Lec9
Source file ⇒ Lec9.Rmd
Today
- Chapter 10 Joining two data tables
- Chapter 8 more ggplot2
Chapter 10: Joining two Tables
Grades <- read.file("http://tiny.cc/mosaic/grades.csv")## Reading data with read.csv()head(Grades,10)## sid grade sessionID
## 1 S31185 D+ session1784
## 2 S31185 B+ session1785
## 3 S31185 A- session1791
## 4 S31185 B+ session1792
## 5 S31185 B- session1794
## 6 S31185 C+ session1795
## 7 S31185 B- session1796
## 8 S31185 NC session1807
## 9 S31188 C session1801
## 10 S31188 C+ session1802Courses <- read.file("http://tiny.cc/mosaic/courses.csv")## Reading data with read.csv()head(Courses,10)## sessionID dept level sem enroll iid
## 1 session1784 M 100 FA1991 22 inst265
## 2 session1785 k 100 FA1991 52 inst458
## 3 session1791 J 100 FA1993 22 inst223
## 4 session1792 J 300 FA1993 20 inst235
## 5 session1794 J 200 FA1993 22 inst234
## 6 session1795 J 200 SP1994 26 inst230
## 7 session1796 k 200 IT1994 25 inst466
## 8 session1801 C 100 SP1997 38 inst144
## 9 session1802 o 300 SP1997 16 inst390
## 10 session1807 W 100 SP1998 43 inst138Joins
A join is a data verb that combines two tables.
These care called the left and the right tables. There are several kinds of join.
- All involve establishing a correspondance — a match — between each case in the left table and zero or more cases in the right table.
The various joins differ in how they handle multiple matches or missing matches.
Example: Average class size
Goal: Figure out the average class size seen by each student.
- enroll comes from Courses table.
- Student (sid) comes from Grades.
- sessionID is in both tables.
joined_tables <- Grades %>%
select(sid, sessionID) %>%
left_join(Courses %>% select(enroll, sessionID), by=c("sessionID"="sessionID"))
head(joined_tables, 10) ## sid sessionID enroll
## 1 S31185 session1784 22
## 2 S31185 session1785 52
## 3 S31185 session1791 22
## 4 S31185 session1792 20
## 5 S31185 session1794 22
## 6 S31185 session1795 26
## 7 S31185 session1796 25
## 8 S31185 session1807 43
## 9 S31188 session1801 38
## 10 S31188 session1802 16Task for you
Make the following plot of the average class size for each student. 
Note: To figure out how to remove the tick mark labels (which aren’t legible) a good resource is the Cookbook for R
Most popular joins:
- left_join() The output has all the cases from the Left, even if there is no match in the right. If there is no match in the right you get NA
- inner_join() the output has only the cases from the Left with a match in the Right
inner joins are usefull for filtering. For example suppose I want to know the departments where the most students get good grades.
good_grades <- Grades %>%
filter(grade %in% c("A","A-"))
head(good_grades)## sid grade sessionID
## 1 S31185 A- session1791
## 2 S31188 A- session1810
## 3 S31188 A session2644
## 4 S31188 A- session3139
## 5 S31188 A session3141
## 6 S31188 A- session3187good_depts <- good_grades %>%
inner_join(Courses) %>%
group_by(dept) %>%
tally(sort=TRUE)## Joining by: "sessionID"head(good_depts)## Source: local data frame [6 x 2]
##
## dept n
## (chr) (int)
## 1 m 226
## 2 d 221
## 3 O 208
## 4 W 186
## 5 q 167
## 6 k 153Chap 8 (More ggplot2)
It is important to be able to look up the syntax and aesetics for the different geoms. Here is a good resourse from ggplot2.org
Example
The data table Birth78 in MosaicData package a day by day record of the number of births in the United States in 1978.
head(Births78)## date births dayofyear wday
## 1 1978-01-01 7701 1 Sun
## 2 1978-01-02 7527 2 Mon
## 3 1978-01-03 8825 3 Tues
## 4 1978-01-04 8859 4 Wed
## 5 1978-01-05 9043 5 Thurs
## 6 1978-01-06 9208 6 FriTask for you
Make a new table DailyBirths that gives the average of births on the different days of the week for each month of the year. You will need to use the function lubridate::month(date) which gives you the month from date. For example:
head(Births78$date)## [1] "1978-01-01 UTC" "1978-01-02 UTC" "1978-01-03 UTC" "1978-01-04 UTC"
## [5] "1978-01-05 UTC" "1978-01-06 UTC"head(month(Births78$date))## [1] 1 1 1 1 1 1We will rename wday as Wday using rename():
myBirths78 <- Births78 %>% rename(Wday=wday)
head(myBirths78)## date births dayofyear Wday
## 1 1978-01-01 7701 1 Sun
## 2 1978-01-02 7527 2 Mon
## 3 1978-01-03 8825 3 Tues
## 4 1978-01-04 8859 4 Wed
## 5 1978-01-05 9043 5 Thurs
## 6 1978-01-06 9208 6 Fri- Make the following plot:
- You can find a data set listing US federal holidays at
http://tiny.cc/dcf/US-Holidays.csv. Read it in as follows:1
Holidays <- read.csv("http://tiny.cc/dcf/US-Holidays.csv") %>%
mutate(date = lubridate::dmy(date))Filter out cases that are not in the month of November or December
my_months=c(11,12)
myHolidays <- Holidays %>% filter(month(date) %in% my_months , year(date)==1978) - Add a layer to your plot from (1). Draw a vertical bar at each date which is a holiday in November or January. You’ll use the
geom_vline()glyph. You can give adata =argument togeom_vline()to tell it to plot out the information fromHolidaysrather thanMyTwoYears.
Add a text label to each of the vertical bars to identify which holiday it is. Use the
geom_text()glyph.2
Next your layer your graph with a horizontal line with yintercept equal to the average number of births in for a day in 1978. Make the following plot:
- Use
annotate()to label the horizontal line as average
plot4 + annotate("text", label="avg", x="Sun", y=9000)