Lab 2 Practice

Last updated: 2019-09-20

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An Intro to R data programming

Base R tools:
* classes
* numeric summaries
* basic plots

New R tools:
* tidyverse (is a collection of R packages)
* ggplot2 package: advanced graphics
* dplyr package: data manipulation, working with data frames

Part 1: Object Type

icecream <- read.table("data/icecream.txt")
dim(icecream)

[1] 200   5

copier <- read.table("data/CH01PR20.txt")
dim(copier)

[1] 45  2

Understand Dataframe

class(icecream)

[1] "data.frame"

class(copier)

[1] "data.frame"

head(icecream)

   id female ice_cream video puzzle
1  70      0         2    47     57
2 121      1         1    63     61
3  86      0         3    58     31
4 141      0         3    53     56
5 172      0         1    53     61
6 113      0         1    63     61

head(copier)

colnames(copier)=c("minutes","number")
head(copier)

  minutes number
1      20      2
2      60      4
3      46      3
4      41      2
5      12      1
6     137     10

#alternative way
copier <- setNames(copier,c("minutes","number"))
head(copier)

  minutes number
1      20      2
2      60      4
3      46      3
4      41      2
5      12      1
6     137     10

dim(icecream)

[1] 200   5

names(icecream)

[1] "id"        "female"    "ice_cream" "video"     "puzzle"

class(icecream$video)

[1] "integer"

head(iris)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

class(iris$Species)

[1] "factor"

class(iris$Sepal.Length)

[1] "numeric"

Class type can be changed

class(icecream$video)

[1] "integer"

x=as.numeric(icecream$video)
class(x)

[1] "numeric"

Check the difference of the following to different types

  [1] 47 63 58 53 53 63 53 39 58 50 53 63 61 55 31 50 50 58 55 53 66 72 55
 [24] 61 39 39 61 58 39 55 47 64 66 72 61 61 66 66 36 39 42 58 55 50 63 69
 [47] 49 63 53 47 57 47 50 55 69 26 33 56 58 44 58 69 34 36 36 50 55 42 65
 [70] 44 39 58 63 74 58 45 49 63 39 42 55 61 66 63 44 63 53 42 34 61 47 66
 [93] 69 44 47 63 66 69 39 61 69 66 33 50 61 42 50 51 50 58 61 39 46 59 55
[116] 42 55 58 58 39 50 50 39 48 34 58 44 50 47 29 50 54 50 47 44 67 58 44
[139] 42 44 44 50 39 44 53 48 55 44 40 34 42 58 50 53 58 55 54 47 42 61 53
[162] 51 63 61 55 40 61 47 55 53 50 47 31 61 35 54 55 53 58 56 50 39 63 50
[185] 66 58 53 42 55 53 42 50 55 34 50 42 36 55 58 53

y=as.factor(x)
class(y)

[1] "factor"

  [1] 47 63 58 53 53 63 53 39 58 50 53 63 61 55 31 50 50 58 55 53 66 72 55
 [24] 61 39 39 61 58 39 55 47 64 66 72 61 61 66 66 36 39 42 58 55 50 63 69
 [47] 49 63 53 47 57 47 50 55 69 26 33 56 58 44 58 69 34 36 36 50 55 42 65
 [70] 44 39 58 63 74 58 45 49 63 39 42 55 61 66 63 44 63 53 42 34 61 47 66
 [93] 69 44 47 63 66 69 39 61 69 66 33 50 61 42 50 51 50 58 61 39 46 59 55
[116] 42 55 58 58 39 50 50 39 48 34 58 44 50 47 29 50 54 50 47 44 67 58 44
[139] 42 44 44 50 39 44 53 48 55 44 40 34 42 58 50 53 58 55 54 47 42 61 53
[162] 51 63 61 55 40 61 47 55 53 50 47 31 61 35 54 55 53 58 56 50 39 63 50
[185] 66 58 53 42 55 53 42 50 55 34 50 42 36 55 58 53
34 Levels: 26 29 31 33 34 35 36 39 40 42 44 45 46 47 48 49 50 51 53 ... 74

z=as.character(x)
class(z)

