I - 3 R Basics continued - factors and data frames : Introduction to Data Management practices

Overview

Teaching: 40 min
Exercises: 20 min

Questions

How do I get started with tabular data (e.g. spreadsheets) in R?

What are some best practices for reading data into R?

How do I save tabular data generated in R?

Objectives

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

Describe what a data frame is.

Summarize the contents of a data frame.

Use indexing to subset specific portions of data frames.

Describe what a factor is.

Convert between strings and factors.

Reorder and rename factors.

Change how character strings are handled in a data frame.

COVID-19 example data

In this lesson we will use data on 29 positive SARS-CoV-2 samples taken from as many individuals. The data come from a scientific study Links to an external site. where researchers evaluated the performance of a new and more cost-effective method, “COVseq”, for obtaining viral genome sequences. The researchers were particularly interested in the method’s ability to detect new viral variants.

We will use two datasets that are stored in comma-separated values (CSV) files: covid_samples.csv and covseq_miseq.csv. The first dataset is mainly describing the SARS-CoV-2 samples. The second dataset contains some results from sequencing the samples using the COVseq method on an Illumina MiSeq instrument. This particular set-up was one of three sequencing strategies examined by the researchers.

SARS-CoV-2 data used in this lesson.

Loading the samples data

We will start with the samples dataset. This dataset has 29 rows that each represents one positive SARS-CoV-2 sample (and consequently one individual). The dataset has the following columns:

Column	Description
patient_id	code for the sampled individual
collection_date	date when the sample was taken
country	country
region	geographic region
age	age of the person when the sample was taken
disease_outcome	outcome of the disease (dead/recovered/NA)
sex	sex of the person (female/male)
ct	cycle threshold (Ct) value from real time PCR assays, i.e. the number of amplification cycles needed to detect the virus

Downloading the data

We are going to use the R function download.file() to download the CSV file that contains the data, and we will then use read_csv() to load the content of the CSV file into R.

Inside the download.file command, the first entry is a character string with the source URL (“https://nbisweden.github.io/module-r-intro-dm-practices/data/covid_samples.csv”). This source URL downloads a CSV file from GitHub. The text after the comma (“data_raw/covid_samples.csv”) is the destination of the file on your computer. You’ll need to have a folder called “data_raw” where you’ll download the file. So this command downloads a file from the web, names it “covid_samples.csv” and adds it to a preexisting folder named “data_raw”.

download.file(
  url = "https://nbisweden.github.io/module-r-intro-dm-practices/data/covid_samples.csv",
  destfile = "data_raw/covid_samples.csv")

Reading the data into R

The file has now been downloaded to the destination you specified, but R has not yet loaded the data from the file into memory. To do this, we can use the read_csv() function from the tidyverse package.

Packages in R are basically sets of additional functions that let you do more stuff. The functions we’ve been using so far, like round(), sqrt(), and c(), come built into R; packages give you access to additional functions. Before you use a package for the first time you need to install it on your machine, and then you load it into every subsequent R session when you need it.

If you have followed the setup instructions, you should now have the tidyverse package installed. If not, you can install the package by typing install.packages("tidyverse") straight into the console. In fact, it’s better to write this in the console than in our script for any package, as there’s no need to re-install packages every time we run the script. Then, to load the package type:

## load the tidyverse packages, incl. dplyr
library(tidyverse)

Now we can use the functions from the tidyverse package. Let’s use read_csv() to read the data into a data frame (we will learn more about data frames later):

samples <- read_csv("data_raw/covid_samples.csv")

Rows: 29 Columns: 8

── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr  (5): patient_id, country, region, disease_outcome, sex
dbl  (2): age, ct
date (1): collection_date

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

You will see the text Column specification, followed by the delimiter and the columns’ data types. When you execute read_csv on a data file, it looks through the first 1000 rows of each column and guesses its data type. For example, in this dataset, read_csv() reads patient_id as chr (short for “character”) and age as dbl (short for “double”, a numeric data type), and . You have the option to specify the data type for a column manually by using the col_types argument in read_csv.

