Exploratory Data Analysis in R

1 Introduction

      The objective of this document is to introduce the necessary functions from tidyverse package for data manipulation and data visualization. There are basically six functions - select(), filter(), mutate(), arrange(), group_by(), and summarize() - from dplyr package of tidyverse ecosystem that are very much necessary for data manipulation. These six functions can be used for 80% of data manipulation problems. Additionally, this handout also introduces ggplot functions from tidyverse. ggplot is considered very effective for data visualization.

2 Loading Necessary Packages

library(tidyverse)
library(janitor)
library(lubridate)

3 Data Set for Classroom Practice

# Loading Data 

product <- read_csv(
  "https://raw.githubusercontent.com/msharifbd/DATA/main/Al-Bundy_raw-data.csv"
)

4 Clean the Data Set

# To clean the names of the variables
product <- product %>%
  janitor::clean_names() %>% # this function cleans the names of the variables
  dplyr::rename_all(toupper) # All variable names in upper case

5 Meta Data

      Once you load a data set in R, your next job should be to learn about some characteristics about the data.

glimpse(product)
Rows: 14,967
Columns: 14
$ INVOICE_NO  <dbl> 52389, 52390, 52391, 52392, 52393, 52394, 52395, 52396, 52…
$ DATE        <chr> "1/1/2014", "1/1/2014", "1/1/2014", "1/1/2014", "1/1/2014"…
$ COUNTRY     <chr> "United Kingdom", "United States", "Canada", "United State…
$ PRODUCT_ID  <dbl> 2152, 2230, 2160, 2234, 2222, 2173, 2200, 2238, 2191, 2237…
$ SHOP        <chr> "UK2", "US15", "CAN7", "US6", "UK4", "US15", "GER2", "CAN5…
$ GENDER      <chr> "Male", "Male", "Male", "Female", "Female", "Male", "Femal…
$ SIZE_US     <dbl> 11.0, 11.5, 9.5, 9.5, 9.0, 10.5, 9.0, 10.0, 10.5, 9.0, 10.…
$ SIZE_EUROPE <chr> "44", "44-45", "42-43", "40", "39-40", "43-44", "39-40", "…
$ SIZE_UK     <dbl> 10.5, 11.0, 9.0, 7.5, 7.0, 10.0, 7.0, 9.5, 10.0, 7.0, 9.5,…
$ UNIT_PRICE  <dbl> 159, 199, 149, 159, 159, 159, 179, 169, 139, 149, 129, 169…
$ DISCOUNT    <dbl> 0.0, 0.2, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1…
$ YEAR        <dbl> 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014…
$ MONTH       <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ SALE_PRICE  <dbl> 159.0, 159.2, 119.2, 159.0, 159.0, 159.0, 179.0, 169.0, 13…

      It can be seen that there are 14,967 Rows (also called observations) and 14 columns (also called variables). The name of the first variable is INVOICE_ID, which type is chr, which means it is character type.

6 Changing the Types of Variables

      Sometimes we might need to change the type of the variable; e.g., converting an integer variable to a character variable. In such case, we need to write code.

# Changing the types of Variables 
product <- product %>% 
  mutate(
    DATE = mdy(DATE),
    PRODUCT_ID = as.character(PRODUCT_ID),
    SIZE_US = as.character(SIZE_US),
    MONTH = as.character(MONTH),
    INVOICE_NO = as.character(INVOICE_NO)
  )
glimpse(product)
Rows: 14,967
Columns: 14
$ INVOICE_NO  <chr> "52389", "52390", "52391", "52392", "52393", "52394", "523…
$ DATE        <date> 2014-01-01, 2014-01-01, 2014-01-01, 2014-01-01, 2014-01-0…
$ COUNTRY     <chr> "United Kingdom", "United States", "Canada", "United State…
$ PRODUCT_ID  <chr> "2152", "2230", "2160", "2234", "2222", "2173", "2200", "2…
$ SHOP        <chr> "UK2", "US15", "CAN7", "US6", "UK4", "US15", "GER2", "CAN5…
$ GENDER      <chr> "Male", "Male", "Male", "Female", "Female", "Male", "Femal…
$ SIZE_US     <chr> "11", "11.5", "9.5", "9.5", "9", "10.5", "9", "10", "10.5"…
$ SIZE_EUROPE <chr> "44", "44-45", "42-43", "40", "39-40", "43-44", "39-40", "…
$ SIZE_UK     <dbl> 10.5, 11.0, 9.0, 7.5, 7.0, 10.0, 7.0, 9.5, 10.0, 7.0, 9.5,…
$ UNIT_PRICE  <dbl> 159, 199, 149, 159, 159, 159, 179, 169, 139, 149, 129, 169…
$ DISCOUNT    <dbl> 0.0, 0.2, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1…
$ YEAR        <dbl> 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014…
$ MONTH       <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1"…
$ SALE_PRICE  <dbl> 159.0, 159.2, 119.2, 159.0, 159.0, 159.0, 179.0, 169.0, 13…

