Data Project 2
Jeff Nieman
March 7, 2016
The first dataset I analyzed was my own. I wanted to explore whether those who went out to eat consumed more calories than those who did not - broken down by age group and gender. I also wanted to explore the percentage of calories eaten out (for those who did) also by age group and gender.
calories <- read.csv('https://raw.githubusercontent.com/jeffnieman11/Data607_Project2/master/calories%20and%20restaurants.csv', header = TRUE, na.strings="")
calories## Gender Age Sample.Size Percent.Reporting
## 1 Male 2 to 5 411 43
## 2 <NA> 6 to 11 590 38
## 3 <NA> 12 to 19 585 56
## 4 <NA> 20 to 39 882 67
## 5 <NA> 40 to 59 576 56
## 6 <NA> 60 + 756 51
## 7 Female 2 to 5 423 38
## 8 <NA> 6 to 11 556 46
## 9 <NA> 12 to 19 567 57
## 10 <NA> 20 to 39 832 61
## 11 <NA> 40 to 59 830 53
## 12 <NA> 60 + 745 43
## Total.Intake.kcal.for.all.individuals
## 1 1648
## 2 2087
## 3 2531
## 4 2767
## 5 2613
## 6 2181
## 7 1520
## 8 1878
## 9 1809
## 10 2015
## 11 1829
## 12 1611
## intake.kcal.from.restaurants.for.all.individuals
## 1 212
## 2 273
## 3 736
## 4 922
## 5 598
## 6 414
## 7 177
## 8 353
## 9 516
## 10 583
## 11 426
## 12 295
## Percentage.from.Restaurants Total.intake.kcal.for.restaurant.consumers
## 1 13 1646
## 2 13 2128
## 3 29 2766
## 4 33 2816
## 5 23 2672
## 6 19 2236
## 7 12 1573
## 8 19 1972
## 9 29 1837
## 10 29 2086
## 11 23 1946
## 12 18 1725
## Intake.kcal.from.restaurants.for.restaurant.consumers
## 1 498
## 2 726
## 3 1315
## 4 1386
## 5 1076
## 6 805
## 7 465
## 8 765
## 9 906
## 10 958
## 11 811
## 12 679
## Percentage.from.restaurant.for.restaurant.consumers
## 1 30
## 2 34
## 3 48
## 4 49
## 5 40
## 6 36
## 7 30
## 8 39
## 9 49
## 10 46
## 11 42
## 12 39
## Total.intake.kcal.for.non.consumers
## 1 1650
## 2 2062
## 3 2232
## 4 2671
## 5 2539
## 6 2121
## 7 1487
## 8 1797
## 9 1771
## 10 1905
## 11 1700
## 12 1523require(tidyr)## Loading required package: tidyr## Warning: package 'tidyr' was built under R version 3.2.3require(zoo)## Loading required package: zoo## Warning: package 'zoo' was built under R version 3.2.3##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericrequire(dplyr)## Loading required package: dplyr##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionrequire(ggplot2)## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 3.2.3require(stringr)## Loading required package: stringrcalories1 <- na.locf(calories)
# Compare calories of those who eat out with those who do not
caloriesshort <- calories1[,c(1:5,8,11)]
colnames(caloriesshort) <- c("Gender", "Age", "Sample", "Percent.EatOut", "AvgCals.All", "AvgCals.EatOut", "AvgCals.NoEatOut")
caloriesshort$Sample <- as.numeric(as.character(caloriesshort$Sample))
caloriesshort$Percent.EatOut <- as.numeric((as.character(caloriesshort$Percent.EatOut)))
caloriesshort$AvgCals.All <- as.numeric(as.character(caloriesshort$AvgCals.All))
caloriesshort$AvgCals.EatOut <- as.numeric(as.character(caloriesshort$AvgCals.EatOut))
caloriesshort$AvgCals.NoEatOut <- as.numeric(as.character(caloriesshort$AvgCals.NoEatOut))
calories2 <- mutate(caloriesshort, Sample.EatOut =round(Sample*Percent.EatOut/100))
calories3 <- mutate(calories2, Sample.NoEatOut = Sample - Sample.EatOut)
calories4 <- mutate(calories3, TotalCals.EatOut = Sample.EatOut*AvgCals.EatOut)
calories5 <- mutate(calories4, TotalCals.NoEatOut = Sample.NoEatOut*AvgCals.NoEatOut)
#Create a ratio by the groups for calories of those who went out and those who did not, and plot to see who goes above 100% and compare.
calories6 <- calories5 %>%
group_by(Gender) %>%
summarise(AvgCals.EatOut = sum(TotalCals.EatOut)/sum(Sample.EatOut), AvgCals.NoEatOut = sum(TotalCals.NoEatOut)/sum(Sample.NoEatOut))
calories6$Ratio <- calories6$AvgCals.EatOut/calories6$AvgCals.NoEatOut
calories6$Ratio <- as.numeric(calories6$Ratio)
calories6## Source: local data frame [2 x 4]
##
## Gender AvgCals.EatOut AvgCals.NoEatOut Ratio
## (chr) (dbl) (dbl) (dbl)
## 1 Female 1902.074 1690.649 1.125055
## 2 Male 2496.634 2212.769 1.128285ggplot(calories6, aes(x=Gender, y=Ratio)) + geom_bar(stat = "identity", position = "dodge")
# Conclusion : Both men and women eat more calories when they go out and the percentage of increase is nearly identical.
calories7 <- calories5 %>%
group_by(Age) %>%
summarise(AvgCals.EatOut = sum(TotalCals.EatOut)/sum(Sample.EatOut), AvgCals.NoEatOut = sum(TotalCals.NoEatOut)/sum(Sample.NoEatOut))
calories7$Ratio <- calories7$AvgCals.EatOut/calories7$AvgCals.NoEatOut
calories7$Ratio <- as.numeric(calories7$Ratio)
calories7## Source: local data frame [6 x 4]
##
## Age AvgCals.EatOut AvgCals.NoEatOut Ratio
## (chr) (dbl) (dbl) (dbl)
## 1 12 to 19 2305.068 2007.481 1.148239
## 2 2 to 5 1611.228 1563.899 1.030263
## 3 20 to 39 2478.566 2267.449 1.093108
## 4 40 to 59 2253.337 2030.120 1.109953
## 5 6 to 11 2044.800 1942.631 1.052593
## 6 60 + 2004.385 1801.314 1.112735ggplot(calories7, aes(x=Age, y=Ratio)) + geom_bar(stat="identity", position = "dodge")
# Conclusion: All age groups eat more calories when they go out - but only slightly for the 2 kid groups and highest for the teenage group 12-19.
