Starting with Data

Overview

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?
• How are dates represented in R and how can I change the format?

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 values from data frames.
• Change how character strings are handled in a data frame.
• Examine and change date formats.

What are data frames?

---

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 Clicks YouTube data

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We will be working with a dataset of 200 Youtube posts. Here is some background to the dataset.

An initial dataset of 314 posts were returned by the YouTube API in response to the following query: “Query: clicks south africa* hair (ad OR advertisement) -click” covering videos posted during the period 2020 - 2023 The dataset was prepared using a spreadsheet and OpenRefine to identify missing values, change variable names to “snake case”, change tags and topics to lowercase, and to use semicolons to separate tags and topics. Owing to the ambiguity of the keyword “clicks”, many irrelevant results were returned. The dataset was reviewed and any posts which were not related to the controversy, or which did not relate to issues about body politics and racism were excluded. This resulted in a dataset of 200 posts selected for computational analysis. In case you need a refresher, here are some details about how YouTube API derives the variables in this dataset.

We will be using a subset of the dataset that was prepared in OpenRefine (data/youtube-27082024-open-refine-200-na.csv). In this dataset, the missing data is encoded as “na”, each row holds information for a single YouTube video, and the columns represent |

column_name	description
‘position’
‘randomise’
‘channel_id’
‘channel_title’
‘video_id’
‘url’
‘published_at’
‘published_at_sql’
‘year’
‘month’
‘day’
‘video_title’
‘video_description’
‘tags’
‘video_category_label’
‘topic_categories’
‘duration_sec’
‘definition’
‘caption’
‘default_language’
‘default_l_audio_language’
‘thumbnail_maxres’
‘licensed_content’
‘location_description’
‘view_count’
‘like_count’
‘favorite_count’
‘comment_count’
‘controversy’
‘clicks’
‘body_politics’
‘eff_lead’

Importing data

---

You are going to 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 proceeding, however, this is a good opportunity to talk about conflicts. Certain packages we load can end up introducing function names that are already in use by pre-loaded R packages. For instance, when we load the tidyverse package below, we will introduce two conflicting functions: filter() and lag(). This happens because filter and lag are already functions used by the stats package (already pre-loaded in R). What will happen now is that if we, for example, call the filter() function, R will use the dplyr::filter() version and not the stats::filter() one. This happens because, if conflicted, by default R uses the function from the most recently loaded package. Conflicted functions may cause you some trouble in the future, so it is important that we are aware of them so that we can properly handle them, if we want.

To do so, we just need the following functions from the conflicted package:

• conflicted::conflict_scout(): Shows us any conflicted functions.
  
• conflict_prefer("function", "package_prefered"): Allows us to choose the default function we want from now on.

It is also important to know that we can, at any time, just call the function directly from the package we want, such as stats::filter().

Even with the use of an RStudio project, it can be difficult to learn how to specify paths to file locations. Enter the here package! The here package creates paths relative to the top-level directory (your RStudio project). These relative paths work regardless of where the associated source file lives inside your project, like analysis projects with data and reports in different subdirectories. This is an important contrast to using setwd(), which depends on the way you order your files on your computer.

Image credit: Allison Horst

Before we can use the read_csv() and here() functions, we need to load the tidyverse and here packages.

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

R

library(tidyverse)
library(here)

videos <- read_csv(
  here("data", "youtube-27082024-open-refine-200-na.csv"), 
  na = "na")

In the above code, we notice the here() function takes folder and file names as inputs (e.g., "data", "youtube-27082024-open-refine-200-na.csv"), each enclosed in quotations ("") and separated by a comma. The here() will accept as many names as are necessary to navigate to a particular file (e.g., here("analysis", "data", "surveys", "clean", "youtube-27082024-open-refine-200-na.csv")).

The here() function can accept the folder and file names in an alternate format, using a slash (“/”) rather than commas to separate the names. The two methods are equivalent, so that here("data", "youtube-27082024-open-refine-200-na.csv") and here("data/youtube-27082024-open-refine-200-na.csv") produce the same result. (The slash is used on all operating systems; backslashes are not used.)

