Class 6 Data Wrangling with R & Descriptive Analytics

Author

Affiliation

Dr Wei Miao

UCL School of Management

Published

October 15, 2025

1 Data Wrangling with R

1.1 Data Frame Basics

A data frame is the R object that we will deal with most of the time in the MSc programme. You can think of a data.frame as a spreadsheet.
- Each row stands for an observation; usually a record for a customer.
- Each column stands for a variable; each column should have a unique name.
- Each column must contain the same data type, but different columns can store different data types.

1.2 Tidyverse in R

The tidyverse is a collection of R packages designed for data science. It includes packages for data manipulation, visualisation, and more.

1.3 Install and Load the `dplyr` package

In R, we use the dplyr package for data cleaning and manipulation.¹

Code

pacman::p_load(dplyr)

Load a CSV-format dataset called data_full using read.csv()

Code

data_full <- read.csv("https://www.dropbox.com/scl/fi/2q7ppqtyca0pd3j486osl/data_full.csv?rlkey=gsyk51q27vd1skek4qpn5ikgm&dl=1")

To browse the whole dataset, simply click the object in the Environment pane.

1.4 First Look at the Dataset

Which variables does the dataset have? What are the data types of each variable?

Code

glimpse(data_full)

Rows: 2,000
Columns: 22
$ ID                  <int> 5524, 2174, 4141, 6182, 5324, 7446, 965, 6177, 485…
$ MntWines            <int> 635, 11, 426, 11, 173, 520, 235, 76, 14, 28, 5, 6,…
$ MntFruits           <int> 88, 1, 49, 4, 43, 42, 65, 10, 0, 0, 5, 16, 61, 2, …
$ MntMeatProducts     <int> 546, 6, 127, 20, 118, 98, 164, 56, 24, 6, 6, 11, 4…
$ MntFishProducts     <int> 172, 2, 111, 10, 46, 0, 50, 3, 3, 1, 0, 11, 225, 3…
$ MntSweetProducts    <int> 88, 1, 21, 3, 27, 42, 49, 1, 3, 1, 2, 1, 112, 5, 1…
$ MntGoldProds        <int> 88, 6, 42, 5, 15, 14, 27, 23, 2, 13, 1, 16, 30, 14…
$ NumDealsPurchases   <int> 3, 2, 1, 2, 5, 2, 4, 2, 1, 1, 1, 1, 1, 3, 1, 1, 3,…
$ NumWebPurchases     <int> 8, 1, 8, 2, 5, 6, 7, 4, 3, 1, 1, 2, 3, 6, 1, 7, 3,…
$ NumCatalogPurchases <int> 10, 1, 2, 0, 3, 4, 3, 0, 0, 0, 0, 0, 4, 1, 0, 6, 0…
$ NumStorePurchases   <int> 4, 2, 10, 4, 6, 10, 7, 4, 2, 0, 2, 3, 8, 5, 3, 12,…
$ NumWebVisitsMonth   <int> 7, 5, 4, 6, 5, 6, 6, 8, 9, 20, 7, 8, 2, 6, 8, 3, 8…
$ Complain            <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ Response            <int> 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,…
$ Year_Birth          <int> 1957, 1954, 1965, 1984, 1981, 1967, 1971, 1985, 19…
$ Education           <chr> "Graduation", "Graduation", "Graduation", "Graduat…
$ Marital_Status      <chr> "Single", "Single", "Together", "Together", "Marri…
$ Income              <int> 58138, 46344, 71613, 26646, 58293, 62513, 55635, 3…
$ Kidhome             <int> 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1,…
$ Teenhome            <int> 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1,…
$ Dt_Customer         <chr> "04/09/2012", "08/03/2014", "21/08/2013", "10/02/2…
$ Recency             <int> 58, 38, 26, 26, 94, 16, 34, 32, 19, 68, 11, 59, 82…

1.5 First Few Rows of the Dataset

We can use slice_head() to view the first few rows of the dataset. Similarly, slice_tail() can be used to view the last few rows of the dataset.

