Exercise 9

Margaret Hallerud

October 17, 2017

Import file grazing_error.txt

setwd("~/WILD4580/data/exercise_dat")
graz<-read.delim("grazing_error.txt", sep = " ")

How many observations and variables are in the file?

dim(graz)

## [1] 86  5

86 observations
5 variables

What are the variable names? which are categorical and which are numeric?

names(graz)

## [1] "Geology"           "Management"        "Width2Depth_Ratio"
## [4] "Avg_Bank_Angle"    "PC_Predator_Taxa"

str(graz)

## 'data.frame':    86 obs. of  5 variables:
##  $ Geology          : Factor w/ 3 levels "Granitci","granitic",..: 2 2 2 2 2 2 2 2 2 2 ...
##  $ Management       : Factor w/ 5 levels "catle","cattle",..: 2 2 3 2 3 3 3 2 3 4 ...
##  $ Width2Depth_Ratio: num  16.1 5.4 11.3 11.5 16.2 15 17.2 7 54.9 21.1 ...
##  $ Avg_Bank_Angle   : num  123.6 86.6 83.3 88.4 82.4 ...
##  $ PC_Predator_Taxa : Factor w/ 86 levels ".","10.07","10.35",..: 65 60 43 4 49 59 9 20 83 71 ...

variable names: Geology, Management, Width2Depth_Ratio, Avg_Bank_Angle, PC_Predator_Taxa
categorical (a.k.a. factor): Geology, Management, PC_Predator_Taxa
numeric: Width2Depth_Ratio, Avg_Bank_Angle

Determine whether the first 4 variables are factors or numeric

class(graz$Geology)

## [1] "factor"

class(graz$Management)

## [1] "factor"

class(graz$Width2Depth_Ratio)

## [1] "numeric"

class(graz$Avg_Bank_Angle)

## [1] "numeric"

Geology and Management (first two variables) are factors
Width2Depth_Ratio and Avg_Bank_Angle (third and fourth variables) are numeric

Are there any obvious anomalies or errors in the data?

Error #1 PC_Predator_Taxa is numeric, NOT categorical/factor- there should not be near the same number of categories as observations

str(graz$PC_Predator_Taxa)

##  Factor w/ 86 levels ".","10.07","10.35",..: 65 60 43 4 49 59 9 20 83 71 ...

Error #2 obvious outliers in numeric Width2Depth_Ratio and Avg_Bank_Angle

min(graz$Width2Depth_Ratio)

## [1] 2.94

max(graz$Width2Depth_Ratio)

## [1] 54.9

min(graz$Avg_Bank_Angle)

## [1] 37.71

max(graz$Avg_Bank_Angle)

## [1] 129129.2

Error #3 “Granitci” is considered a unique level in Geology, but is really just a misspelling of “granitic”

str(graz$Geology)

##  Factor w/ 3 levels "Granitci","granitic",..: 2 2 2 2 2 2 2 2 2 2 ...

Error #4 Two misspellings- “catle” and “shepp” causing two categories in Management which should be merged with “cattle” and “sheep”

str(graz$Management)

##  Factor w/ 5 levels "catle","cattle",..: 2 2 3 2 3 3 3 2 3 4 ...

Correct errors listed previously

Correction #1 set PC_Predator_Taxa as a numeric variable

pred2<-as.numeric(graz$PC_Predator_Taxa)
graz$PC_Predator_Taxa<-pred2
str(graz)

## 'data.frame':    86 obs. of  5 variables:
##  $ Geology          : Factor w/ 3 levels "Granitci","granitic",..: 2 2 2 2 2 2 2 2 2 2 ...
##  $ Management       : Factor w/ 5 levels "catle","cattle",..: 2 2 3 2 3 3 3 2 3 4 ...
##  $ Width2Depth_Ratio: num  16.1 5.4 11.3 11.5 16.2 15 17.2 7 54.9 21.1 ...
##  $ Avg_Bank_Angle   : num  123.6 86.6 83.3 88.4 82.4 ...
##  $ PC_Predator_Taxa : num  65 60 43 4 49 59 9 20 83 71 ...

Correction #2B Correct Width2Depth_Ratio outlier- caused by misplaced decimal point

which(graz$Width2Depth_Ratio==54.9) #find outlier

## [1] 9

graz$Width2Depth_Ratio[9] <- 5.49 #correct
graz$Width2Depth_Ratio #check for correction, value consistency, etc.

