DATA607_Project2
Harpreet Shoker
Environment setup
library(stringr)
library(dplyr)## Warning: package 'dplyr' was built under R version 3.4.2##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyr)## Warning: package 'tidyr' was built under R version 3.4.3library(knitr)
library(tibble)## Warning: package 'tibble' was built under R version 3.4.3library(ggplot2)For 1st data set i have picked World population data from discussion by steven which has data set showing the population of the world’s countries from 1980 to 2010
1 Data - World Population
Loading Data set for World population
countryData <- read.csv(file="https://raw.githubusercontent.com/Harpreet1984/DATA607/master/populationbycountry19802010millions.csv", header=TRUE, sep=",")
countryDataTidy <- countryData %>% gather ("Year", "population", 2:32) #using gather()## Warning: attributes are not identical across measure variables;
## they will be droppedhead(countryDataTidy)## X Year population
## 1 North America X1980 320.27638
## 2 Bermuda X1980 0.05473
## 3 Canada X1980 24.5933
## 4 Greenland X1980 0.05021
## 5 Mexico X1980 68.34748
## 6 Saint Pierre and Miquelon X1980 0.00599Tidying Data
Removing all the rows that have NA and – as populations group data based on country. Calculate percentage change for the entire duration for each country.
countryDataFilteredGrouped <- countryDataTidy %>% filter (!population %in% c("--", NA) ) %>% group_by(X) %>% summarise( populationChange = (last(as.numeric(population)) - first(as.numeric(population)))/first(as.numeric(population)))
countryDataFilteredGrouped## # A tibble: 229 x 2
## X populationChange
## <fct> <dbl>
## 1 Afghanistan 0.936
## 2 Africa 1.12
## 3 Albania 0.118
## 4 Algeria 0.839
## 5 American Samoa 1.05
## 6 Angola 0.938
## 7 Antigua and Barbuda 0.265
## 8 Argentina 0.457
## 9 Armenia -0.122
## 10 Aruba 0.749
## # ... with 219 more rowsData Analaysis
Find the country that has the maximum population growth during this duration
countryWithMaxPopulationGrowth <- countryDataFilteredGrouped %>% filter(populationChange == max(populationChange) )
countryWithMaxPopulationGrowth## # A tibble: 1 x 2
## X populationChange
## <fct> <dbl>
## 1 United Arab Emirates 3.97So here we found that the country that has maximum population growth during the entire duration is United Arab emirates
Find the country that has the minimum population growth during this duration
countryWithMinPopulationGrowth <- countryDataFilteredGrouped %>% filter(populationChange == min(populationChange) )
countryWithMinPopulationGrowth## # A tibble: 1 x 2
## X populationChange
## <fct> <dbl>
## 1 Montserrat -0.565From the above result we can conclude that population percentage has decreased for montserrat by 5.64 %
2 Data - Time Spent (Male vs Female)
For 2nd data set Time spent as discussed by Nicholas this dataset on time use by gender and by country has variables include eating, sleeping, employment, travel, school, study, walking the dog, etc
Laoding Data
myurl <- "https://raw.githubusercontent.com/Harpreet1984/DATA607/master/TimeUse%20(1).csv"
time_info <- read.csv(myurl, header= TRUE,sep=",",stringsAsFactors=FALSE)