#Compare % of calories from restaurants
calrest <- calories1[,c(1:4,8:10)]
colnames(calrest) <- c("Gender", "Age", "Sample", "Percent.EatOut", "AvgCals.EatOut", "AvgCals.FromEatOut","Percent.Cals.EatOut")
calrest$Sample <- as.numeric(as.character(calrest$Sample))
calrest$Percent.EatOut <- as.numeric((as.character(calrest$Percent.EatOut)))
calrest$AvgCals.EatOut <- as.numeric(as.character(calrest$AvgCals.EatOut))
calrest$AvgCals.FromEatOut <- as.numeric(as.character((calrest$AvgCals.FromEatOut)))
calrest1 <- mutate(calrest, Sample.EatOut =round(Sample*Percent.EatOut/100))
calrest2 <- mutate(calrest1, TotalCals.EatOut = Sample.EatOut*AvgCals.EatOut)
calrest3 <- mutate(calrest2, TotalCals.FromEatOut = Sample.EatOut*AvgCals.FromEatOut)
calrest3## Gender Age Sample Percent.EatOut AvgCals.EatOut AvgCals.FromEatOut
## 1 Male 2 to 5 411 43 1646 498
## 2 Male 6 to 11 590 38 2128 726
## 3 Male 12 to 19 585 56 2766 1315
## 4 Male 20 to 39 882 67 2816 1386
## 5 Male 40 to 59 576 56 2672 1076
## 6 Male 60 + 756 51 2236 805
## 7 Female 2 to 5 423 38 1573 465
## 8 Female 6 to 11 556 46 1972 765
## 9 Female 12 to 19 567 57 1837 906
## 10 Female 20 to 39 832 61 2086 958
## 11 Female 40 to 59 830 53 1946 811
## 12 Female 60 + 745 43 1725 679
## Percent.Cals.EatOut Sample.EatOut TotalCals.EatOut TotalCals.FromEatOut
## 1 30 177 291342 88146
## 2 34 224 476672 162624
## 3 48 328 907248 431320
## 4 49 591 1664256 819126
## 5 40 323 863056 347548
## 6 36 386 863096 310730
## 7 30 161 253253 74865
## 8 39 256 504832 195840
## 9 49 323 593351 292638
## 10 46 508 1059688 486664
## 11 42 440 856240 356840
## 12 39 320 552000 217280calrest4 <- calrest3 %>%
group_by(Gender) %>%
summarise(Percent.Cals.EatOut = 100*sum(TotalCals.FromEatOut)/sum(TotalCals.EatOut))
calrest4## Source: local data frame [2 x 2]
##
## Gender Percent.Cals.EatOut
## (chr) (dbl)
## 1 Female 42.52349
## 2 Male 42.62998ggplot(calrest4, aes(x=Gender, y=Percent.Cals.EatOut)) + geom_bar(stat="identity", position = "dodge")
calrest5 <- calrest3 %>%
group_by(Age) %>%
summarise(Percent.Cals.EatOut = 100*sum(TotalCals.FromEatOut)/sum(TotalCals.EatOut))
calrest5## Source: local data frame [6 x 2]
##
## Age Percent.Cals.EatOut
## (chr) (dbl)
## 1 12 to 19 48.24460
## 2 2 to 5 29.93252
## 3 20 to 39 47.93748
## 4 40 to 59 40.96956
## 5 6 to 11 36.52191
## 6 60 + 37.31266ggplot(calrest5, aes(x=Age, y=Percent.Cals.EatOut)) + geom_bar(stat="identity", position = "dodge")
The second data I analyzed was from James on the relationship between college education and jobs. I also looked at the other variables for any relationship.
jobs <- read.csv('https://raw.githubusercontent.com/jeffnieman11/Data607_Project2/dea005e80fd96bd01fa1ac7cfafa0b027d973683/jobs%20and%20county.csv', header=TRUE)
jobs## County LandArea NatAmenity College1970 College1980 College1990
## 1 Autauga 599 4 0.064 0.121 0.145
## 2 Baldwin 1578 4 0.065 0.121 0.168
## 3 Barbour 891 4 0.073 0.092 0.118
## 4 Bibb 625 3 0.042 0.049 0.047
## 5 Blount 639 4 0.027 0.053 0.070
## College2000 Jobs1970 Jobs1980 Jobs1990 Jobs2000
## 1 0.180 6853 11278 11471 16289
## 2 0.231 19749 27861 40809 70247
## 3 0.109 9448 9755 12163 15197
## 4 0.071 3965 4276 5564 6098
## 5 0.096 7587 9490 11811 16503# After downloading the data I used the gather function to tidy up the data and extracted only the numerical component for the year.
jobs1 <- gather(jobs, "Jobs", "n", 8:11)
jobs2 <- gather(jobs1, "College", "m", 4:7)
jobs2$Jobs <- str_extract_all(jobs2$Jobs, "[0-9]{4}")
jobs2$College <- str_extract_all(jobs2$College, "[0-9]{4}")
jobs2$College <- as.numeric(as.character(jobs2$College))
jobs2$Jobs <- as.numeric(as.character(jobs2$Jobs))
jobs2## County LandArea NatAmenity Jobs n College m
## 1 Autauga 599 4 1970 6853 1970 0.064
## 2 Baldwin 1578 4 1970 19749 1970 0.065
## 3 Barbour 891 4 1970 9448 1970 0.073
## 4 Bibb 625 3 1970 3965 1970 0.042
## 5 Blount 639 4 1970 7587 1970 0.027
## 6 Autauga 599 4 1980 11278 1970 0.064
## 7 Baldwin 1578 4 1980 27861 1970 0.065
## 8 Barbour 891 4 1980 9755 1970 0.073
## 9 Bibb 625 3 1980 4276 1970 0.042
## 10 Blount 639 4 1980 9490 1970 0.027
## 11 Autauga 599 4 1990 11471 1970 0.064
## 12 Baldwin 1578 4 1990 40809 1970 0.065
## 13 Barbour 891 4 1990 12163 1970 0.073
## 14 Bibb 625 3 1990 5564 1970 0.042
## 15 Blount 639 4 1990 11811 1970 0.027
## 16 Autauga 599 4 2000 16289 1970 0.064
## 17 Baldwin 1578 4 2000 70247 1970 0.065
## 18 Barbour 891 4 2000 15197 1970 0.073
## 19 Bibb 625 