Data Wrangling with dplyr
Last updated on 2024-11-19 | Edit this page
Estimated time: 40 minutes
- This lesson works better if you have graphics demonstrating dplyr commands. You can modify this Google Slides deck and use it for your workshop.
- For this lesson make sure that learners are comfortable using pipes.
- There is also sometimes some confusion on what the arguments of
group_byshould be, and when to usefilter()andselect().
Overview
Questions
- How can I select specific rows and/or columns from a dataframe?
- How can I combine multiple commands into a single command?
- How can I create new columns or remove existing columns from a dataframe?
Objectives
- Describe the purpose of an R package and the
dplyrpackage. - Select certain columns in a dataframe with the
dplyrfunctionselect. - Select certain rows in a dataframe according to filtering conditions
with the
dplyrfunctionfilter. - Link the output of one
dplyrfunction to the input of another function with the ‘pipe’ operator%>%. - Add new columns to a dataframe that are functions of existing
columns with
mutate. - Use the split-apply-combine concept for data analysis.
- Use
summarize,group_by, andcountto split a dataframe into groups of observations, apply a summary statistics for each group, and then combine the results.
dplyr is a package for making tabular
data wrangling easier by using a limited set of functions that can be
combined to extract and summarize insights from your data.
Like readr,
dplyr is a part of the tidyverse. These
packages were loaded in R’s memory when we called
library(tidyverse) earlier.
Note
The packages in the tidyverse, namely
dplyr, tidyr
and ggplot2 accept both the British
(e.g. summarise) and American (e.g. summarize)
spelling variants of different function and option names. For this
lesson, we utilize the American spellings of different functions;
however, feel free to use the regional variant for where you are
teaching.
What is an R package?
The package dplyr provides easy tools
for the most common data wrangling tasks. It is built to work directly
with dataframes, with many common tasks optimized by being written in a
compiled language (C++) (not all R packages are written in R!).
There are also packages available for a wide range of tasks including
building plots (ggplot2, which we’ll see
later), downloading data from the NCBI database, or performing
statistical analysis on your data set. Many packages such as these are
housed on, and downloadable from, the Comprehensive
R Archive Network
(CRAN) using install.packages. This function makes the
package accessible by your R installation with the command
library(), as you did with tidyverse
earlier.
To easily access the documentation for a package within R or RStudio,
use help(package = "package_name").
To learn more about dplyr after the
workshop, you may want to check out this handy
data transformation with dplyr
cheatsheet.
Note
There are alternatives to the tidyverse packages for
data wrangling, including the package data.table.
See this comparison
for example to get a sense of the differences between using
base, tidyverse, and
data.table.
Learning dplyr
To make sure everyone will use the same dataset for this lesson, we’ll read again the SAFI dataset that we downloaded earlier.
R
library(tidyverse)
library(here)
videos <- read_csv(
here("data", "youtube-27082024-open-refine-200-na.csv"),
na = "na")
R
## inspect the data
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>, …R
## preview the data
# view(videos)
We’re going to learn some of the most common
dplyr functions:
-
select(): subset columns -
filter(): subset rows on conditions -
mutate(): create new columns by using information from other columns -
group_by()andsummarize(): create summary statistics on grouped data -
arrange(): sort results -
count(): count discrete values
Selecting columns and filtering rows
To select columns of a dataframe, use select(). The
first argument to this function is the dataframe (videos),
and the subsequent arguments are the columns to keep, separated by
commas. Alternatively, if you are selecting columns adjacent to each
other, you can use a : to select a range of columns, read
as “select columns from ___ to ___.” You may have done something similar
in the past using subsetting. select() is essentially doing
the same thing as subsetting, using a package (dplyr)
instead of R’s base functions.
