R Tutorial
An introduction to R
Introduction
This tutorial is will introduce the reader to , a free, open-source statistical computing environment often used with RStudio, a integrated development environment for .
Download
- Download at https://www.r-project.org/
- Download
RStudio
at https://rstudio.com/products/rstudio/download/
Calculator
can be used as a super awesome calculator
## [1] 8
## [1] 8
## [1] 8
## [1] 8
## [1] 8
Functions
has many useful built in functions
## [1] 1 2 3 4 5 6 7 8 9 10
## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"
## [1] 1 2 1 2 1 2 1 2 1 2
## [1] 1 2 3 4 5 1 2 3 4 5
## [1] 1 1 2 2 3 3 4 4 5 5
## [1] 1 2 3 4 5 1 2
## [1] 5 10 15 20 25 30 35 40 45 50
## [1] 5.00 16.25 27.50 38.75 50.00
## [1] "1-20" "2-21" "3-22" "4-23" "5-24" "6-25" "7-26" "8-27" "9-28" "10-29" "1-30"
## [1] "1-2-3-4-5-6-7-8-9-10"
## [1] "x1" "x2" "x3" "x4" "x5" "x6" "x7" "x8" "x9" "x10"
## [1] 1
## [1] 10
## [1] 1 10
## [1] 5.5
## [1] 3.02765
Custom Functions
Users can also create their own functions
customFunction1 <- function(x, y) {
z <- 100 * x / (x + y)
paste(z, "%")
}
customFunction1(x = 10, y = 90)
## [1] "10 %"
customFunction2 <- function(x) {
mymin <- mean(x - sd(x))
mymax <- mean(x) + sd(x)
print(paste("Min =", mymin))
print(paste("Max =", mymax))
}
customFunction2(x = 1:10)
## [1] "Min = 2.47234964590251"
## [1] "Max = 8.52765035409749"
for
loops and if
else
statements
## [1] 3 6 9 12 15 18 21 24 27 30
## [1] 1 0 1 0 1 0 1 0 1 0
for(i in 1:length(xx)) {
if((xx[i] %% 2) == 0) {
print(paste(xx[i],"is Even"))
} else {
print(paste(xx[i],"is Odd"))
}
}
## [1] "3 is Odd"
## [1] "6 is Even"
## [1] "9 is Odd"
## [1] "12 is Even"
## [1] "15 is Odd"
## [1] "18 is Even"
## [1] "21 is Odd"
## [1] "24 is Even"
## [1] "27 is Odd"
## [1] "30 is Even"
## [1] "Odd" "Even" "Odd" "Even" "Odd" "Even" "Odd" "Even" "Odd" "Even"
## [1] "3 is Odd" "6 is Even" "9 is Odd" "12 is Even" "15 is Odd" "18 is Even" "21 is Odd" "24 is Even" "27 is Odd" "30 is Even"
Objects
Information can be stored in user defined objects, in multiple forms:
c()
: a string of valuesmatrix()
: a two dimensional matrix in one formatdata.frame()
: a two dimensional matrix where each column can be a different formatlist()
:
A string…
## [1] 1 2 3 4 5 6 7 8 9 10
## [1] 1 2 3 4 5 6 7 8 9 10
A matrix…
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 2 3 4 5 6 7 8 9 10
## [2,] 11 12 13 14 15 16 17 18 19 20
## [3,] 21 22 23 24 25 26 27 28 29 30
## [4,] 31 32 33 34 35 36 37 38 39 40
## [5,] 41 42 43 44 45 46 47 48 49 50
## [6,] 51 52 53 54 55 56 57 58 59 60
## [7,] 61 62 63 64 65 66 67 68 69 70
## [8,] 71 72 73 74 75 76 77 78 79 80
## [9,] 81 82 83 84 85 86 87 88 89 90
## [10,] 91 92 93 94 95 96 97 98 99 100
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 11 21 31 41 51 61 71 81 91
