-`r paste0("Created: ",format(Sys.time(),format='%d-%b-%Y'))` • SciLifeLab • NBIS +`r paste0("Created: ",format(Sys.time(),format='%d-%b-%Y'))` • SciLifeLab • NBIS
-[NBIS](https://nbis.se/) • [SciLifeLab](https://www.scilifelab.se/) +2023 • [NBIS](https://nbis.se/) • [SciLifeLab](https://www.scilifelab.se/)
Graphics from
-`r paste0("Created: ",format(Sys.time(),format='%d-%b-%Y'))` • SciLifeLab • NBIS
+`r paste0("Created: ",format(Sys.time(),format='%d-%b-%Y'))` • SciLifeLab • NBIS
It is NOT absolutely necessary to have Docker installed, but we are working on shipping the working course as a Docker container ready to run. In order to be able to use this functionality, please make sure you have Docker Desktop installed and running on your machine.
Install git
In order to use RStudio’s version control features, install Git on your machine. This step is also NOT necessary, but to our experience learning and using git makes your life much easier.
-Install Zoom
-Go to Zoom website and follow installation instructions. Most likely, your University has already paid some pro plan and you should consult your local IT to get university account details.
-02-Nov-2020Mononline +23-Oct-2023MonTripplet room
-03-Nov-2020Tueonline +24-Oct-2023TueTripplet room
-04-Nov-2020Wedonline +25-Oct-2023WedTripplet room
-05-Nov-2020Thuonline +26-Oct-2023ThuTripplet room
-06-Nov-2020Frionline +27-Oct-2023FriTripplet room
- MK: Marcin Kierczak, SDL: Sebastian DiLorenzo, RF: Roy Francis, JOW: Jakub Orzechowski Westholm, LM: Lokeshwaran Manoharan, DB: Dimitris Bampalikis, PU: Per Unneberg, M-GH: Mun-Gwan Hong, LS: Lucile Soler + NR: Nima Rafati, PA: PRasoon Agarwal, MK: Marcin Kierczak, SDL: Sebastian DiLorenzo, RF: Roy Francis, GD: Guilherme Dias, MR: Miguel Redondo
diff --git a/2310-canvas/index.html b/2310-canvas/index.html index c3ec556..0a3c9c0 100644 --- a/2310-canvas/index.html +++ b/2310-canvas/index.html @@ -201,7 +201,7 @@-Last updated on 06-Oct-2023 at 01:47:59 PM . +Last updated on 06-Oct-2023 at 01:51:10 PM .
-
-mode(X)
length(X)-
-X[X>6]## [1] 7 10 8 11 9 12-
-X[,c(3,2,1)]## [,1] [,2] [,3]
@@ -364,10 +364,10 @@ 2.1 Exercise
Add a vector with three zeros as a fifth row to the matrix
-
-
+
X.2 <- rbind(X, rep(0, 3))
X.2
@@ -383,10 +383,10 @@ 2.1 Exercise
Replace all values the first two columns in your matrix with NA.
-
-
+
X[,1:2] <- NA
X
@@ -401,10 +401,10 @@ 2.1 Exercise
Replace all values in the matrix with 0 and convert it to a vector
-
-
+
X[] <- 0
as.vector(X)
@@ -438,10 +438,10 @@ 2.1 Exercise
E. A - B
F. A == B
-
-
+
A <- matrix(1:4, ncol = 2, nrow = 2)
B <- matrix(5:8, ncol = 2, nrow = 2)
@@ -486,10 +486,10 @@ 2.1 Exercise
Generate a 10 by 10 matrix with random numbers. Add row and column names and calculate mean and median over rows and save these in a new matrix.
-
-
+
e <- rnorm(n = 100)
E <- matrix(e, nrow = 10, ncol = 10)
@@ -499,12 +499,12 @@ 2.1 Exercise
E.medians <- apply(E, MARGIN = 1, median)
E.mm <- rbind(E.means, E.medians)
E.mm
-## A B C D E F
-## E.means -0.24554257 0.09255232 0.1776671 -0.02575399 0.04905815 0.2151623
-## E.medians -0.08656111 0.12959063 0.1369226 -0.21832978 0.09551396 0.4012007
-## G H I J
-## E.means -0.12206824 0.3077895 0.1633312 -0.1194803
-## E.medians 0.02903108 0.6740534 0.2727073 0.1281230
+## A B C D E F
+## E.means 0.5990676 0.15178714 0.07305545 -0.3111931 0.333125527 0.05136066
+## E.medians 0.7662251 0.05512929 0.15630451 -0.2596826 -0.001697912 -0.19751099
+## G H I J
+## E.means -0.1603784 0.5072262 0.3574913 -0.1305142
+## E.medians 0.1574162 0.4147138 0.7171831 -0.6067180
@@ -520,7 +520,7 @@ 3 Dataframes
## 'data.frame': 10 obs. of 3 variables:
## $ vector1: int 1 2 3 4 5 6 7 8 9 10
## $ vector2: chr "a" "b" "c" "d" ...
-## $ vector3: num -3.673 -14.961 -20.694 15.702 0.104 ...
+## $ vector3: num -10.9 -9.5 3.84 -10.46 12.75 ...
In the above example, we can see that the dataframe dfr contains 10 observations for three variables that all have different modes, column 1 is an integer vector, column 2 a vector with factors and column 3 a numeric vector. It is noteworthy that the second column is a factor even though we just gave it a character vector.
3.1 Exercise
@@ -528,93 +528,93 @@ 3.1 Exercise
Figure out what is going on with the second column in dfr dataframe described above and modify the creation of the dataframe so that the second column is stored as a character vector rather than a factor. Hint: Check the help for data.frame to find an argument that turns off the factor conversion.
-
-
+
dfr <- data.frame(vector1, vector2, vector3, stringsAsFactors = FALSE)
str(dfr)
## 'data.frame': 10 obs. of 3 variables:
## $ vector1: int 1 2 3 4 5 6 7 8 9 10
## $ vector2: chr "a" "b" "c" "d" ...
-## $ vector3: num -3.673 -14.961 -20.694 15.702 0.104 ...
+## $ vector3: num -10.9 -9.5 3.84 -10.46 12.75 ...
- One can select columns from a data frame using either the name or the position. Use both methods to print the last two columns from the dfr data frame.
-
-
+
dfr[,2:3]
dfr[,c("vector2", "vector3")]
-## vector2 vector3
-## 1 a -3.6731018
-## 2 b -14.9606909
-## 3 c -20.6937346
-## 4 d 15.7020847
-## 5 e 0.1044213
-## 6 f 1.4622225
-## 7 g 13.5802968
-## 8 h 18.7472758
-## 9 i -5.9545288
-## 10 j -7.9457016
-## vector2 vector3
-## 1 a -3.6731018
-## 2 b -14.9606909
-## 3 c -20.6937346
-## 4 d 15.7020847
-## 5 e 0.1044213
-## 6 f 1.4622225
-## 7 g 13.5802968
-## 8 h 18.7472758
-## 9 i -5.9545288
-## 10 j -7.9457016
+## vector2 vector3
+## 1 a -10.901683
+## 2 b -9.499812
+## 3 c 3.842289
+## 4 d -10.462964
+## 5 e 12.752113
+## 6 f -11.463670
+## 7 g 3.925227
+## 8 h 24.011192
+## 9 i -2.241737
+## 10 j 4.802361
+## vector2 vector3
+## 1 a -10.901683
+## 2 b -9.499812
+## 3 c 3.842289
+## 4 d -10.462964
+## 5 e 12.752113
+## 6 f -11.463670
+## 7 g 3.925227
+## 8 h 24.011192
+## 9 i -2.241737
+## 10 j 4.802361
- Print all letters in the vector2 column of the data frame where the vector3 column has a positive value.
-
-
+
dfr[dfr$vector3>0,2]
dfr$vector2[dfr$vector3>0]
-## [1] "d" "e" "f" "g" "h"
-## [1] "d" "e" "f" "g" "h"
+## [1] "c" "e" "g" "h" "j"
+## [1] "c" "e" "g" "h" "j"
- Create a new vector combining the all columns of dfr separated by a underscore.
-
-
+
paste(dfr$vector1, dfr$vector2, dfr$vector3, sep = "_")
-## [1] "1_a_-3.67310183964819" "2_b_-14.9606908784505" "3_c_-20.693734567916"
-## [4] "4_d_15.7020846777396" "5_e_0.104421342925342" "6_f_1.46222251410756"
-## [7] "7_g_13.5802967890314" "8_h_18.7472757525284" "9_i_-5.95452880120009"
-## [10] "10_j_-7.94570155184973"
+## [1] "1_a_-10.9016825376144" "2_b_-9.49981214747461" "3_c_3.8422894323332"
+## [4] "4_d_-10.4629640340051" "5_e_12.752112945608" "6_f_-11.4636702975827"
+## [7] "7_g_3.92522673996526" "8_h_24.0111919155462" "9_i_-2.24173696275685"
+## [10] "10_j_4.80236116240453"
- There is a dataframe of car information that comes with the base installation of R. Have a look at this data by typing
mtcars. How many rows and columns does it have?
-
-
+
dim(mtcars)
ncol(mtcars)
@@ -628,10 +628,10 @@ 3.1 Exercise
Re-arrange the row names of this dataframe and save as a vector.
-
-
+
car.names <- sample(row.names(mtcars))
@@ -640,58 +640,58 @@ 3.1 Exercise
Create a dataframe containing the vector from the previous question and two vectors with random numbers named random1 and random2.
