This is your first R coding homework of this course. Please complete this homework in a RMarkdown document. You may either modify this document or start a new one. Knit this document to form an html file which you will then submit to Canvas. Please do not submit your .Rmd file. Make sure your html file is properly formatted (i.e., clear sections for each problem, tabbing, a table of contents). Make sure all code chunks are included in your homework under the corresponding problem. Any text answers required should be written under the coresopnding problem.

Problem 1

Create a new variable called my_num that contains 6 numbers of your choice
Multiply my_num by 4
Create a second variable called my_char that contains 5 character strings of your choice
Combine the two variables my_num and my_char into a variable called both (hint: you can ues c() to combine two variables together)
What is the length of both my_num and my_char?
What is the class of both my_num and my_char? Are the classes the same? If not, why?

#a
my_num <- c(2, 4, 6, 8, 10, 12)
my_num

## [1]  2  4  6  8 10 12

#b
my_num_4 <- (my_num)*4
my_num_4

## [1]  8 16 24 32 40 48

#c
my_char <- c("apple", "banana", "cherry", "date", "elderberry")
my_char

## [1] "apple"      "banana"     "cherry"     "date"       "elderberry"

#d
both <- c(my_num,my_char)
both

##  [1] "2"          "4"          "6"          "8"          "10"        
##  [6] "12"         "apple"      "banana"     "cherry"     "date"      
## [11] "elderberry"

#e
length(my_num) # 6

## [1] 6

length(my_char) # 5

## [1] 5

#f
class(my_num) # numeric

## [1] "numeric"

class(my_char) # character

## [1] "character"

Problem 2

Create a vector with elements 1 2 3 4 5 6 and call it x
Create another vector with elements 10 20 30 40 50 and call it y
What happens if you try to add x and y together? Why?
Append the value 60 onto the vector y (hint: you can use the c() function)
Add x and y together
Multiply x and y together. How does R performs operations on vectors of the same length?

x <- c(1, 2, 3, 4, 5, 6)
x

## [1] 1 2 3 4 5 6

y <- c(10,20,30,40,50)
y

## [1] 10 20 30 40 50

(x)+(y) # you get, warning message In (x) + (y):longer object length is

## Warning in (x) + (y): longer object length is not a multiple of shorter object
## length

## [1] 11 22 33 44 55 16

# not a multiple of shorter object length
y <- c(y, 60)
y

## [1] 10 20 30 40 50 60

(x)+(y)

## [1] 11 22 33 44 55 66

(x)*(y)

## [1]  10  40  90 160 250 360

# R performs operations on vectors of the same length element-wise.
# In the case of addition, each element of x is added to the corresponding element of y. 
# In the case of multiplication, each element of x is multiplied by the corresponding element of y.

Problem 3

Create a vector first_ten that has the values 10 through 20 in it using the : operator.
Create a vector next_ten that has the values 21 through 30 in it using the seq() function.
Create a vector all_numbers by combining the previous two vectors (hint: you can ues c() to combine two vectors together)
Create a variable eleventh that contains the 11th element in all_numbers
Create a vector some_numbers that contains the 2nd through the 5th elements of all_numbers
Create a vector some_numbers_2 that contains everything except the 1st, 14th, and 23rd element of all_numbers

first_ten <- 10:20
first_ten

##  [1] 10 11 12 13 14 15 16 17 18 19 20

next_ten <- seq(from=21, to = 30)
next_ten

##  [1] 21 22 23 24 25 26 27 28 29 30

all_numbers <- c(first_ten, next_ten)
all_numbers

##  [1] 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

eleventh <- all_numbers [11]
eleventh

## [1] 20

some_numbers <- all_numbers [2:5]
some_numbers

## [1] 11 12 13 14

some_numbers_2 <- all_numbers [-c(1, 14, 23)]
some_numbers_2

##  [1] 11 12 13 14 15 16 17 18 19 20 21 22 24 25 26 27 28 29 30

Problem 4

Create a vector phone_numbers that contains the numbers 8, 6, 7, 5, 3, 0, 9
Using indexing create a vector prefix that has the first three elements of phone_numbers
Using indexing create a vector small that has the values of phone_numbers that are less than or equal to 5
Using indexing create a vector large that has the values of phone_numbers that are strictly greater than 5
Replace the values in phone_numbers that are larger than 5 with the number 5
Replace every odd-numbered value in phone_numbers with the number 0