[1] "character"

  [1] "47" "63" "58" "53" "53" "63" "53" "39" "58" "50" "53" "63" "61" "55"
 [15] "31" "50" "50" "58" "55" "53" "66" "72" "55" "61" "39" "39" "61" "58"
 [29] "39" "55" "47" "64" "66" "72" "61" "61" "66" "66" "36" "39" "42" "58"
 [43] "55" "50" "63" "69" "49" "63" "53" "47" "57" "47" "50" "55" "69" "26"
 [57] "33" "56" "58" "44" "58" "69" "34" "36" "36" "50" "55" "42" "65" "44"
 [71] "39" "58" "63" "74" "58" "45" "49" "63" "39" "42" "55" "61" "66" "63"
 [85] "44" "63" "53" "42" "34" "61" "47" "66" "69" "44" "47" "63" "66" "69"
 [99] "39" "61" "69" "66" "33" "50" "61" "42" "50" "51" "50" "58" "61" "39"
[113] "46" "59" "55" "42" "55" "58" "58" "39" "50" "50" "39" "48" "34" "58"
[127] "44" "50" "47" "29" "50" "54" "50" "47" "44" "67" "58" "44" "42" "44"
[141] "44" "50" "39" "44" "53" "48" "55" "44" "40" "34" "42" "58" "50" "53"
[155] "58" "55" "54" "47" "42" "61" "53" "51" "63" "61" "55" "40" "61" "47"
[169] "55" "53" "50" "47" "31" "61" "35" "54" "55" "53" "58" "56" "50" "39"
[183] "63" "50" "66" "58" "53" "42" "55" "53" "42" "50" "55" "34" "50" "42"
[197] "36" "55" "58" "53"

Change the variable ice_cream to factor

icecream$ice_cream

  [1] 2 1 3 3 1 1 1 1 1 1 1 1 3 3 2 2 3 1 3 1 1 1 1 3 1 1 3 1 3 2 1 3 3 1 3
 [36] 3 1 3 2 1 1 1 1 2 3 2 3 1 1 1 1 3 1 1 1 3 2 1 1 1 1 3 2 1 3 1 1 2 2 1
 [71] 1 3 1 1 1 2 3 3 1 3 3 2 1 2 1 3 3 3 1 3 1 2 3 2 2 2 1 3 2 3 3 3 2 3 1
[106] 1 3 2 2 3 1 2 3 2 3 1 3 3 2 2 2 1 1 1 2 3 1 1 3 2 1 2 1 1 2 3 1 1 1 1
[141] 2 3 1 1 1 3 1 1 2 2 2 3 1 1 1 1 1 1 1 3 2 1 3 2 1 2 1 2 1 2 2 1 3 3 2
[176] 2 1 1 3 2 1 1 3 1 3 2 1 1 3 2 2 3 1 3 1 1 1 1 1 3

class(icecream$ice_cream)

[1] "integer"

icecream$ice_cream=as.factor(icecream$ice_cream)
icecream$ice_cream

  [1] 2 1 3 3 1 1 1 1 1 1 1 1 3 3 2 2 3 1 3 1 1 1 1 3 1 1 3 1 3 2 1 3 3 1 3
 [36] 3 1 3 2 1 1 1 1 2 3 2 3 1 1 1 1 3 1 1 1 3 2 1 1 1 1 3 2 1 3 1 1 2 2 1
 [71] 1 3 1 1 1 2 3 3 1 3 3 2 1 2 1 3 3 3 1 3 1 2 3 2 2 2 1 3 2 3 3 3 2 3 1
[106] 1 3 2 2 3 1 2 3 2 3 1 3 3 2 2 2 1 1 1 2 3 1 1 3 2 1 2 1 1 2 3 1 1 1 1
[141] 2 3 1 1 1 3 1 1 2 2 2 3 1 1 1 1 1 1 1 3 2 1 3 2 1 2 1 2 1 2 2 1 3 3 2
[176] 2 1 1 3 2 1 1 3 1 3 2 1 1 3 2 2 3 1 3 1 1 1 1 1 3
Levels: 1 2 3

class(icecream$ice_cream)

[1] "factor"

Part 2: Exploratory Data Analysis

(1) Numerical summaries:
-mean, median, five number summary, standard deviation, IQR, correlation, etc.

