R-Workshop-James

path2data <- "../data/"

PART 1: INSTALLATION, SETTINGS, AND DATA MANAGEMENT

TOPIC 1: Projects & Directories in R Studio

getwd() #get the current working directory

[1] "/Users/rick/github/psu-psychology/r-bootcamp/talks"

/Users/rick/github/psu-psychology/r-bootcamp/talks

#setwd("~/Dropbox/James Work Files/R Workshop/2017") #change the working directory

Since ~/Dropbox/James Work Files/R Workshop/2017 is specific to James’ computer, it won’t work for others. When using an RStudio project, I don’t change my working directory. Instead, I just make sure I give relevant functions information about the directories where other resources can be found.

TOPIC 2: Installing Packages & Loading into Active Library of Resources

Install packages via syntax

# Can install by evaluating chunk, but not by "knitting"
install.packages("multilevel") #Downloading a package to my computer
#loading packages into working library
library("multilevel")

---

Understanding How R Searches for Information

search()
detach(package:multilevel)
search()

---

Obtaining Help

#You may inquire about a function using any of the following:
##If you know the exact name:
?search

help(search)

##If want to search by part of the name
apropos("searc")

[1] "help.search"         "hsearch_db"          "hsearch_db_concepts"
[4] "hsearch_db_keywords" "RSiteSearch"         "search"             
[7] "searchpaths"        

help.search

hsearch_db

hsearch_db_concepts

hsearch_db_keywords

RSiteSearch

search

searchpaths

??sear

Another good source of help is StackOverflow.

TOPIC 3: Data Types & Structures in R

Numbers

x <- 2
x

[1] 2

y = c(1:3); y

[1] 1 2 3

z = c("Porsche 911", "Porsche 944", "Porsche 911", "BMW 335xi")
z

[1] "Porsche 911" "Porsche 944" "Porsche 911" "BMW 335xi"  

Porsche 911

Porsche 944

Porsche 911

BMW 335xi

g=sqrt(x); g

[1] 1.414214

is.numeric(x)

[1] TRUE

is.numeric(z)

[1] FALSE

---

Strings

#String Data as character:
z

[1] "Porsche 911" "Porsche 944" "Porsche 911" "BMW 335xi"  

Porsche 911

Porsche 944

Porsche 911

BMW 335xi

#String Data as factor:
z2=factor(z)
z2

[1] Porsche 911 Porsche 944 Porsche 911 BMW 335xi  
Levels: BMW 335xi Porsche 911 Porsche 944

#Compute the Length of a String (or Numeric) Variable:
nchar(x)

[1] 1

nchar(y)

[1] 1 1 1

nchar(y)

[1] 1 1 1

nchar(z)

[1] 11 11 11  9

#nchar(z2) Throws error during rendering

---

Logical Data

##Assumes values of TRUE or FALSE
###TRUE is considered equal to 1
###FALSE is considered equal to 0
TRUE*5

[1] 5

sqrt(TRUE)

[1] 1

t=TRUE
# you can test if a variable type is logical using:
is.logical(x)

[1] FALSE

is.logical(t)

[1] TRUE

# Logical data types also used as input to functions (see Day 2 examples)
2==2

[1] TRUE

2==3

[1] FALSE

---

Vectors

#Vectors - 1 dimensional collections of same type data
v1=1:5; v1 #creating vector of numbers

[1] 1 2 3 4 5

v2=c(1,2,3,4,5); v2

[1] 1 2 3 4 5

v3=c("Porsche 911", "Ford Mustang GT", "Plymouth Baracuda", "Chevrolet Camaro", "Honda Pilot LX")
v1; v2; v3

[1] 1 2 3 4 5

[1] 1 2 3 4 5

[1] "Porsche 911"       "Ford Mustang GT"   "Plymouth Baracuda"
[4] "Chevrolet Camaro"  "Honda Pilot LX"   

Porsche 911

Ford Mustang GT

Plymouth Baracuda

Chevrolet Camaro

Honda Pilot LX

#Matrices - 2 dimensional collections of same type data
m=matrix(1:20, nrow=5); m

     [,1] [,2] [,3] [,4]
[1,]    1    6   11   16
[2,]    2    7   12   17
[3,]    3    8   13   18
[4,]    4    9   14   19
[5,]    5   10   15   20

---

Arrays & Data Frames

#Arrays - multidimensional collection of same type data
#example of 3D array
a=array(1:20, dim=c(2,5,2)); a

, , 1

     [,1] [,2] [,3] [,4] [,5]
[1,]    1    3    5    7    9
[2,]    2    4    6    8   10

, , 2

     [,1] [,2] [,3] [,4] [,5]
[1,]   11   13   15   17   19
[2,]   12   14   16   18   20

#Creating a data frame from vectors
eng=c("Flat-6", "V-8", "V-8", "V-8", "V-6")
doors=c(2,2,2,2,4)
data1=data.frame(v2, v3, eng, doors)

# Viewing content of data framees
#   Look at the "enviroment" tab in the upper left panel
#   Click on one of the data frames listed under Data (e.g., "data1") 
#   Or, simply type:

data1

# Obtain a list of the variable names in a data frame 
names(data1)

[1] "v2"    "v3"    "eng"   "doors"

v2

v3

eng

doors

---

# Change the names of the variables in a data frame
data2=data.frame(id=v2, model=v3, eng=eng, doors=doors) #creates a new data frame 
data1

data2

data3=data1 #make a copy of the original dataframe

install.packages("plyr")
library(plyr)
data3=rename(data3, replace=c("v2"="id","v3" = "model"))  #renames specific variables
data3
names(data1)=c("id","model", "eng", "doors")  #replaces names of all variables in existing data frame
data1

TOPIC 4: Reading Data Files into R

Reading Data - From R Data Sets

##List of avaialble data sets
data()

library(multilevel)

Loading required package: nlme

Loading required package: MASS

#List data in the multilevel package
data(package="multilevel")

#load the univ data frame into R environment
data(univbct, package="multilevel")
d=univbct

#Confirm it is loaded as a data frame
class(d)

[1] "data.frame"

data.frame

---

Saving data frames as comma-separated value (CSV)

#Saving a data frame as a .csv file (to be read into SPSS, Excel, Text Editor, etc.)
write.table(d, file = paste0(path2data, "d2.csv"), sep=",",row.names=F)
write.table(d, paste0(path2data, "d1.csv"), sep=",", row.names=FALSE) 

#save the data as a text file to be read into SPSS
install.packages("foreign")
library("foreign")
write.foreign(univbct,
              datafile=paste0(path2data, "univbct.csv"),
              codefile=paste0(path2data, "univbct.sps"),
              package="SPSS")
file.show(paste0(path2data, "univbct.csv"))
file.show(paste0(path2data, "univbct.sps"))

---

Reading data from SPSS

library("foreign")
demo1=read.spss(file=paste0(path2data, "demo1.sav"), 
                use.value.labels=TRUE, 
                to.data.frame=TRUE,
                use.missings=TRUE)
summary(demo1)

     SUBNUM            TIME        BTN            COMPANY   
 Min.   :  1.00   Min.   :0   Min.   :   4.0   A      :246  
 1st Qu.: 75.75   1st Qu.:0   1st Qu.: 377.8   HHC    :210  
 Median :150.50   Median :1   Median :1022.0   B      :207  
 Mean   :150.50   Mean   :1   Mean   :1860.3   D      :114  
 3rd Qu.:225.25   3rd Qu.:2   3rd Qu.:3066.0   C      : 84  
 Max.   :300.00   Max.   :2   Max.   :4042.0   SVC    : 24  
                                               (Other): 15  
    MARITAL          GENDER         HOWLONG           RANK      
 Min.   :1.000   Min.   :1.000   Min.   :0.000   Min.   :11.00  
 1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:13.00  
 Median :2.000   Median :1.000   Median :2.000   Median :14.00  
 Mean   :1.711   Mean   :1.039   Mean   :2.371   Mean   :15.26  
 3rd Qu.:2.000   3rd Qu.:1.000   3rd Qu.:4.000   3rd Qu.:16.00  
 Max.   :5.000   Max.   :2.000   Max.   :5.000   Max.   :32.00  
 NA's   :6       NA's   :51      NA's   :18      NA's   :48     
    EDUCATE           AGE       
 Min.   :1.000   Min.   :18.00  
 1st Qu.:2.000   1st Qu.:20.00  
 Median :2.000   Median :24.00  
 Mean   :2.663   Mean   :25.75  
 3rd Qu.:3.000   3rd Qu.:30.00  
 Max.   :6.000   Max.   :44.00  
 NA's   :9       NA's   :9      

demo2=read.spss(file=paste0(path2data, "demo2.sav"),
                use.value.labels=T,
                to.data.frame=T,
                use.missings=FALSE)
summary(demo2) #oops, GENDER = 999 was a missing values code