phone_numbers <- c(8,6,7,5,3,0,9)
phone_numbers

## [1] 8 6 7 5 3 0 9

prefix <- phone_numbers [1:3]
prefix

## [1] 8 6 7

small <- phone_numbers [phone_numbers <= 5]
small

## [1] 5 3 0

large <- phone_numbers [phone_numbers >5]
large

## [1] 8 6 7 9

phone_numbers[phone_numbers > 5] <- 5
phone_numbers

## [1] 5 5 5 5 3 0 5

phone_numbers[seq_along(phone_numbers) %% 2 == 1] <- 0 
phone_numbers

## [1] 0 5 0 5 0 0 0

phone_numbers <- c(8,6,7,5,3,0,9)
phone_numbers[which(phone_numbers == 3)] <- 0
  phone_numbers[which(phone_numbers == 5)] <- 0
  phone_numbers[which(phone_numbers == 7)] <- 0
  phone_numbers[which(phone_numbers == 9)] <- 0
  print(phone_numbers)

## [1] 8 6 0 0 0 0 0

Problem 5

Import the Monuments.xlsx dataset and call the output mon. Print out the first 5 observations of this data.
How many observations are there in this dataset?
How many variables are there in this dataset?
Display all the variable names of the mon dataset
Write the mon R data.frame as a CSV file to a location of your choice. Call the file “monuments_new.csv”.

getwd()

## [1] "C:/Users/samah/OneDrive/Desktop/HW#5"

library(readr)

## Warning: package 'readr' was built under R version 4.3.3

Monuments_file<-read_csv("Monuments file.csv")

## Rows: 84 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): name, neighborhood, policeDistrict, Location 1
## dbl (2): zipCode, councilDistrict
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

library(readr)
mon<- read_csv("Monuments file.csv")

## Rows: 84 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): name, neighborhood, policeDistrict, Location 1
## dbl (2): zipCode, councilDistrict
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

head(mon,5)

## # A tibble: 5 × 6
##   name          zipCode neighborhood councilDistrict policeDistrict `Location 1`
##   <chr>           <dbl> <chr>                  <dbl> <chr>          <chr>       
## 1 James Cardin…   21201 Downtown                  11 CENTRAL        "408 CHARLE…
## 2 The Battle M…   21202 Downtown                  11 CENTRAL         <NA>       
## 3 Negro Heroes…   21202 Downtown                  11 CENTRAL         <NA>       
## 4 Star Bangled…   21202 Downtown                  11 CENTRAL        "100 HOLLID…
## 5 Flame at the…   21202 Downtown                  11 CENTRAL        "50 MARKET …

nrow(mon)

## [1] 84

ncol(mon)

## [1] 6

colnames(mon)

## [1] "name"            "zipCode"         "neighborhood"    "councilDistrict"
## [5] "policeDistrict"  "Location 1"

library(readr)
write_csv(mon,"C:/Users/samah/OneDrive/Desktop/monuments new.csv")

Problem 6

Read in the kaggleCarAuction.csv using readr and name the dataset “cars”. Make sure each variable in your dataset has an appropriate type based on the values it contains using col_type. You must specify data types for each variable in your data set to get full credit (don’t just let readr guess for you). Hint: open your data in excel first and look at all your variables!
Run spec on your imported data to show that your variables have appropriate data types
What is the dimension of the cars dataset?
Print out the last 5 observations of this dataset

cars <- read_csv(file= "kaggleCarAuction.csv")

## Rows: 72983 Columns: 34
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (24): PurchDate, Auction, Make, Model, Trim, SubModel, Color, Transmissi...
## dbl (10): RefId, IsBadBuy, VehYear, VehicleAge, VehOdo, BYRNO, VNZIP1, VehBC...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

cars

## # A tibble: 72,983 × 34
##    RefId IsBadBuy PurchDate Auction VehYear VehicleAge Make       Model    Trim 
##    <dbl>    <dbl> <chr>     <chr>     <dbl>      <dbl> <chr>      <chr>    <chr>
##  1     1        0 12/7/2009 ADESA      2006          3 MAZDA      MAZDA3   i    
##  2     2        0 12/7/2009 ADESA      2004          5 DODGE      1500 RA… ST   
##  3     3        0 12/7/2009 ADESA      2005          4 DODGE      STRATUS… SXT  
##  4     4        0 12/7/2009 ADESA      2004          5 DODGE      NEON     SXT  
##  5     5        0 12/7/2009 ADESA      2005          4 FORD       FOCUS    ZX3  
##  6     6        0 12/7/2009 ADESA      2004          5 MITSUBISHI GALANT … ES   
##  7     7        0 12/7/2009 ADESA      2004          5 KIA        SPECTRA  EX   
##  8     8        0 12/7/2009 ADESA      2005          4 FORD       TAURUS   SE   
##  9     9        0 12/7/2009 ADESA      2007          2 KIA        SPECTRA  EX   
## 10    10        0 12/7/2009 ADESA      2007          2 FORD       FIVE HU… SEL  
## # ℹ 72,973 more rows
## # ℹ 25 more variables: SubModel <chr>, Color <chr>, Transmission <chr>,
## #   WheelTypeID <chr>, WheelType <chr>, VehOdo <dbl>, Nationality <chr>,
## #   Size <chr>, TopThreeAmericanName <chr>,
## #   MMRAcquisitionAuctionAveragePrice <chr>,
## #   MMRAcquisitionAuctionCleanPrice <chr>,
## #   MMRAcquisitionRetailAveragePrice <chr>, …