Traditional R

mean(icecream$video)

[1] 51.85

median(icecream$video)

[1] 53

sd(icecream$video)

[1] 9.900891

var(icecream$video)

[1] 98.02764

summary(icecream$video)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  26.00   44.00   53.00   51.85   58.00   74.00

IQR(icecream$video)

[1] 14

cor(copier$minutes,copier$number)

[1] 0.978517

summary(icecream)

       id             female      ice_cream     video      
 Min.   :  1.00   Min.   :0.000   1:95      Min.   :26.00  
 1st Qu.: 50.75   1st Qu.:0.000   2:47      1st Qu.:44.00  
 Median :100.50   Median :1.000   3:58      Median :53.00  
 Mean   :100.50   Mean   :0.545             Mean   :51.85  
 3rd Qu.:150.25   3rd Qu.:1.000             3rd Qu.:58.00  
 Max.   :200.00   Max.   :1.000             Max.   :74.00  
     puzzle     
 Min.   :26.00  
 1st Qu.:46.00  
 Median :52.00  
 Mean   :52.41  
 3rd Qu.:61.00  
 Max.   :71.00

summary(copier)

    minutes           number      
 Min.   :  3.00   Min.   : 1.000  
 1st Qu.: 36.00   1st Qu.: 2.000  
 Median : 74.00   Median : 5.000  
 Mean   : 76.27   Mean   : 5.111  
 3rd Qu.:111.00   3rd Qu.: 7.000  
 Max.   :156.00   Max.   :10.000

Advanced summary statistics (tidyverse)
“tibbles” instead of R’s traditional data.frame. Tibbles are data frames, but they tweak some older behaviours to make life a little easier.

Install and load R package “dplyr”

#install.packages("dplyr")
library(dplyr)


Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

pipe
Use %>%(pipe). You can read it as a series of imperative statements: group, then summarise, then filter. A good way to pronounce %>% when reading code is “then”.Behind the scenes, x %>% f(y) turns into f(x, y), and x %>% f(y) %>% g(z) turns into g(f(x, y), z) and so on. You can use the pipe to rewrite multiple operations in a way that you can read left-to-right, top-to-bottom.

dplyr verbs
‘filter’,‘arrange’,‘mutate’,‘summarise’,‘group_by’

filter: select cases based on their values

head(icecream)

   id female ice_cream video puzzle
1  70      0         2    47     57
2 121      1         1    63     61
3  86      0         3    58     31
4 141      0         3    53     56
5 172      0         1    53     61
6 113      0         1    63     61

icecream <- as_tibble(icecream)
icecream

# A tibble: 200 x 5
      id female ice_cream video puzzle
   <int>  <int> <fct>     <int>  <int>
 1    70      0 2            47     57
 2   121      1 1            63     61
 3    86      0 3            58     31
 4   141      0 3            53     56
 5   172      0 1            53     61
 6   113      0 1            63     61
 7    50      0 1            53     61
 8    11      0 1            39     36
 9    84      0 1            58     51
10    48      0 1            50     51
# … with 190 more rows

icecream %>% filter(female==0)

# A tibble: 91 x 5
      id female ice_cream video puzzle
   <int>  <int> <fct>     <int>  <int>
 1    70      0 2            47     57
 2    86      0 3            58     31
 3   141      0 3            53     56
 4   172      0 1            53     61
 5   113      0 1            63     61
 6    50      0 1            53     61
 7    11      0 1            39     36
 8    84      0 1            58     51
 9    48      0 1            50     51
10    75      0 1            53     61
# … with 81 more rows

icecream %>% filter(female==1, video<50)

