If you are using the package for the first time, you will first have to install it.
# install.packages("survival")
# install.packages("memisc")If you have already downloaded this package in the current version of R, you will only have to load the package.
library(survival)## Warning: package 'survival' was built under R version 4.0.4library(memisc)## Warning: package 'memisc' was built under R version 4.0.3## Loading required package: lattice## Loading required package: MASS## Warning: package 'MASS' was built under R version 4.0.4##
## Attaching package: 'memisc'## The following objects are masked from 'package:stats':
##
## contr.sum, contr.treatment, contrasts## The following object is masked from 'package:base':
##
## as.arrayLoad a data set from a package.
You can use the double colon symbol (:), to return the pbc object from the package survival. We store this data set to an object with the name pbc.
pbc <- survival::pbcWhat is the mean and standard deviation for the variable age of the pbc data set?
mean(x = pbc$age)## [1] 50.74155mean(x = pbc$age, na.rm = TRUE)## [1] 50.74155sd(x = pbc$age)## [1] 10.44721What is the mean and variance for the variable chol of the pbc data set?
mean(x = pbc$chol)## [1] NAmean(x = pbc$chol, na.rm = TRUE)## [1] 369.5106var(x = pbc$chol, na.rm = TRUE)## [1] 53798.27What is the median and interquartile range for the variable age of the pbc data set?
median(x = pbc$age)## [1] 51.00068IQR(x = pbc$age)## [1] 15.40862What is the min and max of the variable age of the pbc data set?
min(x = pbc$age)## [1] 26.27789max(x = pbc$age)## [1] 78.43943range(x = pbc$age)## [1] 26.27789 78.43943What are the 10th, 25th, 50th, 75th and 90th percentiles for serum bilirubin of the pbc data set?
quantile(x = pbc$bili, probs = c(0.1, 0.25, 0.5, 0.75, 0.9))## 10% 25% 50% 75% 90%
## 0.60 0.80 1.40 3.40 8.03The functions colMeans() and rowMeans() allow us to calculate the mean for each column or column in a matrix or data.frame, e.g.:
colMeans(x = data.frame(bili = pbc$bili, chol = pbc$chol), na.rm = TRUE)## bili chol
## 3.220813 369.510563rowMeans(x = data.frame(bili = pbc$bili, chol = pbc$chol), na.rm = TRUE)## [1] 137.75 151.55 88.70 122.90 141.20 124.40 161.50 140.15 282.60 106.30
## [11] 130.20 119.80 140.85 0.80 115.90 102.35 138.35 94.70 117.85 189.55
## [21] 126.30 137.20 206.20 229.05 149.35 566.60 98.30 119.60 185.35 131.80
## [31] 150.35 131.90 105.40 182.40 157.60 86.15 170.55 193.15 141.35 1.30
## [41] 6.80 2.10 181.05 151.15 0.60 243.85 158.25 130.45 0.80 129.05
## [51] 138.40 310.00 2.60 144.65 208.90 249.55 131.15 121.35 164.90 302.45
## [61] 108.30 151.65 477.25 187.55 128.60 214.20 233.55 87.35 336.00 0.60
## [71] 129.60 160.25 66.35 283.20 345.55 203.10 125.30 221.15 157.90 127.10
## [81] 231.20 238.25 125.65 131.70 132.05 802.50 173.05 148.30 205.00 