Eric Hare, Andee Kaplan, Carson Sievert
June 10, 2015
During a ten week sensory experiment, 12 individuals were asked to assess taste of french fries on several scales (how potato-y, buttery, grassy, rancid, paint-y do the fries taste?)
French fries were fried in one of three different oils, and each week individuals had to assess six batches of french fries (all three oils, replicated twice)
What code would be needed to plot each of the ratings over time as a different color?
We want to change this wide format:
to this long format:
Keys/Identifiers:
Values/Measures:
french_fries_long <- gather(french_fries, key = variable, value = rating, potato:painty) head(french_fries_long)
## time treatment subject rep variable rating ## 1 1 1 3 1 potato 2.9 ## 2 1 1 3 2 potato 14.0 ## 3 1 1 10 1 potato 11.0 ## 4 1 1 10 2 potato 9.9 ## 5 1 1 15 1 potato 1.2 ## 6 1 1 15 2 potato 8.8
french_fries_long_sub <- french_fries_long[ french_fries_long$time == 10,] qplot(variable, rating, data = french_fries_long_sub, fill = variable, geom = "boxplot")
In certain applications, we may wish to take a long dataset and convert it to a wide dataset (Perhaps displaying in a table).
head(french_fries_long)
## time treatment subject rep variable rating ## 1 1 1 3 1 potato 2.9 ## 2 1 1 3 2 potato 14.0 ## 3 1 1 10 1 potato 11.0 ## 4 1 1 10 2 potato 9.9 ## 5 1 1 15 1 potato 1.2 ## 6 1 1 15 2 potato 8.8
We use the spread function from tidyr to do this:
french_fries_wide <- spread(french_fries_long, key = variable, value = rating) head(french_fries_wide)
## time treatment subject rep potato buttery grassy rancid painty ## 1 1 1 3 1 2.9 0.0 0.0 0.0 5.5 ## 2 1 1 3 2 14.0 0.0 0.0 1.1 0.0 ## 3 1 1 10 1 11.0 6.4 0.0 0.0 0.0 ## 4 1 1 10 2 9.9 5.9 2.9 2.2 0.0 ## 5 1 1 15 1 1.2 0.1 0.0 1.1 5.1 ## 6 1 1 15 2 8.8 3.0 3.6 1.5 2.3
Read in the billboard top 100 music data, which contains N'Sync and Backstreet Boys songs that entered the billboard charts in the year 2000
billboard <- read.csv("http://heike.github.io/rwrks/03a-r-format/data/billboard.csv")
Use tidyr to convert this data into a long format appropriate for plotting a time series (date on the x axis, chart position on the y axis)
Use ggplot2 to create this time series plot:
library(dplyr) french_fries_split <- group_by(french_fries_long, variable) # SPLIT french_fries_apply <- summarise(french_fries_split, rating = mean(rating, na.rm = TRUE)) # APPLY + COMBINE french_fries_apply
## Source: local data frame [5 x 2] ## ## variable rating ## 1 potato 6.9525180 ## 2 buttery 1.8236994 ## 3 grassy 0.6641727 ## 4 rancid 3.8522302 ## 5 painty 2.5217579
%>% (pipe) operatorx %>% f(y) is shorthand for f(x, y)french_fries %>% gather(key = variable, value = rating, potato:painty) %>% group_by(variable) %>% summarise(rating = mean(rating, na.rm = TRUE))
## Source: local data frame [5 x 2] ## ## variable rating ## 1 potato 6.9525180 ## 2 buttery 1.8236994 ## 3 grassy 0.6641727 ## 4 rancid 3.8522302 ## 5 painty 2.5217579
There are five primary dplyr verbs, representing distinct data analysis tasks:
