Starting with Data

Overview

Teaching: 30 min
Exercises: 10 min

Questions

• What is a data.frame?

• How can I read a complete csv file into R?

• How can I get basic summary information about my dataset?

Objectives

• Describe what a data frame is.

• Load external data from a .csv file into a data frame.

• Summarize the contents of a data frame.

• Subset and extract values from data frames.

What are data frames and tibbles?

Data frames are the de facto data structure for tabular data in R, and what we use for data processing, statistics, and plotting.

A data frame is the representation of data in the format of a table where the columns are vectors that all have the same length. Data frames are analogous to the more familiar spreadsheet in programs such as Excel, with one key difference. Because columns are vectors, each column must contain a single type of data (e.g., characters, integers, factors). For example, here is a figure depicting a data frame comprising a numeric, a character, and a logical vector.

Data frames can be created by hand, but most commonly they are generated by the functions read_csv() or read_table(); in other words, when importing spreadsheets from your hard drive (or the web). We will now demonstrate how to import tabular data using read_csv().

Presentation of the SAFI Data

SAFI (Studying African Farmer-Led Irrigation) is a study looking at farming and irrigation methods in Tanzania and Mozambique. The survey data was collected through interviews conducted between November 2016 and June 2017. For this lesson, we will be using a subset of the available data. For information about the full dataset, see the dataset description.

We will be using a subset of the cleaned version of the dataset that was produced through cleaning in OpenRefine (data/SAFI_clean.csv). Each row holds information for a single interview respondent, and the columns represent:

Importing data

You are going load the data in R’s memory using the function read_csv() from the readr package, which is part of the tidyverse; learn more about the tidyverse collection of packages here. readr gets installed as part as the tidyverse installation. When you load the tidyverse (library(tidyverse)), the core packages (the packages used in most data analyses) get loaded, including readr.

Before we can use the read_csv() we need to load the tidyverse package.

Also, if you recall, the missing data is encoded as “NULL” in the dataset. We’ll tell it to the function, so R will automatically convert all the “NULL” entries in the dataset into NA.

The specific path we are using here is dependent on the specific setup. If you have followed the recommendations for structuring your project-folder, it should be this command:

library(tidyverse)

interviews <- read_csv("data/SAFI_clean.csv", na = "NULL")

If you were to type in the code above, it is likely that the read.csv() function would appear in the automatically populated list of functions. This function is different from the read_csv() function, as it is included in the “base” packages that come pre-installed with R. Overall, read.csv() behaves similar to read_csv(), with a few notable differences. First, read.csv() coerces column names with spaces and/or special characters to different names (e.g. interview date becomes interview.date). Second, read.csv() stores data as a data.frame, where read_csv() stores data as a tibble. We prefer tibbles because they have nice printing properties among other desirable qualities. Read more about tibbles here.

The second statement in the code above creates a data frame but doesn’t output any data because, as you might recall, assignments (<-) don’t display anything. (Note, however, that read_csv may show informational text about the data frame that is created.) If we want to check that our data has been loaded, we can see the contents of the data frame by typing its name: interviews in the console.

interviews
## Try also
## view(interviews)
## head(interviews)

# A tibble: 131 × 6
   key_ID village  interview_date      no_membrs respondent_wall_type no_meals
    <dbl> <chr>    <dttm>                  <dbl> <chr>                   <dbl>
 1      1 God      2016-11-17 00:00:00         3 muddaub                     2
 2      1 God      2016-11-17 00:00:00         7 muddaub                     2
 3      3 God      2016-11-17 00:00:00        10 burntbricks                 2
 4      4 God      2016-11-17 00:00:00         7 burntbricks                 2
 5      5 God      2016-11-17 00:00:00         7 burntbricks                 2
 6      6 God      2016-11-17 00:00:00         3 muddaub                     2
 7      7 God      2016-11-17 00:00:00         6 muddaub                     3
 8      8 Chirodzo 2016-11-16 00:00:00        12 burntbricks                 2
 9      9 Chirodzo 2016-11-16 00:00:00         8 burntbricks                 3
10     10 Chirodzo 2016-12-16 00:00:00        12 burntbricks                 3
# ℹ 121 more rows

Note

read_csv() assumes that fields are delimited by commas. However, in several countries, the comma is used as a decimal separator and the semicolon (;) is used as a field delimiter. If you want to read in this type of files in R, you can use the read_csv2 function. It behaves exactly like read_csv but uses different parameters for the decimal and the field separators. If you are working with another format, they can be both specified by the user. Check out the help for read_csv() by typing ?read_csv to learn more. There is also the read_tsv() for tab-separated data files, and read_delim() allows you to specify more details about the structure of your file.

