Commentary :

This code performs the following actions:

Calculates the mean of the 'unit_price' and 'unit_cost' columns of the data frame 'df_product', rounding the results to the nearest integer.
price_mean <- round(mean(df_product$unit_price, na.rm = TRUE))
cost_mean <- round(mean(df_product$unit_cost, na.rm = TRUE))

round() function is used to round off the mean values to nearest integers.
na.rm=TRUE is used to remove any missing values from the calculations.

Replaces any missing values in the 'unit_price' and 'unit_cost' columns of 'df_product' with their respective mean values.
df_product_2 <- df_product %>%
replace_na(list(unit_price = price_mean, unit_cost = cost_mean))

replace_na() is used from the 'tidyr' package to replace missing values with the mean values calculated earlier.

The %>% operator (also known as the pipe operator) is used to chain the two actions together, where the output of the first action is used as input for the second action.

Uses the sapply() function to calculate the number of missing values in each column of the modified 'df_product_2' data frame.

sapply(df_product_2, function(x) sum(is.na(x)))
sapply() function is used to apply a function (function(x) sum(is.na(x))) to each column of 'df_product_2' data frame.

The sum(is.na(x)) part of the function counts the number of missing values (NA) in each column (x) of 'df_product_2' data frame.

Commentary :

This code performs the following actions:

Calculates the median of the 'unit_price' and 'unit_cost' columns of the data frame 'df_product', rounding the results to the nearest integer.
price_median <- round(median(df_product$unit_price, na.rm = TRUE))
cost_median <- round(median(df_product$unit_cost, na.rm = TRUE))

median() function is used to calculate the median values of the 'unit_price' and 'unit_cost' columns.

round() function is used to round off the median values to nearest integers.

na.rm=TRUE is used to remove any missing values from the calculations.

Replaces any missing values in the 'unit_price' and 'unit_cost' columns of 'df_product' with their respective median values.
df_product_3 <- df_product %>%
replace_na(list(unit_price = price_median, unit_cost = cost_median))

replace_na() is used from the 'tidyr' package to replace missing values with the median values calculated earlier

The %>% operator (also known as the pipe operator) is used to chain the two actions together, where the output of the first action is used as input for the second action.

Uses the sapply() function to calculate the number of missing values in each column of the modified 'df_product_3' data frame.
sapply(df_product_3, function(x) sum(is.na(x)))

sapply() function is used to apply a function (function(x) sum(is.na(x))) to each column of 'df_product_3' data frame.

The sum(is.na(x)) part of the function counts the number of missing values (NA) in each column (x) of 'df_product_3' data frame.

Commentary :

This code performs the following actions:

Groups the 'df_product' data frame by the 'category_small_cd' column.
df_product_4 <- df_product %>%
group_by(category_small_cd)

%>% operator is used to chain the two actions together, where the output of the first action is used as input for the second action.

'group_by()' function from the 'dplyr' package is used to group the 'df_product' data frame by the 'category_small_cd' column.

Calculates the median of the 'unit_price' and 'unit_cost' columns for each group in the grouped data frame, rounding the results to the nearest integer, and summarizes the results into a new data frame.
summarise(price_median = round(median(unit_price, na.rm = TRUE)), cost_median = round(median(unit_cost, na.rm = TRUE)), .groups = "drop")

'summarise()' function is used from the 'dplyr' package to calculate the median of the 'unit_price' and 'unit_cost' columns for each group in the grouped data frame.

'round()' function is used to round off the median values to nearest integers.

na.rm=TRUE is used to remove any missing values from the calculations.

.groups="drop" argument is used to remove the grouping information from the output.

Joins the summarized data frame with the original 'df_product' data frame by the 'category_small_cd' column.
inner_join(df_product, by = "category_small_cd")

'inner_join()' function is used from the 'dplyr' package to join the summarized data frame with the original 'df_product' data frame by the 'category_small_cd' column.

The resulting data frame will have all the columns from both data frames.

Replaces any missing values in the 'unit_price' and 'unit_cost' columns of the joined data frame with their respective median values for the corresponding 'category_small_cd' group.
mutate(unit_price = ifelse(is.na(unit_price), price_median, unit_price), unit_cost = ifelse(is.na(unit_cost), cost_median, unit_cost))

'mutate()' function is used from the 'dplyr' package to create new columns with modified values in the joined data frame.

ifelse() function is used to check if a value is missing (NA) in the 'unit_price' and 'unit_cost' columns and replace it with the corresponding median value for the 'category_small_cd' group it belongs to.

