1. Read the dataset from “https://www.stats.govt.nz/assets/Uploads/Government-finance-statistics-general-government/Government-finance-statistics-general-government-Year-ended-June-2020/Download-data/government-finance-statistics-general-government-year-ended-june-2020-csv.csv” and find the datatypes of the variables. If there are some charecter variables, convert them to factors in the next step. Show the data is read properly by printing first few rows.
#Read the dataset
data = read.csv("https://www.stats.govt.nz/assets/Uploads/Government-finance-statistics-general-government/Government-finance-statistics-general-government-Year-ended-June-2020/Download-data/government-finance-statistics-general-government-year-ended-june-2020-csv.csv")
head(data)## Series_reference Period Data_value STATUS UNITS MAGNTUDE
## 1 GFSA.SGS01G01Z90 2009.06 3374 FINAL Dollars 6
## 2 GFSA.SGS01G01Z90 2010.06 -2971 FINAL Dollars 6
## 3 GFSA.SGS01G01Z90 2011.06 -13101 REVISED Dollars 6
## 4 GFSA.SGS01G01Z90 2012.06 -3367 REVISED Dollars 6
## 5 GFSA.SGS01G01Z90 2013.06 -476 REVISED Dollars 6
## 6 GFSA.SGS01G01Z90 2014.06 1715 REVISED Dollars 6
## Subject
## 1 Government Financial Statistics - GFS
## 2 Government Financial Statistics - GFS
## 3 Government Financial Statistics - GFS
## 4 Government Financial Statistics - GFS
## 5 Government Financial Statistics - GFS
## 6 Government Financial Statistics - GFS
## Group Series_title_1
## 1 General Government, Operating Statement, Net Balances Net operating balance
## 2 General Government, Operating Statement, Net Balances Net operating balance
## 3 General Government, Operating Statement, Net Balances Net operating balance
## 4 General Government, Operating Statement, Net Balances Net operating balance
## 5 General Government, Operating Statement, Net Balances Net operating balance
## 6 General Government, Operating Statement, Net Balances Net operating balance
## Series_title_2 Series_title_3 Series_title_4 Series_title_5
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA#find the datatypes of the variables.
str(data)## 'data.frame': 2012 obs. of 13 variables:
## $ Series_reference: chr "GFSA.SGS01G01Z90" "GFSA.SGS01G01Z90" "GFSA.SGS01G01Z90" "GFSA.SGS01G01Z90" ...
## $ Period : num 2009 2010 2011 2012 2013 ...
## $ Data_value : int 3374 -2971 -13101 -3367 -476 1715 4244 6080 8016 9134 ...
## $ STATUS : chr "FINAL" "FINAL" "REVISED" "REVISED" ...
## $ UNITS : chr "Dollars" "Dollars" "Dollars" "Dollars" ...
## $ MAGNTUDE : int 6 6 6 6 6 6 6 6 6 6 ...
## $ Subject : chr "Government Financial Statistics - GFS" "Government Financial Statistics - GFS" "Government Financial Statistics - GFS" "Government Financial Statistics - GFS" ...
## $ Group : chr "General Government, Operating Statement, Net Balances" "General Government, Operating Statement, Net Balances" "General Government, Operating Statement, Net Balances" "General Government, Operating Statement, Net Balances" ...
## $ Series_title_1 : chr "Net operating balance" "Net operating balance" "Net operating balance" "Net operating balance" ...
## $ Series_title_2 : chr "" "" "" "" ...
## $ Series_title_3 : chr "" "" "" "" ...
## $ Series_title_4 : chr "" "" "" "" ...
## $ Series_title_5 : logi NA NA NA NA NA NA ...If there are some charecter variables, convert them to factors in the next step. Show the data is read properly by printing first few rows.
#Read the dataset
data = read.csv("https://www.stats.govt.nz/assets/Uploads/Government-finance-statistics-general-government/Government-finance-statistics-general-government-Year-ended-June-2020/Download-data/government-finance-statistics-general-government-year-ended-june-2020-csv.csv", stringsAsFactors = T)
#printing first few rows.
head(data)## Series_reference Period Data_value STATUS UNITS MAGNTUDE
## 1 GFSA.SGS01G01Z90 2009.06 3374 FINAL Dollars 6
## 2 GFSA.SGS01G01Z90 2010.06 -2971 FINAL Dollars 6
## 3 GFSA.SGS01G01Z90 2011.06 -13101 REVISED Dollars 6
## 4 GFSA.SGS01G01Z90 2012.06 -3367 REVISED Dollars 6
## 5 GFSA.SGS01G01Z90 2013.06 -476 REVISED Dollars 6
## 6 GFSA.SGS01G01Z90 2014.06 1715 REVISED Dollars 6
## Subject
## 1 Government Financial Statistics - GFS
## 2 Government Financial Statistics - GFS
## 3 Government Financial Statistics - GFS
## 4 Government Financial Statistics - GFS
## 5 Government Financial Statistics - GFS
## 6 Government Financial Statistics - GFS
## Group Series_title_1
## 1 General Government, Operating Statement, Net Balances Net operating balance
## 2 General Government, Operating Statement, Net Balances Net operating balance
## 3 General Government, Operating Statement, Net Balances Net operating balance
## 4 General Government, Operating Statement, Net Balances Net operating balance
## 5 General Government, Operating Statement, Net Balances Net operating balance
## 6 General Government, Operating Statement, Net Balances Net operating balance
## Series_title_2 Series_title_3 Series_title_4 Series_title_5
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA#find the datatypes of the variables.
str(data)## 'data.frame': 2012 obs. of 13 variables:
## $ Series_reference: Factor w/ 167 levels "GFSA.SGS01G01Z90",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Period : num 2009 2010 2011 2012 2013 ...