3 2000 6098 1970 0.042
## 20 Blount 639 4 2000 16503 1970 0.027
## 21 Autauga 599 4 1970 6853 1980 0.121
## 22 Baldwin 1578 4 1970 19749 1980 0.121
## 23 Barbour 891 4 1970 9448 1980 0.092
## 24 Bibb 625 3 1970 3965 1980 0.049
## 25 Blount 639 4 1970 7587 1980 0.053
## 26 Autauga 599 4 1980 11278 1980 0.121
## 27 Baldwin 1578 4 1980 27861 1980 0.121
## 28 Barbour 891 4 1980 9755 1980 0.092
## 29 Bibb 625 3 1980 4276 1980 0.049
## 30 Blount 639 4 1980 9490 1980 0.053
## 31 Autauga 599 4 1990 11471 1980 0.121
## 32 Baldwin 1578 4 1990 40809 1980 0.121
## 33 Barbour 891 4 1990 12163 1980 0.092
## 34 Bibb 625 3 1990 5564 1980 0.049
## 35 Blount 639 4 1990 11811 1980 0.053
## 36 Autauga 599 4 2000 16289 1980 0.121
## 37 Baldwin 1578 4 2000 70247 1980 0.121
## 38 Barbour 891 4 2000 15197 1980 0.092
## 39 Bibb 625 3 2000 6098 1980 0.049
## 40 Blount 639 4 2000 16503 1980 0.053
## 41 Autauga 599 4 1970 6853 1990 0.145
## 42 Baldwin 1578 4 1970 19749 1990 0.168
## 43 Barbour 891 4 1970 9448 1990 0.118
## 44 Bibb 625 3 1970 3965 1990 0.047
## 45 Blount 639 4 1970 7587 1990 0.070
## 46 Autauga 599 4 1980 11278 1990 0.145
## 47 Baldwin 1578 4 1980 27861 1990 0.168
## 48 Barbour 891 4 1980 9755 1990 0.118
## 49 Bibb 625 3 1980 4276 1990 0.047
## 50 Blount 639 4 1980 9490 1990 0.070
## 51 Autauga 599 4 1990 11471 1990 0.145
## 52 Baldwin 1578 4 1990 40809 1990 0.168
## 53 Barbour 891 4 1990 12163 1990 0.118
## 54 Bibb 625 3 1990 5564 1990 0.047
## 55 Blount 639 4 1990 11811 1990 0.070
## 56 Autauga 599 4 2000 16289 1990 0.145
## 57 Baldwin 1578 4 2000 70247 1990 0.168
## 58 Barbour 891 4 2000 15197 1990 0.118
## 59 Bibb 625 3 2000 6098 1990 0.047
## 60 Blount 639 4 2000 16503 1990 0.070
## 61 Autauga 599 4 1970 6853 2000 0.180
## 62 Baldwin 1578 4 1970 19749 2000 0.231
## 63 Barbour 891 4 1970 9448 2000 0.109
## 64 Bibb 625 3 1970 3965 2000 0.071
## 65 Blount 639 4 1970 7587 2000 0.096
## 66 Autauga 599 4 1980 11278 2000 0.180
## 67 Baldwin 1578 4 1980 27861 2000 0.231
## 68 Barbour 891 4 1980 9755 2000 0.109
## 69 Bibb 625 3 1980 4276 2000 0.071
## 70 Blount 639 4 1980 9490 2000 0.096
## 71 Autauga 599 4 1990 11471 2000 0.180
## 72 Baldwin 1578 4 1990 40809 2000 0.231
## 73 Barbour 891 4 1990 12163 2000 0.109
## 74 Bibb 625 3 1990 5564 2000 0.071
## 75 Blount 639 4 1990 11811 2000 0.096
## 76 Autauga 599 4 2000 16289 2000 0.180
## 77 Baldwin 1578 4 2000 70247 2000 0.231
## 78 Barbour 891 4 2000 15197 2000 0.109
## 79 Bibb 625 3 2000 6098 2000 0.071
## 80 Blount 639 4 2000 16503 2000 0.096# I then minimized the data to those where the years for job count and college percentage were the same and added some column names.
jobs3 <- subset(jobs2, Jobs==College)
jobs4 <- jobs3[,c(1:5,7)]
colnames(jobs4) <- c("County", "LandArea", "NatAmenity", "Year", "Jobs","College" )
jobs4## County LandArea NatAmenity Year Jobs College
## 1 Autauga 599 4 1970 6853 0.064
## 2 Baldwin 1578 4 1970 19749 0.065
## 3 Barbour 891 4 1970 9448 0.073
## 4 Bibb 625 3 1970 3965 0.042
## 5 Blount 639 4 1970 7587 0.027
## 26 Autauga 599 4 1980 11278 0.121
## 27 Baldwin 1578 4 1980 27861 0.121
## 28 Barbour 891 4 1980 9755 0.092
## 29 Bibb 625 3 1980 4276 0.049
## 30 Blount 639 4 1980 9490 0.053
## 51 Autauga 599 4 1990 11471 0.145
## 52 Baldwin 1578 4 1990 40809 0.168
## 53 Barbour 891 4 1990 12163 0.118
## 54 Bibb 625 3 1990 5564 0.047
## 55 Blount 639 4 1990 11811 0.070
## 76 Autauga 599 4 2000 16289 0.180
## 77 Baldwin 1578 4 2000 70247 0.231
## 78 Barbour 891 4 2000 15197 0.109
## 79 Bibb 625 3 2000 6098 0.071
## 80 Blount 639 4 2000 16503 0.096# I then plotted to see the relationships between college and jobs, Land area and jobs and natural amenity and jobs.
ggplot(jobs4, aes(College, Jobs)) + geom_line()
ggplot(jobs4, aes(LandArea, Jobs)) + geom_point()
ggplot(jobs4, aes(NatAmenity, Jobs)) + geom_point()
# Conclusion - the graph shows a general upward trend for job growth as college education increases. Land area is less conclusive although the largest county has the highest rate in each year. Ironically the county with the natural amenity has the lowest jobs. My final analysis was the data provided by Valerie Briot. I looked at the highest average downloads for apps - for free apps and for paid apps, along with the average downloads for each app across both platforms and for each platform.
apps <- read.csv('https://raw.githubusercontent.com/jeffnieman11/Data607_Project2/master/Mobile%20App%20downloads.csv', header=TRUE, skip=2)