7 Create a New Data Set with Some Variables

7.1 1st (First) verb - select ()

      The select () function is used to select some columns from your data set. For example, if you want to select all variables except SIZE_EUROPE and SIZE_UK from your data set. Then you should write the following code (We created a new data set called product2)

product2 <- product %>% 
  select(
   -SIZE_EUROPE, - SIZE_UK  
  )  # 1st Verb
glimpse(product2)
Rows: 14,967
Columns: 12
$ INVOICE_NO <chr> "52389", "52390", "52391", "52392", "52393", "52394", "5239…
$ DATE       <date> 2014-01-01, 2014-01-01, 2014-01-01, 2014-01-01, 2014-01-01…
$ COUNTRY    <chr> "United Kingdom", "United States", "Canada", "United States…
$ PRODUCT_ID <chr> "2152", "2230", "2160", "2234", "2222", "2173", "2200", "22…
$ SHOP       <chr> "UK2", "US15", "CAN7", "US6", "UK4", "US15", "GER2", "CAN5"…
$ GENDER     <chr> "Male", "Male", "Male", "Female", "Female", "Male", "Female…
$ SIZE_US    <chr> "11", "11.5", "9.5", "9.5", "9", "10.5", "9", "10", "10.5",…
$ UNIT_PRICE <dbl> 159, 199, 149, 159, 159, 159, 179, 169, 139, 149, 129, 169,…
$ DISCOUNT   <dbl> 0.0, 0.2, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1,…
$ YEAR       <dbl> 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014,…
$ MONTH      <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",…
$ SALE_PRICE <dbl> 159.0, 159.2, 119.2, 159.0, 159.0, 159.0, 179.0, 169.0, 139…

8 count () Function to deal with categorical variables

      To know the frequency of different categorical variables, we can use the `count() function. For example - we want to know whether the dataset includes information about United States; we should write -

# Number of Countries 
product %>% 
  count(COUNTRY) 
# A tibble: 4 × 2
  COUNTRY            n
  <chr>          <int>
1 Canada          2952
2 Germany         4392
3 United Kingdom  1737
4 United States   5886
# Number of Years 
product %>% 
  count(YEAR)
# A tibble: 3 × 2
   YEAR     n
  <dbl> <int>
1  2014  2753
2  2015  4848
3  2016  7366
# Number of Invoices 
product %>% 
  count(INVOICE_NO)
# A tibble: 13,389 × 2
   INVOICE_NO     n
   <chr>      <int>
 1 52389          1
 2 52390          1
 3 52391          1
 4 52392          1
 5 52393          1
 6 52394          1
 7 52395          1
 8 52396          1
 9 52397          1
10 52398          1
# ℹ 13,379 more rows

QUESTIONS - 1. How many products are available in the dataset? 2. How many shoe sizes are available in the dataset (use SIZE_US variable)

9 Create a new data set that satisfies some rows conditions

9.1 2nd (Second) verb - filter ()

      If we want to subset our dataset by rows, then filter () is used. For example - we want to create a data set that will include only observations for United States, then we should write the following code. The name of the dataset is given US.