-
-
+
random1 <- rnorm(length(car.names))
random2 <- rnorm(length(car.names))
mtcars2 <- data.frame(car.names, random1, random2)
mtcars2
-## car.names random1 random2
-## 1 Cadillac Fleetwood -1.44813327 0.667170062
-## 2 Dodge Challenger 0.96631403 2.008901916
-## 3 Lincoln Continental -1.24482608 1.234904817
-## 4 Ferrari Dino 0.32215738 0.557701150
-## 5 Mazda RX4 0.68248952 -0.256977364
-## 6 Hornet Sportabout -1.36452485 -0.122377111
-## 7 Hornet 4 Drive 0.14282002 1.151213614
-## 8 Merc 280C -0.74864226 -0.572289892
-## 9 Porsche 914-2 -0.42486810 0.469073416
-## 10 Toyota Corolla 0.33444879 -0.006593141
-## 11 Lotus Europa -0.09669209 0.845589521
-## 12 Fiat X1-9 0.04680125 1.296786601
-## 13 Duster 360 0.25989000 -1.868213411
-## 14 Merc 450SE 0.08523512 -1.131824480
-## 15 Datsun 710 -0.61680456 0.990348348
-## 16 Toyota Corona 0.64418868 -1.429888072
-## 17 Mazda RX4 Wag -0.42520871 -1.901486559
-## 18 AMC Javelin 0.31805239 0.244901805
-## 19 Merc 450SL 0.52704018 0.565871001
-## 20 Maserati Bora -0.78967614 -1.204599521
-## 21 Merc 450SLC -0.46386014 1.271466484
-## 22 Valiant -0.97997104 0.700639409
-## 23 Camaro Z28 -1.35616513 -0.668927045
-## 24 Merc 240D -0.54350915 0.559449212
-## 25 Volvo 142E -0.23098041 -0.379186839
-## 26 Chrysler Imperial -0.39391475 -1.135718869
-## 27 Merc 230 0.11678336 -1.160498175
-## 28 Fiat 128 1.46967913 0.225667450
-## 29 Honda Civic -1.53703259 0.302538307
-## 30 Pontiac Firebird 0.81357753 0.123373178
-## 31 Ford Pantera L 1.77159819 -1.797125812
-## 32 Merc 280 -0.87424899 1.542585670
+## car.names random1 random2
+## 1 Duster 360 -0.51963778 -2.0976367
+## 2 Porsche 914-2 -0.83836114 -0.9760807
+## 3 Lincoln Continental 2.23228617 -0.2299046
+## 4 Dodge Challenger 0.57749968 -0.9185632
+## 5 Honda Civic -0.56946337 -0.6738820
+## 6 Merc 450SE -0.56754256 -0.1971689
+## 7 Chrysler Imperial 0.00891405 0.1225101
+## 8 Hornet Sportabout 0.21089074 1.5508003
+## 9 Ferrari Dino -0.79289084 0.3598078
+## 10 Ford Pantera L -1.28226974 0.4235203
+## 11 Valiant 1.00974593 0.8587322
+## 12 Fiat X1-9 -0.84624097 -0.5940864
+## 13 Merc 240D -0.12018175 -0.1634045
+## 14 Merc 230 -0.92161299 -0.3301632
+## 15 Volvo 142E 0.61962166 1.6107189
+## 16 Merc 280 -0.55260068 -1.0145372
+## 17 Hornet 4 Drive 0.16531456 -0.8925392
+## 18 Mazda RX4 Wag 0.17341684 0.1038194
+## 19 Toyota Corona -0.78093402 -0.3310758
+## 20 Merc 280C -1.91560657 -1.2993199
+## 21 Toyota Corolla -1.59888672 -0.5347388
+## 22 Merc 450SL -0.17677231 -0.5140271
+## 23 Fiat 128 -0.49031051 -0.0412338
+## 24 Cadillac Fleetwood 0.10629939 0.1013671
+## 25 Merc 450SLC -0.38067489 1.0606843
+## 26 Datsun 710 0.28154234 0.2470850
+## 27 Pontiac Firebird -0.82031740 -0.1619482
+## 28 Camaro Z28 1.97980384 0.6128899
+## 29 Mazda RX4 -1.13884251 0.3669082
+## 30 AMC Javelin -0.23773187 0.4884641
+## 31 Maserati Bora -0.37314495 -0.3861996
+## 32 Lotus Europa -0.66125625 1.3205477
- Now you have two dataframes that both contains information on a set of cars. A collaborator asks you to create a new dataframe with all this information combined. Create a merged dataframe ensuring that rows match correctly.
-
-
+
mt.merged <- merge(mtcars, mtcars2, by.x = "row.names", by.y = "car.names")
mt.merged
@@ -728,67 +728,67 @@ 3.1 Exercise
## 30 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## 31 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## 32 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
-## random1 random2
-## 1 0.31805239 0.244901805
-## 2 -1.44813327 0.667170062
-## 3 -1.35616513 -0.668927045
-## 4 -0.39391475 -1.135718869
-## 5 -0.61680456 0.990348348
-## 6 0.96631403 2.008901916
-## 7 0.25989000 -1.868213411
-## 8 0.32215738 0.557701150
-## 9 1.46967913 0.225667450
-## 10 0.04680125 1.296786601
-## 11 1.77159819 -1.797125812
-## 12 -1.53703259 0.302538307
-## 13 0.14282002 1.151213614
-## 14 -1.36452485 -0.122377111
-## 15 -1.24482608 1.234904817
-## 16 -0.09669209 0.845589521
-## 17 -0.78967614 -1.204599521
-## 18 0.68248952 -0.256977364
-## 19 -0.42520871 -1.901486559
-## 20 0.11678336 -1.160498175
-## 21 -0.54350915 0.559449212
-## 22 -0.87424899 1.542585670
-## 23 -0.74864226 -0.572289892
-## 24 0.08523512 -1.131824480
-## 25 0.52704018 0.565871001
-## 26 -0.46386014 1.271466484
-## 27 0.81357753 0.123373178
-## 28 -0.42486810 0.469073416
-## 29 0.33444879 -0.006593141
-## 30 0.64418868 -1.429888072
-## 31 -0.97997104 0.700639409
-## 32 -0.23098041 -0.379186839
+## random1 random2
+## 1 -0.23773187 0.4884641
+## 2 0.10629939 0.1013671
+## 3 1.97980384 0.6128899
+## 4 0.00891405 0.1225101
+## 5 0.28154234 0.2470850
+## 6 0.57749968 -0.9185632
+## 7 -0.51963778 -2.0976367
+## 8 -0.79289084 0.3598078
+## 9 -0.49031051 -0.0412338
+## 10 -0.84624097 -0.5940864
+## 11 -1.28226974 0.4235203
+## 12 -0.56946337 -0.6738820
+## 13 0.16531456 -0.8925392
+## 14 0.21089074 1.5508003
+## 15 2.23228617 -0.2299046
+## 16 -0.66125625 1.3205477
+## 17 -0.37314495 -0.3861996
+## 18 -1.13884251 0.3669082
+## 19 0.17341684 0.1038194
+## 20 -0.92161299 -0.3301632
+## 21 -0.12018175 -0.1634045
+## 22 -0.55260068 -1.0145372
+## 23 -1.91560657 -1.2993199
+## 24 -0.56754256 -0.1971689
+## 25 -0.17677231 -0.5140271
+## 26 -0.38067489 1.0606843
+## 27 -0.82031740 -0.1619482
+## 28 -0.83836114 -0.9760807
+## 29 -1.59888672 -0.5347388
+## 30 -0.78093402 -0.3310758
+## 31 1.00974593 0.8587322
+## 32 0.61962166 1.6107189
- Calculate the mean value for the two columns that you added to the mtcars data frame. Check out the function
colMeans().
-
-
+
colMeans(mtcars2[, c("random1", "random2")])
## random1 random2
-## -0.15743696 0.03507736
+## -0.25687327 -0.06652045
Try to modify so you get the mean by cylinder instead. Check out the function aggregate().
-
-
+
aggregate(mtcars2$random1, by=list(mtcars$cyl), FUN=mean)
-## Group.1 x
-## 1 4 -0.22804376
-## 2 6 -0.06755036
-## 3 8 -0.14690349
+## Group.1 x
+## 1 4 -0.1538138
+## 2 6 -0.2654711
+## 3 8 -0.3335497
@@ -851,10 +851,10 @@ 4.1 Exercise
Create a list containing 1 character vector, a numeric vector, a character matrix.
-
-
+
list.2 <- list(vec1 = c("hi", "ho", "merry", "christmas"),
vec2 = 4:19,
@@ -880,10 +880,10 @@ 4.1 Exercise
dfr <- data.frame(letters, LETTERS, letters == LETTERS)
Add this dataframe to the list created above.
-
-
+
list.2[[4]] <- dfr
@@ -892,10 +892,10 @@ 4.1 Exercise
Remove the the second entry in your list.
-
-
+
list.2[-2]
## $vec1
@@ -942,10 +942,10 @@ 4.1 Exercise
Create a new list that contain 20 sublists, with each entry holding a numeric vector.
-
-
+
vec1 <- rnorm(1000)
list.a <- split(vec1, 1:20)
@@ -955,10 +955,10 @@ 4.1 Exercise
How long is your list, and how long are each of the vectors that are part of the list?
-
-
+
length(list.a)
lapply(list.a, FUN = "length")
@@ -1028,121 +1028,121 @@ 4.1 Exercise
Figure out what the main differences are between the function lapply() and sapply are and use both of them with the function summary on your newly created list. What are the pros and cons of the two approaches to calculate the same summary statistics?
-
-
+
lapply(X = list.a, FUN = "summary")
sapply(X = list.a, FUN = "summary")
## $`1`
-## Min. 1st Qu. Median Mean 3rd Qu. Max.
