dblogr/

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 .

R Project Logo
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Calculator

can be used as a super awesome calculator

# 5 + 3 = 8
5 + 3 
## [1] 8
# 24 / (1 + 2) = 8
24 / (1 + 2) 
## [1] 8
# 2 * 2 * 2 = 8
2^3 
## [1] 8
# 8 * 8 = 64
sqrt(64) 
## [1] 8
# -log10(0.05 / 5000000) = 8
-log10(0.05 / 5000000) 
## [1] 8

Functions

has many useful built in functions

1:10
##  [1]  1  2  3  4  5  6  7  8  9 10
as.character(1:10)
##  [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10"
rep(1:2, times = 5)
##  [1] 1 2 1 2 1 2 1 2 1 2
rep(1:5, times = 2)
##  [1] 1 2 3 4 5 1 2 3 4 5
rep(1:5, each = 2)
##  [1] 1 1 2 2 3 3 4 4 5 5
rep(1:5, length.out = 7)
## [1] 1 2 3 4 5 1 2
seq(5, 50, by = 5)
##  [1]  5 10 15 20 25 30 35 40 45 50
seq(5, 50, length.out = 5)
## [1]  5.00 16.25 27.50 38.75 50.00
paste(1:10, 20:30, sep = "-")
##  [1] "1-20"  "2-21"  "3-22"  "4-23"  "5-24"  "6-25"  "7-26"  "8-27"  "9-28"  "10-29" "1-30"
paste(1:10, collapse = "-")
## [1] "1-2-3-4-5-6-7-8-9-10"
paste0("x", 1:10)
##  [1] "x1"  "x2"  "x3"  "x4"  "x5"  "x6"  "x7"  "x8"  "x9"  "x10"
min(1:10)
## [1] 1
max(1:10)
## [1] 10
range(1:10)
## [1]  1 10
mean(1:10)
## [1] 5.5
sd(1:10)
## [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

xx <- NULL #creates and empty object
for(i in 1:10) {
  xx[i] <- i*3
}
xx
##  [1]  3  6  9 12 15 18 21 24 27 30
xx %% 2 #gives the remainder when divided by 2
##  [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"
# or
ifelse(xx %% 2 == 0, "Even", "Odd")
##  [1] "Odd"  "Even" "Odd"  "Even" "Odd"  "Even" "Odd"  "Even" "Odd"  "Even"
paste(xx, ifelse(xx %% 2 == 0, "is Even", "is Odd"))
##  [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"
##  [9] "27 is Odd"  "30 is Even"

Objects

Information can be stored in user defined objects, in multiple forms:

  • c(): a string of values
  • matrix(): a two dimensional matrix in one format
  • data.frame(): a two dimensional matrix where each column can be a different format
  • list():

A string…

xc <- 1:10
xc
##  [1]  1  2  3  4  5  6  7  8  9 10
xc <- c(1,2,3,4,5,6,7,8,9,10)
xc
##  [1]  1  2  3  4  5  6  7  8  9 10

A matrix…

xm <- matrix(1:100, nrow = 10, ncol = 10, byrow = T)
xm
##       [,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
xm <- matrix(1:100, nrow = 10, ncol = 10, byrow = F)
xm
##       [,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…

xl <- list(xc, xm, xd)
xl[[1]]
##  [1]  1  2  3  4  5  6  7  8  9 10
xl[[2]]
##       [,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
xl[[3]]
##    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

xc[5] # 5th element in xc
## [1] 5
xd$x3[5] # 5th element in col "x3"
## [1] 1
xd[5,"x3"] # row 5, col "x3"
## [1] 1
xd$x3 # all of col "x3"
##  [1] 1 1 1 1 1 2 2 2 3 3
xd[,"x3"] # all rows, col "x3"
##  [1] 1 1 1 1 1 2 2 2 3 3
xd[3,] # row 3, all cols
##   x1 x2 x3 x4 x5 x6 x7        x8 x9  x10
## 3 cc  3  1  1  3  2 15 0.4771213 27 TRUE
xd[c(2,4),c("x4","x5")] # rows 2 & 4, cols "x4" & "x5"
##   x4 x5
## 2  2  2
## 4  2  4
xl[[3]]$x1 # 3rd object in the list, col "x1
##  [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
xd$x3 <- as.character(xd$x3)
xd$x3
##  [1] "1" "1" "1" "1" "1" "2" "2" "2" "3" "3"
xd$x3 <- as.numeric(xd$x3)
xd$x3
##  [1] 1 1 1 1 1 2 2 2 3 3
xd$x3 <- as.factor(xd$x3)
xd$x3
##  [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 1 2 3
xd$x3 <- factor(xd$x3, levels = c("3","2","1"))
xd$x3
##  [1] 1 1 1 1 1 2 2 2 3 3
## Levels: 3 2 1
xd$x10
##  [1]  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
as.numeric(xd$x10) # TRUE = 1, FALSE = 0
##  [1] 1 1 1 0 0 1 1 0 0 0
sum(xd$x10)
## [1] 5