spec(cars)

## cols(
##   RefId = col_double(),
##   IsBadBuy = col_double(),
##   PurchDate = col_character(),
##   Auction = col_character(),
##   VehYear = col_double(),
##   VehicleAge = col_double(),
##   Make = col_character(),
##   Model = col_character(),
##   Trim = col_character(),
##   SubModel = col_character(),
##   Color = col_character(),
##   Transmission = col_character(),
##   WheelTypeID = col_character(),
##   WheelType = col_character(),
##   VehOdo = col_double(),
##   Nationality = col_character(),
##   Size = col_character(),
##   TopThreeAmericanName = col_character(),
##   MMRAcquisitionAuctionAveragePrice = col_character(),
##   MMRAcquisitionAuctionCleanPrice = col_character(),
##   MMRAcquisitionRetailAveragePrice = col_character(),
##   MMRAcquisitonRetailCleanPrice = col_character(),
##   MMRCurrentAuctionAveragePrice = col_character(),
##   MMRCurrentAuctionCleanPrice = col_character(),
##   MMRCurrentRetailAveragePrice = col_character(),
##   MMRCurrentRetailCleanPrice = col_character(),
##   PRIMEUNIT = col_character(),
##   AUCGUART = col_character(),
##   BYRNO = col_double(),
##   VNZIP1 = col_double(),
##   VNST = col_character(),
##   VehBCost = col_double(),
##   IsOnlineSale = col_double(),
##   WarrantyCost = col_double()
## )

dim(cars)   # [1] obs = 72983   var = 34

## [1] 72983    34

tail(cars, 5)

## # A tibble: 5 × 34
##   RefId IsBadBuy PurchDate Auction VehYear VehicleAge Make  Model Trim  SubModel
##   <dbl>    <dbl> <chr>     <chr>     <dbl>      <dbl> <chr> <chr> <chr> <chr>   
## 1 73010        1 12/2/2009 ADESA      2001          8 MERC… SABLE GS    4D SEDA…
## 2 73011        0 12/2/2009 ADESA      2007          2 CHEV… MALI… LS    4D SEDA…
## 3 73012        0 12/2/2009 ADESA      2005          4 JEEP  GRAN… Lar   4D WAGO…
## 4 73013        0 12/2/2009 ADESA      2006          3 CHEV… IMPA… LS    4D SEDA…
## 5 73014        0 12/2/2009 ADESA      2006          3 MAZDA MAZD… s     4D SEDA…
## # ℹ 24 more variables: Color <chr>, Transmission <chr>, WheelTypeID <chr>,
## #   WheelType <chr>, VehOdo <dbl>, Nationality <chr>, Size <chr>,
## #   TopThreeAmericanName <chr>, MMRAcquisitionAuctionAveragePrice <chr>,
## #   MMRAcquisitionAuctionCleanPrice <chr>,
## #   MMRAcquisitionRetailAveragePrice <chr>,
## #   MMRAcquisitonRetailCleanPrice <chr>, MMRCurrentAuctionAveragePrice <chr>,
## #   MMRCurrentAuctionCleanPrice <chr>, MMRCurrentRetailAveragePrice <chr>, …

Problem 7

Import the whaledata.txt file using base R. Name the output whale. Print the first 5 observations of this dataset. (hint: Make sure you look at this file before importing).
Import the data whaledatanamed.txt using base R. Name this output whale_2. Explicitly specify the data types of each variable in whaledatanamed.txt in your import call. Print the first 5 observations of this dataset. (hint: Make sure you look at this file before importing).
Write out the whale_2 R object as a space delimited file using base R to a location of your choice call the file “whales_new.txt”.