-## -1.766054 -0.681966 -0.008311 0.087600 0.830620 3.315326
+## Min. 1st Qu. Median Mean 3rd Qu. Max.
+## -2.65715 -0.43213 -0.02789 0.03397 0.44572 1.83699
##
## $`2`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
-## -2.03075 -0.81358 -0.24613 -0.07786 0.66369 2.10973
+## -1.77516 -0.66814 0.02863 -0.13046 0.39037 1.19403
##
## $`3`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
-## -2.17546 -0.83929 -0.10869 -0.06314 0.59801 2.18636
+## -1.93486 -0.59654 0.15336 0.05606 0.69708 2.15068
##
## $`4`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
-## -2.30808 -0.74603 -0.10848 -0.01814 0.55251 1.92269
+## -1.58925 -0.53745 0.15880 0.09996 0.57787 3.03450
##
## $`5`
-## Min. 1st Qu. Median Mean 3rd Qu. Max.
-## -2.5504 -1.0259 -0.2931 -0.2617 0.3798 2.1361
+## Min. 1st Qu. Median Mean 3rd Qu. Max.
+## -1.99516 -0.46280 -0.12086 0.02156 0.65667 1.68003
##
## $`6`
-## Min. 1st Qu. Median Mean 3rd Qu. Max.
-## -3.05613 -0.76552 0.03826 -0.01098 0.83433 1.97229
+## Min. 1st Qu. Median Mean 3rd Qu. Max.
+## -1.904748 -0.535934 0.002088 -0.023797 0.421573 2.242569
##
## $`7`
-## Min. 1st Qu. Median Mean 3rd Qu. Max.
-## -2.3633 -0.4799 0.2227 0.1152 0.8357 2.4224
+## Min. 1st Qu. Median Mean 3rd Qu. Max.
+## -2.04307 -0.73349 -0.08943 -0.10205 0.53890 2.91787
##
## $`8`
-## Min. 1st Qu. Median Mean 3rd Qu. Max.
-## -2.5143047 -0.8263967 -0.1073021 -0.0000911 0.6904748 2.5439957
+## Min. 1st Qu. Median Mean 3rd Qu. Max.
+## -1.3765 -0.1955 0.1387 0.3450 0.5920 2.9896
##
## $`9`
-## Min. 1st Qu. Median Mean 3rd Qu. Max.
-## -1.82496 -0.65804 0.03671 0.03022 0.77678 2.16232
+## Min. 1st Qu. Median Mean 3rd Qu. Max.
+## -2.6595 -0.8450 -0.2505 -0.1827 0.2979 1.9612
##
## $`10`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
-## -2.18713 -0.34877 0.01290 0.05342 0.49224 2.36651
+## -2.11826 -0.66521 -0.16212 -0.07021 0.48821 2.84256
##
## $`11`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
-## -1.90644 -0.46933 0.02577 0.05749 0.60110 2.43533
+## -1.90946 -0.49854 0.08619 0.16520 0.90361 2.44786
##
## $`12`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
-## -1.97763 -0.67915 -0.07988 -0.10761 0.39855 2.10142
+## -2.38377 -0.76251 -0.09665 -0.11372 0.49522 2.08282
##
## $`13`
-## Min. 1st Qu. Median Mean 3rd Qu. Max.
-## -2.142916 -0.699941 0.057180 0.004829 0.654341 2.758331
+## Min. 1st Qu. Median Mean 3rd Qu. Max.
+## -3.18173 -0.63043 -0.04757 -0.03419 0.64939 2.94251
##
## $`14`
-## Min. 1st Qu. Median Mean 3rd Qu. Max.
-## -3.37926 -0.93112 0.13271 0.09597 1.03250 3.10498
+## Min. 1st Qu. Median Mean 3rd Qu. Max.
+## -2.125521 -0.608177 0.002613 -0.044199 0.466461 2.298878
##
## $`15`
-## Min. 1st Qu. Median Mean 3rd Qu. Max.
-## -1.3363 -0.4849 0.0431 0.1765 0.6987 2.0955
+## Min. 1st Qu. Median Mean 3rd Qu. Max.
+## -1.87155 -0.60158 -0.05991 -0.03074 0.75115 1.54973
##
## $`16`
-## Min. 1st Qu. Median Mean 3rd Qu. Max.
-## -3.69483 -0.59866 -0.14105 0.02977 0.69764 2.41164
+## Min. 1st Qu. Median Mean 3rd Qu. Max.
+## -2.3666 -0.8189 -0.1627 -0.1134 0.5041 2.3821
##
## $`17`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
-## -2.6063 -0.4881 0.1792 0.1992 0.9987 2.3219
+## -2.1244 -1.1477 -0.2055 -0.3112 0.3473 1.4335
##
## $`18`
-## Min. 1st Qu. Median Mean 3rd Qu. Max.
-## -1.946909 -0.442994 0.009251 0.179721 1.059416 2.168144
+## Min. 1st Qu. Median Mean 3rd Qu. Max.
+## -2.05235 -0.65864 0.12911 -0.04662 0.60199 1.81103
##
## $`19`
## Min. 1st Qu. Median Mean 3rd Qu. Max.
-## -2.36211 -0.56554 0.17238 0.04702 0.54974 2.38857
+## -3.88083 -0.90693 -0.09098 -0.15290 0.68146 1.98788
##
## $`20`
-## Min. 1st Qu. Median Mean 3rd Qu. Max.
-## -1.75336 -0.38955 0.09874 0.12315 0.60477 2.92935
+## Min. 1st Qu. Median Mean 3rd Qu. Max.
+## -2.810970 -0.707554 0.007753 -0.098714 0.486686 2.796566
##
-## 1 2 3 4 5 6
-## Min. -1.766054378 -2.03074546 -2.17545928 -2.30807938 -2.5503676 -3.05612550
-## 1st Qu. -0.681966235 -0.81358378 -0.83929144 -0.74602611 -1.0259090 -0.76552235
-## Median -0.008310549 -0.24612555 -0.10868902 -0.10848198 -0.2930507 0.03825579
-## Mean 0.087599754 -0.07785591 -0.06314266 -0.01814389 -0.2617145 -0.01098460
-## 3rd Qu. 0.830620345 0.66369467 0.59800900 0.55251483 0.3798131 0.83433066
-## Max. 3.315326351 2.10972946 2.18635670 1.92268554 2.1361147 1.97228802
-## 7 8 9 10 11 12
-## Min. -2.3632885 -2.514304704 -1.8249573 -2.18712844 -1.90643663 -1.97763393
-## 1st Qu. -0.4798859 -0.826396709 -0.6580380 -0.34877151 -0.46933008 -0.67915067
-## Median 0.2226939 -0.107302133 0.0367091 0.01290206 0.02577231 -0.07987882
-## Mean 0.1152315 -0.000091117 0.0302250 0.05341740 0.05748696 -0.10760926
-## 3rd Qu. 0.8356591 0.690474825 0.7767807 0.49223522 0.60109835 0.39854692
-## Max. 2.4224288 2.543995694 2.1623157 2.36650924 2.43533106 2.10141959
-## 13 14 15 16 17
-## Min. -2.142915847 -3.37926274 -1.33634063 -3.69483183 -2.6062575
-## 1st Qu. -0.699941237 -0.93111644 -0.48486624 -0.59866360 -0.4880606
-## Median 0.057179740 0.13271128 0.04309686 -0.14105234 0.1792345
-## Mean 0.004828511 0.09596553 0.17650953 0.02977361 0.1991963
-## 3rd Qu. 0.654341123 1.03249907 0.69869783 0.69764429 0.9986868
-## Max. 2.758330498 3.10498354 2.09553220 2.41163781 2.3219140
-## 18 19 20
-## Min. -1.946908663 -2.36211477 -1.75335569
-## 1st Qu. -0.442993984 -0.56554453 -0.38955334
-## Median 0.009250932 0.17238316 0.09873916
-## Mean 0.179721377 0.04702163 0.12314927
-## 3rd Qu. 1.059416465 0.54974325 0.60477201
-## Max. 2.168144055 2.38856689 2.92935257
+## 1 2 3 4 5 6
+## Min. -2.65715230 -1.77516438 -1.93485705 -1.58925339 -1.9951561 -1.904748128
+## 1st Qu. -0.43213493 -0.66814062 -0.59653839 -0.53745178 -0.4628037 -0.535934459
+## Median -0.02788531 0.02863069 0.15335571 0.15879776 -0.1208648 0.002088196
+## Mean 0.03397383 -0.13045737 0.05605841 0.09996303 0.0215649 -0.023796599
+## 3rd Qu. 0.44572123 0.39037353 0.69707675 0.57787257 0.6566727 0.421573510
+## Max. 1.83699359 1.19403349 2.15068058 3.03450103 1.6800308 2.242568971
+## 7 8 9 10 11 12
+## Min. -2.04307144 -1.3764763 -2.6594711 -2.11826172 -1.90945762 -2.38377086
+## 1st Qu. -0.73349190 -0.1954625 -0.8449523 -0.66521245 -0.49854426 -0.76251253
+## Median -0.08943154 0.1387238 -0.2505236 -0.16211731 0.08619236 -0.09665442
+## Mean -0.10205195 0.3450137 -0.1827192 -0.07020622 0.16519536 -0.11372100
+## 3rd Qu. 0.53889665 0.5920247 0.2978909 0.48820819 0.90361065 0.49522021
+## Max. 2.91786665 2.9896040 1.9612242 2.84256225 2.44786359 2.08282481
+## 13 14 15 16 17 18
+## Min. -3.18172685 -2.125521226 -1.87155090 -2.3665691 -2.1243513 -2.05235183
+## 1st Qu. -0.63043547 -0.608177346 -0.60157596 -0.8188821 -1.1476649 -0.65864364
+## Median -0.04757012 0.002613426 -0.05991466 -0.1626641 -0.2055332 0.12911330
+## Mean -0.03418954 -0.044198574 -0.03074161 -0.1134420 -0.3112278 -0.04661865
+## 3rd Qu. 0.64938918 0.466460629 0.75114710 0.5040776 0.3472937 0.60199068
+## Max. 2.94251143 2.298878053 1.54972870 2.3821001 1.4335347 1.81103353
+## 19 20
+## Min. -3.88082468 -2.81097029
+## 1st Qu. -0.90693431 -0.70755417
+## Median -0.09097676 0.00775270
+## Mean -0.15289995 -0.09871364
+## 3rd Qu. 0.68145534 0.48668644
+## Max. 1.98788284 2.79656635
diff --git a/2310-canvas/lab_datatypes.html b/2310-canvas/lab_datatypes.html
index 276f893..fe9f04c 100644
--- a/2310-canvas/lab_datatypes.html
+++ b/2310-canvas/lab_datatypes.html
@@ -271,16 +271,16 @@ 1.1 Data types
Numeric = Any number that is not a complex number.
