tidyr: pivot_opposite()

Converting data from one form to another is one of the most common activities related to ammunition data. tidyr, the R-package, included in tidyverse, provides basic functions for processing records in wide or long forms.

In this article we will look at examples of one of the main functions of the tidyr kernel – the pivot_wider() function to convert ordinary long data into data in a broad form.

pivot_longer() lengthens the data by increasing the number of rows and decreasing the number of columns.

Let’s start loading the clean R packs. Let’s also look at the version of the tidyr package. We need a clean version 1.0.0 or higher.

Library

We will benefit from the global growth of mobile subscriptions over many years, starting with the Tidytuesday project to rebuild the data framework itself.

mobile <- readr::read_csv(‘https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-11-10/mobile.csv’)

Our data in regular or long form, with each variable in a separate column.

## Disassembled with speaker specifications:## cols(## entity = col_character(),## code = col_character(),## code = col_double(),## year = col_double(),## total_pop = col_double(),## gdp_per_cap = col_double(),## mobile_subs = col_double(),## continent = col_character()## ).

How can I reconfigure the longitude data with a variable using the function pivot_wider()?

We will first focus on the overlay of the data frame on a variable, mobile_subs. We will try to convert data specifically to a larger view so that the year variable in the columns and mobile_subs are the values in the larger data frame.

http://31.220.61.170/wp-content/uploads/2020/11/1605774250_366_How-To-Reshape-Tidy-Data-to-Wide-Data-with-pivot_wider.png

pivot_wider(): exact data for extended data

We can change the shape to Tidyr with the pivot_wider() function. First we select only the variables we are interested in replacing, then we use the function pivot_wider() with the arguments names_from and values_from.

We specify which variable or column should be used as column name in the larger data, reformulated with the name_of. And which variable or column should have values in a broader form with the argument values_from.

mobile %>%
select(code,continent,year,mobile_subs) %>%
pivot_ opposite(names_of=year, values_of=mobile_subs)

Now we get the same data frame in a larger image over the years as column names and mobile_subs as values.

# A tibble: 248 x 30# # # # continent code `1990 `1991 `1992 `1993 `1994 `1995 `1996` # # # # # 1 AFG Asia 0 0 0 0 # # # # 2 ALB Europe 0 0 0 0.0744## 3 AFG Africa 0,00181 0,0180 0,0176 0,0172 0,00475 0,0162 0,0398### 4 ASM Oceania 0 0 1,41 1,77 2,32 2,36 2,41### 5 AS Europe 0 0 0 1.31 1,28 1,25 4,42 8,53### 6 AGO Africa 0 0,00821 0,0132 0,0140 0,0225### 7 AIA America NA### 8 ATG America 0 NA 1,72### 9 ARG America 0,0367 0,0753 0,138 0,328 0,698 1,16 1,88### 10 ARM Asia 0,00947### #…. …238 additional lines and 21 variables : `1997` , `1998` ,

Note that when rotating with a variable, it is a good idea to keep the Dataaframe simple when needed. For example, if there are multiple variables in your own data, running a single variable will give a very wide data frame. And that may not be necessary.

Here is an example of using a mobile data frame where the only variable is rotated, but the data frame contains multiple variables.

mobile %>%
pivot_ against (names_of=year, values_of=mobile_subs)

We get a larger data frame with more than 6000 columns, and most values are in NA.

## #Chat: 6.184 x 33### code of the essence total_pop gdp_per_cap continent ## AFG 13032161 NA Asia 0 NA
## 2 Afghanistan AFG 14069854 NA Asia NA 0 NA
## 3 Afghan AFG 15472076 NA Asia NA 0 NA
## 4 Afghan AFG 17053213 NA 0
## 5 Afghan AFG 18553819 NA Asia NA
## 6 Afghans AFG 19789880 NA NA
## 7 Afghans… AFG 20684982 NA Asia NA
## 8 Afghans AFG 21299350 NA Asia NA
## 9 Afghans AFG 21752257 NA Asia NA
## 10 Afghans AFG 22227543 NA Asia NA
## ## ## ## … …with 6 174 additional lines and 24 variables: `1994` , `1995` ,

How long can I resist with the pivot_resistance function?) Rotating widthways with multiple variables ?

