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Chapter 9, Advanced dataframe manipulation

#Task 1: Downloading the dataframe

pirates.errors <- read.table("http://nathanieldphillips.com/wp-content/uploads/2015/05/pirate_survey_witherrors.txt", header = T , sep= "\t", stringsAsFactors=F)
#Task 2: Cleaning up the dataframe
pirates.errors$sex [!(pirates.errors$sex %in% c("male","female","other/NA"))] <- NA
table(pirates.errors$sex)
## 
## female   male 
##    466    490
pirates.errors$headband [!(pirates.errors$headband %in% c ("no","yes"))] <- NA
table(pirates.errors$headband) # what is a headband and sometimes should become NA
## 
##  no yes 
##  97 893
pirates.errors$age [!(pirates.errors$age %in% seq (1, 100,1))] <- NA
table(pirates.errors$age) # all values lower than 0 and higher than 100 should be marked as NA
## 
##  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 
##  1  1  5  1  2  4  5  7  5 19 20 33 35 38 55 73 52 63 55 58 69 70 57 56 40 
## 34 35 36 37 38 39 40 41 42 43 45 46 48 
## 45 32 32 12 10  5  5  6  4  2  1  1  1
pirates.errors$tattoos [!(pirates.errors$tattoos %in% seq (1, 130, 1))] <- NA
table(pirates.errors$tattoos) 
## 
##   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18 
##   5  13  24  24  39  60  73 124 119 110 128  91  73  46  21  15   9   6 
##  19 
##   2
pirates.errors$favorite.pirate [!(pirates.errors$favorite.pirate %in% c ("Anicetus", "Blackbeard", "Edward Low", "Hook", "Jack Sparrow", "Lewis Scot") )] <- NA
table(pirates.errors$favorite.pirate) 
## 
##     Anicetus   Blackbeard   Edward Low         Hook Jack Sparrow 
##          117          100          113          114          450 
##   Lewis Scot 
##           96
pirates.errors$sword.type [!(pirates.errors$sword.type %in% c ("cutlass", "sabre", "scimitar"))] <- NA # I was not sure whether banana should be marked as NA or not, I decided to leave it out
table(pirates.errors$sword.type)
## 
##  cutlass    sabre scimitar 
##      842       62       57
#Task 3
pirates <- read.table("http://nathanieldphillips.com/wp-content/uploads/2015/05/pirate_survey_noerrors.txt", sep = "\t", header = T, stringsAsFactors = F)
# Mean number of treasure tchests foudn by males, females and other
# I often used the function view to look at the data, but I had to leave it out, because otherwise the document would not knit

aggregate(formula= tchests.found ~ sex, FUN= mean, na.rm= T, data= pirates)
##      sex tchests.found
## 1 female      7.353319
## 2   male      7.128049
## 3  other      8.048780
# Task 4 Calculating the median sword.speed for each sword.type
aggregate(formula= sword.speed ~ sword.type, FUN= median, na.rm= T, data= pirates)
##   sword.type sword.speed
## 1     banana   2.5859139
## 2    cutlass   0.4848266
## 3      sabre   1.7393120
## 4   scimitar   1.7559671
 # highest banana, lowest cutlass
# Task 5 first way
aggregated.headband <-aggregate(formula= sword.speed ~ headband, FUN= median, na.rm= T, data= pirates)
# Pirates not wearing a headband had a higher sword.speed (no = 1.0780988)
# Task 5 second way 
aggregated.swordspeed <- aggregate(formula=sword.speed ~ headband+sword.type,  FUN= median,na.rm= T, data= pirates)    
# There are some differences between the conclusions you draw from the first way compared to the second way. This time you would derive from the table that those pirates wearing a headband and using the sword type banana have the highest number for the column word speed (7.9722183) and those pirates not wearing a headband and using the sword type cutlass have the lowest number for word speed  (0.3408127). Compared to the first way this is different as the fact of wearing a headband was just the other way around: those pirates wearing a headband had a higher sword speed than those not wearing one (no = 1.078098,  yes= 0.5375353).