Uses the sapply() function to calculate the number of missing values in each column of the modified 'df_product_4' data frame.
sapply(df_product_4, function(x) sum(is.na(x)))

sapply() function is used to apply a function (function(x) sum(is.na(x))) to each column of 'df_product_4' data frame.

The sum(is.na(x)) part of the function counts the number of missing values (NA) in each column (x) of 'df_product_4' data frame.

Commentary :

This code performs some data manipulation on a data frame called df_receipt, which likely contains information about sales transactions. Here is a breakdown of what each line of code is doing:

df_receipt_2019 <- df_receipt %>%
  filter(20190101 <= sales_ymd & sales_ymd <= 20191231) %>%
  group_by(customer_id) %>% summarise(amount_2019 = sum(amount), .groups = "drop")

This line of code creates a new data frame called df_receipt_2019 which filters the original df_receipt data frame to only include sales transactions that occurred between January 1, 2019 and December 31, 2019. It then groups the data by customer_id and calculates the total amount spent by each customer during that time period, storing the result in a new column called amount_2019.

df_receipt_all <- df_receipt %>%
  group_by(customer_id) %>%
  summarise(amount_all = sum(amount), .groups = "drop")

This line of code creates a new data frame called df_receipt_all which groups the df_receipt data frame by customer_id and calculates the total amount spent by each customer across all transactions, storing the result in a new column called amount_all.

df_sales_rate <- left_join(df_customer["customer_id"], df_receipt_2019, by = "customer_id") %>%
  left_join(df_receipt_all, by = "customer_id") %>%
  replace_na(list(amount_2019 = 0, amount_all = 0)) %>%
  mutate(amount_rate = ifelse(amount_all == 0, 0, amount_2019 / amount_all)) %>%
  filter(amount_rate > 0) %>%
  slice(1:10)

This line of code creates a new data frame called df_sales_rate by joining together the df_customer data frame (which likely contains information about customer demographics) with the df_receipt_2019 and df_receipt_all data frames on the customer_id column. It then replaces any missing values in the amount_2019 and amount_all columns with 0, calculates a new column called amount_rate which represents the proportion of a customer's total spending that occurred in 2019 (i.e. amount_2019 / amount_all), filters the data to only include customers who spent money in 2019 (i.e. amount_rate > 0), and finally selects the top 10 customers by their amount_rate values (i.e. slice(1:10)).

Overall, this code is identifying the top 10 customers who spent the highest proportion of their total spending in 2019, which could be useful for targeted marketing or customer retention efforts.

Commentary :

This code performs the following tasks:

It creates a new data frame called df_geocode_1 by selecting three columns from the df_geocode data frame (postal_cd, longitude, and latitude).

The df_geocode_1 data frame is grouped by postal_cd, and the mean values of longitude and latitude for each group are computed using the summarise() function. The resulting data frame contains one row for each unique value of postal_cd, with columns postal_cd, m_longitude, and m_latitude.

The df_customer data frame is joined with the df_geocode_1 data frame using the inner_join() function, which matches rows based on the common postal_cd column. The resulting data frame contains all columns from both data frames.

The head() function is used to display the first 10 rows of the resulting data frame (df_customer_1). These rows include information about the customers and their geographical locations (latitude and longitude).

Commentary :

This code performs the following tasks:

It defines a custom function called calc_distance() which takes four parameters x1, y1, x2, and y2. The function calculates the distance between two geographical locations (specified by their latitude and longitude) using the Haversine formula. The calculated distance is returned in kilometers.

The df_customer_1 data frame is joined with the df_store data frame using the inner_join() function. The join is performed on the application_store_cd column from df_customer_1 and the store_cd column from df_store.

The resulting data frame is then renamed to reflect the two different address columns, using the rename() function.

A new column called distance is added to the resulting data frame using the mutate() function. The calc_distance() function is used to calculate the distance between the customer and the store. The function is called with four arguments: the mean latitude and longitude values from df_customer_1 (m_latitude and m_longitude) and the latitude and longitude values from df_store. The resulting distance values are stored in the distance column.