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 different kind of data frame called 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: videos in the console.

R

videos
## Try also
## view(videos)
## head(videos)

OUTPUT

# A tibble: 200 × 32
   position randomise channel_id               channel_title      video_id url
      <dbl>     <dbl> <chr>                    <chr>              <chr>    <chr>
 1      112       409 UCI3RT5PGmdi1KVp9FG_CneA eNCA               iPUAl1j… http…
 2       50       702 UCI3RT5PGmdi1KVp9FG_CneA eNCA               YUmIAd_… http…
 3      149       313 UCMwDXpWEVQVw4ZF7z-E4NoA StellenboschNews … v8XfpOi… http…
 4      167       384 UCsqKkYLOaJ9oBwq9rxFyZMw SOUTH AFRICAN POL… lnLdo2k… http…
 5      195       606 UC5G5Dy8-mmp27jo6Frht7iQ Umgosi Entertainm… XN6toca… http…
 6      213       423 UCC1udUghY9dloGMuvZzZEzA The Tea World      rh2Nz78… http…
 7      145       452 UCaCcVtl9O3h5en4m-_edhZg Celeb LaLa Land    1l5GZ0N… http…
 8      315       276 UCAurTjb6Ewz21vjfTs1wZxw NOSIPHO NZAMA      j4Y022C… http…
 9      190       321 UCBlX1mnsIFZRqsyRNvpW_rA Zandile Mhlambi    gf2YNN6… http…
10      214       762 UClY87IoUANFZtswyC9GeecQ Beauty recipes     AGJmRd4… http…
# ℹ 190 more rows
# ℹ 26 more variables: published_at <dttm>, published_at_sql <chr>, year <dbl>,
#   month <dbl>, day <dbl>, video_title <chr>, video_description <chr>,
#   tags <chr>, video_category_label <chr>, topic_categories <chr>,
#   duration_sec <dbl>, definition <chr>, caption <lgl>,
#   default_language <chr>, default_l_audio_language <chr>,
#   thumbnail_maxres <chr>, licensed_content <dbl>, …

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

R

class(videos)

OUTPUT

[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’), channel_title is a column of characters (<chr>) and the published_at is a column in the “date and time” format (<dttm>).

Inspecting data frames

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When calling a tbl_df object (like videos 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(videos) - 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(videos) - returns the number of rows
• ncol(videos) - returns the number of columns

Content:

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

Names:

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

Summary:

• str(videos) - structure of the object and information about the class, length and content of each column
• summary(videos) - summary statistics for each column
• glimpse(videos) - 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.

Subsetting data frames

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Our videos 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 access some specific data from it, we need to specify the “coordinates” (i.e., indices) we want from it. Row numbers come first, followed by column numbers.

Tip

Subsetting a tibble with [ 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.

R

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

OUTPUT

# A tibble: 1 × 1
  position
     <dbl>
1      112

R

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

OUTPUT

# A tibble: 1 × 1
  url
  <chr>
1 https://www.youtube.com/watch?v=iPUAl1jywdU

R

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

OUTPUT

  [1] 112  50 149 167 195 213 145 315 190 214 263  30  63  65   2   3   4   7
 [19]   9  13  14  16  17  24  36  66  72 132 118 299  40  43  46  54  58  80
 [37]  88  93  99 103 123 124 152 169 174 181 200 215 217 218 227 236 244 250
 [55] 253 272 282 284 291 257   1  29  38  92 135 139 144 153 170 176 183 187
 [73] 194 197 207 221 226 262 271 273 278 290 294 295 306 307 311  11  44  57
 [91]  60  64  69  70  78  96 107 121 130 308   5   8  10  21  23  25  28  34
[109]  52  56  61  62  71  73  74  81  86  91  98 100 110 120 126 131 133 136
[127] 141 142 143 146 150 154 159 161 164 166 173 178 182 184 191 192 202 208
[145] 210 219 220 223 231 233 234 238 243 245 261 264 275 276 277 283 285 288
[163] 292 300 304 305 309 313   6  15  18  19  22  27  31  33  35  37  39  41
[181]  42  67  77  82  83  85  87  94 158 168 193 254 293 104 155 162 204 209
[199] 246 255