Internal structure of an object can be checked with str()

str(xc) # c()
##  num [1:10] 1 2 3 4 5 6 7 8 9 10
str(xm) # matrix()
##  int [1:10, 1:10] 1 2 3 4 5 6 7 8 9 10 ...
str(xd) # data.frame()
## '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 ...
str(xl) # list()
## 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.

install.packages("scales")
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

scales::rescale(1:10, to = c(1,30))
##  [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$Data1 * 1000
xx
##    Group Data1 Data2 NewData1 NewData2
## 1      X     1    10       11     1000
## 2      X     2    20       22     2000
## 3      Y     3    30       33     3000
## 4      Y     4    40       44     4000
## 5      Y     5    50       55     5000
## 6      X     6    60       66     6000
## 7      X     7    70       77     7000
## 8      X     8    80       88     8000
## 9      Y     9    90       99     9000
## 10     Y    10   100      110    10000
xx$Data1 < 5 # which are less than 5
##  [1]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
xx[xx$Data1 < 5,]
##   Group Data1 Data2 NewData1 NewData2
## 1     X     1    10       11     1000
## 2     X     2    20       22     2000
## 3     Y     3    30       33     3000
## 4     Y     4    40       44     4000
xx[xx$Group == "X", c("Group","Data2","NewData1")]
##   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
filter(xx, Data1 < 5)
##   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
xx %>% filter(Data1 < 5)
##   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
xx %>% filter(Group == "X") %>% 
  select(Group, NewColName=Data2, NewData1)
##   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.
xx %>% left_join(xs, by = "Group")
##    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

xx <- read.csv("data_r_tutorial.csv")
write.csv(xx, "data_r_tutorial.csv", row.names = F)

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

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
yy <- yy %>% spread(key = Location, value = Mean_DTF)
yy
## # 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
yy <- yy %>% gather(key = TraitName, value = Value, 2:4)
yy
## # 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
yy <- yy %>% spread(key = Name, value = Value)
yy
## # 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()

# A basic scatter plot
plot(x = xd$x8, y = xd$x9)

# Adjust color and shape of the points
plot(x = xd$x8, y = xd$x9, col = "darkred", pch = 0)

plot(x = xd$x8, y = xd$x9, col = xd$x4, pch = xd$x4)

# Adjust plot type 
plot(x = xd$x8, y = xd$x9, type = "line")

# Adjust linetype
plot(x = xd$x8, y = xd$x9, type = "line", lty = 2)

# Plot lines and points
plot(x = xd$x8, y = xd$x9, type = "both")

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] 77.152603 17.092284 82.804121 79.468784 54.324774 75.743599  1.793080  4.619851 26.168223 68.417082 30.216503
##  [12] 68.630146 85.309261 20.936962 81.251742 46.118445 42.034681 69.848978 31.255699 84.730194 76.444621 18.308148
##  [23] 72.755251 44.169507 19.144339 30.492828 22.726455 63.109458 18.425065 44.167590 36.429344 22.822045  5.156477
##  [34] 96.382447 25.275699 84.939285 44.397066 79.381456 74.267450 34.921348 76.048172 33.277575 80.392451 38.031192
##  [45] 44.190326  2.963671 95.780402 72.315340 63.287432 98.000000 82.488955 35.181922 88.042699 20.282898 57.795733
##  [56] 92.848927 22.700094 41.120234 65.184459 62.091439 23.092097 19.718216 92.560617 33.177140 64.359985 55.000833
##  [67] 75.696104  4.788791 72.715144 34.641761 54.345250  8.075174 13.803461 88.163802 89.162206 58.469454 81.294373
##  [78] 95.614294 49.950071 23.270368 86.283575 42.168688 96.499614 52.968176 41.485576 96.947674  7.754029 31.765841
##  [89] 87.191858 21.813328  1.951203 23.539873 12.963345 24.592010 68.064925 57.625059 59.104732 92.447522 93.954402
## [100] 79.367076
plot(x = ru)

order(ru)
##   [1]   7  91  46   8  68  33  87  72  93  73   2  22  29  25  62  54  14  90  57  27  32  61  80  92  94  35   9  11
##  [29]  26  19  88  64  42  70  40  52  31  44  58  85  17  82  30  24  45  37  16  79  84   5  71  66  96  55  76  97
##  [57]  60  28  49  65  59  95  10  12  18  48  69  23  39  67   6  41  21   1 100  38   4  43  15  77  51   3  20  36
##  [85]  13  81  89  53  74  75  98  63  56  99  78  47  34  83  86  50
ru<- ru[order(ru)]
ru
##   [1]  1.793080  1.951203  2.963671  4.619851  4.788791  5.156477  7.754029  8.075174 12.963345 13.803461 17.092284
##  [12] 18.308148 18.425065 19.144339 19.718216 20.282898 20.936962 21.813328 22.700094 22.726455 22.822045 23.092097
##  [23] 23.270368 23.539873 24.592010 25.275699 26.168223 30.216503 30.492828 31.255699 31.765841 33.177140 33.277575
##  [34] 34.641761 34.921348 35.181922 36.429344 38.031192 41.120234 41.485576 42.034681 42.168688 44.167590 44.169507
##  [45] 44.190326 44.397066 46.118445 49.950071 52.968176 54.324774 54.345250 55.000833 57.625059 57.795733 58.469454
##  [56] 59.104732 62.091439 63.109458 63.287432 64.359985 65.184459 68.064925 68.417082 68.630146 69.848978 72.315340
##  [67] 72.715144 72.755251 74.267450 75.696104 75.743599 76.048172 76.444621 77.152603 79.367076 79.381456 79.468784
##  [78] 80.392451 81.251742 81.294373 82.488955 82.804121 84.730194 84.939285 85.309261 86.283575 87.191858 88.042699
##  [89] 88.163802 89.162206 92.447522 92.560617 92.848927 93.954402 95.614294 95.780402 96.382447 96.499614 96.947674
## [100] 98.000000
plot(x = ru)

