In the last section, you were asked to convert the “smoker” column to logical values. The solution is fairly simple: ``
birthweight <- read.csv("birthweight.csv")birthweight$smoker == "yes"## [1] FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE
## [13] TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE FALSE TRUE TRUE FALSE
## [25] FALSE FALSE TRUE TRUE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE
## [37] TRUE FALSE FALSE TRUE TRUE FALSEbirthweight$smoker <- (birthweight$smoker == "yes")birthweight## ID birth.date location length birthweight head.circumference
## 1 1107 1/25/1967 General 52 3.23 36
## 2 697 2/6/1967 Silver Hill 48 3.03 35
## 3 1683 2/14/1967 Silver Hill 53 3.35 33
## 4 27 3/9/1967 Silver Hill 53 3.55 37
## 5 1522 3/13/1967 Memorial 50 2.74 33
## 6 569 3/23/1967 Memorial 50 2.51 35
## 7 365 4/23/1967 Memorial 52 3.53 37
## 8 808 5/5/1967 Silver Hill 48 2.92 33
## 9 1369 6/4/1967 Silver Hill 49 3.18 34
## 10 1023 6/7/1967 Memorial 52 3.00 35
## 11 822 6/14/1967 Memorial 50 3.42 35
## 12 1272 6/20/1967 Memorial 53 2.75 32
## 13 1262 6/25/1967 Silver Hill 53 3.19 34
## 14 575 7/12/1967 Memorial 50 2.78 30
## 15 1016 7/13/1967 Silver Hill 53 4.32 36
## 16 792 9/7/1967 Memorial 53 3.64 38
## 17 820 10/7/1967 General 52 3.77 34
## 18 752 10/19/1967 General 49 3.32 36
## 19 619 11/1/1967 Memorial 52 3.41 33
## 20 1764 12/7/1967 Silver Hill 58 4.57 39
## 21 1081 12/14/1967 Silver Hill 54 3.63 38
## 22 516 1/8/1968 Silver Hill 47 2.66 33
## 23 272 1/10/1968 Memorial 52 3.86 36
## 24 321 1/21/1968 Silver Hill 48 3.11 33
## 25 1636 2/2/1968 Silver Hill 51 3.93 38
## 26 1360 2/16/1968 General 56 4.55 34
## 27 1388 2/22/1968 Memorial 51 3.14 33
## 28 1363 4/2/1968 General 48 2.37 30
## 29 1058 4/24/1968 Silver Hill 53 3.15 34
## 30 755 4/25/1968 Memorial 53 3.20 33
## 31 462 6/19/1968 Silver Hill 58 4.10 39
## 32 300 7/18/1968 Silver Hill 46 2.05 32
## 33 1088 7/24/1968 General 51 3.27 36
## 34 57 8/12/1968 Memorial 51 3.32 38
## 35 553 8/17/1968 Silver Hill 54 3.94 37
## 36 1191 9/7/1968 General 53 3.65 33
## 37 431 9/16/1968 Silver Hill 48 1.92 30
## 38 1313 9/27/1968 Silver Hill 43 2.65 32
## 39 1600 10/9/1968 General 53 2.90 34
## 40 532 10/25/1968 General 53 3.59 34
## 41 223 12/11/1968 General 50 3.87 33
## 42 1187 12/19/1968 Silver Hill 53 4.07 38
## weeks.gestation smoker maternal.age maternal.cigarettes maternal.height
## 1 38 FALSE 31 0 164
## 2 39 FALSE 27 0 162
## 3 41 FALSE 27 0 164
## 4 41 TRUE 37 25 161
## 5 39 TRUE 21 17 156
## 6 39 TRUE 22 7 159
## 7 40 TRUE 26 25 170
## 8 34 FALSE 26 0 167
## 9 38 TRUE 31 25 162
## 10 38 TRUE 30 12 165
## 11 38 FALSE 20 0 157
## 12 40 TRUE 37 50 168
## 13 41 TRUE 27 35 163
## 14 37 TRUE 19 7 165
## 15 40 FALSE 19 0 171
## 16 40 TRUE 20 2 170
## 17 40 FALSE 24 0 157
## 18 