     SUBNUM         TIME        BTN          COMPANY       MARITAL     
 Min.   :301   Min.   :0   Min.   :   4   A      :156   Min.   :1.000  
 1st Qu.:349   1st Qu.:0   1st Qu.: 404   HHC    :144   1st Qu.:1.000  
 Median :398   Median :1   Median :1022   B      :141   Median :2.000  
 Mean   :398   Mean   :1   Mean   :1755   D      : 69   Mean   :1.756  
 3rd Qu.:447   3rd Qu.:2   3rd Qu.:3066   C      : 42   3rd Qu.:2.000  
 Max.   :495   Max.   :2   Max.   :4042   SVC    : 15   Max.   :5.000  
                                          (Other): 18   NA's   :6      
     GENDER          HOWLONG           RANK         EDUCATE    
 Min.   :  1.00   Min.   :0.000   Min.   :11.0   Min.   :1.00  
 1st Qu.:  1.00   1st Qu.:2.000   1st Qu.:13.0   1st Qu.:2.00  
 Median :  1.00   Median :2.000   Median :14.0   Median :2.00  
 Mean   : 88.03   Mean   :2.446   Mean   :14.7   Mean   :2.49  
 3rd Qu.:  1.00   3rd Qu.:3.000   3rd Qu.:15.0   3rd Qu.:2.00  
 Max.   :999.00   Max.   :5.000   Max.   :31.0   Max.   :6.00  
                  NA's   :6       NA's   :27     NA's   :3     
      AGE       
 Min.   :18.00  
 1st Qu.:21.00  
 Median :24.00  
 Mean   :25.68  
 3rd Qu.:29.00  
 Max.   :46.00  
 NA's   :3      

demo2=read.spss(file=paste0(path2data, "demo2.sav"),
                use.value.labels=T,
                to.data.frame=T,
                use.missings=T)
names(demo1); names(demo2)

 [1] "SUBNUM"  "TIME"    "BTN"     "COMPANY" "MARITAL" "GENDER"  "HOWLONG"
 [8] "RANK"    "EDUCATE" "AGE"    

SUBNUM

TIME

BTN

COMPANY

MARITAL

GENDER

HOWLONG

RANK

EDUCATE

AGE

 [1] "SUBNUM"  "TIME"    "BTN"     "COMPANY" "MARITAL" "GENDER"  "HOWLONG"
 [8] "RANK"    "EDUCATE" "AGE"    

SUBNUM

TIME

BTN

COMPANY

MARITAL

GENDER

HOWLONG

RANK

EDUCATE

AGE

#Reading data (csv)
data1=read.csv(paste0(path2data, "data1.csv"), header=T)
data2=read.csv(paste0(path2data, "data2.csv"))

---

#Now click on "Environment" tab and the "data1" dataframe
#NA (not available) is automatically inserted by R for any missing data
head(data1) # display first 6 cases

tail(data1) # display last 6 cases

summary(data1) # display summary

     SUBNUM            TIME      JOBSAT1           COMMIT1       
 Min.   :  1.00   Min.   :0   Min.   :  1.000   Min.   :  1.000  
 1st Qu.: 75.75   1st Qu.:0   1st Qu.:  2.667   1st Qu.:  3.333  
 Median :150.50   Median :1   Median :  3.667   Median :  3.667  
 Mean   :150.50   Mean   :1   Mean   : 49.763   Mean   : 46.794  
 3rd Qu.:225.25   3rd Qu.:2   3rd Qu.:  4.000   3rd Qu.:  4.333  
 Max.   :300.00   Max.   :2   Max.   :999.000   Max.   :999.000  
                                                                 
     READY1          JOBSAT2         COMMIT2          READY2     
 Min.   :  1.00   Min.   :1.000   Min.   :1.000   Min.   :1.000  
 1st Qu.:  2.75   1st Qu.:2.667   1st Qu.:3.000   1st Qu.:2.750  
 Median :  3.25   Median :3.333   Median :3.667   Median :3.250  
 Mean   : 56.18   Mean   :3.272   Mean   :3.498   Mean   :3.176  
 3rd Qu.:  3.75   3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:3.750  
 Max.   :999.00   Max.   :5.000   Max.   :5.000   Max.   :5.000  
                  NA's   :66      NA's   :48      NA's   :54     
    JOBSAT3         COMMIT3          READY3           JSAT      
 Min.   :1.000   Min.   :1.333   Min.   :1.000   Min.   :1.000  
 1st Qu.:3.000   1st Qu.:3.000   1st Qu.:2.750   1st Qu.:2.667  
 Median :3.333   Median :3.667   Median :3.250   Median :3.333  
 Mean   :3.355   Mean   :3.556   Mean   :3.241   Mean   :3.308  
 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  
 NA's   :51      NA's   :48      NA's   :48      NA's   :53     
     COMMIT          READY      
 Min.   :1.000   Min.   :1.000  
 1st Qu.:3.000   1st Qu.:2.750  
 Median :3.667   Median :3.250  
 Mean   :3.573   Mean   :3.161  
 3rd Qu.:4.000   3rd Qu.:3.750  
 Max.   :5.000   Max.   :5.000  
 NA's   :45      NA's   :50     

summary(data2)

     SUBNUM         TIME      JOBSAT1         COMMIT1          READY1    
 Min.   :301   Min.   :0   Min.   :1.000   Min.   :1.000   Min.   :1.00  
 1st Qu.:349   1st Qu.:0   1st Qu.:2.667   1st Qu.:3.000   1st Qu.:2.25  
 Median :398   Median :1   Median :3.333   Median :3.667   Median :3.00  
 Mean   :398   Mean   :1   Mean   :3.137   Mean   :3.543   Mean   :2.92  
 3rd Qu.:447   3rd Qu.:2   3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:3.50  
 Max.   :495   Max.   :2   Max.   :5.000   Max.   :5.000   Max.   :4.75  
                           NA's   :39      NA's   :45      NA's   :48    
    JOBSAT2         COMMIT2          READY2         JOBSAT3     
 Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
 1st Qu.:2.667   1st Qu.:3.000   1st Qu.:2.500   1st Qu.:3.000  
 Median :3.333   Median :3.667   Median :3.000   Median :3.333  
 Mean   :3.207   Mean   :3.422   Mean   :3.007   Mean   :3.313  
 3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:3.750   3rd Qu.:4.000  
 Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :5.000  
 NA's   :24      NA's   :21      NA's   :33      NA's   :45     
    COMMIT3          READY3           JSAT           COMMIT     
 Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
 1st Qu.:3.000   1st Qu.:2.750   1st Qu.:2.667   1st Qu.:3.000  
 Median :3.667   Median :3.250   Median :3.333   Median :3.667  
 Mean   :3.508   Mean   :3.165   Mean   :3.219   Mean   :3.490  
 3rd Qu.:4.000   3rd Qu.:3.750   3rd Qu.:4.000   3rd Qu.:4.000  
 Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :5.000  
 NA's   :36      NA's   :57      NA's   :36      NA's   :34     
     READY     
 Min.   :1.00  
 1st Qu.:2.50  
 Median :3.25  
 Mean   :3.03  
 3rd Qu.:3.75  
 Max.   :5.00  
 NA's   :46    