The resulting data frame is then subsetted using the select() function to include only the customer_id, customer_address, store_address, and distance columns.

Finally, the slice() function is used to display the first 10 rows of the resulting data frame. These rows include information about the customers, their addresses, the store addresses, and the distances between the customers and the stores.

Commentary :

This code performs the following steps:

Calculate the total sales amount for each customer in the df_receipt dataframe and store the results in a new dataframe df_sales_amount using the group_by() and summarise() functions.

df_sales_amount <- df_receipt %>% group_by(customer_id) %>% summarise(sum_amount = sum(amount), .groups = "drop")

Perform a left join between the df_customer and df_sales_amount dataframes based on the customer_id column, then use the mutate() function to replace any NA values in the sum_amount column with 0, and sort the resulting dataframe by descending order of the sum_amount and customer_id columns, and keep only the unique rows based on the customer_name and postal_cd columns.

df_customer_u <- left_join(df_customer, df_sales_amount, by = "customer_id") %>% mutate(sum_amount = ifelse(is.na(sum_amount), 0, sum_amount)) %>% arrange(desc(sum_amount), customer_id) %>% distinct(customer_name, postal_cd, .keep_all = TRUE)

Print out the number of rows in the original df_customer dataframe, the resulting df_customer_u dataframe, and the difference between them to see how many duplicate rows were removed.

print(paste( "df_customer_cnt:", nrow(df_customer), "df_customer_u_cnt:", nrow(df_customer_u), "diff:", nrow(df_customer) - nrow(df_customer_u)))

Commentary :

This code performs the following operations:

It performs an inner join between df_customer and df_customer_u, using the columns "customer_name" and "postal_cd" as the join keys.
The resulting dataframe is assigned to df_customer_n.

The column "customer_id.x" in df_customer_n is renamed to "customer_id".

The column "customer_id.y" in df_customer_u is renamed to "integration_id".

The number of unique values in the "customer_id" and "integration_id" columns of df_customer_n is calculated and the difference between these counts is printed.

The purpose of this code is to compare the number of unique customer IDs in the original df_customer dataframe with the number of unique customer IDs in the df_customer_u dataframe, which was created by joining df_customer with the total sales amount per customer (df_sales_amount). The assumption is that there may be duplicate customer names and postal codes in df_customer, which would result in a larger number of unique customer IDs in df_customer compared to df_customer_u. The difference between the two counts printed at the end of the code represents the number of duplicate customer IDs that were removed by the join operation.

Commentary :

This code is splitting a dataset into training and testing sets using the rsample package in R.

set.seed(71) sets the random seed to ensure that the results are reproducible.

df_sales_customer is created by grouping df_receipt by customer_id and calculating the sum of amount for each customer. Then, only the customers with a positive sum are kept.

df_tmp is created by joining df_customer and df_sales_customer on customer_id.

split is created using initial_split() function from rsample package. The prop parameter is set to 0.8, which means that the dataset is split into 80% training and 20% testing data.

df_customer_train and df_customer_test are the training and testing sets, respectively, obtained from split.

Finally, print() statements are used to confirm the proportion of training and testing data.

Commentary :

This code is an example of how to create time-based cross-validation folds using the createTimeSlices function from the caret package.

The first step of the code is to summarize the sales amount by month for the whole dataset using group_by and summarise functions. Then, the resulting data frame is renamed to have more meaningful column names.

The createTimeSlices function is then used to create three time-based folds for the data. The function takes the vector of time points (in this case, the sales month) and splits it into a set of training and testing indices for each fold. The initialWindow parameter specifies the size of the initial training window, the horizon parameter specifies the size of the testing window, the skip parameter specifies the amount of time to skip between each fold, and the fixedWindow parameter indicates whether the size of the testing window should be fixed. 

Finally, the df_train and df_test data frames are created for each fold using the indices generated by createTimeSlices. These data frames can be used for time-based cross-validation of a model.

Commentary :

The code is performing downsampling using the recipes package in R.

First, the total sales amount for each customer is calculated and joined with customer information. Then, a binary variable called is_buy_flag is created to indicate whether a customer has made a purchase or not based on the presence of sales amount.

