Midterm RIASEC-data
Rebekka Herz
- Dezember 2015
I downloaded the datasets (RIASEC) and (Big5) and created a new R-project called “Midterm_RH”. This Markdown Document will focus on the RIASEC-dataset.
1. Import the data into R with the read.table function
riasec <- read.table("~/Documents/Psychologie/7. Semester/R/Blockseminar R/Midterm/RIASEC/data.csv", sep="\t", header = TRUE) # I set the header = TRUE because this excludes the headers from the other variables and data
head(riasec) #yes! it worked!## implementation R1 R2 R3 R4 R5 R6 R7 R8 I1 I2 I3 I4 I5 I6 I7 I8 A1 A2 A3
## 1 2 3 1 4 2 1 2 1 1 5 4 3 4 2 5 2 4 2 5 5
## 2 2 1 1 1 1 1 1 1 1 4 4 3 1 2 4 2 2 5 3 4
## 3 2 3 2 1 1 1 1 2 1 5 2 3 3 4 1 4 2 1 2 1
## 4 2 3 2 1 2 2 3 1 2 5 4 4 5 4 4 4 3 4 5 3
## 5 2 -1 2 3 2 3 2 1 3 5 2 4 4 4 3 4 3 1 1 2
## 6 2 3 1 3 4 3 4 3 3 3 4 3 3 2 3 3 4 2 3 4
## A4 A5 A6 A7 A8 S1 S2 S3 S4 S5 S6 S7 S8 E1 E2 E3 E4 E5 E6 E7 E8 C1 C2 C3
## 1 5 5 4 2 5 4 4 3 4 4 4 3 3 2 2 3 1 4 1 1 4 1 1 1
## 2 3 3 5 1 3 1 3 3 1 3 2 2 3 1 1 1 1 1 1 1 1 1 1 1
## 3 1 2 1 3 1 4 5 4 4 4 2 2 4 3 2 2 3 4 2 4 2 4 3 2
## 4 4 3 5 4 3 3 4 3 4 4 5 3 2 1 1 4 2 3 4 2 2 2 4 3
## 5 1 2 2 4 3 3 2 3 4 2 3 3 2 3 4 2 1 4 3 4 2 3 4 4
## 6 2 3 3 4 4 2 2 3 2 2 2 3 2 3 2 3 2 4 2 4 4 3 4 3
## C4 C5 C6 C7 C8 accuracy elapse country fromsearch age gender
## 1 1 2 1 1 2 90 222 PT 0 -1 -1
## 2 1 1 1 1 1 100 102 US 0 -1 -1
## 3 3 3 4 4 4 95 264 US 1 -1 -1
## 4 2 1 3 2 1 60 189 SG 0 -1 -1
## 5 2 4 3 3 3 90 197 US 0 -1 -1
## 6 3 3 3 3 3 80 247 US 1 -1 -12. Basic Inspections
What’s the size (rows, cols) of the riasec-dataset
nrow(riasec) #This dataset contains 8855 rows of data## [1] 8855ncol(riasec) #This dataset is made up of columns, i.e. 55 variables## [1] 55names(riasec) #What are the names of the variables?## [1] "implementation" "R1" "R2" "R3"
## [5] "R4" "R5" "R6" "R7"
## [9] "R8" "I1" "I2" "I3"
## [13] "I4" "I5" "I6" "I7"
## [17] "I8" "A1" "A2" "A3"
## [21] "A4" "A5" "A6" "A7"
## [25] "A8" "S1" "S2" "S3"
## [29] "S4" "S5" "S6" "S7"
## [33] "S8" "E1" "E2" "E3"
## [37] "E4" "E5" "E6" "E7"
## [41] "E8" "C1" "C2" "C3"
## [45] "C4" "C5" "C6" "C7"
## [49] "C8" "accuracy" "elapse" "country"
## [53] "fromsearch" "age" "gender"We now know, that this dataset contains 8855 rows of data and 55 columns, i.e. 55 different variables. The names() function shows us the names of all 55 variables.
What do we know about the background of this dataset
On the homegape and in the codebook, we find some additional information about the background and variables of this dataset:
- data was collected from an interactive version of the public domain RIASEC markers (a.k.a. Holland Codes or Holland Occupational Themes) from the Interest Item Pool, maintained by James Rounds of the University of Illinois at Urbana-Champaign.
- The Holland Occupational Themes is a theory of personality which focuses on career and vocational choices. It groups people on the basis of their suitability for six different categories of occupation. The six categories are represented by the RIASEC acronym.