Code

slice_head(data_full, n = 3)

1.6 Extract a Single Column

We can use the $ operator to extract a single column from a data frame.
For example, data_full$Income extracts the Income column from the data_full data frame, returning a vector of income values.

Code

# extract the Income column and show the first 5 values
data_full$Income[1:5]

[1] 58138 46344 71613 26646 58293

Code

# compute the mean of the Income column, ignoring missing values
mean(data_full$Income, na.rm = TRUE)

[1] 52139.7

1.7 Common Data Wrangling Operations

Filter rows (filter)
Sort rows (arrange)
Select columns (select)
Generate new columns (mutate)
Group-wise aggregation (group_by)
Merge datasets (join)

1.8 Subset Rows Based on Conditions: `filter`

We can use filter() to select rows that meet certain logical criteria.

Important: To keep the new subset of data in RStudio, assign it to a new object.

Example: From data_full, find customers who are single

Code

# keep only single customers
filter(data_full, Marital_Status == "Single" )

1.9 The Pipe Operator

1.9.1 Pipe Operator

%>% passes the object in front as the first argument of the subsequent function.²

The tidyverse is designed to work with the pipe operator, using a consistent syntax.

1.10 Example of the Pipe Operator

Code

# without using pipe
filter(data_full, Marital_Status == 'Single')

# with pipe 
data_full %>% filter(Marital_Status == 'Single')

1.11 Why Do We Need Pipe Operator for Data Wrangling?

Exercise: find single customers who have a PhD without using the pipe.

Code

filter(filter(data_full, Marital_Status == "Single"), Education == "PhD")

Exercise: find single customers who have a PhD using the pipe.

Code

data_full_single_PhD <- data_full %>%
  filter(Marital_Status == 'Single') %>%
  filter(Education == 'PhD')
  
## You can even continue with more filter steps with more pipe operators

The pipe works like a conveyor belt in a factory, passing the intermediate outputs from the previous data wrangling step to the next step for further processing until you finish your data wrangling task.

This makes your code more readable and easier to debug. You can chain multiple (as many as) data wrangling steps together in a single pipeline.

1.12 Sort Rows: `arrange`

arrange() orders the rows by the values of selected columns.
Ascending order by default; for descending order, put a minus sign in front of the variable.
Allows multiple sorting variables, separated by a comma.
Example: sort customers based on income in descending order.

Code

data_full %>% 
  arrange(-Income) %>%
  slice_head(n = 10)

Exercise: sort customers based on income in descending order and age in ascending order.

Code

data_full %>% 
  mutate(Age = 2025 - Year_Birth) %>%
  arrange(-Income, Age) %>%
  slice_head(n = 10) # only show the first 10 rows using head(10)

1.13 Generate New Variables: `mutate`

mutate() generates new variables in the dataset while preserving existing variables.
Example: create a new variable named Age from Year_Birth.

Code

data_full %>%
  mutate(Age = 2025 - Year_Birth) %>%
  slice_head(n = 10)

Exercise: create a new variable named totalkids, which is the sum of Kidhome and Teenhome.

Code

data_full %>%
  mutate(totalkids = Kidhome + Teenhome) %>%
  slice_head(n = 10)

1.14 Aggregation by Groups: `group_by`

group_by() allows us to aggregate data by group and compute statistics for each group.

Code

# group by marital status
data_full %>%
    group_by(Marital_Status)

Internally, the dataset is already grouped based on the specified variable(s).

1.15 Aggregation by Groups: `group_by() %>% summarise()`

The summarise() function is typically used after group_by() to create a new data frame of summary statistics for each group.
It collapses each group into a single row.
Inside summarise(), you can compute various summary statistics (e.g., mean() for mean, sum() for sum, n() for count, sd() for standard deviation) for each group. The result is a new data frame with one row per group, containing the grouping variables and the calculated summary statistics.

