Chapter 1: R for Advanced Analytics

Activity 1: Create an R Markdown File to Read a CSV File and Write a Summary of Data

1. Start the RStudio and navigate to Files | New Files | R Markdown.

2. On the New R Markdown window, provide the Title and Author name, as illustrated in the following screenshot. Ensure that you select the Word option under the Default Output Format section:

   

   Figure 1.13: Creating a new R Markdown file in Rstudio

3. Now, use the read.csv() method to read the bank-full.csv file:

   

   Figure 1.14: Using the read.csv method to read the data

4. Finally, print the summary into a word file using the summary method:

   

   Figure 1.15: Final output after using the summary method

Activity 2: Create a List of Two Matrices and Access the Values

1. Create two matrices of size 10 x 4 and 4 x 5 by randomly generated numbers from a binomial distribution (use rbinom method). Call the matrix mat_A and mat_B, respectively:

   mat_A <- matrix(rbinom(n = 40, size = 100, prob = 0.4),nrow = 10, ncol=4)
   mat_B <- matrix(rbinom(n = 20, size = 100, prob = 0.4),nrow = 4, ncol=5)

2. Now, store the two matrices in a list:

   list_of_matrices <- list(mat_A = mat_A, mat_B =mat_B)

3. Using the list, access the row 4 and column 2 of mat_A and store it in variable A, and access row 2 and column 1 of mat_B and store it in variable B:

   A <- list_of_matrices[["mat_A"]][4,2]
   B <- list_of_matrices[["mat_B"]][2,1]

4. Multiply the A and B matrices and subtract from row 2 and column 1 of mat_A:

   list_of_matrices[["mat_A"]][2,1] - (A*B)

   The output is as follows:

   ## [1] -1554

Activity 3: Create a DataFrame with Five Summary Statistics for All Numeric Variables from Bank Data Using dplyr and tidyr

1. Import the dplyr and tidyr packages in the system:

   library(dplyr)
   library(tidyr)
   Warning: package 'tidyr' was built under R version 3.2.5

2. Create the df DataFrame and import the file into it:

   df <- tbl_df(df_bank_detail)

3. Extract all numeric variables from bank data using select(), and compute min, 1st quartile, 3rd quartile, median, mean, max, and standard deviation using the summarise_all() method:

   df_wide <- df %>%
     select(age, balance, duration, pdays) %>% 
     summarise_all(funs(min = min, 
                         q25 = quantile(., 0.25), 
                         median = median, 
                         q75 = quantile(., 0.75), 
                         max = max,
                         mean = mean, 
                         sd = sd))

4. The result is a wide data frame. 4 variable, 7 measures:

   dim(df_wide)
   ## [1]  1 28

5. Store the result in a DataFrame of wide format named df_wide, reshape it using the tidyr functions, and, finally, convert the wide format to deep, use the gather, separate, and spread functions of the tidyr package:

   df_stats_tidy <- df_wide %>% gather(stat, val) %>%
     separate(stat, into = c("var", "stat"), sep = "_") %>%
     spread(stat, val) %>%
     select(var,min, q25, median, q75, max, mean, sd) # reorder columns
   print(df_stats_tidy)

   The output is as follows:

   ## # A tibble: 4 x 8
   ##        var   min   q25 median   q75    max       mean         sd
   ## *    <chr> <dbl> <dbl>  <dbl> <dbl>  <dbl>      <dbl>      <dbl>
   ## 1      age    18    33     39    48     95   40.93621   10.61876
   ## 2  balance -8019    72    448  1428 102127 1362.27206 3044.76583
   ## 3 duration     0   103    180   319   4918  258.16308  257.52781
   ## 4    pdays    -1    -1     -1    -1    871   40.19783  100.12875