- The RIASEC Markers Scales from the Interest Item Pool were developed by Liao, Armstrong and Rounds (2008) to be used in psychological research.
About the test
- The test contains 48 tasks
- Each item has to be rated on a scale of (1) dislike (2) slightly dislike (3) neither like not dislike (4) slightly enjoy (5) enjoy.
- the test takes about 5-10 minutes to complete.
Measures
Variables in this dataset
- 48 likert rated statements
- gender
- age
- country
- time elapsed
- self-rated accuracy
Summary Statistics
Summary statistics of all variables
summary(riasec)## implementation R1 R2 R3
## Min. :1.000 Min. :-1.00 Min. :-1.000 Min. :-1.000
## 1st Qu.:2.000 1st Qu.: 1.00 1st Qu.: 1.000 1st Qu.: 1.000
## Median :2.000 Median : 2.00 Median : 2.000 Median : 1.000
## Mean :1.759 Mean : 2.42 Mean : 2.159 Mean : 1.769
## 3rd Qu.:2.000 3rd Qu.: 3.00 3rd Qu.: 3.000 3rd Qu.: 2.000
## Max. :2.000 Max. : 5.00 Max. : 5.000 Max. : 5.000
##
## R4 R5 R6 R7
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.: 1.000
## Median : 2.000 Median : 1.000 Median : 2.000 Median : 2.000
## Mean : 2.256 Mean : 1.664 Mean : 2.321 Mean : 1.898
## 3rd Qu.: 3.000 3rd Qu.: 2.000 3rd Qu.: 3.000 3rd Qu.: 3.000
## Max. : 5.000 Max. : 5.000 Max. : 5.000 Max. : 5.000
##
## R8 I1 I2 I3
## Min. :-1.000 Min. :-1.000 Min. :-1.00 Min. :-1.000
## 1st Qu.: 1.000 1st Qu.: 3.000 1st Qu.: 2.00 1st Qu.: 2.000
## Median : 2.000 Median : 4.000 Median : 4.00 Median : 3.000
## Mean : 1.958 Mean : 3.432 Mean : 3.34 Mean : 3.083
## 3rd Qu.: 3.000 3rd Qu.: 4.000 3rd Qu.: 4.00 3rd Qu.: 4.000
## Max. : 5.000 Max. : 5.000 Max. : 5.00 Max. : 5.000
##
## I4 I5 I6 I7
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.: 2.000 1st Qu.: 2.000 1st Qu.: 2.000 1st Qu.: 1.000
## Median : 3.000 Median : 3.000 Median : 3.000 Median : 3.000
## Mean : 2.927 Mean : 2.874 Mean : 2.995 Mean : 2.715
## 3rd Qu.: 4.000 3rd Qu.: 4.000 3rd Qu.: 4.000 3rd Qu.: 4.000
## Max. : 5.000 Max. : 5.000 Max. : 5.000 Max. : 5.000
##
## I8 A1 A2 A3
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.: 2.000
## Median : 3.000 Median : 2.000 Median : 3.000 Median : 3.000
## Mean : 2.612 Mean : 2.505 Mean : 2.811 Mean : 3.062
## 3rd Qu.: 4.000 3rd Qu.: 4.000 3rd Qu.: 4.000 3rd Qu.: 4.000
## Max. : 5.000 Max. : 5.000 Max. : 5.000 Max. : 5.000
##
## A4 A5 A6 A7
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.: 2.000 1st Qu.: 2.000 1st Qu.: 2.000 1st Qu.: 1.000
## Median : 3.000 Median : 4.000 Median : 4.000 Median : 3.000
## Mean : 3.026 Mean : 3.227 Mean : 3.298 Mean : 2.688
## 3rd Qu.: 4.000 3rd Qu.: 5.000 3rd Qu.: 5.000 3rd Qu.: 4.000
## Max. : 5.000 Max. : 5.000 Max. : 5.000 Max. : 5.000
##
## A8 S1 S2 S3
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.: 2.000 1st Qu.: 2.000 1st Qu.: 3.000 1st Qu.: 2.000
## Median : 3.000 Median : 4.000 Median : 4.000 Median : 3.000
## Mean : 2.883 Mean : 3.342 Mean : 3.453 Mean : 3.012
## 3rd Qu.: 4.000 3rd Qu.: 4.000 3rd Qu.: 4.000 3rd Qu.: 4.000
## Max. : 5.000 Max. : 5.000 Max. : 5.000 Max. : 5.000
##
## S4 S5 S6 S7
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.: 2.000 1st Qu.: 2.000 1st Qu.: 2.000 1st Qu.: 2.000