R

## first column of the tibble
videos[1]

OUTPUT

# A tibble: 200 × 1
   position
      <dbl>
 1      112
 2       50
 3      149
 4      167
 5      195
 6      213
 7      145
 8      315
 9      190
10      214
# ℹ 190 more rows

R

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

OUTPUT

# A tibble: 3 × 1
  published_at
  <dttm>
1 2020-09-14 16:16:44
2 2020-09-11 05:34:52
3 2020-09-10 11:59:51

R

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

OUTPUT

# A tibble: 1 × 32
  position randomise channel_id channel_title video_id url   published_at
     <dbl>     <dbl> <chr>      <chr>         <chr>    <chr> <dttm>
1      149       313 UCMwDXpWE… Stellenbosch… v8XfpOi… http… 2020-09-10 11:59:51
# ℹ 25 more variables: published_at_sql <chr>, year <dbl>, month <dbl>,
#   day <dbl>, video_title <chr>, video_description <chr>, tags <chr>,
#   video_category_label <chr>, topic_categories <chr>, duration_sec <dbl>,
#   definition <chr>, caption <lgl>, default_language <chr>,
#   default_l_audio_language <chr>, thumbnail_maxres <chr>,
#   licensed_content <dbl>, location_description <chr>, view_count <dbl>,
#   like_count <dbl>, favorite_count <dbl>, comment_count <dbl>, …

R

## equivalent to head_videos <- head(videos)
head_videos <- videos[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:

R

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

OUTPUT

# A tibble: 200 × 31
   randomise channel_id         channel_title video_id url   published_at
       <dbl> <chr>              <chr>         <chr>    <chr> <dttm>
 1       409 UCI3RT5PGmdi1KVp9… eNCA          iPUAl1j… http… 2020-09-14 16:16:44
 2       702 UCI3RT5PGmdi1KVp9… eNCA          YUmIAd_… http… 2020-09-11 05:34:52
 3       313 UCMwDXpWEVQVw4ZF7… Stellenbosch… v8XfpOi… http… 2020-09-10 11:59:51
 4       384 UCsqKkYLOaJ9oBwq9… SOUTH AFRICA… lnLdo2k… http… 2020-09-07 11:08:43
 5       606 UC5G5Dy8-mmp27jo6… Umgosi Enter… XN6toca… http… 2020-09-08 12:45:36
 6       423 UCC1udUghY9dloGMu… The Tea World rh2Nz78… http… 2020-09-09 15:03:43
 7       452 UCaCcVtl9O3h5en4m… Celeb LaLa L… 1l5GZ0N… http… 2020-09-08 18:43:58
 8       276 UCAurTjb6Ewz21vjf… NOSIPHO NZAMA j4Y022C… http… 2021-06-22 10:18:51
 9       321 UCBlX1mnsIFZRqsyR… Zandile Mhla… gf2YNN6… http… 2020-09-07 18:50:42
10       762 UClY87IoUANFZtswy… Beauty recip… AGJmRd4… http… 2022-11-26 09:08:54
# ℹ 190 more rows
# ℹ 25 more variables: published_at_sql <chr>, year <dbl>, month <dbl>,
#   day <dbl>, video_title <chr>, video_description <chr>, tags <chr>,
#   video_category_label <chr>, topic_categories <chr>, duration_sec <dbl>,
#   definition <chr>, caption <lgl>, default_language <chr>,
#   default_l_audio_language <chr>, thumbnail_maxres <chr>,
#   licensed_content <dbl>, location_description <chr>, view_count <dbl>, …