330.80
## [91] 165.00 103.70 177.15 102.10 17.40 1.00 211.00 120.00 230.90 90.15
## [101] 200.45 124.45 95.25 152.05 232.55 2.10 106.30 63.70 60.25 243.95
## [111] 266.75 134.50 190.35 131.10 151.85 230.50 478.25 196.75 318.30 164.25
## [121] 76.15 149.30 5.10 125.80 158.65 135.10 134.25 16.20 210.45 896.20
## [131] 122.40 224.95 166.25 289.35 131.70 131.90 200.05 216.65 164.55 145.55
## [141] 173.45 182.50 167.45 292.00 154.85 1.20 144.60 511.10 130.00 1.00
## [151] 230.45 294.15 108.75 85.20 110.30 191.75 143.30 226.70 159.75 108.80
## [161] 252.15 131.60 116.65 8.50 100.00 742.85 188.45 128.70 204.65 195.60
## [171] 0.50 103.15 119.50 0.50 141.90 3.20 129.45 0.60 198.90 241.35
## [181] 124.70 0.60 100.75 342.50 128.40 113.50 411.00 93.85 180.65 2.30
## [191] 558.25 154.45 471.40 147.25 175.35 113.70 133.30 143.35 197.05 120.35
## [201] 117.80 111.75 74.75 127.85 192.25 106.80 0.60 199.95 126.35 173.45
## [211] 1.30 116.60 200.25 202.45 640.95 0.50 309.70 0.50 108.30 214.90
## [221] 180.45 188.25 231.05 155.00 137.35 111.75 159.15 107.85 97.75 152.65
## [231] 260.70 133.70 257.45 289.45 674.50 127.25 221.80 140.30 150.40 116.20
## [241] 160.20 177.95 238.00 176.95 136.80 194.55 859.05 162.40 121.65 149.80
## [251] 113.75 123.55 125.05 115.05 96.85 168.55 140.25 207.55 140.05 118.80
## [261] 189.10 162.40 216.55 179.70 175.75 159.25 114.30 175.20 188.80 224.50
## [271] 161.00 113.25 165.10 1.60 287.10 110.00 159.00 171.80 99.25 163.30
## [281] 96.45 152.65 206.55 146.15 126.90 156.00 189.70 159.35 210.00 147.70
## [291] 171.10 277.30 101.25 503.30 324.20 164.40 138.10 170.55 172.20 5.20
## [301] 197.00 167.85 186.50 109.75 214.45 119.80 136.90 123.20 130.20 217.85
## [311] 124.50 291.20 0.70 1.40 0.70 0.70 0.80 0.70 5.00 0.40
## [321] 1.30 1.10 0.60 0.60 1.80 1.50 1.20 1.00 0.70 3.50
## [331] 3.10 12.60 2.80 7.10 0.60 2.10 1.80 16.00 0.60 5.40
## [341] 9.00 0.90 11.10 8.90 0.50 0.60 3.40 0.90 1.40 2.10
## [351] 15.00 0.60 1.30 1.30 1.60 2.20 3.00 0.80 0.80 1.80
## [361] 5.50 18.00 0.60 2.70 0.90 1.30 1.10 13.80 4.40 16.00
## [371] 7.30 0.60 0.70 0.70 1.70 9.50 2.20 1.80 3.30 2.90
## [381] 1.70 14.00 0.80 1.30 0.70 1.70 13.60 0.90 0.70 3.00
## [391] 1.20 0.40 0.70 2.00 1.40 1.60 0.50 7.30 8.10 0.50
## [401] 4.20 0.80 2.50 4.60 1.00 4.50 1.10 1.90 0.70 1.50
## [411] 0.60 1.00 0.70 1.20 0.90 1.60 0.80 0.70The functions colSums() and rowSums() allow us to calculate the sum for each column or column in a matrix or data.frame, e.g.:
colSums(x = data.frame(bili = pbc$bili, chol = pbc$chol), na.rm = TRUE)## bili chol