kable(time_info)| SEX | GEO.ACL00 | Total | Personal.care | Sleep | Eating | Other.and.or.unspecified.personal.care | Employment..related.activities.and.travel.as.part.of.during.main.and.second.job | Main.and.second.job.and.related.travel | Activities.related.to.employment.and.unspecified.employment | Study | School.and.university.except.homework | Homework | Free.time.study | Household.and.family.care | Food.management.except.dish.washing | Dish.washing | Cleaning.dwelling | Household.upkeep.except.cleaning.dwelling | Laundry | Ironing | Handicraft.and.producing.textiles.and.other.care.for.textiles | Gardening..other.pet.care | Tending.domestic.animals | Caring.for.pets | Walking.the.dog | Construction.and.repairs | Shopping.and.services | Childcare..except.teaching..reading.and.talking | Teaching..reading.and.talking.with.child | Household.management.and.help.family.member | Leisure..social.and.associative.life | Organisational.work | Informal.help.to.other.households | Participatory.activities | Visiting.and.feasts | Other.social.life | Entertainment.and.culture | Resting | Walking.and.hiking | Sports.and.outdoor.activities.except.walking.and.hiking | Computer.games | Computing | Hobbies.and.games.except.computing.and.computer.games | Reading.books | Reading..except.books | TV.and.video | Radio.and.music | Unspecified.leisure | Travel.except.travel.related.to.jobs | Travel.to.from.work | Travel.related.to.study | Travel.related.to.shopping.and.services | Transporting.a.child | Travel.related.to.other.household.purposes | Travel.related.to.leisure..social.and.associative.life | Unspecified.travel | Unspecified.time.use |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Males | Belgium | 24:00 | 10:45 | 8:15 | 1:49 | 0:42 | 3:07 | 3:05 | 0:02 | 0:11 | 0:05 | 0:03 | 0:03 | 2:28 | 0:22 | 0:10 | 0:08 | 0:18 | 0:01 | 0:01 | 0:00 | 0:19 | 0:00 | 0:03 | 0:05 | 0:19 | 0:24 | 0:05 | 0:04 | 0:08 | 5:58 | 0:07 | 0:00 | 0:03 | 0:32 | 0:23 | 0:10 | 0:27 | 0:12 | 0:15 | 0:05 | 0:22 | 0:13 | 0:06 | 0:22 | 2:35 | 0:05 | 0:01 | 1:30 | 0:25 | 0:02 | 0:16 | 0:03 | 0:00 | 0:15 | 0:30 | 0:01 |
| Males | Bulgaria | 24:00 | 11:54 | 9:08 | 2:07 | 0:39 | 3:32 | 3:27 | 0:04 | 0:03 | 0:02 | 0:01 | 0:00 | 2:37 | 0:15 | 0:05 | 0:06 | 0:22 | 0:01 | 0:00 | 0:00 | 0:36 | 0:32 | 0:01 | 0:02 | 0:16 | 0:13 | 0:02 | 0:05 | 0:01 | 4:46 | 0:00 | 0:09 | 0:01 | 0:04 | 0:37 | 0:01 | 0:10 | 0:16 | 0:10 | 0:00 | 0:01 | 0:11 | 0:06 | 0:15 | 2:41 | 0:06 | 0:01 | 1:07 | 0:23 | 0:00 | 0:12 | 0:01 | 0:06 | 0:21 | 0:03 | 0:02 |
| Males | Germany (including former GDR from 1991) | 24:00 | 10:40 | 8:08 | 1:43 | 0:49 | 3:27 | 3:21 | 0:06 | 0:15 | 0:06 | 0:05 | 0:04 | 2:22 | 0:16 | 0:08 | 0:11 | 0:14 | 0:02 | 0:01 | 0:00 | 0:17 | 0:01 | 0:03 | 0:03 | 0:19 | 0:29 | 0:05 | 0:05 | 0:09 | 5:42 | 0:09 | 0:08 | 0:04 | 0:17 | 0:45 | 0:14 | 0:16 | 0:13 | 0:15 | 0:05 | 0:16 | 0:18 | 0:06 | 0:31 | 1:58 | 0:05 | 0:00 | 1:29 | 0:27 | 0:02 | 0:16 | 0:02 | 0:05 | 0:34 | 0:03 | 0:05 |