R

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

OUTPUT

# A tibble: 75 × 32
   position randomise channel_id               channel_title      video_id url
      <dbl>     <dbl> <chr>                    <chr>              <chr>    <chr>
 1      112       409 UCI3RT5PGmdi1KVp9FG_CneA eNCA               iPUAl1j… http…
 2       50       702 UCI3RT5PGmdi1KVp9FG_CneA eNCA               YUmIAd_… http…
 3      149       313 UCMwDXpWEVQVw4ZF7z-E4NoA StellenboschNews … v8XfpOi… http…
 4      167       384 UCsqKkYLOaJ9oBwq9rxFyZMw SOUTH AFRICAN POL… lnLdo2k… http…
 5      195       606 UC5G5Dy8-mmp27jo6Frht7iQ Umgosi Entertainm… XN6toca… http…
 6      213       423 UCC1udUghY9dloGMuvZzZEzA The Tea World      rh2Nz78… http…
 7      154       806 UCzvl9exW-o8IsySPU-ezgoQ TheKingzRSA        m4KitiL… http…
 8      159       496 UC5G5Dy8-mmp27jo6Frht7iQ Umgosi Entertainm… QigpfXd… http…
 9      161       678 UCFnUvf-Lg1nIc5wvl9lx8zQ Mzansi Hotspot     cuXJVNI… http…
10      164       181 UCITau47AeZDY9sO77TA3OUA ITV News           aqSmGuy… http…
# ℹ 65 more rows
# ℹ 26 more variables: published_at <dttm>, published_at_sql <chr>, year <dbl>,
#   month <dbl>, day <dbl>, video_title <chr>, video_description <chr>,
#   tags <chr>, video_category_label <chr>, topic_categories <chr>,
#   duration_sec <dbl>, definition <chr>, caption <lgl>,
#   default_language <chr>, default_l_audio_language <chr>,
#   thumbnail_maxres <chr>, licensed_content <dbl>, …

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

R

videos["channel_title"]       # Result is a tibble

videos[, "channel_title"]     # Result is a tibble

videos[["channel_title"]]     # Result is a vector

videos$channel_title          # 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 (videos_100) containing only the data in row 100 of the videos dataset.

Now, continue using videos for each of the following activities:

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 (videos_last) from that last row.

3. Using the number of rows in the videos 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 videos_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(videos), keeping just the first through 6th rows of the videos dataset.

Show me the solution

R

## 1.
videos_100 <- videos[100, ]
## 2.
# Saving `n_rows` to improve readability and reduce duplication
n_rows <- nrow(videos)
videos_last <- videos[n_rows, ]
## 3.
videos_middle <- videos[median(1:n_rows), ]

ERROR

Error in `videos[median(1:n_rows), ]`:
! Can't subset rows with `median(1:n_rows)`.
✖ Can't convert from `i` <double> to <integer> due to loss of precision.

R

## 4.
videos_head <- videos[-(7:n_rows), ]

Formatting Dates

---

One of the most common issues that new (and experienced!) R users have is converting date and time information into a variable that is appropriate and usable during analyses. A best practice for dealing with date data is to ensure that each component of your date is available as a separate variable. In our dataset, we have a column published_at which contains information about the year, month, and day that the interview was conducted. Let’s convert those dates into three separate columns.

R

str(videos)

We are going to use the package lubridate, , which is included in the tidyverse installation and should be loaded by default. However, if we deal with older versions of tidyverse (2022 and ealier), we can manually load it by typing library(lubridate).

If necessary, start by loading the required package:

R

library(lubridate)

The lubridate function ymd() takes a vector representing year, month, and day, and converts it to a Date vector. Date is a class of data recognized by R as being a date and can be manipulated as such. The argument that the function requires is flexible, but, as a best practice, is a character vector formatted as “YYYY-MM-DD”.

Let’s extract our published_at column and inspect the structure:

R

dates <- videos$published_at
str(dates)

OUTPUT

 POSIXct[1:200], format: "2020-09-14 16:16:44" "2020-09-11 05:34:52" "2020-09-10 11:59:51" ...