We can see the contents of the first few lines of the data by typing its name: samples. By default, this will show show you as many rows and columns of the data as fit on your screen. If you want to see the first 20 rows, you could type print(samples, n = 20). We can also extract the first few lines of this data using the function head():

head(samples)

# A tibble: 6 × 8
  patient_id collection_date country region   age disease_outcome sex       ct
  <chr>      <date>          <chr>   <chr>  <dbl> <chr>           <chr>  <dbl>
1 OAS-29_1   2020-03-31      Italy   Turin     48 dead            female  41.5
2 OAS-29_10  2020-03-31      Italy   Turin     35 <NA>            male    15.3
3 OAS-29_11  2020-03-31      Italy   Turin     59 recovered       male    25.3
4 OAS-29_12  2020-03-31      Italy   Turin     60 recovered       female  27  
5 OAS-29_13  2020-03-31      Italy   Turin     83 dead            female  25.3
6 OAS-29_14  2020-04-01      Italy   Turin     21 dead            male    31  

Unlike the print() function, head() returns the extracted data. You could use it to assign the first 20 rows of samples to an object using samples_head20 <- head(samples, 20). This can be useful if you want to try out complex computations on a subset of your data before you apply them to the whole data set. There is a similar function that lets you extract the last few lines of the dataset. It is called (you might have guessed it) tail().

To open the dataset in RStudio’s Data Viewer, use the view() function:

view(samples)

Note

read_csv() assumes that fields are delineated by commas, however, in several countries, the comma is used as a decimal separator and the semicolon (;) is used as a field delineator. If you want to read in this type of files in R, you can use the read_csv2() function. It behaves like read_csv() but uses different parameters for the decimal and the field separators. There is also the read_tsv() for tab separated data files and read_delim() for less common formats. Check out the help for read_csv() by typing ?read_csv to learn more.

In addition to the above versions of the csv format, you should develop the habits of looking at some parameters of your csv files. For instance, the character encoding, control characters used for line ending, date format (if the date is not splitted into three variables), and the presence of unexpected newlines Links to an external site. are important characteristics of your data files. Those parameters will ease up the import step of your data in R.

What are data frames?

When we loaded the data into R, it got stored as an object of class tibble, which is a special kind of data frame (the difference is not important for our purposes, but you can learn more about tibbles here Links to an external site.). Data frames are the de facto data structure for most tabular data, and what we use for statistics and plotting. Data frames can be created by hand, but most commonly they are generated by functions like read_csv(); in other words, when importing spreadsheets from your hard drive or the web.

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. 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.

We can see this also when inspecting the structure of a data frame with the function str():

str(samples)

spec_tbl_df [29 × 8] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ patient_id     : chr [1:29] "OAS-29_1" "OAS-29_10" "OAS-29_11" "OAS-29_12" ...
 $ collection_date: Date[1:29], format: "2020-03-31" "2020-03-31" ...
 $ country        : chr [1:29] "Italy" "Italy" "Italy" "Italy" ...
 $ region         : chr [1:29] "Turin" "Turin" "Turin" "Turin" ...
 $ age            : num [1:29] 48 35 59 60 83 21 44 55 81 63 ...
 $ disease_outcome: chr [1:29] "dead" NA "recovered" "recovered" ...
 $ sex            : chr [1:29] "female" "male" "male" "female" ...
 $ ct             : num [1:29] 41.5 15.3 25.3 27 25.3 ...
 - attr(*, "spec")=
  .. cols(
  ..   patient_id = col_character(),
  ..   collection_date = col_date(format = ""),
  ..   country = col_character(),
  ..   region = col_character(),
  ..   age = col_double(),
  ..   disease_outcome = col_character(),
  ..   sex = col_character(),
  ..   ct = col_double()
  .. )
 - attr(*, "problems")=<externalptr> 

Inspecting data frames

We already saw how the functions head() and str() can be useful to check the content and the structure of a data frame. Here is a non-exhaustive list of functions to get a sense of the content/structure of the data. Let’s try them out!

Size:
- dim(samples) - returns a vector with the number of rows in the first element, and the number of columns as the second element (the dimensions of the object)
- nrow(samples) - returns the number of rows
- ncol(samples) - returns the number of columns
Content:
- head(samples) - shows the first 6 rows
- tail(samples) - shows the last 6 rows
Names:
- names(samples) - returns the column names (synonym of colnames() for data.frame objects)
- rownames(samples) - returns the row names
Summary:
- str(samples) - structure of the object and information about the class, length and content of each column
- summary(samples) - summary statistics for each column

Note: most of these functions are “generic”, they can be used on other types of objects besides data.frame.