| Males | Estonia | 24:00 | 10:35 | 8:24 | 1:19 | 0:52 | 4:27 | 4:20 | 0:07 | 0:06 | 0:03 | 0:02 | 0:02 | 2:33 | 0:21 | 0:06 | 0:09 | 0:22 | 0:01 | 0:00 | 0:01 | 0:16 | 0:04 | 0:01 | 0:05 | 0:29 | 0:20 | 0:06 | 0:04 | 0:06 | 5:02 | 0:02 | 0:15 | 0:01 | 0:04 | 0:26 | 0:05 | 0:21 | 0:10 | 0:13 | 0:01 | 0:02 | 0:05 | 0:14 | 0:23 | 2:29 | 0:11 | 0:00 | 1:12 | 0:28 | 0:01 | 0:13 | 0:01 | 0:07 | 0:22 | 0:01 | 0:04 |
| Males | Spain | 24:00 | 11:11 | 8:36 | 1:47 | 0:48 | 4:21 | 4:17 | 0:03 | 0:18 | 0:06 | 0:07 | 0:04 | 1:37 | 0:19 | 0:04 | 0:07 | 0:06 | 0:01 | 0:00 | 0:00 | 0:09 | 0:03 | 0:01 | 0:03 | 0:06 | 0:19 | 0:07 | 0:04 | 0:06 | 5:16 | 0:01 | 0:07 | 0:03 | 0:12 | 0:45 | 0:07 | 0:24 | 0:39 | 0:14 | 0:02 | 0:09 | 0:10 | 0:04 | 0:13 | 2:00 | 0:05 | 0:00 | 1:16 | 0:31 | 0:02 | 0:07 | 0:02 | 0:03 | 0:28 | 0:02 | 0:02 |
| Males | France | 24:00 | 11:44 | 8:45 | 2:18 | 0:41 | 3:48 | 3:46 | 0:02 | 0:15 | 0:09 | 0:05 | 0:01 | 2:24 | 0:16 | 0:08 | 0:11 | 0:08 | 0:01 | 0:01 | 0:00 | 0:18 | 0:03 | 0:05 | : | 0:32 | 0:30 | 0:05 | 0:04 | 0:04 | 4:44 | 0:01 | 0:10 | 0:07 | 0:21 | 0:20 | 0:05 | 0:06 | 0:20 | 0:17 | : | 0:07 | 0:14 | 0:01 | 0:22 | 2:08 | 0:04 | 0:00 | 1:03 | 0:24 | 0:02 | : | 0:02 | : | : | 0:35 | 0:02 |
| Males | Italy | 24:00 | 11:16 | 8:17 | 1:57 | 1:02 | 4:15 | 4:11 | 0:04 | 0:11 | 0:04 | 0:06 | 0:01 | 1:35 | 0:11 | 0:05 | 0:09 | 0:06 | 0:00 | 0:00 | 0:00 | 0:16 | 0:01 | 0:01 | 0:02 | 0:06 | 0:22 | 0:04 | 0:07 | 0:04 | 5:05 | 0:02 | 0:07 | 0:04 | 0:17 | 0:42 | 0:06 | 0:32 | 0:23 | 0:15 | 0:02 | 0:07 | 0:12 | 0:04 | 0:17 | 1:52 | 0:04 | 0:00 | 1:35 | 0:32 | 0:02 | 0:12 | 0:02 | 0:03 | 0:36 | 0:08 | 0:03 |
| Males | Latvia | 24:00 | 10:46 | 8:35 | 1:33 | 0:37 | 5:00 | 4:55 | 0:06 | 0:09 | 0:05 | 0:02 | 0:02 | 1:50 | 0:16 | 0:04 | 0:06 | 0:21 | 0:01 | 0:00 | 0:00 | 0:17 | 0:03 | 0:01 | 0:03 | 0:17 | 0:12 | 0:02 | 0:02 | 0:06 | 4:45 | 0:00 | 0:11 | 0:01 | 0:20 | 0:14 | 0:05 | 0:23 | 0:12 | 0:19 | 0:02 | 0:03 | 0:05 | 0:09 | 0:17 | 2:18 | 0:06 | 0:00 | 1:28 | 0:37 | 0:02 | 0:12 | 0:01 | 0:07 | 0:26 | 0:02 | 0:02 |
| Males | Lithuania | 24:00 | 10:53 | 8:28 | 1:32 | 0:53 | 4:45 | 4:43 | 0:03 | 0:09 | 0:07 | 0:02 | 0:01 | 2:09 | 0:20 | 0:04 | 0:10 | 0:29 | 0:01 | 0:00 | 0:01 | 0:11 | 0:12 | 0:01 | 0:02 | 0:17 | 0:13 | 0:03 | 0:04 | 0:01 | 4:47 | 0:00 | 0:15 | 0:02 | 0:21 | 0:12 | 0:01 | 0:15 | 0:08 | 0:13 | 0:03 | 0:05 | 0:04 | 0:05 | 0:18 | 2:36 | 0:09 | 0:00 | 1:13 | 0:28 | 0:01 | 0:13 | 0:01 | 0:06 | 0:23 | 0:02 | 0:03 |
| Males | Poland | 24:00 | 10:44 | 8:21 | 1:33 | 0:50 | 4:01 | 3:58 | 0:03 | 0:14 | 0:07 | 0:06 | 0:01 | 2:22 | 0:25 | 0:06 | 0:09 | 0:20 | 0:01 | 0:01 | 0:00 | 0:12 | 0:02 | 0:02 | 0:07 | 0:19 | 0:21 | 0:05 | 0:10 | 0:01 | 5:20 | 0:01 | 0:16 | 0:09 | 0:22 | 0:28 | 0:02 | 0:15 | 0:13 | 0:12 | 0:06 | 0:05 | 0:07 | 0:07 | 0:14 | 2:34 | 0:10 | 0:00 | 1:13 | 0:23 | 0:02 | 0:14 | 0:01 | 0:04 | 0:27 | 0:01 | 0:05 |
| Males | Slovenia | 24:00 | 10:31 | 8:18 | 1:33 | 0:40 | 3:53 | 3:49 | 0:04 | 0:15 | 0:05 | 0:09 | 0:01 | 2:38 | 0:16 | 0:04 | 0:08 | 0:24 | 0:00 | 0:00 | 0:01 | 0:32 | 0:15 | 0:02 | 0:04 | 0:24 | 0:16 | 0:05 | 0:07 | 0:02 | 5:31 | 0:02 | 0:10 | 0:03 | 0:06 | 0:53 | 0:05 | 0:38 | 0:19 | 0:17 | 0:01 | 0:06 | 0:09 | 0:04 | 0:19 | 2:12 | 0:07 | 0:00 | 1:10 | 0:21 | 0:02 | 0:11 | 0:02 | 0:05 | 0:28 | 0:02 | 0:02 |