---

Handling missing values

#Note: I used 999 to represent missing data for JOBSAT1 COMMIT1 and READY1  
#R needs to be told that 999 is not a legitimate value, but is user-defined missing value
data1$JOBSAT1[data1$JOBSAT1==999]=NA #Explain what the heck this means!
data1$COMMIT1[data1$COMMIT1==999]=NA 
data1$READY1[data1$READY1==999]=NA 
summary(data1)

     SUBNUM            TIME      JOBSAT1         COMMIT1     
 Min.   :  1.00   Min.   :0   Min.   :1.000   Min.   :1.000  
 1st Qu.: 75.75   1st Qu.:0   1st Qu.:2.667   1st Qu.:3.000  
 Median :150.50   Median :1   Median :3.333   Median :3.667  
 Mean   :150.50   Mean   :1   Mean   :3.297   Mean   :3.663  
 3rd Qu.:225.25   3rd Qu.:2   3rd Qu.:4.000   3rd Qu.:4.000  
 Max.   :300.00   Max.   :2   Max.   :5.000   Max.   :5.000  
                              NA's   :42      NA's   :39     
     READY1         JOBSAT2         COMMIT2          READY2     
 Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
 1st Qu.:2.500   1st Qu.:2.667   1st Qu.:3.000   1st Qu.:2.750  
 Median :3.000   Median :3.333   Median :3.667   Median :3.250  
 Mean   :3.066   Mean   :3.272   Mean   :3.498   Mean   :3.176  
 3rd Qu.:3.750   3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:3.750  
 Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :5.000  
 NA's   :48      NA's   :66      NA's   :48      NA's   :54     
    JOBSAT3         COMMIT3          READY3           JSAT      
 Min.   :1.000   Min.   :1.333   Min.   :1.000   Min.   :1.000  
 1st Qu.:3.000   1st Qu.:3.000   1st Qu.:2.750   1st Qu.:2.667  
 Median :3.333   Median :3.667   Median :3.250   Median :3.333  
 Mean   :3.355   Mean   :3.556   Mean   :3.241   Mean   :3.308  
 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  
 NA's   :51      NA's   :48      NA's   :48      NA's   :53     
     COMMIT          READY      
 Min.   :1.000   Min.   :1.000  
 1st Qu.:3.000   1st Qu.:2.750  
 Median :3.667   Median :3.250  
 Mean   :3.573   Mean   :3.161  
 3rd Qu.:4.000   3rd Qu.:3.750  
 Max.   :5.000   Max.   :5.000  
 NA's   :45      NA's   :50     

summary(data2)

     SUBNUM         TIME      JOBSAT1         COMMIT1          READY1    
 Min.   :301   Min.   :0   Min.   :1.000   Min.   :1.000   Min.   :1.00  
 1st Qu.:349   1st Qu.:0   1st Qu.:2.667   1st Qu.:3.000   1st Qu.:2.25  
 Median :398   Median :1   Median :3.333   Median :3.667   Median :3.00  
 Mean   :398   Mean   :1   Mean   :3.137   Mean   :3.543   Mean   :2.92  
 3rd Qu.:447   3rd Qu.:2   3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:3.50  
 Max.   :495   Max.   :2   Max.   :5.000   Max.   :5.000   Max.   :4.75  
                           NA's   :39      NA's   :45      NA's   :48    
    JOBSAT2         COMMIT2          READY2         JOBSAT3     
 Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
 1st Qu.:2.667   1st Qu.:3.000   1st Qu.:2.500   1st Qu.:3.000  
 Median :3.333   Median :3.667   Median :3.000   Median :3.333  
 Mean   :3.207   Mean   :3.422   Mean   :3.007   Mean   :3.313  
 3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:3.750   3rd Qu.:4.000  
 Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :5.000  
 NA's   :24      NA's   :21      NA's   :33      NA's   :45     
    COMMIT3          READY3           JSAT           COMMIT     
 Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
 1st Qu.:3.000   1st Qu.:2.750   1st Qu.:2.667   1st Qu.:3.000  
 Median :3.667   Median :3.250   Median :3.333   Median :3.667  
 Mean   :3.508   Mean   :3.165   Mean   :3.219   Mean   :3.490  
 3rd Qu.:4.000   3rd Qu.:3.750   3rd Qu.:4.000   3rd Qu.:4.000  
 Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :5.000  
 NA's   :36      NA's   :57      NA's   :36      NA's   :34     
     READY     
 Min.   :1.00  
 1st Qu.:2.50  
 Median :3.25  
 Mean   :3.03  
 3rd Qu.:3.75  
 Max.   :5.00  
 NA's   :46    

---

#The above can be tedious if you have a large number of variables
### it is eaiser if you copy & paste code
#Or, if 999 doens't hold any meaning for ANY of the variables
data1=read.csv(paste0(path2data, "data1.csv"), na.strings=c(".", "999","9","-9"))
summary(data1)

     SUBNUM         TIME      JOBSAT1         COMMIT1          READY1     
 Min.   :  1   Min.   :0   Min.   :1.000   Min.   :1.000   Min.   :1.000  
 1st Qu.: 76   1st Qu.:0   1st Qu.:2.667   1st Qu.:3.000   1st Qu.:2.500  
 Median :151   Median :1   Median :3.333   Median :3.667   Median :3.000  
 Mean   :151   Mean   :1   Mean   :3.297   Mean   :3.663   Mean   :3.066  
 3rd Qu.:226   3rd Qu.:2   3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:3.750  
 Max.   :300   Max.   :2   Max.   :5.000   Max.   :5.000   Max.   :5.000  
 NA's   :3                 NA's   :42      NA's   :39      NA's   :48     
    JOBSAT2         COMMIT2          READY2         JOBSAT3     
 Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
 1st Qu.:2.667   1st Qu.:3.000   1st Qu.:2.750   1st Qu.:3.000  
 Median :3.333   Median :3.667   Median :3.250   Median :3.333  
 Mean   :3.272   Mean   :3.498   Mean   :3.176   Mean   :3.355  
 3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:3.750   3rd Qu.:4.000  
 Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :5.000  
 NA's   :66      NA's   :48      NA's   :54      NA's   :51     
    COMMIT3          READY3           JSAT           COMMIT     
 Min.   :1.333   Min.   :1.000   Min.   :1.000   Min.   :1.000  
 1st Qu.:3.000   1st Qu.:2.750   1st Qu.:2.667   1st Qu.:3.000  
 Median :3.667   Median :3.250   Median :3.333   Median :3.667  
 Mean   :3.556   Mean   :3.241   Mean   :3.308   Mean   :3.573  
 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  
 NA's   :48      NA's   :48      NA's   :53      NA's   :45     
     READY      
 Min.   :1.000  
 1st Qu.:2.750  
 Median :3.250  
 Mean   :3.161  
 3rd Qu.:3.750  
 Max.   :5.000  
 NA's   :50     

#OR, you could write a function
my999isNA=function(x) {x[x==999]=NA; x}

---

#Now we will apply this missing data function to the proper variables in data2
#To do this, we use the "lapply" function which allows us to apply the same function over a list or array

data1=read.csv(paste0(path2data, "data1.csv")) #reread data1 as a data.frame with missing data 
names(data1)

 [1] "SUBNUM"  "TIME"    "JOBSAT1" "COMMIT1" "READY1"  "JOBSAT2" "COMMIT2"
 [8] "READY2"  "JOBSAT3" "COMMIT3" "READY3"  "JSAT"    "COMMIT"  "READY"  

SUBNUM

TIME

JOBSAT1

COMMIT1

READY1

JOBSAT2

COMMIT2

READY2

JOBSAT3

COMMIT3

READY3

JSAT

COMMIT

READY

summary(data1)