Code

# compute the average income for each marital status group
data_full %>%
  group_by(Marital_Status) %>% 
  summarise(avg_income = mean(Income, na.rm = TRUE)) %>%
  ungroup()

What if you replace summarise() with mutate()?

1.16 Comparison of `summarise()` and `mutate()`

mutate() will add a new column to the original data frame. The new column will contain the group-wise calculation, repeated for each row within the group. The number of rows in the data frame remains unchanged.
In contrast, summarise() collapses the data frame to one row per group, containing only the grouping variables and the newly created summary statistics.

1.17 Aggregation by Groups: `group_by()` Multiple Groups

You can also group by multiple variables. For example, we can compute the average income for each combination of Marital_Status and Education.

Code

# compute the average income for each marital, education group
data_full %>%
  group_by(Marital_Status,Education) %>% 
  summarise(avg_income = mean(Income, na.rm = TRUE)) %>% 
  ungroup() %>%
  slice_head(n = 5)

2 Data Cleaning

2.1 Missing Values

In R, missing values are represented by the symbol NA (i.e., not available).
Most statistical models cannot handle missing values, so we need to deal with them in R.
If there are just a few missing values: remove them from analysis.
If there are many missing values: need to collect better data or replace them with appropriate values:
- mean/median/statistical imputation

2.2 Outliers

Outliers are data points that are significantly different from other data points in the dataset, such as unusually large and small values.
Winsorisation is a common method to deal with outliers. It replaces the extreme values with the nearest non-extreme value, usually the 99th or 1st percentile (or other thresholds as appropriate).

3 Descriptive Analytics

3.1 Two Major Tasks of Descriptive Analytics

You can think of descriptive analytics as creating a dashboard to display the key information you would like to know for your business. For instance:

Describe the data for your business purposes
- “How much do our customers spend each month on average?”
- “What percentage of our customers are unprofitable?”
- “What is the difference between the retention rates across different demographic groups?”
Conduct statistical tests (such as t-tests) for hypothesis testing.

Is there a significant difference in the average spending between different age/gender groups?
- Based on our test mailing, can we conclude that ad-copy A works better than ad-copy B?

3.2 Example of Descriptive Analytics Dashboard

3.3 Summary Statistics

Summary statistics are used to summarise a set of observations, in order to communicate the largest amount of information as simply as possible.
There are two main types of summary statistics used in evaluation:
- measures of central tendency: number of observations, mean, min, 25th percentile, median, 75th percentile, max, etc.
- measures of dispersion: range and standard deviation.

3.4 Summary Statistics with R

In R, a powerful package to report summary statistics is called modelsummary.
datasummary_skim() is a shortcut to compute basic summary statistics.
For more features, refer to the package tutorial here

Code

pacman::p_load(modelsummary)
data_full %>%
  datasummary_skim(type = "numeric")

	Unique	Missing Pct.	Mean	SD	Min	Median	Max
ID	2000	0	5599.2	3242.0	0.0	5492.0	11191.0
MntWines	738	0	306.1	338.3	0.0	176.5	1493.0
MntFruits	157	0	26.4	39.9	0.0	8.0	199.0
MntMeatProducts	532	0	167.9	225.3	0.0	68.0	1725.0
MntFishProducts	179	0	37.6	54.6	0.0	12.0	259.0
MntSweetProducts	175	0	27.5	41.8	0.0	8.0	263.0
MntGoldProds	207	0	43.8	51.7	0.0	24.0	362.0
NumDealsPurchases	15	0	2.3	2.0	0.0	2.0	15.0
NumWebPurchases	15	0	4.1	2.8	0.0	4.0	27.0
NumCatalogPurchases	14	0	2.7	3.0	0.0	2.0	28.0
NumStorePurchases	14	0	5.8	3.3	0.0	5.0	13.0
NumWebVisitsMonth	15	0	5.3	2.5	0.0	6.0	20.0
Complain	2	0	0.0	0.1	0.0	0.0	1.0
Response	2	0	0.2	0.4	0.0	0.0	1.0
Year_Birth	59	0	1968.8	12.0	1893.0	1970.0	1996.0
Income	1783	1	52139.7	21492.4	1730.0	51518.0	162397.0
Kidhome	3	0	0.4	0.5	0.0	0.0	2.0
Teenhome	3	0	0.5	0.5	0.0	0.0	2.0
Recency	100	0	49.2	29.0	0.0	50.0	99.0