R
# to select columns throughout the dataframe
select(videos, channel_title, view_count, )
# to do the same thing with subsetting
videos[c("channel_title","view_count","comment_count")]
# to select a series of connected columns
select(videos,"view_count":"comment_count")
# to select columns by name as well as a series of connected columns
select(videos,"channel_title","published_at_sql","view_count":"comment_count")
To choose rows based on specific criteria, we can use the
filter() function. The argument after the dataframe is the
condition we want our final dataframe to adhere to (e.g. channel_title
name is SABC News):
R
# filters observations where channel title is "SABC News"
filter(videos, channel_title == "SABC News")
OUTPUT
# A tibble: 22 × 32
position randomise channel_id channel_title video_id url
<dbl> <dbl> <chr> <chr> <chr> <chr>
1 30 785 UC8yH-uI81UUtEMDsowQyx1g SABC News PNom9RIla7o https:…
2 63 418 UC8yH-uI81UUtEMDsowQyx1g SABC News EZDazXhf-Pk https:…
3 65 486 UC8yH-uI81UUtEMDsowQyx1g SABC News 9ewtc8eUY7k https:…
4 9 380 UC8yH-uI81UUtEMDsowQyx1g SABC News naLVyfMrFGs https:…
5 14 500 UC8yH-uI81UUtEMDsowQyx1g SABC News pO8dkGtjxQc https:…
6 17 647 UC8yH-uI81UUtEMDsowQyx1g SABC News rRg8J3lqaPc https:…
7 36 663 UC8yH-uI81UUtEMDsowQyx1g SABC News TLAxmusluVw https:…
8 66 541 UC8yH-uI81UUtEMDsowQyx1g SABC News P82g09FOSQ0 https:…
9 72 810 UC8yH-uI81UUtEMDsowQyx1g SABC News N2d1eBI5Zjc https:…
10 132 404 UC8yH-uI81UUtEMDsowQyx1g SABC News JXz_3WWo3ew https:…
# ℹ 12 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>, …We can also specify multiple conditions within the
filter() function. We can combine conditions using either
“and” or “or” statements. In an “and” statement, an observation (row)
must meet every criteria to be included in the
resulting dataframe. To form “and” statements within dplyr, we can pass
our desired conditions as arguments in the filter()
function, separated by commas:
R
# filters observations with "and" operator (comma)
# output dataframe satisfies ALL specified conditions
filter(videos, channel_title == "SABC News",
view_count > 1000,
comment_count > 20)
OUTPUT
# A tibble: 16 × 32
position randomise channel_id channel_title video_id url
<dbl> <dbl> <chr> <chr> <chr> <chr>
1 30 785 UC8yH-uI81UUtEMDsowQyx1g SABC News PNom9RIla7o https:…
2 65 486 UC8yH-uI81UUtEMDsowQyx1g SABC News 9ewtc8eUY7k https:…
3 9 380 UC8yH-uI81UUtEMDsowQyx1g SABC News naLVyfMrFGs https:…
4 36 663 UC8yH-uI81UUtEMDsowQyx1g SABC News TLAxmusluVw https:…
5 66 541 UC8yH-uI81UUtEMDsowQyx1g SABC News P82g09FOSQ0 https:…
6 72 810 UC8yH-uI81UUtEMDsowQyx1g SABC News N2d1eBI5Zjc https:…
7 11 209 UC8yH-uI81UUtEMDsowQyx1g SABC News 752qt6_0J0k https:…
8 5 636 UC8yH-uI81UUtEMDsowQyx1g SABC News AqQ-ukdgnWc https:…
9 23 628 UC8yH-uI81UUtEMDsowQyx1g SABC News CAwHz26tboE https:…
10 34 569 UC8yH-uI81UUtEMDsowQyx1g SABC News I5Qra7UUnJs https:…
11 62 461 UC8yH-uI81UUtEMDsowQyx1g SABC News mTfSl9KnNPo https:…
12 73 659 UC8yH-uI81UUtEMDsowQyx1g SABC News _aH8qJLdKtI https:…
13 18 597 UC8yH-uI81UUtEMDsowQyx1g SABC News B2SsMOgP8sQ https:…
14 22 769 UC8yH-uI81UUtEMDsowQyx1g SABC News dGDBWhcPTuI https:…
15 67 33 UC8yH-uI81UUtEMDsowQyx1g SABC News fs_xxqx70mA https:…
16 85 574 UC8yH-uI81UUtEMDsowQyx1g SABC News FIgI7GIcsuc https:…
