#1 Download the dataframe pirate_survey_witherrors.txt from http://nathanieldphillips.com/wp-content/uploads/2015/05/pirate_survey_witherrors.txt. The data are stored in a tab-separated text file with headers. Load the dataframe into an object called pirates.errors. Because it’s tab-separated, use sep = “\t”.
pirates.errors <- read.table(file = "http://nathanieldphillips.com/wp-content/uploads/2015/05/pirate_survey_witherrors.txt",header= T, sep="\t", stringsAsFactors=F)
#pirates.errors
#2 Let’s clean up the dataframe. Some of the values don’t seem to be appropriate. For example, when I look at the column sex, I see some bad values. For each of the columns, try to figure out which values are appropriate (hint: use table()), and recode all inappropriate values as NA.
table(pirates.errors$sex)
##
## depends on who is offering female
## 1 466
## male other
## 490 41
## sure I'll have some yes please!
## 1 1
pirates.errors$sex[!(pirates.errors$sex %in% c("male", "female", "other/NA"))] <- NA
pirates.errors$headband[!(pirates.errors$headband %in% c("yes", "no"))] <- NA
pirates.errors$age[!(pirates.errors$age %in% c(1:120))] <- NA
pirates.errors$tchests.found[!(pirates.errors$tchests.found %in% c(1:99))] <- NA
pirates.errors$parrots.lifetime[!(pirates.errors$parrots.lifetime %in% c(1:99))] <- NA
table(pirates.errors$favorite.pirate)
##
## Anicetus Blackbeard Edward Low Hook Jack Sparrow
## 117 100 113 114 450
## Lewis Scot your mom
## 96 10
pirates.errors$favorite.pirate[!(pirates.errors$favorite.pirate %in% c("Anicetus","Blackbeard","Edward Low", "Hook", "Jack Sparrow", "Lewis Scot"))] <- NA
table(pirates.errors$sword.type)
##
## banana cutlass sabre scimitar
## 39 842 62 57
#3 A fellow pirate captain wants to know if there is a relationship between the sex of my pirates and the number of treasure chests they have found. Using aggregate() figure out the mean number of treasure chests found by males, females, and other.
pirates <- read.table(file = "http://nathanieldphillips.com/wp-content/uploads/2015/05/pirate_survey_noerrors.txt" , header= T, sep="\t", stringsAsFactors=F)
aggregate (formula= tchests.found ~ sex, FUN=mean, data=pirates)
## sex tchests.found
## 1 female 7.353319
## 2 male 7.128049
## 3 other 8.048780
#4 Each pirate only uses one kind of sword - and their speed with their preferred sword is represented in sword.speed Which sword types tend to have the fastest (i.e.; smallest) sword speed? Test this by calculating the median sword.speed for each sword type
aggregate(formula= sword.speed ~ sword.type, FUN=median , data=pirates)
## sword.type sword.speed
## 1 banana 2.5859139
## 2 cutlass 0.4848266
## 3 sabre 1.7393120
## 4 scimitar 1.7559671
#5 Is there a relationship between whether or not a pirate wears a headband and their speed with their sword? Test this in two ways.
#First, calculate the median sword speed, separately for each level of headband use using aggregate(). What is your conclusion?
aggregate(formula= sword.speed ~ headband, FUN=median , data=pirates)
## headband sword.speed
## 1 no 1.0780988
## 2 yes 0.5375353
# -> pirates wearing a headband seem to be faster
aggregate(formula= sword.speed ~ sword.type + sex, FUN=median , data=pirates)
## sword.type sex sword.speed
## 1 banana female 2.6029223
## 2 cutlass female 0.4602335
## 3 sabre female 1.5953447
## 4 scimitar female 1.7907291
## 5 banana male 3.6661735
## 6 cutlass male 0.4960470
## 7 sabre male 2.0730914
## 8 scimitar male 1.6260470
## 9 banana other 1.5442277
## 10 cutlass other 0.6131719
## 11 sabre other 0.7028357
## 12 scimitar other 4.7844982
#Second, calculate the median sword speed for all combinations of both sex AND sword.type. -> Doesn't it mean HEADBAND instead of sex? Is there a mistake in the WPA?
aggregate(formula= sword.speed ~ sword.type + headband, FUN=median , data=pirates)
## sword.type headband sword.speed
## 1 banana no 2.1272891
## 2 cutlass no 0.3408127
## 3 sabre no 1.0295643
## 4 scimitar no 0.8387161
## 5 banana yes 7.9722183
## 6 cutlass yes 0.4897835
## 7 sabre yes 2.3635005
## 8 scimitar yes 4.9336595
# -> seems that the pirates without a headband are faster
#6 Does a pirate’s favorite pirate say anything about them? Do pirates whose favorite pirate is Hook have more tattoos on average or a faster sword speed than those whose favorite pirate is Blackbeard? Using dplyr, create the following aggregated dataframe which shows aggregated data depending on the pirates’ favorite pirate. Here are the four basic steps
#install.packages("dplyr")
#library("dplyr")
#pirates %>% group_by(favorite.pirate) %>% summarise (number.tattoos=mean(tattoos), mean.speed=mean(sword.speed))
#7 Is there a relationship between a pirate’s age and whether or not they went to Jack Sparrow’s School of Fashion and Piratry? Solve this in 2 steps.
log.vec <- pirates$college == "JSSFP"
aggregate(formula= age ~ log.vec + id, FUN=mean, data=pirates)
## log.vec id age
## 1 TRUE 1 35
## 2 FALSE 2 21
## 3 FALSE 3 27
## 4 FALSE 4 19
## 5 FALSE 5 31
## 6 FALSE 6 21
## 7 TRUE 7 31
## 8 TRUE 8 35
## 9 FALSE 9 26
## 10 FALSE 10 19
## 11 FALSE 11 20
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#didn't know how to finish
#8 Is there a relationship between the number of tattoos a pirate has and the number of chests he/she has found?
plot(x=pirates$tattoos, y=pirates$tchests.found, xlab="tattoos", ylab= "tchests")
pirates$tattoos.cut5 <- cut(pirates$tattoos, breaks=4)
aggregate(formula= tchests.found ~ tattoos.cut5, FUN=median, data=pirates)
## tattoos.cut5 tchests.found
## 1 (-0.019,4.75] 3
## 2 (4.75,9.5] 5
## 3 (9.5,14.2] 5
## 4 (14.2,19] 8
plot(x=pirates$tattoos.cut5, y=pirates$tchests.found, main= "Number of tattoos and chests found", xlab="tattoos", ylab="median chests found" )
