Visualization Data Exploratory Project: Analysis of Samsung Sales Data
2025-06-13
Visualization Data Exploratory Project
The Impact of 5G Features on Samsung Sales: Data Analysis and Global Trends
Group 7:
- Naomy Athaya Jovany 5052241002
- Ghisele Valerin Sharent Milano 5052241005
- Kiranna Indy 5052241014
Introduction and Problem Statement
The rapid advancement of technology has brought 5G connectivity as an innovation in the smartphone industry. 5G is the new global standard for telecommunications network, designed to give more benefits to the users like delivering faster speeds, lower latency, and greater network capacity compared to the previous generations. Mobile phone manufacturers such as Samsung has begun to implement this technology. However, understanding how this technological shift influences consumer behavior and market outcomes is essential for strategic decision-making. This analysis aims to explore the impact of 5G support on Samsung’s smartphone sales and revenue, providing insights into whether 5G adoption affects business advantages.
Research Question
- How do Samsung 5G and non-5G phone sales compare since the launch of the 5G network?
- Do Regional 5G Coverage affects Sales?
- Do models that have 5g generate more revenue than models that don’t?
Library Installments and Imports
The first step we take is to import the potential libraries that will be used, namely as follows:
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.4.3## Warning: package 'ggplot2' was built under R version 4.4.3## Warning: package 'tibble' was built under R version 4.4.3## Warning: package 'tidyr' was built under R version 4.4.3## Warning: package 'readr' was built under R version 4.4.3## Warning: package 'purrr' was built under R version 4.4.3## Warning: package 'dplyr' was built under R version 4.4.3## Warning: package 'stringr' was built under R version 4.4.3## Warning: package 'forcats' was built under R version 4.4.3## Warning: package 'lubridate' was built under R version 4.4.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(dplyr)
library (ggplot2)
library(gridExtra)## Warning: package 'gridExtra' was built under R version 4.4.3##
## Attaching package: 'gridExtra'
##
## The following object is masked from 'package:dplyr':
##
## combinelibrary("knitr")## Warning: package 'knitr' was built under R version 4.4.3Data Imports
We’re using a dataset with CSV format. And for that reason, we’re importing the dataset to Rstudio using read.csv and head command to give overview of our data including the variables and their data types. We’ve named it as samsung_sales.
samsung_sales <- read.csv("C:/Users/KOKO MEME/Downloads/Expanded_Dataset.csv")
head(samsung_sales)## Year Quarter Product.Model X5G.Capability Units.Sold Revenue....