# Task 6
require(dplyr)
## Loading required package: dplyr
## 
## Attaching package: 'dplyr'
## 
## The following object is masked from 'package:stats':
## 
##     filter
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
favorite.pirates.agg <- pirates %>%
group_by(favorite.pirate) %>% 
  summarise(frequency = n() ,tattoos.mean=mean(tattoos, na.rm= T), sword.speed.med = median(sword.speed, na.rm= T)
    )
favorite.pirates.agg 
## Source: local data frame [6 x 4]
## 
##   favorite.pirate frequency tattoos.mean sword.speed.med
## 1        Anicetus       120     9.100000       0.4776414
## 2      Blackbeard       100     9.620000       0.7313608
## 3      Edward Low       114     9.342105       0.5371732
## 4            Hook       115     9.713043       0.6046085
## 5    Jack Sparrow       453     9.607064       0.5391035
## 6      Lewis Scot        98     9.081633       0.5539311
# Task 7
aggregate.college <- aggregate(formula= college ~ age, FUN= median, data= pirates, na.rm= T)
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]):
## argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]):
## argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]):
## argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]):
## argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]):
## argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]):
## argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]):
## argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]):
## argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]):
## argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]):
## argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]):
## argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]):
## argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]):
## argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]):
## argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]):
## argument is not numeric or logical: returning NA
## Warning in mean.default(sort(x, partial = half + 0L:1L)[half + 0L:1L]):
## argument is not numeric or logical: returning NA
aggregate.college
##    age college
## 1    9    CCCC
## 2   10    CCCC
## 3   11    CCCC
## 4   12    CCCC
## 5   13    <NA>
## 6   14    <NA>
## 7   15    CCCC
## 8   16    <NA>
## 9   17    CCCC
## 10  18    CCCC
## 11  19    <NA>
## 12  20    CCCC
## 13  21    CCCC
## 14  22    <NA>
## 15  23    CCCC
## 16  24    CCCC
## 17  25    CCCC
## 18  26    <NA>
## 19  27    <NA>
## 20  28    <NA>
## 21  29    CCCC
## 22  30    CCCC
## 23  31    <NA>
## 24  32    <NA>
## 25  33   JSSFP
## 26  34   JSSFP
## 27  35   JSSFP
## 28  36    <NA>
## 29  37    <NA>
## 30  38    <NA>
## 31  39   JSSFP
## 32  40   JSSFP
## 33  41    <NA>
## 34  42    <NA>
## 35  43    <NA>
## 36  45   JSSFP
## 37  46   JSSFP
## 38  48   JSSFP
# Task 8
plot(x=pirates$tattoos, y=pirates$tchests.found, main="scatterplot", xlab= "tattoos", ylab= "tchests found", pch= 16, cex=1, col="lightgray", type= "p")

pirates$tattoos.cut5 <- cut(pirates$tattoos, seq(0,20,5))
pirates$tattoos.cut5
