Homework_01

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Feb 20, 2024

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Homework 01 R Basics Due by 11:59pm, Saturday, 1.27.24 S&DS 230 (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 LINK (make the word LINK at left link to the New York Times home page AND make it bold) Make this line look like R Code Below 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 , 3 , 4 , 5 , 6 , 7 ) print (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) ## [1] 3 for (i in 1 : 10 ) { x <- 1 + 1 } x <- 1 y <- c ( 3 , 4 Question 1 and 2
(3) Data handling 36 pts (3.1) Insert a new R code chunk below. (3.2) Read the .csv stored HERE into a new data frame and call is “wb”. This is the World Bank data I discussed in class two. (3.3) Get the dimension of wb. wb <- read.csv ( "http://reuningscherer.net/S&DS230/data/WB.2016.csv" , header= TRUE ) dim (wb) ## [1] 217 29 (3.4) Get the variable names of wb. colnames (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" (3.5) Show the first 6 lines of wb. head (wb, n = 6 ) ## 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 Question 3.1, 3.2, 3.3, 3.4, 3.5
## 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 Question 3.1, 3.2, 3.3, 3.4, 3.5
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## 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 NA (3.6) Get the data type of each variable. sapply (wb, class) ## Country Code Population Rural GNI ## "character" "character" "integer" "numeric" "integer" ## IncomeTop10 Imports Exports Military Cell ## "numeric" "numeric" "numeric" "numeric" "numeric" ## Fertility66 Fertility16 Measles InfMort LifeExp ## "numeric" "numeric" "integer" "numeric" "numeric" ## PM2.5 Diesel CO2 EnergyUse FossilPct ## "numeric" "numeric" "numeric" "numeric" "numeric" ## Forest94 Forest14 Deforestation GunTotal GunHomicide ## "numeric" "numeric" "character" "numeric" "numeric" ## GunSuicide GunUnint GunUndet GunsPer100 ## "numeric" "numeric" "numeric" "numeric" (3.7) What is the data type of the variable Pop ? str (wb $ Population) Question 3.5, 3.6, 3.7, 3.8
## int [1:217] 34656032 2876101 40606052 55599 77281 28813463 100963 43847430 2924816 104822 ... (3.8) Create a new object called subset that has only the variables Country, GNI, Exports, and Imports AND only for countries where GNI is greater than 70000. You’ll need to use the na.omit() function (use help(na.omit) ) to eliminate countries missing data for any of the four variables you retain. You should end up with exactly three countries in subset . Question 3.1, 3.2, 3.3, 3.4, 3.5
subset <- na.omit (wb[wb $ GNI > 70000 , c ( "Country" , "GNI" , "Imports" , "Exports" )]) subset ## Country GNI Imports Exports ## 116 Luxembourg 71590 186.16333 221.26778 ## 147 Norway 82010 33.27319 34.13664 ## 189 Switzerland 82080 54.58890 65.81131 (3.9) Get summary statistics for cell phone lines per 100 people (called Cell ). The function you want is summary() . summary (wb $ Cell) ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## 10.21 81.69 110.66 106.78 127.97 321.80 17 (3.10) Store the results from (3.9) in a new object called stats . Incidentally, stats will be a vector! stats <- summary (wb $ Cell) stats ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## 10.21 81.69 110.66 106.78 127.97 321.80 17 (3.11) Get the length of stats . The function you want is length() . length (stats) ## [1] 7 (3.12) Get r to show the following elements of stats : 1,2,3,5,6 stats[ c ( 1 : 6 )] ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 10.21264 81.68643 110.66193 106.78417 127.97427 321.80304 (4) Plots 16 pts (4.1) Using the wb dataset 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= "Cellular Subscriptions per 100k Population" , main= "Cellular Subscriptions by Population" , pch= 19 , col= "blue" ) Question 3.8, 3.9, 3.10, 3.11, 3.12, 4.1
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(4.2) 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" ) chickwts ## weight feed ## 1 179 horsebean ## 2 160 horsebean ## 3 136 horsebean ## 4 227 horsebean ## 5 217 horsebean ## 6 168 horsebean ## 7 108 horsebean ## 8 124 horsebean ## 9 143 horsebean ## 10 140 horsebean ## 11 309 linseed ## 12 229 linseed ## 13 181 linseed ## 14 141 linseed Question 4.1, 4.2
## 15 260 linseed ## 16 203 linseed ## 17 148 linseed ## 18 169 linseed ## 19 213 linseed ## 20 257 linseed ## 21 244 linseed ## 22 271 linseed ## 23 243 soybean ## 24 230 soybean ## 25 248 soybean ## 26 327 soybean ## 27 329 soybean ## 28 250 soybean ## 29 193 soybean ## 30 271 soybean ## 31 316 soybean ## 32 267 soybean ## 33 199 soybean ## 34 171 soybean ## 35 158 soybean ## 36 248 soybean ## 37 423 sunflower ## 38 340 sunflower ## 39 392 sunflower ## 40 339 sunflower ## 41 341 sunflower ## 42 226 sunflower ## 43 320 sunflower ## 44 295 sunflower ## 45 334 sunflower ## 46 322 sunflower ## 47 297 sunflower ## 48 318 sunflower ## 49 325 meatmeal ## 50 257 meatmeal ## 51 303 meatmeal ## 52 315 meatmeal ## 53 380 meatmeal ## 54 153 meatmeal ## 55 263 meatmeal ## 56 242 meatmeal ## 57 206 meatmeal ## 58 344 meatmeal ## 59 258 meatmeal ## 60 368 casein ## 61 390 casein ## 62 379 casein ## 63 260 casein ## 64 404 casein Question 4.2
## 65 318 casein ## 66 352 casein ## 67 359 casein ## 68 216 casein ## 69 222 casein ## 70 283 casein ## 71 332 casein boxplot (chickwts $ weight ~ chickwts $ feed, xlab= "Feed type" , ylab= "Weight at 6 weeks (gm)" , main= "Chickwts Data Analysis" , col= 1 : 6 , varwidth= TRUE , notch= FALSE , cex.axis = 0.5 ) (5) Lists 12 pts The code below creates a list called aList (5.1) 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 the sum() 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 ]]) mySum ## [1] 39 Question 4.2, 5.1
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(5.2) What is the difference between what is returned from the following two commands? aList[[ 3 ]][ 2 ] ## [[1]] ## [1] 14 13 12 aList[[ 3 ]][[ 2 ]] ## [1] 14 13 12 The first command with a single bracket around 2 calls for the same type as aList, which is a list consisting of vectors. This is why we have an extra [[1]] at the top of the element. The second command with double brackets around 2 calls for a single element in the list, which is a vector of 14 13 12. Question 5.2

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