## 1 2019 Q1 Galaxy S10 No 26396 4212951
## 2 2019 Q1 Galaxy Note10 No 25671 7240266
## 3 2019 Q1 Galaxy S20 No 16573 25608332
## 4 2019 Q1 Galaxy Note20 No 7177 21984416
## 5 2019 Q1 Galaxy S21 No 45633 16342438
## 6 2019 Q1 Galaxy A32 5G Yes 15912 17178327
## Market.Share.... Regional.5G.Coverage.... X5G.Subscribers..millions.
## 1 1.04 57.36 39.55
## 2 2.82 85.80 42.58
## 3 -0.03 47.02 3.78
## 4 0.84 25.70 23.41
## 5 2.36 89.13 44.43
## 6 5.41 59.12 12.14
## Avg.5G.Speed..Mbps. Preference.for.5G.... Region
## 1 293.10 55.87 Asia-Pacific
## 2 67.46 37.26 Latin America
## 3 77.25 84.66 Middle East & Africa
## 4 105.27 40.03 North America
## 5 206.17 76.88 Latin America
## 6 179.15 80.79 Middle East & AfricaHere is the summarized version of our uncleaned datasets.
summary(samsung_sales)## Year Quarter Product.Model X5G.Capability
## Min. :2019 Length:1000 Length:1000 Length:1000
## 1st Qu.:2020 Class :character Class :character Class :character
## Median :2021 Mode :character Mode :character Mode :character
## Mean :2021
## 3rd Qu.:2023
## Max. :2024
## Units.Sold Revenue.... Market.Share.... Regional.5G.Coverage....
## Min. : 5309 Min. : 2987436 Min. :-0.490 Min. : 25.34
## 1st Qu.:19327 1st Qu.:14607494 1st Qu.: 2.635 1st Qu.: 50.40
## Median :33689 Median :28012005 Median : 3.760 Median : 67.05
## Mean :32843 Mean :30197332 Mean : 3.724 Mean : 66.89
## 3rd Qu.:43911 3rd Qu.:41803911 3rd Qu.: 5.282 3rd Qu.: 83.21
## Max. :64883 Max. :84264944 Max. : 6.950 Max. :103.92
## X5G.Subscribers..millions. Avg.5G.Speed..Mbps. Preference.for.5G....
## Min. :-0.89 Min. : 50.37 Min. :37.14
## 1st Qu.:18.41 1st Qu.:120.41 1st Qu.:53.27
## Median :29.91 Median :177.39 Median :66.96
## Mean :30.15 Mean :179.23 Mean :67.14
## 3rd Qu.:44.36 3rd Qu.:238.86 3rd Qu.:80.99
## Max. :54.94 Max. :298.70 Max. :94.84
## Region
## Length:1000
## Class :character
## Mode :character
##
##
## Data Pre-processing
1. Checking missing value
First thing to do data pre-processing is checking missing values. Missing values could greatly affect the data visualization, that is why we are trying to remove it by using summarise-across-everything command.
samsung_sales %>%
summarise(across(everything(), ~sum(is.na(.))))## Year Quarter Product.Model X5G.Capability Units.Sold Revenue....
## 1 0 0 0 0 0 0
## Market.Share.... Regional.5G.Coverage.... X5G.Subscribers..millions.
## 1 0 0 0
## Avg.5G.Speed..Mbps. Preference.for.5G.... Region
## 1 0 0 02. Checking duplicates
The next step is to check duplicates. First of all, we want to know how many duplicates we have, then we want to see in which row those duplicates exist using filter command. Once we know all of that, we make a new variable called samsung_sales_clean which is a clean version of samsung_sales with pipeline and distinct command to search the unique rows.