Character = Text
-In many cases the mode of on entry is determined by the content so if you save the value 5.1 as a variable in R, the variable will automatically be recognised as numeric. If you instead have a text string like “hello world” it will have the mode character. Below you will also see examples of how you can specify the mode and not rely on R inferring the right mode based on content.
+In many cases the mode of an entry is determined by the content. So if you save the value 5.1 as a variable in R, the variable will automatically be recognised as numeric. If you instead have a text string like “hello world” it will have the mode character. Below you will also see examples of how you can specify the mode and not rely on R inferring the right mode based on content.
2 Exercises
In all exercises during this course, it is important that you try to figure out what the expected result would be, prior to running the commands. You should then verify that this will indeed be the
-result by running the command. In case there is a discrepency between your expectations and the actual output make sure you understand why before you move forward. If you cannot figure out
-how to, or which command to run you can click the key to reveal example code including expected output. If you are trying out things on your own have a hard time understanding what is going on, ask the TAs or your someone sitting next to you who might have wrapped their head around the issue.
-Also note that in many cases there multiple solutions that solve the problem equally well.
+result by running the command. In case there is a discrepency between your expectations and the actual output make sure you understand why, before you move forward. If you cannot figure out
+how to, or which command to run you can click the key to reveal example code including expected output. If you are trying out things on your own have a hard time understanding what is going on, ask the TAs or someone sitting next to you who might have wrapped their head around the issue.
+Also note that in many cases there are multiple solutions that solve the problem equally well.
We do recommend to write all code in a Rmarkdown document in R-studio as that will at the end of the course be your own R tutorial with comments and code solutions.
@@ -305,10 +305,10 @@ 2.1 Working with variables
Create variables var1 and var2 and initialize them with two integers of choice.
-
-
+
var1 <- 11
var2 <- 34
@@ -318,10 +318,10 @@ 2.1 Working with variables
Add the two variables and save them as a new variable named var3 and print the result.
-
-
+
var3 <- var1 + var2
var3
@@ -332,10 +332,10 @@ 2.1 Working with variables
Check the class, mode, and type for var1, var2, var3 and π (is found under the variable name pi in R)
-
-
+
mode(var1)
class(var1)
@@ -346,10 +346,10 @@ 2.1 Working with variables
-
-
+
mode(pi)
class(pi)
@@ -363,10 +363,10 @@ 2.1 Working with variables
Create two character variables containing a text of choice. Check the mode, class, and type of the first one.
-
-
+
text1 <- "test1"
text2 <- "test2"
@@ -381,10 +381,10 @@ 2.1 Working with variables
Add var1 to it. What is the result and why?
-
-
+
text1+var1
## Error in text1 + var1: non-numeric argument to binary operator
@@ -394,10 +394,10 @@ 2.1 Working with variables
Convert var3 to an integer, cast an integer variable to double, cast a string to a double.
-
-
+
as.integer(var3)
i <- 175
@@ -412,10 +412,10 @@ 2.1 Working with variables
Report floor and ceiling of π and round π to 3 decimal places.
-
-
+
floor(pi)
ceiling(pi)
@@ -429,10 +429,10 @@ 2.1 Working with variables
Is floor of π an integer?
-
-
+
is.integer(floor(pi))
## [1] FALSE
@@ -442,10 +442,10 @@ 2.1 Working with variables
Treat "3.56437" string as number.
-
-
+
as.numeric('3.56437')
## [1] 3.56437
@@ -455,51 +455,23 @@ 2.1 Working with variables
Divide ∞ by - ∞
-
-
+
Inf/-Inf
## [1] NaN
-- Create two freely chosen complex numbers.
-
-
-- Check that they are complex indeed.
-- Add, multiply and divide one by another.
-- Add an integer to their sum.
-
-
-
-
-
-
-c1 <- 23 + 4i
-c2 <- -15 - 7i
-is.complex(c1)
-is.complex(c2)
-c1 + c2
-c1 / c2
-c1 + c2 + 7
-## [1] TRUE
-## [1] TRUE
-## [1] 8-3i
-## [1] -1.361314+0.368613i
-## [1] 15-3i
-
-
-
- Print a truth table for OR (for three distinct logical values). Read about truth tables here.
-
-
+
x <- c(NA, FALSE, TRUE)
names(x) <- as.character(x)
@@ -510,14 +482,14 @@ 2.1 Working with variables
## TRUE TRUE TRUE TRUE
-
+
- Multiply a logical TRUE by a logical FALSE. Rise the logical true to the 7-th power.
-
-
+
TRUE * FALSE
T^7
@@ -525,7 +497,7 @@ 2.1 Working with variables
## [1] 1
-
+
- Create two character variables containing two verses of your favorite song.
@@ -540,10 +512,10 @@ 2.1 Working with variables
- Extract substring starting at the 5th character and 5 characters long.
-
-
+
line1 <- "Hello darkness my old friend"
line2 <- "I've come to talk to you again"
@@ -563,18 +535,28 @@ 2.1 Working with variables
2.2 R Environment
-- Get help for the t.test, table, locator and identify functions,
-- Check for all occurences of fisher.test in the docs,
-- Which package contains the plot.ecdf function. What does it do?
-- Find package ‘reshape’-related questions on StackOverflow,
-- Search on the internet on how to load an XML file into R,
-- Install the ‘cgmisc’ package from GitHub,
-- Look up the ‘cgmisc’ vignette,
-- See all the demos available for you and run one you like,
-- Run examples for the fisher.test,
-- Check out CRANs view for genetics,
-- Install a CRAN package of choice,
-- Install the R-Forge package ‘bigRR’
+- Get help for the t.test, table, locator and identify functions.
+
+- Check for all occurrences of select in the help.
+
+- Which package contains the plot.ecdf function. What does it do?
+
+- Find package ‘reshape’-related questions on StackOverflow.
+
+- Search on the internet on how to load an XML file into R.
+
+- Install the ‘dplyr’ package from GitHub.
+
+- Look up the ‘dplyr’ vignette.
+
+- See all the demos available for you and run one you like.
+
+- Run examples for the fisher.test.
+
+- Check out CRANs view for genetics.
+
+- Install a CRAN package of choice.
+- Install the R-Forge package ‘bigRR’ or ‘Matrix’.
diff --git a/2310-canvas/lab_ggplot2_files/figure-html/unnamed-chunk-47-1.png b/2310-canvas/lab_ggplot2_files/figure-html/unnamed-chunk-47-1.png
index ce185bd..3b946cd 100644
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diff --git a/2310-canvas/lab_graphics.html b/2310-canvas/lab_graphics.html
index 5c449a4..7438bcd 100644
--- a/2310-canvas/lab_graphics.html
+++ b/2310-canvas/lab_graphics.html
@@ -405,36 +405,37 @@ 4.1 Prepare input data
4.2 Prepare reference data
-Go to WHO website (http://www.who.int/childgrowth/standards/en/) and find out the link to the dataset of your concern, e.g. Weight for age, percentiles for girls have the following address: http://www.who.int/entity/childgrowth/standards/tab_wfa_girls_p_0_5.txt
-Load the data using URL from the previous point and the read.table() function.
-ERROR: The URL below is broken therefore everything downstream is set to eval=FALSE. This needs to be fixed.
-uri <- "http://www.who.int/entity/childgrowth/standards/tab_wfa_girls_p_0_5.txt"
-#uri <- "http://www.who.int/entity/childgrowth/standards/second_set/tab_hcfa_girls_p_0_5.txt"
-#uri <- "http://www.who.int/entity/childgrowth/standards/tab_lhfa_girls_p_0_2.txt"
-myData <-read.table(uri, header=T, sep='\t')
+Go to WHO website (http://www.who.int/childgrowth/standards/en/) and find out the link to the dataset of your concern, e.g. Weight for age, percentiles for girls have the following address: http://www.who.int/entity/childgrowth/standards/wfa_girls_p_0_5.xlsx
+library(readxl)
+uri <- "https://cdn.who.int/media/docs/default-source/child-growth/child-growth-standards/indicators/weight-for-age/tab_wfa_girls_p_0_5.xlsx?sfvrsn=666fe445_7"
+local_file_path <- "data/wfa_girls_p_0_5.xlsx" ## give the local path of the downloaded file
+download.file(url = uri, destfile = local_file_path, mode = "wb")
+myData <-read_excel(local_file_path)
4.3 Built empty plot
Create an empty plot to show your and WHO data,
-
-
+
plot(1, xlim=c(0, max(myData$Month)), type='n', bty='n', ylim=c(0, max(myData[,c(5:19)])), las=1, xlab='Month', ylab='kg')
grid()
+
4.4 Plot reference data
Plot WHO mean and percentiles: P25, P75, P0.1 and P99.9, use different colors and line types to make the plot pretty.