     SUBNUM            TIME      JOBSAT1           COMMIT1       
 Min.   :  1.00   Min.   :0   Min.   :  1.000   Min.   :  1.000  
 1st Qu.: 75.75   1st Qu.:0   1st Qu.:  2.667   1st Qu.:  3.333  
 Median :150.50   Median :1   Median :  3.667   Median :  3.667  
 Mean   :150.50   Mean   :1   Mean   : 49.763   Mean   : 46.794  
 3rd Qu.:225.25   3rd Qu.:2   3rd Qu.:  4.000   3rd Qu.:  4.333  
 Max.   :300.00   Max.   :2   Max.   :999.000   Max.   :999.000  
                                                                 
     READY1          JOBSAT2         COMMIT2          READY2     
 Min.   :  1.00   Min.   :1.000   Min.   :1.000   Min.   :1.000  
 1st Qu.:  2.75   1st Qu.:2.667   1st Qu.:3.000   1st Qu.:2.750  
 Median :  3.25   Median :3.333   Median :3.667   Median :3.250  
 Mean   : 56.18   Mean   :3.272   Mean   :3.498   Mean   :3.176  
 3rd Qu.:  3.75   3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:3.750  
 Max.   :999.00   Max.   :5.000   Max.   :5.000   Max.   :5.000  
                  NA's   :66      NA's   :48      NA's   :54     
    JOBSAT3         COMMIT3          READY3           JSAT      
 Min.   :1.000   Min.   :1.333   Min.   :1.000   Min.   :1.000  
 1st Qu.:3.000   1st Qu.:3.000   1st Qu.:2.750   1st Qu.:2.667  
 Median :3.333   Median :3.667   Median :3.250   Median :3.333  
 Mean   :3.355   Mean   :3.556   Mean   :3.241   Mean   :3.308  
 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  
 NA's   :51      NA's   :48      NA's   :48      NA's   :53     
     COMMIT          READY      
 Min.   :1.000   Min.   :1.000  
 1st Qu.:3.000   1st Qu.:2.750  
 Median :3.667   Median :3.250  
 Mean   :3.573   Mean   :3.161  
 3rd Qu.:4.000   3rd Qu.:3.750  
 Max.   :5.000   Max.   :5.000  
 NA's   :45      NA's   :50     

data1[3:5]=lapply(data1[3:5],my999isNA)
summary(data1)

     SUBNUM            TIME      JOBSAT1         COMMIT1     
 Min.   :  1.00   Min.   :0   Min.   :1.000   Min.   :1.000  
 1st Qu.: 75.75   1st Qu.:0   1st Qu.:2.667   1st Qu.:3.000  
 Median :150.50   Median :1   Median :3.333   Median :3.667  
 Mean   :150.50   Mean   :1   Mean   :3.297   Mean   :3.663  
 3rd Qu.:225.25   3rd Qu.:2   3rd Qu.:4.000   3rd Qu.:4.000  
 Max.   :300.00   Max.   :2   Max.   :5.000   Max.   :5.000  
                              NA's   :42      NA's   :39     
     READY1         JOBSAT2         COMMIT2          READY2     
 Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
 1st Qu.:2.500   1st Qu.:2.667   1st Qu.:3.000   1st Qu.:2.750  
 Median :3.000   Median :3.333   Median :3.667   Median :3.250  
 Mean   :3.066   Mean   :3.272   Mean   :3.498   Mean   :3.176  
 3rd Qu.:3.750   3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:3.750  
 Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :5.000  
 NA's   :48      NA's   :66      NA's   :48      NA's   :54     
    JOBSAT3         COMMIT3          READY3           JSAT      
 Min.   :1.000   Min.   :1.333   Min.   :1.000   Min.   :1.000  
 1st Qu.:3.000   1st Qu.:3.000   1st Qu.:2.750   1st Qu.:2.667  
 Median :3.333   Median :3.667   Median :3.250   Median :3.333  
 Mean   :3.355   Mean   :3.556   Mean   :3.241   Mean   :3.308  
 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  
 NA's   :51      NA's   :48      NA's   :48      NA's   :53     
     COMMIT          READY      
 Min.   :1.000   Min.   :1.000  
 1st Qu.:3.000   1st Qu.:2.750  
 Median :3.667   Median :3.250  
 Mean   :3.573   Mean   :3.161  
 3rd Qu.:4.000   3rd Qu.:3.750  
 Max.   :5.000   Max.   :5.000  
 NA's   :45      NA's   :50     

TOPIC 5: Merging Data Files

#Merging data by adding variables (e.g, two data.frames, demo1 + data1)
dd1=merge(demo1,data1, by="SUBNUM")
dd1=merge(demo1,data1, by=c("SUBNUM","TIME"), all=TRUE)
      
dd2=merge(demo2,data2, by=c("SUBNUM","TIME"), all=TRUE)
summary(dd1)

     SUBNUM            TIME        BTN            COMPANY   
 Min.   :  1.00   Min.   :0   Min.   :   4.0   A      :246  
 1st Qu.: 75.75   1st Qu.:0   1st Qu.: 377.8   HHC    :210  
 Median :150.50   Median :1   Median :1022.0   B      :207  
 Mean   :150.50   Mean   :1   Mean   :1860.3   D      :114  
 3rd Qu.:225.25   3rd Qu.:2   3rd Qu.:3066.0   C      : 84  
 Max.   :300.00   Max.   :2   Max.   :4042.0   SVC    : 24  
                                               (Other): 15  
    MARITAL          GENDER         HOWLONG           RANK      
 Min.   :1.000   Min.   :1.000   Min.   :0.000   Min.   :11.00  
 1st Qu.:1.000   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:13.00  
 Median :2.000   Median :1.000   Median :2.000   Median :14.00  
 Mean   :1.711   Mean   :1.039   Mean   :2.371   Mean   :15.26  
 3rd Qu.:2.000   3rd Qu.:1.000   3rd Qu.:4.000   3rd Qu.:16.00  
 Max.   :5.000   Max.   :2.000   Max.   :5.000   Max.   :32.00  
 NA's   :6       NA's   :51      NA's   :18      NA's   :48     
    EDUCATE           AGE           JOBSAT1         COMMIT1     
 Min.   :1.000   Min.   :18.00   Min.   :1.000   Min.   :1.000  
 1st Qu.:2.000   1st Qu.:20.00   1st Qu.:2.667   1st Qu.:3.000  
 Median :2.000   Median :24.00   Median :3.333   Median :3.667  
 Mean   :2.663   Mean   :25.75   Mean   :3.297   Mean   :3.663  
 3rd Qu.:3.000   3rd Qu.:30.00   3rd Qu.:4.000   3rd Qu.:4.000  
 Max.   :6.000   Max.   :44.00   Max.   :5.000   Max.   :5.000  
 NA's   :9       NA's   :9       NA's   :42      NA's   :39     
     READY1         JOBSAT2         COMMIT2          READY2     
 Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
 1st Qu.:2.500   1st Qu.:2.667   1st Qu.:3.000   1st Qu.:2.750  
 Median :3.000   Median :3.333   Median :3.667   Median :3.250  
 Mean   :3.066   Mean   :3.272   Mean   :3.498   Mean   :3.176  
 3rd Qu.:3.750   3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:3.750  
 Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :5.000  
 NA's   :48      NA's   :66      NA's   :48      NA's   :54     
    JOBSAT3         COMMIT3          READY3           JSAT      
 Min.   :1.000   Min.   :1.333   Min.   :1.000   Min.   :1.000  
 1st Qu.:3.000   1st Qu.:3.000   1st Qu.:2.750   1st Qu.:2.667  
 Median :3.333   Median :3.667   Median :3.250   Median :3.333  
 Mean   :3.355   Mean   :3.556   Mean   :3.241   Mean   :3.308  
 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  
 NA's   :51      NA's   :48      NA's   :48      NA's   :53     
     COMMIT          READY      
 Min.   :1.000   Min.   :1.000  
 1st Qu.:3.000   1st Qu.:2.750  
 Median :3.667   Median :3.250  
 Mean   :3.573   Mean   :3.161  
 3rd Qu.:4.000   3rd Qu.:3.750  
 Max.   :5.000   Max.   :5.000  
 NA's   :45      NA's   :50     

summary(dd2)