whale <- read.delim(file="whaledata.txt", header=FALSE)
whale

##          V1   V2     V3 V4    V5    V6   V7  V8
## 1       May 1344    low  7 60.37 -4.18  520 415
## 2       May 1633 medium 13 60.38 -4.19  559 405
## 3       May  743 medium 12 60.54 -4.62 1006  88
## 4       May 1050 medium 10 60.29 -4.35  540 409
## 5       May 1764 medium 12 60.41 -5.20 1000  97
## 6       May  580   high 10 60.38 -5.22 1000 173
## 7       May  459 medium  5 60.40 -5.08  993 162
## 8       May  561 medium  8 60.41 -5.00  988 162
## 9       May  709 medium 11 60.44 -4.64  954 245
## 10      May  690 medium 12 60.43 -4.84  984 161
## 11      May  634 medium  9 60.43 -4.75  965 127
## 12      May  422    low 11 60.86 -1.53  120  23
## 13      May 1232 medium 11 61.29 -2.93 1195 217
## 14      May 1232    low 12 61.25 -2.76 1088 261
## 15      May 1451    low 12 61.42 -3.51 1259 109
## 16      May 1441    low 12 61.40 -3.41 1277 111
## 17      May 1566 medium 17 61.38 -3.32 1288 100
## 18      May 1578    low 11 61.52 -3.94 1200 152
## 19      May 1475 medium  7 61.53 -4.02 1171 198
## 20      May 1544 medium  5 61.60 -4.34  832 671
## 21      May 1707 medium  9 61.67 -4.65  400 226
## 22      May  234 medium  4 61.75 -5.07  255 111
## 23      May 1206    low 13 61.61 -6.33  195 133
## 24      May 1092    low 11 61.51 -4.16 1124 232
## 25      May 1134    low 12 61.55 -4.18 1076 306
## 26      May 1079    low 11 61.47 -4.16 1133 166
## 27      May 1232 medium 15 61.59 -4.18 1024 361
## 28      May 1024    low 10 61.39 -4.16 1105 118
## 29      May 1086    low 12 61.43 -4.15 1125 141
## 30      May 1341    low 18 61.64 -4.18  934 394
## 31      May  840    low  0 61.67 -4.18  869 361
## 32      May 1254    low 14 61.72 -4.18  757 361
## 33      May 1087    low 18 60.98 -4.26 1100   0
## 34      May  832    low  7 61.10 -4.20 1100  29
## 35      May 1030    low 11 61.34 -4.16 1100  78
## 36      May  935    low 10 61.14 -4.21 1100  29
## 37      May 1148 medium 21 60.92 -4.40 1100  49
## 38      May  945    low 10 61.19 -4.20 1100   7
## 39      May  816    low 14 60.64 -4.88 1000 102
## 40      May  663    low NA 60.67 -4.83 1016 114
## 41      May  605 medium 10 60.54 -4.96 1000   8
## 42      May  651 medium 11 60.52 -5.05 1000  33
## 43      May  754 medium 14 60.51 -5.14 1000  56
## 44      May  615 medium  8 60.58 -5.00 1000  54
## 45      May  647 medium 10 60.52 -5.14 1000  73
## 46      May 1216   high 17 61.99 -2.26 1593 120
## 47      May  905   high  7 61.94 -2.41 1601 118
## 48      May 1329   high 28 61.59 -3.06 1425 106
## 49      May  787 medium 13 61.33 -3.50 1204 134
## 50      May  803 medium 13 61.36 -3.46 1238 126
## 51  October  545 medium  5 61.29 -3.57 1178 130
## 52  October 1137   high 12 62.10 -2.01 1610 134
## 53  October  948   high  9 61.91 -2.48 1600 110
## 54  October 1041   high  9 62.07 -2.06 1600 123
## 55  October 1454   high 25 62.01 -2.18 1596 125
## 56  October  792   high  1 62.04 -2.12 1592 115
## 57  October 1096   high 10 62.07 -2.01 1584 124
## 58  October  787   high  8 61.62 -3.00 1455 103
## 59  October 1252   high 15 62.01 -2.00 1553 122
## 60  October 1076 medium  6 61.82 -2.00 1502 128
## 61  October 1140 medium  8 61.78 -2.00 1488 135
## 62  October 1419 medium 17 61.72 -2.01 1448 183
## 63  October 1272 medium 11 61.66 -2.02 1406 248
## 64  October 1071 medium  7 61.90 -2.01 1517 151
## 65  October  901   high  8 61.87 -2.58 1587  88
## 66  October 1335 medium 12 61.57 -2.01 1256 356
## 67  October  658 medium  7 61.42 -3.33 1300 104
## 68  October  790 medium 11 61.46 -3.28 1321  95
## 69  October  824   high  9 61.65 -2.96 1484  94
## 70  October 1365 medium 13 61.53 -2.00 1183 394
## 71  October 1506 medium 17 61.49 -2.00 1090 444
## 72  October 1342 medium 11 61.45 -2.00 1007 505
## 73  October 1164   high 19 61.78 -2.74 1549  63
## 74  October 1250 medium  7 61.41 -2.00  901 467
## 75  October 1512    low 16 61.36 -2.00  735 400
## 76  October 1238 medium  5 61.30 -1.99  647 288
## 77  October  521    low  9 60.44 -4.26  687 421
## 78  October  625    low 12 60.45 -4.30  765 397
## 79  October 1871 medium 18 60.83 -4.34 1100  22
## 80  October 1942    low 21 60.79 -4.33 1100  79
## 81  October 1592 medium  9 60.74 -4.33 1084 148
## 82  October 1539 medium 10 60.50 -4.35  905 326
## 83  October 1672 medium 14 60.56 -4.33  934 285
## 84  October 1854 medium 19 60.69 -4.33 1024 213
## 85  October 1579    low 12 60.45 -4.38  900 333
## 86  October  555 medium 18 60.92 -4.98  951 246
## 87  October   60 medium  0 60.92 -5.08  912 364
## 88  October  480 medium 16 60.92 -4.87  993 177
## 89  October 2158 medium 25 60.92 -4.77 1000 171
## 90  October 1583 medium  5 60.92 -4.65 1033 131
## 91  October 1754    low 12 60.92 -4.50 1100 100
## 92  October 1854 medium 15 60.92 -4.58 1054 105
## 93  October  652 medium 13 60.75 -4.53 1100  41
## 94  October  441 medium  6 60.75 -4.62 1100  41
## 95  October  775 medium 20 60.75 -4.99  946 210
## 96  October  506 medium 10 60.75 -4.89 1009 188
## 97  October  695 medium 16 60.75 -4.80 1057 135
## 98  October  519 medium  9 60.75 -4.69 1099  69
## 99  October  563 medium  6 60.58 -4.79 1000  46
## 100 October  659 medium 10 60.58 -4.70 1016  69