| Males | Finland | 24:00 | 10:23 | 8:22 | 1:23 | 0:38 | 3:48 | 3:46 | 0:02 | 0:13 | 0:07 | 0:03 | 0:03 | 2:16 | 0:21 | 0:04 | 0:08 | 0:26 | 0:02 | 0:00 | 0:01 | 0:06 | : | 0:02 | 0:05 | 0:21 | 0:26 | 0:07 | 0:05 | 0:04 | 5:56 | 0:06 | 0:11 | 0:02 | 0:26 | 0:23 | 0:06 | 0:24 | 0:12 | 0:25 | 0:04 | 0:06 | 0:10 | 0:09 | 0:35 | 2:25 | 0:11 | 0:00 | 1:12 | 0:18 | 0:02 | 0:11 | 0:02 | 0:03 | 0:33 | 0:03 | 0:12 |
| Males | United Kingdom | 24:00 | 10:22 | 8:18 | 1:24 | 0:41 | 4:10 | 4:06 | 0:05 | 0:08 | 0:05 | 0:02 | 0:02 | 2:18 | 0:26 | 0:09 | 0:11 | 0:09 | 0:02 | 0:02 | 0:00 | 0:12 | 0:01 | 0:03 | 0:06 | 0:17 | 0:24 | 0:07 | 0:05 | 0:04 | 5:22 | 0:03 | 0:08 | 0:04 | 0:20 | 0:30 | 0:07 | 0:17 | 0:04 | 0:14 | 0:04 | 0:10 | 0:11 | 0:05 | 0:21 | 2:37 | 0:07 | 0:00 | 1:30 | 0:29 | 0:01 | 0:16 | 0:03 | 0:03 | 0:36 | 0:02 | 0:08 |
| Males | Norway | 24:00 | 10:06 | 7:56 | 1:25 | 0:45 | 4:04 | 4:03 | 0:01 | 0:12 | 0:08 | 0:04 | : | 2:21 | 0:25 | 0:08 | 0:14 | 0:04 | 0:02 | 0:00 | 0:01 | 0:10 | : | 0:01 | 0:04 | 0:23 | 0:21 | 0:12 | 0:05 | 0:12 | 5:52 | 0:03 | 0:07 | 0:05 | 0:48 | 0:44 | 0:07 | 0:11 | 0:13 | 0:20 | 0:03 | 0:10 | 0:12 | 0:07 | 0:26 | 2:06 | 0:08 | 0:02 | 1:21 | 0:26 | 0:02 | 0:13 | 0:02 | 0:02 | 0:36 | : | 0:03 |
| Females | Belgium | 24:00 | 11:11 | 8:34 | 1:50 | 0:47 | 1:53 | 1:52 | 0:01 | 0:16 | 0:06 | 0:06 | 0:04 | 4:10 | 0:57 | 0:20 | 0:26 | 0:28 | 0:09 | 0:19 | 0:06 | 0:10 | 0:00 | 0:03 | 0:03 | 0:04 | 0:33 | 0:16 | 0:07 | 0:10 | 5:06 | 0:03 | 0:00 | 0:03 | 0:37 | 0:24 | 0:11 | 0:31 | 0:11 | 0:07 | 0:02 | 0:09 | 0:09 | 0:08 | 0:16 | 2:13 | 0:03 | 0:01 | 1:22 | 0:15 | 0:02 | 0:18 | 0:04 | 0:00 | 0:16 | 0:27 | 0:02 |
| Females | Bulgaria | 24:00 | 11:38 | 9:07 | 1:55 | 0:36 | 2:34 | 2:33 | 0:02 | 0:06 | 0:02 | 0:04 | 0:00 | 5:01 | 1:37 | 0:36 | 0:31 | 0:13 | 0:19 | 0:06 | 0:16 | 0:24 | 0:16 | 0:01 | 0:01 | 0:02 | 0:16 | 0:13 | 0:10 | 0:02 | 3:47 | 0:00 | 0:08 | 0:01 | 0:05 | 0:32 | 0:01 | 0:08 | 0:14 | 0:03 | 0:00 | 0:00 | 0:02 | 0:10 | 0:06 | 2:14 | 0:03 | 0:00 | 0:52 | 0:17 | 0:01 | 0:14 | 0:01 | 0:03 | 0:13 | 0:02 | 0:02 |
| Females | Germany (including former GDR from 1991) | 24:00 | 10:58 | 8:15 | 1:46 | 0:56 | 1:56 | 1:53 | 0:03 | 0:13 | 0:04 | 0:04 | 0:04 | 4:14 | 0:49 | 0:21 | 0:39 | 0:15 | 0:13 | 0:10 | 0:07 | 0:13 | 0:01 | 0:04 | 0:04 | 0:03 | 0:38 | 0:16 | 0:09 | 0:12 | 5:15 | 0:07 | 0:09 | 0:04 | 0:19 | 0:49 | 0:14 | 0:20 | 0:15 | 0:12 | 0:02 | 0:06 | 0:16 | 0:08 | 0:30 | 1:40 | 0:04 | 0:00 | 1:19 | 0:13 | 0:02 | 0:19 | 0:04 | 0:04 | 0:33 | 0:03 | 0:05 |
| Females | Estonia | 24:00 | 10:30 | 8:26 | 1:12 | 0:53 | 3:05 | 3:02 | 0:03 | 0:07 | 0:02 | 0:03 | 0:02 | 4:53 | 1:19 | 0:25 | 0:34 | 0:14 | 0:14 | 0:07 | 0:14 | 0:19 | 0:06 | 0:02 | 0:05 | 0:03 | 0:29 | 0:25 | 0:10 | 0:06 | 4:18 | 0:01 | 0:12 | 0:02 | 0:04 | 0:27 | 0:04 | 0:17 | 0:10 | 0:05 | 0:00 | 0:01 | 0:05 | 0:17 | 0:20 | 2:06 | 0:06 | 0:00 | 1:02 | 0:20 | 0:01 | 0:16 | 0:03 | 0:03 | 0:18 | 0:01 | 0:05 |
| Females | Spain | 24:00 | 11:05 | 8:32 | 1:44 | 0:49 | 2:06 | 2:05 | 0:01 | 0:20 | 0:06 | 0:07 | 0:06 | 4:55 | 1:20 | 0:29 | 0:50 | 0:11 | 0:11 | 0:12 | 0:10 | 0:03 | 0:01 | 0:01 | 0:02 | 0:01 | 0:35 | 0:25 | 0:05 | 0:19 | 4:26 | 0:01 | 0:12 | 0:05 | 0:16 | 0:34 | 0:06 | 0:26 | 0:32 | 0:06 | 0:00 | 0:03 | 0:05 | 0:04 | 0:08 | 1:46 | 0:02 | 0:00 | 1:05 | 0:18 | 0:03 | 0:12 | 0:06 | 0:01 | 0:24 | 0:01 | 0:02 |