Next, the recipe() function is used to create a recipe object. The step_downsample() function is then used to downsample the majority class (customers who did not make a purchase) to match the number of observations in the minority class (customers who made a purchase), using a random seed of 71.

The prep() function is used to prepare the recipe, and juice() function is used to extract the processed dataset. Finally, the resulting downsampling is verified by grouping the data by is_buy_flag and counting the number of observations in each group.

Commentary :

The code above is manipulating a data frame called "df_customer" that contains customer information. Here's what each line of code does:

df_gender_std = unique(df_customer[c("gender_cd", "gender")]) - This line of code creates a new data frame called df_gender_std. It selects two columns from the df_customer data frame, specifically the columns named "gender_cd" and "gender". Then, it uses the unique() function to remove any duplicate rows, creating a data frame that contains all unique combinations of gender codes and gender values in the original data frame.

df_customer_std = df_customer[, colnames(df_customer) != "gender"] - This line of code creates another new data frame called df_customer_std. It copies all columns from the df_customer data frame except for the "gender" column. This is done by selecting all columns whose names are not equal to "gender" using the colnames() function and the logical operator !=. The resulting data frame has the same rows as the original df_customer data frame but without the "gender" column.

Together, these two lines of code split the original df_customer data frame into two parts: one containing only unique gender codes and values, and one containing all other customer information except for the gender column.

Commentary :

The code above is manipulating two data frames called "df_product" and "df_category". Here's what each line of code does:

df_product_full <- inner_join(df_product, df_category[c("category_small_cd", "category_major_name", "category_medium_name", "category_small_name")], by = "category_small_cd") - This line of code creates a new data frame called df_product_full. It uses the inner_join() function to combine the df_product and df_category data frames based on a common column named "category_small_cd". The result is a data frame that contains all columns from both data frames where the "category_small_cd" values match. The df_category data frame is subsetted to only include the columns "category_small_cd", "category_major_name", "category_medium_name", and "category_small_name".

head(df_product_full, n = 3) - This line of code is used to check the content of the df_product_full data frame. It displays the first three rows of the data frame using the head() function. This is a useful way to quickly inspect the resulting data frame to ensure that the join operation was successful and to check the format of the output.

Commentary :

The code above writes a data frame called df_product_full to a CSV file with UTF-8 encoding. Here's what each argument in the write.csv() function does:

df_product_full - This is the data frame that we want to write to a CSV file.

"../data/R_df_product_full_UTF-8_header.csv" - This is the file path and name of the output CSV file. The .. at the beginning of the path means that the file will be saved in the parent directory of the current working directory. R_df_product_full_UTF-8_header.csv is the name of the output file.

row.names=FALSE - This argument specifies that we do not want to include row names in the output CSV file.

fileEncoding = "UTF-8" - This argument specifies the encoding of the output CSV file. UTF-8 is a character encoding that supports a wide range of characters from different languages, so it is a good choice for handling non-English characters.

In summary, the write.csv() function writes the df_product_full data frame to a CSV file with UTF-8 encoding, and saves it in the specified file path and name. The resulting CSV file will not include row names.

Commentary :

The code above writes a data frame called df_product_full to a CSV file with CP932 encoding. Here's what each argument in the write.csv() function does:

df_product_full - This is the data frame that we want to write to a CSV file.

"../data/R_df_product_full_CP932_header.csv" - This is the file path and name of the output CSV file. The .. at the beginning of the path means that the file will be saved in the parent directory of the current working directory. R_df_product_full_CP932_header.csv is the name of the output file.

row.names=FALSE - This argument specifies that we do not want to include row names in the output CSV file.

fileEncoding = "CP932" - This argument specifies the encoding of the output CSV file. CP932 is a character encoding used for Japanese text, which supports a wide range of characters used in Japanese language.

In summary, the write.csv() function writes the df_product_full data frame to a CSV file with CP932 encoding, and saves it in the specified file path and name. The resulting CSV file will not include row names. This encoding is useful for handling Japanese text data.

Commentary :

The code above writes a data frame called df_product_full to a text file with UTF-8 encoding. Here's what each argument in the write.table() function does:

df_product_full - This is the data frame that we want to write to a text file.