     SUBNUM         TIME        BTN          COMPANY       MARITAL     
 Min.   :301   Min.   :0   Min.   :   4   A      :156   Min.   :1.000  
 1st Qu.:349   1st Qu.:0   1st Qu.: 404   HHC    :144   1st Qu.:1.000  
 Median :398   Median :1   Median :1022   B      :141   Median :2.000  
 Mean   :398   Mean   :1   Mean   :1755   D      : 69   Mean   :1.756  
 3rd Qu.:447   3rd Qu.:2   3rd Qu.:3066   C      : 42   3rd Qu.:2.000  
 Max.   :495   Max.   :2   Max.   :4042   SVC    : 15   Max.   :5.000  
                                          (Other): 18   NA's   :6      
     GENDER         HOWLONG           RANK         EDUCATE    
 Min.   :1.000   Min.   :0.000   Min.   :11.0   Min.   :1.00  
 1st Qu.:1.000   1st Qu.:2.000   1st Qu.:13.0   1st Qu.:2.00  
 Median :1.000   Median :2.000   Median :14.0   Median :2.00  
 Mean   :1.022   Mean   :2.446   Mean   :14.7   Mean   :2.49  
 3rd Qu.:1.000   3rd Qu.:3.000   3rd Qu.:15.0   3rd Qu.:2.00  
 Max.   :2.000   Max.   :5.000   Max.   :31.0   Max.   :6.00  
 NA's   :51      NA's   :6       NA's   :27     NA's   :3     
      AGE           JOBSAT1         COMMIT1          READY1    
 Min.   :18.00   Min.   :1.000   Min.   :1.000   Min.   :1.00  
 1st Qu.:21.00   1st Qu.:2.667   1st Qu.:3.000   1st Qu.:2.25  
 Median :24.00   Median :3.333   Median :3.667   Median :3.00  
 Mean   :25.68   Mean   :3.137   Mean   :3.543   Mean   :2.92  
 3rd Qu.:29.00   3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:3.50  
 Max.   :46.00   Max.   :5.000   Max.   :5.000   Max.   :4.75  
 NA's   :3       NA's   :39      NA's   :45      NA's   :48    
    JOBSAT2         COMMIT2          READY2         JOBSAT3     
 Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
 1st Qu.:2.667   1st Qu.:3.000   1st Qu.:2.500   1st Qu.:3.000  
 Median :3.333   Median :3.667   Median :3.000   Median :3.333  
 Mean   :3.207   Mean   :3.422   Mean   :3.007   Mean   :3.313  
 3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:3.750   3rd Qu.:4.000  
 Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :5.000  
 NA's   :24      NA's   :21      NA's   :33      NA's   :45     
    COMMIT3          READY3           JSAT           COMMIT     
 Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
 1st Qu.:3.000   1st Qu.:2.750   1st Qu.:2.667   1st Qu.:3.000  
 Median :3.667   Median :3.250   Median :3.333   Median :3.667  
 Mean   :3.508   Mean   :3.165   Mean   :3.219   Mean   :3.490  
 3rd Qu.:4.000   3rd Qu.:3.750   3rd Qu.:4.000   3rd Qu.:4.000  
 Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :5.000  
 NA's   :36      NA's   :57      NA's   :36      NA's   :34     
     READY     
 Min.   :1.00  
 1st Qu.:2.50  
 Median :3.25  
 Mean   :3.03  
 3rd Qu.:3.75  
 Max.   :5.00  
 NA's   :46    

Merging data by adding rows (subjects)

#let's combine dd1 with dd2
#when you have IDENTICAL columns in both data sets you may use rbind
names(dd1); names(dd2)

 [1] "SUBNUM"  "TIME"    "BTN"     "COMPANY" "MARITAL" "GENDER"  "HOWLONG"
 [8] "RANK"    "EDUCATE" "AGE"     "JOBSAT1" "COMMIT1" "READY1"  "JOBSAT2"
[15] "COMMIT2" "READY2"  "JOBSAT3" "COMMIT3" "READY3"  "JSAT"    "COMMIT" 
[22] "READY"  

SUBNUM

TIME

BTN

COMPANY

MARITAL

GENDER

HOWLONG

RANK

EDUCATE

AGE

JOBSAT1

COMMIT1

READY1

JOBSAT2

COMMIT2

READY2

JOBSAT3

COMMIT3

READY3

JSAT

COMMIT

READY

 [1] "SUBNUM"  "TIME"    "BTN"     "COMPANY" "MARITAL" "GENDER"  "HOWLONG"
 [8] "RANK"    "EDUCATE" "AGE"     "JOBSAT1" "COMMIT1" "READY1"  "JOBSAT2"
[15] "COMMIT2" "READY2"  "JOBSAT3" "COMMIT3" "READY3"  "JSAT"    "COMMIT" 
[22] "READY"  

SUBNUM

TIME

BTN

COMPANY

MARITAL

GENDER

HOWLONG

RANK

EDUCATE

AGE

JOBSAT1

COMMIT1

READY1

JOBSAT2

COMMIT2

READY2

JOBSAT3

COMMIT3

READY3

JSAT

COMMIT

READY

dd3=rbind(dd1,dd2)
summary(dd3)

     SUBNUM         TIME        BTN          COMPANY       MARITAL     
 Min.   :  1   Min.   :0   Min.   :   4   A      :402   Min.   :1.000  
 1st Qu.:124   1st Qu.:0   1st Qu.: 404   HHC    :354   1st Qu.:1.000  
 Median :248   Median :1   Median :1022   B      :348   Median :2.000  
 Mean   :248   Mean   :1   Mean   :1819   D      :183   Mean   :1.729  
 3rd Qu.:372   3rd Qu.:2   3rd Qu.:3066   C      :126   3rd Qu.:2.000  
 Max.   :495   Max.   :2   Max.   :4042   SVC    : 39   Max.   :5.000  
                                          (Other): 33   NA's   :12     
     GENDER         HOWLONG         RANK          EDUCATE     
 Min.   :1.000   Min.   :0.0   Min.   :11.00   Min.   :1.000  
 1st Qu.:1.000   1st Qu.:1.0   1st Qu.:13.00   1st Qu.:2.000  
 Median :1.000   Median :2.0   Median :14.00   Median :2.000  
 Mean   :1.033   Mean   :2.4   Mean   :15.04   Mean   :2.595  
 3rd Qu.:1.000   3rd Qu.:4.0   3rd Qu.:16.00   3rd Qu.:3.000  
 Max.   :2.000   Max.   :5.0   Max.   :32.00   Max.   :6.000  
 NA's   :102     NA's   :24    NA's   :75      NA's   :12     
      AGE           JOBSAT1         COMMIT1          READY1    
 Min.   :18.00   Min.   :1.000   Min.   :1.000   Min.   :1.00  
 1st Qu.:21.00   1st Qu.:2.667   1st Qu.:3.000   1st Qu.:2.50  
 Median :24.00   Median :3.333   Median :3.667   Median :3.00  
 Mean   :25.72   Mean   :3.235   Mean   :3.617   Mean   :3.01  
 3rd Qu.:30.00   3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:3.75  
 Max.   :46.00   Max.   :5.000   Max.   :5.000   Max.   :5.00  
 NA's   :12      NA's   :81      NA's   :84      NA's   :96    
    JOBSAT2         COMMIT2          READY2         JOBSAT3     
 Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
 1st Qu.:2.667   1st Qu.:3.000   1st Qu.:2.500   1st Qu.:3.000  
 Median :3.333   Median :3.667   Median :3.250   Median :3.333  
 Mean   :3.246   Mean   :3.468   Mean   :3.109   Mean   :3.338  
 3rd Qu.:4.000   3rd Qu.:4.000   3rd Qu.:3.750   3rd Qu.:4.000  
 Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :5.000  
 NA's   :90      NA's   :69      NA's   :87      NA's   :96     
    COMMIT3          READY3           JSAT           COMMIT     
 Min.   :1.000   Min.   :1.000   Min.   :1.000   Min.   :1.000  
 1st Qu.:3.000   1st Qu.:2.750   1st Qu.:2.667   1st Qu.:3.000  
 Median :3.667   Median :3.250   Median :3.333   Median :3.667  
 Mean   :3.537   Mean   :3.212   Mean   :3.273   Mean   :3.540  
 3rd Qu.:4.000   3rd Qu.:3.750   3rd Qu.:4.000   3rd Qu.:4.000  
 Max.   :5.000   Max.   :5.000   Max.   :5.000   Max.   :5.000  
 NA's   :84      NA's   :105     NA's   :89      NA's   :79     
     READY     
 Min.   :1.00  
 1st Qu.:2.50  
 Median :3.25  
 Mean   :3.11  
 3rd Qu.:3.75  
 Max.   :5.00  
 NA's   :96    