head(whale, 5)

##    V1   V2     V3 V4    V5    V6   V7  V8
## 1 May 1344    low  7 60.37 -4.18  520 415
## 2 May 1633 medium 13 60.38 -4.19  559 405
## 3 May  743 medium 12 60.54 -4.62 1006  88
## 4 May 1050 medium 10 60.29 -4.35  540 409
## 5 May 1764 medium 12 60.41 -5.20 1000  97

whale_2 <- read.delim(file="whaledatanamed.txt", header=TRUE)
whale_2

##       month time.at.station water.noise number.whales latitude longitude depth
## 1       May            1344         low             7    60.37     -4.18   520
## 2       May            1633      medium            13    60.38     -4.19   559
## 3       May             743      medium            12    60.54     -4.62  1006
## 4       May            1050      medium            10    60.29     -4.35   540
## 5       May            1764      medium            12    60.41     -5.20  1000
## 6       May             580        high            10    60.38     -5.22  1000
## 7       May             459      medium             5    60.40     -5.08   993
## 8       May             561      medium             8    60.41     -5.00   988
## 9       May             709      medium            11    60.44     -4.64   954
## 10      May             690      medium            12    60.43     -4.84   984
## 11      May             634      medium             9    60.43     -4.75   965
## 12      May             422         low            11    60.86     -1.53   120
## 13      May            1232      medium            11    61.29     -2.93  1195
## 14      May            1232         low            12    61.25     -2.76  1088
## 15      May            1451         low            12    61.42     -3.51  1259
## 16      May            1441         low            12    61.40     -3.41  1277
## 17      May            1566      medium            17    61.38     -3.32  1288
## 18      May            1578         low            11    61.52     -3.94  1200
## 19      May            1475      medium             7    61.53     -4.02  1171
## 20      May            1544      medium             5    61.60     -4.34   832
## 21      May            1707      medium             9    61.67     -4.65   400
## 22      May             234      medium             4    61.75     -5.07   255
## 23      May            1206         low            13    61.61     -6.33   195
## 24      May            1092         low            11    61.51     -4.16  1124
## 25      May            1134         low            12    61.55     -4.18  1076
## 26      May            1079         low            11    61.47     -4.16  1133
## 27      May            1232      medium            15    61.59     -4.18  1024
## 28      May            1024         low            10    61.39     -4.16  1105
## 29      May            1086         low            12    61.43     -4.15  1125
## 30      May            1341         low            18    61.64     -4.18   934
## 31      May             840         low             0    61.67     -4.18   869
## 32      May            1254         low            14    61.72     -4.18   757
## 33      May            1087         low            18    60.98     -4.26  1100
## 34      May             832         low             7    61.10     -4.20  1100
## 35      May            1030         low            11    61.34     -4.16  1100
## 36      May             935         low            10    61.14     -4.21  1100
## 37      May            1148      medium            21    60.92     -4.40  1100
## 38      May             945         low            10    61.19     -4.20  1100
## 39      May             816         low            14    60.64     -4.88  1000
## 40      May             663         low            NA    60.67     -4.83  1016
## 41      May             605      medium            10    60.54     -4.96  1000
## 42      May             651      medium            11    60.52     -5.05  1000
## 43      May             754      medium            14    60.51     -5.14  1000
## 44      May             615      medium             8    60.58     -5.00  1000
## 45      May             647      medium            10    60.52     -5.14  1000
## 46      May            1216        high            17    61.99     -2.26  1593
## 47      May             905        high             7    61.94     -2.41  1601
## 48      May            1329        high            28    61.59     -3.06  1425
## 49      May             787      medium            13    61.33     -3.50  1204
## 50      May             803      medium            13    61.36     -3.46  1238
## 51  October             545      medium             5    61.29     -3.57  1178
## 52  October            1137        high            12    62.10     -2.01  1610
## 53  October             948        high             9    61.91     -2.48  1600
## 54  October            1041        high             9    62.07     -2.06  1600
## 55  October            1454        high            25    62.01     -2.18  1596
## 56  October             792        high             1    62.04     -2.12  1592
## 57  October            1096        high            10    62.07     -2.01  1584
## 58  