# A tibble: 6 × 5
type country year_1999 year_2000 year_2001
<chr> <chr> <dbl> <dbl> <dbl>
1 population Afghanistan 19987071 20595360 21295360
2 cases Afghanistan 745 2666 NA
3 population Brazil 172006362 174594898 178594898
4 cases Brazil 37737 80488 12452
5 population China 1272915272 1280428583 1310428583
6 cases China 212258 213766 313766
Answer
The data frame exhibits several issues related to its organization. Instead of utilizing separate columns for individual variables, the “type” column specifies the nature of the values within each row. Columns 3 and 4, which both contain doubles, indicate the year of the values in those columns.
Let’s use pivot_longer from dplyr package to take care of this. Recap:
pivot_longer(data, cols, names_to, values_to))
Arguments:
data data frame to pivot
cols columns to pivot into longer format.
names_to name of new column to create from the information stored in the column names of = the pivoted columns
values_to name of the new column to create from the data stored in cell values of the pivoted columns
Edit the following code to produce the output below:
# A tibble: 18 × 4
type country year value
<chr> <chr> <chr> <dbl>
1 population Afghanistan year_1999 19987071
2 population Afghanistan year_2000 20595360
3 population Afghanistan year_2001 21295360
4 cases Afghanistan year_1999 745
5 cases Afghanistan year_2000 2666
6 cases Afghanistan year_2001 NA
7 population Brazil year_1999 172006362
8 population Brazil year_2000 174594898
9 population Brazil year_2001 178594898
10 cases Brazil year_1999 37737
11 cases Brazil year_2000 80488
12 cases Brazil year_2001 12452
13 population China year_1999 1272915272
14 population China year_2000 1280428583
15 population China year_2001 1310428583
16 cases China year_1999 212258
17 cases China year_2000 213766
18 cases China year_2001 313766
However, year_2000 isn’t really a value of year. It would be better for the values to contains the numeric value only. We can remove the “recorded_” prefix with pivot_longer()’s names_prefix argument. As stated in the help file ?pivot_longer, this argument accepts a regular expression and is used to remove matching text from the start of each variable name. Note that I used the negative indexing to exclude the first two columns in pivoting, rather than passing the names of the columns.
# A tibble: 18 × 4
type country year value
<chr> <chr> <chr> <dbl>
1 population Afghanistan 1999 19987071
2 population Afghanistan 2000 20595360
3 population Afghanistan 2001 21295360
4 cases Afghanistan 1999 745
5 cases Afghanistan 2000 2666
6 cases Afghanistan 2001 NA
7 population Brazil 1999 172006362
8 population Brazil 2000 174594898
9 population Brazil 2001 178594898
10 cases Brazil 1999 37737
11 cases Brazil 2000 80488
12 cases Brazil 2001 12452
13 population China 1999 1272915272
14 population China 2000 1280428583
15 population China 2001 1310428583
16 cases China 1999 212258
17 cases China 2000 213766
18 cases China 2001 313766
It’s probably more useful to store year as a integer. We can tell pivot_longer() our desired type for the names_to column by using the optional names_transform argument.
# A tibble: 18 × 4
type country year value
<chr> <chr> <int> <dbl>
1 population Afghanistan 1999 19987071
2 population Afghanistan 2000 20595360
3 population Afghanistan 2001 21295360
4 cases Afghanistan 1999 745
5 cases Afghanistan 2000 2666
6 cases Afghanistan 2001 NA
7 population Brazil 1999 172006362
8 population Brazil 2000 174594898
9 population Brazil 2001 178594898
10 cases Brazil 1999 37737
11 cases Brazil 2000 80488
12 cases Brazil 2001 12452
13 population China 1999 1272915272
14 population China 2000 1280428583
15 population China 2001 1310428583
16 cases China 1999 212258
17 cases China 2000 213766
18 cases China 2001 313766
We can also utilize a similar conversion for the values_to column as well. Edit the following code to convert the value to integer ( to save memory da!). See here to learn about the parameter.
For downstream analysis we should make our table wider, For this we use pivot_wider() which adds columns making the data frame wider.
