The pig feed data again
my_url <- "http://ritsokiguess.site/datafiles/pigs1.txt"
pigs <- read_table(my_url)
pigs
Make longer (as before)
pigs %>% pivot_longer(-pig, names_to="feed",
values_to="weight") -> pigs_longer
pigs_longer
Make wider two ways 1/2
pivot_wider is inverse of pivot_longer:
pigs_longer %>%
pivot_wider(names_from=feed, values_from=weight)
we are back where we started.
Make wider 2/2
Or
pigs_longer %>%
pivot_wider(names_from=pig, values_from=weight)
Disease presence and absence at two locations
Frequencies of plants observed with and without disease at two locations:
Species Disease present Disease absent
Location X Location Y Location X Location Y
A 44 12 38 10
B 28 22 20 18
This has two rows of headers, so I rewrote the data file:
Species present_x present_y absent_x absent_y
A 44 12 38 10
B 28 22 20 18
Read into data frame called prevalence.
my_url <- "http://ritsokiguess.site/STAC32/disease.txt"
prevalence <- read_table(my_url)
prevalence
Lengthen and separate
prevalence %>%
pivot_longer(-Species, names_to = "column",
values_to = "freq") %>%
separate_wider_delim(column, "_",
names = c("disease", "location"))
Making longer, the better way
prevalence %>%
pivot_longer(-Species, names_to=c("disease", "location"),
names_sep="_",
values_to="frequency") -> prevalence_longer
prevalence_longer
Making wider, different ways
prevalence_longer %>%
pivot_wider(names_from=c(Species, location), values_from=frequency)
prevalence_longer %>%
pivot_wider(names_from=location, values_from=frequency)
Interlude
pigs_longer %>%
group_by(feed) %>%
summarize(weight_mean=mean(weight))
What if summary is more than one number?
eg. quartiles:
pigs_longer %>%
group_by(feed) %>%
summarize(r=quantile(weight, c(0.25, 0.75)))
Following the hint…
pigs_longer %>%
group_by(feed) %>%
reframe(r=quantile(weight, c(0.25, 0.75)))
this also works
pigs_longer %>%
group_by(feed) %>%
summarize(r=quantile(weight, c(0.25, 0.75)))
pigs_longer %>%
group_by(feed) %>%
summarize(r=list(quantile(weight, c(0.25, 0.75)))) %>%
unnest(r)
or, even better, use enframe:
quantile(pigs_longer$weight, c(0.25, 0.75))
enframe(quantile(pigs_longer$weight, c(0.25, 0.75)))
A nice look
Run this one line at a time to see how it works:
pigs_longer %>%
group_by(feed) %>%
summarize(r=list(enframe(quantile(weight, c(0.25, 0.75))))) %>%
unnest(r) %>%
pivot_wider(names_from=name, values_from=value) -> d
d
A hairy one
18 people receive one of three treatments. At 3 different times (pre, post, followup) two variables y and z are measured on each person:
my_url <- "http://ritsokiguess.site/STAC32/repmes.txt"
repmes0 <- read_table(my_url)
repmes0
repmes0 %>% mutate(id=str_c(treatment, ".", rep)) %>%
select(-rep) %>%
select(id, everything()) -> repmes
repmes
repmes %>% pivot_longer(contains("_"),
names_to=c("time", ".value"),
names_sep="_"
)
Attempt 1
repmes %>% pivot_longer(contains("_"),
names_to=c("time", "var"),
names_sep="_",
values_to = "vvv"
)
This is too long! We wanted a column called y and a column called z, but they have been pivoted-longer too.
Attempt 2
repmes %>% pivot_longer(contains("_"),
names_to=c("time", ".value"),
names_sep="_"
) -> repmes3
repmes3
This has done what we wanted.
make a graph
ggplot(repmes3, aes(x=fct_inorder(time), y=y,
colour=treatment, group = id)) +
geom_point() + geom_line()
![]()
A so-called spaghetti plot. The three measurements for each person are joined by lines, and the lines are coloured by treatment.
or do the plot with means
repmes3 %>% group_by(treatment, ftime=fct_inorder(time)) %>%
summarize(mean_y=mean(y)) %>%
ggplot(aes(x=ftime, y=mean_y, colour=treatment,
group=treatment)) +
geom_point() + geom_line()
![]()
On average, the two real treatments go up and level off, but the control group is very different.