October             787        high             8    61.62     -3.00  1455
## 59  October            1252        high            15    62.01     -2.00  1553
## 60  October            1076      medium             6    61.82     -2.00  1502
## 61  October            1140      medium             8    61.78     -2.00  1488
## 62  October            1419      medium            17    61.72     -2.01  1448
## 63  October            1272      medium            11    61.66     -2.02  1406
## 64  October            1071      medium             7    61.90     -2.01  1517
## 65  October             901        high             8    61.87     -2.58  1587
## 66  October            1335      medium            12    61.57     -2.01  1256
## 67  October             658      medium             7    61.42     -3.33  1300
## 68  October             790      medium            11    61.46     -3.28  1321
## 69  October             824        high             9    61.65     -2.96  1484
## 70  October            1365      medium            13    61.53     -2.00  1183
## 71  October            1506      medium            17    61.49     -2.00  1090
## 72  October            1342      medium            11    61.45     -2.00  1007
## 73  October            1164        high            19    61.78     -2.74  1549
## 74  October            1250      medium             7    61.41     -2.00   901
## 75  October            1512         low            16    61.36     -2.00   735
## 76  October            1238      medium             5    61.30     -1.99   647
## 77  October             521         low             9    60.44     -4.26   687
## 78  October             625         low            12    60.45     -4.30   765
## 79  October            1871      medium            18    60.83     -4.34  1100
## 80  October            1942         low            21    60.79     -4.33  1100
## 81  October            1592      medium             9    60.74     -4.33  1084
## 82  October            1539      medium            10    60.50     -4.35   905
## 83  October            1672      medium            14    60.56     -4.33   934
## 84  October            1854      medium            19    60.69     -4.33  1024
## 85  October            1579         low            12    60.45     -4.38   900
## 86  October             555      medium            18    60.92     -4.98   951
## 87  October              60      medium             0    60.92     -5.08   912
## 88  October             480      medium            16    60.92     -4.87   993
## 89  October            2158      medium            25    60.92     -4.77  1000
## 90  October            1583      medium             5    60.92     -4.65  1033
## 91  October            1754         low            12    60.92     -4.50  1100
## 92  October            1854      medium            15    60.92     -4.58  1054
## 93  October             652      medium            13    60.75     -4.53  1100
## 94  October             441      medium             6    60.75     -4.62  1100
## 95  October             775      medium            20    60.75     -4.99   946
## 96  October             506      medium            10    60.75     -4.89  1009
## 97  October             695      medium            16    60.75     -4.80  1057
## 98  October             519      medium             9    60.75     -4.69  1099
## 99  October             563      medium             6    60.58     -4.79  1000
## 100 October             659      medium            10    60.58     -4.70  1016
##     gradient
## 1        415
## 2        405
## 3         88
## 4        409
## 5         97
## 6        173
## 7        162
## 8        162
## 9        245
## 10       161
## 11       127
## 12        23
## 13       217
## 14       261
## 15       109
## 16       111
## 17       100
## 18       152
## 19       198
## 20       671
## 21       226
## 22       111
## 23       133
## 24       232
## 25       306
## 26       166
## 27       361
## 28       118
## 29       141
## 30       394
## 31       361
## 32       361
## 33         0
## 34        29
## 35        78
## 36        29
## 37        49
## 38         7
## 39       102
## 40       114
## 41         8
## 42        33
## 43        56
## 44        54
## 45        73
## 46       120
## 47       118
## 48       106
## 49       134
## 50       126
## 51       130
## 52       134
## 53       110
## 54       123
## 55       125
## 56       115
## 57       124
## 58       103
## 59       122
## 60       128
## 61       135
## 62       183
## 63       248
## 64       151
## 65        88
## 66       356
## 67       104
## 68        95
## 69        94
## 70       394
## 71       444
## 72       505
## 73        63
## 74       467
## 75       400
## 76       288
## 77       421
## 78       397
## 79        22
## 80        79
## 81       148
## 82       326
## 83       285
## 84       213
## 85       333
## 86       246
## 87       364
## 88       177
## 89       171
## 90       131
## 91       100
## 92       105
## 93        41
## 94        41
## 95       210
## 96       188
## 97       135
## 98        69
## 99        46
## 100       69