40 TRUE 27 12 152
## 19 39 TRUE 23 25 181
## 20 41 TRUE 32 12 173
## 21 38 FALSE 18 0 172
## 22 35 TRUE 20 35 170
## 23 39 TRUE 30 25 170
## 24 37 FALSE 28 0 158
## 25 38 FALSE 29 0 165
## 26 44 FALSE 20 0 162
## 27 41 TRUE 22 7 160
## 28 37 TRUE 20 7 163
## 29 40 FALSE 29 0 167
## 30 41 FALSE 21 0 155
## 31 41 FALSE 35 0 172
## 32 35 TRUE 41 7 166
## 33 40 FALSE 24 0 168
## 34 39 TRUE 23 17 157
## 35 42 FALSE 24 0 175
## 36 42 FALSE 21 0 165
## 37 33 TRUE 20 7 161
## 38 33 FALSE 24 0 149
## 39 39 FALSE 19 0 165
## 40 40 TRUE 31 12 163
## 41 45 TRUE 28 25 163
## 42 44 FALSE 20 0 174
## maternal.prepregnant.weight paternal.age paternal.education
## 1 57 NA NA
## 2 62 27 14
## 3 62 37 14
## 4 66 46 NA
## 5 53 24 12
## 6 52 23 14
## 7 62 30 10
## 8 64 25 12
## 9 57 32 16
## 10 64 38 14
## 11 48 22 14
## 12 61 31 16
## 13 51 31 16
## 14 60 20 14
## 15 62 19 12
## 16 59 24 12
## 17 50 31 16
## 18 48 37 12
## 19 69 23 16
## 20 70 38 14
## 21 50 20 12
## 22 57 23 12
## 23 78 40 16
## 24 54 39 10
## 25 61 NA NA
## 26 57 23 10
## 27 53 24 16
## 28 47 20 10
## 29 60 30 16
## 30 55 25 14
## 31 58 31 16
## 32 57 37 14
## 33 53 29 16
## 34 48 NA NA
## 35 66 30 12
## 36 61 21 10
## 37 50 20 10
## 38 45 26 16
## 39 57 NA NA
## 40 49 41 12
## 41 54 30 16
## 42 68 26 14
## paternal.cigarettes paternal.height low.birthweight geriatric.pregnancy
## 1 NA NA 0 FALSE
## 2 0 178 0 FALSE
## 3 0 170 0 FALSE
## 4 0 175 0 TRUE
## 5 7 179 0 FALSE
## 6 25 NA 1 FALSE
## 7 25 181 0 FALSE
## 8 25 175 0 FALSE
## 9 50 194 0 FALSE
## 10 50 180 0 FALSE
## 11 0 179 0 FALSE
## 12 0 173 0 TRUE
## 13 25 185 0 FALSE
## 14 0 183 0 FALSE
## 15 0 183 0 FALSE
## 16 12 185 0 FALSE
## 17 0 173 0 FALSE
## 18 25 170 0 FALSE
## 19 2 181 0 FALSE
## 20 25 180 0 FALSE
## 21 7 172 0 FALSE
## 22 50 186 1 FALSE
## 23 50 178 0 FALSE
## 24 0 171 0 FALSE
## 25 NA NA 0 FALSE
## 26 35 179 0 FALSE
## 27 12 176 0 FALSE
## 28 35 185 1 FALSE
## 29 NA 182 0 FALSE
## 30 25 183 0 FALSE
## 31 25 185 0 TRUE
## 32 25 173 1 TRUE
## 33 0 181 0 FALSE
## 34 NA NA 0 FALSE
## 35 0 184 0 FALSE
## 36 25 185 0 FALSE
## 37 35 180 1 FALSE
## 38 0 169 1 FALSE
## 39 NA NA 0 FALSE
## 40 50 191 0 FALSE
## 41 0 183 0 FALSE
## 42 25 189 0 FALSEConverting the dates (currently stored as characters) to a more usable format is not as simple, and will require a function.
R is filled with functions. We have already used a few:
read.csv(), class(), dim(),
mean(), colnames(), as.logical(),
and as.numeric(). To find the documentation on a function,
use the ? character. This opens a page containing details
on the function in the help pane.
What if we want to do this for all three fields, but we don’t want to write the code three times?
Take a few minutes to dissect the following function, working from the inside out. What does each piece do?
