## Assigning the number in new column based on row count

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I would like to know how to increase number based on whether in every 10 row.

For example

```test_table <- data.frame(row=c(rev(0:99)))

row
1   99
2   98
3   97
4   96
5   95
6   94
7   93
8   92
9   91
10  90
11  89
```

I want to add new column when we passed every 10nth row in `row` column.

So I tried

```library(dplyr)
test_table%>%
mutate(No=cumsum(n()/10==10))
```

which gives every `No` column to be `1`. What I look for is the number incrasing from 0,1,2,3 in every 10 row change in data. How can I do that ?

```        row No
1    99  1
2    98  1
3    97  1
4    96  1
5    95  1
6    94  1
7    93  1
8    92  1
9    91  1
10   90  1
11   89  1
```

expected output

```   row No
1    99  0
2    98  0
3    97  0
4    96  0
5    95  0
6    94  0
7    93  0
8    92  0
9    91  0
10   90  0
11   89  1
```

You can do a modular division on `row_number`:

```test_table %>% mutate(No = (row_number() - 1) %/% 10)

#    row No
#1    99  0
#2    98  0
#3    97  0
#4    96  0
#5    95  0
#6    94  0
#7    93  0
#8    92  0
#9    91  0
#10   90  0
#11   89  1
# ...
```

Ways to count values in a worksheet, Count cells in a column based on single or multiple conditions by using the Count the total number of cells in a range by using ROWS and COLUMNS functions. Count Click Subtotal in the Data tab to add a subtotal row in your Excel Data. Tip: In the above formula, B1:B13 is the column range you want to number based on, and A1 is the cell you typed 1 into, and “Total” is the value you do not want to number. 3. Then drag the autofill handle to the column range you want to number.

You can try using `floor` after dividing `row_number()` by `10`. This will increase the `No` at every 10th row:

```library(dplyr)

test_table <- data.frame(row=c(rev(0:99)))

test_table%>%
mutate(No=floor((row_number()-1)/10))

#     row No
# 1    99  0
# 2    98  0
# 3    97  0
# 4    96  0
# 5    95  0
# 6    94  0
# 7    93  0
# 8    92  0
# 9    91  0
# 10   90  0
# 11   89  1
# 12   88  1
# 13   87  1
# 14   86  1
# 15   85  1
# 16   84  1
# 17   83  1
# 18   82  1
# 19   81  1
# 20   80  2
# 21   79  2
#......so on
```

4 data wrangling tasks in R for advanced beginners, (R adds its own row numbers if you don't include row names.) to perform in R is adding a new column to a data frame based on one or more other columns. There are four ranking window functions supported in SQL Server; ROW_NUMBER(), RANK(), DENSE_RANK() and NTILE(). All these functions are used to calculate ROWID for the provided rows window in their own way.

Here is a base R solution.

```test_table\$No <- (seq_len(nrow(test_table)) - 1) %/% 10

#    row No
# 1   99  0
# 2   98  0
# 3   97  0
# 4   96  0
# 5   95  0
# 6   94  0
# 7   93  0
# 8   92  0
# 9   91  0
# 10  90  0
# 11  89  1
# 12  88  1
# 13  87  1
# 14  86  1
# 15  85  1
```

Create New Columns Based on Operations, A pivotal part of data analysis is to create new columns based on some operation​. Let's get the tips dataset from the Python seaborn visualization library and assign it to the DataFrame df_tips . size, count of people eating meal. Preview the first 5 rows of df_tips using the pandas series head() method. Pandas : Sort a DataFrame based on column names or row index labels using Dataframe.sort_index() Python Pandas : Replace or change Column & Row index names in DataFrame; Python Pandas : How to convert lists to a dataframe; Pandas : Find duplicate rows in a Dataframe based on all or selected columns using DataFrame.duplicated() in Python

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