| Females | France | 24:00 | 11:53 | 8:55 | 2:11 | 0:46 | 2:17 | 2:16 | 0:01 | 0:14 | 0:08 | 0:05 | 0:01 | 4:34 | 1:01 | 0:25 | 0:58 | 0:07 | 0:07 | 0:15 | 0:08 | 0:09 | 0:02 | 0:05 | : | 0:04 | 0:41 | 0:20 | 0:08 | 0:04 | 4:05 | 0:01 | 0:09 | 0:05 | 0:24 | 0:20 | 0:05 | 0:06 | 0:17 | 0:06 | : | 0:02 | 0:11 | 0:01 | 0:22 | 1:55 | 0:02 | 0:00 | 0:54 | 0:15 | 0:01 | : | 0:05 | : | : | 0:32 | 0:03 |
| Females | Italy | 24:00 | 11:12 | 8:19 | 1:52 | 1:01 | 1:52 | 1:50 | 0:02 | 0:14 | 0:04 | 0:09 | 0:01 | 5:20 | 1:19 | 0:35 | 1:24 | 0:06 | 0:10 | 0:20 | 0:08 | 0:06 | 0:01 | 0:01 | 0:02 | 0:01 | 0:36 | 0:18 | 0:10 | 0:05 | 4:06 | 0:01 | 0:13 | 0:08 | 0:26 | 0:23 | 0:04 | 0:31 | 0:17 | 0:06 | 0:00 | 0:02 | 0:05 | 0:06 | 0:10 | 1:29 | 0:03 | 0:00 | 1:14 | 0:15 | 0:02 | 0:17 | 0:06 | 0:01 | 0:27 | 0:06 | 0:03 |
| Females | Latvia | 24:00 | 10:53 | 8:44 | 1:26 | 0:43 | 3:29 | 3:26 | 0:04 | 0:12 | 0:07 | 0:04 | 0:01 | 3:56 | 1:06 | 0:22 | 0:27 | 0:15 | 0:09 | 0:03 | 0:09 | 0:20 | 0:06 | 0:02 | 0:04 | 0:01 | 0:21 | 0:14 | 0:07 | 0:08 | 4:08 | 0:00 | 0:11 | 0:03 | 0:24 | 0:11 | 0:04 | 0:20 | 0:15 | 0:06 | 0:00 | 0:01 | 0:04 | 0:13 | 0:16 | 1:55 | 0:04 | 0:00 | 1:20 | 0:24 | 0:03 | 0:20 | 0:03 | 0:06 | 0:23 | 0:01 | 0:03 |
| Females | Lithuania | 24:00 | 10:56 | 8:35 | 1:26 | 0:56 | 3:31 | 3:29 | 0:02 | 0:10 | 0:05 | 0:03 | 0:02 | 4:29 | 1:18 | 0:22 | 0:38 | 0:21 | 0:11 | 0:04 | 0:11 | 0:15 | 0:17 | 0:02 | 0:02 | 0:02 | 0:21 | 0:16 | 0:09 | 0:01 | 3:45 | 0:00 | 0:11 | 0:04 | 0:22 | 0:10 | 0:02 | 0:13 | 0:08 | 0:05 | 0:00 | 0:02 | 0:02 | 0:10 | 0:13 | 1:59 | 0:04 | 0:00 | 1:05 | 0:20 | 0:02 | 0:19 | 0:01 | 0:03 | 0:19 | 0:01 | 0:04 |
| Females | Poland | 24:00 | 11:03 | 8:35 | 1:34 | 0:54 | 2:15 | 2:14 | 0:01 | 0:14 | 0:06 | 0:06 | 0:01 | 4:45 | 1:30 | 0:29 | 0:34 | 0:14 | 0:14 | 0:07 | 0:05 | 0:10 | 0:01 | 0:02 | 0:06 | 0:02 | 0:30 | 0:22 | 0:17 | 0:02 | 4:32 | 0:00 | 0:14 | 0:15 | 0:23 | 0:26 | 0:02 | 0:12 | 0:12 | 0:04 | 0:01 | 0:02 | 0:06 | 0:13 | 0:12 | 2:03 | 0:06 | 0:00 | 1:06 | 0:14 | 0:03 | 0:19 | 0:03 | 0:02 | 0:25 | 0:01 | 0:05 |
| Females | Slovenia | 24:00 | 10:32 | 8:25 | 1:26 | 0:41 | 2:42 | 2:39 | 0:03 | 0:19 | 0:06 | 0:11 | 0:01 | 4:56 | 1:24 | 0:28 | 0:39 | 0:16 | 0:09 | 0:16 | 0:07 | 0:25 | 0:11 | 0:02 | 0:03 | 0:02 | 0:21 | 0:19 | 0:11 | 0:03 | 4:27 | 0:00 | 0:06 | 0:05 | 0:07 | 0:50 | 0:04 | 0:30 | 0:18 | 0:08 | 0:00 | 0:02 | 0:05 | 0:09 | 0:14 | 1:44 | 0:05 | 0:00 | 1:02 | 0:16 | 0:03 | 0:14 | 0:03 | 0:03 | 0:23 | 0:01 | 0:02 |
| Females | Finland | 24:00 | 10:38 | 8:32 | 1:19 | 0:47 | 2:33 | 2:32 | 0:02 | 0:16 | 0:09 | 0:05 | 0:02 | 3:56 | 0:55 | 0:15 | 0:26 | 0:23 | 0:13 | 0:05 | 0:10 | 0:08 | : | 0:03 | 0:07 | 0:04 | 0:32 | 0:21 | 0:08 | 0:05 | 5:17 | 0:04 | 0:12 | 0:03 | 0:30 | 0:25 | 0:05 | 0:20 | 0:13 | 0:15 | 0:01 | 0:02 | 0:10 | 0:14 | 0:33 | 2:02 | 0:08 | 0:00 | 1:07 | 0:14 | 0:02 | 0:14 | 0:03 | 0:02 | 0:31 | 0:02 | 0:12 |