US <- product %>% 
  filter(
    COUNTRY == "United States"
  )   # 2nd Verb

glimpse(US)
Rows: 5,886
Columns: 14
$ INVOICE_NO  <chr> "52390", "52392", "52394", "52397", "52399", "52399", "523…
$ DATE        <date> 2014-01-01, 2014-01-01, 2014-01-01, 2014-01-02, 2014-01-0…
$ COUNTRY     <chr> "United States", "United States", "United States", "United…
$ PRODUCT_ID  <chr> "2230", "2234", "2173", "2191", "2197", "2213", "2206", "2…
$ SHOP        <chr> "US15", "US6", "US15", "US13", "US1", "US11", "US2", "US15…
$ GENDER      <chr> "Male", "Female", "Male", "Male", "Male", "Female", "Femal…
$ SIZE_US     <chr> "11.5", "9.5", "10.5", "10.5", "10", "9.5", "9.5", "8", "1…
$ SIZE_EUROPE <chr> "44-45", "40", "43-44", "43-44", "43", "40", "40", "41", "…
$ SIZE_UK     <dbl> 11.0, 7.5, 10.0, 10.0, 9.5, 7.5, 7.5, 7.5, 10.5, 7.5, 9.5,…
$ UNIT_PRICE  <dbl> 199, 159, 159, 139, 129, 169, 139, 139, 149, 159, 129, 169…
$ DISCOUNT    <dbl> 0.2, 0.0, 0.0, 0.0, 0.0, 0.1, 0.0, 0.0, 0.5, 0.0, 0.0, 0.1…
$ YEAR        <dbl> 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014…
$ MONTH       <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1"…
$ SALE_PRICE  <dbl> 159.2, 159.0, 159.0, 139.0, 129.0, 152.1, 139.0, 139.0, 74…
Germany <- product %>% 
  filter(
    COUNTRY == "Germany" & YEAR %in% c ('2014', '2015')
  ) 
glimpse(Germany)
Rows: 2,229
Columns: 14
$ INVOICE_NO  <chr> "52395", "52401", "52401", "52408", "52409", "52414", "524…
$ DATE        <date> 2014-01-02, 2014-01-03, 2014-01-03, 2014-01-04, 2014-01-0…
$ COUNTRY     <chr> "Germany", "Germany", "Germany", "Germany", "Germany", "Ge…
$ PRODUCT_ID  <chr> "2200", "2235", "2197", "2206", "2157", "2235", "2239", "2…
$ SHOP        <chr> "GER2", "GER1", "GER1", "GER2", "GER2", "GER1", "GER2", "G…
$ GENDER      <chr> "Female", "Male", "Female", "Male", "Male", "Male", "Femal…
$ SIZE_US     <chr> "9", "10.5", "8.5", "8.5", "12", "9.5", "8.5", "10.5", "10…
$ SIZE_EUROPE <chr> "39-40", "43-44", "39", "41-42", "45", "42-43", "39", "43-…
$ SIZE_UK     <dbl> 7.0, 10.0, 6.5, 8.0, 11.5, 9.0, 6.5, 10.0, 9.5, 8.0, 10.0,…
$ UNIT_PRICE  <dbl> 179, 169, 179, 149, 149, 169, 129, 169, 199, 149, 169, 189…
$ DISCOUNT    <dbl> 0.0, 0.5, 0.2, 0.2, 0.2, 0.3, 0.5, 0.5, 0.0, 0.2, 0.5, 0.1…
$ YEAR        <dbl> 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014…
$ MONTH       <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1"…
$ SALE_PRICE  <dbl> 179.0, 84.5, 143.2, 119.2, 119.2, 118.3, 64.5, 84.5, 199.0…
count (Germany, YEAR)
# A tibble: 2 × 2
   YEAR     n
  <dbl> <int>
1  2014   710
2  2015  1519

QUESTIONS - 1. Filter those observations that belong to United States and Germany and that are related to Male Gender.

10 Average Price of Shoes

10.1 3rd (Third) verb - summarize ()

      The summarize () function is used to calculate different statistics such as mean, median, standard deviation, maximum, and minimum value. For example, we want to calculate the average price of all products -

product %>% 
  summarize(AVG_PRICE = mean(SALE_PRICE)) # 3rd Verb
# A tibble: 1 × 1
  AVG_PRICE
      <dbl>
1      144.