##    [1] (15,20] (5,10]  (10,15] (5,10]  (10,15] (5,10]  (10,15] (5,10] 
##    [9] (10,15] (10,15] (5,10]  (10,15] (10,15] (5,10]  (5,10]  (5,10] 
##   [17] (10,15] (5,10]  (0,5]   (10,15] (5,10]  (10,15] (15,20] (0,5]  
##   [25] (5,10]  (5,10]  (5,10]  (10,15] (5,10]  (10,15] (5,10]  (5,10] 
##   [33] (5,10]  (5,10]  (10,15] (10,15] (5,10]  (10,15] <NA>    (10,15]
##   [41] (5,10]  (10,15] (5,10]  (5,10]  (0,5]   (15,20] (10,15] (5,10] 
##   [49] (10,15] (0,5]   (5,10]  (15,20] (10,15] (5,10]  (0,5]   (0,5]  
##   [57] (5,10]  (10,15] (5,10]  (10,15] (5,10]  (10,15] (10,15] (0,5]  
##   [65] (10,15] (5,10]  (5,10]  (5,10]  (10,15] (0,5]   (0,5]   (10,15]
##   [73] (5,10]  (0,5]   (5,10]  (10,15] (10,15] (5,10]  (5,10]  <NA>   
##   [81] (5,10]  (0,5]   (5,10]  (5,10]  (0,5]   (0,5]   <NA>    (10,15]
##   [89] (5,10]  (5,10]  (15,20] (5,10]  (5,10]  (5,10]  (0,5]   (15,20]
##   [97] (10,15] (0,5]   (10,15] (5,10]  (10,15] (5,10]  (10,15] (5,10] 
##  [105] (5,10]  (15,20] (5,10]  (10,15] (10,15] (5,10]  (10,15] (5,10] 
##  [113] (5,10]  (10,15] (10,15] (10,15] (5,10]  (5,10]  (10,15] (5,10] 
##  [121] (10,15] (10,15] (10,15] (10,15] (5,10]  (10,15] (5,10]  (10,15]
##  [129] (10,15] (5,10]  (10,15] (10,15] (5,10]  (5,10]  (5,10]  (0,5]  
##  [137] (5,10]  (10,15] (5,10]  (5,10]  (10,15] (5,10]  (0,5]   (10,15]
##  [145] (0,5]   (5,10]  (10,15] (5,10]  (5,10]  (10,15] <NA>    (5,10] 
##  [153] (0,5]   (10,15] (5,10]  (5,10]  (5,10]  (10,15] (5,10]  (5,10] 
##  [161] (5,10]  (10,15] (5,10]  (5,10]  (5,10]  (5,10]  (5,10]  (5,10] 
##  [169] (0,5]   (0,5]   (0,5]   (10,15] (5,10]  (10,15] (5,10]  (10,15]
##  [177] (15,20] (10,15] (5,10]  (0,5]   (10,15] (10,15] (5,10]  (5,10] 
##  [185] (5,10]  (5,10]  (10,15] (5,10]  (10,15] (10,15] (10,15] (10,15]
##  [193] (5,10]  (10,15] (15,20] (5,10]  (10,15] (5,10]  (0,5]   (10,15]
##  [201] (5,10]  (0,5]   (15,20] (5,10]  (0,5]   (10,15] (5,10]  (10,15]
##  [209] (0,5]   (10,15] (5,10]  (10,15] (5,10]  (5,10]  (5,10]  (5,10] 
##  [217] (0,5]   (5,10]  (5,10]  (5,10]  (10,15] (10,15] (10,15] (10,15]
##  [225] (0,5]   (10,15] (5,10]  (5,10]  (5,10]  (10,15] (5,10]  (5,10] 
##  [233] (10,15] (5,10]  (5,10]  (10,15] (10,15] (5,10]  (5,10]  (0,5]  
##  [241] (10,15] (10,15] (0,5]   (10,15] (0,5]   (10,15] (5,10]  (10,15]
##  [249] (5,10]  (5,10]  (10,15] (5,10]  (0,5]   (10,15] (0,5]   (5,10] 
##  [257] (5,10]  (0,5]   (5,10]  (5,10]  (10,15] (5,10]  (10,15] (10,15]
##  [265] (0,5]   (5,10]  (15,20] (10,15] (0,5]   (0,5]   (5,10]  (10,15]
##  [273] (0,5]   (5,10]  (5,10]  (10,15] (10,15] (10,15] (5,10]  (10,15]
##  [281] (10,15] (10,15] (10,15] (0,5]   (0,5]   (10,15] (5,10]  (5,10] 
##  [289] (5,10]  (10,15] (0,5]   (10,15] (10,15] (10,15] (0,5]   (0,5]  
##  [297] (5,10]  (5,10]  (5,10]  (5,10]  (5,10]  (10,15] (5,10]  (5,10] 
##  [305] (10,15] (5,10]  (10,15] (10,15] (5,10]  (0,5]   (10,15] (10,15]
##  [313] (5,10]  (10,15] (5,10]  (10,15] (10,15] (5,10]  (0,5]   (5,10] 
##  [321] (10,15] (10,15] (5,10]  (10,15] (10,15] (10,15] (10,15] (5,10] 
##  [329] (5,10]  (5,10]  (5,10]  (10,15] (0,5]   (10,15] (10,15] (10,15]
##  [337] (5,10]  (5,10]  (5,10]  (5,10]  (5,10]  (5,10]  (5,10]  (5,10] 
##  [345] (10,15] (10,15] (10,15] (5,10]  (5,10]  (5,10]  (15,20] (5,10] 
##  [353] (10,15] (10,15] (10,15] (10,15] (5,10]  (10,15] (0,5]   (10,15]
##  [361] (5,10]  (5,10]  (5,10]  (10,15] (5,10]  (5,10]  (0,5]   (5,10] 
##  [369] (5,10]  (5,10]  (5,10]  (5,10]  (10,15] (10,15] (5,10]  (5,10] 
##  [377] (10,15] (10,15] (10,15] (10,15] (5,10]  (5,10]  (5,10]  (10,15]
##  [385] (10,15] (0,5]   (5,10]  (10,15] (0,5]   (5,10]  (10,15] (5,10] 
##  [393] (10,15] (5,10]  (5,10]  (10,15] (10,15] (10,15] (10,15] (5,10] 
##  [401] (10,15] (10,15] (5,10]  (10,15] (10,15] (10,15] (5,10]  (5,10] 
##  [409] (0,5]   (10,15] (5,10]  (5,10]  (5,10]  (10,15] (5,10]  (5,10] 
##  [417] (5,10]  (10,15] (5,10]  (5,10]  (5,10]  (0,5]   (5,10]  (10,15]
##  [425] (5,10]  (10,15] (10,15] (5,10]  (10,15] (5,10]  (5,10]  (0,5]  
##  [433] (5,10]  (0,5]   (10,15] (5,10]  (5,10]  (10,15] (5,10]  (10,15]
##  [441] (5,10]  (10,15] (5,10]  (15,20] (5,10]  (5,10]  (5,10]  (10,15]
##  [449] (5,10]  (10,15] (10,15] (5,10]  (10,15] (15,20] (5,10]  (10,15]
##  [457] (10,15] (5,10]  (10,15] (5,10]  (5,10]  (5,10]  (5,10]  (5,10] 
##  [465] (10,15] (5,10]  (5,10]  (0,5]   (5,10]  (10,15] (5,10]  (15,20]
##  [473] (0,5]   (5,10]  (10,15] (10,15] (5,10]  (0,5]   (5,10]  (5,10] 
##  [481] (10,15] (10,15] (10,15] (5,10]  (0,5]   (10,15] (10,15] (5,10] 
##  [489] (10,15] (5,10]  (5,10]  (10,15] (5,10]  (5,10]  (0,5]   (5,10] 
##  [497] (5,10]  (15,20] (5,10]  (5,10]  (10,15] (5,10]  (10,15] (0,5]  
##  [505] (5,10]  (5,10]  (10,15] (10,15] (10,15] (5,10]  (5,10]  (0,5]  
##  [513] (5,10]  (10,15] (0,5]   (0,5]   (10,15] (5,10]  (5,10]  (5,10] 
##  [521] (10,15] (5,10]  (5,10]  (5,10]  (5,10]  (10,15] (5,10]  (15,20]
##  [529] (10,15] (10,15] (10,15] (5,10]  (10,15] (5,10]  (10,15] (10,15]
##  [537] (5,10]  (10,15] (10,15] <NA>    (5,10]  (15,20] (10,15] (10,15]
##  [545] (10,15] (10,15] (10,15] (10,15] (10,15] (10,15] (5,10]  (5,10] 
##  [553] (5,10]  (5,10]  (5,10]  (10,15] (10,15] (5,10]  (5,10]  (10,15]
##  [561] (10,15] (10,15] (10,15] (5,10]  (10,15] (0,5]   (10,15] (5,10] 
##  [569] (5,10]  (10,15] (0,5]   (5,10]  (10,15] (10,15] (5,10]  (5,10] 
##  [577] (10,15] (10,15] (5,10]  (10,15] (5,10]  (5,10]  (5,10]  (0,5]  
##  [585] (5,10]  (10,15] (5,10]  (5,10]  (5,10]  (5,10]  (10,15] (5,10] 
##  [593] (5,10]  (5,10]  (10,15] (10,15] (10,15] (5,10]  (10,15] (5,10] 
##  [601] (5,10]  (5,10]  (5,10]  (0,5]   (5,10]  (10,15] (10,15] (10,15]
##  [609] (10,15] <NA>    (10,15] (5,10]  (15,20] (5,10]  (0,5]   (5,10] 
##  [617] (0,5]   (5,10]  (0,5]   (5,10]  (5,10]  (5,10]  (10,15] (5,10] 
##  [625] (5,10]  (5,10]  (5,10]  (5,10]  (5,10]  (5,10]  (0,5]   (10,15]
##  [633] (5,10]  (10,15] (5,10]  (10,15] (15,20] (10,15] (5,10]  (10,15]
##  [641] (5,10]  (5,10]  (5,10]  (10,15] (5,10]  (5,10]  (10,15] (0,5]  
##  [649] (10,15] (10,15] (5,10]  (0,5]   (10,15] (10,15] (5,10]  (5,10] 
##  [657] (10,15] (10,15] (5,10]  (10,15] (5,10]  (10,15] (0,5]   (10,15]
##  [665] (5,10]  (5,10]  (0,5]   (5,10]  (5,10]  (5,10]  (15,20] (5,10] 
##  [673] (5,10]  (15,20] (5,10]  (10,15] (5,10]  (5,10]  (10,15] (10,15]
##  [681] (5,10]  (5,10]  (5,10]  (5,10]  (5,10]  (5,10]  (5,10]  (15,20]
##  [689] (10,15] (5,10]  (5,10]  (15,20] (10,15] (0,5]   (5,10]  (5,10] 
##  [697] (5,10]  (5,10]  (10,15] (5,10]  (5,10]  (10,15] (5,10]  (5,10] 
##  [705] (10,15] (5,10]  (5,10]  (5,10]  (10,15] (5,10]  (10,15] (5,10] 
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##  [721] (10,15] (5,10]  (5,10]  (5,10]  (5,10]  (5,10]  (0,5]   (5,10] 