Code

data_full %>%
  datasummary_skim(type = "categorical")

Warning: These variables were omitted because they include more than 50 levels:
Dt_Customer.

		N	%
Education	2n Cycle	185	9.2
	Basic	43	2.1
	Graduation	992	49.6
	Master	327	16.4
	PhD	453	22.6
Marital_Status	Alone	3	0.1
	Divorced	206	10.3
	Married	767	38.4
	Single	436	21.8
	Together	521	26.0
	Widow	67	3.4

4 M&S Descriptive Analytics

Let’s move on to the Quarto document to see how to apply descriptive analytics to the M&S dataset.

4.1 After-Class

(essential) Cheatsheet for dplyr. This cheatsheet provides a quick reference for the most commonly used functions in the dplyr package. It’s very important to familiarise yourself with these functions as you will use them a lot in your future projects.
(optional) Complete the after-class exercise for Week 3. If you still have time, you can also complete the DataCamp exercise on the dplyr package. The link is here.

Footnotes

pacman is a package management tool that makes our lives easier by loading multiple packages at once.↩︎
Starting from R 4.1, base R introduced the native pipe operator |>↩︎

--- date: "`r (first_date + lubridate::dweeks(2))`" title: "Class 6 Data Wrangling with R & Descriptive Analytics" execute: echo: true --- # Data Wrangling with R ## Data Frame Basics - A data frame is the R object that we will deal with most of the time in the MSc programme. You can think of a `data.frame` as a spreadsheet. - Each row stands for an **observation**; usually a record for a customer. - Each column stands for a **variable**; each column should have a unique name. - Each column must contain the same data type, but different columns can store different data types. ## Tidyverse in R - The [`tidyverse`](https://www.tidyverse.org) is a collection of R packages designed for data science. It includes packages for data manipulation, visualisation, and more. ```{r} #| echo: false #| fig-align: 'center' #| out-width: '10cm' knitr::include_graphics('images/Week 3/tidyverse.png') ``` ## Install and Load the `dplyr` package - In R, we use the `dplyr` package for data cleaning and manipulation.[^1] [^1]: `pacman` is a package management tool that makes our lives easier by loading multiple packages at once. ```{r} #| echo: true pacman::p_load(dplyr) ``` - Load a CSV-format dataset called `data_full` using `read.csv()` ```{r} #| echo: true data_full <- read.csv("https://www.dropbox.com/scl/fi/2q7ppqtyca0pd3j486osl/data_full.csv?rlkey=gsyk51q27vd1skek4qpn5ikgm&dl=1") ``` - To browse the whole dataset, simply click the object in the Environment pane. ## First Look at the Dataset - Which variables does the dataset have? What are the data types of each variable? \tiny ```{r} glimpse(data_full) ``` ## First Few Rows of the Dataset - We can use `slice_head()` to view the first few rows of the dataset. Similarly, `slice_tail()` can be used to view the last few rows of the dataset. \vspace{1em} \tiny ```{r} #| echo: true slice_head(data_full, n = 3) ``` ## Extract a Single Column - We can use the `$` operator to extract a single column from a data frame. - For example, `data_full$Income` extracts the `Income` column from the `data_full` data frame, returning a vector of income values. ```{r} #| echo: true # extract the Income column and show the first 5 values data_full$Income[1:5] # compute the mean of the Income column, ignoring missing values mean(data_full$Income, na.rm = TRUE) ``` ## Common Data Wrangling Operations - **Filter rows** (`filter`) - **Sort rows** (`arrange`) - **Select columns** (`select`) - **Generate new columns** (`mutate`) - **Group-wise aggregation** (`group_by`) - Merge datasets (`join`) ## Subset Rows Based on Conditions: `filter` - We can use `filter()` to select rows that meet certain logical criteria. ```{r} #| echo: false #| fig-align: 'center' knitr::include_graphics('images/Week 3/filter.png') ``` - **Important**: To keep the new subset of data in RStudio, assign it to a new object. ::: {.content-visible when-format="beamer"} ## Subset Rows Based on Conditions: `filter` **Example**: From `data_full`, find customers who are single ```{r} #| eval: false #| echo: true # keep only single customers filter(data_full, Marital_Status == "Single" ) ``` ::: ::: {.content-hidden when-format="beamer"} **Example**: From `data_full`, find customers who are single ```{r} # keep only single customers filter(data_full, Marital_Status == "Single" ) ``` ::: ## The Pipe Operator ::: block ### Pipe Operator `%>%` passes the **object in front** as the **first argument** of the **subsequent function**.