10.2 4th (Fourth) verb - group_by ()

      The group_by () function is very useful when it is used with summarize () function. For example, we want to know the average price for each country; then, we should write the following code -

10.3 5th (Fifth) verb - arrange ()

      The arrange ()function allows you to reorder your data set by one or more variables. For example, if you want to reorder the average price in difference countries, you need to execute the following code -

product %>% 
  group_by(COUNTRY) %>% # 4th Verb 
  summarise(AVG_PRICE = mean(SALE_PRICE)) %>% 
  arrange(AVG_PRICE) # 5th Verb
# A tibble: 4 × 2
  COUNTRY        AVG_PRICE
  <chr>              <dbl>
1 Germany             144.
2 United States       144.
3 Canada              144.
4 United Kingdom      146.

QUESTIONS - 1. Calculate the average price for both Gender. Who pays greater price? 2. Calculate the average discount for both Gender. Who gets higher discount? 3. Calculate the average discount for each month. In which month highest discount is provided?

11 Reshaping the Data

11.1 Tidy Data

      There are three interrelated rules which make a dataset tidy:

  • Each variable must have its own column.
  • Each observation must have its own row.
  • Each value must have its own cell.

# An example of a Tidy Dataset 
table1
# A tibble: 6 × 4
  country      year  cases population
  <chr>       <dbl>  <dbl>      <dbl>
1 Afghanistan  1999    745   19987071
2 Afghanistan  2000   2666   20595360
3 Brazil       1999  37737  172006362
4 Brazil       2000  80488  174504898
5 China        1999 212258 1272915272
6 China        2000 213766 1280428583

      Manipulating tidy data is easy. For example, for dataset table1, if we want to measure the rate of, we can do it easily.

table1 %>% 
  mutate(rate = cases / population * 10000)
# A tibble: 6 × 5
  country      year  cases population  rate
  <chr>       <dbl>  <dbl>      <dbl> <dbl>
1 Afghanistan  1999    745   19987071 0.373
2 Afghanistan  2000   2666   20595360 1.29 
3 Brazil       1999  37737  172006362 2.19 
4 Brazil       2000  80488  174504898 4.61 
5 China        1999 212258 1272915272 1.67 
6 China        2000 213766 1280428583 1.67

11.2 Untidy Data

      Untidy data violate the principle of the tidy data. Therefore, we need to apply analytics to transform it into tidy data. There are two important functions from tidyr that can be used to reshape data. The first one is - pivot_wider function and the second one is pivot_longer function. pivot_wider widens a LONG data whereas pivot_longer lengthens a WIDE data.

# Some Built-in Untidy Datasets

table2
# A tibble: 12 × 4
   country      year type            count
   <chr>       <dbl> <chr>           <dbl>
 1 Afghanistan  1999 cases             745
 2 Afghanistan  1999 population   19987071
 3 Afghanistan  2000 cases            2666
 4 Afghanistan  2000 population   20595360
 5 Brazil       1999 cases           37737
 6 Brazil       1999 population  172006362
 7 Brazil       2000 cases           80488
 8 Brazil       2000 population  174504898
 9 China        1999 cases          212258
10 China        1999 population 1272915272
11 China        2000 cases          213766
12 China        2000 population 1280428583
table3
# A tibble: 6 × 3
  country      year rate             
  <chr>       <dbl> <chr>            
1 Afghanistan  1999 745/19987071     
2 Afghanistan  2000 2666/20595360    
3 Brazil       1999 37737/172006362  
4 Brazil       2000 80488/174504898  
5 China        1999 212258/1272915272
6 China        2000 213766/1280428583
table4a
# A tibble: 3 × 3
  country     `1999` `2000`
  <chr>        <dbl>  <dbl>
1 Afghanistan    745   2666
2 Brazil       37737  80488
3 China       212258 213766
table4b
# A tibble: 3 × 3
  country         `1999`     `2000`
  <chr>            <dbl>      <dbl>
1 Afghanistan   19987071   20595360
2 Brazil       172006362  174504898
3 China       1272915272 1280428583