french_fries %>% filter(subject == 3, time == 1)
## time treatment subject rep potato buttery grassy rancid painty ## 1 1 1 3 1 2.9 0.0 0.0 0.0 5.5 ## 2 1 1 3 2 14.0 0.0 0.0 1.1 0.0 ## 3 1 2 3 1 13.9 0.0 0.0 3.9 0.0 ## 4 1 2 3 2 13.4 0.1 0.0 1.5 0.0 ## 5 1 3 3 1 14.1 0.0 0.0 1.1 0.0 ## 6 1 3 3 2 9.5 0.0 0.6 2.8 0.0
french_fries %>% arrange(desc(rancid)) %>% head
## time treatment subject rep potato buttery grassy rancid painty ## 1 9 2 51 1 7.3 2.3 0 14.9 0.1 ## 2 10 1 86 2 0.7 0.0 0 14.3 13.1 ## 3 5 2 63 1 4.4 0.0 0 13.8 0.6 ## 4 9 2 63 1 1.8 0.0 0 13.7 12.3 ## 5 5 2 19 2 5.5 4.7 0 13.4 4.6 ## 6 4 3 63 1 5.6 0.0 0 13.3 4.4
french_fries %>% select(time, treatment, subject, rep, potato) %>% head
## time treatment subject rep potato ## 61 1 1 3 1 2.9 ## 25 1 1 3 2 14.0 ## 62 1 1 10 1 11.0 ## 26 1 1 10 2 9.9 ## 63 1 1 15 1 1.2 ## 27 1 1 15 2 8.8
french_fries %>% mutate(rancid2 = rancid^2) %>% head
## time treatment subject rep potato buttery grassy rancid painty rancid2 ## 1 1 1 3 1 2.9 0.0 0.0 0.0 5.5 0.00 ## 2 1 1 3 2 14.0 0.0 0.0 1.1 0.0 1.21 ## 3 1 1 10 1 11.0 6.4 0.0 0.0 0.0 0.00 ## 4 1 1 10 2 9.9 5.9 2.9 2.2 0.0 4.84 ## 5 1 1 15 1 1.2 0.1 0.0 1.1 5.1 1.21 ## 6 1 1 15 2 8.8 3.0 3.6 1.5 2.3 2.25
french_fries %>% group_by(time, treatment) %>% summarise(mean_rancid = mean(rancid), sd_rancid = sd(rancid))
## Source: local data frame [30 x 4] ## Groups: time ## ## time treatment mean_rancid sd_rancid ## 1 1 1 2.758333 3.212870 ## 2 1 2 1.716667 2.714801 ## 3 1 3 2.600000 3.202037 ## 4 2 1 3.900000 4.374730 ## 5 2 2 2.141667 3.117540 ## 6 2 3 2.495833 3.378767 ## 7 3 1 4.650000 3.933358 ## 8 3 2 2.895833 3.773532 ## 9 3 3 3.600000 3.592867 ## 10 4 1 2.079167 2.394737 ## .. ... ... ... ...
Read in the flights data:
flights <- read.csv("http://heike.github.io/rwrks/03a-r-format/data/flights.csv")
This dataset contains information on over 300,000 flights that departed from New York City in the year 2013.
arr_delay) based on the destination airport (dest). Sort this data frame in descending order, so the destination airport with the largest delay is first.Dates are deceptively hard to work with in R.
Example: 02/05/2012. Is it February 5th, or May 2nd?
Other things are difficult too:
The lubridate package helps tackle some of these issues.
library(lubridate) now() today() now() + hours(4) today() - days(2)
## [1] "2015-06-10 15:28:30 CDT" ## [1] "2015-06-10" ## [1] "2015-06-10 19:28:30 CDT" ## [1] "2015-06-08"
ymd("2013-05-14") mdy("05/14/2013") dmy("14052013") ymd_hms("2013:05:14 14:50:30", tz = "America/Chicago")
## [1] "2013-05-14 UTC" ## [1] "2013-05-14 UTC" ## [1] "2013-05-14 UTC" ## [1] "2013-05-14 14:50:30 CDT"
flights data, create a new column Date using lubridate. You will need to paste together the columns year, month, and day in order to do this. See the paste function.