## [2,] 2 12 22 32 42 52 62 72 82 92
## [3,] 3 13 23 33 43 53 63 73 83 93
## [4,] 4 14 24 34 44 54 64 74 84 94
## [5,] 5 15 25 35 45 55 65 75 85 95
## [6,] 6 16 26 36 46 56 66 76 86 96
## [7,] 7 17 27 37 47 57 67 77 87 97
## [8,] 8 18 28 38 48 58 68 78 88 98
## [9,] 9 19 29 39 49 59 69 79 89 99
## [10,] 10 20 30 40 50 60 70 80 90 100
A data frame…
xd <- data.frame(
x1 = c("aa","bb","cc","dd","ee",
"ff","gg","hh","ii","jj"),
x2 = 1:10,
x3 = c(1,1,1,1,1,2,2,2,3,3),
x4 = rep(c(1,2), times = 5),
x5 = rep(1:5, times = 2),
x6 = rep(1:5, each = 2),
x7 = seq(5, 50, by = 5),
x8 = log10(1:10),
x9 = (1:10)^3,
x10 = c(T,T,T,F,F,T,T,F,F,F)
)
xd
## x1 x2 x3 x4 x5 x6 x7 x8 x9 x10
## 1 aa 1 1 1 1 1 5 0.0000000 1 TRUE
## 2 bb 2 1 2 2 1 10 0.3010300 8 TRUE
## 3 cc 3 1 1 3 2 15 0.4771213 27 TRUE
## 4 dd 4 1 2 4 2 20 0.6020600 64 FALSE
## 5 ee 5 1 1 5 3 25 0.6989700 125 FALSE
## 6 ff 6 2 2 1 3 30 0.7781513 216 TRUE
## 7 gg 7 2 1 2 4 35 0.8450980 343 TRUE
## 8 hh 8 2 2 3 4 40 0.9030900 512 FALSE
## 9 ii 9 3 1 4 5 45 0.9542425 729 FALSE
## 10 jj 10 3 2 5 5 50 1.0000000 1000 FALSE
A list…
## [1] 1 2 3 4 5 6 7 8 9 10
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 11 21 31 41 51 61 71 81 91
## [2,] 2 12 22 32 42 52 62 72 82 92
## [3,] 3 13 23 33 43 53 63 73 83 93
## [4,] 4 14 24 34 44 54 64 74 84 94
## [5,] 5 15 25 35 45 55 65 75 85 95
## [6,] 6 16 26 36 46 56 66 76 86 96
## [7,] 7 17 27 37 47 57 67 77 87 97
## [8,] 8 18 28 38 48 58 68 78 88 98
## [9,] 9 19 29 39 49 59 69 79 89 99
## [10,] 10 20 30 40 50 60 70 80 90 100
## x1 x2 x3 x4 x5 x6 x7 x8 x9 x10
## 1 aa 1 1 1 1 1 5 0.0000000 1 TRUE
## 2 bb 2 1 2 2 1 10 0.3010300 8 TRUE
## 3 cc 3 1 1 3 2 15 0.4771213 27 TRUE
## 4 dd 4 1 2 4 2 20 0.6020600 64 FALSE
## 5 ee 5 1 1 5 3 25 0.6989700 125 FALSE
## 6 ff 6 2 2 1 3 30 0.7781513 216 TRUE
## 7 gg 7 2 1 2 4 35 0.8450980 343 TRUE
## 8 hh 8 2 2 3 4 40 0.9030900 512 FALSE
## 9 ii 9 3 1 4 5 45 0.9542425 729 FALSE
## 10 jj 10 3 2 5 5 50 1.0000000 1000 FALSE
Selecting Data
## [1] 5
## [1] 1
## [1] 1
## [1] 1 1 1 1 1 2 2 2 3 3
## [1] 1 1 1 1 1 2 2 2 3 3
## x1 x2 x3 x4 x5 x6 x7 x8 x9 x10
## 3 cc 3 1 1 3 2 15 0.4771213 27 TRUE
## x4 x5
## 2 2 2
## 4 2 4
## [1] "aa" "bb" "cc" "dd" "ee" "ff" "gg" "hh" "ii" "jj"
regexpr
xx <- data.frame(Name = c("Item 1 (detail 1)",
"Item 20 (detail 20)",
"Item 300 (detail 300)"),
Item = NA,
Detail = NA)
xx$Detail <- substr(xx$Name, regexpr("\\(", xx$Name)+1, regexpr("\\)", xx$Name)-1)
xx$Item <- substr(xx$Name, 1, regexpr("\\(", xx$Name)-2)
xx
## Name Item Detail
## 1 Item 1 (detail 1) Item 1 detail 1
## 2 Item 20 (detail 20) Item 20 detail 20
## 3 Item 300 (detail 300) Item 300 detail 300
Data Formats
Data can also be saved in many formats:
- numeric
- integer
- character
- factor
- logical
## [1] "1" "1" "1" "1" "1" "2" "2" "2" "3" "3"
## [1] 1 1 1 1 1 2 2 2 3 3
## [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 1 2 3
## [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 3 2 1
## [1] TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
## [1] 1 1 1 0 0 1 1 0 0 0
## [1] 5
Internal structure of an object can be checked with
str()