# A tibble: 41 x 5
      id female ice_cream video puzzle
   <int>  <int> <fct>     <int>  <int>
 1     8      1 2            44     48
 2   129      1 2            47     51
 3     1      1 2            39     41
 4    47      1 2            33     41
 5    65      1 1            42     56
 6     4      1 2            39     51
 7   131      1 3            46     66
 8   106      1 1            42     41
 9    37      1 2            39     51
10    73      1 1            39     56
# … with 31 more rows

iris %>% filter(Species=="setosa", Sepal.Width>4)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.7         4.4          1.5         0.4  setosa
2          5.2         4.1          1.5         0.1  setosa
3          5.5         4.2          1.4         0.2  setosa

iris %>% filter(Species=="versicolor", Petal.Length<4)

   Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
1           4.9         2.4          3.3         1.0 versicolor
2           5.2         2.7          3.9         1.4 versicolor
3           5.0         2.0          3.5         1.0 versicolor
4           5.6         2.9          3.6         1.3 versicolor
5           5.6         2.5          3.9         1.1 versicolor
6           5.7         2.6          3.5         1.0 versicolor
7           5.5         2.4          3.8         1.1 versicolor
8           5.5         2.4          3.7         1.0 versicolor
9           5.8         2.7          3.9         1.2 versicolor
10          5.0         2.3          3.3         1.0 versicolor
11          5.1         2.5          3.0         1.1 versicolor

# Question: filter iris dataset for Species equal to "setosa" or "virginica"

arrange: reorder cases

icecream %>% arrange(video) # order 'video' column in ascending order

# A tibble: 200 x 5
      id female ice_cream video puzzle
   <int>  <int> <fct>     <int>  <int>
 1    15      0 3            26     42
 2    45      1 2            29     26
 3    38      0 2            31     56
 4    51      1 3            31     39
 5    67      0 2            33     32
 6    47      1 2            33     41
 7   134      0 2            34     46
 8   133      0 1            34     31
 9    44      1 2            34     46
10    46      1 2            34     41
# … with 190 more rows

icecream %>% arrange(desc(puzzle)) # order 'puzzle' column in descending order

# A tibble: 200 x 5
      id female ice_cream video puzzle
   <int>  <int> <fct>     <int>  <int>
 1    95      0 3            61     71
 2   192      0 3            66     71
 3   183      0 1            55     71
 4   100      1 3            69     71
 5   180      1 3            58     71
 6   139      1 1            55     71
 7    59      1 1            55     71
 8    23      1 2            58     71
 9   143      0 1            72     66
10   154      0 3            61     66
# … with 190 more rows

as_tibble(iris) %>% arrange(Petal.Length)

# A tibble: 150 x 5
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
 1          4.6         3.6          1           0.2 setosa 
 2          4.3         3            1.1         0.1 setosa 
 3          5.8         4            1.2         0.2 setosa 
 4          5           3.2          1.2         0.2 setosa 
 5          4.7         3.2          1.3         0.2 setosa 
 6          5.4         3.9          1.3         0.4 setosa 
 7          5.5         3.5          1.3         0.2 setosa 
 8          4.4         3            1.3         0.2 setosa 
 9          5           3.5          1.3         0.3 setosa 
10          4.5         2.3          1.3         0.3 setosa 
# … with 140 more rows

as_tibble(iris) %>% arrange(desc(Sepal.Length))

# A tibble: 150 x 5
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species  
          <dbl>       <dbl>        <dbl>       <dbl> <fct>    
 1          7.9         3.8          6.4         2   virginica
 2          7.7         3.8          6.7         2.2 virginica
 3          7.7         2.6          6.9         2.3 virginica
 4          7.7         2.8          6.7         2   virginica
 5          7.7         3            6.1         2.3 virginica
 6          7.6         3            6.6         2.1 virginica
 7          7.4         2.8          6.1         1.9 virginica
 8          7.3         2.9          6.3         1.8 virginica
 9          7.2         3.6          6.1         2.5 virginica
10          7.2         3.2          6           1.8 virginica
# … with 140 more rows

