2017-08-16 18:08:07
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"
#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] ".rs.getCompletionsSearchPath" ".rs.objectsOnSearchPath" ## [3] ".rs.recursiveSearch" ".rs.rpc.search" ## [5] "help.search" "hsearch_db" ## [7] "hsearch_db_concepts" "hsearch_db_keywords" ## [9] "RSiteSearch" "search" ## [11] "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"
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"
#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"
#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
## v2 v3 eng doors ## 1 1 Porsche 911 Flat-6 2 ## 2 2 Ford Mustang GT V-8 2 ## 3 3 Plymouth Baracuda V-8 2 ## 4 4 Chevrolet Camaro V-8 2 ## 5 5 Honda Pilot LX V-6 4
# Obtain a list of the variable names in a data frame names(data1)
## [1] "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
## v2 v3 eng doors ## 1 1 Porsche 911 Flat-6 2 ## 2 2 Ford Mustang GT V-8 2 ## 3 3 Plymouth Baracuda V-8 2 ## 4 4 Chevrolet Camaro V-8 2 ## 5 5 Honda Pilot LX V-6 4
data2
## id model eng doors ## 1 1 Porsche 911 Flat-6 2 ## 2 2 Ford Mustang GT V-8 2 ## 3 3 Plymouth Baracuda V-8 2 ## 4 4 Chevrolet Camaro V-8 2 ## 5 5 Honda Pilot LX V-6 4
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) #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"
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"
## [1] "SUBNUM" "TIME" "BTN" "COMPANY" "MARITAL" "GENDER" "HOWLONG" ## [8] "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
## SUBNUM TIME JOBSAT1 COMMIT1 READY1 JOBSAT2 COMMIT2 READY2 JOBSAT3 ## 1 1 0 1.666667 1.666667 2.75 1 1.666667 1 3 ## 2 1 1 1.666667 1.666667 2.75 1 1.666667 1 3 ## 3 1 2 1.666667 1.666667 2.75 1 1.666667 1 3 ## 4 2 0 3.666667 1.666667 3.00 4 1.333333 2 4 ## 5 2 1 3.666667 1.666667 3.00 4 1.333333 2 4 ## 6 2 2 3.666667 1.666667 3.00 4 1.333333 2 4 ## COMMIT3 READY3 JSAT COMMIT READY ## 1 3.000000 3.00 1.666667 1.666667 2.75 ## 2 3.000000 3.00 1.000000 1.666667 1.00 ## 3 3.000000 3.00 3.000000 3.000000 3.00 ## 4 1.333333 1.75 3.666667 1.666667 3.00 ## 5 1.333333 1.75 4.000000 1.333333 2.00 ## 6 1.333333 1.75 4.000000 1.333333 1.75
tail(data1) # display last 6 cases
## SUBNUM TIME JOBSAT1 COMMIT1 READY1 JOBSAT2 COMMIT2 READY2 JOBSAT3 ## 895 299 0 2.333333 4.333333 2.75 3.666667 3.666667 2.75 4 ## 896 299 1 2.333333 4.333333 2.75 3.666667 3.666667 2.75 4 ## 897 299 2 2.333333 4.333333 2.75 3.666667 3.666667 2.75 4 ## 898 300 0 2.666667 3.666667 2.25 2.666667 4.333333 3.75 3 ## 899 300 1 2.666667 3.666667 2.25 2.666667 4.333333 3.75 3 ## 900 300 2 2.666667 3.666667 2.25 2.666667 4.333333 3.75 3 ## COMMIT3 READY3 JSAT COMMIT READY ## 895 3.333333 3.00 2.333333 4.333333 2.75 ## 896 3.333333 3.00 3.666667 3.666667 2.75 ## 897 3.333333 3.00 4.000000 3.333333 3.00 ## 898 4.333333 3.25 2.666667 3.666667 2.25 ## 899 4.333333 3.25 2.666667 4.333333 3.75 ## 900 4.333333 3.25 3.000000 4.333333 3.25
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"