^[Starting from R 4.1, base R introduced the native pipe operator `|>`] ::: ```{r} #| echo: false #| fig-align: 'center' knitr::include_graphics('images/Week 3/pipeillustration.png') ``` - The tidyverse is designed to work with the pipe operator, using a consistent syntax. ## Example of the Pipe Operator ```{r} #| eval: false #| echo: true # without using pipe filter(data_full, Marital_Status == 'Single') # with pipe data_full %>% filter(Marital_Status == 'Single') ``` ## Why Do We Need Pipe Operator for Data Wrangling? - **Exercise**: find **single** customers who have a **PhD** without using the pipe. ::: {.content-visible when-format="beamer"} ```{r} #| eval: false #| echo: true # Write the code below ``` ::: ::: {.content-hidden when-format="beamer"} ```{r} #| eval: false #| echo: true filter(filter(data_full, Marital_Status == "Single"), Education == "PhD") ``` ::: ::: {.content-visible when-format="beamer"} ## Why Do We Need Pipe Operator for Data Wrangling? ::: - **Exercise**: find **single** customers who have a **PhD** using the pipe. ```{r} #| eval: false #| echo: true data_full_single_PhD <- data_full %>% filter(Marital_Status == 'Single') %>% filter(Education == 'PhD') ## You can even continue with more filter steps with more pipe operators ``` ::: {.content-visible when-format="beamer"} ## Why Do We Need Pipe Operator for Data Wrangling? ::: - The pipe works like a conveyor belt in a factory, passing the intermediate outputs from the previous data wrangling step to the next step for further processing until you finish your data wrangling task. ```{r} #| echo: false #| fig-align: 'center' knitr::include_graphics('images/Week 3/pipe.png') ``` - This makes your code more readable and easier to debug. You can chain multiple (as many as) data wrangling steps together in a single pipeline. ## Sort Rows: `arrange` - **`arrange()`** orders the rows by the values of selected columns. - Ascending order by default; for descending order, put a minus sign in front of the variable. - Allows multiple sorting variables, separated by a comma. - **Example**: sort customers based on income in descending order. ::: {.content-visible when-format="beamer"} ```{r} #| eval: false #| echo: true data_full %>% arrange(-Income) ``` ::: ::: {.content-hidden when-format="beamer"} ```{r} data_full %>% arrange(-Income) %>% slice_head(n = 10) ``` ::: - **Exercise**: sort customers based on income in descending order and age in ascending order. ::: {.content-visible when-format='html'} ```{r} #| eval: true #| echo: true data_full %>% mutate(Age = 2025 - Year_Birth) %>% arrange(-Income, Age) %>% slice_head(n = 10) # only show the first 10 rows using head(10) ``` ::: ## Generate New Variables: `mutate` - **`mutate()`** generates new variables in the dataset while preserving existing variables. - **Example**: create a new variable named `Age` from `Year_Birth`. ::: {.content-visible when-format="beamer"} ```{r} #| eval: false #| echo: true data_full %>% mutate(Age = 2025 - Year_Birth) ``` ::: ::: {.content-hidden when-format="beamer"} ```{r} data_full %>% mutate(Age = 2025 - Year_Birth) %>% slice_head(n = 10) ``` ::: - **Exercise**: create a new variable named `totalkids`, which is the sum of `Kidhome` and `Teenhome`. ::: {.content-hidden when-format="beamer"} ```{r} data_full %>% mutate(totalkids = Kidhome + Teenhome) %>% slice_head(n = 10) ``` ::: ## Aggregation by Groups: `group_by` - `group_by()` allows us to aggregate data by group and compute statistics for each group. ```{r} #| eval: false #| echo: true # group by marital status data_full %>% group_by(Marital_Status) ``` - Internally, the dataset is already grouped based on the specified variable(s). ```{r} #| echo: false #| fig-align: 'center' knitr::include_graphics('images/Week 3/group_by.png') ``` ## Aggregation by Groups: `group_by() %>% summarise()` - The `summarise()` function is typically used after `group_by()` to create a new data frame of summary statistics for each group. - It collapses each group into a single row. - Inside `summarise()`, you can compute various summary statistics (e.g., `mean()` for mean, `sum()` for sum, `n()` for count, `sd()` for standard deviation) for each group. The result is a new data frame with one row per group, containing the grouping variables and the calculated summary statistics. ```{r} #| eval: false #| echo: true # compute the average income for each marital status group data_full %>% group_by(Marital_Status) %>% summarise(avg_income = mean(Income, na.rm = TRUE)) %>% ungroup() ``` - What if you replace `summarise()` with `mutate()`? ## Comparison of `summarise()` and `mutate()` - `mutate()` will add a new column to the original data frame. The new column will contain