#Then display the mean and median for at least two attributes.
#check your working directory
#import your Rdataset.csv into R studio
homework <- read.csv(file = "CATestscores.csv", header = TRUE, sep = ",")
summary(homework)## X district school county
## Min. : 1.0 Min. :61382 Length:420 Length:420
## 1st Qu.:105.8 1st Qu.:64308 Class :character Class :character
## Median :210.5 Median :67760 Mode :character Mode :character
## Mean :210.5 Mean :67473
## 3rd Qu.:315.2 3rd Qu.:70419
## Max. :420.0 Max. :75440
## grades students teachers calworks
## Length:420 Min. : 81.0 Min. : 4.85 Min. : 0.000
## Class :character 1st Qu.: 379.0 1st Qu.: 19.66 1st Qu.: 4.395
## Mode :character Median : 950.5 Median : 48.56 Median :10.520
## Mean : 2628.8 Mean : 129.07 Mean :13.246
## 3rd Qu.: 3008.0 3rd Qu.: 146.35 3rd Qu.:18.981
## Max. :27176.0 Max. :1429.00 Max. :78.994
## lunch computer expenditure income
## Min. : 0.00 Min. : 0.0 Min. :3926 Min. : 5.335
## 1st Qu.: 23.28 1st Qu.: 46.0 1st Qu.:4906 1st Qu.:10.639
## Median : 41.75 Median : 117.5 Median :5215 Median :13.728
## Mean : 44.71 Mean : 303.4 Mean :5312 Mean :15.317
## 3rd Qu.: 66.86 3rd Qu.: 375.2 3rd Qu.:5601 3rd Qu.:17.629
## Max. :100.00 Max. :3324.0 Max. :7712 Max. :55.328
## english read math
## Min. : 0.000 Min. :604.5 Min. :605.4
## 1st Qu.: 1.941 1st Qu.:640.4 1st Qu.:639.4
## Median : 8.778 Median :655.8 Median :652.5
## Mean :15.768 Mean :655.0 Mean :653.3
## 3rd Qu.:22.970 3rd Qu.:668.7 3rd Qu.:665.9
## Max. :85.540 Max. :704.0 Max. :709.5mean(homework$english)## [1] 15.76816median(homework$english)## [1] 8.777634mean(homework$math)## [1] 653.3426median(homework$math)## [1] 652.45#2. Create a new data frame with a subset of the columns and rows. #Make sure to rename it.
df.CA <- data.frame(homework[sample(1:nrow(homework), 10), c(11:12)])
names(df.CA) <- c("expenditure", "income")
row.names(df.CA) <- 1:10
df.CA## expenditure income
## 1 5220.370 15.96800
## 2 5447.345 7.38500
## 3 6180.149 49.93900
## 4 4842.608 9.97200
## 5 5482.677 13.24300
## 6 5179.645 15.29700
## 7 7593.406 35.81000
## 8 5179.411 14.06200
## 9 5741.463 41.73411
## 10 5501.955 8.97800#3. Create new column names for the new data frame.
df.CA <- setNames(df.CA, c("Spending Money", "Income"))
df.CA## Spending Money Income
## 1 5220.370 15.96800
## 2 5447.345 7.38500
## 3 6180.149 49.93900
## 4 4842.608 9.97200
## 5 5482.677 13.24300
## 6 5179.645 15.29700
## 7 7593.406 35.81000
## 8 5179.411 14.06200
## 9 5741.463 41.73411
## 10 5501.955 8.97800#4. Use the summary function to create an overview of your new data frame. #The print the mean and median for the same two attributes. Please compare.
summary(df.CA)## Spending Money Income
## Min. :4843 Min. : 7.385
## 1st Qu.:5190 1st Qu.:10.790
## Median :5465 Median :14.680
## Mean :5637 Mean :21.239
## 3rd Qu.:5682 3rd Qu.:30.849
## Max. :7593 Max. :49.939#Mean and Median of Spending Money
mean(df.CA$`Spending Money`)## [1] 5636.903median(df.CA$`Spending Money`)## [1] 5465.011mean(df.CA$Income)## [1] 21.23881median(df.CA$Income)## [1] 14.6795#5. For at least 3 values in a column please rename so that every value in that #column is renamed.For example, suppose I have 20 values of the letter “e” in one column. Rename those values so #that all 20 would show as “excellent”.
