Original post published to DataScience+

In this post I will show how to collect data from a webpage and to analyze or visualize in R. For this task I will use the rvest package and will get the data from Wikipedia. I got the idea to write this post from Fisseha Berhane.
I will gain access to the prevalence of obesity in United States from Wikipedia page, then I will plot it in the map. Lets begin with loading the required packages.

## LOAD THE PACKAGES ####
library(rvest)

library(ggplot2)

library(dplyr)

library(scales)

Download the data from Wikipedia.

## LOAD THE DATA ####
obesity = read_html("https://en.wikipedia.org/wiki/Obesity_in_the_United_States")

obesity = obesity %>%

html_nodes("table") %>%

.[[1]]%>%

html_table(fill=T)

The first line of code is calling the data from Wikipedia and the second line of codes is transforming the table that we are interested into dataframe in R.
The head of our data.

head(obesity)
State and District of Columbia Obese adults Overweight (incl. obese) adults

1 Alabama 30.1% 65.4%

2 Alaska 27.3% 64.5%

3 Arizona 23.3% 59.5%

4 Arkansas 28.1% 64.7%

5 California 23.1% 59.4%

6 Colorado 21.0% 55.0%

Obese children and adolescents Obesity rank

1 16.7% 3

2 11.1% 14

3 12.2% 40

4 16.4% 9

5 13.2% 41

6 9.9% 51

The dataframe looks good, now we need to clean it from making ready to plot.

## CLEAN THE DATA ####
str(obesity)

'data.frame': 51 obs. of 5 variables:

$ State and District of Columbia : chr "Alabama" "Alaska" "Arizona" "Arkansas" ...

$ Obese adults : chr "30.1%" "27.3%" "23.3%" "28.1%" ...

$ Overweight (incl. obese) adults: chr "65.4%" "64.5%" "59.5%" "64.7%" ...

$ Obese children and adolescents : chr "16.7%" "11.1%" "12.2%" "16.4%" ...

$ Obesity rank : int 3 14 40 9 41 51 49 43 22 39 ...

# remove the % and make the data numeric

for(i in 2:4){

obesity[,i] = gsub("%", "", obesity[,i])

obesity[,i] = as.numeric(obesity[,i])

}

# check data again

str(obesity)

'data.frame': 51 obs. of 5 variables:

$ State and District of Columbia : chr "Alabama" "Alaska" "Arizona" "Arkansas" ...

$ Obese adults : num 30.1 27.3 23.3 28.1 23.1 21 20.8 22.1 25.9 23.3 ...

$ Overweight (incl. obese) adults: num 65.4 64.5 59.5 64.7 59.4 55 58.7 55 63.9 60.8 ...

$ Obese children and adolescents : num 16.7 11.1 12.2 16.4 13.2 9.9 12.3 14.8 22.8 14.4 ...

$ Obesity rank : int 3 14 40 9 41 51 49 43 22 39 ...

Fix the names of variables by removing the spaces.

names(obesity)
[1] "State and District of Columbia" "Obese adults" 

[3] "Overweight (incl. obese) adults" "Obese children and adolescents" 

[5] "Obesity rank"

names(obesity) = make.names(names(obesity))

names(obesity)

[1] "State.and.District.of.Columbia" "Obese.adults" 

[3] "Overweight..incl..obese..adults" "Obese.children.and.adolescents" 

[5] "Obesity.rank"

Now, it's time to load the map data.

# load the map data
states = map_data("state")

str(states)

'data.frame': 15537 obs. of 6 variables:

$ long : num -87.5 -87.5 -87.5 -87.5 -87.6 ...

$ lat : num 30.4 30.4 30.4 30.3 30.3 ...

$ group : num 1 1 1 1 1 1 1 1 1 1 ...

$ order : int 1 2 3 4 5 6 7 8 9 10 ...

$ region : chr "alabama" "alabama" "alabama" "alabama" ...

$ subregion: chr NA NA NA NA ...

Merge two datasets (obesity and states) by region, therefore we first need to create a new variable (region) in obesity dataset.

# create a new variable name for state
obesity$region = tolower(obesity$State.and.District.of.Columbia)

Merge the datasets.

states = merge(states, obesity, by="region", all.x=T)
str(states)

'data.frame': 15537 obs. of 11 variables:

$ region : chr "alabama" "alabama" "alabama" "alabama" ...

$ long : num -87.5 -87.5 -87.5 -87.5 -87.6 ...

$ lat : num 30.4 30.4 30.4 30.3 30.3 ...

$ group : num 1 1 1 1 1 1 1 1 1 1 ...

$ order : int 1 2 3 4 5 6 7 8 9 10 ...

$ subregion : chr NA NA NA NA ...

$ State.and.District.of.Columbia : chr "Alabama" "Alabama" "Alabama" "Alabama" ...

$ Obese.adults : num 30.1 30.1 30.1 30.1 30.1 30.1 30.1 30.1 30.1 30.1 ...

$ Overweight..incl..obese..adults: num 65.4 65.4 65.4 65.4 65.4 65.4 65.4 65.4 65.4 65.4 ...

$ Obese.children.and.adolescents : num 16.7 16.7 16.7 16.7 16.7 16.7 16.7 16.7 16.7 16.7 ...

$ Obesity.rank : int 3 3 3 3 3 3 3 3 3 3 ...

Plot the data

Finally we will plot the prevalence of obesity in adults.

## MAKE THE PLOT ####
# adults

ggplot(states, aes(x = long, y = lat, group = group, fill = Obese.adults)) + 

geom_polygon(color = "white") +

scale_fill_gradient(name = "Percent", low = "#feceda", high = "#c81f49", guide = "colorbar", na.value="black", breaks = pretty_breaks(n = 5)) +

labs(title="Prevalence of Obesity in Adults") +

coord_map()

Here is the plot in adults:

Similarly, we can plot the prevalence of obesity in children.

# children
ggplot(states, aes(x = long, y = lat, group = group, fill = Obese.children.and.adolescents)) + 

geom_polygon(color = "white") +

scale_fill_gradient(name = "Percent", low = "#feceda", high = "#c81f49", guide = "colorbar", na.value="black", breaks = pretty_breaks(n = 5)) +

labs(title="Prevalence of Obesity in Children") +

coord_map()

Here is the plot in children:

If you like to show the name of State in the map use the code below to create a new dataset.

statenames = states %>% 
group_by(region) %>%

summarise(

long = mean(range(long)), 

lat = mean(range(lat)), 

group = mean(group), 

Obese.adults = mean(Obese.adults), 

Obese.children.and.adolescents = mean(Obese.children.and.adolescents)

)

After you add this code to ggplot code above

geom_text(data=statenames, aes(x = long, y = lat, label = region), size=3)

That's all. I hope you learned something useful today.