## num [1:10] 1 2 3 4 5 6 7 8 9 10
## int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...
## 'data.frame': 10 obs. of 10 variables:
## $ x1 : chr "aa" "bb" "cc" "dd" ...
## $ x2 : int 1 2 3 4 5 6 7 8 9 10
## $ x3 : Factor w/ 3 levels "3","2","1": 3 3 3 3 3 2 2 2 1 1
## $ x4 : num 1 2 1 2 1 2 1 2 1 2
## $ x5 : int 1 2 3 4 5 1 2 3 4 5
## $ x6 : int 1 1 2 2 3 3 4 4 5 5
## $ x7 : num 5 10 15 20 25 30 35 40 45 50
## $ x8 : num 0 0.301 0.477 0.602 0.699 ...
## $ x9 : num 1 8 27 64 125 216 343 512 729 1000
## $ x10: logi TRUE TRUE TRUE FALSE FALSE TRUE ...
## List of 3
## $ : num [1:10] 1 2 3 4 5 6 7 8 9 10
## $ : int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...
## $ :'data.frame': 10 obs. of 10 variables:
## ..$ x1 : chr [1:10] "aa" "bb" "cc" "dd" ...
## ..$ x2 : int [1:10] 1 2 3 4 5 6 7 8 9 10
## ..$ x3 : num [1:10] 1 1 1 1 1 2 2 2 3 3
## ..$ x4 : num [1:10] 1 2 1 2 1 2 1 2 1 2
## ..$ x5 : int [1:10] 1 2 3 4 5 1 2 3 4 5
## ..$ x6 : int [1:10] 1 1 2 2 3 3 4 4 5 5
## ..$ x7 : num [1:10] 5 10 15 20 25 30 35 40 45 50
## ..$ x8 : num [1:10] 0 0.301 0.477 0.602 0.699 ...
## ..$ x9 : num [1:10] 1 8 27 64 125 216 343 512 729 1000
## ..$ x10: logi [1:10] TRUE TRUE TRUE FALSE FALSE TRUE ...
Packages
Additional libraries can be installed and loaded for use.
library(scales)
xx <- data.frame(Values = 1:10)
xx$Rescaled <- rescale(x = xx$Values, to = c(1,30))
xx
## Values Rescaled
## 1 1 1.000000
## 2 2 4.222222
## 3 3 7.444444
## 4 4 10.666667
## 5 5 13.888889
## 6 6 17.111111
## 7 7 20.333333
## 8 8 23.555556
## 9 9 26.777778
## 10 10 30.000000
libraries can also be used without having to load them
## [1] 1.000000 4.222222 7.444444 10.666667 13.888889 17.111111 20.333333 23.555556 26.777778 30.000000
Data Wrangling
R for Data Science - https://r4ds.had.co.nz/
xx <- data.frame(Group = c("X","X","Y","Y","Y","X","X","X","Y","Y"),
Data1 = 1:10,
Data2 = seq(10, 100, by = 10))
xx$NewData1 <- xx$Data1 + xx$Data2
xx$NewData2 <- xx$Data1 * 1000
xx
## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
## 5 Y 5 50 55 5000
## 6 X 6 60 66 6000
## 7 X 7 70 77 7000
## 8 X 8 80 88 8000
## 9 Y 9 90 99 9000
## 10 Y 10 100 110 10000
## [1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
## Group Data2 NewData1
## 1 X 10 11
## 2 X 20 22
## 6 X 60 66
## 7 X 70 77
## 8 X 80 88
Data wrangling with tidyverse
and pipes
(%>%
)
library(tidyverse) # install.packages("tidyverse")
xx <- data.frame(Group = c("X","X","Y","Y","Y","Y","Y","X","X","X")) %>%
mutate(Data1 = 1:10,
Data2 = seq(10, 100, by = 10),
NewData1 = Data1 + Data2,
NewData2 = Data1 * 1000)
xx
## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
## 5 Y 5 50 55 5000
## 6 Y 6 60 66 6000
## 7 Y 7 70 77 7000
## 8 X 8 80 88 8000
## 9 X 9 90 99 9000
## 10 X 10 100 110 10000
## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
## Group Data1 Data2 NewData1 NewData2
## 1 X 1 10 11 1000
## 2 X 2 20 22 2000
## 3 Y 3 30 33 3000
## 4 Y 4 40 44 4000
## Group NewColName NewData1
## 1 X 10 11
## 2 X 20 22
## 3 X 80 88
## 4 X 90 99
## 5 X 100 110
xs <- xx %>%
group_by(Group) %>%
summarise(Data2_mean = mean(Data2),
Data2_sd = sd(Data2),
NewData2_mean = mean(NewData2),
NewData2_sd = sd(NewData2))
xs
## # A tibble: 2 × 5
## Group Data2_mean Data2_sd NewData2_mean NewData2_sd
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 X 60 41.8 6000 4183.
