Descriptive Statistics

Descriptive stat with basic summary function

Input

summary(mydata)

Descriptive statistics with visualization

Creating histogram for “year”

Input

hist(mydata$year)

Creating a histogram using gpplot 2

Input

g <- ggplot(mydata, aes(x=year))
g <- g + geom_histogram(binwidth=.9) + scale_y_continuous(trans='log2')
g <- g + theme_light()
g <- g + labs(x="years", y="Frequency", face="bold")
g
  • ggplot(mydata, aes(x=year)) creates a plot using mydata aes defines the x, y and many other axis
  • geom_histogram defines de plot as a histogram, binwidth defines de width of the bars
  • scale_y_continuous(trans='log2') transforms the scale of the graph to log2 delete + scale_y_continuous(trans='log2') and check what happens
  • theme_light() changes the theme. There are various themes like black and white or color blind.
  • labs(x="years", y="Frequency", face="bold") changes the x and y labels in the plot

Create barplot for “era”

Input

barplot(table(mydata$era))

More visualization functions with “ggplot2”

Try to run this command:

Input

ggplot(data=mydata, aes(x=year, y=AverageTemperature)) + geom_line()

Can we do better?

What if we can manipulate the data so that we get the average temperature of each year?

group_by( ): Aggregates data based on the values from one or more columns

Input

grouped_data <- mydata  %>%
    group_by(year) %>%
    summarise(avg_temp = mean(AverageTemperature))

Input

head(grouped_data)

Plotting the grouped data

Input

ggplot(data=grouped_data, aes(x=year, y=avg_temp)) +
  geom_line()

If we are on track, try to:

1. load carbon dioxide data

2. remove NA

3. Change the column “CarbonDioxide” to numeric

4. Change the column “year” to numeric

5. view the data using the head( ) and tail( ) commands

6. get the mean CarbonDioxide emission of each year

Don’t spoil the fun. The stick figure is watching you

View in GitHub