I will focus on how to use the Nest and Unnest functions utilizing data from FiveThirtyEight,“How Popular Is Donald Trump?” from the trump-approval-ratings data set.
(Source: https://data.fivethirtyeight.com/)

#Nest & Tibble {.tabset .tabset-fade}

Nest creates a list of data frames containing all the nested variables.
(Source: https://tidyr.tidyverse.org/reference/nest.html)

Tibble, or tbl_df, is a modern reimagining of the data.frame, keeping what time has proven to be effective, and throwing out what is not. Tibbles are data.frames that are lazy and surly: they do less (i.e. they don’t change variable names or types, and don’t do partial matching) and complain more (e.g. when a variable does not exist). This forces you to confront problems earlier, typically leading to cleaner, more expressive code.
(Source: https://tibble.tidyverse.org/)

Nest Arguments

data - A data frame.

… - A selection of columns. If empty, all variables are selected. You can supply bare variable names, select all variables between x and z with x:z, exclude y with -y. For more options, see the dplyr::select() documentation. See also the section on selection rules below.

.key - The name of the new column, as a string or symbol.

This argument is passed by expression and supports quasiquotation (you can unquote strings and symbols). The name is captured from the expression with rlang::ensym() (note that this kind of interface where symbols do not represent actual objects is now discouraged in the tidyverse; we support it here for backward compatibility).

##Example

The first step is to load the data into R.

library(tidyverse)
library(DT)
poll <- read.csv("https://raw.githubusercontent.com/IsARam/Data607TidyVerse/master/approval_polllist%5B1%5D.csv", sep = ",", na.strings = "NA", strip.white = TRUE, stringsAsFactors = FALSE)
datatable(poll, extensions = 'Buttons', options = list(
  dom = 'Bfrtip',
  buttons = c('copy', 'csv', 'excel', 'pdf', 'print')
  )
)

The next step is to use the nest function to create a list of data frames containing all the nested variables by pollster, in the below example. Use the group_by and arrange functions to achieve result.

npoll<-poll %>% group_by(pollster) %>% nest()
npoll2<- arrange(npoll, (pollster))
head(npoll2,10)
FALSE # A tibble: 10 x 2
FALSE    pollster                                             data              
FALSE    <chr>                                                <list>            
FALSE  1 ABC News/Washington Post                             <tibble [31 x 21]>
FALSE  2 America First Policies                               <tibble [2 x 21]> 
FALSE  3 American Research Group                              <tibble [54 x 21]>
FALSE  4 AP-NORC                                              <tibble [32 x 21]>
FALSE  5 Cards Against Humanity/Survey Sampling International <tibble [20 x 21]>
FALSE  6 CBS News                                             <tibble [35 x 21]>
FALSE  7 Civiqs                                               <tibble [2 x 21]> 
FALSE  8 Civis Analytics                                      <tibble [4 x 21]> 
FALSE  9 CNN/Opinion Research Corp.                           <tibble [6 x 21]> 
FALSE 10 CNN/SSRS                                             <tibble [57 x 21]>

A tibble is displayed in the data column for each groupped pollster, sorted alphabetically in ascending order.

#Unnest {.tabset .tabset-fade}

Unnest is the inverse operation of nest. If you have a list-column, this makes each element of the list its own row. unnest() can handle list-columns that contain atomic vectors, lists, or data frames (but not a mixture of the different types).
(Source: https://tidyr.tidyverse.org/reference/unnest.html)

##Unnest Arguments

data - A data frame.

… - Specification of columns to unnest. Use bare variable names or functions of variables. If omitted, defaults to all list-cols.

.drop - Should additional list columns be dropped? By default, unnest will drop them if unnesting the specified columns requires the rows to be duplicated.

.id - Data frame identifier - if supplied, will create a new column with name .id, giving a unique identifier. This is most useful if the list column is named.

.sep - If non-NULL, the names of unnested data frame columns will combine the name of the original list-col with the names from nested data frame, separated by .sep.

.preserve - Optionally, list-columns to preserve in the output. These will be duplicated in the same way as atomic vectors. This has dplyr::select semantics so you can preserve multiple variables with .preserve = c(x, y) or .preserve = starts_with(“list”).

##Example

Using the nested table npoll from the example in the Nest section we can Unnest the table. This will display the orginal data table structure. The pollster column is now the first column and maintains the ascending alphabetically sort.

unpoll<- npoll2 %>% unnest()
datatable(unpoll, extensions = 'Buttons', options = list(
  dom = 'Bfrtip',
  buttons = c('copy', 'csv', 'excel', 'pdf', 'print')
  )
)

Rajwant Part2 Start:

Working with forcats package

This package give some tool tool to work with Categorical Variables Count entries in a factor, similar to table or group_by with summareise . Here I am using fct_count , group_by and table to get the count of factor variable.

fct_count(poll$grade)
## # A tibble: 11 x 2
##    f         n
##    <fct> <int>
##  1 ""      240
##  2 A       127
##  3 A-      294
##  4 A+       85
##  5 B      2154
##  6 B-      549
##  7 B+     2465
##  8 C        47
##  9 C-       60
## 10 C+     1620
## 11 D-      432
group_by(poll,grade)%>%summarise(count  = n()) 
## # A tibble: 11 x 2
##    grade count
##    <chr> <int>
##  1 ""      240
##  2 A       127
##  3 A-      294
##  4 A+       85
##  5 B      2154
##  6 B-      549
##  7 B+     2465
##  8 C        47
##  9 C-       60
## 10 C+     1620
## 11 D-      432
table(poll$grade)
## 
##         A   A-   A+    B   B-   B+    C   C-   C+   D- 
##  240  127  294   85 2154  549 2465   47   60 1620  432

You can use sort = TRUE if you want to sort your ouput.

fct_count(poll$grade,sort = TRUE)
## # A tibble: 11 x 2
##    f         n
##    <fct> <int>
##  1 B+     2465
##  2 B      2154
##  3 C+     1620
##  4 B-      549
##  5 D-      432
##  6 A-      294
##  7 ""      240
##  8 A       127
##  9 A+       85
## 10 C-       60
## 11 C        47

Lets say you only want to find out only top 3 category of grades, and put rest of them in Other. We can use fct_lump to do this, here n = number of groups here I am giving n = 3 to see how it works.

fact_lump_result <- fct_lump(poll$grade,n=3)
head(fact_lump_result,n=30)
##  [1] B     Other B+    B     B+    Other C+    B     B+    C+    B    
## [12] B     B     B     C+    B+    B     B+    B     C+    Other B+   
## [23] B     Other C+    B+    B+    B     B     B    
## Levels: B B+ C+ Other
class(fact_lump_result)
## [1] "factor"
# Now we will pass this in fct_count using pipe and sort the reult. 
(fct_lump(poll$grade,n=3))  %>%fct_count(sort=TRUE)
## # A tibble: 4 x 2
##   f         n
##   <fct> <int>
## 1 B+     2465
## 2 B      2154
## 3 Other  1834
## 4 C+     1620