col_type = “cci”

letter type
c character
i integer
n number
d double
l logical
D date
T date time
t time
? guess the type
_ or - skip the column

Your Turn 2

Set the 4 column types to be: integer, double, character, and unknown (guess)

read_csv(
  "a,b,c,d
  1,2,3,4
  5,6,7,8", 
  col_types = ""
)

Your Turn 2

Set the 4 column types to be: integer, double, character, and unknown (guess)

read_csv(
  "a,b,c,d
  1,2,3,4
  5,6,7,8", 
  col_types = "idc?"
)
# A tibble: 2 × 4
      a     b c         d
  <int> <dbl> <chr> <dbl>
1     1     2 3         4
2     5     6 7         8

haven

Function Software
read_sas() SAS
read_xpt() SAS
read_spss() SPSS
read_sav() SPSS
read_por() SPSS
read_stata() Stata
read_dta() Stata

Heads up!

haven is not a core member of the tidyverse. That means you need to load it with library(haven).

Your Turn 3

There are several versions of the diabetes file besides CSV. Pick a file format you or your colleagues use and import them using the corresponding function from haven.

Your Turn 3

library(haven)
diabetes <- read_sas("diabetes.sas7bdat")

Your Turn 3

diabetes
# A tibble: 403 × 19
      id  chol stab_glu   hdl ratio glyhb location     age
   <dbl> <dbl>    <dbl> <dbl> <dbl> <dbl> <chr>      <dbl>
 1  1000   203       82    56  3.60  4.31 Buckingham    46
 2  1001   165       97    24  6.90  4.44 Buckingham    29
 3  1002   228       92    37  6.20  4.64 Buckingham    58
 4  1003    78       93    12  6.5   4.63 Buckingham    67
 5  1005   249       90    28  8.90  7.72 Buckingham    64
 6  1008   248       94    69  3.60  4.81 Buckingham    34
 7  1011   195       92    41  4.80  4.84 Buckingham    30
 8  1015   227       75    44  5.20  3.94 Buckingham    37
 9  1016   177       87    49  3.60  4.84 Buckingham    45
10  1022   263       89    40  6.60  5.78 Buckingham    55
# ℹ 393 more rows
# ℹ 11 more variables: gender <chr>, height <dbl>,
#   weight <dbl>, frame <chr>, bp_1s <dbl>, bp_1d <dbl>, …

Writing data

Function Writes
write_csv() Comma separated values
write_excel_csv() CSV that you plan to open in Excel
write_delim() General delimited files
write_file() A single string, written as is
write_lines() A vector of strings, one string per line
write_tsv() Tab delimited values
write_rds() A data type used by R to save objects
write_xpt() SAS transport format, .xpt
write_sas() SAS .sas7bdat files (experimental)
write_sav() SPSS .sav files
write_stata() Stata .dta files

Writing data

write_csv(diabetes, file = "diabetes-clean.csv")

Your Turn 4

R has a few data file types, such as RDS and .Rdata. Save diabetes as "diabetes.Rds".

Your Turn 4

R has a few data file types, such as RDS and .Rdata. Save diabetes as "diabetes.Rds".

write_rds(diabetes, "diabetes.Rds")