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"
## [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"
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)
## SUBNUM TIME BTN COMPANY MARITAL GENDER HOWLONG RANK EDUCATE AGE ## 1 1 0 1022 HHC 1 1 2 12 2 20 ## 2 1 1 1022 HHC 1 1 2 12 2 20 ## 3 1 2 1022 HHC 1 1 2 12 2 20 ## 4 10 0 3066 C 1 1 3 13 2 22 ## 5 10 1 3066 C 1 1 3 13 2 22 ## 6 10 2 3066 C 1 1 3 13 2 22 ## JOBSAT1 COMMIT1 READY1 JOBSAT2 COMMIT2 READY2 JOBSAT3 COMMIT3 READY3 ## 1 1.666667 1.666667 2.75 1.000000 1.666667 1 3 3 3 ## 2 1.666667 1.666667 2.75 1.000000 1.666667 1 3 3 3 ## 3 1.666667 1.666667 2.75 1.000000 1.666667 1 3 3 3 ## 4 1.000000 3.666667 2.00 1.333333 3.333333 3 3 3 3 ## 5 1.000000 3.666667 2.00 1.333333 3.333333 3 3 3 3 ## 6 1.000000 3.666667 2.00 1.333333 3.333333 3 3 3 3 ## JSAT COMMIT READY ## 1 1.666667 1.666667 2.75 ## 2 1.000000 1.666667 1.00 ## 3 3.000000 3.000000 3.00 ## 4 1.000000 3.666667 2.00 ## 5 1.333333 3.333333 3.00 ## 6 3.000000 3.000000 3.00
## SUBNUM TIME BTN COMPANY MARITAL GENDER HOWLONG RANK EDUCATE AGE ## 1480 494 0 4042 B 1 1 0 11 2 22 ## 1481 494 1 4042 B 1 1 0 11 2 22 ## 1482 494 2 4042 B 1 1 0 11 2 22 ## 1483 495 0 1022 B 1 1 3 13 2 19 ## 1484 495 1 1022 B 1 1 3 13 2 19 ## 1485 495 2 1022 B 1 1 3 13 2 19 ## JOBSAT1 COMMIT1 READY1 JOBSAT2 COMMIT2 READY2 JOBSAT3 COMMIT3 ## 1480 3 4.333333 3.75 2.333333 3.333333 3.25 3.666667 4.000000 ## 1481 3 4.333333 3.75 2.333333 3.333333 3.25 3.666667 4.000000 ## 1482 3 4.333333 3.75 2.333333 3.333333 3.25 3.666667 4.000000 ## 1483 4 4.000000 3.75 4.000000 4.000000 4.00 3.000000 3.333333 ## 1484 4 4.000000 3.75 4.000000 4.000000 4.00 3.000000 3.333333 ## 1485 4 4.000000 3.75 4.000000 4.000000 4.00 3.000000 3.333333 ## READY3 JSAT COMMIT READY ## 1480 4.00 3.000000 4.333333 3.75 ## 1481 4.00 2.333333 3.333333 3.25 ## 1482 4.00 3.666667 4.000000 4.00 ## 1483 3.25 4.000000 4.000000 3.75 ## 1484 3.25 4.000000 4.000000 4.00 ## 1485 3.25 3.000000 3.333333 3.25
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"
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"
#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"
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"
class(dd4$GENDER2)
## [1] "factor"
class(dd4$GENDER3)
## [1] "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)
## vars n mean sd median trimmed mad min max range ## SUBNUM 1 1485 248.00 142.94 248.00 248.00 183.84 1 495 494 ## TIME 2 1485 1.00 0.82 1.00 1.00 1.48 0 2 2 ## BTN 3 1485 1818.73 1403.35 1022.00 1767.29 1509.29 4 4042 4038 ## COMPANY* 4 1485 3.26 2.10 2.00 3.12 1.48 1 8 7 ## STATUS 5 1473 1.73 0.80 2.00 1.61 0.00 1 5 4 ## GENDER 6 1383 1.03 0.18 1.00 1.00 0.00 1 2 1 ## TENURE 