col_types
You can also specify which types the columns in your imported data frame should have. You can do this with col_types
. If set to NULL
, the default, functions from the readr
package will try to find the correct types themselves. You can manually set the types with a string, where each character denotes the class of the column: c
haracter, d
ouble, i
nteger and l
ogical. _
skips the column as a whole.
potatoes.txt
(view), a flat file with tab-delimited records and without column names, is again available in your workspace.
This is a part of the course
“Introduction to Importing Data in R”
Exercise instructions
- In the second
read_tsv()
call, edit thecol_types
argument to import all columns as characters (c
). Store the resulting data frame inpotatoes_char
. - Print out the structure of
potatoes_char
and verify whether all column types arechr
, short forcharacter
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Column names
properties <- c("area", "temp", "size", "storage", "method",
"texture", "flavor", "moistness")
# Import all data, but force all columns to be character: potatoes_char
potatoes_char <- read_tsv("potatoes.txt", col_types = "iiiiiddd", col_names = properties)
# Print out structure of potatoes_char
___
This exercise is part of the course
Introduction to Importing Data in R
In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.
In addition to base R, there are dedicated packages to easily and efficiently import flat file data. We'll talk about two such packages: readr and data.table.
Exercise 1: readr: read_csv & read_tsvExercise 2: read_csvExercise 3: read_tsvExercise 4: readr: read_delimExercise 5: read_delimExercise 6: skip and n_maxExercise 7: col_typesExercise 8: col_types with collectorsExercise 9: data.table: freadExercise 10: freadExercise 11: fread: more advanced useExercise 12: Dedicated classesWhat is DataCamp?
Learn the data skills you need online at your own pace—from non-coding essentials to data science and machine learning.