apps## App.Name Type Paid.Free Release.Date X2010 X2011
## 1 Candy Crush Saga Game Free 4/12/2012 NA NA
## 2 Fruit Ninja Game Free 4/21/2010 4 8
## 3 Angry Birds Game Free 12/11/2009 10 124
## 4 Subway Surfers Game Free 5/24/2012 NA NA
## 5 Despicable Me: Minion Rush Game Free 6/10/2013 NA NA
## 6 Clash of Clans Game Free 8/2/2012 NA NA
## 7 Temple Run Game Free 8/4/2011 NA 18
## 8 Angry Birds Rio Game Free 3/22/2011 NA 104
## 9 Temple Run 2 Game Free 1/16/2013 NA NA
## 10 Words With Friends Game Free 9-Jul 143 156
## 11 Minecraft: Pocket Edition Game Paid 11/7/2011 NA 4
## 12 NBA 2K16 Game Paid 10/14/2015 NA NA
## X2012 X2013 X2014 X2015 Release.Date.1 X2010.1 X2011.1 X2012.1 X2013.1
## 1 8 56 60 76 11/4/2012 NA NA 2 53
## 2 58 102 126 148 7/10/2010 1 9 64 108
## 3 320 547 648 627 11/19/2010 2 108 312 538
## 4 23 123 202 303 5/24/2012 NA NA 26 128
## 5 NA 16 58 128 6/10/2013 NA NA NA 18
## 6 24 123 234 345 10/7/2013 NA NA NA 4
## 7 102 246 306 378 3/27/2012 NA NA 108 254
## 8 204 382 485 324 3/22/2011 NA 108 205 398
## 9 NA 230 403 503 1/16/2013 NA NA NA 253
## 10 100 93 85 86 9-Jul 146 173 112 105
## 11 120 240 320 340 11/7/2011 NA 8 154 285
## 12 NA NA NA 245 10/14/2015 NA NA NA NA
## X2014.1 X2015.1
## 1 64 72
## 2 132 165
## 3 647 656
## 4 236 329
## 5 64 294
## 6 143 256
## 7 302 402
## 8 476 389
## 9 493 523
## 10 95 92
## 11 369 352
## 12 NA 345# After downloading the data I began by creating separate data frames for apple and android platforms.
appleapps <- apps[,1:10]
appleapps## App.Name Type Paid.Free Release.Date X2010 X2011
## 1 Candy Crush Saga Game Free 4/12/2012 NA NA
## 2 Fruit Ninja Game Free 4/21/2010 4 8
## 3 Angry Birds Game Free 12/11/2009 10 124
## 4 Subway Surfers Game Free 5/24/2012 NA NA
## 5 Despicable Me: Minion Rush Game Free 6/10/2013 NA NA
## 6 Clash of Clans Game Free 8/2/2012 NA NA
## 7 Temple Run Game Free 8/4/2011 NA 18
## 8 Angry Birds Rio Game Free 3/22/2011 NA 104
## 9 Temple Run 2 Game Free 1/16/2013 NA NA
## 10 Words With Friends Game Free 9-Jul 143 156
## 11 Minecraft: Pocket Edition Game Paid 11/7/2011 NA 4
## 12 NBA 2K16 Game Paid 10/14/2015 NA NA
## X2012 X2013 X2014 X2015
## 1 8 56 60 76
## 2 58 102 126 148
## 3 320 547 648 627
## 4 23 123 202 303
## 5 NA 16 58 128
## 6 24 123 234 345
## 7 102 246 306 378
## 8 204 382 485 324
## 9 NA 230 403 503
## 10 100 93 85 86
## 11 120 240 320 340
## 12 NA NA NA 245googleapps <- apps[,c(1:3,11:17)]
googleapps## App.Name Type Paid.Free Release.Date.1 X2010.1
## 1 Candy Crush Saga Game Free 11/4/2012 NA
## 2 Fruit Ninja Game Free 7/10/2010 1
## 3 Angry Birds Game Free 11/19/2010 2
## 4 Subway Surfers Game Free 5/24/2012 NA
## 5 Despicable Me: Minion Rush Game Free 6/10/2013 NA
## 6 Clash of Clans Game Free 10/7/2013 NA
## 7 Temple Run Game Free 3/27/2012 NA
## 8 Angry Birds Rio Game Free 3/22/2011 NA
## 9 Temple Run 2 Game Free 1/16/2013 NA
## 10 Words With Friends Game Free 9-Jul 146
## 11 Minecraft: Pocket Edition Game Paid 11/7/2011 NA
## 12 NBA 2K16 Game Paid 10/14/2015 NA
## X2011.1 X2012.1 X2013.1 X2014.1 X2015.1
## 1 NA 2 53 64 72
## 2 9 64 108 132 165
## 3 108 312 538 647 656
## 4 NA 26 128 236 329
## 5 NA NA 18 64 294
## 6 NA NA 4 143 256
## 7 NA 108 254 302 402
## 8 108 205 398 476 389
## 9 NA NA 253 493 523
## 10 173 112 105 95 92
## 11 8 154 285 369 352
## 12 NA NA NA NA 345# For the apple apps I made the data tidy.
appleapp1 <- gather(appleapps, "Year", "Downloads", 5:10)
appleapp1$Year <- str_extract_all(appleapp1$Year, "[0-9]{4}")
appleapp1$Source <- "Apple Store"
colnames(appleapp1) <- c("App.Name", "Type", "Paid.Free", "Release.Date", "Year", "Downloads", "Source")
appleapp1$Release.Date <- as.character.Date(appleapp1$Release.Date)
# I then summarized the data for average downloads in the apple platforms (fulfilling the assignment request).
appleapp2 <- appleapp1 %>%
group_by(App.Name) %>%
summarise(Avg.Downloads = mean(Downloads, na.rm = TRUE))
appleapp2## Source: local data frame [12 x 2]
##
## App.Name Avg.Downloads
## (fctr) (dbl)
## 1 Angry Birds 379.33333
## 2 Angry Birds Rio 299.80000
## 3 Candy Crush Saga 50.00000
## 4 Clash of Clans 181.50000
## 5 Despicable Me: Minion Rush 67.33333
## 6 Fruit Ninja 74.33333
## 7 Minecraft: Pocket Edition 204.80000
## 8 NBA 2K16 245.00000
## 9 Subway Surfers 162.75000
## 10 Temple Run 210.00000
## 11 Temple Run 2 378.66667
## 12 Words With Friends 110.50000# For the android apps I once again made the data tidy.
googleapp1 <- gather(googleapps, "Year", "Downloads", 5:10)
googleapp1$Year <- str_extract_all(googleapp1$Year, "[0-9]{4}")
googleapp1$Source <- "Google Play"
colnames(googleapp1) <- c("App.Name", "Type", "Paid.Free", "Release.Date", "Year", "Downloads", "Source")
googleapp1$Release.Date <- as.character.Date(googleapp1$Release.Date)
# I then summarized the data for average downloads in the android platforms (again fulfilling the assignment request).
googleapp2 <- googleapp1 %>%
group_by(App.Name) %>%
summarise(Avg.Downloads = mean(Downloads, na.rm = TRUE))
googleapp2## Source: local data frame [12 x 2]
##
## App.Name Avg.Downloads
## (fctr) (dbl)
## 1 Angry Birds 377.16667
## 2 Angry Birds Rio 315.20000
## 3 Candy Crush Saga 47.75000
## 4 Clash of Clans 134.33333
## 5 Despicable Me: Minion Rush 125.33333
## 6 Fruit Ninja 79.83333
## 7 Minecraft: Pocket Edition 233.60000
## 8 NBA 2K16 345.00000
## 9 Subway Surfers 179.75000
## 10 Temple Run 266.50000
## 11 Temple Run 2 423.00000
## 12 Words With Friends 120.50000# I combined the two data frames into one and summarized the average downloads for each app with both platforms combined (fulfilling the assignment request for across providers).