## 2 Y 50 15.8 5000 1581.
## Group Data1 Data2 NewData1 NewData2 Data2_mean Data2_sd NewData2_mean NewData2_sd
## 1 X 1 10 11 1000 60 41.83300 6000 4183.300
## 2 X 2 20 22 2000 60 41.83300 6000 4183.300
## 3 Y 3 30 33 3000 50 15.81139 5000 1581.139
## 4 Y 4 40 44 4000 50 15.81139 5000 1581.139
## 5 Y 5 50 55 5000 50 15.81139 5000 1581.139
## 6 Y 6 60 66 6000 50 15.81139 5000 1581.139
## 7 Y 7 70 77 7000 50 15.81139 5000 1581.139
## 8 X 8 80 88 8000 60 41.83300 6000 4183.300
## 9 X 9 90 99 9000 60 41.83300 6000 4183.300
## 10 X 10 100 110 10000 60 41.83300 6000 4183.300
Read/Write data
For excel sheets, the package readxl
can be used to read
in sheets of data.
library(readxl) # install.packages("readxl")
xx <- read_xlsx("data_r_tutorial.xlsx", sheet = "Data")
Tidy Data
- Tutorial 1 - https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html
- Tutorial 2 - https://r4ds.had.co.nz/tidy-data.html
yy <- xx %>%
group_by(Name, Location) %>%
summarise(Mean_DTF = round(mean(DTF),1)) %>%
arrange(Location)