homework <- cbind(homework, school = factor(NA, levels = c("a", "b", "c")))#6 Display enough rows to see examples of all of steps 1-5 above I
showed
head(homework, 20)## X district school county grades students
## 1 1 75119 Sunol Glen Unified Alameda KK-08 195
## 2 2 61499 Manzanita Elementary Butte KK-08 240
## 3 3 61549 Thermalito Union Elementary Butte KK-08 1550
## 4 4 61457 Golden Feather Union Elementary Butte KK-08 243
## 5 5 61523 Palermo Union Elementary Butte KK-08 1335
## 6 6 62042 Burrel Union Elementary Fresno KK-08 137
## 7 7 68536 Holt Union Elementary San Joaquin KK-08 195
## 8 8 63834 Vineland Elementary Kern KK-08 888
## 9 9 62331 Orange Center Elementary Fresno KK-08 379
## 10 10 67306 Del Paso Heights Elementary Sacramento KK-06 2247
## 11 11 65722 Le Grand Union Elementary Merced KK-08 446
## 12 12 62174 West Fresno Elementary Fresno KK-08 987
## 13 13 71795 Allensworth Elementary Tulare KK-08 103
## 14 14 72181 Sunnyside Union Elementary Tulare KK-08 487
## 15 15 72298 Woodville Elementary Tulare KK-08 649
## 16 16 72041 Pixley Union Elementary Tulare KK-08 852
## 17 17 63594 Lost Hills Union Elementary Kern KK-08 491
## 18 18 63370 Buttonwillow Union Elementary Kern KK-08 421
## 19 19 64709 Lennox Elementary Los Angeles KK-08 6880
## 20 20 63560 Lamont Elementary Kern KK-08 2688
## teachers calworks lunch computer expenditure income english read
## 1 10.90 0.5102 2.0408 67 6384.911 22.690001 0.000000 691.6
## 2 11.15 15.4167 47.9167 101 5099.381 9.824000 4.583333 660.5
## 3 82.90 55.0323 76.3226 169 5501.955 8.978000 30.000002 636.3
## 4 14.00 36.4754 77.0492 85 7101.831 8.978000 0.000000 651.9
## 5 71.50 33.1086 78.4270 171 5235.988 9.080333 13.857677 641.8
## 6 6.40 12.3188 86.9565 25 5580.147 10.415000 12.408759 605.7
## 7 10.00 12.9032 94.6237 28 5253.331 6.577000 68.717949 604.5
## 8 42.50 18.8063 100.0000 66 4565.746 8.174000 46.959461 605.5
## 9 19.00 32.1900 93.1398 35 5355.548 7.385000 30.079157 608.9
## 10 108.00 78.9942 87.3164 0 5036.211 11.613333 40.275921 611.9
## 11 21.00 18.6099 85.8744 86 4547.692 8.931000 52.914799 612.8
## 12 47.00 71.7131 98.6056 56 5447.345 7.385000 54.609932 616.6
## 13 5.00 22.4299 98.1308 25 6567.149 5.335000 42.718445 612.8
## 14 24.34 24.6094 77.1484 0 4818.613 8.279000 20.533880 610.0
## 15 36.00 14.6379 76.2712 31 5621.456 9.630000 80.123260 611.9
## 16 42.07 24.2142 94.2957 80 6026.360 7.454000 49.413143 614.8
## 17 28.92 11.2016 97.7597 100 6723.238 6.216000 85.539719 611.7
## 18 25.50 8.5511 77.9097 50 5589.885 7.764000 58.907364 614.9
## 19 303.03 21.2824 94.9712 960 5064.616 7.022000 77.005814 619.1
## 20 135.00 23.4375 93.2292 139 5433.593 5.699000 49.813988 621.3
## math school
## 1 690.0 <NA>
## 2 661.9 <NA>
## 3 650.9 <NA>
## 4 643.5 <NA>
## 5 639.9 <NA>
## 6 605.4 <NA>
## 7 609.0 <NA>
## 8 612.5 <NA>
## 9 616.1 <NA>
## 10 613.4 <NA>
## 11 618.7 <NA>
## 12 616.0 <NA>
## 13 619.8 <NA>
## 14 622.6 <NA>
## 15 621.0 <NA>
## 16 619.9 <NA>
## 17 624.4 <NA>
## 18 621.7 <NA>
## 19 620.5 <NA>
## 20 619.3 <NA>