apps1 <- rbind(googleapp1,appleapp1)
apps1## App.Name Type Paid.Free Release.Date Year Downloads
## 1 Candy Crush Saga Game Free 11/4/2012 2010 NA
## 2 Fruit Ninja Game Free 7/10/2010 2010 1
## 3 Angry Birds Game Free 11/19/2010 2010 2
## 4 Subway Surfers Game Free 5/24/2012 2010 NA
## 5 Despicable Me: Minion Rush Game Free 6/10/2013 2010 NA
## 6 Clash of Clans Game Free 10/7/2013 2010 NA
## 7 Temple Run Game Free 3/27/2012 2010 NA
## 8 Angry Birds Rio Game Free 3/22/2011 2010 NA
## 9 Temple Run 2 Game Free 1/16/2013 2010 NA
## 10 Words With Friends Game Free 9-Jul 2010 146
## 11 Minecraft: Pocket Edition Game Paid 11/7/2011 2010 NA
## 12 NBA 2K16 Game Paid 10/14/2015 2010 NA
## 13 Candy Crush Saga Game Free 11/4/2012 2011 NA
## 14 Fruit Ninja Game Free 7/10/2010 2011 9
## 15 Angry Birds Game Free 11/19/2010 2011 108
## 16 Subway Surfers Game Free 5/24/2012 2011 NA
## 17 Despicable Me: Minion Rush Game Free 6/10/2013 2011 NA
## 18 Clash of Clans Game Free 10/7/2013 2011 NA
## 19 Temple Run Game Free 3/27/2012 2011 NA
## 20 Angry Birds Rio Game Free 3/22/2011 2011 108
## 21 Temple Run 2 Game Free 1/16/2013 2011 NA
## 22 Words With Friends Game Free 9-Jul 2011 173
## 23 Minecraft: Pocket Edition Game Paid 11/7/2011 2011 8
## 24 NBA 2K16 Game Paid 10/14/2015 2011 NA
## 25 Candy Crush Saga Game Free 11/4/2012 2012 2
## 26 Fruit Ninja Game Free 7/10/2010 2012 64
## 27 Angry Birds Game Free 11/19/2010 2012 312
## 28 Subway Surfers Game Free 5/24/2012 2012 26
## 29 Despicable Me: Minion Rush Game Free 6/10/2013 2012 NA
## 30 Clash of Clans Game Free 10/7/2013 2012 NA
## 31 Temple Run Game Free 3/27/2012 2012 108
## 32 Angry Birds Rio Game Free 3/22/2011 2012 205
## 33 Temple Run 2 Game Free 1/16/2013 2012 NA
## 34 Words With Friends Game Free 9-Jul 2012 112
## 35 Minecraft: Pocket Edition Game Paid 11/7/2011 2012 154
## 36 NBA 2K16 Game Paid 10/14/2015 2012 NA
## 37 Candy Crush Saga Game Free 11/4/2012 2013 53
## 38 Fruit Ninja Game Free 7/10/2010 2013 108
## 39 Angry Birds Game Free 11/19/2010 2013 538
## 40 Subway Surfers Game Free 5/24/2012 2013 128
## 41 Despicable Me: Minion Rush Game Free 6/10/2013 2013 18
## 42 Clash of Clans Game Free 10/7/2013 2013 4
## 43 Temple Run Game Free 3/27/2012 2013 254
## 44 Angry Birds Rio Game Free 3/22/2011 2013 398
## 45 Temple Run 2 Game Free 1/16/2013 2013 253
## 46 Words With Friends Game Free 9-Jul 2013 105
## 47 Minecraft: Pocket Edition Game Paid 11/7/2011 2013 285
## 48 NBA 2K16 Game Paid 10/14/2015 2013 NA
## 49 Candy Crush Saga Game Free 11/4/2012 2014 64
## 50 Fruit Ninja Game Free 7/10/2010 2014 132
## 51 Angry Birds Game Free 11/19/2010 2014 647
## 52 Subway Surfers Game Free 5/24/2012 2014 236
## 53 Despicable Me: Minion Rush Game Free 6/10/2013 2014 64
## 54 Clash of Clans Game Free 10/7/2013 2014 143
## 55 Temple Run Game Free 3/27/2012 2014 302
## 56 Angry Birds Rio Game Free 3/22/2011 2014 476
## 57 Temple Run 2 Game Free 1/16/2013 2014 493
## 58 Words With Friends Game Free 9-Jul 2014 95
## 59 Minecraft: Pocket Edition Game Paid 11/7/2011 2014 369
## 60 NBA 2K16 Game Paid 10/14/2015 2014 NA
## 61 Candy Crush Saga Game Free 11/4/2012 2015 72
## 62 Fruit Ninja Game Free 7/10/2010 2015 165
## 63 Angry Birds Game Free 11/19/2010 2015 656
## 64 Subway Surfers Game Free 5/24/2012 2015 329
## 65 Despicable Me: Minion Rush Game Free 6/10/2013 2015 294
## 66 Clash of Clans Game Free 10/7/2013 2015 256
## 67 Temple Run Game Free 3/27/2012 2015 402
## 68 Angry Birds Rio Game Free 3/22/2011 2015 389
## 69 Temple Run 2 Game Free 1/16/2013 2015 523
## 70 Words With Friends Game Free 9-Jul 2015 92
## 71 Minecraft: Pocket Edition Game Paid 11/7/2011 2015 352
## 72 NBA 2K16 Game Paid 10/14/2015 2015 345
## 73 Candy Crush Saga Game Free 4/12/2012 2010 NA
## 74 Fruit Ninja Game Free 4/21/2010 2010 4
## 75 Angry Birds Game Free 12/11/2009 2010 10
## 76 Subway Surfers Game Free 5/24/2012 2010 NA
## 77 Despicable Me: Minion Rush Game Free 6/10/2013 2010 NA
## 78 Clash of Clans Game Free 8/2/2012 2010 NA
## 79 Temple Run Game Free 8/4/2011 2010 NA
## 80 Angry Birds Rio Game Free 3/22/2011 2010 NA
## 81 Temple Run 2 Game Free 1/16/2013 2010 NA
## 82 Words With Friends Game Free 9-Jul 2010 143
## 83 Minecraft: Pocket Edition Game Paid 11/7/2011 2010 NA
## 84 NBA 2K16 Game Paid 10/14/2015 2010 NA
## 85 Candy Crush Saga Game Free 4/12/2012 2011 NA
## 86 Fruit Ninja Game Free 4/21/2010 2011 8
## 87 Angry Birds Game Free 12/11/2009 2011 124
## 88 Subway Surfers Game Free 5/24/2012 2011 NA
## 89 Despicable Me: Minion Rush Game Free 6/10/2013 2011 NA
## 90 Clash of Clans Game Free 8/2/2012 2011 NA
## 91 Temple Run Game Free 8/4/2011 2011 18
## 92 Angry Birds Rio Game Free 3/22/2011 2011 104
## 93 Temple Run 2 Game Free 1/16/2013 2011 NA
## 94 Words With Friends Game Free 9-Jul 2011 156
## 95 Minecraft: Pocket Edition Game Paid 11/7/2011 2011 4
## 96 NBA 2K16 Game Paid 10/14/2015 2011 NA
## 97 Candy Crush Saga Game Free 4/12/2012 2012 8
## 98 Fruit Ninja Game Free 4/21/2010 2012 58
## 99 Angry Birds Game Free 12/11/2009 2012 320
## 100 Subway Surfers Game Free 5/24/2012 2012 23
## 101 Despicable Me: Minion Rush Game Free 6/10/2013 2012 NA
## 102 Clash of Clans Game Free 8/2/2012 2012 24
## 103 Temple Run Game Free 8/4/2011 2012 102
## 104 Angry Birds Rio Game Free 3/22/2011 2012 204
## 105 Temple Run 2 Game Free 