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Step:::1
…………First of all we have to run libraries………………..
library(tidyverse)## -- Attaching packages ---------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.0 v purrr 0.3.3
## v tibble 2.1.3 v dplyr 0.8.5
## v tidyr 1.0.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0## -- Conflicts ------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(datasets)Step:::2 ……..we run data from datasets library and check the structure of dataset. ………..Here the worldphone dataset represent a matrices.
WorldPhones## N.Amer Europe Asia S.Amer Oceania Africa Mid.Amer
## 1951 45939 21574 2876 1815 1646 89 555
## 1956 60423 29990 4708 2568 2366 1411 733
## 1957 64721 32510 5230 2695 2526 1546 773
## 1958 68484 35218 6662 2845 2691 1663 836
## 1959 71799 37598 6856 3000 2868 1769 911
## 1960 76036 40341 8220 3145 3054 1905 1008
## 1961 79831 43173 9053 3338 3224 2005 1076str(WorldPhones)## num [1:7, 1:7] 45939 60423 64721 68484 71799 ...
## - attr(*, "dimnames")=List of 2
## ..$ : chr [1:7] "1951" "1956" "1957" "1958" ...
## ..$ : chr [1:7] "N.Amer" "Europe" "Asia" "S.Amer" ...is.matrix(WorldPhones)## [1] TRUEStep:::3 ……..Since Worldphone dataset represent a matrics. So I want to convert it into dataframe. ……….So, For this we can do,
#create a variable
year <- c('1951','1956','1957','1958','1959','1960','1961')
w1 <- WorldPhones%>%as_tibble()%>%rownames_to_column(var = 'Year')
w1 <- cbind(w1,year)%>%select(9:2)
w1 ## year Mid.Amer Africa Oceania S.Amer Asia Europe N.Amer
## 1 1951 555 89 1646 1815 2876 21574 45939
## 2 1956 733 1411 2366 2568 4708 29990 60423
## 3 1957 773 1546 2526 2695 5230 32510 64721
## 4 1958 836 1663 2691 2845 6662 35218 68484
## 5 1959 911 1769 2868 3000 6856 37598 71799
## 6 1960 1008 1905 3054 3145 8220 40341 76036
## 7 1961 1076 2005 3224 3338 9053 43173 79831ii.. ………….Here we will see worldphone datasets easily convert to dataframe. ……………..very interesting!!! isn’t it?
year <- c('1951','1956','1957','1958','1959','1960','1961')
w2 <- data.frame(WorldPhones)%>%rownames_to_column('Year')
w2## Year N.Amer Europe Asia S.Amer Oceania Africa Mid.Amer
## 1 1951 45939 21574 2876 1815 1646 89 555
## 2 1956 60423 29990 4708 2568 2366 1411 733
## 3 1957 64721 32510 5230 2695 2526 1546 773
## 4 1958 68484 35218 6662 2845 2691 1663 836
## 5 1959 71799 37598 6856 3000 2868 1769 911
## 6 1960 76036 40341 8220 3145 3054 1905 1008
## 7 1961 79831 43173 9053 3338 3224 2005 1076……..this method almost same with method(ii) but have little bit different….and we see that exactly same with worldphone data. just rows are convert to coloumn with a variable (Year). It’s magic of code,isn’t it?
……..in method(ii) we use data.frame but in method(iii) use as.data.frame()..But why? ……..Because data.frame() can be used to build a data frame while as.data.frame() can only be used to coerce other object to a data frame.
w3 <- as.data.frame(WorldPhones)%>%rownames_to_column('Year')
w3## Year N.Amer Europe Asia S.Amer Oceania Africa Mid.Amer
## 1 1951 45939 21574 2876 1815 1646 89 555
## 2 1956 60423 29990 4708 2568 2366 1411 733
## 3 1957 64721 32510 5230 2695 2526 1546 773
## 4 1958 68484 35218 6662 2845 2691 1663 836
## 5 1959 71799 37598 6856 3000 2868 1769 911
## 6 1960 76036 40341 8220 3145 3054 1905 1008
## 7 1961 79831 43173 9053 3338 3224 2005 1076