yy
## # A tibble: 9 × 3
## # Groups: Name [3]
## Name Location Mean_DTF
## <chr> <chr> <dbl>
## 1 CDC Maxim AGL Jessore, Bangladesh 86.7
## 2 ILL 618 AGL Jessore, Bangladesh 79.3
## 3 Laird AGL Jessore, Bangladesh 76.8
## 4 CDC Maxim AGL Metaponto, Italy 134.
## 5 ILL 618 AGL Metaponto, Italy 138.
## 6 Laird AGL Metaponto, Italy 137.
## 7 CDC Maxim AGL Saskatoon, Canada 52.5
## 8 ILL 618 AGL Saskatoon, Canada 47
## 9 Laird AGL Saskatoon, Canada 56.8
## # A tibble: 3 × 4
## # Groups: Name [3]
## Name `Jessore, Bangladesh` `Metaponto, Italy` `Saskatoon, Canada`
## <chr> <dbl> <dbl> <dbl>
## 1 CDC Maxim AGL 86.7 134. 52.5
## 2 ILL 618 AGL 79.3 138. 47
## 3 Laird AGL 76.8 137. 56.8
## # A tibble: 9 × 3
## # Groups: Name [3]
## Name TraitName Value
## <chr> <chr> <dbl>
## 1 CDC Maxim AGL Jessore, Bangladesh 86.7
## 2 ILL 618 AGL Jessore, Bangladesh 79.3
## 3 Laird AGL Jessore, Bangladesh 76.8
## 4 CDC Maxim AGL Metaponto, Italy 134.
## 5 ILL 618 AGL Metaponto, Italy 138.
## 6 Laird AGL Metaponto, Italy 137.
## 7 CDC Maxim AGL Saskatoon, Canada 52.5
## 8 ILL 618 AGL Saskatoon, Canada 47
## 9 Laird AGL Saskatoon, Canada 56.8
## # A tibble: 3 × 4
## TraitName `CDC Maxim AGL` `ILL 618 AGL` `Laird AGL`
## <chr> <dbl> <dbl> <dbl>
## 1 Jessore, Bangladesh 86.7 79.3 76.8
## 2 Metaponto, Italy 134. 138. 137.
## 3 Saskatoon, Canada 52.5 47 56.8
Base Plotting
We will start with some basic plotting using the base function
plot()
Now lets create some random and normally distributed data to make some more complicated plots
# 100 random uniformly distributed numbers ranging from 0 - 100
ru <- runif(100, min = 0, max = 100)
ru
## [1] 92.802355 32.508834 14.030417 30.335811 86.835025 77.912915 5.238022 58.283525 27.343554 82.834092 59.624567 99.683549 46.285572 75.134858
## [15] 10.830570 21.392290 77.919337 36.722081 36.452919 48.631926 90.915227 21.443425 96.195847 15.962305 92.544430 97.000347 82.106469 25.210997
## [29] 74.753497 99.694554 29.891154 39.173467 3.872677 12.261735 68.584759 61.002512 93.100063 57.374420 94.760051 73.930278 59.569481 9.380330
## [43] 25.666884 76.515440 76.777547 90.552324 28.379938 35.394553 85.884346 15.963192 46.222303 96.574730 21.471466 9.494254 25.619582 36.266962
## [57] 60.344791 79.953196 39.012411 33.207714 34.926042 33.746388 96.632477 39.121491 50.495049 21.863308 59.459481 79.511460 89.577023 53.394697
## [71] 3.965710 7.407184 44.819578 85.697127 53.486370 99.664998 35.506292 59.381866 13.694541 79.603244 30.833089 61.055980 68.244548 12.437711
## [85] 24.857254 11.473762 30.708561 81.608344 80.777127 24.635950 13.477504 2.689561 82.867214 11.555085 39.090877 6.949948 79.680829 80.039933
## [99] 29.220791 30.022653
## [1] 92 33 71 7 96 72 42 54 15 86 94 34 84 91 79 3 24 50 16 22 53 66 90 85 28 55 43 9 47 99 31 100 4 87 81 2
## [37] 60 62 61 48 77 56 19 18 59 95 64 32 73 51 13 20 65 70 75 38 8 78 67 41 11 57 36 82 83 35 40 29 14 44 45 6
## [73] 17 68 80 97 58 98 89 88 27 10 93 74 49 5 69 46 21 25 1 37 39 23 52 63 26 76 12 30
## [1] 2.689561 3.872677 3.965710 5.238022 6.949948 7.407184 9.380330 9.494254 10.830570 11.473762 11.555085 12.261735 12.437711 13.477504
## [15] 13.694541 14.030417 15.962305 15.963192 21.392290 21.443425 21.471466 21.863308 24.635950 24.857254 25.210997 25.619582 25.666884 27.343554
## [29] 28.379938 29.220791 29.891154 30.022653 30.335811 30.708561 30.833089 32.508834 33.207714 33.746388 34.926042 35.394553 35.506292 36.266962
## [43] 36.452919 36.722081 39.012411 39.090877 39.121491 39.173467 44.819578 46.222303 46.285572 48.631926 50.495049 53.394697 53.486370 57.374420
## [57] 58.283525 59.381866 59.459481 59.569481 59.624567 60.344791 61.002512 61.055980 68.244548 68.584759 73.930278 74.753497 75.134858 76.515440
## [71] 76.777547 77.912915 77.919337 79.511460 79.603244 79.680829 79.953196 80.039933 80.777127 81.608344 82.106469 82.834092 82.867214 85.697127
## [85] 85.884346 86.835025 89.577023 90.552324 90.915227 92.544430 92.802355 93.100063 94.760051 96.195847 96.574730 96.632477 97.000347 99.664998
## [99] 99.683549 99.694554
# 100 normally distributed numbers with a mean of 50 and sd of 10
nd <- rnorm(100, mean = 50, sd = 10)