the group-wise calculation, repeated for each row within the group. The number of rows in the data frame remains unchanged. - In contrast, `summarise()` collapses the data frame to one row per group, containing only the grouping variables and the newly created summary statistics. ## Aggregation by Groups: `group_by()` Multiple Groups - You can also group by multiple variables. For example, we can compute the average income for each combination of `Marital_Status` and `Education`. ```{r} #| message: false #| eval: false #| echo: true # compute the average income for each marital, education group data_full %>% group_by(Marital_Status,Education) %>% summarise(avg_income = mean(Income, na.rm = TRUE)) %>% ungroup() %>% slice_head(n = 5) ``` # Data Cleaning ## Missing Values - In R, missing values are represented by the symbol `NA` (i.e., not available). - Most statistical models cannot handle missing values, so we need to deal with them in R. - If there are just a few missing values: remove them from analysis. - If there are many missing values: need to collect better data or replace them with appropriate values: - mean/median/statistical imputation ## Outliers - **Outliers** are data points that are significantly different from other data points in the dataset, such as unusually large and small values. - **Winsorisation** is a common method to deal with outliers. It replaces the extreme values with the nearest non-extreme value, usually the 99th or 1st percentile (or other thresholds as appropriate). # Descriptive Analytics ```{r} #| echo: false pacman::p_load(dplyr, modelsummary) data_full <- read.csv("https://www.dropbox.com/scl/fi/2q7ppqtyca0pd3j486osl/data_full.csv?rlkey=gsyk51q27vd1skek4qpn5ikgm&dl=1") ``` ## Two Major Tasks of Descriptive Analytics - You can think of descriptive analytics as **creating a dashboard** to display the key information you would like to know for your business. For instance: 1. Describe the data for your business purposes - "How much do our customers spend each month on average?" - "What percentage of our customers are unprofitable?" - "What is the difference between the retention rates across different demographic groups?" 2. Conduct statistical tests (such as t-tests) for hypothesis testing. - Is there a significant difference in the average spending between different age/gender groups? - Based on our test mailing, can we conclude that ad-copy A works better than ad-copy B? ## Example of Descriptive Analytics Dashboard ```{r} #| echo: false #| fig-align: 'center' #| out-width: 9cm knitr::include_graphics("images/Week 3/YoutubeStudio2025.png") ``` ## Summary Statistics - **Summary statistics** are used to summarise a set of observations, in order to communicate the largest amount of information as simply as possible. - There are two main types of summary statistics used in evaluation: - **measures of central tendency**: number of observations, mean, min, 25th percentile, median, 75th percentile, max, etc. - **measures of dispersion:** range and standard deviation. ## Summary Statistics with R - In R, a powerful package to report summary statistics is called `modelsummary`. - `datasummary_skim()` is a shortcut to compute basic summary statistics. - For more features, refer to the package tutorial [here](https://vincentarelbundock.github.io/modelsummary/articles/datasummary.html) ::: {.content-visible when-format="beamer"} ```{r} #| eval: false #| echo: true pacman::p_load(modelsummary) ## Summary statistics for numeric variables data_full %>% datasummary_skim(type = "numeric") ## Summary statistics for categorical variables data_full %>% datasummary_skim(type = "categorical") ``` ::: ::: {.content-visible when-format="html"} ```{r} #| eval: true #| echo: true pacman::p_load(modelsummary) data_full %>% datasummary_skim(type = "numeric") data_full %>% datasummary_skim(type = "categorical") ``` ::: # M&S Descriptive Analytics Let's move on to the Quarto document to see how to apply descriptive analytics to the M&S dataset. ## After-Class - (essential) [Cheatsheet for `dplyr`](https://rstudio.github.io/cheatsheets/html/data-transformation.html). This cheatsheet provides a quick reference for the most commonly used functions in the `dplyr` package. It's very important to familiarise yourself with these functions as you will use them a lot in your future projects. - (optional) Complete the after-class exercise for Week 3. If you still have time, you can also complete the DataCamp exercise on the dplyr package. The link is [here](https://app.datacamp.com/search?q=dplyr&type=practice&type=assessment&type=exercise&technology=R&topic=Data+Manipulation).