1/16/2013 2012 NA
## 106 Words With Friends Game Free 9-Jul 2012 100
## 107 Minecraft: Pocket Edition Game Paid 11/7/2011 2012 120
## 108 NBA 2K16 Game Paid 10/14/2015 2012 NA
## 109 Candy Crush Saga Game Free 4/12/2012 2013 56
## 110 Fruit Ninja Game Free 4/21/2010 2013 102
## 111 Angry Birds Game Free 12/11/2009 2013 547
## 112 Subway Surfers Game Free 5/24/2012 2013 123
## 113 Despicable Me: Minion Rush Game Free 6/10/2013 2013 16
## 114 Clash of Clans Game Free 8/2/2012 2013 123
## 115 Temple Run Game Free 8/4/2011 2013 246
## 116 Angry Birds Rio Game Free 3/22/2011 2013 382
## 117 Temple Run 2 Game Free 1/16/2013 2013 230
## 118 Words With Friends Game Free 9-Jul 2013 93
## 119 Minecraft: Pocket Edition Game Paid 11/7/2011 2013 240
## 120 NBA 2K16 Game Paid 10/14/2015 2013 NA
## 121 Candy Crush Saga Game Free 4/12/2012 2014 60
## 122 Fruit Ninja Game Free 4/21/2010 2014 126
## 123 Angry Birds Game Free 12/11/2009 2014 648
## 124 Subway Surfers Game Free 5/24/2012 2014 202
## 125 Despicable Me: Minion Rush Game Free 6/10/2013 2014 58
## 126 Clash of Clans Game Free 8/2/2012 2014 234
## 127 Temple Run Game Free 8/4/2011 2014 306
## 128 Angry Birds Rio Game Free 3/22/2011 2014 485
## 129 Temple Run 2 Game Free 1/16/2013 2014 403
## 130 Words With Friends Game Free 9-Jul 2014 85
## 131 Minecraft: Pocket Edition Game Paid 11/7/2011 2014 320
## 132 NBA 2K16 Game Paid 10/14/2015 2014 NA
## 133 Candy Crush Saga Game Free 4/12/2012 2015 76
## 134 Fruit Ninja Game Free 4/21/2010 2015 148
## 135 Angry Birds Game Free 12/11/2009 2015 627
## 136 Subway Surfers Game Free 5/24/2012 2015 303
## 137 Despicable Me: Minion Rush Game Free 6/10/2013 2015 128
## 138 Clash of Clans Game Free 8/2/2012 2015 345
## 139 Temple Run Game Free 8/4/2011 2015 378
## 140 Angry Birds Rio Game Free 3/22/2011 2015 324
## 141 Temple Run 2 Game Free 1/16/2013 2015 503
## 142 Words With Friends Game Free 9-Jul 2015 86
## 143 Minecraft: Pocket Edition Game Paid 11/7/2011 2015 340
## 144 NBA 2K16 Game Paid 10/14/2015 2015 245
## Source
## 1 Google Play
## 2 Google Play
## 3 Google Play
## 4 Google Play
## 5 Google Play
## 6 Google Play
## 7 Google Play
## 8 Google Play
## 9 Google Play
## 10 Google Play
## 11 Google Play
## 12 Google Play
## 13 Google Play
## 14 Google Play
## 15 Google Play
## 16 Google Play
## 17 Google Play
## 18 Google Play
## 19 Google Play
## 20 Google Play
## 21 Google Play
## 22 Google Play
## 23 Google Play
## 24 Google Play
## 25 Google Play
## 26 Google Play
## 27 Google Play
## 28 Google Play
## 29 Google Play
## 30 Google Play
## 31 Google Play
## 32 Google Play
## 33 Google Play
## 34 Google Play
## 35 Google Play
## 36 Google Play
## 37 Google Play
## 38 Google Play
## 39 Google Play
## 40 Google Play
## 41 Google Play
## 42 Google Play
## 43 Google Play
## 44 Google Play
## 45 Google Play
## 46 Google Play
## 47 Google Play
## 48 Google Play
## 49 Google Play
## 50 Google Play
## 51 Google Play
## 52 Google Play
## 53 Google Play
## 54 Google Play
## 55 Google Play
## 56 Google Play
## 57 Google Play
## 58 Google Play
## 59 Google Play
## 60 Google Play
## 61 Google Play
## 62 Google Play
## 63 Google Play
## 64 Google Play
## 65 Google Play
## 66 Google Play
## 67 Google Play
## 68 Google Play
## 69 Google Play
## 70 Google Play
## 71 Google Play
## 72 Google Play
## 73 Apple Store
## 74 Apple Store
## 75 Apple Store
## 76 Apple Store
## 77 Apple Store
## 78 Apple Store
## 79 Apple Store
## 80 Apple Store
## 81 Apple Store
## 82 Apple Store
## 83 Apple Store
## 84 Apple Store
## 85 Apple Store
## 86 Apple Store
## 87 Apple Store
## 88 Apple Store
## 89 Apple Store
## 90 Apple Store
## 91 Apple Store
## 92 Apple Store
## 93 Apple Store
## 94 Apple Store
## 95 Apple Store
## 96 Apple Store
## 97 Apple Store
## 98 Apple Store
## 99 Apple Store
## 100 Apple Store
## 101 Apple Store
## 102 Apple Store
## 103 Apple Store
## 104 Apple Store
## 105 Apple Store
## 106 Apple Store
## 107 Apple Store
## 108 Apple Store
## 109 Apple Store
## 110 Apple Store
## 111 Apple Store
## 112 Apple Store
## 113 Apple Store
## 114 Apple Store
## 115 Apple Store
## 116 Apple Store
## 117 Apple Store
## 118 Apple Store
## 119 Apple Store
## 120 Apple Store
## 121 Apple Store
## 122 Apple Store
## 123 Apple Store
## 124 Apple Store
## 125 Apple Store
## 126 Apple Store
## 127 Apple Store
## 128 Apple Store
## 129 Apple Store
## 130 Apple Store
## 131 Apple Store
## 132 Apple Store
## 133 Apple Store
## 134 Apple Store
## 135 Apple Store
## 136 Apple Store
## 137 Apple Store
## 138 Apple Store
## 139 Apple Store
## 140 Apple Store
## 141 Apple Store
## 142 Apple Store
## 143 Apple Store
## 144 Apple Storeapps2 <- apps1 %>%
group_by(App.Name) %>%
summarise(Avg.Downloads = mean(Downloads, na.rm = TRUE))
apps2## Source: local data frame [12 x 2]
##
## App.Name Avg.Downloads
## (fctr) (dbl)
## 1 Angry Birds 378.25000
## 2 Angry Birds Rio 307.50000
## 3 Candy Crush Saga 48.87500
## 4 Clash of Clans 161.28571
## 5 Despicable Me: Minion Rush 96.33333
## 6 Fruit Ninja 77.08333
## 7 Minecraft: Pocket Edition 219.20000
## 8 NBA 2K16 295.00000
## 9 Subway Surfers 171.25000
## 10 Temple Run 235.11111
## 11 Temple Run 2 400.83333
## 12 Words With Friends 115.50000# I then created a subet of the data for free apps and summarized the average downloads for each free app.