# 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>, location_description <chr>,
# view_count <dbl>, like_count <dbl>, favorite_count <dbl>, …We can also form “and” statements with the &
operator instead of commas:
R
# filters observations with "&" logical operator
# output dataframe satisfies ALL specified conditions
filter(videos, channel_title == "SABC News" &
view_count > 1000 &
comment_count > 20)
OUTPUT
# A tibble: 16 × 32
position randomise channel_id channel_title video_id url
<dbl> <dbl> <chr> <chr> <chr> <chr>
1 30 785 UC8yH-uI81UUtEMDsowQyx1g SABC News PNom9RIla7o https:…
2 65 486 UC8yH-uI81UUtEMDsowQyx1g SABC News 9ewtc8eUY7k https:…
3 9 380 UC8yH-uI81UUtEMDsowQyx1g SABC News naLVyfMrFGs https:…
4 36 663 UC8yH-uI81UUtEMDsowQyx1g SABC News TLAxmusluVw https:…
5 66 541 UC8yH-uI81UUtEMDsowQyx1g SABC News P82g09FOSQ0 https:…
6 72 810 UC8yH-uI81UUtEMDsowQyx1g SABC News N2d1eBI5Zjc https:…
7 11 209 UC8yH-uI81UUtEMDsowQyx1g SABC News 752qt6_0J0k https:…
8 5 636 UC8yH-uI81UUtEMDsowQyx1g SABC News AqQ-ukdgnWc https:…
9 23 628 UC8yH-uI81UUtEMDsowQyx1g SABC News CAwHz26tboE https:…
10 34 569 UC8yH-uI81UUtEMDsowQyx1g SABC News I5Qra7UUnJs https:…
11 62 461 UC8yH-uI81UUtEMDsowQyx1g SABC News mTfSl9KnNPo https:…
12 73 659 UC8yH-uI81UUtEMDsowQyx1g SABC News _aH8qJLdKtI https:…
13 18 597 UC8yH-uI81UUtEMDsowQyx1g SABC News B2SsMOgP8sQ https:…
14 22 769 UC8yH-uI81UUtEMDsowQyx1g SABC News dGDBWhcPTuI https:…
15 67 33 UC8yH-uI81UUtEMDsowQyx1g SABC News fs_xxqx70mA https:…
16 85 574 UC8yH-uI81UUtEMDsowQyx1g SABC News FIgI7GIcsuc https:…
# 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>, location_description <chr>,
# view_count <dbl>, like_count <dbl>, favorite_count <dbl>, …In an “or” statement, observations must meet at least one of the specified conditions. To form “or” statements we use the logical operator for “or,” which is the vertical bar (|):
R
# filters observations with "|" logical operator
# output dataframe satisfies AT LEAST ONE of the specified conditions
filter(videos, channel_title == "SABC News" | channel_title == "eNCA")
OUTPUT
# A tibble: 42 × 32
position randomise channel_id channel_title video_id url
<dbl> <dbl> <chr> <chr> <chr> <chr>
1 112 409 UCI3RT5PGmdi1KVp9FG_CneA eNCA iPUAl1jywdU https:…
2 50 702 UCI3RT5PGmdi1KVp9FG_CneA eNCA YUmIAd_O0U4 https:…
3 30 785 UC8yH-uI81UUtEMDsowQyx1g SABC News PNom9RIla7o https:…
4 63 418 UC8yH-uI81UUtEMDsowQyx1g SABC News EZDazXhf-Pk https:…
5 65 486 UC8yH-uI81UUtEMDsowQyx1g SABC News 9ewtc8eUY7k https:…
6 2 944 UCI3RT5PGmdi1KVp9FG_CneA eNCA li3_91gCQHc https:…
7 3 269 UCI3RT5PGmdi1KVp9FG_CneA eNCA kvQRfnD1h64 https:…
8 4 518 UCI3RT5PGmdi1KVp9FG_CneA eNCA 3BkmO0M56lA https:…
9 7 417 UCI3RT5PGmdi1KVp9FG_CneA eNCA hZBwMrCCp4A https:…
10 9 380 UC8yH-uI81UUtEMDsowQyx1g SABC News naLVyfMrFGs https:…
# ℹ 32 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>, …Pipes
What if you want to select and filter at the same time? There are three ways to do this: use intermediate steps, nested functions, or pipes.
With intermediate steps, you create a temporary dataframe and use that as input to the next function, like this:
R
videos2 <- filter(videos, channel_title == "SABC News")
videos_SABC_metrics <- select(videos2, channel_title,view_count:comment_count)
This is readable, but can clutter up your workspace with lots of objects that you have to name individually. With multiple steps, that can be hard to keep track of.
