The goal of this assignment is to give you practice in preparing different datasets for downstream analysis work.
Your task is to:
(1) Choose any three of the “wide” datasets identified in the Week 6 Discussion items. (You may use your own dataset; please don’t use my Sample Post dataset, since that was used in your Week 6 assignment!)
For each of the three chosen datasets:
Create a .CSV file (or optionally, a MySQL database!) that includes all of the information included in the dataset. You’re encouraged to use a “wide” structure similar to how the information appears in the discussion item, so that you can practice tidying and transformations as described below.
Read the information from your .CSV file into R, and use tidyr and dplyr as needed to tidy and transform your data. [Most of your grade will be based on this step!]
Perform the analysis requested in the discussion item.
Your code should be in an R Markdown file, posted to rpubs.com, and should include narrative descriptions of your data cleanup work, analysis, and conclusions.
Wide Data Set # 3
Website reference has been added. This site was used and referenced to turn wide dataset into a long dataset using rshape2 tools:
head(airquality)
## Ozone Solar.R Wind Temp Month Day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 NA NA 14.3 56 5 5
## 6 28 NA 14.9 66 5 6
# Changing the header names to lowercase based on website author recommendation that it enhances readability
colnames(airquality)<-tolower(colnames(airquality))
head(airquality)
## ozone solar.r wind temp month day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 NA NA 14.3 56 5 5
## 6 28 NA 14.9 66 5 6
In the rshape2 package, the melt function will turn the column variable names into rows. In the first iteration, it will be applied on all the default argument values, even the month and day. No Id variables will be identified. For the 2nd iteration, Id variables will be stated along with column names. This is very similiar to the gather function in the tidyr package.
# requires reshape2 package
library(reshape2)
airquality_long<- melt(airquality)
## No id variables; using all as measure variables
head(airquality_long)
## variable value
## 1 ozone 41
## 2 ozone 36
## 3 ozone 12
## 4 ozone 18
## 5 ozone NA
## 6 ozone 28
airquality2_long<-melt(airquality, id.vars = c("month", "day"))
head(airquality2_long)
## month day variable value
## 1 5 1 ozone 41
## 2 5 2 ozone 36
## 3 5 3 ozone 12
## 4 5 4 ozone 18
## 5 5 5 ozone NA
## 6 5 6 ozone 28
airquality3_long<- melt(airquality, id.vars = c("month", "day"),
variable.name = "climatetype", value.name = "climatemeasurement")
head(airquality3_long)
## month day climatetype climatemeasurement
## 1 5 1 ozone 41
## 2 5 2 ozone 36
## 3 5 3 ozone 12
## 4 5 4 ozone 18
## 5 5 5 ozone NA
## 6 5 6 ozone 28
NA values have been removed.
airquality4_long<-na.omit(airquality3_long)
head(airquality4_long)
## month day climatetype climatemeasurement
## 1 5 1 ozone 41
## 2 5 2 ozone 36
## 3 5 3 ozone 12
## 4 5 4 ozone 18
## 6 5 6 ozone 28
## 7 5 7 ozone 23
str(airquality4_long)
## 'data.frame': 568 obs. of 4 variables:
## $ month : int 5 5 5 5 5 5 5 5 5 5 ...
## $ day : int 1 2 3 4 6 7 8 9 11 12 ...
## $ climatetype : Factor w/ 4 levels "ozone","solar.r",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ climatemeasurement: num 41 36 12 18 28 23 19 8 7 16 ...
## - attr(*, "na.action")= 'omit' Named int 5 10 25 26 27 32 33 34 35 36 ...
## ..- attr(*, "names")= chr "5" "10" "25" "26" ...
summary(airquality4_long)
## month day climatetype climatemeasurement
## Min. :5.000 Min. : 1.00 ozone :116 Min. : 1.00
## 1st Qu.:6.000 1st Qu.: 8.00 solar.r:146 1st Qu.: 13.00
## Median :7.000 Median :16.00 wind :153 Median : 66.00
## Mean :7.044 Mean :15.83 temp :153 Mean : 80.06
## 3rd Qu.:8.000 3rd Qu.:23.00 3rd Qu.: 91.00
## Max. :9.000 Max. :31.00 Max. :334.00