+
-
-
+
lines(myData$M, col='grey', lty=1)
lines(myData$P25, col='blue', lty=2)
@@ -447,11 +448,12 @@ 4.4 Plot reference data
4.5 Plot input data
Plot your data on top of the percentiles, mind the units so that they match with the WHO ones
+
-
-
+
points(xpoints, weight/1000, pch=3, type='l', cex=.5)
points(xpoints, weight/1000, pch=3, type='p', cex=.5)
@@ -461,11 +463,12 @@ 4.5 Plot input data
4.6 Add annotation
Add descriptions of the confidence lines in the margins
+
-
-
+
mtext(text = c('P0.1','P25','P75','P99.9'), side = 4, at=myData[dim(myData)[1], c('P01','P25','P75','P999')], las=1, cex=.8)
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index 7f84319..0721639 100644
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diff --git a/2310-canvas/lab_loadingdata.html b/2310-canvas/lab_loadingdata.html
index ad8a935..aee5077 100644
--- a/2310-canvas/lab_loadingdata.html
+++ b/2310-canvas/lab_loadingdata.html
@@ -267,10 +267,10 @@ 2 scan()
words <- scan(what=character())
We will read in this book chapter. Read the manual for scan and read the text file named book_chapter.txt into R, first as vector and then as a list, with each word in the chapter saved as a entry in the vector or as a single vector in a list.
-
-
+
shelley.vec <- scan(file="https://raw.githubusercontent.com/NBISweden/workshop-r/master/data/lab_loadingdata/book_chapter.txt", what=character())
str(shelley.vec)
@@ -286,14 +286,14 @@ 2 scan()
- Identify the longest word in your vector.
-
-
+
sort(nchar(shelley.vec), decreasing=TRUE)
-which(nchar(shelley.vec) == max(nchar(shelley.vec)))
-shelley.vec[381]
+longest_wotd_index <- which(nchar(shelley.vec) == max(nchar(shelley.vec)))
+shelley.vec[longest_wotd_index]
## [1] 690 12 11 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [19] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [37] 9 9 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
@@ -318,7 +318,6 @@ 2 scan()
## [379] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [397] 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [415] 1 1 1 1 1 1
-## [1] 381
## [1] "By the sacred earth on which I kneel, by the shades that wander near me, by the deep and eternal grief that I feel, I swear; and by thee, O Night, and the spirits that preside over thee, to pursue the daemon who caused this misery, until he or I shall perish in mortal conflict. For this purpose I will preserve my life; to execute this dear revenge will I again behold the sun and tread the green herbage of earth, which otherwise should vanish from my eyes forever. And I call on you, spirits of the dead, and on you, wandering ministers of vengeance, to aid and conduct me in my work. Let the cursed and hellish monster drink deep of agony; let him feel the despair that now torments me."
@@ -326,10 +325,10 @@ 2 scan()
- Go back and fix the way you read in the text to make sure that you get a vector with all words in chapter as individual entries also filter any non-letter characters and now identify the longest word.
-
-
+
shelley.vec2 <- scan(file="https://raw.githubusercontent.com/NBISweden/workshop-r/master/data/lab_loadingdata/book_chapter.txt", what='character', sep=' ', quote=NULL)
shelley.filt2 <- gsub(pattern='[^[:alnum:] ]', replacement="", x=shelley.vec2)
@@ -342,13 +341,13 @@ 2 scan()
3 read.table()
-This is the by far most common way to get data into R. As the function creates a data frame at import it will only work for data set that fits those criteria, meaning that the data needs to have a set of columns of equal length that are separated with a common string eg. tab, comma, semicolon etc.
-In this code block with first import the data from normalized.txt and accept the defaults for all other arguments in the function. With this settings R will read it as a tab delimited file and will use the first row of the data as colnames (header) and the first column as rownames.
+This is by far the most common way to get data into R. As the function creates a data frame at import it will only work for data set that fits those criteria, meaning that the data needs to have a set of columns of equal length that are separated with a common string eg. tab, comma, semicolon etc.
+In this code block with first import the data from normalized.txt and accept the defaults for all other arguments in the function. With this settings R will read it as a tab delimited file and will use the first row of the data as colnames (header) and the first column as rownames.
-
-
+
expr.At <- read.table("https://raw.githubusercontent.com/NBISweden/workshop-r/master/data/lab_loadingdata/normalized.txt")
head(expr.At)
@@ -361,12 +360,12 @@ 3 read.table()
## AT1G01070.1 8.230858 17.871625 14.924906 7.9996663 8.824486 13.8554244
-One does however not have to have all data as a file an the local disk, instead one can read data from online resources. The following command will read in a file from a web server.
+You don’t need to store all data on your local disk; you can simply read it from online resources. The following command will read in a file from a web server.
-
-
+
url <- 'http://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data'
abalone <- read.table(url, header=FALSE , sep=',')
@@ -381,46 +380,39 @@ 3 read.table()
-- Read this example data to R using the
read.table() function. This files consist of gene expression values. Once you have the object in R validate that it looks okay and export it using the write.table function.
+- Read this example data to R using the
read.table() function. This file consists of gene expression values. Once you have the object in R, validate that it looks okay and export it using the write.table function.
-
-
+
-ed <- read.table("https://raw.githubusercontent.com/NBISweden/workshop-r/master/data/lab_loadingdata/example.data", sep=":")
+ed <- read.table("https://raw.githubusercontent.com/NBISweden/workshop-r/master/data/lab_loadingdata/example.data", sep=":", header = T)
head(ed)
str(ed)
-## V1 V2 V3 V4
-## 1 bZIP29_1 bZIP29_2 bZIP29_3 WT_1
-## 2 <NA> 14.1671426761817 12.9727029171751 14.8181737869517
-## 3 1.41723379939617 1.20145379585993 <NA> <NA>
-## 4 41.8629060744716 41.1998530830302 42.6965659118674 37.528635786519
-## 5 102.422396502516 98.3189689612045 92.0684061403395 104.210417827802
-## 6 <NA> 4.00484598619978 3.58953430232514 3.70454344673793
-## V5 V6
-## 1 WT_2 WT_3
-## 2 <NA> <NA>
-## 3 1.096828754225 <NA>
-## 4 34.8492408728761 48.6456870599298
-## 5 97.4183357161658 86.065446336799
-## 6 2.64236018063295 6.27033804098888
-## 'data.frame': 18946 obs. of 6 variables:
-## $ V1: chr "bZIP29_1" NA "1.41723379939617" "41.8629060744716" ...
-## $ V2: chr "bZIP29_2" "14.1671426761817" "1.20145379585993" "41.1998530830302" ...
-## $ V3: chr "bZIP29_3" "12.9727029171751" NA "42.6965659118674" ...
-## $ V4: chr "WT_1" "14.8181737869517" NA "37.528635786519" ...
-## $ V5: chr "WT_2" NA "1.096828754225" "34.8492408728761" ...
-## $ V6: chr "WT_3" NA NA "48.6456870599298" ...
+## bZIP29_1 bZIP29_2 bZIP29_3 WT_1 WT_2 WT_3
+## 1 NA 14.167143 12.972703 14.818174 NA NA
+## 2 1.417234 1.201454 NA NA 1.096829 NA
+## 3 41.862906 41.199853 42.696566 37.528636 34.849241 48.645687
+## 4 102.422397 98.318969 92.068406 104.210418 97.418336 86.065446
+## 5 NA 4.004846 3.589534 3.704543 2.642360 6.270338
+## 6 8.230858 17.871625 14.924906 7.999666 NA NA
+## 'data.frame': 18945 obs. of 6 variables:
+## $ bZIP29_1: num NA 1.42 41.86 102.42 NA ...
+## $ bZIP29_2: num 14.2 1.2 41.2 98.3 4 ...
+## $ bZIP29_3: num 12.97 NA 42.7 92.07 3.59 ...
+## $ WT_1 : num 14.8 NA 37.5 104.2 3.7 ...
+## $ WT_2 : num NA 1.1 34.85 97.42 2.64 ...
+## $ WT_3 : num NA NA 48.65 86.07 6.27 ...
Encode all NA values as “missing”, at export.
-
-
+
write.table(x=ed, na="missing", file="example_mis.data")
@@ -429,30 +421,39 @@ 3 read.table()
- Read in the file you just created and double-check that you have the same data as earlier.
-
-
+
-df.test <- read.table("example_mis.data", na.strings="missing")
+df.test <- read.table("example_mis.data", na.strings="missing", header = T)
- Analysing genome annotation in R using read.table
For this exercise we will load a GTF file into R and calculate some basic summary statistics from the file. In the first part we will use basic manipulations of data frames to extract the information. In the second part you get a try out a library designed to work with annotation data, that stores the information in a more complex format, that allow for easy manipulation and calculation of summaries from genome annotation files.
-For those not familiar with the gtf format it is a file format containing annotation information for a genome. It does not contain the actual DNA sequence of the organism, but instead refers to positions along the genome.
+For those not familiar with the GTF format it is a file format containing annotation information for a genome. It does not contain the actual DNA sequence of the organism, but instead refers to positions along the genome. It is like an address book for location of genes in a genome.
+Note: There are different versions of GTF/GFF format which you can read more in AGAT tutorial.
A valid GTF file should contain the following tab delimited fields (taken from the ensembl home page).
-- seqname - name of the chromosome or scaffold; chromosome names can be given with or without the ‘chr’ prefix.
-- source - name of the program that generated this feature, or the data source (database or project name)
-- feature - feature type name, e.g. Gene, Variation, Similarity
-- start - Start position of the feature, with sequence numbering starting at 1.
