Chapter 4 Import your data

4.1 from CSV/TXT

You can import data in csv (comma seperate) or text files that use a different delimiter to seperate data. There two groups of functions you can use:

  • Base R functions

  • readr package functions

read.table( ) is a multipurpose function in R for importing data, and this function has two special cases: read.csv( ) and read.delim( ).

  • read.csv( ) is a wrapper for read.table with some default settings: sep = “,” ; header = TRUE.

  • read.delim( ) is a wrapper for read.table with some default settings: sep = “” ; header = TRUE.

readr package functions you need to library(readr)

readr functions are 10 times faster than built-in functions.

  • read_csv( ): you can specify

col_types = list(col_double(), col_character())

col_names = c(“a”,“b”)

  • read_delim( )

  • read_table( )

However, I found R-studio Import Dataset function under Environment tab (Upper right panel) most useful when importing data. It offers an user interface and you will know how your data look like when you choose different settings.

You may want to copy the code automatically generated by R-Studio and paste it in your script.

A little more about the path to directory: it is helpful to get to know your working directory, you can get this information by getwd( ). When you know where you are, you can specify your folder either relative to your present location or with the absolute path.

participant_1 <- read.csv("data/participant_1.csv")
#stringAsFactor = FALSE

participant_2 <- read.csv("data/participant_2.csv")

## from TXT
participant_1_tab <- read.delim("~/OneDrive_Backup/OneDrive - Western Sydney University/JuqiangCHEN/310_Tutorial/DH_Data_wrangling/data/participant_1_tab.txt")

4.2 from EXCEL

library(readxl)
excel_test <- read_excel("data/excel_test.xlsx", sheet = "participant_1")

4.3 Batch import

rbind(participant_1, participant_2)

all = rbind(participant_1, participant_2)

# what about 10 more people?

participant_3 <- read.csv("data/participant_3.csv")

# get all the filenames of the folder

getwd() # get working directory

paste0(getwd(), "/data") #specify the directory for the data

list.files(paste0(getwd(), "/data"),full.names=T) # full.names=T means getting the full path

filename = list.files(paste0(getwd(), "/data"),full.names=T, pattern = ".csv") # get only csv files

# now you need a loop
df = data.frame()
bin = data.frame()
for (i in 1:length(filename)){
  # length(filename) = how many files
  bin = read.csv(filename[i])
  df = rbind(df, bin)
}

4.4 Take a glimpse of the dataframe

R has some basic functions that can help us have a quick look at the data frame that we import.

  • summary( )

1 For each factor variable, the levels are printed.

2 For each numeric variable, the minimun, first quantile, median, mean, third quantile and the maximun values are shown.

  • str( ) structure

This gives the information about the types of variables that your dataframe contain.

Alternatively, you can hover your mouse over the head of the table, it will give you basic information about the variable type of the column.

There are also other packages that offer data summer like the describe ( ), similar to summary ( ), and contents ( ), similar to str ( ) in the Hmisc package.

summary(all)

str(all)

#library(Hmisc)

describe(all)

contents(all)