7 1461 2.40 1.56 2.00 2.38 1.48 0 5 5 ## RANK 8 1410 15.04 3.02 14.00 14.51 1.48 11 32 21 ## EDUCATE 9 1473 2.59 1.09 2.00 2.37 0.00 1 6 5 ## AGE 10 1473 25.72 6.12 24.00 25.01 5.93 18 46 28 ## JOBSAT1 11 1404 3.24 0.98 3.33 3.29 0.99 1 5 4 ## COMMIT1 12 1401 3.62 0.85 3.67 3.66 0.99 1 5 4 ## READY1 13 1389 3.01 0.83 3.00 3.07 0.74 1 5 4 ## JOBSAT2 14 1395 3.25 0.94 3.33 3.31 0.99 1 5 4 ## COMMIT2 15 1416 3.47 0.87 3.67 3.52 0.49 1 5 4 ## READY2 16 1398 3.11 0.83 3.25 3.15 0.74 1 5 4 ## JOBSAT3 17 1389 3.34 0.85 3.33 3.39 0.99 1 5 4 ## COMMIT3 18 1401 3.54 0.75 3.67 3.57 0.49 1 5 4 ## READY3 19 1380 3.21 0.81 3.25 3.27 0.74 1 5 4 ## JSAT 20 1396 3.27 0.93 3.33 3.33 0.99 1 5 4 ## COMMIT 21 1406 3.54 0.83 3.67 3.58 0.49 1 5 4 ## READY 22 1389 3.11 0.83 3.25 3.16 0.74 1 5 4 ## GENDER2* 23 1383 1.03 0.18 1.00 1.00 0.00 1 2 1 ## GENDER3 24 1383 0.03 0.18 0.00 0.00 0.00 0 1 1 ## LEAVE 25 1406 2.46 0.83 2.33 2.42 0.49 1 5 4 ## skew kurtosis se ## SUBNUM 0.00 -1.20 3.71 ## TIME 0.00 -1.50 0.02 ## BTN 0.20 -1.48 36.42 ## COMPANY* 0.52 -1.11 0.05 ## STATUS 1.67 3.97 0.02 ## GENDER 5.26 25.73 0.00 ## TENURE 0.19 -0.85 0.04 ## RANK 1.84 4.18 0.08 ## EDUCATE 1.50 0.75 0.03 ## AGE 0.92 0.16 0.16 ## JOBSAT1 -0.47 -0.50 0.03 ## COMMIT1 -0.48 0.14 0.02 ## READY1 -0.47 -0.26 0.02 ## JOBSAT2 -0.60 -0.23 0.03 ## COMMIT2 -0.69 0.64 0.02 ## READY2 -0.41 -0.09 0.02 ## JOBSAT3 -0.55 0.16 0.02 ## COMMIT3 -0.48 0.69 0.02 ## READY3 -0.50 0.09 0.02 ## JSAT -0.55 -0.20 0.02 ## COMMIT -0.57 0.56 0.02 ## READY -0.46 -0.09 0.02 ## GENDER2* 5.26 25.73 0.00 ## GENDER3 5.26 25.73 0.00 ## LEAVE 0.57 0.56 0.02
psych::describe(dd4,na.rm=F)
## vars n mean sd median trimmed mad min max range ## SUBNUM 1 1032 249.52 143.06 248.50 250.14 182.36 1 495 494 ## TIME 2 1032 1.00 0.82 1.00 1.00 1.48 0 2 2 ## BTN 3 1032 1805.08 1400.20 1022.00 1750.15 1509.29 4 4042 4038 ## COMPANY* 4 1032 3.29 2.08 2.50 3.16 2.22 1 8 7 ## STATUS 5 1032 1.75 0.81 2.00 1.63 0.00 1 5 4 ## GENDER 6 1032 1.03 0.16 1.00 1.00 0.00 1 2 1 ## TENURE 7 1032 2.42 1.58 2.00 2.41 1.48 0 5 5 ## RANK 8 1032 15.14 3.18 14.00 14.61 1.48 11 32 21 ## EDUCATE 9 1032 2.65 1.13 2.00 2.44 0.00 1 6 5 ## AGE 10 1032 25.68 6.03 24.00 24.99 5.93 18 45 27 ## JOBSAT1 11 1032 3.20 0.99 3.33 3.26 0.99 1 5 4 ## COMMIT1 12 1032 3.63 0.84 3.67 3.66 0.99 1 5 4 ## READY1 13 1032 3.02 0.79 3.00 3.08 0.74 1 5 4 ## JOBSAT2 14 1032 3.23 0.93 3.33 3.29 0.99 1 5 4 ## COMMIT2 15 1032 3.47 0.86 3.67 3.53 0.49 1 5 4 ## READY2 16 1032 3.13 0.83 3.25 3.18 0.74 1 5 4 ## JOBSAT3 17 1032 3.28 0.87 3.33 3.34 0.99 1 5 4 ## COMMIT3 18 1032 3.52 0.76 3.67 3.54 0.49 1 5 4 ## READY3 19 1032 3.21 0.81 3.25 3.26 0.74 1 5 4 ## JSAT 20 1032 3.24 0.93 3.33 3.30 0.99 1 5 4 ## COMMIT 21 1032 3.54 0.82 3.67 3.58 0.49 1 5 4 ## READY 22 1032 3.12 0.81 3.25 3.17 0.74 1 5 4 ## GENDER2* 23 1032 1.03 0.16 1.00 1.00 0.00 1 