| Females | United Kingdom | 24:00 | 10:43 | 8:27 | 1:26 | 0:50 | 2:24 | 2:21 | 0:03 | 0:09 | 0:04 | 0:03 | 0:02 | 4:15 | 0:59 | 0:18 | 0:38 | 0:12 | 0:11 | 0:11 | 0:05 | 0:07 | 0:01 | 0:05 | 0:06 | 0:04 | 0:39 | 0:23 | 0:10 | 0:05 | 4:55 | 0:03 | 0:11 | 0:05 | 0:30 | 0:31 | 0:06 | 0:23 | 0:03 | 0:07 | 0:01 | 0:04 | 0:11 | 0:07 | 0:18 | 2:09 | 0:05 | 0:00 | 1:25 | 0:17 | 0:01 | 0:22 | 0:09 | 0:02 | 0:34 | 0:01 | 0:10 |
| Females | Norway | 24:00 | 10:27 | 8:10 | 1:20 | 0:56 | 2:38 | 2:37 | 0:01 | 0:15 | 0:09 | 0:06 | : | 3:47 | 0:51 | 0:21 | 0:33 | 0:02 | 0:11 | 0:04 | 0:12 | 0:09 | : | 0:01 | 0:03 | 0:04 | 0:27 | 0:25 | 0:09 | 0:14 | 5:40 | 0:01 | 0:08 | 0:05 | 1:01 | 0:56 | 0:06 | 0:12 | 0:13 | 0:14 | 0:01 | 0:05 | 0:10 | 0:11 | 0:28 | 1:39 | 0:07 | 0:02 | 1:11 | 0:18 | 0:02 | 0:14 | 0:03 | 0:01 | 0:33 | : | 0:03 |
Tidying and analysis
timedata <- time_info
library(tibble)
timedata <- as_data_frame(timedata)
timedata <- timedata %>% rename(Country = GEO.ACL00)Converting the time spent on personal care into minutes by removing the colon and then calculating mean of total time(personal care)
timedata_PC <- timedata %>%
separate ("Personal.care" , c("PC_Min", "PC_sec"), sep=":")
timedata_PC <- timedata_PC %>%
mutate(PC_TotalSec= (as.numeric(PC_Min) * 60) + as.numeric(PC_sec))
timedata_PC <- timedata_PC %>%
group_by(SEX) %>% summarise(mean= mean(PC_TotalSec))
kable(timedata_PC)| SEX | mean |
|---|---|
| Females | 658.5000 |
| Males | 650.7143 |
Analysis:- Here from the above results we see that Females spent more time in Personal care compared to Males
Converting the time on eating in minutes by removing the colon and calculating mean of total time(eating)
timedata_Eat <- timedata %>%
separate ("Eating" , c("E_Min", "E_sec"), sep=":")
timedata_Eat <- timedata_Eat %>%
mutate(E_TotalSec= (as.numeric(E_Min) * 60) + as.numeric(E_sec))
timedata_Eat <- timedata_Eat %>%
group_by(Country,SEX) %>% summarise(mean= mean(E_TotalSec))
timedata_Eat## # A tibble: 28 x 3
## # Groups: Country [?]
## Country SEX mean
## <chr> <chr> <dbl>
## 1 Belgium Females 110
## 2 Belgium Males 109
## 3 Bulgaria Females 115
## 4 Bulgaria Males 127
## 5 Estonia Females 72.0
## 6 Estonia Males 79.0
## 7 Finland Females 79.0
## 8 Finland Males 83.0
## 9 France Females 131
## 10 France Males 138
## # ... with 18 more rowsAnalysis :- From the above subset we can infer that Males spend more time in eating compared to females for most of the countries.
timedata_Sleep <- timedata %>%
separate ("Sleep" , c("S_Min", "S_sec"), sep=":")
timedata_Sleep <- timedata_Sleep %>%
mutate(S_TotalSec= (as.numeric(S_Min) * 60) + as.numeric(S_sec))
timedata_Sleep <- timedata_Sleep %>%
group_by(Country,SEX) %>% summarise(mean= mean(S_TotalSec))
timedata_Sleep## # A tibble: 28 x 3
## # Groups: Country [?]
## Country SEX mean
## <chr> <chr> <dbl>
## 1 Belgium Females 514
## 2 Belgium Males 495
## 3 Bulgaria Females 547
## 4 Bulgaria Males 548
## 5 Estonia Females 506
## 6 Estonia Males 504
## 7 Finland Females 512
## 8 Finland Males 502
## 9 France Females 535
## 10 France Males 525
## # ... with 18 more rowsAnalysis :- From the above subset we can infer that Males spend less time in sleeping compared to females for most of the countries.