You can also nest functions (i.e. one function inside of another), like this:
R
videos_SABC_metrics <- select(filter(videos, channel_title == "SABC News"),
channel_title,view_count:comment_count)
videos_SABC_metrics
OUTPUT
# A tibble: 22 × 5
channel_title view_count like_count favorite_count comment_count
<chr> <dbl> <dbl> <dbl> <dbl>
1 SABC News 22079 125 0 105
2 SABC News 3339 18 0 7
3 SABC News 20674 122 0 100
4 SABC News 19715 68 0 132
5 SABC News 14485 52 0 19
6 SABC News 2329 18 0 7
7 SABC News 25313 133 0 131
8 SABC News 11466 55 0 28
9 SABC News 7297 74 0 34
10 SABC News 3147 12 0 2
# ℹ 12 more rowsThis is handy, but can be difficult to read if too many functions are nested, as R evaluates the expression from the inside out (in this case, filtering, then selecting).
The last option, pipes, are a recent addition to R. Pipes
let you take the output of one function and send it directly to the
next, which is useful when you need to do many things to the same
dataset. There are two Pipes in R: 1) %>% (called
magrittr pipe; made available via the
magrittr package, installed automatically
with dplyr) or 2) |>
(called native R pipe and it comes preinstalled with R v4.1.0 onwards).
Both the pipes are, by and large, function similarly with a few
differences (For more information, check: https://www.tidyverse.org/blog/2023/04/base-vs-magrittr-pipe/).
The choice of which pipe to be used can be changed in the Global
settings in R studio and once that is done, you can type the pipe
with:
- Ctrl + Shift + M if you have a PC or Cmd + Shift + M if you have a Mac.
R
# the following example is run using magrittr pipe but the output will be same with the native pipe
videos %>%
filter(channel_title == "SABC News") %>%
select(channel_title,view_count:comment_count)
OUTPUT
# A tibble: 22 × 5
channel_title view_count like_count favorite_count comment_count
<chr> <dbl> <dbl> <dbl> <dbl>
1 SABC News 22079 125 0 105
2 SABC News 3339 18 0 7
3 SABC News 20674 122 0 100
4 SABC News 19715 68 0 132
5 SABC News 14485 52 0 19
6 SABC News 2329 18 0 7
7 SABC News 25313 133 0 131
8 SABC News 11466 55 0 28
9 SABC News 7297 74 0 34
10 SABC News 3147 12 0 2
# ℹ 12 more rowsR
#videos |>
# filter(channel_title == "SABC News") |>
# select(channel_title,view_count:comment_count)
In the above code, we use the pipe to send the videos
dataset first through filter() to keep rows where
channel_title is “SABC News”, then through
select() to keep only the columns from
channel_title to respondent_wall_type. Since
%>% takes the object on its left and passes it as the
first argument to the function on its right, we don’t need to explicitly
include the dataframe as an argument to the filter() and
select() functions any more.
Some may find it helpful to read the pipe like the word “then”. For
instance, in the above example, we take the dataframe
videos, then we filter for rows with
channel_title == "SABC News", then we
select columns view_count:comment_count. The
dplyr functions by themselves are somewhat
simple, but by combining them into linear workflows with the pipe, we
can accomplish more complex data wrangling operations.
If we want to create a new object with this smaller version of the data, we can assign it a new name:
R
videos_SABC_metrics <- videos %>%
filter(channel_title == "SABC News") %>%
select(view_count:comment_count)
videos_SABC_metrics
OUTPUT
# A tibble: 22 × 4
view_count like_count favorite_count comment_count
<dbl> <dbl> <dbl> <dbl>
1 22079 125 0 105
2 3339 18 0 7
3 20674 122 0 100
4 19715 68 0 132
5 14485 52 0 19
6 2329 18 0 7
7 25313 133 0 131
8 11466 55 0 28
9 7297 74 0 34
10 3147 12 0 2
# ℹ 12 more rowsNote that the final dataframe (videos_SABC_metrics) is
the leftmost part of this expression.
Exercise
Using pipes, subset the videos data to include videos
where the video is categorised as “News & Politics” and retain only
the metrics columns with values (views, likes and comments).