## Median : 3.000 Median : 4.000 Median : 3.000 Median : 3.000
## Mean : 2.952 Mean : 3.302 Mean : 2.839 Mean : 3.121
## 3rd Qu.: 4.000 3rd Qu.: 4.000 3rd Qu.: 4.000 3rd Qu.: 4.000
## Max. : 5.000 Max. : 5.000 Max. : 5.000 Max. : 5.000
##
## S8 E1 E2 E3
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.: 2.000
## Median : 3.000 Median : 2.000 Median : 2.000 Median : 3.000
## Mean : 2.561 Mean : 1.994 Mean : 2.194 Mean : 2.695
## 3rd Qu.: 4.000 3rd Qu.: 3.000 3rd Qu.: 3.000 3rd Qu.: 4.000
## Max. : 5.000 Max. : 5.000 Max. : 5.000 Max. : 5.000
##
## E4 E5 E6 E7
## Min. :-1.000 Min. :-1.00 Min. :-1.000 Min. :-1.000
## 1st Qu.: 1.000 1st Qu.: 2.00 1st Qu.: 1.000 1st Qu.: 1.000
## Median : 2.000 Median : 3.00 Median : 2.000 Median : 2.000
## Mean : 2.203 Mean : 2.84 Mean : 2.454 Mean : 2.285
## 3rd Qu.: 3.000 3rd Qu.: 4.00 3rd Qu.: 3.000 3rd Qu.: 3.000
## Max. : 5.000 Max. : 5.00 Max. : 5.000 Max. : 5.000
##
## E8 C1 C2 C3
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.: 1.000
## Median : 3.000 Median : 2.000 Median : 2.000 Median : 2.000
## Mean : 2.636 Mean : 2.103 Mean : 2.296 Mean : 2.268
## 3rd Qu.: 4.000 3rd Qu.: 3.000 3rd Qu.: 3.000 3rd Qu.: 3.000
## Max. : 5.000 Max. : 5.000 Max. : 5.000 Max. : 5.000
##
## C4 C5 C6 C7
## Min. :-1.000 Min. :-1.000 Min. :-1.000 Min. :-1.000
## 1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.: 1.000
## Median : 2.000 Median : 2.000 Median : 3.000 Median : 2.000
## Mean : 2.293 Mean : 2.478 Mean : 2.636 Mean : 2.064
## 3rd Qu.: 3.000 3rd Qu.: 4.000 3rd Qu.: 4.000 3rd Qu.: 3.000
## Max. : 5.000 Max. : 5.000 Max. : 5.000 Max. : 5.000
##
## C8 accuracy elapse country
## Min. :-1.000 Min. : -1 Min. : -1.0 US :5389
## 1st Qu.: 1.000 1st Qu.: 23 1st Qu.: 84.0 CA : 610
## Median : 2.000 Median : 85 Median : 160.0 GB : 480
## Mean : 2.132 Mean : 242582 Mean : 371.1 AU : 338
## 3rd Qu.: 3.000 3rd Qu.: 95 3rd Qu.: 230.0 MY : 274
## Max. : 5.000 Max. :2147483647 Max. :509296.0 (Other):1763
## NA's : 1
## fromsearch age gender
## Min. :0.0000 Min. :-1.0000 Min. : -1.000
## 1st Qu.:0.0000 1st Qu.:-1.0000 1st Qu.: -1.000
## Median :0.0000 Median :-1.0000 Median : -1.000
## Mean :0.4247 Mean :-0.3964 Mean : 6.892
## 3rd Qu.:1.0000 3rd Qu.:-1.0000 3rd Qu.: -1.000
## Max. :1.0000 Max. : 3.0000 Max. :100.000
## When we look at the summary statistics, we notice that there are a few issues with the data.
- all missing data have been reported as (-1)
- accuracy: missing data reported as (-1), the maximum is a really big number. The range of reported data should be between 0-100.
- elapse: missing data reported as (-1), largest number really big.
- country: 1 NA
- age: the range should be between 0 and probably a maximum of 100. but the median is 2.0. So most participants did not report age. Maybe this variable should be excluded from analysis.
- gender: a big portion of the data is missing because even the third quantile is (-1) but the maximum is 100. It may be better to exclude gender from further analysis.
Let’s get rid of messy data
So let’s get rolling and clean this dataset up.