This corrects the issue that each variable does not have its own column. For example column type can be two different columns; seperate column for population and another column for case.
Recap:
pivot_wider(data, names from ="", values_from ="")
data data frame to pivot
names_from column whose values are the names of the new columns
values_from column whose values are the values of the new columns
Now write your code to create a wider table describe above.
Hint
Step 1: Identify the column whose values will supply the column names (name_from).
Step 2: Identify the column whose values will supply the column values. (values_from)
wide_table1 <-pivot_wider(table1, names_from = type, values_from = value )
wide_table1
# A tibble: 9 × 4
country year population cases
<chr> <int> <int> <int>
1 Afghanistan 1999 19987071 745
2 Afghanistan 2000 20595360 2666
3 Afghanistan 2001 21295360 NA
4 Brazil 1999 172006362 37737
5 Brazil 2000 174594898 80488
6 Brazil 2001 178594898 12452
7 China 1999 1272915272 212258
8 China 2000 1280428583 213766
9 China 2001 1310428583 313766
Now create a new column, cases_per_cap, that is the number of cases divided by the total population that year. saved it to a new data frame table2.
# ??? recall which function we use to create new colstable2 <- ???(wide_table1, cases_per_cap = ... ) # ... should be a vector of length n# where n is the number of rows in wide_table1
This next example demonstrates how to use the case_when() function. case_when() is very useful when creating a new categorical variable based on already existing numeric variables.
This function does not have any explicit parameters. Instead, there can be any number of logical statements (e.g. cases / population < 0.0001) with a corresponding ~.
~ creates a formula in R. On the other side of the ~ is the value of the variable if the logical statement evaluates to TRUE.
Make sure only one formula evaluates as TRUE for each observation. The code chunk below demonstrates the syntax for case_when().
(new_table <-mutate(table2, cases_per_cap =case_when( cases / population <0.0001~"low", cases / population >=0.0001& cases / population <0.0003~"medium", cases / population >=0.0003~"high")))
# A tibble: 9 × 5
country year population cases cases_per_cap
<chr> <int> <int> <int> <chr>
1 Afghanistan 1999 19987071 745 low
2 Afghanistan 2000 20595360 2666 medium
3 Afghanistan 2001 21295360 NA <NA>
4 Brazil 1999 172006362 37737 medium
5 Brazil 2000 174594898 80488 high
6 Brazil 2001 178594898 12452 low
7 China 1999 1272915272 212258 medium
8 China 2000 1280428583 213766 medium
9 China 2001 1310428583 313766 medium
This example creates a new variable that designates the level of cases per capita as low, medium, or high. The new variable is a string instead of numeric. This variable is perfect for using the factor data type, as it has inherent ordered levels.
Write code below to turn this variable into a factor. Remember that when using mutate, if you set something equal to a variable name already in use, it will overwrite that variable.
Recap: ggplot2 uses the theoretical framework called the grammar of graphics as the basis for its organization. There are three essential components to a graphic
data: the clean data containing the variable(s) of interest
geom: the type of geometric object in the plot e.g. line, point, bar, box
aes: aesthetic attributes of the geometric object e.g. x/y variables, color, shape, size can be mapped (coordinated) to variables
Example 1: Create simple scatter plot that shows the relationship between engine displacement and city miles per gallon for the cars whithin mpg dataset. You may find the data visualization cheatsheet helpful here.
In order to capture the relationship better we can fit a line to this points. This can be done by adding geom_smooth.
plot1 +geom_smooth()
Example 2: Let’s plot two different variables on the same axis. Note that we need to make sure that both variables have the same unit.
ggplot(data = mpg)+geom_point(aes(x = displ, y = cty), color ='red') +geom_point(aes(x = displ, y = hwy), color ='blue')
Example 3: We want to differentiate between different fuel type. we can use this categorical variable as the color! This will automatically add a legend to our plot.
ggplot(data = mpg) +geom_point(aes(x = displ, y = cty, color = fl))
Alternatively, we can also use alpha to differentiate between different fuel type. It provides different shades and is better suited for black/white plots.
Warning: Using alpha for a discrete variable is not advised.
Properties of geom
This section will explore other visual properties of the geom (aesthetics) like size, shape.