# custom function takes a vector of dates and returns a data frame with columns day, month, and year
split_MMDDYYYY <- function(date_vector){
date_list = lapply(seq(1:3), function(i){
as.integer(sapply(strsplit(date_vector, split = "/"), '[[', i))
})
names(date_list) = c("month", "day", "year")
as.data.frame(do.call("cbind", date_list))
}split_MMDDYYYY(birthweight$birth.date)## month day year
## 1 1 25 1967
## 2 2 6 1967
## 3 2 14 1967
## 4 3 9 1967
## 5 3 13 1967
## 6 3 23 1967
## 7 4 23 1967
## 8 5 5 1967
## 9 6 4 1967
## 10 6 7 1967
## 11 6 14 1967
## 12 6 20 1967
## 13 6 25 1967
## 14 7 12 1967
## 15 7 13 1967
## 16 9 7 1967
## 17 10 7 1967
## 18 10 19 1967
## 19 11 1 1967
## 20 12 7 1967
## 21 12 14 1967
## 22 1 8 1968
## 23 1 10 1968
## 24 1 21 1968
## 25 2 2 1968
## 26 2 16 1968
## 27 2 22 1968
## 28 4 2 1968
## 29 4 24 1968
## 30 4 25 1968
## 31 6 19 1968
## 32 7 18 1968
## 33 7 24 1968
## 34 8 12 1968
## 35 8 17 1968
## 36 9 7 1968
## 37 9 16 1968
## 38 9 27 1968
## 39 10 9 1968
## 40 10 25 1968
## 41 12 11 1968
## 42 12 19 1968birthweight <- cbind(birthweight, split_MMDDYYYY(birthweight$birth.date))The cbind() function combines objects by column. Now
that we have the birth month in its own column, we can get the mean of
birth weight by month.
tapply(birthweight$birthweight, birthweight$month, mean)## 1 2 3 4 5 6 7 8
## 3.215000 3.600000 2.933333 3.062500 2.920000 3.273333 3.105000 3.630000
## 9 10 11 12
## 2.965000 3.395000 3.410000 4.035000For this example experiment, we also have placental miRNA expression data. These values are not from real samples, but have been manipulated to roughly reproduce the effect observed by Maccani et al. 2010.
download.file("https://raw.githubusercontent.com/ucdavis-bioinformatics-training/2022_February_Introduction_to_R_for_Bioinformatics/main/miRNA.csv", "miRNA.csv")
mir <- read.csv("miRNA.csv", row.names = 1)mir## sample.27 sample.1522 sample.569 sample.365 sample.1369 sample.1023
## miR-16 46 56 47 54 56 59
## miR-21 52 43 40 35 59 47
## miR-146a 98 97 87 96 84 96
## miR-182 53 45 63 41 46 50
## sample.1272 sample.1262 sample.575 sample.792 sample.752 sample.619
## miR-16 49 55 62 63 46 52
## miR-21 42 45 55 45 42 43
## miR-146a 88 97 96 104 103 92
## miR-182 49 50 62 51 64 58
## sample.1764 sample.516 sample.272 sample.1388 sample.1363 sample.300
## miR-16 46 61 49 46 61 60
## miR-21 40 51 43 44 47 48
## miR-146a 98 97 91 105 77 89
## miR-182 57 59 55 60 60 65
## sample.57 sample.431 sample.532 sample.223 sample.1107 sample.697
## miR-16 46 70 60 60 57 68
## miR-21 39 51 44 46 49 47
## miR-146a 105 84 94 87 116 98
## miR-182 40 48 49 52 48 57
## sample.1683 sample.808 sample.822 sample.1016 sample.820 sample.1081
## miR-16 49 59 54 69 58 55
## miR-21 48 56 52 41 55 52
## miR-146a 98 101 86 98 102 93
## miR-182 55 74 49 51 53 52
## sample.321 sample.1636 sample.1360 sample.1058 sample.755 sample.462
## miR-16 68 63 70 77 56 65
## miR-21 46 39 57 55 46 58
## miR-146a 125 104 111 124 101 101
## miR-182 60 43 46 56 50 60
## sample.1088 sample.553 sample.1191 sample.1313 sample.1600 sample.1187
## miR-16 42 63 66 64 50 57
## miR-21 54 54 48 47 44 46
## miR-146a 107 106 102 104 111 86
## miR-182 63 60 50 42 67 43In this object, the rows are the gene identifiers, and the columns are the samples.
mir <- as.data.frame(t(mir))
mir$ID <- gsub("sample.", "", rownames(mir))
experiment <- merge(birthweight, mir)What did these lines of code do? Explore each of
them, looking up the function help using ? as
necessary.