- reassign all values with -1 (missing data) to NA
- gender is supposed to have the following values: 1 if “Male”, 2 if “Female, 3 if”Other" (-1 if missed). (implementation 1) –> we may want to remove this variable because when we look at the summary statistics we see that there are 6743 NA’s and there’s a maximum of 100.00. There is a lot of messy data in this column. So, we’ll remove this variable.
- age: delete this variable (too many missing values)
- accuracy: The range of reported data should be between 0-100. There are a values > 100. Let’s set all values > 100 to NA.
- elapse: The description of the test told us that the test should take between 5-10 mins to complete. This means that the mean should be between 300-600 s. The mean 489.2 at the moment. That’s pretty good. Let’s set all values above 1200 to NA
#missing data
riasec[riasec == -1] <- NA #set all missing values to NA
head(riasec) #test to see if it worked## implementation R1 R2 R3 R4 R5 R6 R7 R8 I1 I2 I3 I4 I5 I6 I7 I8 A1 A2 A3
## 1 2 3 1 4 2 1 2 1 1 5 4 3 4 2 5 2 4 2 5 5
## 2 2 1 1 1 1 1 1 1 1 4 4 3 1 2 4 2 2 5 3 4
## 3 2 3 2 1 1 1 1 2 1 5 2 3 3 4 1 4 2 1 2 1
## 4 2 3 2 1 2 2 3 1 2 5 4 4 5 4 4 4 3 4 5 3
## 5 2 NA 2 3 2 3 2 1 3 5 2 4 4 4 3 4 3 1 1 2
## 6 2 3 1 3 4 3 4 3 3 3 4 3 3 2 3 3 4 2 3 4
## A4 A5 A6 A7 A8 S1 S2 S3 S4 S5 S6 S7 S8 E1 E2 E3 E4 E5 E6 E7 E8 C1 C2 C3
## 1 5 5 4 2 5 4 4 3 4 4 4 3 3 2 2 3 1 4 1 1 4 1 1 1
## 2 3 3 5 1 3 1 3 3 1 3 2 2 3 1 1 1 1 1 1 1 1 1 1 1
## 3 1 2 1 3 1 4 5 4 4 4 2 2 4 3 2 2 3 4 2 4 2 4 3 2
## 4 4 3 5 4 3 3 4 3 4 4 5 3 2 1 1 4 2 3 4 2 2 2 4 3
## 5 1 2 2 4 3 3 2 3 4 2 3 3 2 3 4 2 1 4 3 4 2 3 4 4
## 6 2 3 3 4 4 2 2 3 2 2 2 3 2 3 2 3 2 4 2 4 4 3 4 3
## C4 C5 C6 C7 C8 accuracy elapse country fromsearch age gender
## 1 1 2 1 1 2 90 222 PT 0 NA NA
## 2 1 1 1 1 1 100 102 US 0 NA NA
## 3 3 3 4 4 4 95 264 US 1 NA NA
## 4 2 1 3 2 1 60 189 SG 0 NA NA
## 5 2 4 3 3 3 90 197 US 0 NA NA
## 6 3 3 3 3 3 80 247 US 1 NA NA#messy data
#gender
summary(riasec$gender) #it's so messy that we are going to remove this variable## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.00 22.00 29.00 32.09 40.00 100.00 6743riasec$gender <- NULL
#age
summary(riasec$age) #again, this looks nothing like what we would expect. So we are going to delete this column as well. ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 1.000 2.000 1.515 2.000 3.000 6730riasec$age <- NULL
#accuracy
summary(riasec$accuracy)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000e+00 8.000e+01 9.000e+01 3.195e+05 9.800e+01 2.147e+09 2132riasec$accuracy[riasec$accuracy > 100] <- NA #we set all values over 100 to NA. We can see that now instead of 2132 NA's we have 2137 NA's. And the minimum is 1.0 which is good, because everyone who answered with 0, didn't want their data to be used (see codebook)
summary(riasec$accuracy) #the maximum is 100.0 which means that there are no values > 100.0. They have all been reassigned to NA. ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.0 80.0 90.0 86.2 98.0 100.0 2137#elapse
summary(riasec$elapse)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 3.0 145.0 187.0 489.2 262.0 509300.0 2132plot(riasec$elapse) #we can see that there is one person who is really far off. 
riasec$elapse[riasec$elapse > 1200] <- NA #set all values > 100 to NA
summary(riasec$elapse)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 3.0 144.0 185.0 225.8 254.0 1193.0 2288plot(riasec$elapse) #this looks way better. 