## 1346.3 104941.0rowSums(x = data.frame(bili = pbc$bili, chol = pbc$chol), na.rm = TRUE)## [1] 275.5 303.1 177.4 245.8 282.4 248.8 323.0 280.3 565.2 212.6
## [11] 260.4 239.6 281.7 0.8 231.8 204.7 276.7 189.4 235.7 379.1
## [21] 252.6 274.4 412.4 458.1 298.7 1133.2 196.6 239.2 370.7 263.6
## [31] 300.7 263.8 210.8 364.8 315.2 172.3 341.1 386.3 282.7 1.3
## [41] 6.8 2.1 362.1 302.3 0.6 487.7 316.5 260.9 0.8 258.1
## [51] 276.8 620.0 2.6 289.3 417.8 499.1 262.3 242.7 329.8 604.9
## [61] 216.6 303.3 954.5 375.1 257.2 428.4 467.1 174.7 672.0 0.6
## [71] 259.2 320.5 132.7 566.4 691.1 406.2 250.6 442.3 315.8 254.2
## [81] 462.4 476.5 251.3 263.4 264.1 1605.0 346.1 296.6 410.0 661.6
## [91] 330.0 207.4 354.3 204.2 17.4 1.0 422.0 240.0 461.8 180.3
## [101] 400.9 248.9 190.5 304.1 465.1 2.1 212.6 127.4 120.5 487.9
## [111] 533.5 269.0 380.7 262.2 303.7 461.0 956.5 393.5 636.6 328.5
## [121] 152.3 298.6 5.1 251.6 317.3 270.2 268.5 16.2 420.9 1792.4
## [131] 244.8 449.9 332.5 578.7 263.4 263.8 400.1 433.3 329.1 291.1
## [141] 346.9 365.0 334.9 584.0 309.7 1.2 289.2 1022.2 260.0 1.0
## [151] 460.9 588.3 217.5 170.4 220.6 383.5 286.6 453.4 319.5 217.6
## [161] 504.3 263.2 233.3 8.5 200.0 1485.7 376.9 257.4 409.3 391.2
## [171] 0.5 206.3 239.0 0.5 283.8 3.2 258.9 0.6 397.8 482.7
## [181] 249.4 0.6 201.5 685.0 256.8 227.0 822.0 187.7 361.3 2.3
## [191] 1116.5 308.9 942.8 294.5 350.7 227.4 266.6 286.7 394.1 240.7
## [201] 235.6 223.5 149.5 255.7 384.5 213.6 0.6 399.9 252.7 346.9
## [211] 1.3 233.2 400.5 404.9 1281.9 0.5 619.4 0.5 216.6 429.8
## [221] 360.9 376.5 462.1 310.0 274.7 223.5 318.3 215.7 195.5 305.3
## [231] 521.4 267.4 514.9 578.9 1349.0 254.5 443.6 280.6 300.8 232.4
## [241] 320.4 355.9 476.0 353.9 273.6 389.1 1718.1 324.8 243.3 299.6
## [251] 227.5 247.1 250.1 230.1 193.7 337.1 280.5 415.1 280.1 237.6
## [261] 378.2 324.8 433.1 359.4 351.5 318.5 228.6 350.4 377.6 449.0
## [271] 322.0 226.5 330.2 1.6 574.2 220.0 318.0 343.6 198.5 326.6
## [281] 192.9 305.3 413.1 292.3 253.8 312.0 379.4 318.7 420.0 295.4
## [291] 342.2 554.6 202.5 1006.6 648.4 328.8 276.2 341.1 344.4 5.2
## [301] 394.0 335.7 373.0 219.5 428.9 239.6 273.8 246.4 260.4 435.7
## [311] 249.0 582.4 0.7 1.4 0.7 0.7 0.8 0.7 5.0 0.4
## [321] 1.3 1.1 0.6 0.6 1.8 1.5 1.2 1.0 0.7 3.5
## [331] 3.1 12.6 2.8 7.1 0.6 2.1 1.8 16.0 0.6 5.4
## [341] 9.0 0.9 11.1 8.9 0.5 0.6 3.4 0.9 1.4 2.1
## [351] 15.0 0.6 1.3 1.3 1.6 2.2 3.0 0.8 0.8 1.8
## [361] 5.5 18.0 0.6 2.7 0.9 1.3 1.1 13.8 4.4 16.0
## [371] 7.3 0.6 0.7 0.7 1.7 9.5 2.2 1.8 3.3 2.9
## [381] 1.7 14.0 0.8 1.3 0.7 1.7 13.6 0.9 0.7 3.0
## [391] 1.2 0.4 0.7 2.0 1.4 1.6 0.5 7.3 8.1 0.5
## [401] 4.2 0.8 2.5 4.6 1.0 4.5 1.1 1.9 0.7 1.5
## [411] 0.6 1.0 0.7 1.2 0.9 1.6 0.8 0.7What is the correlation between serum bilirubin and serum cholesterol of the pbc data set?
cor(x = pbc$bili, pbc$chol, use = "complete.obs", method = "pearson")## [1] 0.3971289cor(x = pbc$bili, pbc$chol, use = "complete.obs", method = "spearman")## [1] 0.4024538What is the correlation matrix for the variables serum bilirubin, serum cholesterol and alkaline of the pbc data set?