3. Data -sales-tax credits from the government of Canada
Loading data
craCreditBenefit <- read.csv(file="https://raw.githubusercontent.com/Harpreet1984/DATA607/master/CRA_Credit_Benefit.csv", header=TRUE, sep=",")
kable(craCreditBenefit)| Province | GT_freq | GT_amount | X5K_freq | X5K_amount | X5K_10K_freq | X5K_10K_amount | X10K_15K_freq | X10K_15K_amount | X15K_20K_freq | X15K_20K_amount | X20K_25K_freq | X20K_25K_amount | X25K_30K_freq | X25K_30K_amount | X30K_35K_freq | X30K_35K_amount | X35K_40K_freq | X35K_40K_amount | X40K_45K_freq | X40K_45K_amount | X45K_50K_freq | X45K_50K_amount | X50K_55K_freq | X50K_55K_amount | X55K_60K_freq | X55K_60K_amount | X60K_freq | X60K_amount |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Newfoundland_and_Labrador | 160300 | $65,756 | 26410 | $7,044 | 16150 | $5,278 | 19530 | $8,527 | 28520 | $13,023 | 18290 | $8,540 | 18820 | $9,207 | 15370 | $7,518 | 12620 | $5,157 | 3980 | $1,274 | 550 | $169 | 60 | $18 | NA | NA | ||
| Prince_Edward_Island | 45850 | $18,648 | 8000 | $2,115 | 4220 | $1,363 | 4900 | $2,003 | 7240 | $3,332 | 5820 | $2,776 | 5250 | $2,520 | 4790 | $2,321 | 4020 | $1,676 | 1260 | $422 | 290 | $102 | 50 | $14 | 10 | $4 | NA | |
| Nova_Scotia | 302210 | $122,873 | 59800 | $17,694 | 38340 | $13,202 | 29460 | $12,610 | 44640 | $20,540 | 35870 | $16,942 | 29900 | $14,249 | 29700 | $14,352 | 25480 | $10,312 | 7530 | $2,501 | 1280 | $406 | 190 | $57 | 20 | $7 | NA | |
| New_Brunswick | 249780 | $102,431 | 43160 | $11,986 | 29500 | $9,489 | 24660 | $11,372 | 39740 | $18,130 | 29700 | $13,994 | 27650 | $13,379 | 25490 | $12,383 | 21760 | $8,996 | 6670 | $2,243 | 1240 | $395 | 200 | $59 | 20 | $4 | NA | |
| Quebec | 2698620 | $1,082,380 | 495090 | $132,486 | 288830 | $101,067 | 353700 | $138,867 | 429720 | $194,299 | 291000 | $135,812 | 256360 | $124,147 | 258750 | $127,158 | 234200 | $97,567 | 73060 | $25,216 | 15330 | $5,039 | 2390 | $662 | 170 | $51 | 30 | $8 |
| Ontario | 3982840 | $1,647,108 | 852320 | $249,084 | 426320 | $151,182 | 573760 | $243,898 | 563100 | $270,404 | 440530 | $217,190 | 362380 | $179,196 | 328470 | $162,303 | 308840 | $129,493 | 99930 | $35,557 | 23370 | $7,656 | 3460 | $1,028 | 300 | $97 | 50 | $18 |
| Manitoba | 385370 | $162,874 | 101660 | $35,032 | 35830 | $12,788 | 44610 | $19,386 | 48710 | $23,418 | 40510 | $19,740 | 34860 | $17,185 | 33840 | $16,759 | 31830 | $13,517 | 10040 | $3,816 | 2780 | $999 | 620 | $202 | 70 | $23 | 20 | $10 |
| Saskatchewan | 291210 | $125,501 | 72980 | $26,349 | 23750 | $8,961 | 29560 | $12,701 | 43070 | $20,642 | 33870 | $16,671 | 27420 | $13,623 | 26620 | $13,072 | 24050 | $9,945 | 7540 | $2,728 | 1890 | $662 | 410 | $126 | 50 | $16 | 10 | $5 |
| Alberta | 921590 | $384,471 | 208080 | $65,556 | 80350 | $28,722 | 91240 | $39,264 | 124420 | $59,999 | 121870 | $57,588 | 91450 | $43,595 | 87090 | $42,645 | 83290 | $34,831 | 25190 | $9,331 | 6970 | $2,429 | 1380 | $433 | 200 | $62 | 50 | $14 |
| British_Columbia | 1390640 | $561,578 | 304940 | $89,447 | 124170 | $42,769 | 195460 | $79,598 | 199330 | $93,329 | 159180 | $75,739 | 134750 | $64,798 | 118750 | $56,608 | 111800 | $45,130 | 34040 | $11,603 | 7160 | $2,253 | 970 | $270 | 80 | $26 | 20 | $7 |
| Northwest_Territories | 10740 | $4,478 | 2890 | $998 | 1420 | $541 | 1350 | $571 | 1370 | $664 | 1160 | $558 | 860 | $420 | 690 | $337 | 720 | $289 | 200 | $71 | 60 | $21 | 20 | $7 | NA | NA | ||
| Yukon | 9110 | $3,788 | 1600 | $442 | 730 | $259 | 1030 | $423 | 1470 | $710 | 1290 | $620 | 970 | $462 | 920 | $441 | 840 | $337 | 230 | $75 | 40 | $15 | NA | NA | NA | |||
| Nunavut | 8530 | $4,383 | 1680 | $567 | 1740 | $727 | 1220 | $724 | 1060 | $694 | 800 | $508 | 620 | $397 | 540 | $328 | 490 | $265 | 220 | $107 | 110 | $48 | 40 | $16 | NA | NA | ||
| Outside_Canada | 920 | $436 | 350 | $159 | 120 | $44 | 130 | $63 | 90 | $46 | 80 | $44 | 50 | $28 | 50 | $27 | 30 | $15 | NA | NA | NA | NA | NA |
tidying data set
craCreditBenefitAmountAnalysis <- craCreditBenefit %>% select(Province, X5K_amount, X5K_10K_amount, X10K_15K_amount, X15K_20K_amount, X20K_25K_amount, X25K_30K_amount, X30K_35K_amount, X35K_40K_amount, X40K_45K_amount, X45K_50K_amount, X50K_55K_amount, X55K_60K_amount, X60K_amount)
