Convert one column into multiple columns - r

Question

I am a novice. I have a data set with one column and many rows. I want to convert this column into 5 columns. For example my data set looks like this:
Column
----
City
Nation
Area
Metro Area
Urban Area
Shanghai
China
24,000,000
1230040
4244234
New york
America
343423
23423434
343434
Etc
The output should look like this
City | Nation | Area | Metro City | Urban Area
----- ------- ------ ------------ -----------
Shangai China 2400000 1230040 4244234
New york America 343423 23423434 343434
The first 5 rows of the data set (City, Nation,Area, etc) need to be the names of the 5 columns and i want the rest of the data to get populated under these 5 columns. Please help.

Answer 1

Here is a one liner (considering that your column is character, i.e. df$column <- as.character(df$column))
setNames(data.frame(matrix(unlist(df[-c(1:5),]), ncol = 5, byrow = TRUE)), c(unlist(df[1:5,])))
# City Nation Area Metro_Area Urban_Area
#1 Shanghai China 24,000,000 1230040 4244234
#2 New_york America 343423 23423434 343434

Answer 2

I'm going to go out on a limb and guess that the data you're after is from the URL: https://en.wikipedia.org/wiki/List_of_largest_cities.
If this is the case, I would suggest you actually try re-reading the data (not sure how you got the data into R in the first place) since that would probably make your life easier.
Here's one way to read the data in:
library(rvest)
URL <- "https://en.wikipedia.org/wiki/List_of_largest_cities"
XPATH <- '//*[#id="mw-content-text"]/table[2]'
cities <- URL %>%
read_html() %>%
html_nodes(xpath=XPATH) %>%
html_table(fill = TRUE)
Here's what the data currently looks like. Still needs to be cleaned up (notice that some of the columns which had names in merged cells from "rowspan" and the sorts):
head(cities[[1]])
## City Nation Image Population Population Population
## 1 Image City proper Metropolitan area Urban area[7]
## 2 Shanghai China 24,256,800[8] 34,750,000[9] 23,416,000[a]
## 3 Karachi Pakistan 23,500,000[10] 25,400,000[11] 25,400,000
## 4 Beijing China 21,516,000[12] 24,900,000[13] 21,009,000
## 5 Dhaka Bangladesh 16,970,105[14] 15,669,000 18,305,671[15][not in citation given]
## 6 Delhi India 16,787,941[16] 24,998,000 21,753,486[17]
From there, the cleanup might be like:
cities <- cities[[1]][-1, ]
names(cities) <- c("City", "Nation", "Image", "Pop_City", "Pop_Metro", "Pop_Urban")
cities["Image"] <- NULL
head(cities)
cities[] <- lapply(cities, function(x) type.convert(gsub("\\[.*|,", "", x)))
head(cities)
# City Nation Pop_City Pop_Metro Pop_Urban
# 2 Shanghai China 24256800 34750000 23416000
# 3 Karachi Pakistan 23500000 25400000 25400000
# 4 Beijing China 21516000 24900000 21009000
# 5 Dhaka Bangladesh 16970105 15669000 18305671
# 6 Delhi India 16787941 24998000 21753486
# 7 Lagos Nigeria 16060303 13123000 21000000
str(cities)
# 'data.frame': 163 obs. of 5 variables:
# $ City : Factor w/ 162 levels "Abidjan","Addis Ababa",..: 133 74 12 41 40 84 66 148 53 102 ...
# $ Nation : Factor w/ 59 levels "Afghanistan",..: 13 41 13 7 25 40 54 31 13 25 ...
# $ Pop_City : num 24256800 23500000 21516000 16970105 16787941 ...
# $ Pop_Metro: int 34750000 25400000 24900000 15669000 24998000 13123000 13520000 37843000 44259000 17712000 ...
# $ Pop_Urban: num 23416000 25400000 21009000 18305671 21753486 ...