experiment## ID birth.date location length birthweight head.circumference
## 1 27 3/9/1967 Silver Hill 53 3.55 37
## 2 57 8/12/1968 Memorial 51 3.32 38
## 3 223 12/11/1968 General 50 3.87 33
## 4 272 1/10/1968 Memorial 52 3.86 36
## 5 300 7/18/1968 Silver Hill 46 2.05 32
## 6 321 1/21/1968 Silver Hill 48 3.11 33
## 7 365 4/23/1967 Memorial 52 3.53 37
## 8 431 9/16/1968 Silver Hill 48 1.92 30
## 9 462 6/19/1968 Silver Hill 58 4.10 39
## 10 516 1/8/1968 Silver Hill 47 2.66 33
## 11 532 10/25/1968 General 53 3.59 34
## 12 553 8/17/1968 Silver Hill 54 3.94 37
## 13 569 3/23/1967 Memorial 50 2.51 35
## 14 575 7/12/1967 Memorial 50 2.78 30
## 15 619 11/1/1967 Memorial 52 3.41 33
## 16 697 2/6/1967 Silver Hill 48 3.03 35
## 17 752 10/19/1967 General 49 3.32 36
## 18 755 4/25/1968 Memorial 53 3.20 33
## 19 792 9/7/1967 Memorial 53 3.64 38
## 20 808 5/5/1967 Silver Hill 48 2.92 33
## 21 820 10/7/1967 General 52 3.77 34
## 22 822 6/14/1967 Memorial 50 3.42 35
## 23 1016 7/13/1967 Silver Hill 53 4.32 36
## 24 1023 6/7/1967 Memorial 52 3.00 35
## 25 1058 4/24/1968 Silver Hill 53 3.15 34
## 26 1081 12/14/1967 Silver Hill 54 3.63 38
## 27 1088 7/24/1968 General 51 3.27 36
## 28 1107 1/25/1967 General 52 3.23 36
## 29 1187 12/19/1968 Silver Hill 53 4.07 38
## 30 1191 9/7/1968 General 53 3.65 33
## 31 1262 6/25/1967 Silver Hill 53 3.19 34
## 32 1272 6/20/1967 Memorial 53 2.75 32
## 33 1313 9/27/1968 Silver Hill 43 2.65 32
## 34 1360 2/16/1968 General 56 4.55 34
## 35 1363 4/2/1968 General 48 2.37 30
## 36 1369 6/4/1967 Silver Hill 49 3.18 34
## 37 1388 2/22/1968 Memorial 51 3.14 33
## 38 1522 3/13/1967 Memorial 50 2.74 33
## 39 1600 10/9/1968 General 53 2.90 34
## 40 1636 2/2/1968 Silver Hill 51 3.93 38
## 41 1683 2/14/1967 Silver Hill 53 3.35 33
## 42 1764 12/7/1967 Silver Hill 58 4.57 39
## weeks.gestation smoker maternal.age maternal.cigarettes maternal.height
## 1 41 TRUE 37 25 161
## 2 39 TRUE 23 17 157
## 3 45 TRUE 28 25 163
## 4 39 TRUE 30 25 170
## 5 35 TRUE 41 7 166
## 6 37 FALSE 28 0 158
## 7 40 TRUE 26 25 170
## 8 33 TRUE 20 7 161
## 9 41 FALSE 35 0 172
## 10 35 TRUE 20 35 170
## 11 40 TRUE 31 12 163
## 12 42 FALSE 24 0 175
## 13 39 TRUE 22 7 159
## 14 37 TRUE 19 7 165
## 15 39 TRUE 23 25 181
## 16 39 FALSE 27 0 162
## 17 40 TRUE 27 12 152
## 18 41 FALSE 21 0 155
## 19 40 TRUE 20 2 170
## 20 34 FALSE 26 0 167
## 21 40 FALSE 24 0 157
## 22 38 FALSE 20 0 157
## 23 40 FALSE 19 0 171
## 24 38 TRUE 30 12 165
## 25 40 FALSE 29 0 167
## 26 38 FALSE 18 0 172
## 27 40 FALSE 24 0 168
## 28 38 FALSE 31 0 164
## 29 44 FALSE 20 0 174
## 30 42 FALSE 21 0 165
## 31 41 TRUE 27 35 163
## 32 40 TRUE 37 50 168
## 33 33 FALSE 24 0 149
## 34 44 FALSE 20 0 162
## 35 37 TRUE 20 7 163
## 36 38 TRUE 31 25 162
## 37 41 TRUE 22 7 160
## 38 39 TRUE 21 17 156
## 39 39 FALSE 19 0 165
## 40 38 FALSE 29 0 165
## 41 41 FALSE 27 0 164
## 42 