##  [729] (5,10]  (10,15] (5,10]  (10,15] (5,10]  (10,15] (5,10]  (5,10] 
##  [737] (5,10]  (10,15] (5,10]  (10,15] (5,10]  (0,5]   (10,15] (5,10] 
##  [745] (0,5]   (5,10]  (5,10]  (5,10]  (5,10]  (0,5]   (5,10]  (5,10] 
##  [753] (10,15] (5,10]  (10,15] (10,15] (5,10]  (10,15] (5,10]  (10,15]
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##  [793] (10,15] (5,10]  (5,10]  (10,15] (5,10]  (5,10]  (0,5]   (0,5]  
##  [801] (5,10]  (10,15] (0,5]   (5,10]  (5,10]  (5,10]  (10,15] (5,10] 
##  [809] (0,5]   (5,10]  (10,15] (10,15] (10,15] (10,15] (10,15] (10,15]
##  [817] (10,15] (5,10]  (5,10]  (10,15] (15,20] (10,15] (10,15] (5,10] 
##  [825] (5,10]  (5,10]  (10,15] (5,10]  (10,15] (0,5]   (5,10]  (10,15]
##  [833] (10,15] (10,15] (5,10]  (5,10]  (5,10]  (10,15] (15,20] (5,10] 
##  [841] (10,15] (5,10]  (15,20] (0,5]   (5,10]  (10,15] (5,10]  (5,10] 
##  [849] (10,15] (5,10]  (0,5]   (10,15] (10,15] (5,10]  (5,10]  (5,10] 
##  [857] (10,15] (10,15] (5,10]  (10,15] (10,15] (5,10]  (5,10]  (5,10] 
##  [865] (5,10]  (5,10]  (5,10]  (0,5]   (10,15] (5,10]  (0,5]   (5,10] 
##  [873] (5,10]  (10,15] (10,15] (5,10]  (10,15] (5,10]  (10,15] (10,15]
##  [881] (5,10]  (10,15] (15,20] (10,15] (10,15] (5,10]  (10,15] (10,15]
##  [889] (10,15] (0,5]   (10,15] (5,10]  (5,10]  (10,15] (5,10]  (5,10] 
##  [897] (10,15] (5,10]  (5,10]  (10,15] (10,15] (5,10]  (5,10]  (5,10] 
##  [905] (5,10]  (5,10]  (0,5]   (5,10]  (10,15] (5,10]  (5,10]  (10,15]
##  [913] (15,20] (10,15] (10,15] (5,10]  (10,15] (5,10]  (5,10]  (10,15]
##  [921] (10,15] (0,5]   (5,10]  (5,10]  (5,10]  (5,10]  (15,20] (5,10] 
##  [929] (5,10]  (10,15] (5,10]  (5,10]  (5,10]  (5,10]  (0,5]   (5,10] 
##  [937] (5,10]  (5,10]  (10,15] (0,5]   (5,10]  (5,10]  (15,20] (5,10] 
##  [945] (5,10]  (5,10]  (10,15] (10,15] (5,10]  (5,10]  (5,10]  (5,10] 
##  [953] (10,15] (10,15] (0,5]   (5,10]  (5,10]  (10,15] (5,10]  (0,5]  
##  [961] (10,15] (0,5]   (10,15] (5,10]  (5,10]  (5,10]  (5,10]  (10,15]
##  [969] (5,10]  (5,10]  (0,5]   (10,15] (0,5]   (5,10]  (5,10]  (5,10] 
##  [977] (5,10]  (0,5]   (5,10]  (10,15] (0,5]   (5,10]  (10,15] (10,15]
##  [985] <NA>    (5,10]  (5,10]  (10,15] (10,15] (10,15] (5,10]  (5,10] 
##  [993] (10,15] (5,10]  (10,15] (5,10]  (10,15] (10,15] (5,10]  <NA>   
## Levels: (0,5] (5,10] (10,15] (15,20]
aggregate.tchests <- aggregate(formula=tchests.found ~ tattoos.cut5, FUN= median, data= pirates, na.rm= T)
aggregate.tchests
##   tattoos.cut5 tchests.found
## 1        (0,5]           4.0
## 2       (5,10]           5.0
## 3      (10,15]           5.0
## 4      (15,20]           7.5
plot(x=pirates$tattoos.cut5, y=pirates$tchests.found, main="scatterplot", xlab= "Tattoos", ylab= "median tchests found", pch= 16, cex=1, col="lightgray", type= "p", xlim=c(0,5), ylim=c(0,9))