##  [1] 16.10  5.40 11.30 11.50 16.20 15.00 17.20  7.00  5.49 21.10 12.20
## [12] 17.10  5.80 17.10 10.70 12.10 11.10 26.40 11.80 16.90 23.30 17.30
## [23] 23.00  6.00 12.20 19.90 15.40 20.80  6.40 21.53 22.62  8.18 21.39
## [34] 10.02 14.04 22.90 11.18 18.67 18.75  7.19 19.73 10.85 23.28 20.90
## [45] 29.05 10.94 25.51 13.18  5.09  4.62  4.61 15.89  3.58 28.48 18.22
## [56]  2.94 17.58 10.76  7.71 18.80 10.89  5.98 17.35 17.17 11.55 22.81
## [67] 23.36 17.98 11.18 27.97 12.50 31.92 18.21 12.52 39.82 26.39 13.46
## [78] 17.47  6.88 17.93 11.10 12.05  6.14 18.04 29.62  9.63

Correction #2A Correct Avg_Bank_Angle outlier- caused by double entry

which(graz$Avg_Bank_Angle==max(graz$Avg_Bank_Angle))

## [1] 73

graz$Avg_Bank_Angle[73]<-129.2
graz$Avg_Bank_Angle

##  [1] 123.57  86.58  83.35  88.40  82.40 104.58 114.82  77.04 106.69 110.19
## [11] 125.32  68.35  64.70 102.20 106.15 123.24  93.56 103.29  77.79 106.00
## [21]  70.70  95.50 110.44  80.00 102.54 108.47 114.70 106.89 108.42 134.67
## [31] 149.78  63.38 103.95  88.13 106.70  84.72  88.58  86.43  98.21  79.32
## [41] 110.13  92.21  74.67  96.05  91.11  84.21  98.52  99.30  52.09  37.71
## [51]  57.92  84.88  53.13 113.28  84.09  62.73 112.64  86.77  87.26  81.08
## [61]  82.73  92.29  90.96  94.70  71.58 135.00  98.04 106.54 102.48 150.30
## [71]  87.05  97.27 129.20  97.71 122.00 126.60 107.93 124.70  83.48  93.43
## [81]  73.92  70.96  90.42 121.40 113.93  86.46

Correction #3 Fit misspelling into existing factors

which(graz$Geology=="Granitci")

## [1] 81

graz$Geology[81]<-"granitic"
graz$Geology

##  [1] granitic granitic granitic granitic granitic granitic granitic
##  [8] granitic granitic granitic granitic granitic granitic granitic
## [15] granitic granitic granitic granitic granitic granitic granitic
## [22] granitic granitic granitic granitic granitic granitic granitic
## [29] granitic volcanic volcanic volcanic granitic granitic granitic
## [36] granitic granitic granitic granitic granitic volcanic volcanic
## [43] granitic granitic granitic granitic granitic volcanic granitic
## [50] granitic granitic granitic granitic volcanic granitic granitic
## [57] granitic granitic granitic granitic granitic granitic granitic
## [64] volcanic volcanic volcanic volcanic volcanic granitic volcanic
## [71] granitic volcanic granitic volcanic volcanic volcanic volcanic
## [78] volcanic granitic granitic granitic granitic granitic volcanic
## [85] volcanic granitic
## Levels: Granitci granitic volcanic

Correction #4A Merge “catle” misspelling into “cattle” category

which(graz$Management=="catle")

## [1] 28

graz$Management[28]<-"cattle"

Correction #4B Match “shepp” into “sheep” category

which(graz$Management=="shepp")

## [1] 18

graz$Management[18]<-"sheep"
graz$Management

##  [1] cattle    cattle    reference cattle    reference reference reference
##  [8] cattle    reference sheep     sheep     sheep     sheep     reference
## [15] reference reference cattle    sheep     cattle    reference cattle   
## [22] sheep     reference sheep     reference reference cattle    cattle   
## [29] cattle    cattle    cattle    cattle    cattle    cattle    cattle   
## [36] reference cattle    reference reference reference sheep     cattle   
## [43] reference reference reference reference sheep     sheep     reference
## [50] reference reference reference reference sheep     reference reference
## [57] reference reference reference reference reference reference reference
## [64] reference reference cattle    cattle    cattle    cattle    cattle   
## [71] cattle    cattle    reference reference cattle    reference cattle   
## [78] cattle    reference reference reference cattle    cattle    sheep    
## [85] sheep     cattle   
## Levels: catle cattle reference sheep shepp

Corrected dataset

There were 6 total errors in the R dataframe
Saving this corrected dataset as grazing_impacts.RData:

save(graz, file="grazing_impacts.RData")