freeapps <- subset(apps1, Paid.Free == "Free")
freeapps## App.Name Type Paid.Free Release.Date Year Downloads
## 1 Candy Crush Saga Game Free 11/4/2012 2010 NA
## 2 Fruit Ninja Game Free 7/10/2010 2010 1
## 3 Angry Birds Game Free 11/19/2010 2010 2
## 4 Subway Surfers Game Free 5/24/2012 2010 NA
## 5 Despicable Me: Minion Rush Game Free 6/10/2013 2010 NA
## 6 Clash of Clans Game Free 10/7/2013 2010 NA
## 7 Temple Run Game Free 3/27/2012 2010 NA
## 8 Angry Birds Rio Game Free 3/22/2011 2010 NA
## 9 Temple Run 2 Game Free 1/16/2013 2010 NA
## 10 Words With Friends Game Free 9-Jul 2010 146
## 13 Candy Crush Saga Game Free 11/4/2012 2011 NA
## 14 Fruit Ninja Game Free 7/10/2010 2011 9
## 15 Angry Birds Game Free 11/19/2010 2011 108
## 16 Subway Surfers Game Free 5/24/2012 2011 NA
## 17 Despicable Me: Minion Rush Game Free 6/10/2013 2011 NA
## 18 Clash of Clans Game Free 10/7/2013 2011 NA
## 19 Temple Run Game Free 3/27/2012 2011 NA
## 20 Angry Birds Rio Game Free 3/22/2011 2011 108
## 21 Temple Run 2 Game Free 1/16/2013 2011 NA
## 22 Words With Friends Game Free 9-Jul 2011 173
## 25 Candy Crush Saga Game Free 11/4/2012 2012 2
## 26 Fruit Ninja Game Free 7/10/2010 2012 64
## 27 Angry Birds Game Free 11/19/2010 2012 312
## 28 Subway Surfers Game Free 5/24/2012 2012 26
## 29 Despicable Me: Minion Rush Game Free 6/10/2013 2012 NA
## 30 Clash of Clans Game Free 10/7/2013 2012 NA
## 31 Temple Run Game Free 3/27/2012 2012 108
## 32 Angry Birds Rio Game Free 3/22/2011 2012 205
## 33 Temple Run 2 Game Free 1/16/2013 2012 NA
## 34 Words With Friends Game Free 9-Jul 2012 112
## 37 Candy Crush Saga Game Free 11/4/2012 2013 53
## 38 Fruit Ninja Game Free 7/10/2010 2013 108
## 39 Angry Birds Game Free 11/19/2010 2013 538
## 40 Subway Surfers Game Free 5/24/2012 2013 128
## 41 Despicable Me: Minion Rush Game Free 6/10/2013 2013 18
## 42 Clash of Clans Game Free 10/7/2013 2013 4
## 43 Temple Run Game Free 3/27/2012 2013 254
## 44 Angry Birds Rio Game Free 3/22/2011 2013 398
## 45 Temple Run 2 Game Free 1/16/2013 2013 253
## 46 Words With Friends Game Free 9-Jul 2013 105
## 49 Candy Crush Saga Game Free 11/4/2012 2014 64
## 50 Fruit Ninja Game Free 7/10/2010 2014 132
## 51 Angry Birds Game Free 11/19/2010 2014 647
## 52 Subway Surfers Game Free 5/24/2012 2014 236
## 53 Despicable Me: Minion Rush Game Free 6/10/2013 2014 64
## 54 Clash of Clans Game Free 10/7/2013 2014 143
## 55 Temple Run Game Free 3/27/2012 2014 302
## 56 Angry Birds Rio Game Free 3/22/2011 2014 476
## 57 Temple Run 2 Game Free 1/16/2013 2014 493
## 58 Words With Friends Game Free 9-Jul 2014 95
## 61 Candy Crush Saga Game Free 11/4/2012 2015 72
## 62 Fruit Ninja Game Free 7/10/2010 2015 165
## 63 Angry Birds Game Free 11/19/2010 2015 656
## 64 Subway Surfers Game Free 5/24/2012 2015 329
## 65 Despicable Me: Minion Rush Game Free 6/10/2013 2015 294
## 66 Clash of Clans Game Free 10/7/2013 2015 256
## 67 Temple Run Game Free 3/27/2012 2015 402
## 68 Angry Birds Rio Game Free 3/22/2011 2015 389
## 69 Temple Run 2 Game Free 1/16/2013 2015 523
## 70 Words With Friends Game Free 9-Jul 2015 92
## 73 Candy Crush Saga Game Free 4/12/2012 2010 NA
## 74 Fruit Ninja Game Free 4/21/2010 2010 4
## 75 Angry Birds Game Free 12/11/2009 2010 10
## 76 Subway Surfers Game Free 5/24/2012 2010 NA
## 77 Despicable Me: Minion Rush Game Free 6/10/2013 2010 NA
## 78 Clash of Clans Game Free 8/2/2012 2010 NA
## 79 Temple Run Game Free 8/4/2011 2010 NA
## 80 Angry Birds Rio Game Free 3/22/2011 2010 NA
## 81 Temple Run 2 Game Free 1/16/2013 2010 NA
## 82 Words With Friends Game Free 9-Jul 2010 143
## 85 Candy Crush Saga Game Free 4/12/2012 2011 NA
## 86 Fruit Ninja Game Free 4/21/2010 2011 8
## 87 Angry Birds Game Free 12/11/2009 2011 124
## 88 Subway Surfers Game Free 5/24/2012 2011 NA
## 89 Despicable Me: Minion Rush Game Free 6/10/2013 2011 NA
## 90 Clash of Clans Game Free 8/2/2012 2011 NA
## 91 Temple Run Game Free 8/4/2011 2011 18
## 92 Angry Birds Rio Game Free 3/22/2011 2011 104
## 93 Temple Run 2 Game Free 1/16/2013 2011 NA
## 94 Words With Friends Game Free 9-Jul 2011 156
## 97 Candy Crush Saga Game Free 4/12/2012 2012 8
## 98 Fruit Ninja Game Free 4/21/2010 2012 58
## 99 Angry Birds Game Free 12/11/2009 2012 320
## 100 Subway Surfers Game Free 5/24/2012 2012 23
## 101 Despicable Me: Minion Rush Game Free 6/10/2013 2012 NA
## 102 Clash of Clans Game Free 8/2/2012 2012 24
## 103 Temple Run Game Free 8/4/2011 2012 102
## 104 Angry Birds Rio Game Free 3/22/2011 2012 204
## 105 Temple Run 2 Game Free 1/16/2013 2012 NA
## 106 Words With Friends Game Free 9-Jul 2012 100
## 109 Candy Crush Saga Game Free 4/12/2012 2013 56
## 110 Fruit Ninja Game Free 4/21/2010 2013 102
## 111 Angry Birds Game Free 12/11/2009 2013 547
## 112 Subway Surfers Game Free 5/24/2012 2013 123
## 113 Despicable Me: Minion Rush Game Free 6/10/2013 2013 16
## 114 Clash of Clans Game Free 8/2/2012 2013 123
## 115 Temple Run Game Free 8/4/2011 2013 246
## 116 Angry Birds Rio Game Free 3/22/2011 2013 382
## 117 Temple Run 2 Game Free 1/16/2013 2013 230
## 118 Words With Friends Game Free 9-Jul 2013 93
## 121 Candy Crush Saga Game Free 4/12/2012 2014 60
## 122 Fruit Ninja Game Free 4/21/2010 2014 126
## 123 Angry Birds Game Free 12/11/2009 2014 648
## 124 Subway Surfers Game Free 5/24/2012 2014 202
## 125 Despicable Me: Minion Rush Game Free 6/10/2013 2014 58
## 126 Clash of Clans Game Free 8/2/2012 2014 234
## 127 Temple Run Game Free 8/4/2011 2014 306
## 128 Angry Birds Rio Game Free 3/22/2011 2014 485
## 129 Temple Run 2 Game Free 1/16/2013 2014 403
## 