R
videos %>%
filter(video_category_label == "News & Politics") %>%
select(view_count,like_count,comment_count)
OUTPUT
# A tibble: 85 × 3
view_count like_count comment_count
<dbl> <dbl> <dbl>
1 939 12 NA
2 910 7 NA
3 213 6 2
4 22079 125 105
5 3339 18 7
6 20674 122 100
7 6745 10 NA
8 6087 47 0
9 11359 84 0
10 34911 299 NA
# ℹ 75 more rowsMutate
Frequently you’ll want to create new columns based on the values in
existing columns, for example to do unit conversions, or to find the
ratio of values in two columns. For this we’ll use
mutate().
We might be interested in the ratio of likes to comments:
R
videos %>%
mutate(ratio = like_count / comment_count)
OUTPUT
# A tibble: 200 × 33
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 27 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>, …We may be interested in investigating whether mentioning the EFF in the video title had any effect on the ratio of likes to comments.
To look at this relationship, we will first remove data from our dataset where comments were not recorded. These cases are recorded as “NA” in the dataset.
To remove these cases, we could insert a filter() in the
chain:
R
videos %>%
filter(!is.na(comment_count)) %>%
mutate(ratio = like_count / comment_count)
OUTPUT
# A tibble: 184 × 33
position randomise channel_id channel_title video_id url
<dbl> <dbl> <chr> <chr> <chr> <chr>
1 149 313 UCMwDXpWEVQVw4ZF7z-E4NoA StellenboschNews … v8XfpOi… http…
2 167 384 UCsqKkYLOaJ9oBwq9rxFyZMw SOUTH AFRICAN POL… lnLdo2k… http…
3 195 606 UC5G5Dy8-mmp27jo6Frht7iQ Umgosi Entertainm… XN6toca… http…
4 213 423 UCC1udUghY9dloGMuvZzZEzA The Tea World rh2Nz78… http…
5 145 452 UCaCcVtl9O3h5en4m-_edhZg Celeb LaLa Land 1l5GZ0N… http…
6 315 276 UCAurTjb6Ewz21vjfTs1wZxw NOSIPHO NZAMA j4Y022C… http…
7 190 321 UCBlX1mnsIFZRqsyRNvpW_rA Zandile Mhlambi gf2YNN6… http…
8 214 762 UClY87IoUANFZtswyC9GeecQ Beauty recipes AGJmRd4… http…
9 263 952 UCYeHXDmIJDiF1DVQM0qfNWQ Mama Shirat 19uG9pR… http…
10 30 785 UC8yH-uI81UUtEMDsowQyx1g SABC News PNom9RI… http…
# ℹ 174 more rows
# ℹ 27 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>, …The ! symbol negates the result of the
is.na() function. Thus, if is.na() returns a
value of TRUE (because the comment_count is
missing), the ! symbol negates this and says we only want
values of FALSE, where `comment_count is
not missing.
Exercise
Create a new dataframe from the videos data that meets
the following criteria: contains only the channel_title
column and, for all the videos which received at least one comment, a
new column called ratio containing a value that is equal to
the likes divided by the comments. Only the rows where
ratio is greater than 20 should be shown in the final
dataframe.
Hint: think about how the commands should be ordered to produce this data frame!
R
videos_ratio <- videos %>%
filter(comment_count>0) %>%
mutate(ratio = view_count/comment_count) %>%
filter(ratio > 20) %>%
select(channel_title, ratio)
videos_ratio
OUTPUT
# A tibble: 120 × 2
channel_title ratio
<chr> <dbl>
1 The Tea World 143.
2 Celeb LaLa Land 260.
3 Zandile Mhlambi 106.
4 Beauty recipes 3707.
5 Mama Shirat 650.
6 SABC News 210.
7 SABC News 477
8 SABC News 207.
9 SABC News 149.
10 SABC News 762.
# ℹ 110 more rowsSplit-apply-combine data analysis and the summarize() function
Many data analysis tasks can be approached using the
split-apply-combine paradigm: split the data into groups, apply
some analysis to each group, and then combine the results.
dplyr makes this very easy through the use
of the group_by() function.
The summarize() function
group_by() is often used together with
summarize(), which collapses each group into a single-row
summary of that group. group_by() takes as arguments the
column names that contain the categorical variables for
which you want to calculate the summary statistics. So to compute the
average household size by channel_title:
R
videos %>%
group_by(channel_title) %>%
summarize(mean_views = mean(view_count))
OUTPUT
# A tibble: 119 × 2
channel_title mean_views
<chr> <dbl>
1 2nacheki 54470.