head(riasec) #let's take a look at the head of our clean dataset. ## implementation R1 R2 R3 R4 R5 R6 R7 R8 I1 I2 I3 I4 I5 I6 I7 I8 A1 A2 A3
## 1 2 3 1 4 2 1 2 1 1 5 4 3 4 2 5 2 4 2 5 5
## 2 2 1 1 1 1 1 1 1 1 4 4 3 1 2 4 2 2 5 3 4
## 3 2 3 2 1 1 1 1 2 1 5 2 3 3 4 1 4 2 1 2 1
## 4 2 3 2 1 2 2 3 1 2 5 4 4 5 4 4 4 3 4 5 3
## 5 2 NA 2 3 2 3 2 1 3 5 2 4 4 4 3 4 3 1 1 2
## 6 2 3 1 3 4 3 4 3 3 3 4 3 3 2 3 3 4 2 3 4
## A4 A5 A6 A7 A8 S1 S2 S3 S4 S5 S6 S7 S8 E1 E2 E3 E4 E5 E6 E7 E8 C1 C2 C3
## 1 5 5 4 2 5 4 4 3 4 4 4 3 3 2 2 3 1 4 1 1 4 1 1 1
## 2 3 3 5 1 3 1 3 3 1 3 2 2 3 1 1 1 1 1 1 1 1 1 1 1
## 3 1 2 1 3 1 4 5 4 4 4 2 2 4 3 2 2 3 4 2 4 2 4 3 2
## 4 4 3 5 4 3 3 4 3 4 4 5 3 2 1 1 4 2 3 4 2 2 2 4 3
## 5 1 2 2 4 3 3 2 3 4 2 3 3 2 3 4 2 1 4 3 4 2 3 4 4
## 6 2 3 3 4 4 2 2 3 2 2 2 3 2 3 2 3 2 4 2 4 4 3 4 3
## C4 C5 C6 C7 C8 accuracy elapse country fromsearch
## 1 1 2 1 1 2 90 222 PT 0
## 2 1 1 1 1 1 100 102 US 0
## 3 3 3 4 4 4 95 264 US 1
## 4 2 1 3 2 1 60 189 SG 0
## 5 2 4 3 3 3 90 197 US 0
## 6 3 3 3 3 3 80 247 US 1Demographic variables
Let’s take a look at the count of some demographic variables.
How many people participated from each country?
str(riasec$country) #this tells us that this variable has 127 levels, i.e. 127 countries from which people participated. ## Factor w/ 127 levels "A1","A2","AE",..: 102 122 122 109 122 122 122 44 122 122 ...table(riasec$country)#this gives us an overview of how many people from each country participated##
## A1 A2 AE AF AI AL AP AR AS AT AU AZ BA BD BE
## 2 2 14 1 1 6 6 10 1 14 338 2 3 1 29
## BG BH BM BN BO BR BS BY CA CH CL CN CO CR CV
## 10 2 1 3 1 28 1 3 610 35 7 26 8 6 1
## CW CY CZ DE DK DO EC EE EG ES EU FI FR GB GF
## 1 5 20 79 14 1 1 5 18 39 9 25 45 480 1
## GH GR GT GU GY HK HN HR HU ID IE IL IM IN IQ
## 4 21 2 2 1 66 1 13 7 39 65 15 1 115 2
## IR IS IT JE JM JO JP KE KG KR KW LB LK LT LU
## 12 4 39 1 16 2 14 9 1 17 2 14 1 6 3
## LV LY MA MD ME MK MM MN MO MT MU MW MX MY MZ
## 2 1 5 1 1 1 1 1 2 6 1 1 16 274 1
## NG NL NO NZ OM PF PH PK PL PR PS PT QA RO RS
## 2 73 18 95 40 1 51 12 22 4 1 17 1 32 10
## RU SA SE SG SI SK SL SV SY TH TJ TR TT TW TZ
## 10 11 37 124 36 7 1 1 1 22 1 22 11 3 2
## UA US UY VE VI VN ZA
## 3 5389 3 7 2 11 47summary(riasec$country) #this function allows us to see the different countries in order (most participants to least). The country with the most participants was the US, the second largest participation rate was found in Canada and GB is next. ## US CA GB AU MY SG IN NZ DE