samsung_sales %>%
duplicated() %>%
sum()## [1] 640samsung_sales %>% filter(duplicated(.))## Year Quarter Product.Model X5G.Capability Units.Sold Revenue....
## 1 2019 Q1 Galaxy S10 No 26396 4212951
## 2 2019 Q1 Galaxy Note10 No 25671 7240266
## 3 2019 Q1 Galaxy S20 No 16573 25608332
## 4 2019 Q1 Galaxy Note20 No 7177 21984416
## 5 2019 Q1 Galaxy S21 No 45633 16342438
## 6 2019 Q1 Galaxy A32 5G Yes 15912 17178327
## 7 2019 Q1 Galaxy A52 5G Yes 7231 48639810
## 8 2019 Q1 Galaxy A73 5G Yes 58711 70175779
## 9 2019 Q1 Galaxy Z Fold2 5G Yes 40641 51959556
## 10 2019 Q1 Galaxy Z Flip3 5G Yes 38119 49899541
## 11 2019 Q1 Galaxy S22 5G Yes 36255 12091674
## 12 2019 Q1 Galaxy Z Fold3 5G Yes 19643 36029499
## 13 2019 Q1 Galaxy A14 5G Yes 12612 57161552
## 14 2019 Q1 Galaxy S23 5G Yes 22722 45310947
## 15 2019 Q1 Galaxy Z Flip5 5G Yes 8453 46937282
## 16 2019 Q2 Galaxy S10 No 19160 6660065
## 17 2019 Q2 Galaxy Note10 No 38253 53195937
## 18 2019 Q2 Galaxy S20 No 17404 14950209
## 19 2019 Q2 Galaxy Note20 No 27841 9787427
## 20 2019 Q2 Galaxy S21 No 10047 10691517
## 21 2019 Q2 Galaxy A32 5G Yes 25054 40495944
## 22 2019 Q2 Galaxy A52 5G Yes 37263 29763310
## 23 2019 Q2 Galaxy A73 5G Yes 39191 19631909
## 24 2019 Q2 Galaxy Z Fold2 5G Yes 11320 36678246
## 25 2019 Q2 Galaxy Z Flip3 5G Yes 31105 12818738
## 26 2019 Q2 Galaxy S22 5G Yes 24185 52192973
## 27 2019 Q2 Galaxy Z Fold3 5G Yes 8422 11292488
## 28 2019 Q2 Galaxy A14 5G Yes 56397 18390884
## 29 2019 Q2 Galaxy S23 5G Yes 36613 63070768
## 30 2019 Q2 Galaxy Z Flip5 5G Yes 12030 33549566
## 31 2019 Q3 Galaxy S10 No 27112 27051338
## 32 2019 Q3 Galaxy Note10 No 28788 10130231
## 33 2019 Q3 Galaxy S20 No 29329 11066723
## 34 2019 Q3 Galaxy Note20 No 12535 28466176
## 35 2019 Q3 Galaxy S21 No 20683 15828499
## 36 2019 Q3 Galaxy A32 5G Yes 57404 22248957
## 37 2019 Q3 Galaxy A52 5G Yes 56869 78970161
## 38 2019 Q3 Galaxy A73 5G Yes 6542 13222481
## 39 2019 Q3 Galaxy Z Fold2 5G Yes 57951 41600297
## 40 2019 Q3 Galaxy Z Flip3 5G Yes 25004 28940719
## 41 2019 Q3 Galaxy S22 5G Yes 15756 19796586
## 42 2019 Q3 Galaxy Z Fold3 5G Yes 56418 53656998
## 43 2019 Q3 Galaxy A14 5G Yes 30819 60274563
## 44 2019 Q3 Galaxy S23 5G Yes 18284 26788293
## 45 2019 Q3 Galaxy Z Flip5 5G Yes 48617 6134803
## 46 2019 Q4 Galaxy S10 No 30040 34166065
## 47 2019 Q4 Galaxy Note10 No 40978 52202051
## 48 2019 Q4 Galaxy S20 No 46113 23485210
## 49 2019 Q4 Galaxy Note20 No 8438 35324704
## 50 2019 Q4 Galaxy S21 No 23500 33337346
## 51 2019 Q4 Galaxy A32 5G Yes 40164 21739756
## 52 2019 Q4 Galaxy A52 5G Yes 53367 69827344
## 53 2019 Q4 Galaxy A73 5G Yes 37612 34802847
## 54 2019 Q4 Galaxy Z Fold2 5G Yes 31445 62662105
## 55 2019 Q4 Galaxy