"../data/R_df_product_full_UTF-8_noh.csv" - This is the file path and name of the output text file. The .. at the beginning of the path means that the file will be saved in the parent directory of the current working directory. R_df_product_full_UTF-8_noh.csv is the name of the output file.

row.names = FALSE - This argument specifies that we do not want to include row names in the output text file.

col.names = FALSE - This argument specifies that we do not want to include column names in the output text file.

sep = "," - This argument specifies the delimiter to be used between the columns in the output text file. In this case, a comma is used as the delimiter.

fileEncoding = "UTF-8" - This argument specifies the encoding of the output text file. UTF-8 is a character encoding that supports a wide range of characters from different languages, so it is a good choice for handling non-English characters.

In summary, the write.table() function writes the df_product_full data frame to a text file with UTF-8 encoding, and saves it in the specified file path and name. The resulting text file will not include row or column names and will have a comma as a delimiter.

Commentary :

The code above reads a CSV file called R_df_product_full_UTF-8_header.csv into a data frame called df_product_full. Here's what each argument in the read.csv() function does:

"../data/R_df_product_full_UTF-8_header.csv" - This is the file path and name of the input CSV file. The .. at the beginning of the path means that the file is located in the parent directory of the current working directory.

colClasses = c_class - This argument specifies the class of each column in the data frame. c_class is a vector that specifies the class of each column in the data frame. In this case, the first and fifth to ninth columns are set to NA, indicating that their classes should be determined automatically. The second to fourth columns are set to character, indicating that they should be treated as character columns.

fileEncoding = "UTF-8" - This argument specifies the encoding of the input CSV file. UTF-8 is a character encoding that supports a wide range of characters from different languages.

head(df_product_full, 3) - This command prints the first three rows of the df_product_full data frame to the console.

In summary, the read.csv() function reads the R_df_product_full_UTF-8_header.csv file into a data frame called df_product_full, using the specified column classes and file encoding. The resulting data frame is then printed to the console using the head() function.

Commentary :

The code above reads a CSV file called R_df_product_full_UTF-8_noh.csv into a data frame called df_product_full, then assigns new column names to the data frame. Here's what each argument in the read.csv() function does:

"../data/R_df_product_full_UTF-8_noh.csv" - This is the file path and name of the input CSV file. The .. at the beginning of the path means that the file is located in the parent directory of the current working directory.

col.names = c_names - This argument specifies the column names to be used in the data frame. c_names is a character vector that lists the new column names in the same order as they appear in the input file.

colClasses = c_class - This argument specifies the class of each column in the data frame. c_class is a vector that specifies the class of each column in the data frame. In this case, the first column is set to NA, indicating that its class should be determined automatically. The second to fourth columns are set to character, indicating that they should be treated as character columns. The fifth and sixth columns are set to NA, indicating that their classes should be determined automatically. The last three columns are set to NA, indicating that they should be treated as missing values.

header = FALSE - This argument specifies that the input CSV file does not have a header row.

fileEncoding = "UTF-8" - This argument specifies the encoding of the input CSV file. UTF-8 is a character encoding that supports a wide range of characters from different languages.

head(df_product_full, 3) - This command prints the first three rows of the df_product_full data frame to the console.

In summary, the read.csv() function reads the R_df_product_full_UTF-8_noh.csv file into a data frame called df_product_full, using the specified column names, column classes, header setting, and file encoding. The resulting data frame is then printed to the console using the head() function.

Commentary :

The code above writes the contents of the data frame df_product_full to a tab-separated value (TSV) file called R_df_product_full_UTF-8_header.tsv. Here's what each argument in the write.table() function does:

df_product_full - This is the data frame to be written to the TSV file.

"../data/R_df_product_full_UTF-8_header.tsv" - This is the file path and name of the output TSV file. The .. at the beginning of the path means that the file will be located in the parent directory of the current working directory.

row.names = FALSE - This argument specifies that the row names should not be included in the output file.

sep = "\t" - This argument specifies the delimiter to be used between columns. In this case, the delimiter is a tab character.

fileEncoding = "UTF-8" - This argument specifies the encoding of the output TSV file. UTF-8 is a character encoding that supports a wide range of characters from different languages.

In summary, the write.table() function writes the contents of the df_product_full data frame to a TSV file called R_df_product_full_UTF-8_header.tsv, using tabs as delimiters and UTF-8 as the file encoding.