11.2.1 pivot_longer()

table4a %>% 
  pivot_longer(c(`1999`, `2000`), names_to = "year", values_to = "cases")
# A tibble: 6 × 3
  country     year   cases
  <chr>       <chr>  <dbl>
1 Afghanistan 1999     745
2 Afghanistan 2000    2666
3 Brazil      1999   37737
4 Brazil      2000   80488
5 China       1999  212258
6 China       2000  213766
table4b %>% 
  pivot_longer(c(`1999`, `2000`), names_to = "year", values_to = "population")
# A tibble: 6 × 3
  country     year  population
  <chr>       <chr>      <dbl>
1 Afghanistan 1999    19987071
2 Afghanistan 2000    20595360
3 Brazil      1999   172006362
4 Brazil      2000   174504898
5 China       1999  1272915272
6 China       2000  1280428583
tidy4a <- table4a %>% 
  pivot_longer(c(`1999`, `2000`), names_to = "year", values_to = "cases")
tidy4b <- table4b %>% 
  pivot_longer(c(`1999`, `2000`), names_to = "year", values_to = "population")
left_join(tidy4a, tidy4b,
          by = c ('country', 'year'))
# A tibble: 6 × 4
  country     year   cases population
  <chr>       <chr>  <dbl>      <dbl>
1 Afghanistan 1999     745   19987071
2 Afghanistan 2000    2666   20595360
3 Brazil      1999   37737  172006362
4 Brazil      2000   80488  174504898
5 China       1999  212258 1272915272
6 China       2000  213766 1280428583

11.2.2 pivot_wider()

table2 %>%
    pivot_wider(names_from = type, values_from = count)
# A tibble: 6 × 4
  country      year  cases population
  <chr>       <dbl>  <dbl>      <dbl>
1 Afghanistan  1999    745   19987071
2 Afghanistan  2000   2666   20595360
3 Brazil       1999  37737  172006362
4 Brazil       2000  80488  174504898
5 China        1999 212258 1272915272
6 China        2000 213766 1280428583

11.3 Identify Total Sales by Gender of Each Product (PRODUCT_ID)

11.3.1 6th (Sixth) verb - mutate ()

      The function mutate () is used to create new variables (columns). For example, we want to know the TOTAL_SALE, which is the sum of the sum of sale by gender; then, we should write the following code -

product %>%
  count(PRODUCT_ID, GENDER) %>%
  arrange(-n) %>% 
  pivot_wider(
    names_from = GENDER,
    values_from = n
  ) %>% 
  rename_all(toupper) %>% 
  rename_at(vars(c("MALE","FEMALE")), ~paste0(.x,"_SALE")) %>% 
  mutate(
    TOTAL_SALE = MALE_SALE + FEMALE_SALE
  ) %>% # 6th Verb
  arrange(-TOTAL_SALE)
# A tibble: 96 × 4
   PRODUCT_ID MALE_SALE FEMALE_SALE TOTAL_SALE
   <chr>          <int>       <int>      <int>
 1 2190             132          75        207
 2 2226             123          81        204
 3 2213             114          90        204
 4 2192             135          66        201
 5 2158             120          78        198
 6 2172             114          75        189
 7 2179             102          87        189
 8 2239              87         102        189
 9 2225             117          69        186
10 2183             123          60        183
# ℹ 86 more rows

QUESTIONS - 1. Identify Total Sales by Gender of Each Shoe Size (SIZE_US)

12 Bar Chart

12.1 Create a Bar Chart of Sale of Shoes by Gender of Different Sizes

product %>%
  count(SIZE_US, GENDER) %>%
  pivot_wider(
    names_from = "GENDER",
    values_from = "n"
  ) %>%
  rename_all(toupper) %>%
  replace(is.na(.),0) %>%
  mutate(
    TOTAL_SALES = FEMALE + MALE
  ) %>%
  pivot_longer(
    cols = c("FEMALE", "MALE"),
    names_to = "GENDER",
    values_to = "GENDERSALES"
  )%>%
  ggplot(aes(x=reorder(SIZE_US,as.numeric(SIZE_US)), y= GENDERSALES, fill = GENDER))+
  geom_bar(stat = "identity")+
  labs(x = "SHOE SIZE",
       y = "TOTAL SALES",
       title = "SALES OF DIFFERENT SIZES OF SHOE")+
  geom_text(aes(label = GENDERSALES),
            position = position_stack(vjust = 0.5),
            color = "black",
            size = 2
  )+
  theme(legend.title = element_blank())