## $ Data_value : int 3374 -2971 -13101 -3367 -476 1715 4244 6080 8016 9134 ...
## $ STATUS : Factor w/ 3 levels "FINAL","PROVISIONAL",..: 1 1 3 3 3 3 3 3 3 3 ...
## $ UNITS : Factor w/ 1 level "Dollars": 1 1 1 1 1 1 1 1 1 1 ...
## $ MAGNTUDE : int 6 6 6 6 6 6 6 6 6 6 ...
## $ Subject : Factor w/ 1 level "Government Financial Statistics - GFS": 1 1 1 1 1 1 1 1 1 1 ...
## $ Group : Factor w/ 26 levels "General Government, Balance Sheet, Financial Assets",..: 17 17 17 17 17 17 17 17 17 17 ...
## $ Series_title_1 : Factor w/ 57 levels "ACC outstanding claims liability",..: 30 30 30 30 30 30 30 30 30 30 ...
## $ Series_title_2 : Factor w/ 47 levels "","Cash","Customs and other import duties",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Series_title_3 : Factor w/ 12 levels "","Additions",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Series_title_4 : Factor w/ 6 levels "","Additions",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Series_title_5 : logi NA NA NA NA NA NA ...**2. Create a 3*3 matrix A using the following data [4,5,7,8,0,9,5,4,8]. Find the transpose of the matrix.**
#Create a 3*3 matrix A
A = matrix(c(4,5,7,8,0,9,5,4,8),3,3)
A## [,1] [,2] [,3]
## [1,] 4 8 5
## [2,] 5 0 4
## [3,] 7 9 8#Find the transpose of the matrix
A_trans = t(A)
A_trans## [,1] [,2] [,3]
## [1,] 4 5 7
## [2,] 8 0 9
## [3,] 5 4 83. Create a 33 matrix B using the following data [14,52,75,89,10,91,51,44,28]. Using matrix A and B find A + B, A - B and AB.
#Create a 3*3 matrix B
B = matrix(c(14,52,75,89,10,91,51,44,28),3,3)
B## [,1] [,2] [,3]
## [1,] 14 89 51
## [2,] 52 10 44
## [3,] 75 91 28#A + B
A + B## [,1] [,2] [,3]
## [1,] 18 97 56
## [2,] 57 10 48
## [3,] 82 100 36#A-B
A-B## [,1] [,2] [,3]
## [1,] -10 -81 -46
## [2,] -47 -10 -40
## [3,] -68 -82 -20#A*B
A*B## [,1] [,2] [,3]
## [1,] 56 712 255
## [2,] 260 0 176
## [3,] 525 819 224#A/B
A/B## [,1] [,2] [,3]
## [1,] 0.28571429 0.08988764 0.09803922
## [2,] 0.09615385 0.00000000 0.09090909
## [3,] 0.09333333 0.09890110 0.285714294. Create 2 vectors with 7 elements each and perform all the vector operations.
V1 = c(1,2,3,4,5,6,7)
V2 = c(7,6,5,4,3,2,1)
V1+V2## [1] 8 8 8 8 8 8 8V1-V2## [1] -6 -4 -2 0 2 4 6V1*V2## [1] 7 12 15 16 15 12 7V1/V2## [1] 0.1428571 0.3333333 0.6000000 1.0000000 1.6666667 3.0000000 7.0000000cbind(V1,V2)## V1 V2
## [1,] 1 7
## [2,] 2 6
## [3,] 3 5
## [4,] 4 4
## [5,] 5 3
## [6,] 6 2
## [7,] 7 1rbind(V1,V2)## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## V1 1 2 3 4 5 6 7
## V2 7 6 5 4 3 2 1as.numeric(V1)## [1] 1 2 3 4 5 6 7as.character(V2)## [1] "7" "6" "5" "4" "3" "2" "1"data.frame(V1,V2)## V1 V2
## 1 1 7
## 2 2 6
## 3 3 5
## 4 4 4
## 5 5 3
## 6 6 2
## 7 7 15. Create the following vectors and create a data frame using those vectors. Also find the charectristics of the data frame.
id,name,salary,start_date,dept
1,Rick,623.3,2012-01-01,IT
2,Dan,515.2,2013-09-23,Operations
3,Michelle,611,2014-11-15,IT
4,Ryan,729,2014-05-11,HR
5,Gary,843.25,2015-03-27,Finance
6,Nina,578,2013-05-21,IT
7,Simon,632.8,2013-07-30,Operations
8,Guru,722.5,2014-06-17,Finance
v1 = c("id","name","salary","start_date","dept")
v2 = c(1,"Rick",623.3,2012-01-01,"IT")
v3 = c(2,"Dan",515.2,2013-09-23,"Operations")
v4 = c(3,"Michelle",611,2014-11-15,"IT")
v5 = c(4,"Ryan",729,2014-05-11,"HR")
v6 = c(5,"Gary",843.25,2015-03-27,"Finance")
v7 = c(6,"Nina",578,2013-05-21,"IT")
v8 = c(7,"Simon",632.8,2013-07-30,"Operations")
v9 = c(8,"Guru",722.5,2014-06-17,"Finance")
# create a data frame using those vectors.
data_frame = data.frame(rbind(v1,v2,v3,v4,v5,v6,v7,v8,v9))
data_frame## X1 X2 X3 X4 X5
## v1 id name salary start_date dept
## v2 1 Rick 623.3 2010 IT
## v3 2 Dan 515.2 1981 Operations
## v4 3 Michelle 611 1988 IT
## v5 4 Ryan 729 1998 HR
## v6 5 Gary 843.25 1985 Finance
## v7 6 Nina 578 1987 IT
## v8 7 Simon 632.8 1976 Operations
## v9 8 Guru 722.5 1991 Finance