library(tidyverse, verbose = FALSE)
df = readxl::read_excel('datasets.xlsx', sheet = 'wrangling', range = 'p7:T11')
df
str(df)

tibble [4 × 5] (S3: tbl_df/tbl/data.frame)
 $ Education: chr [1:4] "SSC" "HSC" "Bachelor" "Masters"
 $ Football : num [1:4] 34 4 10 5
 $ Hockey   : num [1:4] 25 18 19 16
 $ Cricket  : num [1:4] 20 15 26 23
 $ Baseball : num [1:4] 7 9 21 28

# We will subset this dataframe (tibble) in different ways
# Select only Football and Hockey columns
df %>% select(c(Football, Hockey))

# Filter only SSC row
df %>% filter(Education == 'SSC')

# Filter SSC and HSC rows
df %>% filter(Education == 'SSC' | Education == 'HSC')

# Alternate way of indexing using [row, column], 1:4 indicates 1 to 4
# Example, select first two columns and three rows
df[1:3, 1:2]

# Select only Education column (first column), i.e. all acolumns
df[,1]

# Select all columns except first two columns
df[, -c(1, 2)]

# Now we will convert the first column as row names to make it a contingency table
# First remove the first column
df[, -1]

# Extract the values of the first column
df$Education

# Use this values as the row names
# Give a name (cont.table) to the contingency table
cont.table = df %>% remove_rownames %>% column_to_rownames(var = 'Education')
cont.table

# We can also bring these row names to column i.e. convert to a dataframe.
# See the changes in the dataframe from 4*4 to 4*5
dataframe = cont.table %>% rownames_to_column(var = 'Education')
dataframe

Education	Football	Hockey	Cricket	Baseball
<chr>	<dbl>	<dbl>	<dbl>	<dbl>
SSC	34	25	20	7
HSC	4	18	15	9
Bachelor	10	19	26	21
Masters	5	16	23	28