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→tidyr Package
== tidyr Package ==
== tidyr Package ==
http://vita.had.co.nz/papers/tidy-data.pdf
http://vita.had.co.nz/papers/tidy-data.pdf
<source lang="rsplus">
<source lang="rsplus">
# gather()
# gather()
If we have a dataset with multiple variables stored in one column, for example:<br />
If we have a dataset with multiple variables stored in one column, for example:<br />
grade, male_1, female_1, male_2, female_2, are colums storing grades for two diferent classes.
grade, male_1, female_1, male_2, female_2, are colums storing grades for two diferent classes.
<source lang="rsplus">
<source lang="rsplus">
res <- gather(students2, sex_class, count, -grade)
res <- gather(students2, sex_class, count, -grade)
2 Sally final C <NA> C <NA> <NA></nowiki>
2 Sally final C <NA> C <NA> <NA></nowiki>
This code will tidy the data:
<source lang="rsplus">
<source lang="rsplus">
students3 %>%
students3 %>%
gather(class, grade, class1:class5, na.rm = TRUE) %>%
gather(class, grade, class1:class5, na.rm = TRUE) %>%
print
print
</source>
spread()<br />
Spreads a colum into its variables.
<source lang="rsplus">
students3 %>%
gather(class, grade, class1:class5, na.rm = TRUE) %>%
spread(test, grade) %>%
print
</source>
To change the values from clas1, clas2... to 1, 2...
<source lang="rsplus">
students3 %>%
gather(class, grade, class1:class5, na.rm = TRUE) %>%
spread(test, grade) %>%
mutate(class= extract_numeric(class)) %>%
print
</source>
Tidy multiple observational units in one table (id, name, sex repeated...):
<nowiki>
> students4
id name sex class midterm final
1 168 Brian F 1 B B
2 168 Brian F 5 A C
3 588 Sally M 1 A C
4 588 Sally M 3 B C
5 710 Jeff M 2 D E
6 710 Jeff M 4 A C
7 731 Roger F 2 C A
8 731 Roger F 5 B A
9 908 Karen M 3 C C
10 908 Karen M 4 A A</nowiki>
To solve this will breake the dataset in two:<br />
(id, name, and sex)<br />
(id, class, midterm, final)<br />
<source lang="rsplus">
student_info <- students4 %>%
select(id, name, sex) %>%
unique() %>%
print
gradebook <- students4 %>%
select(id, class, midterm, final) %>%
print
</source>
If a single observation is stored in multiple tables:
<nowiki>
> passed
name class final
1 Brian 1 B
2 Roger 2 A
3 Roger 5 A
4 Karen 4 A
> failed
name class final
1 Brian 5 C
2 Sally 1 C
3 Sally 3 C
4 Jeff 2 E
5 Jeff 4 C
6 Karen 3 C</nowiki>
<source lang="rsplus">
# first we need to add to each table the variable passed or failed accordingly.
passed <- mutate(passed, status="passed")
failed <- mutate(failed, status="failed")
</source>
Example: dataset -> http://research.collegeboard.org/programs/sat/data/cb-seniors-2013
<source lang="rsplus">
# Load dataset in variable sat
sat %>%
select(-contains("total")) %>%
gather(part_sex, count, -score_range) %>%
separate(part_sex, c("part", "sex")) %>%
### <Your call to group_by()> %>%
mutate(###,
###
) %>% print
# The same adding total and prop
sat %>%
select(-contains("total")) %>%
gather(part_sex, count, -score_range) %>%
separate(part_sex, c("part", "sex")) %>%
group_by(part, sex) %>%
mutate(total = sum(count),
prop = count/total
) %>% print
</source>
</source>