12.2 Create a Bar Chart of Sale of Shoes by Gender of Different Sizes in different countries

product %>%
  count(SIZE_US, GENDER, COUNTRY) %>%
  ggplot(aes(x=reorder(SIZE_US,as.numeric(SIZE_US)), y= n, fill = GENDER))+
  geom_bar(stat = "identity")+
  labs(x = "SHOE SIZE",
       y = "TOTAL SALES",
       title = "SALES OF DIFFERENT SIZES OF SHOE BY GENDER IN DIFFERENT COUNTRIES"
  )+
  geom_text(
    aes(label = n),
    position = position_stack(vjust = 0.5),
    color = "black",
    size = 2
  )+
  facet_wrap(~ COUNTRY, nrow = 2, ncol = 2
  )+
  theme(legend.position="top",
        legend.title = element_blank())+
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

12.3 Create a Bar Chart for Product ID (PRODUCT_ID) 2190 Sales by shoes sizes

product %>% 
  filter(
    PRODUCT_ID == "2190"
  ) %>% 
  count(SIZE_US) %>% 
  mutate(SIZE_US = (str_c ("SIZE_", SIZE_US))) %>% 
  ggplot(aes(x = reorder(SIZE_US,n), y = n))+
  geom_bar(stat="identity", color = "blue", fill = "orange")+
  coord_flip()+
  geom_text(aes(label = n), stat = "identity", hjust = -0.2)+ # Here also try to use vjust and take out coord_flip()
  xlab("SHOE SIZE")+
  ylab("SALES (UNIT)")+
  ggtitle("DISTRIBUTION of SALES for PRODUCT ID 2190")

QUESTIONS - 1. Create a Bar Chart for Product ID (PRODUCT_ID) 2190 Sales by Gender of Different Shoes Sizes

13 Relationship between Shoe Size and Price

product %>%
  ggplot(
    aes(x = as.numeric(SIZE_US), y = UNIT_PRICE)
  )+
  geom_smooth(se = FALSE)+
  xlab("SHOE SIZE (US)")+
  ylab("PRICE")

product %>%
  ggplot(
    aes(x = as.numeric(SIZE_US), y = UNIT_PRICE, color = COUNTRY)
  )+
  geom_smooth(se = FALSE)+
  xlab("SHOE SIZE (US)")+
  ylab("PRICE")+
  theme(legend.title = element_blank())

product %>%
  ggplot(
    aes(x = as.numeric(SIZE_US), y = UNIT_PRICE)
  )+
  geom_smooth(se = FALSE, color = "red")+
  xlab("SHOE SIZE (US)")+
  ylab("PRICE")+
  facet_wrap(~COUNTRY)

product %>%
  ggplot(
    aes(x = as.numeric(SIZE_US), y = UNIT_PRICE)
  )+
  geom_smooth(se = FALSE)+
  xlab("SHOE SIZE (US)")+
  ylab("PRICE")+
  facet_wrap(~GENDER)

product %>%
  ggplot(
    aes(x = as.numeric(SIZE_US), y = UNIT_PRICE, color = GENDER)
  )+
  geom_smooth(se = FALSE)+
  xlab("SHOE SIZE (US)")+
  ylab("PRICE")+
  theme(legend.title = element_blank())

13.1 Relationship between Shoe Size and Price in Different Gender and in Different Countries

product %>%
  ggplot(
    aes(x = as.numeric(SIZE_US), y = UNIT_PRICE, color = GENDER)
  )+
  geom_smooth(se = FALSE)+
  xlab("SHOE SIZE (US)")+
  ylab("PRICE")+
  facet_wrap(~COUNTRY)+
  theme(legend.title = element_blank())

QUESTIONS - 1. Do the Above Analyses for the Relationship between Shoe Size and Discount

14 Relationship between Sales and Month

product %>%
  count(MONTH) %>%
  ggplot(aes(x = reorder(MONTH,as.numeric(MONTH) ), y = n))+
  geom_point(color = "red")+
  labs(x = "MONTH",
       y = "TOTAL SALES", title = "SALES IN DIFFERENT MONTHS")

QUESTIONS - 1. Visualize the Relationship between Sales and Month in different countries

15 Conclusion

      Data science is the number 1 most promising job in the US in recent years1. Many disciplines around the world are incorporating the knowledge of data science in their day to day operations. The skills employers most frequently seek in data science job posting are Python, R, and SQL. It is hoped that the preliminary discussion in this project will help you to get some idea about R in data science.

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Footnotes

  1. https://www.techrepublic.com/article/why-data-scientist-is-the-most-promising-job-of-2019/↩︎