cor(x = data.frame(pbc$bili, pbc$chol, pbc$albumin),
use = "complete.obs")## pbc.bili pbc.chol pbc.albumin
## pbc.bili 1.0000000 0.39712889 -0.31310846
## pbc.chol 0.3971289 1.00000000 -0.06973277
## pbc.albumin -0.3131085 -0.06973277 1.00000000What is the variance-covariance matrix for the above variables?
var(x = data.frame(pbc$bili, pbc$chol, pbc$albumin),
use = "complete.obs")## pbc.bili pbc.chol pbc.albumin
## pbc.bili 20.7116508 419.201667 -0.5752636
## pbc.chol 419.2016672 53798.271973 -6.5295880
## pbc.albumin -0.5752636 -6.529588 0.1629782cov(x = data.frame(pbc$bili, pbc$chol, pbc$albumin),
use = "complete.obs")## pbc.bili pbc.chol pbc.albumin
## pbc.bili 20.7116508 419.201667 -0.5752636
## pbc.chol 419.2016672 53798.271973 -6.5295880
## pbc.albumin -0.5752636 -6.529588 0.1629782A (co)variance matrix can be converted to a (pearson) correlation matrix with the help of the function cov2cor():
cov2cor(V = var(x = data.frame(pbc$bili, pbc$chol, pbc$albumin),
use = "complete.obs"))## pbc.bili pbc.chol pbc.albumin
## pbc.bili 1.0000000 0.39712889 -0.31310846
## pbc.chol 0.3971289 1.00000000 -0.06973277
## pbc.albumin -0.3131085 -0.06973277 1.00000000What is the percentage of placebo and treatment patients in the pbc data set? (In order to use the percent() function you will need to load the memisc package)
percent(x = pbc$trt)## 1 2 N
## 50.64103 49.35897 312.00000What is the percentage of females and males in the pbc data set?
percent(x = pbc$sex)## m f N
## 10.52632 89.47368 418.00000What are the frequencies of each combination for the variables trt and sex in the pbc data set?
table(trt = pbc$trt, sex = pbc$sex)## sex
## trt m f
## 1 21 137
## 2 15 139To add summaries (e.g. the sum) for each column and/or row use the addmargins() function:
tab <- table(trt = pbc$trt, sex = pbc$sex)
addmargins(A = tab)## sex
## trt m f Sum
## 1 21 137 158
## 2 15 139 154
## Sum 36 276 312We can also change the function (e.g use the mean):
addmargins(A = tab, FUN = mean)## Margins computed over dimensions
## in the following order:
## 1: trt
## 2: sex## sex
## trt m f mean
## 1 21 137 79
## 2 15 139 77
## mean 18 138 78What are the percentages of each combination for the variables trt and sex in the pbc data set?
prop.table(x = tab)## sex
## trt m f
## 1 0.06730769 0.43910256
## 2 0.04807692 0.44551282For tables with more that 2 dimensions use:
ftable(x = table(trt = pbc$trt, sex = pbc$sex, ascites = pbc$ascites))## ascites 0 1
## trt sex
## 1 m 20 1
## f 124 13
## 2 m 13 2
## f 131 8ftable(x = data.frame(trt = pbc$trt, sex = pbc$sex, ascites = pbc$ascites))## ascites 0 1
## trt sex
## 1 m 20 1
## f 124 13
## 2 m 13 2
## f 131 8With the help of the arguments row.vars and col.vars we can determine which variables are given in the rows and which in the columns:
ftable(x = table(trt = pbc$trt, sex = pbc$sex, ascites = pbc$ascites),
row.vars = c(3, 2))## trt 1 2
## ascites sex
## 0 m 20 13
## f 124 131
## 1 m 1 2
## f 13 8Check if there are any missing values in the serum cholesterol variable of the pbc data set:
is.na(pbc$chol)## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE TRUE TRUE TRUE FALSE FALSE TRUE FALSE FALSE FALSE
## [49] TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
## [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [169] FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE
## [181] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [205] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE
## [217] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [313] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [325] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [337] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [349] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [361] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [373] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [385] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [397] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [409] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUECheck if there are any complete cases in the serum cholesterol variable of the pbc data set:
complete.cases(pbc$chol)## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [13] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [25] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [37] TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE FALSE TRUE TRUE TRUE
## [49] FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## [73] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [85] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE
## [97] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## [109] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [121] TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
## [133] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [145] TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
## [157] TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE
## [169] TRUE TRUE FALSE TRUE TRUE FALSE TRUE FALSE TRUE FALSE TRUE TRUE
## [181] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## [193] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [205] TRUE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE
## [217] TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [229] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [241] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [253] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [265] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE
## [277] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [289] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
## [301] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSEObtain the dimensions of a matrix or data frame. We can use the function dim():
dim(pbc)## [1] 418 20Outliers: e.g. let’s assume that patients with serum bilirun values > 25 are outliers.
serum bilirun outliers:pbc_out_bili <- pbc[pbc$bili > 25, ]Calculate the mean and median of the serum bilirun variable without the outliers:
pbc_no_out_bili <- pbc[pbc$bili <= 25, ]
mean(pbc_no_out_bili$bili)## [1] 3.107692median(pbc_no_out_bili$bili)## [1] 1.35Calculate the mean and median of the serum bilirubin variable without the missing values in the serum cholesterol variable:
pbc_no_mis_chol <- pbc[complete.cases(pbc$chol) == TRUE, ]
mean(pbc_no_mis_chol$bili)## [1] 3.276056median(pbc_no_mis_chol$bili)## [1] 1.4