2 Absolute Controversy 102
3 African Diaspora News Channel 18255
4 Al Jazeera English 113704
5 Ayanda Mafuyeka 29.5
6 Azana Jezile 65647
7 BANELE NOCUZE 210
8 Banetsi Tshetlo 7
9 Beauty recipes 263208
10 Blackish Blue TV 281
# ℹ 109 more rowsYou may also have noticed that the output from these calls doesn’t
run off the screen anymore. It’s one of the advantages of
tbl_df over dataframe.
You can also group by multiple columns:
R
videos %>%
group_by(channel_title, video_category_label) %>%
summarize(mean_views = mean(view_count))
OUTPUT
`summarise()` has grouped output by 'channel_title'. You can override using the
`.groups` argument.OUTPUT
# A tibble: 120 × 3
# Groups: channel_title [119]
channel_title video_category_label mean_views
<chr> <chr> <dbl>
1 2nacheki News & Politics 54470.
2 Absolute Controversy People & Blogs 102
3 African Diaspora News Channel News & Politics 18255
4 Al Jazeera English News & Politics 113704
5 Ayanda Mafuyeka People & Blogs 29.5
6 Azana Jezile Howto & Style 65647
7 BANELE NOCUZE People & Blogs 210
8 Banetsi Tshetlo Entertainment 7
9 Beauty recipes Howto & Style 263208
10 Blackish Blue TV News & Politics 281
# ℹ 110 more rowsNote that the output is a grouped tibble. To obtain an ungrouped
tibble, use the ungroup function:
R
videos %>%
group_by(channel_title, video_category_label) %>%
summarize(mean_views = mean(view_count)) %>%
ungroup()
OUTPUT
`summarise()` has grouped output by 'channel_title'. You can override using the
`.groups` argument.OUTPUT
# A tibble: 120 × 3
channel_title video_category_label mean_views
<chr> <chr> <dbl>
1 2nacheki News & Politics 54470.
2 Absolute Controversy People & Blogs 102
3 African Diaspora News Channel News & Politics 18255
4 Al Jazeera English News & Politics 113704
5 Ayanda Mafuyeka People & Blogs 29.5
6 Azana Jezile Howto & Style 65647
7 BANELE NOCUZE People & Blogs 210
8 Banetsi Tshetlo Entertainment 7
9 Beauty recipes Howto & Style 263208
10 Blackish Blue TV News & Politics 281
# ℹ 110 more rowsWhen grouping both by channel_title and
default_l_audio_language, we see rows in our table for
videos where the creator did not specify their default audio language.
We can exclude those data from our table using a filter step.
R
videos %>%
filter(!is.na(default_l_audio_language)) %>%
group_by(channel_title, default_l_audio_language) %>%
summarize(mean_views = mean(view_count))
OUTPUT
`summarise()` has grouped output by 'channel_title'. You can override using the
`.groups` argument.OUTPUT
# A tibble: 59 × 3
# Groups: channel_title [59]
channel_title default_l_audio_lang…¹ mean_views
<chr> <chr> <dbl>
1 2nacheki en-US 54470.
