Basic R
Objects
## [1] 8
## [1] 5000
## [1] 25
x <- 4+4
#Look at the workspace!
x
## [1] 8
#Look at the console!
x <- 4+10
y <- 5
nums.vector <- c(7, 8, 45, 3, 2, 67)
cols.vector <- c("red", "blue", "green", "pink")
my.list <- list(x, y, nums.vector, cols.vector)
my.matrix <- cbind(x, y, nums.vector, cols.vector)
## Warning in cbind(x, y, nums.vector, cols.vector): number of rows of result is
## not a multiple of vector length (arg 4)
## [1] "numeric"
## [1] "character"
## [1] "list"
## [1] "matrix" "array"
## [[1]]
## [1] 14
##
## [[2]]
## [1] 5
##
## [[3]]
## [1] 7 8 45 3 2 67
##
## [[4]]
## [1] "red" "blue" "green" "pink"
## x y nums.vector cols.vector
## [1,] "14" "5" "7" "red"
## [2,] "14" "5" "8" "blue"
## [3,] "14" "5" "45" "green"
## [4,] "14" "5" "3" "pink"
## [5,] "14" "5" "2" "red"
## [6,] "14" "5" "67" "blue"
Data Frames and indexing
mtcars #built-in data frame, go to the help window to learn about it
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
## [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
## [31] 15.0 21.4
## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
## [16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
## [31] 15.0 21.4
## mpg cyl disp hp drat wt qsec vs am gear carb
## Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4
Data Import/Export
mydata <- read.csv("data_input/Example01.csv")
mydata
## Strain Media Bio.1 Bio.2 Bio.3 Bio.4
## 1 WT TSB 0.305 0.320 0.304 0.315
## 2 MutA TSB 0.161 0.156 0.171 0.187
## 3 MutB TSB 0.285 0.297 0.279 0.277
## 4 MutAB TSB 0.020 0.023 0.015 0.028
## 5 WT CDM 0.210 0.226 0.201 0.219
## 6 MutA CDM 0.059 0.055 0.073 0.085
## 7 MutB CDM 0.061 0.053 0.080 0.086
## 8 MutAB CDM 0.010 0.004 0.025 0.020
mydata$Average <-rowMeans(mydata[c("Bio.1", "Bio.2", "Bio.3","Bio.4")])
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
write_csv(mydata, "data_output/Example01_Edited.csv")
write_rds(mydata, "data_output/Example01_Edited.rds")
mydata_Edited <- read_rds("data_output/Example01_Edited.rds")