-- end - End position of the feature, with sequence numbering starting at 1.
-- score - A floating point value.
-- strand - defined as + (forward) or - (reverse).
-- frame - One of ‘0’, ‘1’ or ‘2’. ‘0’ indicates that the first base of the feature is the first base of a codon, ‘1’ that the second base is the first base of a codon, and so on..
-- attribute - A semicolon-separated list of tag-value pairs, providing additional information about each feature.
+- seqname - Name of the chromosome or scaffold; chromosome names can be given with or without the ‘chr’ prefix
+
+- source - Name of the program that generated this feature, or the data source (database or project name)
+
+- feature - Feature type name, e.g. gene, transcript, exon, CDS, start_codon, end_codon
+
+- start - Start position of the feature, with sequence numbering starting at 1
+
+- end - End position of the feature, with sequence numbering starting at 1
+
+- score - A floating point value
+
+- strand - Defined as + (forward) or - (reverse)
+
+- frame - One of ‘0’, ‘1’ or ‘2’. ‘0’ indicates that the first base of the feature is the first base of a codon, ‘1’ that the second base is the first base of a codon, and so on..
+
+- attribute - A semicolon-separated list of tag-value pairs, providing additional information about each feature
@@ -504,369 +505,244 @@ 3 read.table()
-The last column can contain a large number of attributes that are comma-separated.
-As these files for many organisms are large we will in this exercise use the latest version of Drosophila melanogaster genome annotation available at ftp://ftp.ensembl.org/pub/release-86/gtf/drosophila_melanogaster that is small enough for analysis even on a laptop.
-Download the file named Drosophila_melanogaster.BDGP6.86.gtf.gz to your computer. Unzip this file and keep track of where your store the file.
-With this done read this file into R using the function read.table() and add meaningful column names to the table.
+The last column can contain a large number of attributes that are semicolon-separated.
+As these files for many organisms are large we will, in this exercise, use the latest version of Drosophila melanogaster genome annotation available at https://ftp.ensembl.org/pub/release-110/gtf/drosophila_melanogaster/ that is small enough for analysis even on a laptop.
+Download the file named Drosophila_melanogaster.BDGP6.46.110.gtf.gz to your computer. Unzip this file and keep track of where your store the file.
+With this done, read this file into R using the function read.table() and add meaningful column names to the table.
-
-
+
-d.gtf <- read.table("Drosophila_melanogaster.BDGP6.86.gtf", header=FALSE, comment.char="#", sep="\t")
+d.gtf <- read.table("Drosophila_melanogaster.BDGP6.46.110.gtf", header=FALSE, comment.char="#", sep="\t")
colnames(d.gtf) <- c("Chromosome","Source","Feature", "Start","End","Score","Strand","Frame","Attribute")
Prior to any analysis you should make sure that your attempt to read in the file has worked as expected. This can for example be done by having a look at the dimension of the stored object and making sure that it has the structure you expect.
-
-
+
dim(d.gtf)
str(d.gtf)
-## [1] 538684 9
-## 'data.frame': 538684 obs. of 9 variables:
+## [1] 567802 9
+## 'data.frame': 567802 obs. of 9 variables:
## $ Chromosome: chr "3R" "3R" "3R" "3R" ...
## $ Source : chr "FlyBase" "FlyBase" "FlyBase" "FlyBase" ...
-## $ Feature : chr "gene" "transcript" "exon" "gene" ...
-## $ Start : int 722370 722370 722370 835381 835381 835381 835381 835381 869486 869486 ...
-## $ End : int 722621 722621 722621 2503907 2503907 835491 835491 835383 869548 869548 ...
+## $ Feature : chr "gene" "transcript" "exon" "exon" ...
+## $ Start : int 17750129 17750129 17758709 17757024 17757024 17757559 17755182 17755182 17754976 17754976 ...
+## $ End : int 17763188 17758978 17758978 17757709 17757561 17757561 17755284 17755284 17755101 17755101 ...
## $ Score : chr "." "." "." "." ...
-## $ Strand : chr "-" "-" "-" "+" ...
+## $ Strand : chr "-" "-" "-" "-" ...
## $ Frame : chr "." "." "." "." ...
-## $ Attribute : chr "gene_id FBgn0085804; gene_name CR41571; gene_source FlyBase; gene_biotype pseudogene;" "gene_id FBgn0085804; transcript_id FBtr0114258; gene_name CR41571; gene_source FlyBase; gene_biotype pseudogene"| __truncated__ "gene_id FBgn0085804; transcript_id FBtr0114258; exon_number 1; gene_name CR41571; gene_source FlyBase; gene_bio"| __truncated__ "gene_id FBgn0267431; gene_name CG45784; gene_source FlyBase; gene_biotype protein_coding;" ...
+## $ Attribute : chr "gene_id FBgn0038542; gene_name TyrR; gene_source FlyBase; gene_biotype protein_coding;" "gene_id FBgn0038542; transcript_id FBtr0344474; gene_name TyrR; gene_source FlyBase; gene_biotype protein_codin"| __truncated__ "gene_id FBgn0038542; transcript_id FBtr0344474; exon_number 1; gene_name TyrR; gene_source FlyBase; gene_biotyp"| __truncated__ "gene_id FBgn0038542; transcript_id FBtr0344474; exon_number 2; gene_name TyrR; gene_source FlyBase; gene_biotyp"| __truncated__ ...
- How many chromosome names can be found in the annotation file?
-
-
+
-levels(d.gtf$Chromosome)
-## NULL
+levels(as.factor(d.gtf$Chromosome))
+## [1] "211000022278279" "211000022278436"
+## [3] "211000022278449" "211000022278760"
+## [5] "211000022278978" "211000022279165"
+## [7] "211000022279188" "211000022279264"
+## [9] "211000022279392" "211000022279681"
+## [11] "211000022280328" "211000022280341"
+## [13] "211000022280347" "211000022280481"
+## [15] "211000022280494" "211000022280703"
+## [17] "2L" "2R"
+## [19] "3L" "3R"
+## [21] "4" "mitochondrion_genome"
+## [23] "rDNA" "Unmapped_Scaffold_4_D1555_D1692"
+## [25] "Unmapped_Scaffold_60_D1601" "Unmapped_Scaffold_8_D1580_D1567"
+## [27] "X" "Y"
+## [29] "Y_mapped_Scaffold_9_D1573"
-- How many exons is there in total and per chromosome?
+- How many exons is there in total and per chromosome? (hint: first extract lines that have
feature == 'exon')
-
-
+
-aggregate(d.gtf$Feature, by=list(d.gtf$Chromosome), summary)
-## Group.1 x.Length x.Class x.Mode
-## 1 211000022278158 3 character character
-## 2 211000022278279 3 character character
-## 3 211000022278282 3 character character
-## 4 211000022278298 3 character character
-## 5 211000022278307 3 character character
-## 6 211000022278309 3 character character
-## 7 211000022278436 3 character character
-## 8 211000022278449 4 character character
-## 9 211000022278498 3 character character
-## 10 211000022278522 3 character character
-## 11 211000022278603 3 character character
-## 12 211000022278604 3 character character
-## 13 211000022278664 3 character character
-## 14 211000022278724 3 character character
-## 15 211000022278750 3 character character
-## 16 211000022278760 7 character character
-## 17 211000022278875 3 character character
-## 18 211000022278877 3 character character
-## 19 211000022278878 3 character character
-## 20 211000022278879 3 character character
-## 21 211000022278880 3 character character
-## 22 211000022278985 3 character character
-## 23 211000022279055 3 character character
-## 24 211000022279108 3 character character
-## 25 211000022279132 3 character character
-## 26 211000022279134 3 character character
-## 27 211000022279165 3 character character
-## 28 211000022279188 12 character character
-## 29 211000022279222 3 character character
-## 30 211000022279264 3 character character
-## 31 211000022279342 3 character character
-## 32 211000022279392 3 character character
-## 33 211000022279446 3 character character
-## 34 211000022279528 3 character character
-## 35 211000022279529 3 character character
-## 36 211000022279531 3 character character
-## 37 211000022279555 3 character character
-## 38 211000022279681 3 character character
-## 39 211000022279708 3 character character
-## 40 211000022280133 3 character character
-## 41 211000022280328 14 character character
-## 42 211000022280341 3 character character
-## 43 211000022280347 3 character character
-## 44 211000022280481 3 character character
-## 45 211000022280494 6 character character
-## 46 211000022280645 3 character character
-## 47 211000022280703 3 character character
-## 48 2L 96740 character character
-## 49 2R 110108 character character
-## 50 3L 100745 character character
-## 51 3R 125133 character character
-## 52 4 7844 character character
-## 53 dmel_mitochondrion_genome 146 character character
-## 54 rDNA 59 character character
-## 55 Unmapped_Scaffold_8 43 character character
-## 56 X 97193 character character
-## 57 Y 504 character character
+d.gtf.exons <- d.gtf[(d.gtf$Feature == 'exon'),]
+aggregate(d.gtf.exons$Feature, by=list(d.gtf.exons$Chromosome), summary)
+## Group.1 x.Length x.Class x.Mode
+## 1 211000022278279 1 character character
+## 2 211000022278436 1 character character
+## 3 211000022278449 2 character character
+## 4 211000022278760 2 character character
+## 5 211000022278978 2 character character
+## 6 211000022279165 1 character character
+## 7 211000022279188 3 character character
+## 8 211000022279264 1 character character
+## 9 211000022279392 1 character character
+## 10 211000022279681 1 character character
+## 11 211000022280328 4 character character
+## 12 211000022280341 1 character character
+## 13 211000022280347 1 character character
+## 14 211000022280481 1 character character
+## 15 211000022280494 2 character character
+## 16 211000022280703 1 character character
+## 17 2L 34310 character character
+## 18 2R 41063 character character
+## 19 3L 36249 character character
+## 20 3R 44924 character character
+## 21 4 3733 character character
+## 22 mitochondrion_genome 38 character character
+## 23 rDNA 27 character character
+## 24 Unmapped_Scaffold_4_D1555_D1692 1 character character
+## 25 Unmapped_Scaffold_60_D1601 1 character character
+## 26 Unmapped_Scaffold_8_D1580_D1567 16 character character
+## 27 X 35931 character character
+## 28 Y 306 character character
+## 29 Y_mapped_Scaffold_9_D1573 1 character character
-
-
+
-by(data=d.gtf$Feature, d.gtf[,"Chromosome"], summary)
-## d.gtf[, "Chromosome"]: 211000022278158
+by(data=d.gtf.exons$Feature, d.gtf.exons[,"Chromosome"], summary)
+## d.gtf.exons[, "Chromosome"]: 211000022278279
## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022278279
-## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022278282
-## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022278298
-## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022278307
-## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022278309
-## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022278436
-## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022278449
-## Length Class Mode
-## 4 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022278498
-## Length Class Mode
-## 3 character character
+## 1 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022278522
+## d.gtf.exons[, "Chromosome"]: 211000022278436
## Length Class Mode
-## 3 character character
+## 1 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022278603
+## d.gtf.exons[, "Chromosome"]: 211000022278449
## Length Class Mode
-## 3 character character
+## 2 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022278604
+## d.gtf.exons[, "Chromosome"]: 211000022278760
## Length Class Mode
-## 3 character character
+## 2 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022278664
+## d.gtf.exons[, "Chromosome"]: 211000022278978
## Length Class Mode
-## 3 character character
+## 2 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022278724
+## d.gtf.exons[, "Chromosome"]: 211000022279165
## Length Class Mode
-## 3 character character
+## 1 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022278750
+## d.gtf.exons[, "Chromosome"]: 211000022279188
## Length Class Mode
## 3 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022278760
+## d.gtf.exons[, "Chromosome"]: 211000022279264
## Length Class Mode
-## 7 character character
+## 1 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022278875
+## d.gtf.exons[, "Chromosome"]: 211000022279392
## Length Class Mode
-## 3 character character
+## 1 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022278877
+## d.gtf.exons[, "Chromosome"]: 211000022279681
## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022278878
-## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022278879
-## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022278880
-## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022278985
-## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022279055
-## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022279108
-## Length Class Mode
-## 3 character character
+## 1 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022279132
+## d.gtf.exons[, "Chromosome"]: 211000022280328
## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022279134
-## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022279165
-## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022279188
-## Length Class Mode
-## 12 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022279222
-## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022279264
-## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022279342
-## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022279392
-## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022279446
-## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022279528
-## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022279529
-## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022279531
-## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022279555
-## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022279681
-## Length Class Mode
-## 3 character character
-## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022279708
-## Length Class Mode
-## 3 character character
+## 4 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022280133
+## d.gtf.exons[, "Chromosome"]: 211000022280341
## Length Class Mode
-## 3 character character
+## 1 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022280328
+## d.gtf.exons[, "Chromosome"]: 211000022280347
## Length Class Mode
-## 14 character character
+## 1 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022280341
+## d.gtf.exons[, "Chromosome"]: 211000022280481
## Length Class Mode
-## 3 character character
+## 1 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022280347
+## d.gtf.exons[, "Chromosome"]: 211000022280494
## Length Class Mode
-## 3 character character
+## 2 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022280481
+## d.gtf.exons[, "Chromosome"]: 211000022280703
## Length Class Mode
-## 3 character character
+## 1 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022280494
+## d.gtf.exons[, "Chromosome"]: 2L
## Length Class Mode
-## 6 character character
+## 34310 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022280645
+## d.gtf.exons[, "Chromosome"]: 2R
## Length Class Mode
-## 3 character character
+## 41063 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 211000022280703
+## d.gtf.exons[, "Chromosome"]: 3L
## Length Class Mode
-## 3 character character
+## 36249 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 2L
+## d.gtf.exons[, "Chromosome"]: 3R
## Length Class Mode
-## 96740 character character
+## 44924 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 2R
+## d.gtf.exons[, "Chromosome"]: 4
## Length Class Mode
-## 110108 character character
+## 3733 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 3L
+## d.gtf.exons[, "Chromosome"]: mitochondrion_genome
## Length Class Mode
-## 100745 character character
+## 38 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 3R
+## d.gtf.exons[, "Chromosome"]: rDNA
## Length Class Mode
-## 125133 character character
+## 27 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: 4
+## d.gtf.exons[, "Chromosome"]: Unmapped_Scaffold_4_D1555_D1692
## Length Class Mode
-## 7844 character character
+## 1 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: dmel_mitochondrion_genome
+## d.gtf.exons[, "Chromosome"]: Unmapped_Scaffold_60_D1601
## Length Class Mode
-## 146 character character
+## 1 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: rDNA
+## d.gtf.exons[, "Chromosome"]: Unmapped_Scaffold_8_D1580_D1567
## Length Class Mode
-## 59 character character
+## 16 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: Unmapped_Scaffold_8
+## d.gtf.exons[, "Chromosome"]: X
## Length Class Mode
-## 43 character character
+## 35931 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: X
+## d.gtf.exons[, "Chromosome"]: Y
## Length Class Mode
-## 97193 character character
+## 306 character character
## ------------------------------------------------------------
-## d.gtf[, "Chromosome"]: Y
+## d.gtf.exons[, "Chromosome"]: Y_mapped_Scaffold_9_D1573
## Length Class Mode
-## 504 character character
+## 1 character character
- Filter the data frame to only retain gene annotations
-
-
+
d.gtf.gene <- d.gtf[d.gtf$Feature == "gene",]
@@ -875,53 +751,53 @@ 3 read.table()
- What is the average gene length of in the Drosophila genome?
-
-
+
mean(abs(d.gtf.gene$Start - d.gtf.gene$End))
-## [1] 5753.282
+## [1] 4616.647
- What fraction of the genes are encoded on the plus strand of the genome.
-
-
+
sum(d.gtf.gene$Strand == "+") / length(d.gtf.gene$Strand)
-## [1] 0.5016231
+## [1] 0.4988467
- What is the median and mean length of the exons found on chromosome 3R in the data set?
-
-
+
d.gtf3R <- d.gtf[d.gtf$Chromosome == "3R",]
exon.position <- d.gtf3R[d.gtf3R$Feature == "exon",c("Start", "End")]
-median(abs(exon.position$Start - exon.position$End))
-mean(abs(exon.position$Start - exon.position$End))
-## [1] 251
-## [1] 468.6693
+exon.med <- median(abs(exon.position$Start - exon.position$End))
+exon.mean <- mean(abs(exon.position$Start - exon.position$End))
+sprintf("The median and mean exon length for 3R is %g and %g, respectively", exon.med, exon.mean)
+## [1] "The median and mean exon length for 3R is 252 and 493.043, respectively"
- Do the same calculations for the chromosomes 2L, 2R, 3L, 4, X and Y using a for loop.
-
-
+
chr <- c("2L","2R","3L","4","X","Y")
for (i in chr) {
@@ -932,12 +808,12 @@ 3 read.table()
txt <- sprintf("The median and mean exon length for %s is %g and %g, respectively", i, exon.med, exon.mean)
print(txt)
}
-## [1] "The median and mean exon length for 2L is 279 and 502.617, respectively"
-## [1] "The median and mean exon length for 2R is 225 and 437.187, respectively"
-## [1] "The median and mean exon length for 3L is 255 and 502.116, respectively"
-## [1] "The median and mean exon length for 4 is 198 and 429.573, respectively"
-## [1] "The median and mean exon length for X is 257 and 526.301, respectively"
-## [1] "The median and mean exon length for Y is 410.5 and 657.055, respectively"
+## [1] "The median and mean exon length for 2L is 279 and 529.126, respectively"
+## [1] "The median and mean exon length for 2R is 227 and 481.08, respectively"
+## [1] "The median and mean exon length for 3L is 258 and 543.524, respectively"
+## [1] "The median and mean exon length for 4 is 200 and 463.665, respectively"
+## [1] "The median and mean exon length for X is 258 and 553.507, respectively"
+## [1] "The median and mean exon length for Y is 616.5 and 1096, respectively"
diff --git a/2310-canvas/lab_loops.html b/2310-canvas/lab_loops.html
index 9b31b28..17f0d48 100644
--- a/2310-canvas/lab_loops.html
+++ b/2310-canvas/lab_loops.html
@@ -252,8 +252,7 @@
1 Introduction
-In programming languages loop structures, either with or without conditions, are used to repeat commands over multiple entities. For
-and while loops as well as if-else statements are also often used in R, but not as often as in many other programming languages. The reason for this is that many needs of the loops are addressed using vectorization or via apply functions.
+In programming languages loop structures, either with or without conditions, are used to repeat commands over multiple entities. for and while loops as well as if-else statements are also often used in R, but not as often as in many other programming languages. The reason for this is that many needs of the loops are addressed using vectorization or via apply functions.
This means that we can multiply all values in a vector in R by two by calling
vec.a <- c(1, 2, 3, 4)
vec.a * 2
@@ -277,13 +276,13 @@ 1 Introduction
2 Exercises
-- Create a 100000 by 10 matrix with the numbers 1:1000000. Make a for-loop that calculates the sum for each row of the matrix. Verify that your results are consistent with what you obtain with the
apply() function to calculate row sums as well as with the built-in rowSums() function. These functions were discussed in the lecture Elements of the programming language - part 2.