class(whale_2)

## [1] "data.frame"

head(whale_2, 5)

##   month time.at.station water.noise number.whales latitude longitude depth
## 1   May            1344         low             7    60.37     -4.18   520
## 2   May            1633      medium            13    60.38     -4.19   559
## 3   May             743      medium            12    60.54     -4.62  1006
## 4   May            1050      medium            10    60.29     -4.35   540
## 5   May            1764      medium            12    60.41     -5.20  1000
##   gradient
## 1      415
## 2      405
## 3       88
## 4      409
## 5       97

library(readr)
write_delim(whale_2,"C:/Users/samah/OneDrive/Desktop/Whales_new.txt")

Problem 8

Read in the beoverunder.sas7bdat dataset into r. Print out the first 5 observations.
Read in the rscfp2019.dta dataset into r. Print out the first 5 observations.
Read in the africa.sav dataset into r. Print out the first 5 observations.

library(haven)

## Warning: package 'haven' was built under R version 4.3.3

library(haven)
beoverunder <-read_sas("beoverunder.sas7bdat")
beoverunder

## # A tibble: 455 × 6
##    site      plot  year burn     planttype  ANPP
##    <chr>    <dbl> <dbl> <chr>    <chr>     <dbl>
##  1 Dry site     1  2002 Frequent Nonpine    1.32
##  2 Dry site     1  2003 Frequent Nonpine    3.78
##  3 Dry site     1  2004 Frequent Nonpine    3.29
##  4 Dry site     1  2005 Frequent Nonpine    4.83
##  5 Dry site     1  2006 Frequent Nonpine    2.21
##  6 Dry site     1  2002 Frequent Pine       8.21
##  7 Dry site     1  2003 Frequent Pine       7.78
##  8 Dry site     1  2004 Frequent Pine       4.43
##  9 Dry site     1  2005 Frequent Pine       2.54
## 10 Dry site     1  2006 Frequent Pine       1.41
## # ℹ 445 more rows

head(beoverunder, 5)

## # A tibble: 5 × 6
##   site      plot  year burn     planttype  ANPP
##   <chr>    <dbl> <dbl> <chr>    <chr>     <dbl>
## 1 Dry site     1  2002 Frequent Nonpine    1.32
## 2 Dry site     1  2003 Frequent Nonpine    3.78
## 3 Dry site     1  2004 Frequent Nonpine    3.29
## 4 Dry site     1  2005 Frequent Nonpine    4.83
## 5 Dry site     1  2006 Frequent Nonpine    2.21