#when you have different columns in your data, you can use rbind.fill
#first let's compute some extra variables and add them to dd1
#Computing new variables in an existing data.frame
dd1$STAY=dd1$JSAT+dd1$COMMIT
#dd3=rbind(dd1,dd2) doesn't work because of differing colums
?rbind.fill
install.packages("plyr")
library(plyr)

dd3=plyr::rbind.fill(dd1,dd2)
head(dd3); tail(dd3)

---

Deleting a variable from a data frame

#let's delete STAY from the previous dd3 data.frame
names(dd3)

 [1] "SUBNUM"  "TIME"    "BTN"     "COMPANY" "MARITAL" "GENDER"  "HOWLONG"
 [8] "RANK"    "EDUCATE" "AGE"     "JOBSAT1" "COMMIT1" "READY1"  "JOBSAT2"
[15] "COMMIT2" "READY2"  "JOBSAT3" "COMMIT3" "READY3"  "JSAT"    "COMMIT" 
[22] "READY"  

SUBNUM

TIME

BTN

COMPANY

MARITAL

GENDER

HOWLONG

RANK

EDUCATE

AGE

JOBSAT1

COMMIT1

READY1

JOBSAT2

COMMIT2

READY2

JOBSAT3

COMMIT3

READY3

JSAT

COMMIT

READY

dd4=dd3[c(1,2,3:22)]
names(dd4)

 [1] "SUBNUM"  "TIME"    "BTN"     "COMPANY" "MARITAL" "GENDER"  "HOWLONG"
 [8] "RANK"    "EDUCATE" "AGE"     "JOBSAT1" "COMMIT1" "READY1"  "JOBSAT2"
[15] "COMMIT2" "READY2"  "JOBSAT3" "COMMIT3" "READY3"  "JSAT"    "COMMIT" 
[22] "READY"  

SUBNUM

TIME

BTN

COMPANY

MARITAL

GENDER

HOWLONG

RANK

EDUCATE

AGE

JOBSAT1

COMMIT1

READY1

JOBSAT2

COMMIT2

READY2

JOBSAT3

COMMIT3

READY3

JSAT

COMMIT

READY

#Renaming a variable in a data.frame
#let's rename HOWLONG to TENURE and MARITAL to STATUS
dd4=plyr::rename(dd4, c(HOWLONG="TENURE", MARITAL="STATUS")) 
names(dd4)

 [1] "SUBNUM"  "TIME"    "BTN"     "COMPANY" "STATUS"  "GENDER"  "TENURE" 
 [8] "RANK"    "EDUCATE" "AGE"     "JOBSAT1" "COMMIT1" "READY1"  "JOBSAT2"
[15] "COMMIT2" "READY2"  "JOBSAT3" "COMMIT3" "READY3"  "JSAT"    "COMMIT" 
[22] "READY"  

SUBNUM

TIME

BTN

COMPANY

STATUS

GENDER

TENURE

RANK

EDUCATE

AGE

JOBSAT1

COMMIT1

READY1

JOBSAT2

COMMIT2

READY2

JOBSAT3

COMMIT3

READY3

JSAT

COMMIT

READY

---

Recoding variables

#Categorical Variables: recode sex into a different, dummy variable
#Only “factor” type variables are assigned value labels
dd4$GENDER2=plyr::revalue(as.factor(dd4$GENDER), c("1"="male","2"="female"))
dd4$GENDER3=(dd4$GENDER-1)
class(dd4$GENDER)

[1] "numeric"

numeric

class(dd4$GENDER2)

[1] "factor"

factor

class(dd4$GENDER3)

[1] "numeric"

numeric

#recode Likert-type items/scales
###let's reverse the overall score on COMMIT so that high scores = more likely to leave
dd4$LEAVE=6-dd4$COMMIT

TOPIC 6: Summarizing & Visualizing Data Frames

Central Tendency

mean(dd3$JSAT); median(dd3$JSAT)

[1] NA

[1] NA

mean(dd3$JSAT,na.rm=TRUE); median(dd3$JSAT,na.rm=TRUE)

[1] 3.272923

[1] 3.333333

#Dispersion
var(dd3$JSAT,na.rm=T)

[1] 0.8622181

sd(dd3$JSAT,na.rm=T)

[1] 0.928557

min(dd3$JSAT, na.rm=T)

[1] 1

max(dd3$JSAT,na.rm=T)

[1] 5

summary(dd3$JSAT,na.rm=T)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  1.000   2.667   3.333   3.273   4.000   5.000      89 

quantile(dd3$JSAT,probs=c(.1,.2,.3,.4,.5,.6,.7,.8,.9),na.rm=T)

     10%      20%      30%      40%      50%      60%      70%      80% 
2.000000 2.333333 3.000000 3.000000 3.333333 3.666667 4.000000 4.000000 
     90% 
4.333333 

Alternative: Hmisc

install.packages("Hmisc")
library("Hmisc")

Hmisc::describe(dd4)

dd4 

 25  Variables      1485  Observations
---------------------------------------------------------------------------
SUBNUM 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
    1485        0      495        1      248    165.1     25.2     50.0 
     .25      .50      .75      .90      .95 
   124.0    248.0    372.0    446.0    470.8 

lowest :   1   2   3   4   5, highest: 491 492 493 494 495
---------------------------------------------------------------------------
TIME 
       n  missing distinct     Info     Mean      Gmd 
    1485        0        3    0.889        1   0.8895 
                            
Value          0     1     2
Frequency    495   495   495
Proportion 0.333 0.333 0.333
---------------------------------------------------------------------------
BTN 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
    1485        0       16    0.965     1819     1566        4      104 
     .25      .50      .75      .90      .95 
     404     1022     3066     4042     4042 
                                                                      
Value          0   100   120   140   300   400   700  1000  1010  1020
Frequency    141    15    42    30   123    48     6    66    21   288
Proportion 0.095 0.010 0.028 0.020 0.083 0.032 0.004 0.044 0.014 0.194
                                        
Value       2000  2010  3070  4000  4040
Frequency     36    51   435    18   165
Proportion 0.024 0.034 0.293 0.012 0.111
---------------------------------------------------------------------------
COMPANY 
       n  missing distinct 
    1485        0        8 
                                                          
Value          A     B     C     D     F   HHC   REC   SVC
Frequency    402   348   126   183    15   354    18    39
Proportion 0.271 0.234 0.085 0.123 0.010 0.238 0.012 0.026
---------------------------------------------------------------------------
STATUS 
       n  missing distinct     Info     Mean      Gmd 
    1473       12        5     0.79    1.729    0.745 
                                        
Value          1     2     3     4     5
Frequency    603   768    21    60    21
Proportion 0.409 0.521 0.014 0.041 0.014
---------------------------------------------------------------------------
GENDER 
       n  missing distinct     Info     Mean      Gmd 
    1383      102        2    0.094    1.033    0.063 
                      
Value          1     2
Frequency   1338    45
Proportion 0.967 0.033
---------------------------------------------------------------------------
TENURE 
       n  missing distinct     Info     Mean      Gmd 
    1461       24        6    0.949      2.4    1.747 
                                              