When we imported the data in R, read_csv() recognized that this column contained date information. We can now use the day(), month() and year() functions to extract this information from the date, and create new columns in our data frame to store it:

R

videos$day <- day(dates)
videos$month <- month(dates)
videos$year <- year(dates)
videos

OUTPUT

# A tibble: 200 × 32
   position randomise channel_id               channel_title      video_id url
      <dbl>     <dbl> <chr>                    <chr>              <chr>    <chr>
 1      112       409 UCI3RT5PGmdi1KVp9FG_CneA eNCA               iPUAl1j… http…
 2       50       702 UCI3RT5PGmdi1KVp9FG_CneA eNCA               YUmIAd_… http…
 3      149       313 UCMwDXpWEVQVw4ZF7z-E4NoA StellenboschNews … v8XfpOi… http…
 4      167       384 UCsqKkYLOaJ9oBwq9rxFyZMw SOUTH AFRICAN POL… lnLdo2k… http…
 5      195       606 UC5G5Dy8-mmp27jo6Frht7iQ Umgosi Entertainm… XN6toca… http…
 6      213       423 UCC1udUghY9dloGMuvZzZEzA The Tea World      rh2Nz78… http…
 7      145       452 UCaCcVtl9O3h5en4m-_edhZg Celeb LaLa Land    1l5GZ0N… http…
 8      315       276 UCAurTjb6Ewz21vjfTs1wZxw NOSIPHO NZAMA      j4Y022C… http…
 9      190       321 UCBlX1mnsIFZRqsyRNvpW_rA Zandile Mhlambi    gf2YNN6… http…
10      214       762 UClY87IoUANFZtswyC9GeecQ Beauty recipes     AGJmRd4… http…
# ℹ 190 more rows
# ℹ 26 more variables: published_at <dttm>, published_at_sql <chr>, year <dbl>,
#   month <dbl>, day <int>, video_title <chr>, video_description <chr>,
#   tags <chr>, video_category_label <chr>, topic_categories <chr>,
#   duration_sec <dbl>, definition <chr>, caption <lgl>,
#   default_language <chr>, default_l_audio_language <chr>,
#   thumbnail_maxres <chr>, licensed_content <dbl>, …

Notice the three new columns at the end of our data frame.

In our example above, the published_at column was read in correctly as a Date variable but generally that is not the case. Date columns are often read in as character variables and one can use the as_date() function to convert them to the appropriate Date/POSIXctformat.

Let’s say we have a vector of dates in character format:

R

char_dates <- c("7/31/2012", "8/9/2014", "4/30/2016")
str(char_dates)

OUTPUT

 chr [1:3] "7/31/2012" "8/9/2014" "4/30/2016"

We can convert this vector to dates as :

R

as_date(char_dates, format = "%m/%d/%Y")

OUTPUT

[1] "2012-07-31" "2014-08-09" "2016-04-30"

Argument format tells the function the order to parse the characters and identify the month, day and year. The format above is the equivalent of mm/dd/yyyy. A wrong format can lead to parsing errors or incorrect results.

For example, observe what happens when we use a lower case y instead of upper case Y for the year.

R

as_date(char_dates, format = "%m/%d/%y")

WARNING

Warning: 3 failed to parse.

OUTPUT

[1] NA NA NA

Here, the %y part of the format stands for a two-digit year instead of a four-digit year, and this leads to parsing errors.

Or in the following example, observe what happens when the month and day elements of the format are switched.

R

as_date(char_dates, format = "%d/%m/%y")

WARNING

Warning: 3 failed to parse.

OUTPUT

[1] NA NA NA

Since there is no month numbered 30 or 31, the first and third dates cannot be parsed.

We can also use functions ymd(), mdy() or dmy() to convert character variables to date.

R

mdy(char_dates)

OUTPUT

[1] "2012-07-31" "2014-08-09" "2016-04-30"

Key Points

• Use read_csv to read tabular data in R.