Lab 5 Graphing Data (Part 2)
B. Sosnovski
10/18/2021
Graphing Data (Part 2)
This is a continuation of the practice of the skills presented in Lab 4, Graphing Data.
General goals
Our general goals for this lab are to do the following:
- Load some data into R
- Make graphs of the quantitative data.
R
Gapminder data
https://www.gapminder.org is an organization that collects some interesting worldwide data. They also make cool visualization tools for looking at the data. There are many good examples, and they have visualization tools built right into their website that you can play around with https://www.gapminder.org/tools/. That’s fun; check it out.
Load the data to R
The data set needed for this lab is included together with these instructions.
Use the following commands to load the data.
library("gapminder")
gapminder_df<-gapminderIf you haven’t installed gapminder in your project, run
the code below.
library(data.table)
gapminder_df <-fread("gapminder.csv")Look at the data frame from gapminder
You can look at the data to see what is in it.
colnames(gapminder_df)## [1] "country" "continent" "year" "lifeExp" "pop" "gdpPercap"Let’s check how big is this data frame.
data_size <- dim(gapminder_df)
data_size## [1] 1704 6There are 1704 rows of data and 6 variables. We see columns for the country, continent, year, life expectancy, population, and GDP per capita.
Example
Enter a code chunk below that displays the data’s first and last rows in gapminder.
Enter your code below:
head(gapminder_df)## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.tail(gapminder_df)## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Zimbabwe Africa 1982 60.4 7636524 789.
## 2 Zimbabwe Africa 1987 62.4 9216418 706.
## 3 Zimbabwe Africa 1992 60.4 10704340 693.
## 4 Zimbabwe Africa 1997 46.8 11404948 792.
## 5 Zimbabwe Africa 2002 40.0 11926563 672.
## 6 Zimbabwe Africa 2007 43.5 12311143 470.Asking Questions with the gapminder data
We will show you how to graph some of the data to answer a few different kinds of questions. Then you will form your questions and see if you can answer them with ggplot2 yourself. You will need to copy and paste the following examples and change them up a little bit.
Life expectancy histogram
How long are people living all around the world according to this data set?
There are many ways we could plot the data to find out. The first way
is a histogram. We have many numbers for life expectancy in the column
lifeExp. This is a big sample, full of numbers for 142
countries across many years.
It’s easy to make a histogram in ggplot to view the distribution. Most of the code should be familiar to you since you already encountered most of it in lab 3.
library(ggplot2)
ggplot(gapminder_df, aes(x = lifeExp))+
geom_histogram(color="white")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
See, that was easy. Next is a code chunk that adds more layers and settings if you want to modify parts of the graph:
ggplot(gapminder_df, aes(x=lifeExp)) +
geom_histogram(color="blue")+
theme_classic(base_size = 15) +
xlab("Life Expectancy") +
ylab("Frequency Count") +
ggtitle("Histogram of Life Expectancy from Gapminder")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Did you notice the changes from the examples presented in lab 3?
The histogram shows a wide range of life expectancies, from below 40 to just over 80. Histograms are helpful; they show you what values happen more often than others.
One final thing about histograms in ggplot is that you may want to
change the bin size. That controls how wide or narrow, or the number of
bars (how they split across the range) in the histogram. You must set
the bins= option in geom_histogram().
ggplot(gapminder_df, aes(x = lifeExp)) +
geom_histogram(color="green", bins=50)+
theme_classic(base_size = 15) +
xlab("Life Expectancy") +
ylab("Frequency Count") +
ggtitle("Histogram of Life Expectancy from Gapminder (w/ 50 Bins)")
Example
Use the code chunk above to create a histogram with different colors
and 40 bins displaying the variable corresponding to the GDP per Capita
variable in gapminder.
Enter your code below:
ggplot(gapminder_df, aes(x = gdpPercap)) +
geom_histogram(color="yellow", bins=40)+
theme_classic(base_size = 15) +
xlab("GDP per Capita") +
ylab("Frequency Count") +
ggtitle("Histogram of GDP per Capita from Gapminder (w/ 40 Bins)")
Note: See, same basic shape, but now breaking up the range into 40 little equal-sized bins, rather than 30, which is the default. You get to choose what you want to do.
Life expectancy by year scatterplot
We can see that we have data for life expectancy and different years. So, does worldwide life expectancy change across the years in the data set? As we go into the future, are people living longer?
Let’s look at this by using a scatter plot. We can set the x-axis as
the years and the y-axis as life expectancy. Then we can use
geom_point() to display a whole bunch of dots and look at
them. Here’s the simple code:
ggplot(gapminder_df, aes( x= year, y= lifeExp))+
geom_point(color="magenta")
Whoa, that’s a lot of dots! Remember that each country is measured each year. So, the bands of dots you see show the life expectancies for the whole range of countries within each year of the database. There is a big spread inside each year. But it looks like groups of dots slowly go up over the years.
One country’s life expectancy by year
I’m (B. Sosnovski), born and raised in Brazil, so maybe I want to
know if life expectancy for Brazilians has been going up over the years.
To find out the answer for one country, we first need to split the
complete data set into another smaller one that only contains Brazil
data. In other words, we want only the rows where the word “Brazil” is
found in the country column. We will use the
filter function from dplyr for this:
# filter rows to contain Brazil
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionsmaller_df <- gapminder_df %>%
filter(country == "Brazil")
head(smaller_df)## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Brazil Americas 1952 50.9 56602560 2109.