Value          0     1     2     3     4     5
Frequency    216   159   495   225   147   219
Proportion 0.148 0.109 0.339 0.154 0.101 0.150
---------------------------------------------------------------------------
RANK 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
    1410       75       15    0.972    15.04    2.979       12       12 
     .25      .50      .75      .90      .95 
      13       14       16       21       22 
                                                                      
Value         11    12    13    14    15    16    17    18    19    21
Frequency     21   147   324   264   279   114    84    18     3    54
Proportion 0.015 0.104 0.230 0.187 0.198 0.081 0.060 0.013 0.002 0.038
                                        
Value         22    23    24    31    32
Frequency     51    42     3     3     3
Proportion 0.036 0.030 0.002 0.002 0.002
---------------------------------------------------------------------------
EDUCATE 
       n  missing distinct     Info     Mean      Gmd 
    1473       12        6    0.617    2.595   0.9586 
                                              
Value          1     2     3     4     5     6
Frequency      9  1068    99   117   168    12
Proportion 0.006 0.725 0.067 0.079 0.114 0.008
---------------------------------------------------------------------------
AGE 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
    1473       12       29    0.994    25.72    6.715       19       19 
     .25      .50      .75      .90      .95 
      21       24       30       35       37 

lowest : 18 19 20 21 22, highest: 42 43 44 45 46
---------------------------------------------------------------------------
JOBSAT1 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
    1404       81       13    0.983    3.235    1.104    1.333    1.667 
     .25      .50      .75      .90      .95 
   2.667    3.333    4.000    4.333    4.667 
                                                                         
Value      1.000000 1.333333 1.666667 2.000000 2.333333 2.666667 3.000000
Frequency        48       39       63       96       78      102      180
Proportion    0.034    0.028    0.045    0.068    0.056    0.073    0.128
                                                                
Value      3.333333 3.666667 4.000000 4.333333 4.666667 5.000000
Frequency       156      141      315       87       54       45
Proportion    0.111    0.100    0.224    0.062    0.038    0.032
---------------------------------------------------------------------------
COMMIT1 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
    1401       84       13    0.982    3.617   0.9408    2.000    2.333 
     .25      .50      .75      .90      .95 
   3.000    3.667    4.000    4.667    5.000 
                                                                         
Value      1.000000 1.333333 1.666667 2.000000 2.333333 2.666667 3.000000
Frequency        12        9       21       45       57       75      165
Proportion    0.009    0.006    0.015    0.032    0.041    0.054    0.118
                                                                
Value      3.333333 3.666667 4.000000 4.333333 4.666667 5.000000
Frequency       189      222      282      102      102      120
Proportion    0.135    0.158    0.201    0.073    0.073    0.086
---------------------------------------------------------------------------
READY1 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
    1389       96       17     0.99     3.01   0.9286     1.50     1.75 
     .25      .50      .75      .90      .95 
    2.50     3.00     3.75     4.00     4.00 
                                                                      
Value       1.00  1.25  1.50  1.75  2.00  2.25  2.50  2.75  3.00  3.25
Frequency     36    33    33    45    66    78   108   141   177   204
Proportion 0.026 0.024 0.024 0.032 0.048 0.056 0.078 0.102 0.127 0.147
                                                    
Value       3.50  3.75  4.00  4.25  4.50  4.75  5.00
Frequency    105   117   183    36    18     6     3
Proportion 0.076 0.084 0.132 0.026 0.013 0.004 0.002
---------------------------------------------------------------------------
JOBSAT2 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
    1395       90       13    0.978    3.246    1.041    1.333    2.000 
     .25      .50      .75      .90      .95 
   2.667    3.333    4.000    4.000    4.667 
                                                                         
Value      1.000000 1.333333 1.666667 2.000000 2.333333 2.666667 3.000000
Frequency        51       30       54       75       99       84      174
Proportion    0.037    0.022    0.039    0.054    0.071    0.060    0.125
                                                                
Value      3.333333 3.666667 4.000000 4.333333 4.666667 5.000000
Frequency       177      168      348       63       33       39
Proportion    0.127    0.120    0.249    0.045    0.024    0.028
---------------------------------------------------------------------------
COMMIT2 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
    1416       69       13    0.981    3.468   0.9529    1.667    2.333 
     .25      .50      .75      .90      .95 
   3.000    3.667    4.000    4.667    5.000 
                                                                         
Value      1.000000 1.333333 1.666667 2.000000 2.333333 2.666667 3.000000
Frequency        39       18       30       18       57       93      207
Proportion    0.028    0.013    0.021    0.013    0.040    0.066    0.146
                                                                
Value      3.333333 3.666667 4.000000 4.333333 4.666667 5.000000
Frequency       213      207      291       96       75       72
Proportion    0.150    0.146    0.206    0.068    0.053    0.051
---------------------------------------------------------------------------
READY2 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
    1398       87       17    0.989    3.109   0.9311     1.50     2.00 
     .25      .50      .75      .90      .95 
    2.50     3.25     3.75     4.00     4.25 
                                                                      
Value       1.00  1.25  1.50  1.75  2.00  2.25  2.50  2.75  3.00  3.25
Frequency     24    30    30    39    69    75   105    75   216   162
Proportion 0.017 0.021 0.021 0.028 0.049 0.054 0.075 0.054 0.155 0.116
                                                    
Value       3.50  3.75  4.00  4.25  4.50  4.75  5.00
Frequency    162   162   156    39    18    15    21
Proportion 0.116 0.116 0.112 0.028 0.013 0.011 0.015
---------------------------------------------------------------------------
JOBSAT3 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
    1389       96       13    0.972    3.338    0.941    1.667    2.000 
     .25      .50      .75      .90      .95 
   3.000    3.333    4.000    4.333    4.667 
                                                                         
Value      1.000000 1.333333 1.666667 2.000000 2.333333 2.666667 3.000000
Frequency        24       33       21       72       72       69      279
Proportion    0.017    0.024    0.015    0.052    0.052    0.050    0.201
                                                                
Value      3.333333 3.666667 4.000000 4.333333 4.666667 5.000000
Frequency       183      138      351       60       42       45
Proportion    0.132    0.099    0.253    0.043    0.030    0.032
---------------------------------------------------------------------------
COMMIT3 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
    1401       84       13    0.974    3.537   0.8182    2.000    2.667 
     .25      .50      .75      .90      .95 
   3.000    3.667    4.000    4.333    4.667 
                                                                         
Value      1.000000 1.333333 1.666667 2.000000 2.333333 2.666667 3.000000
Frequency         9        9       21       33       42       54      261
Proportion    0.006    0.006    0.015    0.024    0.030    0.039    0.186
                                                                
Value      3.333333 3.666667 4.000000 4.333333 4.666667 5.000000
Frequency       204      234      315      102       48       69
Proportion    0.146    0.167    0.225    0.073    0.034    0.049
---------------------------------------------------------------------------
READY3 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
    1380      105       17    0.986    3.212   0.8964     1.50     2.00 
     .25      .50      .75      .90      .95 
    2.75     3.25     3.75     4.00     4.25 
                                                                      
Value       1.00  1.25  1.50  1.75  2.00  2.25  2.50  2.75  3.00  3.25
Frequency     12    24    42    36    36    39    87   102   237   144
Proportion 0.009 0.017 0.030 0.026 0.026 0.028 0.063 0.074 0.172 0.104
                                                    
Value       3.50  3.75  4.00  4.25  4.50  4.75  5.00
Frequency    168   114   231    48    21    21    18
Proportion 0.122 0.083 0.167 0.035 0.015 0.015 0.013
---------------------------------------------------------------------------
JSAT 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
    1396       89       13    0.978    3.273    1.032    1.333    2.000 
     .25      .50      .75      .90      .95 
   2.667    3.333    4.000    4.333    4.667 
                                                                         
Value      1.000000 1.333333 1.666667 2.000000 2.333333 2.666667 3.000000
Frequency        41       34       46       81       83       85      211
Proportion    0.029    0.024    0.033    0.058    0.059    0.061    0.151
                                                                