nd
## [1] 36.40087 45.78597 60.31401 40.70099 57.50942 49.82402 54.87482 39.39473 47.65089 32.46331 71.89692 54.79083 36.50531 42.69851 63.80560 48.87270
## [17] 51.82239 65.96000 57.33696 19.95617 40.62659 52.39003 52.15770 34.66952 38.16409 45.58189 44.40213 42.96977 42.73909 38.22358 41.91128 53.37224
## [33] 40.98749 28.11605 62.90001 39.60940 46.50324 44.45227 51.89265 50.84638 42.20468 44.02733 25.43992 42.31539 57.59406 50.40997 41.48731 48.20806
## [49] 49.01825 64.71523 56.27035 72.43764 28.43785 54.63829 60.50549 40.32896 55.29103 48.08996 58.45714 57.29749 42.07058 49.20348 52.30880 42.86439
## [65] 46.27372 54.16808 56.33031 52.90560 44.34328 45.21385 56.35762 38.76759 48.36693 38.81636 53.36611 46.65844 64.92341 48.02846 51.55214 51.23079
## [81] 58.19041 49.94058 66.99090 62.92916 59.02894 73.42657 50.92235 57.72685 60.76827 50.48101 38.29579 35.50869 58.67315 59.21161 46.85622 41.85587
## [97] 44.95792 51.65683 54.69306 40.81170
## [1] 19.95617 25.43992 28.11605 28.43785 32.46331 34.66952 35.50869 36.40087 36.50531 38.16409 38.22358 38.29579 38.76759 38.81636 39.39473 39.60940
## [17] 40.32896 40.62659 40.70099 40.81170 40.98749 41.48731 41.85587 41.91128 42.07058 42.20468 42.31539 42.69851 42.73909 42.86439 42.96977 44.02733
## [33] 44.34328 44.40213 44.45227 44.95792 45.21385 45.58189 45.78597 46.27372 46.50324 46.65844 46.85622 47.65089 48.02846 48.08996 48.20806 48.36693
## [49] 48.87270 49.01825 49.20348 49.82402 49.94058 50.40997 50.48101 50.84638 50.92235 51.23079 51.55214 51.65683 51.82239 51.89265 52.15770 52.30880
## [65] 52.39003 52.90560 53.36611 53.37224 54.16808 54.63829 54.69306 54.79083 54.87482 55.29103 56.27035 56.33031 56.35762 57.29749 57.33696 57.50942
## [81] 57.59406 57.72685 58.19041 58.45714 58.67315 59.02894 59.21161 60.31401 60.50549 60.76827 62.90001 62.92916 63.80560 64.71523 64.92341 65.96000
## [97] 66.99090 71.89692 72.43764 73.42657
ggplot2
Lets be honest, the base plots are ugly! The ggplot2
package gives the user to create a better, more visually appealing
plots. Additional packages such as ggbeeswarm
and
ggrepel
also contain useful functions to add to the
functionality of ggplot2
.
- ggplot2 - https://ggplot2.tidyverse.org/
- Tutorial 1 - http://r-statistics.co/ggplot2-Tutorial-With-R.html
- Tutorial 2 - https://www.statsandr.com/blog/graphics-in-r-with-ggplot2/
- The R Graph Gallery - https://www.r-graph-gallery.com/ggplot2-package.html
xx <- data.frame(data = c(rnorm(50, mean = 40, sd = 10),
rnorm(50, mean = 60, sd = 5)),
group = factor(rep(1:2, each = 50)),
label = c("Label1", rep(NA, 49), "Label2", rep(NA, 49)))
mp <- ggplot(xx, aes(x = data, fill = group))
mp + geom_histogram(color = "black")
mp2 <- mp + geom_violin() +
geom_boxplot(width = 0.1, fill = "white") +
geom_beeswarm(alpha = 0.5)
library(ggrepel)
mp2 + geom_text_repel(aes(label = label), nudge_x = 0.4)
library(ggpubr)
ggarrange(mp1, mp2, ncol = 2, widths = c(2,1),
common.legend = T, legend = "bottom")
Statistics
- Handbook of Biological Statistics - http://biostathandbook.com/
- R Companion for ^ - https://rcompanion.org/rcompanion/a_02.html
# Prep data
lev_Loc <- c("Saskatoon, Canada", "Jessore, Bangladesh", "Metaponto, Italy")
lev_Name <- c("ILL 618 AGL", "CDC Maxim AGL", "Laird AGL")
dd <- read_xlsx("data_r_tutorial.xlsx", sheet = "Data") %>%
mutate(Location = factor(Location, levels = lev_Loc),
Name = factor(Name, levels = lev_Name))
xx <- dd %>%
group_by(Name, Location) %>%
summarise(Mean_DTF = mean(DTF))
xx %>% spread(Location, Mean_DTF)
