6.1 Read and write data

6.1.1 readr

You must be familiar with read.csv(), read.table() and write.csv() in base R. Here we will introduce a more efficient package from RStudio in 2015 for reading and writing data: readr package. The corresponding functions are read_csv(), read_table() and write_csv(). The commands look quite similar, but readr is different in the following respects:

  1. It is 10x faster. The trick is that readr uses C++ to process the data quickly.

  2. It doesn’t change the column names. The names can start with a number and “.” will not be substituted to “_”. For example:

## # A tibble: 2 x 3
##   `2015` `2016` `2017`
##    <dbl>  <dbl>  <dbl>
## 1      1      2      3
## 2      4      5      6
  1. readr functions do not convert strings to factors by default, are able to parse dates and times and can automatically determine the data types in each column.

  2. The killing character, in my opinion, is that readr provides progress bar. What makes you feel worse than waiting is not knowing how long you have to wait.

The major functions of readr is to turn flat files into data frames:

  • read_csv(): reads comma delimited files
  • read_csv2(): reads semicolon separated files (common in countries where , is used as the decimal place)
  • read_tsv(): reads tab delimited files
  • read_delim(): reads in files with any delimiter
  • read_fwf(): reads fixed width files. You can specify fields either by their widths with fwf_widths() or their position with fwf_positions()
  • read_table(): reads a common variation of fixed width files where columns are separated by white space
  • read_log(): reads Apache style log files

The good thing is that those functions have similar syntax. Once you learn one, the others become easy. Here we will focus on read_csv().

The most important information for read_csv() is the path to your data:

# A tibble: 6 x 19
    age gender income house store_exp online_exp store_trans online_trans    Q1
  <int> <chr>   <dbl> <chr>     <dbl>      <dbl>       <int>        <int> <int>
1    57 Female 1.21e5 Yes        529.       304.           2            2     4
2    63 Female 1.22e5 Yes        478.       110.           4            2     4
3    59 Male   1.14e5 Yes        491.       279.           7            2     5
4    60 Male   1.14e5 Yes        348.       142.          10            2     5
5    51 Male   1.24e5 Yes        380.       112.           4            4     4
6    59 Male   1.08e5 Yes        338.       196.           4            5     4
# ... with 10 more variables: Q2 <int>, Q3 <int>, Q4 <int>, Q5 <int>, Q6 <int>,
#   Q7 <int>, Q8 <int>, Q9 <int>, Q10 <int>, segment <chr>

The function reads the file to R as a tibble. You can consider tibble as next iteration of the data frame. They are different with data frame for the following aspects:

  • It never changes an input’s type (i.e., no more stringsAsFactors = FALSE!)
  • It never adjusts the names of variables
  • It has a refined print method that shows only the first 10 rows and all the columns that fit on the screen. You can also control the default print behavior by setting options.

Refer to http://r4ds.had.co.nz/tibbles.html for more information about ‘tibble’.

When you run read_csv() it prints out a column specification that gives the name and type of each column. To better understanding how readr works, it is helpful to type in some baby data set and check the results:

## # A tibble: 2 x 3
##   `2015` `2016`  `2017`
##   <chr>  <chr>   <chr> 
## 1 100    200     300   
## 2 canola soybean corn

You can also add comments on the top and tell R to skip those lines:

## # A tibble: 7 x 3
##   Date      Food   Mood           
##   <chr>     <chr>  <chr>          
## 1 Monday    carrot happy          
## 2 Tuesday   carrot happy          
## 3 Wednesday carrot happy          
## 4 Thursday  carrot happy          
## 5 Friday    carrot happy          
## 6 Saturday  carrot extremely happy
## 7 Sunday    carrot extremely happy

If you don’t have column names, set col_names = FALSE then R will assign names “X1”,“X2”… to the columns:

## # A tibble: 2 x 3
##   X1       X2     X3             
##   <chr>    <chr>  <chr>          
## 1 Saturday carrot extremely happy
## 2 Sunday   carrot extremely happy

You can also pass col_names a character vector which will be used as the column names. Try to replace col_names=FALSE with col_names=c("Date","Food","Mood") and see what happen.

As mentioned before, you can use read_csv2() to read semicolon separated files:

## Using ',' as decimal and '.' as grouping mark. Use read_delim() for more control.
## # A tibble: 2 x 3
##   X1       X2     X3             
##   <chr>    <chr>  <chr>          
## 1 Saturday carrot extremely happy
## 2 Sunday   carrot extremely happy

Here “\n” is a convenient shortcut for adding a new line.

You can use read_tsv() to read tab delimited files:

## # A tibble: 1 x 10
##   X1    X2    X3    X4    X5    X6    X7    X8    X9   
##   <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 every man   is    a     poet  when  he    is    in   
## # … with 1 more variable: X10 <chr>

Or more generally, you can use read_delim() and assign separating character:

## # A tibble: 1 x 5
##   X1    X2         X3         X4    X5   
##   <chr> <chr>      <chr>      <chr> <chr>
## 1 THE   UNBEARABLE RANDOMNESS OF    LIFE

Another situation you will often run into is the missing value. In marketing survey, people like to use “99” to represent missing. You can tell R to set all observation with value “99” as missing when you read the data:

## # A tibble: 1 x 3
##      Q1    Q2 Q3   
##   <dbl> <dbl> <lgl>
## 1     5     4 NA

For writing data back to disk, you can use write_csv() and write_tsv(). The following two characters of the two functions increase the chances of the output file being read back in correctly:

  • Encode strings in UTF-8
  • Save dates and date-times in ISO8601 format so they are easily parsed elsewhere

For example:

For other data types, you can use the following packages:

  • Haven: SPSS, Stata and SAS data
  • Readxl and xlsx: excel data(.xls and .xlsx)
  • DBI: given data base, such as RMySQL, RSQLite and RPostgreSQL, read data directly from the database using SQL

Some other useful materials:

6.1.2 data.table— enhanced data.frame

What is data.table? It is an R package that provides an enhanced version of data.frame. The most used object in R is data frame. Before we move on, let’s briefly review some basic characters and manipulations of data.frame:

  • It is a set of rows and columns.
  • Each row is of the same length and data type
  • Every column is of the same length but can be of differing data types
  • It has characteristics of both a matrix and a list
  • It uses [] to subset data

We will use the clothes customer data to illustrate. There are two dimensions in []. The first one indicates the row and second one indicates column. It uses a comma to separate them.

## Parsed with column specification:
## cols(
##   age = col_double(),
##   gender = col_character(),
##   income = col_double(),
##   house = col_character(),
##   store_exp = col_double(),
##   online_exp = col_double(),
##   store_trans = col_double(),
##   online_trans = col_double(),
##   Q1 = col_double(),
##   Q2 = col_double(),
##   Q3 = col_double(),
##   Q4 = col_double(),
##   Q5 = col_double(),
##   Q6 = col_double(),
##   Q7 = col_double(),
##   Q8 = col_double(),
##   Q9 = col_double(),
##   Q10 = col_double(),
##   segment = col_character()
## )

Remember that there are usually different ways to conduct the same manipulation. For example, the following code presents three ways to calculate an average number of online transactions for male and female:

There is no gold standard to choose a specific function to manipulate data. The goal is to solve the real problem, not the tool itself. So just use whatever tool that is convenient for you.

The way to use [] is straightforward. But the manipulations are limited. If you need more complicated data reshaping or aggregation, there are other packages to use such as dplyr, reshape2, tidyr etc. But the usage of those packages are not as straightforward as []. You often need to change functions. Keeping related operations together, such as subset, group, update, join etc, will allow for:

  • concise, consistent and readable syntax irrespective of the set of operations you would like to perform to achieve your end goal
  • performing data manipulation fluidly without the cognitive burden of having to change among different functions
  • by knowing precisely the data required for each operation, you can automatically optimize operations effectively

data.table is the package for that. If you are not familiar with other data manipulating packages and are interested in reducing programming time tremendously, then this package is for you.