Z Flip3 5G Yes 34541 23308776
## 56 2019 Q4 Galaxy S22 5G Yes 6719 38311286
## 57 2019 Q4 Galaxy Z Fold3 5G Yes 32919 38143238
## 58 2019 Q4 Galaxy A14 5G Yes 46371 71988710
## 59 2019 Q4 Galaxy S23 5G Yes 40084 17163853
## 60 2019 Q4 Galaxy Z Flip5 5G Yes 20192 14639183
## 61 2020 Q1 Galaxy S10 No 17825 37347188
## 62 2020 Q1 Galaxy Note10 No 21472 34965645
## 63 2020 Q1 Galaxy S20 No 35641 48775290
## 64 2020 Q1 Galaxy Note20 No 18960 25972084
## 65 2020 Q1 Galaxy S21 No 18546 42315160
## 66 2020 Q1 Galaxy A32 5G Yes 42883 18588730
## 67 2020 Q1 Galaxy A52 5G Yes 49662 52134225
## 68 2020 Q1 Galaxy A73 5G Yes 48484 10752134
## 69 2020 Q1 Galaxy Z Fold2 5G Yes 11438 56002244
## 70 2020 Q1 Galaxy Z Flip3 5G Yes 58199 35481161
## 71 2020 Q1 Galaxy S22 5G Yes 40807 28409469
## 72 2020 Q1 Galaxy Z Fold3 5G Yes 27476 29188077
## 73 2020 Q1 Galaxy A14 5G Yes 25491 13470115
## 74 2020 Q1 Galaxy S23 5G Yes 57319 64038833
## 75 2020 Q1 Galaxy Z Flip5 5G Yes 53118 71564620
## 76 2020 Q2 Galaxy S10 No 21197 22480526
## 77 2020 Q2 Galaxy Note10 No 8785 16339172
## 78 2020 Q2 Galaxy S20 No 43775 10852646
## 79 2020 Q2 Galaxy Note20 No 29178 41803911
## 80 2020 Q2 Galaxy S21 No 8601 32922566
## 81 2020 Q2 Galaxy A32 5G Yes 51214 22604101
## 82 2020 Q2 Galaxy A52 5G Yes 58086 50254952
## 83 2020 Q2 Galaxy A73 5G Yes 63974 66783552
## 84 2020 Q2 Galaxy Z Fold2 5G Yes 47829 6077295
## 85 2020 Q2 Galaxy Z Flip3 5G Yes 41163 19005446
## 86 2020 Q2 Galaxy S22 5G Yes 27381 65294983
## 87 2020 Q2 Galaxy Z Fold3 5G Yes 61462 55137531
## 88 2020 Q2 Galaxy A14 5G Yes 10455 27982805
## 89 2020 Q2 Galaxy S23 5G Yes 53896 47982929
## 90 2020 Q2 Galaxy Z Flip5 5G Yes 47830 79207664
## 91 2020 Q3 Galaxy S10 No 27328 31739576
## 92 2020 Q3 Galaxy Note10 No 38772 27841227
## 93 2020 Q3 Galaxy S20 No 7511 31385211
## 94 2020 Q3 Galaxy Note20 No 16212 59842662
## 95 2020 Q3 Galaxy S21 No 44767 6397386
## 96 2020 Q3 Galaxy A32 5G Yes 29628 50812407
## 97 2020 Q3 Galaxy A52 5G Yes 63137 23567298
## 98 2020 Q3 Galaxy A73 5G Yes 56115 16158944
## 99 2020 Q3 Galaxy Z Fold2 5G Yes 33872 60050677
## 100 2020 Q3 Galaxy Z Flip3 5G Yes 56598 9650778
## 101 2020 Q3 Galaxy S22 5G Yes 8082 53518254
## 102 2020 Q3 Galaxy Z Fold3 5G Yes 53108 8982958
## 103 2020 Q3 Galaxy A14 5G Yes 42820 5518854
## 104 2020 Q3 Galaxy S23 5G Yes 55164 9027241
## 105 2020 Q3 Galaxy Z Flip5 5G Yes 12993 7444019
## 106 2020 Q4 Galaxy S10 No 49522 13749933
## 107 2020 Q4 Galaxy Note10 No 35672 43822055
## 108 2020 Q4 Galaxy S20 No 23896 18747121
## 109 2020 Q4 Galaxy Note20 No 7473 19621771
## 110 2020 Q4 Galaxy S21 No 11913 41627181
## 111 2020 Q4 Galaxy A32 5G Yes 39692 34859772
## 112 2020 Q4 Galaxy A52 5G Yes 48805 32349455
## 113 2020 Q4 Galaxy A73 5G Yes 8115 44826834
## 