Note that read_csv() actually loads the data as a tibble. A tibble is an extension of R data frames used by the tidyverse. When the data is read using read_csv(), it is stored in an object of class tbl_df, tbl, and data.frame. You can see the class of an object with

class(interviews)

[1] "spec_tbl_df" "tbl_df"      "tbl"         "data.frame" 

As a tibble, the type of data included in each column is listed in an abbreviated fashion below the column names. For instance, here key_ID is a column of floating point numbers (abbreviated <dbl> for the word ‘double’), village is a column of characters (<chr>) and the interview_date is a column in the “date and time” format (<dttm>).

Inspecting data frames

When calling a tbl_df object (like interviews here), there is already a lot of information about our data frame being displayed such as the number of rows, the number of columns, the names of the columns, and as we just saw the class of data stored in each column. However, there are functions to extract this information from data frames. Here is a non-exhaustive list of some of these functions. Let’s try them out!

Size:

• dim(interviews) - returns a vector with the number of rows as the first element, and the number of columns as the second element (the dimensions of the object)
• nrow(interviews) - returns the number of rows
• ncol(interviews) - returns the number of columns

Content:

• head(interviews) - shows the first 6 rows
• tail(interviews) - shows the last 6 rows

Names:

• names(interviews) - returns the column names (synonym of colnames() for data.frame objects)

Summary:

• str(interviews) - structure of the object and information about the class, length and content of each column
• summary(interviews) - summary statistics for each column
• glimpse(interviews) - returns the number of columns and rows of the tibble, the names and class of each column, and previews as many values will fit on the screen. Unlike the other inspecting functions listed above, glimpse() is not a “base R” function so you need to have the dplyr or tibble packages loaded to be able to execute it.

Note: most of these functions are “generic.” They can be used on other types of objects besides data frames or tibbles.

Indexing and subsetting data frames

Our interviews data frame has rows and columns (it has 2 dimensions). In practice, we may not need the entire data frame; for instance, we may only be interested in a subset of the observations (the rows) or a particular set of variables (the columns). If we want to extract some specific data from it, we need to specify the “coordinates” we want from it. Row numbers come first, followed by column numbers.

Tip

Indexing a tibble with [ or [[ or $ always results in a tibble. However, note this is not true in general for data frames, so be careful! Different ways of specifying these coordinates can lead to results with different classes. This is covered in the Software Carpentry lesson R for Reproducible Scientific Analysis.

## first element in the first column of the tibble
interviews[1, 1]

# A tibble: 1 × 1
  key_ID
   <dbl>
1      1

## first element in the 6th column of the tibble 
interviews[1, 6]

# A tibble: 1 × 1
  no_meals
     <dbl>
1        2

## first column of the tibble (as a vector)
interviews[[1]]

  [1]   1   1   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18
 [19]  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36
 [37]  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  21  54
 [55]  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71 127
 [73] 133 152 153 155 178 177 180 181 182 186 187 195 196 197 198 201 202  72
 [91]  73  76  83  85  89 101 103 102  78  80 104 105 106 109 110 113 118 125
[109] 119 115 108 116 117 144 143 150 159 160 165 166 167 174 175 189 191 192
[127] 126 193 194 199 200

## first column of the tibble
interviews[1]

# A tibble: 131 × 1
   key_ID
    <dbl>
 1      1
 2      1
 3      3
 4      4
 5      5
 6      6
 7      7
 8      8
 9      9
10     10
# ℹ 121 more rows

## first three elements in the 7th column of the tibble
interviews[1:3, 7]

Error in `interviews[1:3, 7]`:
! Can't subset columns past the end.
ℹ Location 7 doesn't exist.
ℹ There are only 6 columns.

## the 3rd row of the tibble
interviews[3, ]

# A tibble: 1 × 6
  key_ID village interview_date      no_membrs respondent_wall_type no_meals
   <dbl> <chr>   <dttm>                  <dbl> <chr>                   <dbl>
1      3 God     2016-11-17 00:00:00        10 burntbricks                 2

## equivalent to head_interviews <- head(interviews)
head_interviews <- interviews[1:6, ]

: is a special function that creates numeric vectors of integers in increasing or decreasing order, test 1:10 and 10:1 for instance.

You can also exclude certain indices of a data frame using the “-” sign:

interviews[, -1]          # The whole tibble, except the first column

# A tibble: 131 × 5
   village  interview_date      no_membrs respondent_wall_type no_meals
   <chr>    <dttm>                  <dbl> <chr>                   <dbl>
 1 God      2016-11-17 00:00:00         3 muddaub                     2
 2 God      2016-11-17 00:00:00         7 muddaub                     2
 3 God      2016-11-17 00:00:00        10 burntbricks                 2
 4 God      2016-11-17 00:00:00         7 burntbricks                 2
 5 God      2016-11-17 00:00:00         7 burntbricks                 2
 6 God      2016-11-17 00:00:00         3 muddaub                     2
 7 God      2016-11-17 00:00:00         6 muddaub                     3
 8 Chirodzo 2016-11-16 00:00:00        12 burntbricks                 2
 9 Chirodzo 2016-11-16 00:00:00         8 burntbricks                 3
10 Chirodzo 2016-12-16 00:00:00        12 burntbricks                 3
# ℹ 121 more rows

interviews[-c(7:131), ]   # Equivalent to head(interviews)

# A tibble: 6 × 6
  key_ID village interview_date      no_membrs respondent_wall_type no_meals
   <dbl> <chr>   <dttm>                  <dbl> <chr>                   <dbl>
1      1 God     2016-11-17 00:00:00         3 muddaub                     2
2      1 God     2016-11-17 00:00:00         7 muddaub                     2
3      3 God     2016-11-17 00:00:00        10 burntbricks                 2
4      4 God     2016-11-17 00:00:00         7 burntbricks                 2
5      5 God     2016-11-17 00:00:00         7 burntbricks                 2
6      6 God     2016-11-17 00:00:00         3 muddaub                     2

tibbles can be subset by calling indices (as shown previously), but also by calling their column names directly:

interviews["village"]       # Result is a tibble

interviews[, "village"]     # Result is a tibble

interviews[["village"]]     # Result is a vector

interviews$village          # Result is a vector

In RStudio, you can use the autocompletion feature to get the full and correct names of the columns.

Exercise

1. Create a tibble (interviews_100) containing only the data in row 100 of the interviews dataset.

2. Notice how nrow() gave you the number of rows in the tibble?

   • Use that number to pull out just that last row in the tibble.
   • Compare that with what you see as the last row using tail() to make sure it’s meeting expectations.
   • Pull out that last row using nrow() instead of the row number.
   • Create a new tibble (interviews_last) from that last row.

3. Using the number of rows in the interviews dataset that you found in question 2, extract the row that is in the middle of the dataset. Store the content of this middle row in an object named interviews_middle. (hint: This dataset has an odd number of rows, so finding the middle is a bit trickier than dividing n_rows by 2. Use the median( ) function and what you’ve learned about sequences in R to extract the middle row!

4. Combine nrow() with the - notation above to reproduce the behavior of head(interviews), keeping just the first through 6th rows of the interviews dataset.

Solution

## 1.
interviews_100 <- interviews[100, ]
## 2.
# Saving `n_rows` to improve readability and reduce duplication
n_rows <- nrow(interviews)
interviews_last <- interviews[n_rows, ]
## 3.
interviews_middle <- interviews[median(1:n_rows), ]
## 4.
interviews_head <- interviews[-(7:n_rows), ]

Key Points

• Use read_csv to read tabular data in R.