2 African Diaspora News Channel en 18255
3 Azana Jezile en-US 65647
4 Beauty recipes en 263208
5 Buhle N en-GB 405
6 By.MonaLisa zxx 3802
7 DoRo Lungani zu 19648
8 Duke University - The Fuqua School of Busi… en 4932
9 E News Mzansi en 2876.
10 Economic Freedom Fighters en-US 7164
# ℹ 49 more rows
# ℹ abbreviated name: ¹default_l_audio_languageOnce the data are grouped, you can also summarize multiple variables at the same time (and not necessarily on the same variable). For instance, we could add a column indicating the minimum household size for each channel_title for each group (members of an irrigation association vs not):
R
videos %>%
filter(!is.na(default_l_audio_language)) %>%
group_by(channel_title, default_l_audio_language) %>%
summarize(mean_views = mean(view_count),
max_views = max(view_count))
OUTPUT
`summarise()` has grouped output by 'channel_title'. You can override using the
`.groups` argument.OUTPUT
# A tibble: 59 × 4
# Groups: channel_title [59]
channel_title default_l_audio_lang…¹ mean_views max_views
<chr> <chr> <dbl> <dbl>
1 2nacheki en-US 54470. 98894
2 African Diaspora News Channel en 18255 18255
3 Azana Jezile en-US 65647 65647
4 Beauty recipes en 263208 263208
5 Buhle N en-GB 405 405
6 By.MonaLisa zxx 3802 3802
7 DoRo Lungani zu 19648 39223
8 Duke University - The Fuqua Scho… en 4932 4932
9 E News Mzansi en 2876. 5066
10 Economic Freedom Fighters en-US 7164 10996
# ℹ 49 more rows
# ℹ abbreviated name: ¹default_l_audio_languageIt is sometimes useful to rearrange the result of a query to inspect
the values. For instance, we can sort on max_views to put
the group with the smallest household first:
R
videos %>%
filter(!is.na(default_l_audio_language)) %>%
group_by(channel_title, default_l_audio_language) %>%
summarize(mean_views = mean(view_count),
max_views = max(view_count)) %>%
arrange(max_views)
OUTPUT
`summarise()` has grouped output by 'channel_title'. You can override using the
`.groups` argument.OUTPUT
# A tibble: 59 × 4
# Groups: channel_title [59]
channel_title default_l_audio_lang…¹ mean_views max_views
<chr> <chr> <dbl> <dbl>
1 Lit News en-GB 19 19
2 Peppers Echo en 23 23
3 GOLDGATOR TV en-GB 30 30
4 TalkOutLoud en-GB 67 67
5 Ile Eko Omoluabi en 103 103
6 Papa Khwatsi TOPIC en-GB 105 105
7 The Upright Man en 113 113
8 Fresh Trendz en 130 130
9 Hidden Truth State of Decay Sout… en-GB 151 151
10 Newcastle Advertiser en-GB 172 172
# ℹ 49 more rows
# ℹ abbreviated name: ¹default_l_audio_languageTo sort in descending order, we need to add the desc()
function. If we want to sort the results by decreasing order of views,
or reverse alphabetical order of the audio language to see languages
other than english:
R
videos %>%
filter(!is.na(default_l_audio_language)) %>%
group_by(channel_title, default_l_audio_language) %>%
summarize(mean_views = mean(view_count),
max_views = max(view_count)) %>%
arrange(desc(max_views))
OUTPUT
`summarise()` has grouped output by 'channel_title'. You can override using the
`.groups` argument.OUTPUT
# A tibble: 59 × 4
# Groups: channel_title [59]
channel_title default_l_audio_language mean_views max_views
<chr> <chr> <dbl> <dbl>
1 SABC News en 38085. 487961
2 Beauty recipes en 263208 263208
3 eNCA en 46485. 138612
4 Eyewitness News en-GB 103949 103949
5 2nacheki en-US 54470. 98894
6 Morexskinglow en 91347 91347
7 News24 en-GB 72606. 73622
8 Azana Jezile en-US 65647 65647
9 Renaldo Gouws en 50694 56890
10 Mama Shirat en-US 41626 41626
# ℹ 49 more rowsR
videos %>%
filter(!is.na(default_l_audio_language)) %>%
group_by(channel_title, default_l_audio_language) %>%
summarize(mean_views = mean(view_count),
max_views = max(view_count)) %>%
arrange(desc(default_l_audio_language))
OUTPUT
`summarise()` has grouped output by 'channel_title'. You can override using the
`.groups` argument.OUTPUT
# A tibble: 59 × 4
# Groups: channel_title [59]
channel_title default_l_audio_language mean_views max_views
<chr> <chr> <dbl> <dbl>
1 By.MonaLisa zxx 3802 3802
2 DoRo Lungani zu 19648 39223
3 NOSIPHO NZAMA zu 597 597
4 2nacheki en-US 54470. 98894
5 Azana Jezile en-US 65647 65647
6 Economic Freedom Fighters en-US 7164 10996
7 Gemini Baby en-US 435 435
8 Grey Net en-US 2619 2619
9 KHATHA WORLDWIDE en-US 490 490
10 Mama Shirat en-US 41626 41626
# ℹ 49 more rowsCounting
When working with data, we often want to know the number of
observations found for each factor or combination of factors. For this
task, dplyr provides count().