## # A tibble: 3 × 4
## # Groups: Name [3]
## Name `Saskatoon, Canada` `Jessore, Bangladesh` `Metaponto, Italy`
## <fct> <dbl> <dbl> <dbl>
## 1 ILL 618 AGL 47 79.3 138.
## 2 CDC Maxim AGL 52.5 86.7 134.
## 3 Laird AGL 56.8 76.8 137.
# Plot
mp1 <- ggplot(dd, aes(x = Location, y = DTF, color = Name, shape = Name)) +
geom_point(size = 2, alpha = 0.7, position = position_dodge(width=0.5))
mp2 <- ggplot(xx, aes(x = Location, y = Mean_DTF,
color = Name, group = Name, shape = Name)) +
geom_point(size = 2.5, alpha = 0.7) +
geom_line(size = 1, alpha = 0.7) +
theme(legend.position = "top")
ggarrange(mp1, mp2, ncol = 2, common.legend = T, legend = "top")
From first glace, it is clear there are differences between genotypes, locations, and genotype x environment (GxE) interactions. Now let’s do a few statistical tests.
## Df Sum Sq Mean Sq F value Pr(>F)
## Name 2 88 44 3.476 0.0395 *
## Location 2 65863 32932 2598.336 < 2e-16 ***
## Name:Location 4 560 140 11.044 2.52e-06 ***
## Residuals 45 570 13
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
As expected, an ANOVA shows statistical significance for genotype (p-value = 0.0395), Location (p-value < 2e-16) and GxE interactions (p-value < 2.52e-06). However, all this tells us is that one genotype is different from the rest, one location is different from the others and that there is GxE interactions. If we want to be more specific, would need to do some multiple comparison tests.
If we only have two things to compare, we could do a t-test.
xx <- dd %>%
filter(Location %in% c("Saskatoon, Canada", "Jessore, Bangladesh")) %>%
spread(Location, DTF)
t.test(x = xx$`Saskatoon, Canada`, y = xx$`Jessore, Bangladesh`)
##
## Welch Two Sample t-test
##
## data: xx$`Saskatoon, Canada` and xx$`Jessore, Bangladesh`
## t = -17.521, df = 32.701, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -32.18265 -25.48402
## sample estimates:
## mean of x mean of y
## 52.11111 80.94444
DTF in Saskatoon, Canada is significantly different (p-value < 2.2e-16) from DTF in Jessore, Bangladesh.
xx <- dd %>%
filter(Name %in% c("ILL 618 AGL", "Laird AGL"),
Location == "Metaponto, Italy") %>%
spread(Name, DTF)
t.test(x = xx$`ILL 618 AGL`, y = xx$`Laird AGL`)
##
## Welch Two Sample t-test
##
## data: xx$`ILL 618 AGL` and xx$`Laird AGL`
## t = 0.38008, df = 8.0564, p-value = 0.7137
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5.059739 7.059739
## sample estimates:
## mean of x mean of y
## 137.8333 136.8333
DTF between ILL 618 AGL and Laird AGL are not significantly different (p-value = 0.7137) in Metaponto, Italy.
pch Plot
xx <- data.frame(x = rep(1:6, times = 5, length.out = 26),
y = rep(5:1, each = 6, length.out = 26),
pch = 0:25)
mp <- ggplot(xx, aes(x = x, y = y, shape = as.factor(pch))) +
geom_point(color = "darkred", fill = "darkblue", size = 5) +
geom_text(aes(label = pch), nudge_x = -0.25) +
scale_shape_manual(values = xx$pch) +
scale_x_continuous(breaks = 6:1) +
scale_y_continuous(breaks = 6:1) +
theme_void() +
theme(legend.position = "none",
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
axis.text = element_blank(),
axis.ticks = element_blank()) +
labs(title = "Plot symbols in R (pch)",
subtitle = "color = \"darkred\", fill = \"darkblue\"",
x = NULL, y = NULL)
ggsave("pch.png", mp, width = 4.5, height = 3, bg = "white")
R Markdown
Tutorials on how to create an R markdown document like this one can be found here: