(1) RMarkdown Practice (24 points) Change the markdown code below as indicated.
Make this line bold
Make this line italics
Make this line a third level header
Make this line a bullet point
- Make this line an indented (or level two) bullet point
# Make this line look like R CodeBelow this line, insert a new R chunk, create a vector called xvec that contains the integers 2 through 7, and have R display what is in xvec.
xvec<- c(2:7)
xvec## [1] 2 3 4 5 6 7(2) R Syntax Practice (12 points) Modify the R code below to follow good R Syntax practices
x <- 5
x <- c(1, 2, 3)
length(x)
for (i in 1:10) {
x <-1+1
}
x <- 1
y <- c(3, 4)(3) Data handling 36 pts
- Insert a new R code chunk below this line. Then in the chunk, do the following:
- Read the .csv stored HERE into a new data frame and call is “wb”. This is the world bank data we discussed in class two.
- Get the dimension of wb.
- Get the variable names of wb.
- Show the first 6 lines of wb.
- Get the data type of each variable.
- What is the data type of the variable
Pop?
- Create a new object called
subsetthat has only the variables Country, GNI, Exports, and Imports AND only for countries where GNI is greater than 70000. You’ll need to use thena.omit()function (usehelp(na.omit)) to eliminate countries missing data for any of the four variables you retain. You should end up with exactly three countries insubset. - Get summary statistics for cell phone lines per 100 people (called
Cell). The function you want issummary(). - Store the results from (i) in a new object called
stats. Incidentally,statswill be a vector! - Get the length of
stats. The function you want islength(). - Get r to show the following elements of
stats: 1,2,3,5,6
wb <- read.csv("http://reuningscherer.net/S&DS230/data/WB.2016.csv", header = TRUE)
dim(wb)## [1] 217 29names(wb)## [1] "Country" "Code" "Population" "Rural"
## [5] "GNI" "IncomeTop10" "Imports" "Exports"
## [9] "Military" "Cell" "Fertility66" "Fertility16"
## [13] "Measles" "InfMort" "LifeExp" "PM2.5"
## [17] "Diesel" "CO2" "EnergyUse" "FossilPct"
## [21] "Forest94" "Forest14" "Deforestation" "GunTotal"
## [25] "GunHomicide" "GunSuicide" "GunUnint" "GunUndet"
## [29] "GunsPer100"head(wb)## Country Code Population Rural GNI IncomeTop10 Imports Exports
## 1 Afghanistan AFG 34656032 72.868 580 NA 49.02498 6.89625
## 2 Albania ALB 2876101 41.624 4320 NA 45.74585 28.92342
## 3 Algeria DZA 40606052 28.696 4360 NA 35.27028 21.00176
## 4 American Samoa ASM 55599 12.852 NA NA 93.46505 65.04559
## 5 Andorra AND 77281 15.388 NA NA NA NA
## 6 Angola AGO 28813463 55.181 3450 NA 29.41717 30.01704
## Military Cell Fertility66 Fertility16 Measles InfMort LifeExp PM2.5
## 1 0.954643 62.33542 7.450 4.635 62 53.2 63.673 62.854857
## 2 1.101507 115.15226 5.581 1.713 96 12.0 78.345 14.634008
## 3 6.424474 115.84805 7.676 2.776 94 21.6 76.078 37.230956
## 4 NA NA NA NA NA NA NA 3.763412
## 5 NA 92.04332 NA NA 97 2.4 NA 10.879472
## 6 2.962392 45.12170 7.618 5.694 49 54.6 61.547 36.240479
## Diesel CO2 EnergyUse FossilPct Forest94 Forest14 Deforestation GunTotal
## 1 0.70 0.299445 NA NA 336198 274088 18.47423245 NA
## 2 1.35 1.978763 808.4558 61.42180 1286610 1244430 3.278382727 NA
## 3 0.17 3.717410 1321.0995 99.97792 5365326 4945220 7.830018157 NA
## 4 NA NA NA NA 1650846 2067791 -25.25647462 NA
## 5 NA 5.832170 NA NA 147772 168760 -14.20296132 NA
## 6 0.82 1.291328 545.0405 48.27955 113216 114170 -0.842637083 NA
## GunHomicide GunSuicide GunUnint GunUndet GunsPer100
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NAstr(wb)## 'data.frame': 217 obs. of 29 variables:
## $ Country : chr "Afghanistan" "Albania" "Algeria" "American Samoa" ...
## $ Code : chr "AFG" "ALB" "DZA" "ASM" ...
## $ Population : int 34656032 2876101 40606052 55599 77281 28813463 100963 43847430 2924816 104822 ...
## $ Rural : num 72.9 41.6 28.7 12.9 15.4 ...
## $ GNI : int 580 4320 4360 NA NA 3450 13560 11940 3770 NA ...
## $ IncomeTop10 : num NA NA NA NA NA NA NA 30.9 25.3 NA ...
## $ Imports : num 49 45.7 35.3 93.5 NA ...
## $ Exports : num 6.9 28.9 21 65 NA ...
## $ Military : num 0.955 1.102 6.424 NA NA ...
## $ Cell : num 62.3 115.2 115.8 NA 92 ...
## $ Fertility66 : num 7.45 5.58 7.68 NA NA ...
## $ Fertility16 : num 4.63 1.71 2.78 NA NA ...
## $ Measles : int 62 96 94 NA 97 49 98 90 97 NA ...
## $ InfMort : num 53.2 12 21.6 NA 2.4 54.6 5.1 9.9 11.9 NA ...
## $ LifeExp : num 63.7 78.3 76.1 NA NA ...
## $ PM2.5 : num 62.85 14.63 37.23 3.76 10.88 ...
## $ Diesel : num 0.7 1.35 0.17 NA NA 0.82 NA 1 0.67 NA ...
## $ CO2 : num 0.299 1.979 3.717 NA 5.832 ...
## $ EnergyUse : num NA 808 1321 NA NA ...
## $ FossilPct : num NA 61.4 100 NA NA ...
## $ Forest94 : num 336198 1286610 5365326 1650846 147772 ...
## $ Forest14 : num 274088 1244430 4945220 2067791 168760 ...
## $ Deforestation: chr "18.47423245" "3.278382727" "7.830018157" "-25.25647462" ...
## $ GunTotal : num NA NA NA NA NA NA NA 6.36 NA NA ...
## $ GunHomicide : num NA NA NA NA NA NA NA 2.58 NA NA ...
## $ GunSuicide : num NA NA NA NA NA NA NA 1.57 NA NA ...
## $ GunUnint : num NA NA NA NA NA NA NA 0.05 NA NA ...
## $ GunUndet : num NA NA NA NA NA NA NA 2.57 NA NA ...
## $ GunsPer100 : num NA NA NA NA NA NA NA 10.2 NA NA ...typeof(wb$Population)## [1] "integer"subset <- wb[, c("Country", "GNI", "Exports","Imports")]
subset <- na.omit(subset)
subset <- subset[subset$GNI > 70000, ]
head(subset)## Country GNI Exports Imports
## 116 Luxembourg 71590 221.26778 186.16333
## 147 Norway 82010 34.13664 33.27319
## 189 Switzerland 82080 65.81131 54.58890stats <- summary(wb$Cell)
length(stats)## [1] 7stats[c(1, 2, 3, 5, 6)]## Min. 1st Qu. Median 3rd Qu. Max.
## 10.21264 81.68643 110.66193 127.97427 321.80304(4) Plots 16 pts
- Using the
wbdataset created above, make a scatterplot of “Population” on the x axis and “Cell” on the y axis. Include a main title, axis titles, and a non-default symbol color and symbol type. Hint: check out ?par or see examples from class 1 or class 3 R code.
plot(wb$Population, wb$Cell, xlab = "Population", ylab = "Cellphone lines/100 people", main= "Countries by cellphone lines vs population", pch = "*", col = "orange")
- Use the
data()function to load the “chickwts” dataset that comes with base R’s “datasets” package. Then, create a boxplot of chicken weight by feed type. Ensure the plot has a main title, axis labels, and a unique color for each feed type. You can learn about the dataset by typing?chickwts.
data(chickwts)
plot(chickwts$feed, chickwts$weight, xlab = "Feed Type", ylab = "Chicken weight", main = "Boxplot of Chicken Weight by Feed Type", col = c("red", "orange", "yellow", "green", "blue", "purple"))
(5) Lists 12 pts The code below creates a list called aList
- Compute the sum of the second element of the list’s third element. Store the result into an object named
mySum. You’ll want to use thesum()function.
aList <- list(c(1,5,4), letters[c(1,6,4,9,22,3)], list(c(1,1,1),
c(14,13,12), c(3,2,1)), c(runif(8)))
mySum <- sum(aList[[3]][[2]])- What is the difference between what is returned from the following two commands?
aList[[3]][2]## [[1]]
## [1] 14 13 12aList[[3]][[2]]## [1] 14 13 12The first command retrieve the second item from the third element of the list aList. Because of the single brackets, the item is given as a new list rather than the vector that it is. The second command retrieves the second element of the third element of the list aList. It is given as a vector because of the double brackets