## 2 Brazil Americas 1957 53.3 65551171 2487.
## 3 Brazil Americas 1962 55.7 76039390 3337.
## 4 Brazil Americas 1967 57.6 88049823 3430.
## 5 Brazil Americas 1972 59.5 100840058 4986.
## 6 Brazil Americas 1977 61.5 114313951 6660.# plot the new data contained in smaller_df
ggplot(smaller_df, aes( x= year, y= lifeExp))+
geom_point(color="black")
I would say things are looking good for Brazilians 🙏; their life expectancy has increased over the years!
Example
Enter a code chunk below that shows the life expectance for another country of your choice. Also, add a title and adjust labels for horizontal and vertical axes.
- Here is a list of the countries in the data frame:
listOfCountries <- unique(gapminder_df[,1])
listOfCountries$country## [1] Afghanistan Albania Algeria
## [4] Angola Argentina Australia
## [7] Austria Bahrain Bangladesh
## [10] Belgium Benin Bolivia
## [13] Bosnia and Herzegovina Botswana Brazil
## [16] Bulgaria Burkina Faso Burundi
## [19] Cambodia Cameroon Canada
## [22] Central African Republic Chad Chile
## [25] China Colombia Comoros
## [28] Congo, Dem. Rep. Congo, Rep. Costa Rica
## [31] Cote d'Ivoire Croatia Cuba
## [34] Czech Republic Denmark Djibouti
## [37] Dominican Republic Ecuador Egypt
## [40] El Salvador Equatorial Guinea Eritrea
## [43] Ethiopia Finland France
## [46] Gabon Gambia Germany
## [49] Ghana Greece Guatemala
## [52] Guinea Guinea-Bissau Haiti
## [55] Honduras Hong Kong, China Hungary
## [58] Iceland India Indonesia
## [61] Iran Iraq Ireland
## [64] Israel Italy Jamaica
## [67] Japan Jordan Kenya
## [70] Korea, Dem. Rep. Korea, Rep. Kuwait
## [73] Lebanon Lesotho Liberia
## [76] Libya Madagascar Malawi
## [79] Malaysia Mali Mauritania
## [82] Mauritius Mexico Mongolia
## [85] Montenegro Morocco Mozambique
## [88] Myanmar Namibia Nepal
## [91] Netherlands New Zealand Nicaragua
## [94] Niger Nigeria Norway
## [97] Oman Pakistan Panama
## [100] Paraguay Peru Philippines
## [103] Poland Portugal Puerto Rico
## [106] Reunion Romania Rwanda
## [109] Sao Tome and Principe Saudi Arabia Senegal
## [112] Serbia Sierra Leone Singapore
## [115] Slovak Republic Slovenia Somalia
## [118] South Africa Spain Sri Lanka
## [121] Sudan Swaziland Sweden
## [124] Switzerland Syria Taiwan
## [127] Tanzania Thailand Togo
## [130] Trinidad and Tobago Tunisia Turkey
## [133] Uganda United Kingdom United States
## [136] Uruguay Venezuela Vietnam
## [139] West Bank and Gaza Yemen, Rep. Zambia
## [142] Zimbabwe
## 142 Levels: Afghanistan Albania Algeria Angola Argentina Australia ... ZimbabweEnter your code below:
Multiple countries scatterplot
What if we want to look at a few countries altogether?
We can do this too. We change how we filter the data so that more than one country is allowed, then plot the data. We will also add some nicer color options and make the plot look pretty.
First, the simple code that shows the data for the countries Canada, France, and Spain:
# filter rows to contain countries of choice
smaller_df <- gapminder_df %>%
filter(country %in% c("Canada","France","Spain") == TRUE)
# plot the new data contained in smaller_df
ggplot(smaller_df, aes(x= year, y= lifeExp, group= country))+
geom_point(color="red")
Nice, we can now see three sets of dots, but which countries do they represent? Let’s add a legend and make the graph better looking.
ggplot(smaller_df,aes( x= year, y= lifeExp,
group= country, color = country)) +
geom_point()+
theme_classic(base_size = 15) +
xlab("Year") +
ylab("Life Expectancy") +
ggtitle("Life Expectancy by Year for Three Countries")
geom_line() connecting the dots
We might also want to connect the dots with a line!
To make it easier to see the connection! Remember, ggplot2 draws
layers on top of layers. So, we add a new geom_line()
layer.
ggplot(smaller_df,aes( x= year, y= lifeExp,
group= country, color = country)) +
geom_point()+
geom_line()+
theme_classic(base_size = 15) +
ylab("Life Expectancy") +
xlab("Year") +
ggtitle("Life Expectancy by Year for Three Countries")
Example
Utilizing the list of countries above, create a plot with lines connecting the points to display the life expectancy for 2 countries of your choice that haven’t been featured in the lab so far.
Enter your code below:
References
The material used in this document contains excerpts and modifications from:
- Matthew J. C. Crump, Anjali Krishnan, Stephen Volz, and Alla Chavarga (2018) “Answering questions with data: Lab Manual”. Last compiled on 2019-04-06. https://www.crumplab.com/statisticsLab/