Value      3.333333 3.666667 4.000000 4.333333 4.666667 5.000000
Frequency       172      149      338       70       43       43
Proportion    0.123    0.107    0.242    0.050    0.031    0.031
---------------------------------------------------------------------------
COMMIT 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
    1406       79       13    0.979     3.54   0.9079    2.000    2.667 
     .25      .50      .75      .90      .95 
   3.000    3.667    4.000    4.667    5.000 
                                                                         
Value      1.000000 1.333333 1.666667 2.000000 2.333333 2.666667 3.000000
Frequency        20       12       24       32       52       74      211
Proportion    0.014    0.009    0.017    0.023    0.037    0.053    0.150
                                                                
Value      3.333333 3.666667 4.000000 4.333333 4.666667 5.000000
Frequency       202      221      296      100       75       87
Proportion    0.144    0.157    0.211    0.071    0.053    0.062
---------------------------------------------------------------------------
READY 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
    1389       96       17    0.989     3.11    0.924     1.50     2.00 
     .25      .50      .75      .90      .95 
    2.50     3.25     3.75     4.00     4.25 
                                                                      
Value       1.00  1.25  1.50  1.75  2.00  2.25  2.50  2.75  3.00  3.25
Frequency     24    29    35    40    57    64   100   106   210   170
Proportion 0.017 0.021 0.025 0.029 0.041 0.046 0.072 0.076 0.151 0.122
                                                    
Value       3.50  3.75  4.00  4.25  4.50  4.75  5.00
Frequency    145   131   190    41    19    14    14
Proportion 0.104 0.094 0.137 0.030 0.014 0.010 0.010
---------------------------------------------------------------------------
GENDER2 
       n  missing distinct 
    1383      102        2 
                        
Value        male female
Frequency    1338     45
Proportion  0.967  0.033
---------------------------------------------------------------------------
GENDER3 
       n  missing distinct     Info      Sum     Mean      Gmd 
    1383      102        2    0.094       45  0.03254    0.063 

---------------------------------------------------------------------------
LEAVE 
       n  missing distinct     Info     Mean      Gmd      .05      .10 
    1406       79       13    0.979     2.46   0.9079    1.000    1.333 
     .25      .50      .75      .90      .95 
   2.000    2.333    3.000    3.333    4.000 
                                                                         
Value      1.000000 1.333333 1.666667 2.000000 2.333333 2.666667 3.000000
Frequency        87       75      100      296      221      202      211
Proportion    0.062    0.053    0.071    0.211    0.157    0.144    0.150
                                                                
Value      3.333333 3.666667 4.000000 4.333333 4.666667 5.000000
Frequency        74       52       32       24       12       20
Proportion    0.053    0.037    0.023    0.017    0.009    0.014
---------------------------------------------------------------------------

Alternative: psych

detach("package:Hmisc")
install.packages("psych")
library(psych)

psych::describe(dd4,na.rm=T)

psych::describe(dd4,na.rm=F)

psych::describe(na.omit(dd4))

Simple Distributions

#Frequency Counts
table(dd4$COMPANY)

  A   B   C   D   F HHC REC SVC 
402 348 126 183  15 354  18  39 

#Proportions
prop.table(table(dd4$COMPANY))

         A          B          C          D          F        HHC 
0.27070707 0.23434343 0.08484848 0.12323232 0.01010101 0.23838384 
       REC        SVC 
0.01212121 0.02626263 

#Rounding proportions to 3 decimals
round(prop.table(table(dd4$COMPANY)),3)

    A     B     C     D     F   HHC   REC   SVC 
0.271 0.234 0.085 0.123 0.010 0.238 0.012 0.026 

#Percentages
100*(prop.table(table(dd4$COMPANY)))

        A         B         C         D         F       HHC       REC 
27.070707 23.434343  8.484848 12.323232  1.010101 23.838384  1.212121 
      SVC 
 2.626263 

---

#Cross Tabs & Simple Tables
#install.packages("gmodels")
library(gmodels)
CrossTable(dd4$GENDER,dd4$COMPANY,chisq=TRUE,format="SPSS")

Warning in chisq.test(t, correct = FALSE, ...): Chi-squared approximation
may be incorrect

   Cell Contents
|-------------------------|
|                   Count |
| Chi-square contribution |
|             Row Percent |
|          Column Percent |
|           Total Percent |
|-------------------------|

Total Observations in Table:  1383 

             | dd4$COMPANY 
  dd4$GENDER |        A  |        B  |        C  |        D  |        F  |      HHC  |      REC  |      SVC  | Row Total | 
-------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|
           1 |      357  |      321  |      111  |      165  |        9  |      321  |       18  |       36  |     1338  | 
             |    0.023  |    0.181  |    0.042  |    0.037  |    0.010  |    0.148  |    0.020  |    0.039  |           | 
             |   26.682% |   23.991% |    8.296% |   12.332% |    0.673% |   23.991% |    1.345% |    2.691% |   96.746% | 
             |   95.968% |   99.074% |   94.872% |   98.214% |  100.000% |   94.690% |  100.000% |  100.000% |           | 
             |   25.813% |   23.210% |    8.026% |   11.931% |    0.651% |   23.210% |    1.302% |    2.603% |           | 
-------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|
           2 |       15  |        3  |        6  |        3  |        0  |       18  |        0  |        0  |       45  | 
             |    0.693  |    5.396  |    1.263  |    1.113  |    0.293  |    4.404  |    0.586  |    1.171  |           | 
             |   33.333% |    6.667% |   13.333% |    6.667% |    0.000% |   40.000% |    0.000% |    0.000% |    3.254% | 
             |    4.032% |    0.926% |    5.128% |    1.786% |    0.000% |    5.310% |    0.000% |    0.000% |           | 
             |    1.085% |    0.217% |    0.434% |    0.217% |    0.000% |    1.302% |    0.000% |    0.000% |           | 
-------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|
Column Total |      372  |      324  |      117  |      168  |        9  |      339  |       18  |       36  |     1383  | 
             |   26.898% |   23.427% |    8.460% |   12.148% |    0.651% |   24.512% |    1.302% |    2.603% |           | 
-------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|

 
Statistics for All Table Factors


Pearson's Chi-squared test 
------------------------------------------------------------
Chi^2 =  15.42045     d.f. =  7     p =  0.03097201 


 
       Minimum expected frequency: 0.2928416 
Cells with Expected Frequency < 5: 4 of 16 (25%)

table(dd4$GENDER,dd4$COMPANY)

   
      A   B   C   D   F HHC REC SVC
  1 357 321 111 165   9 321  18  36
  2  15   3   6   3   0  18   0   0

prop.table(table(dd4$GENDER,dd4$COMPANY))

   
              A           B           C           D           F
  1 0.258134490 0.232104121 0.080260304 0.119305857 0.006507592
  2 0.010845987 0.002169197 0.004338395 0.002169197 0.000000000
   
            HHC         REC         SVC
  1 0.232104121 0.013015184 0.026030369
  2 0.013015184 0.000000000 0.000000000

#Histograms
hist(dd4$JSAT)

hist(dd4$JSAT, main="Job Satisfaction Histogram",xlab="Job Satisfaction" )

Correlations using cor (part of stats) or rcorr (part of Hmisc)

cor(dd4[,20:22],use="complete.obs")

            JSAT    COMMIT     READY
JSAT   1.0000000 0.5373179 0.5093204
COMMIT 0.5373179 1.0000000 0.4610560
READY  0.5093204 0.4610560 1.0000000

install.packages("Hmisc")
library(Hmisc)

Hmisc::rcorr(as.matrix(dd4[,c(20:22)]))

       JSAT COMMIT READY
JSAT   1.00   0.54  0.51
COMMIT 0.54   1.00  0.46
READY  0.51   0.46  1.00

n
       JSAT COMMIT READY
JSAT   1396   1385  1369
COMMIT 1385   1406  1375
READY  1369   1375  1389

P
       JSAT COMMIT READY
JSAT         0      0   
COMMIT  0           0   
READY   0    0          

Popular Packages

multilevel

lme4 & nlme

plyr

ggplot2

reshape2

Rcmdr

Hmisc