#Must load dplyr
#Import .xlsx file on my computer
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(readxl)
Table_xlsx_ <- read_excel("~/Desktop/tables/Table(.xlsx).xlsx")
library(dplyr)
#Create a "tibble"
Table_xlsx_ <- tbl_df(Table_xlsx_)
Table_xlsx_
## # A tibble: 15 x 7
## Name Clearfield `State_ College` Dogs Brownfish Pittsburgh
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Steve 2 1 1 10 2
## 2 Nina 2 1 2 11 2
## 3 Gary 2 1 1 12 2
## 4 Daci 2 1 1 10 2
## 5 Jennifer 2 1 1 11 2
## 6 Terry 2 1 1 12 2
## 7 Dave 2 1 1 13 2
## 8 Joe 2 1 1 14 2
## 9 Patty 2 1 2 13 2
## 10 Deb 2 1 1 15 2
## 11 Mark 2 1 2 16 2
## 12 Joey 2 1 2 12 2
## 13 Sonja 2 1 1 12 2
## 14 Lelia 2 1 3 11 2
## 15 Dave 2 1 1 10 2
## # ... with 1 more variables: Harrisburg <dbl>
#Summarize
summary(Table_xlsx_)
## Name Clearfield State_ College Dogs
## Length:15 Min. :2 Min. :1 Min. :1.0
## Class :character 1st Qu.:2 1st Qu.:1 1st Qu.:1.0
## Mode :character Median :2 Median :1 Median :1.0
## Mean :2 Mean :1 Mean :1.4
## 3rd Qu.:2 3rd Qu.:1 3rd Qu.:2.0
## Max. :2 Max. :1 Max. :3.0
## Brownfish Pittsburgh Harrisburg
## Min. :10.00 Min. :2 Min. :1
## 1st Qu.:11.00 1st Qu.:2 1st Qu.:1
## Median :12.00 Median :2 Median :1
## Mean :12.13 Mean :2 Mean :1
## 3rd Qu.:13.00 3rd Qu.:2 3rd Qu.:1
## Max. :16.00 Max. :2 Max. :1
#########################################
#Import .csv file on my computer
library(readxl)
Table_csv_ <- read_excel("~/Desktop/tables/Table(.csv).xlsx")
library(dplyr)
#Create a "tibble"
Table_csv_ <- tbl_df(Table_csv_)
Table_csv_
## # A tibble: 15 x 7
## Name Clearfield `State_ College` Dogs Brownfish Pittsburgh
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Steve 2 1 1 10 2
## 2 Nina 2 1 2 11 2
## 3 Gary 2 1 1 12 2
## 4 Daci 2 1 1 10 2
## 5 Jennifer 2 1 1 11 2
## 6 Terry 2 1 1 12 2
## 7 Dave 2 1 1 13 2
## 8 Joe 2 1 1 14 2
## 9 Patty 2 1 2 13 2
## 10 Deb 2 1 1 15 2
## 11 Mark 2 1 2 16 2
## 12 Joey 2 1 2 12 2
## 13 Sonja 2 1 1 12 2
## 14 Lelia 2 1 3 11 2
## 15 Dave 2 1 1 10 2
## # ... with 1 more variables: Harrisburg <dbl>
#Summarize
summary(Table_csv_)
## Name Clearfield State_ College Dogs
## Length:15 Min. :2 Min. :1 Min. :1.0
## Class :character 1st Qu.:2 1st Qu.:1 1st Qu.:1.0
## Mode :character Median :2 Median :1 Median :1.0
## Mean :2 Mean :1 Mean :1.4
## 3rd Qu.:2 3rd Qu.:1 3rd Qu.:2.0
## Max. :2 Max. :1 Max. :3.0
## Brownfish Pittsburgh Harrisburg
## Min. :10.00 Min. :2 Min. :1
## 1st Qu.:11.00 1st Qu.:2 1st Qu.:1
## Median :12.00 Median :2 Median :1
## Mean :12.13 Mean :2 Mean :1
## 3rd Qu.:13.00 3rd Qu.:2 3rd Qu.:1
## Max. :16.00 Max. :2 Max. :1
#######################################
#Import .txt file on my computer
library(readxl)
Table_txt_ <- read_excel("~/Desktop/tables/Table(.txt).xlsx")
library(dplyr)
#Create a "tibble"
Table_txt_ <- tbl_df(Table_txt_)
Table_txt_
## # A tibble: 15 x 7
## Name Clearfield `State_ College` Dogs Brownfish Pittsburgh
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Steve 2 1 1 10 2
## 2 Nina 2 1 2 11 2
## 3 Gary 2 1 1 12 2
## 4 Daci 2 1 1 10 2
## 5 Jennifer 2 1 1 11 2
## 6 Terry 2 1 1 12 2
## 7 Dave 2 1 1 13 2
## 8 Joe 2 1 1 14 2
## 9 Patty 2 1 2 13 2
## 10 Deb 2 1 1 15 2
## 11 Mark 2 1 2 16 2
## 12 Joey 2 1 2 12 2
## 13 Sonja 2 1 1 12 2
## 14 Lelia 2 1 3 11 2
## 15 Dave 2 1 1 10 2
## # ... with 1 more variables: Harrisburg <dbl>
#Summarize
summary(Table_txt_)
## Name Clearfield State_ College Dogs
## Length:15 Min. :2 Min. :1 Min. :1.0
## Class :character 1st Qu.:2 1st Qu.:1 1st Qu.:1.0
## Mode :character Median :2 Median :1 Median :1.0
## Mean :2 Mean :1 Mean :1.4
## 3rd Qu.:2 3rd Qu.:1 3rd Qu.:2.0
## Max. :2 Max. :1 Max. :3.0
## Brownfish Pittsburgh Harrisburg
## Min. :10.00 Min. :2 Min. :1
## 1st Qu.:11.00 1st Qu.:2 1st Qu.:1
## Median :12.00 Median :2 Median :1
## Mean :12.13 Mean :2 Mean :1
## 3rd Qu.:13.00 3rd Qu.:2 3rd Qu.:1
## Max. :16.00 Max. :2 Max. :1
######################################