# 100 normally distributed numbers with a mean of 50 and sd of 10
nd <- rnorm(100, mean = 50, sd = 10)
nd
##   [1] 32.63671 55.31042 36.94226 53.44871 46.26445 51.32190 50.20777 56.61841 39.78204 58.61444 39.95833 59.36003
##  [13] 36.57054 56.53226 37.53126 42.52957 47.12631 35.59133 47.84774 42.40547 66.68414 70.00763 34.58533 51.74298
##  [25] 38.05416 63.16824 62.12487 45.54744 41.70844 48.34426 62.96012 52.49791 41.10933 47.10056 48.00239 34.58112
##  [37] 35.82234 45.21239 60.17887 52.64009 44.80489 43.66278 55.34933 66.40781 47.05007 47.24586 46.28736 57.33770
##  [49] 63.87857 39.52001 52.36125 50.19096 59.22227 43.36226 57.46042 48.83162 51.69936 50.48656 40.86063 36.55639
##  [61] 46.65618 34.91792 61.90927 52.98910 50.33312 57.20397 60.27683 42.61187 49.70440 54.06991 47.34290 39.39994
##  [73] 56.48333 52.55685 39.72444 22.51617 53.83327 42.39119 61.55854 55.53022 60.80642 77.76619 47.11716 54.15376
##  [85] 38.73536 44.62927 34.11943 49.31376 50.35462 20.54094 47.30898 62.11794 59.19515 50.16351 43.08361 57.76390
##  [97] 67.87251 42.90304 42.15267 37.35808
nd <- nd[order(nd)]
nd
##   [1] 20.54094 22.51617 32.63671 34.11943 34.58112 34.58533 34.91792 35.59133 35.82234 36.55639 36.57054 36.94226
##  [13] 37.35808 37.53126 38.05416 38.73536 39.39994 39.52001 39.72444 39.78204 39.95833 40.86063 41.10933 41.70844
##  [25] 42.15267 42.39119 42.40547 42.52957 42.61187 42.90304 43.08361 43.36226 43.66278 44.62927 44.80489 45.21239
##  [37] 45.54744 46.26445 46.28736 46.65618 47.05007 47.10056 47.11716 47.12631 47.24586 47.30898 47.34290 47.84774
##  [49] 48.00239 48.34426 48.83162 49.31376 49.70440 50.16351 50.19096 50.20777 50.33312 50.35462 50.48656 51.32190
##  [61] 51.69936 51.74298 52.36125 52.49791 52.55685 52.64009 52.98910 53.44871 53.83327 54.06991 54.15376 55.31042
##  [73] 55.34933 55.53022 56.48333 56.53226 56.61841 57.20397 57.33770 57.46042 57.76390 58.61444 59.19515 59.22227
##  [85] 59.36003 60.17887 60.27683 60.80642 61.55854 61.90927 62.11794 62.12487 62.96012 63.16824 63.87857 66.40781
##  [97] 66.68414 67.87251 70.00763 77.76619
plot(x = nd)

hist(x = nd)

hist(nd, breaks = 20, col = "darkgreen")

plot(x = density(nd))

boxplot(x = nd)

boxplot(x = nd, horizontal = T)

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.

library(ggplot2)
mp <- ggplot(xd, aes(x = x8, y = x9))
mp + geom_point()

mp + geom_point(aes(color = x3, shape = x3), size = 4)

mp + geom_line(size = 2)

mp + geom_line(aes(color = x3), size = 2)

mp + geom_smooth(method = "loess")

mp + geom_smooth(method = "lm")

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")

mp + geom_histogram(color = "black", position = "dodge")

mp1 <- mp + geom_histogram(color = "black") + facet_grid(group~.)
mp1

mp + geom_density(alpha = 0.5)

mp <- ggplot(xx, aes(x = group, y = data, fill = group))
mp + geom_boxplot(color = "black")

mp + geom_boxplot() + geom_point()

mp + geom_violin() + geom_boxplot(width = 0.1, fill = "white")

library(ggbeeswarm)
mp + geom_quasirandom()

mp + geom_quasirandom(aes(shape = group))

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

# 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.

summary(aov(DTF ~ Name * Location, data = dd))
##               Df Sum Sq Mean Sq  F value   Pr(>F)    
## Name           2     88      44    3.476   0.0395 *  
## Location       2  65863   32931 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

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© Derek Michael Wright