Challenge 3.1

Based on the output of str(samples), can you answer the following questions?

What is the class of the object samples?
How many rows and how many columns are in this object?

Solution

str(samples)

spec_tbl_df [29 × 8] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ patient_id     : chr [1:29] "OAS-29_1" "OAS-29_10" "OAS-29_11" "OAS-29_12" ...
 $ collection_date: Date[1:29], format: "2020-03-31" "2020-03-31" ...
 $ country        : chr [1:29] "Italy" "Italy" "Italy" "Italy" ...
 $ region         : chr [1:29] "Turin" "Turin" "Turin" "Turin" ...
 $ age            : num [1:29] 48 35 59 60 83 21 44 55 81 63 ...
 $ disease_outcome: chr [1:29] "dead" NA "recovered" "recovered" ...
 $ sex            : chr [1:29] "female" "male" "male" "female" ...
 $ ct             : num [1:29] 41.5 15.3 25.3 27 25.3 ...
 - attr(*, "spec")=
  .. cols(
  ..   patient_id = col_character(),
  ..   collection_date = col_date(format = ""),
  ..   country = col_character(),
  ..   region = col_character(),
  ..   age = col_double(),
  ..   disease_outcome = col_character(),
  ..   sex = col_character(),
  ..   ct = col_double()
  .. )
 - attr(*, "problems")=<externalptr> 

The object samples is of class data.frame, or more specifically a tibble (spec_tbl_df/tbl_df/tbl/data.frame)
Rows and columns: 29 rows and 8 columns

Indexing and subsetting data frames

Our samples data frame has rows and columns (it has 2 dimensions), 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.

# first element in the first column of the data frame
samples[1, 1]   

# A tibble: 1 × 1
  patient_id
  <chr>     
1 OAS-29_1  

# first element in the 6th column
samples[1, 6]   

# A tibble: 1 × 1
  disease_outcome
  <chr>          
1 dead           

# first column of the data frame
samples[, 1]    

# A tibble: 29 × 1
   patient_id
   <chr>     
OAS-29_1  
OAS-29_10 
OAS-29_11 
OAS-29_12 
OAS-29_13 
OAS-29_14 
OAS-29_15 
OAS-29_16 
OAS-29_17 
OAS-29_18 
# … with 19 more rows

# first column of the data frame
samples[1]      

# A tibble: 29 × 1
   patient_id
   <chr>     
OAS-29_1  
OAS-29_10 
OAS-29_11 
OAS-29_12 
OAS-29_13 
OAS-29_14 
OAS-29_15 
OAS-29_16 
OAS-29_17 
OAS-29_18 
# … with 19 more rows

# first three rows of the 6th column
samples[1:3, 6] 

# A tibble: 3 × 1
  disease_outcome
  <chr>          
1 dead           
2 <NA>           
3 recovered      

# the 3rd row of the data frame
samples[3, ]    

# A tibble: 1 × 8
  patient_id collection_date country region   age disease_outcome sex      ct
  <chr>      <date>          <chr>   <chr>  <dbl> <chr>           <chr> <dbl>
1 OAS-29_11  2020-03-31      Italy   Turin     59 recovered       male   25.3

# equivalent to head_samples <- head(samples)
head_samples <- samples[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:

samples[, -1]  # the whole data frame, except the first column

# A tibble: 29 × 7
   collection_date country region   age disease_outcome sex       ct
   <date>          <chr>   <chr>  <dbl> <chr>           <chr>  <dbl>
2020-03-31      Italy   Turin     48 dead            female  41.5
2020-03-31      Italy   Turin     35 <NA>            male    15.3
2020-03-31      Italy   Turin     59 recovered       male    25.3
2020-03-31      Italy   Turin     60 recovered       female  27  
2020-03-31      Italy   Turin     83 dead            female  25.3
2020-04-01      Italy   Turin     21 dead            male    31  
2020-04-01      Italy   Turin     44 recovered       female  33.7
2020-04-01      Italy   Turin     55 recovered       male    39  
2020-03-31      Italy   Turin     81 dead            female  35.7
2020-04-01      Italy   Turin     63 recovered       female  19.3
# … with 19 more rows

samples[-(7:29), ]  # equivalent to head(samples)

# A tibble: 6 × 8
  patient_id collection_date country region   age disease_outcome sex       ct
  <chr>      <date>          <chr>   <chr>  <dbl> <chr>           <chr>  <dbl>
1 OAS-29_1   2020-03-31      Italy   Turin     48 dead            female  41.5
2 OAS-29_10  2020-03-31      Italy   Turin     35 <NA>            male    15.3
3 OAS-29_11  2020-03-31      Italy   Turin     59 recovered       male    25.3
4 OAS-29_12  2020-03-31      Italy   Turin     60 recovered       female  27  
5 OAS-29_13  2020-03-31      Italy   Turin     83 dead            female  25.3
6 OAS-29_14  2020-04-01      Italy   Turin     21 dead            male    31  

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

samples["patient_id"]

# A tibble: 29 × 1
   patient_id
   <chr>     
OAS-29_1  
OAS-29_10 
OAS-29_11 
OAS-29_12 
OAS-29_13 
OAS-29_14 
OAS-29_15 
OAS-29_16 
OAS-29_17 
OAS-29_18 
# … with 19 more rows

samples[, "patient_id"]

# A tibble: 29 × 1
   patient_id
   <chr>     
OAS-29_1  
OAS-29_10 
OAS-29_11 
OAS-29_12 
OAS-29_13 
OAS-29_14 
OAS-29_15 
OAS-29_16 
OAS-29_17 
OAS-29_18 
# … with 19 more rows

When we extract a subset from a data frame of class tibble, we normally get back an object of the same class. To get one-column subsets returned as vectors, we can use double square brackets:

samples[[1]]  # first column as vector

 [1] "OAS-29_1"  "OAS-29_10" "OAS-29_11" "OAS-29_12" "OAS-29_13" "OAS-29_14"
 [7] "OAS-29_15" "OAS-29_16" "OAS-29_17" "OAS-29_18" "OAS-29_19" "OAS-29_2" 
[13] "OAS-29_20" "OAS-29_21" "OAS-29_22" "OAS-29_23" "OAS-29_24" "OAS-29_25"
[19] "OAS-29_26" "OAS-29_27" "OAS-29_28" "OAS-29_29" "OAS-29_3"  "OAS-29_4" 
[25] "OAS-29_5"  "OAS-29_6"  "OAS-29_7"  "OAS-29_8"  "OAS-29_9" 

samples[["patient_id"]]  # named column as vector

 [1] "OAS-29_1"  "OAS-29_10" "OAS-29_11" "OAS-29_12" "OAS-29_13" "OAS-29_14"
 [7] "OAS-29_15" "OAS-29_16" "OAS-29_17" "OAS-29_18" "OAS-29_19" "OAS-29_2" 
[13] "OAS-29_20" "OAS-29_21" "OAS-29_22" "OAS-29_23" "OAS-29_24" "OAS-29_25"
[19] "OAS-29_26" "OAS-29_27" "OAS-29_28" "OAS-29_29" "OAS-29_3"  "OAS-29_4" 
[25] "OAS-29_5"  "OAS-29_6"  "OAS-29_7"  "OAS-29_8"  "OAS-29_9" 

samples[[1, 1]]  # first element in the first column as vector        

[1] "OAS-29_1"

samples[[1, "patient_id"]]  # first element in the named column as vector

[1] "OAS-29_1"

We can also access an individual column as a vector by using a dollar sign, $:

samples$patient_id  # named column as vector

 [1] "OAS-29_1"  "OAS-29_10" "OAS-29_11" "OAS-29_12" "OAS-29_13" "OAS-29_14"
 [7] "OAS-29_15" "OAS-29_16" "OAS-29_17" "OAS-29_18" "OAS-29_19" "OAS-29_2" 
[13] "OAS-29_20" "OAS-29_21" "OAS-29_22" "OAS-29_23" "OAS-29_24" "OAS-29_25"
[19] "OAS-29_26" "OAS-29_27" "OAS-29_28" "OAS-29_29" "OAS-29_3"  "OAS-29_4" 
[25] "OAS-29_5"  "OAS-29_6"  "OAS-29_7"  "OAS-29_8"  "OAS-29_9" 

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

Challenge 3.2

Create a data.frame (samples_20) containing only the data in row 20 of the samples dataset.

Notice how nrow() gave you the number of rows in a data.frame?

Use that number to pull out just that last row in the data frame.

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 data frame (samples_last) from that last row.

Combine nrow() with the - notation above to reproduce the behavior of head(samples), keeping just the first 6 rows of the samples dataset.
Solution
## 1.
samples_20 <- samples[20, ]
## 2.
# Saving `n_rows` to improve readability and reduce duplication
n_rows <- nrow(samples)
samples_last <- samples[n_rows, ]
## 3.
samples_head<- samples[-(7:n_rows), ]

Factors

When we did str(samples) we saw that the columns age, tax_id and ct consist of numeric values and that the column collection_date contains dates. The remaining columns (e.g. patient_id, sex and disease_outcome), however, are of the class character. Arguably, some of these columns (like sex and disease_outcome) contain categorical data, which means that they can only take on a limited number of values.

R has a special class for working with categorical data, called factor. Factors are very useful and actually contribute to making R particularly well suited to working with data. So we are going to spend a little time introducing them.

Once created, factors can only contain a pre-defined set of values, known as levels. Factors are stored as integers associated with labels and they can be ordered or unordered. While factors look (and often behave) like character vectors, they are actually treated as integer vectors by R. So you need to be very careful when treating them as strings.

When importing a data frame with read_csv(), the columns that contain text are not automatically coerced (=converted) into the factor data type, but once we have loaded the data we can do the conversion using the factor() function:

samples$disease_outcome <- factor(samples$disease_outcome)

We can see that the conversion has worked by using the summary() function again. This produces a table with the counts for each factor level:

summary(samples$disease_outcome)

     dead recovered      NA's 
       15        11         3 

By default, R always sorts levels in alphabetical order. For instance, if you have a factor with 2 levels:

disease_outcome <- factor(c("recovered", "dead", "dead", "recovered"))

R will assign 1 to the level "dead" and 2 to the level "recovered" (because d comes before r, even though the first element in this vector is "recovered"). You can see this by using the function levels() and you can find the number of levels using nlevels():

levels(disease_outcome)

[1] "dead"      "recovered"

nlevels(disease_outcome)

[1] 2

Sometimes, the order of the factors does not matter, other times you might want to specify the order because it is meaningful (e.g., “low”, “medium”, “high”), it improves your visualization, or it is required by a particular type of analysis. Here, one way to reorder our levels in the desease_outcome vector would be:

disease_outcome # current order

[1] recovered dead      dead      recovered
Levels: dead recovered

disease_outcome <- factor(disease_outcome, levels = c("recovered", "dead"))
disease_outcome # after re-ordering

[1] recovered dead      dead      recovered
Levels: recovered dead

In R’s memory, these factors are represented by integers (1, 2, 3), but are more informative than integers because factors are self describing: "dead", "recovered" is more descriptive than 1, 2. Which one is “dead”? You wouldn’t be able to tell just from the integer data. Factors, on the other hand, have this information built in. It is particularly helpful when there are many levels (like the species names in our example dataset).

Challenge 3.3

Change the columns disease_outcome and sex in the samples data frame into factors.

Using the functions you have learnt so far, can you find out…

How many levels are there in the sex column?

How many individuals are listed as “dead” in the disease_outcome column?
Solution
samples$disease_outcome <- factor(samples$disease_outcome)
samples$sex <- factor(samples$sex)
nlevels(samples$sex)
[1] 2
summary(samples$disease_outcome)
     dead recovered      NA's 
       15        11         3 
How many levels in the sex column? There are 2 levels.

How many are listed as dead? There are 15 individuals listed as “dead” in the disease_outcome column. column.

Converting factors

If you need to convert a factor to a character vector, you use as.character(x).

as.character(disease_outcome)

[1] "recovered" "dead"      "dead"      "recovered"

In some cases, you may have to convert factors where the levels appear as numbers (such as concentration levels or years) to a numeric vector. For instance, in one part of your analysis the years might need to be encoded as factors (e.g., comparing average weights across years) but in another part of your analysis they may need to be stored as numeric values (e.g., doing math operations on the years). This conversion from factor to numeric is a little trickier. The as.numeric() function returns the index values of the factor, not its levels, so it will result in an entirely new (and unwanted in this case) set of numbers. One method to avoid this is to convert factors to characters, and then to numbers.

Another method is to use the levels() function. Compare:

year_fct <- factor(c(1990, 1983, 1977, 1998, 1990))
as.numeric(year_fct)  # Wrong! And there is no warning...

[1] 3 2 1 4 3

as.numeric(as.character(year_fct))  # Works...

[1] 1990 1983 1977 1998 1990

as.numeric(levels(year_fct))[year_fct]  # The recommended way.

[1] 1990 1983 1977 1998 1990

Notice that in the levels() approach, three important steps occur:

We obtain all the factor levels using levels(year_fct)
We convert these levels to numeric values using as.numeric(levels(year_fct))
We then access these numeric values using the underlying integers of the vector year_fct inside the square brackets

Renaming factors

When your data is stored as a factor, you can use the plot() function to get a quick glance at the number of observations represented by each factor level. Let’s look at the number of males and females captured over the course of the experiment:

plot(samples$disease_outcome)

plot of chunk barplot-1

However, as we saw when we used summary(samples$disease_outcomce), there are 3 individuals for which the information hasn’t been recorded. To show them in the plot, we can turn the missing values into a factor level with the addNA() function. We will also have to give the new factor level a label. We are going to work with a copy of the disease_outcome column, so we’re not modifying the working copy of the data frame:

disease_outcome <- samples$disease_outcome
levels(disease_outcome)

[1] "dead"      "recovered"

We see that the missing values are not among the levels, so let’s add them into a new level:

disease_outcome <- addNA(disease_outcome)
levels(disease_outcome)

[1] "dead"      "recovered" NA

Let us then label the new level with something more sensible:

levels(disease_outcome)[3] <- "unknown"
levels(disease_outcome)

[1] "dead"      "recovered" "unknown"

We see that there are now three labeled levels. We can also see the labels if we look at the vector directly:

disease_outcome

 [1] dead      unknown   recovered recovered dead      dead      recovered
 [8] recovered dead      recovered dead      dead      unknown   recovered
[15] recovered recovered dead      recovered unknown   dead      recovered
[22] recovered dead      dead      dead      dead      dead      dead     
[29] dead     
Levels: dead recovered unknown

Now we can plot the data again, using plot(disease_outcome).

plot of chunk barplot-2

Challenge 3.4

Store a copy of the factor column sex to a new object named sex.

In the new object, rename “female” and “male” to “F” and “M” respectively.

Reorder the factor levels so that “M” comes before “F”.

Create a bar plot of the factor.
Solution
sex = samples$sex
levels(sex)[1:2] <- c("F", "M")
sex <- factor(sex, levels = c("M", "F"))
plot(sex)

Challenge 3.5 (optional)
We have seen how data frames are created when using read_csv(), but they can also be created by hand with the data.frame() function. There are a few mistakes in this hand-crafted data.frame. Can you spot and fix them? Don’t hesitate to experiment!
animal_data <- data.frame(
          animal = c(dog, cat, sea cucumber, sea urchin),
          feel = c("furry", "squishy", "spiny"),
          weight = c(45, 8 1.1, 0.8)
          )
Can you predict the class for each of the columns in the following example? Check your guesses using str(country_climate):

Are they what you expected? Why? Why not?

What would you need to change to ensure that each column had the accurate data type?
 country_climate <- data.frame(
        country = c("Canada", "Panama", "South Africa", "Australia"),
        climate = c("cold", "hot", "temperate", "hot/temperate"),
        temperature = c(10, 30, 18, "15"),
        northern_hemisphere = c(TRUE, TRUE, FALSE, "FALSE"),
        has_kangaroo = c(FALSE, FALSE, FALSE, 1)
        )
Solution

missing quotations around the names of the animals

missing one entry in the feel column (probably for one of the furry animals)

missing one comma in the weight column

country, climate, temperature, and northern_hemisphere are characters; has_kangaroo is numeric

using factor() one could replace character columns with factors columns

removing the quotes in temperature and northern_hemisphere and replacing 1 by TRUE in the has_kangaroo column would give what was probably intended

The automatic conversion of data type is sometimes a blessing, sometimes an annoyance. Be aware that it exists, learn the rules, and double check that data you import in R are of the correct type within your data frame. If not, use it to your advantage to detect mistakes that might have been introduced during data entry (for instance, a letter in a column that should only contain numbers).

Learn more in this RStudio tutorial Links to an external site.

Key Points

It is easy to import data into R from tabular formats including Excel. However, you still need to check that R has imported and interpreted your data correctly.

There are best practices for organizing your data (keeping it tidy) and R is great for this.

Base R has many useful functions for manipulating your data, but all of R’s capabilities are greatly enhanced by software packages developed by the community.