Other than extending the function of [], data.table has the following advantages:

  • Offers fast import, subset, grouping, update, and joins for large data files
  • It is easy to turn data frame to data table
  • Can behave just like a data frame

You need to install and load the package:

Use data.table() to convert the existing data frame sim.dat to data table:

## [1] "data.table" "data.frame"

Calculate mean for counts of online transactions:

## [1] 13.55

You can’t do the same thing using data frame:

If you want to calculate mean by group as before, set “by =” argument:

##    gender    V1
## 1: Female 15.38
## 2:   Male 11.26

You can group by more than one variables. For example, group by “gender” and “house”:

##    gender house     V1
## 1: Female   Yes 11.312
## 2:   Male   Yes  8.772
## 3: Female    No 19.146
## 4:   Male    No 16.486

Assign column names for aggregated variables:

##    gender house    avg
## 1: Female   Yes 11.312
## 2:   Male   Yes  8.772
## 3: Female    No 19.146
## 4:   Male    No 16.486

data.table can accomplish all operations that aggregate() and tapply()can do for data frame.

  • General setting of data.table

Different from data frame, there are three arguments for data table:

It is analogous to SQL. You don’t have to know SQL to learn data table. But experience with SQL will help you understand data table. In SQL, you select column j (use command SELECT) for row i (using command WHERE). GROUP BY in SQL will assign the variable to group the observations.

Let’s review our previous code:

The code above is equal to the following SQL:

R code:

is equal to SQL:

R code:

is equal to SQL:

You can see the analogy between data.table and SQL. Now let’s focus on operations in data table.

  • select row
##    age gender income house store_exp online_exp
## 1:  19 Female  83535    No     227.7       1491
## 2:  18 Female  89416   Yes     209.5       1926
## 3:  19 Female  92813    No     186.7       1042
##    store_trans online_trans Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9
## 1:           1           22  2  1  1  2  4  1  4  2  4
## 2:           3           28  2  1  1  1  4  1  4  2  4
## 3:           2           18  3  1  1  2  4  1  4  3  4
##    Q10 segment
## 1:   1   Style
## 2:   1   Style
## 3:   1   Style
##    age gender income house store_exp online_exp
## 1:  57 Female 120963   Yes     529.1      303.5
## 2:  63 Female 122008   Yes     478.0      109.5
##    store_trans online_trans Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9
## 1:           2            2  4  2  1  2  1  4  1  4  2
## 2:           4            2  4  1  1  2  1  4  1  4  1
##    Q10 segment
## 1:   4   Price
## 2:   4   Price
  • select column

Selecting columns in data.table don’t need $:

## [1] 57 63 59 60 51 59

To return data.table object, put column names in list():

Or you can also put column names in .():

To select all columns from “age” to “income”:

##    age gender income
## 1:  57 Female 120963
## 2:  63 Female 122008

Delete columns using - or !:

  • tabulation

In data table. .N means to count。

## [1] 1000

If you assign the group variable, then it will count by groups:

##    gender   N
## 1: Female 554
## 2:   Male 446
##    gender count
## 1: Female   292
## 2:   Male    86

Order table:

   age gender   income house store_exp online_exp store_trans ...
1:  40 Female 217599.7    No  7023.684   9479.442          10
2:  41 Female       NA   Yes  3786.740   8638.239          14
3:  36   Male 228550.1   Yes  3279.621   8220.555           8
4:  31 Female 159508.1   Yes  5177.081   8005.932          11
5:  43 Female 190407.4   Yes  4694.922   7875.562           6
...

Since data table keep some characters of data frame, they share some operations:

You can also order the table by more than one variable. The following code will order the table by gender, then order within gender by online_exp:

  • Use fread() to import dat

Other than read.csv in base R, we have introduced ‘read_csv’ in ‘readr’. read_csv is much faster and will provide progress bar which makes user feel much better (at least make me feel better). fread() in data.table further increase the efficiency of reading data. The following are three examples of reading the same data file topic.csv. The file includes text data scraped from an agriculture forum with 209670 rows and 6 columns:

It is clear that read_csv() is much faster than read.csv(). fread() is a little faster than read_csv(). As the size increasing, the difference will become for significant. Note that fread() will read file as data.table by default.