craCreditBenefitAmountAnalysisTidy <- craCreditBenefitAmountAnalysis %>% gather("Category", "Amount", 2:14)## Warning: attributes are not identical across measure variables;
## they will be droppedcraCreditBenefitAmountAnalysisTidy$Amount = gsub(",", "",craCreditBenefitAmountAnalysisTidy$Amount)
craCreditBenefitAmountAnalysisTidy$Amount = gsub("\\$", "",craCreditBenefitAmountAnalysisTidy$Amount)
craCreditBenefitAmountAnalysisTidy$Amount = as.numeric (gsub("^$", "0",craCreditBenefitAmountAnalysisTidy$Amount))Analyzing data set
Here we are calculating Maximum Credit Benefit Per province
maxCreditBenefitByProvince <- craCreditBenefitAmountAnalysisTidy %>% group_by(Province) %>% summarise(Amount = max(Amount))
kable(maxCreditBenefitByProvince)| Province | Amount |
|---|---|
| Alberta | 65556 |
| British_Columbia | 93329 |
| Manitoba | 35032 |
| New_Brunswick | 18130 |
| Newfoundland_and_Labrador | 13023 |
| Northwest_Territories | 998 |
| Nova_Scotia | 20540 |
| Nunavut | 727 |
| Ontario | 270404 |
| Outside_Canada | 159 |
| Prince_Edward_Island | 3332 |
| Quebec | 194299 |
| Saskatchewan | 26349 |
| Yukon | 710 |
myggplot <- ggplot(maxCreditBenefitByProvince, aes(x = Province, y = Amount)) +
geom_bar(stat = "identity")
myggplot <- myggplot + theme(axis.text.x = element_text(face="bold", color="#993333",
size=14, angle=90))
myggplot
maxCreditBenefitByCategory <- craCreditBenefitAmountAnalysisTidy %>% group_by(Category) %>% summarise(Amount = max(Amount))
kable(maxCreditBenefitByCategory)| Category | Amount |
|---|---|
| X10K_15K_amount | 243898 |
| X15K_20K_amount | 270404 |
| X20K_25K_amount | 217190 |
| X25K_30K_amount | 179196 |
| X30K_35K_amount | 162303 |
| X35K_40K_amount | 129493 |
| X40K_45K_amount | 35557 |
| X45K_50K_amount | 7656 |
| X50K_55K_amount | 1028 |
| X55K_60K_amount | 97 |
| X5K_10K_amount | 151182 |
| X5K_amount | 249084 |
| X60K_amount | 18 |
library(ggplot2)
myggplot1 <- ggplot(maxCreditBenefitByCategory, aes(x = Category, y = Amount)) +
geom_bar(stat = "identity")
myggplot1 <- myggplot1 + theme(axis.text.x = element_text(face="bold", color="#993333",
size=14, angle=90))
myggplot1
craCreditBenefitFreqAnalysis <- craCreditBenefit %>% select(Province, X5K_freq, X5K_10K_freq, X10K_15K_freq, X15K_20K_freq, X20K_25K_freq, X25K_30K_freq, X30K_35K_freq, X35K_40K_freq, X40K_45K_freq, X45K_50K_freq, X50K_55K_freq, X55K_60K_freq, X60K_freq)
craCreditBenefitFreqAnalysisTidy <- craCreditBenefitFreqAnalysis %>% gather("Category", "Amount", 2:14)
craCreditBenefitFreqAnalysisTidy$Amount = gsub(",", "",craCreditBenefitFreqAnalysisTidy$Amount)
craCreditBenefitFreqAnalysisTidy$Amount = as.numeric (gsub("^$", "0",craCreditBenefitFreqAnalysisTidy$Amount))maxCreditFreqByProvince <- craCreditBenefitFreqAnalysisTidy %>% group_by(Province) %>% summarise(max(Amount))
kable(maxCreditFreqByProvince)| Province | max(Amount) |
|---|---|
| Alberta | 208080 |
| British_Columbia | 304940 |
| Manitoba | 101660 |
| New_Brunswick | NA |
| Newfoundland_and_Labrador | NA |
| Northwest_Territories | NA |
| Nova_Scotia | NA |
| Nunavut | NA |
| Ontario | 852320 |
| Outside_Canada | NA |
| Prince_Edward_Island | NA |
| Quebec | 495090 |
| Saskatchewan | 72980 |
| Yukon | NA |
maxCreditFreqByCategory <- craCreditBenefitFreqAnalysisTidy %>% group_by(Category) %>% summarise(max(Amount))
kable(maxCreditFreqByCategory)| Category | max(Amount) |
|---|---|
| X10K_15K_freq | 573760 |
| X15K_20K_freq | 563100 |
| X20K_25K_freq | 440530 |
| X25K_30K_freq | 362380 |
| X30K_35K_freq | 328470 |
| X35K_40K_freq | 308840 |
| X40K_45K_freq | NA |
| X45K_50K_freq | NA |
| X50K_55K_freq | NA |
| X55K_60K_freq | NA |
| X5K_10K_freq | 426320 |
| X5K_freq | 852320 |
| X60K_freq | NA |
Based on the above graphs, Ontario and montreal province got the maxmimum benefit credits and have highest frequencies. This makes sense as these two provinces are have highest number of working professionals.
Based on the general normal as the income bracket goes up the tax credit benefits decrease. This is confirmed with the graphs, there are substantial drops in the tax credit as the category goes above 40k.