# Question: 1) filter iris dataset for Species equal to "setosa" and 2) sort  in descending order of Sepal.Width

mutate: add new variables that are functions of existing variables

icecream_new <- icecream %>% mutate(puzzle100 = puzzle*100)
icecream_new

# A tibble: 200 x 6
      id female ice_cream video puzzle puzzle100
   <int>  <int> <fct>     <int>  <int>     <dbl>
 1    70      0 2            47     57      5700
 2   121      1 1            63     61      6100
 3    86      0 3            58     31      3100
 4   141      0 3            53     56      5600
 5   172      0 1            53     61      6100
 6   113      0 1            63     61      6100
 7    50      0 1            53     61      6100
 8    11      0 1            39     36      3600
 9    84      0 1            58     51      5100
10    48      0 1            50     51      5100
# … with 190 more rows

# Question: 1) filter icecream dataset for ice_cream equal to 1, 2) create video1000 (video*1000) column, 3) sort in descending order of video1000, 4) assign the dataset to icecream_new2

summarise: condense multiple values to a single value

icecream %>% summarise(Mean_video=mean(video), SD_video=sd(video), SD_median=median(video))

# A tibble: 1 x 3
  Mean_video SD_video SD_median
       <dbl>    <dbl>     <dbl>
1       51.8     9.90        53

group_by: break down a dataset into specified groups of rows

puzzle.summary <- icecream %>% group_by(ice_cream) %>% summarise(Mean=mean(puzzle),
            Variance=var(puzzle))%>%as.data.frame()

iris %>% group_by(Species) %>% summarise(Mean=mean(Sepal.Length), Median=median(Sepal.Length), Variance=var(Sepal.Length))

# A tibble: 3 x 4
  Species     Mean Median Variance
  <fct>      <dbl>  <dbl>    <dbl>
1 setosa      5.01    5      0.124
2 versicolor  5.94    5.9    0.266
3 virginica   6.59    6.5    0.404

# Question: 1) group by Species 2) calculate mean, median, var, min, max of Sepal.Length for each group 3) sort data in descending order of mean 3) convert to a data frame  4) assign the output to "iris_new"

Average and standard deviation by icecream flavor

icecream %>% group_by(ice_cream) %>% 
  summarise(Mean=mean(puzzle),Variance=var(puzzle))

# A tibble: 3 x 3
  ice_cream  Mean Variance
  <fct>     <dbl>    <dbl>
1 1          52.0     99.5
2 2          47.3    117. 
3 3          57.1     99.2

puzzle.summary <-icecream %>% group_by(ice_cream) %>% 
  summarise(Mean=mean(puzzle), Variance=var(puzzle) )
puzzle.summary

# A tibble: 3 x 3
  ice_cream  Mean Variance
  <fct>     <dbl>    <dbl>
1 1          52.0     99.5
2 2          47.3    117. 
3 3          57.1     99.2

class(puzzle.summary)

[1] "tbl_df"     "tbl"        "data.frame"

puzzle.summary <- icecream %>% group_by(ice_cream) %>% 
  summarise(Mean=mean(puzzle),
            Variance=var(puzzle) )%>%as.data.frame()
puzzle.summary

  ice_cream     Mean  Variance
1         1 52.03158  99.45644
2         2 47.31915 117.43941
3         3 57.13793  99.24380

class(puzzle.summary)

[1] "data.frame"

sessionInfo()

R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] dplyr_0.8.3     workflowr_1.4.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2       knitr_1.24       whisker_0.3-2    magrittr_1.5    
 [5] tidyselect_0.2.5 R6_2.4.0         rlang_0.4.0      fansi_0.4.0     
 [9] stringr_1.4.0    tools_3.6.1      xfun_0.9         utf8_1.1.4      
[13] cli_1.1.0        git2r_0.26.1     htmltools_0.3.6  yaml_2.2.0      
[17] rprojroot_1.3-2  digest_0.6.20    assertthat_0.2.1 tibble_2.1.3    
[21] crayon_1.3.4     purrr_0.3.2      vctrs_0.2.0      fs_1.3.1        
[25] zeallot_0.1.0    glue_1.3.1       evaluate_0.14    rmarkdown_1.15  
[29] stringi_1.4.3    compiler_3.6.1   pillar_1.4.2     backports_1.1.4 
[33] pkgconfig_2.0.2

Lab 2 Practice

Donghyung Lee

2019-09-05

Part 1: Object Type

Part 2: Exploratory Data Analysis