+- Create a 100000 by 10 matrix with the numbers 1:1000000. Make a for-loop that calculates the sum for each row of the matrix. Verify that your results are consistent with what you obtain with the
apply() function to calculate row sums as well as with the built-in rowSums() function. These functions were discussed in the lecture Elements of the programming language - part 2.
-
-
+
X <- matrix(1:1000000, nrow = 100000, ncol = 10)
for.sum <- vector()
@@ -312,10 +311,10 @@ 2 Exercises
- Another common loop structure that is used is the while loop, which functions much like a for loop, but will only run as long as a test condition is TRUE. Modify your for loop from exercise 1 and make it into a while loop.
-
-
+
x <- 1
while.sum <- vector("integer", 100000)
@@ -331,10 +330,10 @@ 2 Exercises
- Create a data frame with two numeric and one character vector. Write a loop that loops over the columns and reports the sum of the column values if it is numeric and the total number of characters if it is a character vector.
-
-
+
vector1 <- 1:10
vector2 <- c("Odd", "Loop", letters[1:8])
@@ -349,17 +348,17 @@ 2 Exercises
}
}
sum.vec
-## [1] 55.00000 15.00000 -54.92967
+## [1] 55.00000 15.00000 34.59664
- In question 3 you generated a loop to go over a data frame. Try to convert this code to a function in R. The function should take a single data frame name as argument.
-
-
+
dfr.info <- function(dfr) {
sum.vec <- vector()
@@ -376,8 +375,8 @@ 2 Exercises
Read up on the if-else function in R. If possible use the if-else function to answer question 3.
In all loops that we tried out we have created the variable where the output is saved outside the loop. Why is this?
Advanced: At the lecture an approach to calculate factorials were implemented using recursion (function calling itself). Here we instead will have a go at generating Fibonacci numbers. A fibonacci number is part of a series of number with the following properties:
In all loops that we tried out, we have created the variable where the output is saved outside the loop. Why is this?
Advanced: As explained in the lecture, an approach to calculate factorials were implemented using recursion (function calling itself). Here we instead will have a go at generating Fibonacci numbers. A fibonacci number is part of a series of number with the following properties:
The first two numbers in the Fibonacci sequence are either 1 and 1, or 0 and 1, depending on the chosen starting point of the sequence, and each subsequent number is the sum of the previous two. Hence:
0, 1, 1, 2, 3, 5, 8, 13, 21, ...
diff --git a/2310-canvas/lab_tidyverse.html b/2310-canvas/lab_tidyverse.html
index 13e83b4..6a23d40 100644
--- a/2310-canvas/lab_tidyverse.html
+++ b/2310-canvas/lab_tidyverse.html
@@ -264,10 +264,10 @@ 2 Pipes
print(my_cars)
my_cars <- colMeans(my_cars)
-
-
+
my_cars <- mtcars %>%
select(c(1:4, 7)) %>%
@@ -281,20 +281,20 @@ 2 Pipes
cor(mtcars)
-
-
+
mtcars %>% cor()
cor(mtcars$gear, mtcars$mpg)
-
-
+
mtcars %$% cor(gear, mpg)
@@ -306,10 +306,10 @@ 3 Tibbles
- Convert the
mtcars dataset to a tibble vehicles.
-
-
+
vehicles <- mtcars %>% as_tibble()
@@ -323,10 +323,10 @@ 3 Tibbles
-
-
+
vehicles[['cyl']]
vehicles[[2]]
@@ -340,10 +340,10 @@ 3 Tibbles
- Do the same selection as above, but using pipe and placeholders (use all three ways of accessing a variable).
-
-
+
vehicles %T>%
{print(.[['cyl']])} %T>%
@@ -358,10 +358,10 @@ 3 Tibbles
- Print the tibble.
-
-
+
vehicles
## # A tibble: 32 × 11
@@ -384,10 +384,10 @@ 3 Tibbles
- Print the 30 first rows of the tibble.
-
-
+
vehicles %>% head(n = 30)
## # A tibble: 30 × 11
@@ -410,10 +410,10 @@ 3 Tibbles
- Change the default behaviour of printing a tibble so that at least 15 and at most 30 rows are printed.
-
-
+
options(tibble.print_min = 15, tibble.print_max = 30)
@@ -422,10 +422,10 @@ 3 Tibbles
- Convert
vehicles back to a data.frame called automobiles.
-
-
+
automobiles <- as.data.frame(vehicles)
@@ -442,10 +442,10 @@ 4.1 Selecting columns
- Load the
nycflights13 package (install if necessary)
-
-
+
install.packages('nycflights13')
library(nycflights13)
@@ -455,10 +455,10 @@ 4.1 Selecting columns
- Read about the data in the package docs
-
-
+
?nycflights13
## No documentation for 'nycflights13' in specified packages and libraries:
@@ -469,10 +469,10 @@ 4.1 Selecting columns
- Inspect the
flights tibble.
-
-
+
flights
## # A tibble: 336,776 × 19
@@ -503,10 +503,10 @@ 4.1 Selecting columns
- Select all columns but
carrier and arr_time
-
-
+
flights %>% select(-carrier, -arr_time)
## # A tibble: 336,776 × 17
@@ -536,10 +536,10 @@ 4.1 Selecting columns
- Select
carrier, tailnum and origin
-
-
+
flights %>% select(carrier, tailnum, origin)
## # A tibble: 336,776 × 3
@@ -567,10 +567,10 @@ 4.1 Selecting columns
- Hide columns from
day through carrier
-
-
+
flights %>% select(-(day:carrier))
## # A tibble: 336,776 × 11
@@ -599,10 +599,10 @@ 4.1 Selecting columns
- Select all columns that have to do with
arrival (hint: ?tidyselect)
-
-
+
flights %>% select(contains('arr_'))
## # A tibble: 336,776 × 3
@@ -630,10 +630,10 @@ 4.1 Selecting columns
- Select columns based on a vector
v <- c("arr_time", "sched_arr_time", "arr_delay")
-
-
+
v <- c("arr_time", "sched_arr_time", "arr_delay")
flights %>% select(v) # or
@@ -682,10 +682,10 @@ 4.1 Selecting columns
- Rename column
dest to destination using select() and rename(). What is the difference between the two approaches?
-
-
+
flights %>% select(destination = dest) %>% head()
flights %>% rename(destination = dest) %>% head()
@@ -721,10 +721,10 @@ 4.2 Filtering rows
- Filter only the flights that arrived ahead of schedule
-
-
+
flights %>% filter(arr_delay < 0)
## # A tibble: 188,933 × 19
@@ -755,10 +755,10 @@ 4.2 Filtering rows
- Filter the flights that had departure delay between 10 and 33
-
-
+
flights %>% filter(dep_delay >= 10, dep_delay <= 33) # or
flights %>% filter(between(dep_delay, 10, 33))
@@ -812,10 +812,10 @@ 4.2 Filtering rows
- Fish out all flights with unknown arrival time
-
-
+
flights %>% filter(is.na(arr_time))
## # A tibble: 8,713 × 19
@@ -846,10 +846,10 @@ 4.2 Filtering rows
- Retrieve rows 1234:1258 (hint:
?slice)
-
-
+
flights %>% slice(1234:1258)
## # A tibble: 25 × 19
@@ -889,10 +889,10 @@ 4.2 Filtering rows
- Sample (
?sample_n()) 3 random flights per day in March
-
-
+
flights %>% filter(month == 3) %>%
group_by(day) %>%
@@ -901,21 +901,21 @@ 4.2 Filtering rows
## # Groups: day [31]
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
-## 1 2013 3 1 1631 1630 1 1834 1833
-## 2 2013 3 1 958 959 -1 1254 1333
-## 3 2013 3 1 1821 1658 83 2141 2007
-## 4 2013 3 2 731 736 -5 1038 1115
-## 5 2013 3 2 1722 1730 -8 1953 2046
-## 6 2013 3 2 1636 1640 -4 1821 1855
-## 7 2013 3 3 1323 1330 -7 1607 1629
-## 8 2013 3 3 2248 2251 -3 2400 2357
-## 9 2013 3 3 728 736 -8 1109 1115
-## 10 2013 3 4 906 910 -4 1206 1213
-## 11 2013 3 4 653 700 -7 1012 1025
-## 12 2013 3 4 1927 1929 -2 2250 2247
-## 13 2013 3 5 2036 1755 161 2203 1940
-## 14 2013 3 5 1234 1238 -4 1430 1434
-## 15 2013 3 5 1525 1530 -5 1845 1922
+## 1 2013 3 1 655 700 -5 954 1025
+## 2 2013 3 1 2120 2059 21 2337 2320
+## 3 2013 3 1 1936 1935 1 2229 2334
+## 4 2013 3 2 1555 1600 -5 1826 1915
+## 5 2013 3 2 2022 2029 -7 2150 2218
+## 6 2013 3 2 1521 1444 37 1819 1739
+## 7 2013 3 3 707 715 -8 900 918
+## 8 2013 3 3 1220 1200 20 1320 1303
+## 9 2013 3 3 948 950 -2 1256 1254
+## 10 2013 3 4 1506 1415 51 1659 1607
+## 11 2013 3 4 1623 1600 23 1931 1934
+## 12 2013 3 4 1419 1425 -6 1746 1750
+## 13 2013 3 5 1103 1100 3 1317 1326
+## 14 2013 3 5 1729 1729 0 1932 1937
+## 15 2013 3 5 1556 1603 -7 1722 1716
## # ℹ 78 more rows
## # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
## # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
@@ -926,10 +926,10 @@ 4.2 Filtering rows
- Show 5 most departure-delayed flights in January per carrier
-
-
+
flights %>%
filter(month == 1) %>%
@@ -964,10 +964,10 @@