#Import .csv file from a remote location
csvwebfile <- read.csv("http://www.personal.psu.edu/dlp/alphaheight_weight_dataset.csv")
library(dplyr)
#Create a "tibble"
csvwebfile <- tbl_df(csvwebfile)
csvwebfile
## # A tibble: 200 x 4
## Index Height Weight Gender
## <int> <dbl> <dbl> <fctr>
## 1 1 65.78 112.99 female
## 2 2 71.52 136.49 male
## 3 3 69.40 153.03 male
## 4 4 68.22 142.34 female
## 5 5 67.79 144.30 male
## 6 6 68.70 123.30 male
## 7 7 69.80 141.49 male
## 8 8 70.01 136.46 female
## 9 9 67.90 112.37 male
## 10 10 66.78 120.67 male
## # ... with 190 more rows
#Summarize
summary(csvwebfile)
## Index Height Weight Gender
## Min. : 1.00 Min. :63.43 Min. : 97.9 female: 95
## 1st Qu.: 50.75 1st Qu.:66.52 1st Qu.:119.9 male :105
## Median :100.50 Median :67.94 Median :127.9
## Mean :100.50 Mean :67.95 Mean :127.2
## 3rd Qu.:150.25 3rd Qu.:69.20 3rd Qu.:136.1
## Max. :200.00 Max. :73.90 Max. :159.0
#####################################
#Import .csv webfile
csvwebfile2 <- read.csv("http://www.personal.psu.edu/dlp/w540/datasets/titanicsurvival.csv")
library(dplyr)
#Create a "tibble"
csvwebfile2 <- tbl_df(csvwebfile2)
csvwebfile2
## # A tibble: 2,201 x 4
## Class Age Sex Survive
## <int> <int> <int> <int>
## 1 1 1 1 1
## 2 1 1 1 1
## 3 1 1 1 1
## 4 1 1 1 1
## 5 1 1 1 1
## 6 1 1 1 1
## 7 1 1 1 1
## 8 1 1 1 1
## 9 1 1 1 1
## 10 1 1 1 1
## # ... with 2,191 more rows
#Summarize
summary(csvwebfile2)
## Class Age Sex Survive
## Min. :0.000 Min. :0.0000 Min. :0.0000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:0.000
## Median :1.000 Median :1.0000 Median :1.0000 Median :0.000
## Mean :1.369 Mean :0.9505 Mean :0.7865 Mean :0.323
## 3rd Qu.:3.000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.000
## Max. :3.000 Max. :1.0000 Max. :1.0000 Max. :1.000
#####################################
#Import from spss
library(haven)
EXAMPLE_23_1 <- read_sav("~/Downloads/EXAMPLE_23_1.sav")
library(dplyr)
#Create a “tibble”
EXAMPLE_23_1 <- tbl_df(EXAMPLE_23_1)
EXAMPLE_23_1
## # A tibble: 1,028 x 12
## Illness Item_1 Item_2 Item_3 Item_4 Item_5 Item_6
## <dbl+lbl> <dbl+lbl> <dbl+lbl> <dbl+lbl> <dbl> <dbl+lbl> <dbl+lbl>
## 1 1 4 3 3 3 4 2
## 2 0 3 2 4 3 4 3
## 3 0 4 3 4 3 3 2
## 4 1 5 5 4 5 4 5
## 5 1 2 2 2 2 2 2
## 6 0 3 2 2 3 2 1
## 7 0 2 1 1 2 1 2
## 8 0 3 2 4 4 2 2
## 9 0 2 4 3 3 3 3
## 10 1 1 1 1 1 1 1
## # ... with 1,018 more rows, and 5 more variables: Item_7 <dbl>,
## # Item_8 <dbl+lbl>, Item_9 <dbl+lbl>, Item_10 <dbl+lbl>, Item_11 <dbl>
#Summarize
summary(EXAMPLE_23_1)
## Illness Item_1 Item_2 Item_3
## Min. :0.0000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:0.0000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.00
## Median :0.0000 Median :3.000 Median :3.000 Median :3.00
## Mean :0.3492 Mean :2.855 Mean :2.938 Mean :2.89
## 3rd Qu.:1.0000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.00
## Max. :1.0000 Max. :5.000 Max. :5.000 Max. :5.00
## Item_4 Item_5 Item_6 Item_7
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :2.000 Median :3.000
## Mean :2.832 Mean :2.949 Mean :2.483 Mean :3.022
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## Item_8 Item_9 Item_10 Item_11
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:3.00 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.00
## Median :4.00 Median :4.000 Median :4.000 Median :4.00
## Mean :3.58 Mean :3.601 Mean :3.444 Mean :3.55
## 3rd Qu.:4.00 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.00
## Max. :5.00 Max. :5.000 Max. :5.000 Max. :5.00