rscfp2019 <- read_dta("rscfp2019.dta")
rscfp2019

## # A tibble: 28,885 × 351
##      yy1    y1   wgt hhsex   age agecl  educ  edcl married  kids    lf lifecl
##    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl> <dbl> <dbl>  <dbl>
##  1     1    11 6120.     2    75     6    12     4       2     0     1      5
##  2     1    12 4712.     2    75     6    12     4       2     0     1      5
##  3     1    13 5145.     2    75     6    12     4       2     0     1      5
##  4     1    14 5298.     2    75     6    12     4       2     0     1      5
##  5     1    15 4762.     2    75     6    12     4       2     0     1      5
##  6     2    21 3790.     1    50     3     8     2       1     3     1      3
##  7     2    22 3799.     1    50     3     8     2       1     3     1      3
##  8     2    23 3799.     1    50     3     8     2       1     3     1      3
##  9     2    24 3788.     1    50     3     8     2       1     3     1      3
## 10     2    25 3793.     1    50     3     8     2       1     3     1      3
## # ℹ 28,875 more rows
## # ℹ 339 more variables: famstruct <dbl>, racecl <dbl>, racecl4 <dbl>,
## #   race <dbl>, occat1 <dbl>, occat2 <dbl>, indcat <dbl>, foodhome <dbl>,
## #   foodaway <dbl>, fooddelv <dbl>, rent <dbl>, income <dbl>, wageinc <dbl>,
## #   bussefarminc <dbl>, intdivinc <dbl>, kginc <dbl>, ssretinc <dbl>,
## #   transfothinc <dbl>, penacctwd <dbl>, norminc <dbl>, wsaved <dbl>,
## #   saved <dbl>, savres1 <dbl>, savres2 <dbl>, savres3 <dbl>, savres4 <dbl>, …

#Print the first 5 observations
head(rscfp2019, 5)

## # A tibble: 5 × 351
##     yy1    y1   wgt hhsex   age agecl  educ  edcl married  kids    lf lifecl
##   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl> <dbl> <dbl>  <dbl>
## 1     1    11 6120.     2    75     6    12     4       2     0     1      5
## 2     1    12 4712.     2    75     6    12     4       2     0     1      5
## 3     1    13 5145.     2    75     6    12     4       2     0     1      5
## 4     1    14 5298.     2    75     6    12     4       2     0     1      5
## 5     1    15 4762.     2    75     6    12     4       2     0     1      5
## # ℹ 339 more variables: famstruct <dbl>, racecl <dbl>, racecl4 <dbl>,
## #   race <dbl>, occat1 <dbl>, occat2 <dbl>, indcat <dbl>, foodhome <dbl>,
## #   foodaway <dbl>, fooddelv <dbl>, rent <dbl>, income <dbl>, wageinc <dbl>,
## #   bussefarminc <dbl>, intdivinc <dbl>, kginc <dbl>, ssretinc <dbl>,
## #   transfothinc <dbl>, penacctwd <dbl>, norminc <dbl>, wsaved <dbl>,
## #   saved <dbl>, savres1 <dbl>, savres2 <dbl>, savres3 <dbl>, savres4 <dbl>,
## #   savres5 <dbl>, savres6 <dbl>, savres7 <dbl>, savres8 <dbl>, …

africa <- read_spss("africa.sav")
africa

## # A tibble: 17 × 3
##    COUNTRY  FERTRATE CONTRA
##    <chr>       <dbl>  <dbl>
##  1 Benin        7.10      9
##  2 Botswana     5        33
##  3 Burundi      6.80      9
##  4 Cameroon     6.40      2
##  5 Cote_d       7.40      3
##  6 Ghana        6.40     13
##  7 Kenya        6.70     27
##  8 Lesotho      5.80      7
##  9 Liberia      6.60      7
## 10 Mali         6.70      5
## 11 Nigeria      6.20      6
## 12 Rwanda       8.5      10
## 13 Senegal      6.60     11
## 14 Sudan        5.90      6
## 15 Togo         6.20     34
## 16 Uganda       7.30      5
## 17 Zimbabwe     5.70     43

library(haven)

africa <- read_sav("africa.sav")
africa

## # A tibble: 17 × 3
##    COUNTRY  FERTRATE CONTRA
##    <chr>       <dbl>  <dbl>
##  1 Benin        7.10      9
##  2 Botswana     5        33
##  3 Burundi      6.80      9
##  4 Cameroon     6.40      2
##  5 Cote_d       7.40      3
##  6 Ghana        6.40     13
##  7 Kenya        6.70     27
##  8 Lesotho      5.80      7
##  9 Liberia      6.60      7
## 10 Mali         6.70      5
## 11 Nigeria      6.20      6
## 12 Rwanda       8.5      10
## 13 Senegal      6.60     11
## 14 Sudan        5.90      6
## 15 Togo         6.20     34
## 16 Uganda       7.30      5
## 17 Zimbabwe     5.70     43

head(africa, 5)

## # A tibble: 5 × 3
##   COUNTRY  FERTRATE CONTRA
##   <chr>       <dbl>  <dbl>
## 1 Benin        7.10      9
## 2 Botswana     5        33
## 3 Burundi      6.80      9
## 4 Cameroon     6.40      2
## 5 Cote_d       7.40      3

My Homework 5

Samah Ali

2024-03-21