2 1 ## GENDER3 24 1032 0.03 0.16 0.00 0.00 0.00 0 1 1 ## LEAVE 25 1032 2.46 0.82 2.33 2.42 0.49 1 5 4 ## skew kurtosis se ## SUBNUM -0.04 -1.18 4.45 ## TIME 0.00 -1.50 0.03 ## BTN 0.24 -1.47 43.59 ## COMPANY* 0.49 -1.17 0.06 ## STATUS 1.71 4.12 0.03 ## GENDER 5.93 33.18 0.00 ## TENURE 0.17 -0.90 0.05 ## RANK 1.83 4.08 0.10 ## EDUCATE 1.33 0.21 0.04 ## AGE 0.91 0.17 0.19 ## JOBSAT1 -0.46 -0.61 0.03 ## COMMIT1 -0.42 0.05 0.03 ## READY1 -0.47 -0.08 0.02 ## JOBSAT2 -0.55 -0.39 0.03 ## COMMIT2 -0.68 0.68 0.03 ## READY2 -0.41 -0.18 0.03 ## JOBSAT3 -0.54 0.06 0.03 ## COMMIT3 -0.46 0.70 0.02 ## READY3 -0.47 0.19 0.03 ## JSAT -0.52 -0.33 0.03 ## COMMIT -0.52 0.52 0.03 ## READY -0.44 -0.03 0.03 ## GENDER2* 5.93 33.18 0.00 ## GENDER3 5.93 33.18 0.00 ## LEAVE 0.52 0.52 0.03
psych::describe(na.omit(dd4))
## vars n mean sd median trimmed mad min max range ## SUBNUM 1 1032 249.52 143.06 248.50 250.14 182.36 1 495 494 ## TIME 2 1032 1.00 0.82 1.00 1.00 1.48 0 2 2 ## BTN 3 1032 1805.08 1400.20 1022.00 1750.15 1509.29 4 4042 4038 ## COMPANY* 4 1032 3.29 2.08 2.50 3.16 2.22 1 8 7 ## STATUS 5 1032 1.75 0.81 2.00 1.63 0.00 1 5 4 ## GENDER 6 1032 1.03 0.16 1.00 1.00 0.00 1 2 1 ## TENURE 7 1032 2.42 1.58 2.00 2.41 1.48 0 5 5 ## RANK 8 1032 15.14 3.18 14.00 14.61 1.48 11 32 21 ## EDUCATE 9 1032 2.65 1.13 2.00 2.44 0.00 1 6 5 ## AGE 10 1032 25.68 6.03 24.00 24.99 5.93 18 45 27 ## JOBSAT1 11 1032 3.20 0.99 3.33 3.26 0.99 1 5 4 ## COMMIT1 12 1032 3.63 0.84 3.67 3.66 0.99 1 5 4 ## READY1 13 1032 3.02 0.79 3.00 3.08 0.74 1 5 4 ## JOBSAT2 14 1032 3.23 0.93 3.33 3.29 0.99 1 5 4 ## COMMIT2 15 1032 3.47 0.86 3.67 3.53 0.49 1 5 4 ## READY2 16 1032 3.13 0.83 3.25 3.18 0.74 1 5 4 ## JOBSAT3 17 1032 3.28 0.87 3.33 3.34 0.99 1 5 4 ## COMMIT3 18 1032 3.52 0.76 3.67 3.54 0.49 1 5 4 ## READY3 19 1032 3.21 0.81 3.25 3.26 0.74 1 5 4 ## JSAT 20 1032 3.24 0.93 3.33 3.30 0.99 1 5 4 ## COMMIT 21 1032 3.54 0.82 3.67 3.58 0.49 1 5 4 ## READY 22 1032 3.12 0.81 3.25 3.17 0.74 1 5 4 ## GENDER2* 23 1032 1.03 0.16 1.00 1.00 0.00 1 2 1 ## GENDER3 24 1032 0.03 0.16 0.00 0.00 0.00 0 1 1 ## LEAVE 25 1032 2.46 0.82 2.33 2.42 0.49 1 5 4 ## skew kurtosis se ## SUBNUM -0.04 -1.18 4.45 ## TIME 0.00 -1.50 0.03 ## BTN 0.24 -1.47 43.59 ## COMPANY* 0.49 -1.17 0.06 ## STATUS 1.71 4.12 0.03 ## GENDER 5.93 33.18 0.00 ## TENURE 0.17 -0.90 0.05 ## RANK 1.83 4.08 0.10 ## EDUCATE 1.33 0.21 0.04 ## AGE 0.91 0.17 0.19 ## JOBSAT1 -0.46 -0.61 0.03 ## COMMIT1 -0.42 0.05 0.03 ## READY1 -0.47 -0.08 0.02 ## JOBSAT2 -0.55 -0.39 0.03 ## COMMIT2 -0.68 0.68 0.03 ## READY2 -0.41 -0.18 0.03 ## JOBSAT3 -0.54 0.06 0.03 ## COMMIT3 -0.46 0.70 0.02 ## READY3 -0.47 0.19 0.03 ## JSAT -0.52 -0.33 0.03 ## COMMIT -0.52 0.52 0.03 ## READY -0.44 -0.03 0.03 ## GENDER2* 5.93 33.18 0.00 ## GENDER3 5.93 33.18 0.00 ## LEAVE 0.52 0.52 0.03
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