114 2020 Q4 Galaxy Z Fold2 5G Yes 52951 13524138
## 115 2020 Q4 Galaxy Z Flip3 5G Yes 40353 16476389
## 116 2020 Q4 Galaxy S22 5G Yes 26015 13548942
## 117 2020 Q4 Galaxy Z Fold3 5G Yes 11163 12032305
## 118 2020 Q4 Galaxy A14 5G Yes 36975 33358885
## 119 2020 Q4 Galaxy S23 5G Yes 28163 76740548
## 120 2020 Q4 Galaxy Z Flip5 5G Yes 49246 5030392
## 121 2021 Q1 Galaxy S10 No 44456 14331276
## 122 2021 Q1 Galaxy Note10 No 6548 54168173
## 123 2021 Q1 Galaxy S20 No 36129 28657040
## 124 2021 Q1 Galaxy Note20 No 36476 9769649
## 125 2021 Q1 Galaxy S21 No 34090 7777936
## 126 2021 Q1 Galaxy A32 5G Yes 30278 12377641
## 127 2021 Q1 Galaxy A52 5G Yes 57898 19848318
## 128 2021 Q1 Galaxy A73 5G Yes 44847 14310727
## 129 2021 Q1 Galaxy Z Fold2 5G Yes 6557 41433408
## 130 2021 Q1 Galaxy Z Flip3 5G Yes 58334 67042799
## 131 2021 Q1 Galaxy S22 5G Yes 42025 46339655
## 132 2021 Q1 Galaxy Z Fold3 5G Yes 8612 30287875
## 133 2021 Q1 Galaxy A14 5G Yes 33689 37582038
## 134 2021 Q1 Galaxy S23 5G Yes 60093 79592293
## 135 2021 Q1 Galaxy Z Flip5 5G Yes 50612 24547804
## 136 2021 Q2 Galaxy S10 No 33392 40200176
## 137 2021 Q2 Galaxy Note10 No 20351 10720475
## 138 2021 Q2 Galaxy S20 No 9689 18237818
## 139 2021 Q2 Galaxy Note20 No 19360 21067766
## 140 2021 Q2 Galaxy S21 No 41937 10442281
## 141 2021 Q2 Galaxy A32 5G Yes 26634 17977115
## 142 2021 Q2 Galaxy A52 5G Yes 36653 38555751
## 143 2021 Q2 Galaxy A73 5G Yes 61958 56094434
## 144 2021 Q2 Galaxy Z Fold2 5G Yes 6501 38321093
## 145 2021 Q2 Galaxy Z Flip3 5G Yes 31267 31015491
## 146 2021 Q2 Galaxy S22 5G Yes 27446 66537701
## 147 2021 Q2 Galaxy Z Fold3 5G Yes 60479 80946999
## 148 2021 Q2 Galaxy A14 5G Yes 33576 15758345
## 149 2021 Q2 Galaxy S23 5G Yes 53719 32766472
## 150 2021 Q2 Galaxy Z Flip5 5G Yes 34710 17998845
## 151 2021 Q3 Galaxy S10 No 39031 18608941
## 152 2021 Q3 Galaxy Note10 No 34903 18518248
## 153 2021 Q3 Galaxy S20 No 35996 23537405
## 154 2021 Q3 Galaxy Note20 No 42820 29532856
## 155 2021 Q3 Galaxy S21 No 28881 21441365
## 156 2021 Q3 Galaxy A32 5G Yes 55419 10737538
## 157 2021 Q3 Galaxy A52 5G Yes 7872 5221237
## 158 2021 Q3 Galaxy A73 5G Yes 24758 51101598
## 159 2021 Q3 Galaxy Z Fold2 5G Yes 50384 16798330
## 160 2021 Q3 Galaxy Z Flip3 5G Yes 8281 15827206
## 161 2021 Q3 Galaxy S22 5G Yes 54330 37269680
## 162 2021 Q3 Galaxy Z Fold3 5G Yes 32683 17951924
## 163 2021 Q3 Galaxy A14 5G Yes 58052 45303231
## 164 2021 Q3 Galaxy S23 5G Yes 28022 33044277
## 165 2021 Q3 Galaxy Z Flip5 5G Yes 55987 5330567
## 166 2021 Q4 Galaxy S10 No 5309 30375440
## 167 2021 Q4 Galaxy Note10 No 39415 15774523
## 168 2021 Q4 Galaxy S20 No 7734 10384187
## 169 2021 Q4 Galaxy Note20 No 40180 8451472
## 170 2021 Q4 Galaxy S21 No 39509 57709592
## 171 2021 Q4 Galaxy A32 5G Yes 