## 5389 610 480 338 274 124 115 95 79
## NL HK IE PH ZA FR OM ES ID
## 73 66 65 51 47 45 40 39 39
## IT SE SI CH RO BE BR CN FI
## 39 37 36 35 32 29 28 26 25
## PL TH TR GR CZ EG NO KR PT
## 22 22 22 21 20 18 18 17 17
## JM MX IL AE AT DK JP LB HR
## 16 16 15 14 14 14 14 14 13
## IR PK SA TT VN AR BG RS RU
## 12 12 11 11 11 10 10 10 10
## EU KE CO CL HU SK VE AL AP
## 9 9 8 7 7 7 7 6 6
## CR LT MT CY EE MA GH IS PR
## 6 6 6 5 5 5 4 4 4
## BA BN BY LU TW UA UY A1 A2
## 3 3 3 3 3 3 3 2 2
## AZ BH GT GU IQ JO KW LV MO
## 2 2 2 2 2 2 2 2 2
## NG TZ VI AF AI AS BD BM (Other)
## 2 2 2 1 1 1 1 1 29
## NA's
## 1How many participants were referred to the test through a search engine (“fromsearch” = 1)?
table(riasec$fromsearch) #3761 participants were directed to the test through a search engine. 5094 were referred to the test through another website or sourse. ##
## 0 1
## 5094 3761Graphs
Create a histogram
Let’s create a histogram to visualize how accurate people thought they were:
hist(riasec$accuracy, breaks = 10, col = "Pink") #seems like almost everyone was pretty confident about their accuracy. 
#let's make it look a bit cooler
acc <- hist(riasec$accuracy, breaks = 10, plot = FALSE)
plot(acc, labels = TRUE, border = "Red", col = "Pink", main = "Histogram of participant's self-rated accuracy", xlab = "Accuracy")
Create a boxplot
Is there a difference on how people responded to item E8 depending on whether they were referred from a search engine or another source?
boxplot(E8 ~ fromsearch, data = riasec)
#let's make it look a bit cooler
boxplot(E8 ~ fromsearch,
data = riasec,
col = "turquoise",
notch = TRUE,
main = "Boxplot",
xlab = "Referral source",
ylab = "Item E8",
add = TRUE
)
Correlations
Select columns that apparently belong to together (e.g. E1-E8) and compute the correlation, also show the correlation visually.
E <- data.frame(riasec$E1,riasec$E2, riasec$E3, riasec$E4, riasec$E5, riasec$E6, riasec$E7, riasec$E8)
head(E)## riasec.E1 riasec.E2 riasec.E3 riasec.E4 riasec.E5 riasec.E6 riasec.E7
## 1 2 2 3 1 4 1 1
## 2 1 1 1 1 1 1 1
## 3 3 2 2 3 4 2 4
## 4 1 1 4 2 3 4 2
## 5 3 4 2 1 4 3 4
## 6 3 2 3 2 4 2 4
## riasec.E8
## 1 4
## 2 1
## 3 2
## 4 2
## 5 2
## 6 4cor(na.omit(E)) #calculate the correlations between the E-variables## riasec.E1 riasec.E2 riasec.E3 riasec.E4 riasec.E5 riasec.E6
## riasec.E1 1.0000000 0.3859963 0.4372540 0.2742256 0.4007532 0.3813569
## riasec.E2 0.3859963 1.0000000 0.3462392 0.3414766 0.2763812 0.5549665
## riasec.E3 0.4372540 0.3462392 1.0000000 0.3293392 0.5657515 0.4873247
## riasec.E4 0.2742256 0.3414766 0.3293392 1.0000000 0.2073681 0.5097738
## riasec.E5 0.4007532 0.2763812 0.5657515 0.2073681 1.0000000 0.4203779
## riasec.E6 0.3813569 0.5549665 0.4873247 0.5097738 0.4203779 1.0000000
## riasec.E7 0.4667292 0.3683945 0.4205886 0.2857571 0.3795733 0.3984515
## riasec.E8 0.2804928 0.3722055 0.3692438 0.3406575 0.2808970 0.4743587
## riasec.E7 riasec.E8
## riasec.E1 0.4667292 0.2804928
## riasec.E2 0.3683945 0.3722055
## riasec.E3 0.4205886 0.3692438
## riasec.E4 0.2857571 0.3406575
## riasec.E5 0.3795733 0.2808970
## riasec.E6 0.3984515 0.4743587
## riasec.E7 1.0000000 0.2822687
## riasec.E8 0.2822687 1.0000000pairs(na.omit(E)) #visualize the correlations
#this does not seem to make much sense, but I don't know where the problem is. :( This was an overview of some (very) basic dataset inspections. I’m sorry, I couldn’t really figure out how to sort the correlations stuff and I’m sure there are a easier ways to combine several variables (columns) into one data frame to look at the correlations.