In general, we should know what type of the graphs are suitable for our purpose. One factor that we need to know is the type of the data that we’re plotting in addition of the number of the variables that we’re using.For example:
When having one categorical (discrete) variable one continues (numeric) variable: bar plot, violin plot, boxplot.
The following graph shows a bar plot of hwy for different class of cars. Note that we have a colored plot by using fill. This automate coloring our graphs.
Which class has the max and min fuel consumption?
mpg |>ggplot(aes(x = class, y = hwy, fill= class)) +geom_col()
Replicate the above graph by using color instead of fill. what is the difference? which one do you prefer?
#your code
Now let’s change the name of y axis to highway miles per gallon.
mpg |>ggplot(aes(x = class, y = hwy, fill = class)) +geom_col()+labs( y="highway miles per gallon")
Add a title to the previous graph.
# your code
When creating a graph of only one numeric variable : histogram, density plot, dotplots.
Now, let’s create a bar plot of number of cylinders. The majority of the cars in our dataset has … cylinder?
mpg |>ggplot(aes(x=cyl)) +geom_bar()
We can also change the theme of our graphs, my favirote is theme_minimal().
Explore other themes to see which one you like the best :)
Let’s see how many cars we have for each fuel type. note that we’re using geom_col since we have a categorical ( discrete) data and a numeric variable. n is mapped to count(fuel type) .
mpg |>count(fl) |>ggplot() +geom_col(aes(x = fl, y = n, fill = fl)) +geom_label(aes(x = fl, y = n, label = n)) +labs(title =" Number Of Fuel Types ")+theme_minimal()
The following questions are to be completed and submitted on Canvas in rendered HTML format.
We are using penguins dataset for the following questions. Download the penguins.csv dataset from the Datasets module on Canvas.
Use the read_csv function from readr1 package to read the csv file into your workspace name it to an object called penguins. (1 point)
Provide a brief summary of the dataset using a function from tidyverse (more specifically dplyr). How many rows and columns are there? (2 points)
Write a pipe line that shows summary of the numeric columns only ? (2 points)
Remove the NAs from the data set.(1 point)
Add a new column called size that has categorize the penguins based on the following (4 points)
if the body mass is greater than 4200, penguin size is big
if body mass is lower than or equal than 4200, penguin size is small.
Create a violin plot for the depth of the penguins bill for each size.(2 bonus points)
Create a simple scatter plot to show the relationship between bill_length and bill_depth for different species. (2 points)
Is there a consistent pattern of relationship among species? (1 point)
Create a box plot of the mass_body within each island for different species (3 points)
Do you see any outliers? (1 point)
Change the x and y labels to “Island” and ” Body Mass (g)“, respectively. (1 point)
Add an appropriate title to your graph. (1 point)
Change the theme to theme_classic. (1 point)
Create a colored bar plot that show number of each species in Dream island. (5 points)
General formatting: (3 points). See below for details.
Total possible points: 28 + 2 bonus points
General formating expectations
Code formatting (1 point). Code must appear within an R chunk with the appropriate chunk options (e.g. echo = TRUE unless otherwise specified)
```{r, echo=TRUE}# this is some simple additionx <-2+4x```
[1] 6
or inline with text:
# inline R code:This is some text with inline R code:`r mean(c(1, 2, 3, 4, 5))`.
which renders to: This is some text with inline R code: 3.
Text formatting (1 point) Narrative and word answers should be typeset using regular text (as opposed to using using comments (preceded by #) within an R chunk. The use of of markdown formatting to improve readability is encouraged. Simple examples include:
headers (second-level headers are preceded by ##)
italics (*italics* or _italics_),
bold (**bold**),
typewritter text (`typewritter`)
Labels (1 point) all HTML documents should satisfy the following criteria:
assignments should be named assignment<x>_<y>.html where <x> is replaced by the assignment number and ` is replaced by your student number
Your full name, student number, assignment number, and course number should appear somewhere near the top of the rendered document
Questions should be labeled either by the use of headers or numbered lists.
Important
Submissions in any other format (e.g. Rmd, word document) will incur a 20% penalty.