7653 25848408
## 172 2021 Q4 Galaxy A52 5G Yes 10112 41338292
## 173 2021 Q4 Galaxy A73 5G Yes 63494 10304717
## 174 2021 Q4 Galaxy Z Fold2 5G Yes 40197 47733242
## 175 2021 Q4 Galaxy Z Flip3 5G Yes 28000 39976459
## 176 2021 Q4 Galaxy S22 5G Yes 30537 33782555
## 177 2021 Q4 Galaxy Z Fold3 5G Yes 16890 10789418
## 178 2021 Q4 Galaxy A14 5G Yes 50895 58673135
## 179 2021 Q4 Galaxy S23 5G Yes 14293 84264944
## 180 2021 Q4 Galaxy Z Flip5 5G Yes 18172 7206945
## 181 2022 Q1 Galaxy S10 No 23678 23302026
## 182 2022 Q1 Galaxy Note10 No 24682 30388421
## 183 2022 Q1 Galaxy S20 No 49869 25015562
## 184 2022 Q1 Galaxy Note20 No 5718 47611431
## 185 2022 Q1 Galaxy S21 No 35233 10414206
## 186 2022 Q1 Galaxy A32 5G Yes 41857 71129084
## 187 2022 Q1 Galaxy A52 5G Yes 33814 62983633
## 188 2022 Q1 Galaxy A73 5G Yes 35418 14482116
## 189 2022 Q1 Galaxy Z Fold2 5G Yes 39933 18632947
## 190 2022 Q1 Galaxy Z Flip3 5G Yes 6800 6213700
## 191 2022 Q1 Galaxy S22 5G Yes 6787 52571042
## 192 2022 Q1 Galaxy Z Fold3 5G Yes 34677 15661457
## 193 2022 Q1 Galaxy A14 5G Yes 27735 60038351
## 194 2022 Q1 Galaxy S23 5G Yes 51810 14517046
## 195 2022 Q1 Galaxy Z Flip5 5G Yes 46166 19455487
## 196 2022 Q2 Galaxy S10 No 47075 9011313
## 197 2022 Q2 Galaxy Note10 No 5465 14185150
## 198 2022 Q2 Galaxy S20 No 22036 14156371
## 199 2022 Q2 Galaxy Note20 No 31388 15148576
## 200 2022 Q2 Galaxy S21 No 33806 23699379
## 201 2022 Q2 Galaxy A32 5G Yes 46798 34010965
## 202 2022 Q2 Galaxy A52 5G Yes 15007 56176072
## 203 2022 Q2 Galaxy A73 5G Yes 10585 29685804
## 204 2022 Q2 Galaxy Z Fold2 5G Yes 10415 16056994
## 205 2022 Q2 Galaxy Z Flip3 5G Yes 54159 43416974
## 206 2022 Q2 Galaxy S22 5G Yes 17217 31929960
## 207 2022 Q2 Galaxy Z Fold3 5G Yes 53719 15328592
## 208 2022 Q2 Galaxy A14 5G Yes 58156 45419754
## 209 2022 Q2 Galaxy S23 5G Yes 33117 5432436
## 210 2022 Q2 Galaxy Z Flip5 5G Yes 24948 7564108
## 211 2022 Q3 Galaxy S10 No 10671 9842035
## 212 2022 Q3 Galaxy Note10 No 27006 5136020
## 213 2022 Q3 Galaxy S20 No 35658 14607494
## 214 2022 Q3 Galaxy Note20 No 8330 26191928
## 215 2022 Q3 Galaxy S21 No 32243 28519800
## 216 2022 Q3 Galaxy A32 5G Yes 49871 8902985
## 217 2022 Q3 Galaxy A52 5G Yes 39287 21438285
## 218 2022 Q3 Galaxy A73 5G Yes 43668 32615704
## 219 2022 Q3 Galaxy Z Fold2 5G Yes 64883 70601770
## 220 2022 Q3 Galaxy Z Flip3 5G Yes 29698 9547956
## 221 2022 Q3 Galaxy S22 5G Yes 16005 41833224
## 222 2022 Q3 Galaxy Z Fold3 5G Yes 55071 52340366
## 223 2022 Q3 Galaxy A14 5G Yes 55853 6813707
## 224 2022 Q3 Galaxy S23 5G Yes 47291 19328485
## 225 2022 Q3 Galaxy Z Flip5 5G Yes 30901 11882937
## 226 2022 Q4 Galaxy S10 No 8330 4252117
## 227 2022 Q4 Galaxy Note10 No 16902 45919819
## 228 2022 Q4 Galaxy S20 No 10352 10351377
## 229 2022 