130 Words With Friends Game Free 9-Jul 2014 85
## 133 Candy Crush Saga Game Free 4/12/2012 2015 76
## 134 Fruit Ninja Game Free 4/21/2010 2015 148
## 135 Angry Birds Game Free 12/11/2009 2015 627
## 136 Subway Surfers Game Free 5/24/2012 2015 303
## 137 Despicable Me: Minion Rush Game Free 6/10/2013 2015 128
## 138 Clash of Clans Game Free 8/2/2012 2015 345
## 139 Temple Run Game Free 8/4/2011 2015 378
## 140 Angry Birds Rio Game Free 3/22/2011 2015 324
## 141 Temple Run 2 Game Free 1/16/2013 2015 503
## 142 Words With Friends Game Free 9-Jul 2015 86
## Source
## 1 Google Play
## 2 Google Play
## 3 Google Play
## 4 Google Play
## 5 Google Play
## 6 Google Play
## 7 Google Play
## 8 Google Play
## 9 Google Play
## 10 Google Play
## 13 Google Play
## 14 Google Play
## 15 Google Play
## 16 Google Play
## 17 Google Play
## 18 Google Play
## 19 Google Play
## 20 Google Play
## 21 Google Play
## 22 Google Play
## 25 Google Play
## 26 Google Play
## 27 Google Play
## 28 Google Play
## 29 Google Play
## 30 Google Play
## 31 Google Play
## 32 Google Play
## 33 Google Play
## 34 Google Play
## 37 Google Play
## 38 Google Play
## 39 Google Play
## 40 Google Play
## 41 Google Play
## 42 Google Play
## 43 Google Play
## 44 Google Play
## 45 Google Play
## 46 Google Play
## 49 Google Play
## 50 Google Play
## 51 Google Play
## 52 Google Play
## 53 Google Play
## 54 Google Play
## 55 Google Play
## 56 Google Play
## 57 Google Play
## 58 Google Play
## 61 Google Play
## 62 Google Play
## 63 Google Play
## 64 Google Play
## 65 Google Play
## 66 Google Play
## 67 Google Play
## 68 Google Play
## 69 Google Play
## 70 Google Play
## 73 Apple Store
## 74 Apple Store
## 75 Apple Store
## 76 Apple Store
## 77 Apple Store
## 78 Apple Store
## 79 Apple Store
## 80 Apple Store
## 81 Apple Store
## 82 Apple Store
## 85 Apple Store
## 86 Apple Store
## 87 Apple Store
## 88 Apple Store
## 89 Apple Store
## 90 Apple Store
## 91 Apple Store
## 92 Apple Store
## 93 Apple Store
## 94 Apple Store
## 97 Apple Store
## 98 Apple Store
## 99 Apple Store
## 100 Apple Store
## 101 Apple Store
## 102 Apple Store
## 103 Apple Store
## 104 Apple Store
## 105 Apple Store
## 106 Apple Store
## 109 Apple Store
## 110 Apple Store
## 111 Apple Store
## 112 Apple Store
## 113 Apple Store
## 114 Apple Store
## 115 Apple Store
## 116 Apple Store
## 117 Apple Store
## 118 Apple Store
## 121 Apple Store
## 122 Apple Store
## 123 Apple Store
## 124 Apple Store
## 125 Apple Store
## 126 Apple Store
## 127 Apple Store
## 128 Apple Store
## 129 Apple Store
## 130 Apple Store
## 133 Apple Store
## 134 Apple Store
## 135 Apple Store
## 136 Apple Store
## 137 Apple Store
## 138 Apple Store
## 139 Apple Store
## 140 Apple Store
## 141 Apple Store
## 142 Apple Storefreeapps1 <- freeapps %>%
group_by(App.Name) %>%
summarise(Avg.Downloads = mean(Downloads, na.rm = TRUE))
freeapps1## Source: local data frame [10 x 2]
##
## App.Name Avg.Downloads
## (fctr) (dbl)
## 1 Angry Birds 378.25000
## 2 Angry Birds Rio 307.50000
## 3 Candy Crush Saga 48.87500
## 4 Clash of Clans 161.28571
## 5 Despicable Me: Minion Rush 96.33333
## 6 Fruit Ninja 77.08333
## 7 Subway Surfers 171.25000
## 8 Temple Run 235.11111
## 9 Temple Run 2 400.83333
## 10 Words With Friends 115.50000# Conclusion: The Temple Run 2 app had the most average downloads among the free apps.
# I followed the same procedure for the paid apps.
paidapps <- subset(apps1, Paid.Free == "Paid")
paidapps## App.Name Type Paid.Free Release.Date Year Downloads
## 11 Minecraft: Pocket Edition Game Paid 11/7/2011 2010 NA
## 12 NBA 2K16 Game Paid 10/14/2015 2010 NA
## 23 Minecraft: Pocket Edition Game Paid 11/7/2011 2011 8
## 24 NBA 2K16 Game Paid 10/14/2015 2011 NA
## 35 Minecraft: Pocket Edition Game Paid 11/7/2011 2012 154
## 36 NBA 2K16 Game Paid 10/14/2015 2012 NA
## 47 Minecraft: Pocket Edition Game Paid 11/7/2011 2013 285
## 48 NBA 2K16 Game Paid 10/14/2015 2013 NA
## 59 Minecraft: Pocket Edition Game Paid 11/7/2011 2014 369
## 60 NBA 2K16 Game Paid 10/14/2015 2014 NA
## 71 Minecraft: Pocket Edition Game Paid 11/7/2011 2015 352
## 72 NBA 2K16 Game Paid 10/14/2015 2015 345
## 83 Minecraft: Pocket Edition Game Paid 11/7/2011 2010 NA
## 84 NBA 2K16 Game Paid 10/14/2015 2010 NA
## 95 Minecraft: Pocket Edition Game Paid 11/7/2011 2011 4
## 96 NBA 2K16 Game Paid 10/14/2015 2011 NA
## 107 Minecraft: Pocket Edition Game Paid 11/7/2011 2012 120
## 108 NBA 2K16 Game Paid 10/14/2015 2012 NA
## 119 Minecraft: Pocket Edition Game Paid 11/7/2011 2013 240
## 120 NBA 2K16 Game Paid 10/14/2015 2013 NA
## 131 Minecraft: Pocket Edition Game Paid 11/7/2011 2014 320
## 132 NBA 2K16 Game Paid 10/14/2015 2014 NA
## 143 Minecraft: Pocket Edition Game Paid 11/7/2011 2015 340
## 144 NBA 2K16 Game Paid 10/14/2015 2015 245
## Source
## 11 Google Play
## 12 Google Play
## 23 Google Play
## 24 Google Play
## 35 Google Play
## 36 Google Play
## 47 Google Play
## 48 Google Play
## 59 Google Play
## 60 Google Play
## 71 Google Play
## 72 Google Play
## 83 Apple Store
## 84 Apple Store
## 95 Apple Store
## 96 Apple Store
## 107 Apple Store
## 108 Apple Store
## 119 Apple Store
## 120 Apple Store
## 131 Apple Store
## 132 Apple Store
## 143 Apple Store
## 144 Apple Storepaidapps1 <- paidapps %>%
group_by(App.Name) %>%
summarise(Avg.Downloads = mean(Downloads, na.rm = TRUE))
paidapps1## Source: local data frame [2 x 2]
##
## App.Name Avg.Downloads
## (fctr) (dbl)
## 1 Minecraft: Pocket Edition 219.2
## 2 NBA 2K16 295.0# Conclusion: The NBA 2K16 app had the most average downloads among the paid apps.