41 TRUE 32 12 173
## maternal.prepregnant.weight paternal.age paternal.education
## 1 66 46 NA
## 2 48 NA NA
## 3 54 30 16
## 4 78 40 16
## 5 57 37 14
## 6 54 39 10
## 7 62 30 10
## 8 50 20 10
## 9 58 31 16
## 10 57 23 12
## 11 49 41 12
## 12 66 30 12
## 13 52 23 14
## 14 60 20 14
## 15 69 23 16
## 16 62 27 14
## 17 48 37 12
## 18 55 25 14
## 19 59 24 12
## 20 64 25 12
## 21 50 31 16
## 22 48 22 14
## 23 62 19 12
## 24 64 38 14
## 25 60 30 16
## 26 50 20 12
## 27 53 29 16
## 28 57 NA NA
## 29 68 26 14
## 30 61 21 10
## 31 51 31 16
## 32 61 31 16
## 33 45 26 16
## 34 57 23 10
## 35 47 20 10
## 36 57 32 16
## 37 53 24 16
## 38 53 24 12
## 39 57 NA NA
## 40 61 NA NA
## 41 62 37 14
## 42 70 38 14
## paternal.cigarettes paternal.height low.birthweight geriatric.pregnancy
## 1 0 175 0 TRUE
## 2 NA NA 0 FALSE
## 3 0 183 0 FALSE
## 4 50 178 0 FALSE
## 5 25 173 1 TRUE
## 6 0 171 0 FALSE
## 7 25 181 0 FALSE
## 8 35 180 1 FALSE
## 9 25 185 0 TRUE
## 10 50 186 1 FALSE
## 11 50 191 0 FALSE
## 12 0 184 0 FALSE
## 13 25 NA 1 FALSE
## 14 0 183 0 FALSE
## 15 2 181 0 FALSE
## 16 0 178 0 FALSE
## 17 25 170 0 FALSE
## 18 25 183 0 FALSE
## 19 12 185 0 FALSE
## 20 25 175 0 FALSE
## 21 0 173 0 FALSE
## 22 0 179 0 FALSE
## 23 0 183 0 FALSE
## 24 50 180 0 FALSE
## 25 NA 182 0 FALSE
## 26 7 172 0 FALSE
## 27 0 181 0 FALSE
## 28 NA NA 0 FALSE
## 29 25 189 0 FALSE
## 30 25 185 0 FALSE
## 31 25 185 0 FALSE
## 32 0 173 0 TRUE
## 33 0 169 1 FALSE
## 34 35 179 0 FALSE
## 35 35 185 1 FALSE
## 36 50 194 0 FALSE
## 37 12 176 0 FALSE
## 38 7 179 0 FALSE
## 39 NA NA 0 FALSE
## 40 NA NA 0 FALSE
## 41 0 170 0 FALSE
## 42 25 180 0 FALSE
## month day year miR-16 miR-21 miR-146a miR-182
## 1 3 9 1967 46 52 98 53
## 2 8 12 1968 46 39 105 40
## 3 12 11 1968 60 46 87 52
## 4 1 10 1968 49 43 91 55
## 5 7 18 1968 60 48 89 65
## 6 1 21 1968 68 46 125 60
## 7 4 23 1967 54 35 96 41
## 8 9 16 1968 70 51 84 48
## 9 6 19 1968 65 58 101 60
## 10 1 8 1968 61 51 97 59
## 11 10 25 1968 60 44 94 49
## 12 8 17 1968 63 54 106 60
## 13 3 23 1967 47 40 87 63
## 14 7 12 1967 62 55 96 62
## 15 11 1 1967 52 43 92 58
## 16 2 6 1967 68 47 98 57
## 17 10 19 1967 46 42 103 64
## 18 4 25 1968 56 46 101 50
## 19 9 7 1967 63 45 104 51
## 20 5 5 1967 59 56 101 74
## 21 10 7 1967 58 55 102 53
## 22 6 14 1967 54 52 86 49
## 23 7 13 1967 69 41 98 51
## 24 6 7 1967 59 47 96 50
## 25 4 24 1968 77 55 124 56
## 26 12 14 1967 55 52 93 52
## 27 7 24 1968 42 54 107 63
## 28 1 25 1967 57 49 116 48
## 29 12 19 1968 57 46 86 43
## 30 9 7 1968 66 48 102 50
## 31 6 25 1967 55 45 97 50
## 32 6 20 1967 49 42 88 49
## 33 9 27 1968 64 47 104 42
## 34 2 16 1968 70 57 111 46
## 35 4 2 1968 61 47 77 60
## 36 6 4 1967 56 59 84 46
## 37 2 22 1968 46 44 105 60
## 38 3 13 1967 56 43 97 45
## 39 10 9 1968 50 44 111 67
## 40 2 2 1968 63 39 104 43
## 41 2 14 1967 49 48 98 55
## 42 12 7 1967 46 40 98 57