For example, if we wanted to count the number of rows of data for each
channel_title, we would do:
R
videos %>%
count(channel_title)
OUTPUT
# A tibble: 119 × 2
channel_title n
<chr> <int>
1 2nacheki 2
2 Absolute Controversy 1
3 African Diaspora News Channel 1
4 Al Jazeera English 1
5 Ayanda Mafuyeka 2
6 Azana Jezile 1
7 BANELE NOCUZE 3
8 Banetsi Tshetlo 1
9 Beauty recipes 1
10 Blackish Blue TV 1
# ℹ 109 more rowsFor convenience, count() provides the sort
argument to get results in decreasing order:
R
videos %>%
count(channel_title, sort = TRUE)
OUTPUT
# A tibble: 119 × 2
channel_title n
<chr> <int>
1 SABC News 22
2 eNCA 20
3 Newzroom Afrika 13
4 Umgosi Entertainment 5
5 StellenboschNews Com 4
6 BANELE NOCUZE 3
7 Economic Freedom Fighters 3
8 Renaldo Gouws 3
9 Trade with Free Money 3
10 2nacheki 2
# ℹ 109 more rowsExercise
Which YouTube video categories are used to describe the videos in the sample? (Use the video_category_label column.) Create a tibble of these categories and the number of videos in each of them, arranged in descending order.
R
videos %>%
count(video_category_label, sort=TRUE)
OUTPUT
# A tibble: 10 × 2
video_category_label n
<chr> <int>
1 News & Politics 85
2 People & Blogs 62
3 Entertainment 23
4 Howto & Style 14
5 Comedy 6
6 Education 4
7 Film & Animation 3
8 Autos & Vehicles 1
9 Nonprofits & Activism 1
10 Science & Technology 1Exercise (continued)
Use group_by() and summarize() to find the
mean, min, and max number of views for each YouTube channel
(channel_title). Also add the number of observations (hint: see
?n). Arrange the channels in descending order by the
channel’s maximum views.
R
videos %>%
group_by(channel_title) %>%
summarize(
mean_views = mean(view_count),
min_views = min(view_count),
max_views = max(view_count),
n = n() )%>%
arrange(desc(max_views))
OUTPUT
# A tibble: 119 × 5
channel_title mean_views min_views max_views n
<chr> <dbl> <dbl> <dbl> <int>
1 SABC News 36450. 2329 487961 22
2 Beauty recipes 263208 263208 263208 1
3 SamupertyZuluTV 144836 144836 144836 1
4 eNCA 36810. 910 138612 20
5 Al Jazeera English 113704 113704 113704 1
6 Eyewitness News 103949 103949 103949 1
7 2nacheki 54470. 10047 98894 2
8 Morexskinglow 91347 91347 91347 1
9 Newzroom Afrika 9501. 246 83542 13
10 Kay Monqo 81906 81906 81906 1
# ℹ 109 more rowsExercise (continued)
Excluding those videos that were set to not allow comments (comment_count =NA), what was the total number of comments posted on the videos in each month?
R
# if not already included, add month, year, and day columns
library(lubridate) # load lubridate if not already loaded
videos %>%
filter(!is.na(comment_count)) %>%
mutate(month = month(published_at_sql),
day = day(published_at_sql),
year = year(published_at_sql)) %>%
group_by(year, month) %>%
summarize(total_comments = sum(comment_count)) %>%
arrange(year,month)
OUTPUT
`summarise()` has grouped output by 'year'. You can override using the
`.groups` argument.OUTPUT
# A tibble: 20 × 3
# Groups: year [4]
year month total_comments
<dbl> <dbl> <dbl>
1 2020 1 33
2 2020 2 0
3 2020 4 168
4 2020 7 466
5 2020 8 29
6 2020 9 11215
7 2020 10 1
8 2020 11 1
9 2021 6 0
10 2021 9 17
11 2021 12 64
12 2022 1 14
13 2022 2 386
14 2022 3 4
15 2022 4 8
16 2022 7 76
17 2022 11 71
18 2023 1 7
19 2023 3 6
20 2023 6 241Key Points
- Use the
dplyrpackage to manipulate dataframes. - Use
select()to choose variables from a dataframe. - Use
filter()to choose data based on values. - Use
group_by()andsummarize()to work with subsets of data. - Use
mutate()to create new variables.