We are often interested in transforming a clean data framework into a larger data framework using multiple variables instead of just one. Here is a comic book illustration of the problem of the perestroika.

http://31.220.61.170/wp-content/uploads/2020/11/1605774250_971_How-To-Reshape-Tidy-Data-to-Wide-Data-with-pivot_wider.png

multi-column spin_resistant()

The pivot_reverse() function of tidyr can also be used for this recovery task. This time we give the variables as vectors, and not as the only variable in the value_from argument.

Here, for example, we change the shape of our own data frame into two variables or columns.

mobile %>%
select(code,continent,year,
mobile_subs,gdp_per_cap) %>%
pivot_against(names_from=year,
values_from=c(mobile_subs,
gdp_per_cap)).

Note that by default the function pivot_wider() adds variable names to column names when we rotate our own data over two variables.

## # A tibble: 248 x 58#### continent code mobile_subs_1990 mobile_subs_1991# ## ## 1 AFG Asia 0 0## 2 ALB Europe 0 0## 3 DZA Africa 0.00181 0.0180 0.0176## 4 AFG Oceania 0 0 1,41## 5 A Europe 0 1,31## 6 DZA Africa 0 0## 7 DZA America NA## 8 DZA America 0 DZA## 9 ARG America 0,0367 0,0753 0,138## 10 ARM Asia 0 0 0##…. …238 additional lines and 53 variables : mobile_subs_1993 , ## ## ## ## ## ## ## mobile_subs_1994 , mobile_subs_1995 , mobile_subs_1996 , ## ## ## ## ## mobile_subs_1997 , mobile_subs_1998 , mobile_subs_1999 , ## ## ## ## ## ## ## ## ## ## # ## mobile_subs_2000, mobile_subs_2001 , mobile_subs_2001 , mobile_subs_2002 , ## # mobile_subs_2003 , mobile_subs_2004 , mobile_subs_2005 , ## ## # # # mobile_subs_2006 , mobile_subs_2007 , mobile_subs_2008,## Mobile_subs_2009 , mobile_subs_2010 , mobile_subs_2011,## , mobile_subs_2012 , mobile_subs_2013 , mobile_subs_2014,## , mobile_subs_2015 , mobile_subs_2016 , mobile_subs_2017 , mobile_subs_2017 , ## # gdp_per_cap_1990 , gdp_per_cap_1991 , gdp_per_cap_1992 , ## # gdp_per_cap_1993 , gdp_per_cap_1994 , gdp_per_cap_1995 , ## # gdp_per_cap_1996 , gdp_per_cap_1997 , gdp_per_cap_1998 ,

In the above example we saw how the function pivot_wider() is used to rotate two columns. We can easily add more than two variables, as shown below

mobile %>%
select(code,continent,year,
mobile_subs, gdp_per_cap, total_pop) %>%
pivot_against(names_from=year,
values_from=c(mobile_subs,
gdp_per_cap,
total_pop)).

## # A tibble: 248 x 86#### continent code mobile_subs_1990 mobile_subs_1991# ## ## 1 AFG Asia 0 0## 2 ALB Europe 0 0## 3 DZA Africa 0.00181 0.0180 0.0176## 4 AFG Oceania 0 0 1,41## 5 A Europe 0 1,31## 6 DZA Africa 0 0## 7 DZA America NA## 8 DZA America 0 DZA## 9 ARG America 0,0367 0,0753 0,138## 10 ARM Asia 0 0 0##…. …238 other lines and 81 variables : mobile_subs_1993 , ## ## ## ## ## ## mobile_subs_1994 , mobile_subs_1995 , mobile_subs_1996 , ## ## ## ## mobile_subs_1997 , mobile_subs_1998, mobile_subs_1999,## ## # # gdp_per_cap_1990 , gdp_per_cap_1991 , gdp_per_cap_1992 , ## ## ## ## # total_pop_1992 , total_pop_1993 , total_pop_1994 ,

letgo olx,5 miles