1 Data Wrangling with R

1.1 Data Frame Basics

1.2 Tidyverse in R

1.3 Install and Load the dplyr package

1.4 First Look at the Dataset

1.5 First Few Rows of the Dataset

1.6 Extract a Single Column

1.7 Common Data Wrangling Operations

1.8 Subset Rows Based on Conditions: filter

1.9 The Pipe Operator

1.9.1 Pipe Operator

1.10 Example of the Pipe Operator

1.11 Why Do We Need Pipe Operator for Data Wrangling?

1.12 Sort Rows: arrange

1.13 Generate New Variables: mutate

1.14 Aggregation by Groups: group_by

1.15 Aggregation by Groups: group_by() %>% summarise()

1.16 Comparison of summarise() and mutate()

1.17 Aggregation by Groups: group_by() Multiple Groups

2 Data Cleaning

2.1 Missing Values

2.2 Outliers

3 Descriptive Analytics

3.1 Two Major Tasks of Descriptive Analytics

3.2 Example of Descriptive Analytics Dashboard

3.3 Summary Statistics

3.4 Summary Statistics with R

4 M&S Descriptive Analytics

4.1 After-Class

Footnotes

1.3 Install and Load the `dplyr` package

1.8 Subset Rows Based on Conditions: `filter`

1.12 Sort Rows: `arrange`

1.13 Generate New Variables: `mutate`

1.14 Aggregation by Groups: `group_by`

1.15 Aggregation by Groups: `group_by() %>% summarise()`

1.16 Comparison of `summarise()` and `mutate()`

1.17 Aggregation by Groups: `group_by()` Multiple Groups