Q4 Galaxy Note20 No 20904 41945285
## 230 2022 Q4 Galaxy S21 No 32799 30084812
## 231 2022 Q4 Galaxy A32 5G Yes 17884 11319455
## 232 2022 Q4 Galaxy A52 5G Yes 40963 10902531
## 233 2022 Q4 Galaxy A73 5G Yes 15691 21823040
## 234 2022 Q4 Galaxy Z Fold2 5G Yes 23653 24085328
## 235 2022 Q4 Galaxy Z Flip3 5G Yes 56264 11515466
## 236 2022 Q4 Galaxy S22 5G Yes 47629 40258148
## 237 2022 Q4 Galaxy Z Fold3 5G Yes 22829 33124655
## 238 2022 Q4 Galaxy A14 5G Yes 33785 47573855
## 239 2022 Q4 Galaxy S23 5G Yes 28676 33319539
## 240 2022 Q4 Galaxy Z Flip5 5G Yes 62215 44729536
## 241 2023 Q1 Galaxy S10 No 40947 29431350
## 242 2023 Q1 Galaxy Note10 No 14664 15195951
## 243 2023 Q1 Galaxy S20 No 43817 24176526
## 244 2023 Q1 Galaxy Note20 No 41459 13687955
## 245 2023 Q1 Galaxy S21 No 32918 22481075
## 246 2023 Q1 Galaxy A32 5G Yes 42266 25950100
## 247 2023 Q1 Galaxy A52 5G Yes 35503 31844795
## 248 2023 Q1 Galaxy A73 5G Yes 47694 18903891
## 249 2023 Q1 Galaxy Z Fold2 5G Yes 36500 11340926
## 250 2023 Q1 Galaxy Z Flip3 5G Yes 31535 75369161
## 251 2023 Q1 Galaxy S22 5G Yes 25428 41184239
## 252 2023 Q1 Galaxy Z Fold3 5G Yes 58459 6197608
## 253 2023 Q1 Galaxy A14 5G Yes 39916 30964782
## 254 2023 Q1 Galaxy S23 5G Yes 56785 37101919
## 255 2023 Q1 Galaxy Z Flip5 5G Yes 11658 14130689
## 256 2023 Q2 Galaxy S10 No 28445 23846107
## 257 2023 Q2 Galaxy Note10 No 12074 10953766
## 258 2023 Q2 Galaxy S20 No 24787 11158723
## 259 2023 Q2 Galaxy Note20 No 40418 32030343
## 260 2023 Q2 Galaxy S21 No 34655 5500615
## 261 2023 Q2 Galaxy A32 5G Yes 18720 45175723
## 262 2023 Q2 Galaxy A52 5G Yes 7111 38171300
## 263 2023 Q2 Galaxy A73 5G Yes 22044 31012290
## 264 2023 Q2 Galaxy Z Fold2 5G Yes 14004 7989842
## 265 2023 Q2 Galaxy Z Flip3 5G Yes 55851 19401460
## 266 2023 Q2 Galaxy S22 5G Yes 43275 7555516
## 267 2023 Q2 Galaxy Z Fold3 5G Yes 14970 18515549
## 268 2023 Q2 Galaxy A14 5G Yes 37850 11674639
## 269 2023 Q2 Galaxy S23 5G Yes 12105 51838069
## 270 2023 Q2 Galaxy Z Flip5 5G Yes 43400 25193545
## 271 2023 Q3 Galaxy S10 No 45735 2987436
## 272 2023 Q3 Galaxy Note10 No 14799 4874860
## 273 2023 Q3 Galaxy S20 No 42940 4424999
## 274 2023 Q3 Galaxy Note20 No 46321 12172734
## 275 2023 Q3 Galaxy S21 No 22101 45898246
## 276 2023 Q3 Galaxy A32 5G Yes 18578 35842355
## 277 2023 Q3 Galaxy A52 5G Yes 63804 26099538
## 278 2023 Q3 Galaxy A73 5G Yes 60353 10771601
## 279 2023 Q3 Galaxy Z Fold2 5G Yes 56014 38203294
## 280 2023 Q3 Galaxy Z Flip3 5G Yes 30009 27935762
## 281 2023 Q3 Galaxy S22 5G Yes 11176 41387014
## 282 2023 Q3 Galaxy Z Fold3 5G Yes 35816 11652906
## 283 2023 Q3 Galaxy A14 5G Yes 51399 42332669
## 284 2023 Q3 Galaxy S23 5G Yes 25724 4967037
## 285 2023 Q3 Galaxy Z Flip5 5G Yes 19229 37535868
## 286 2023 Q4 Galaxy S10 No 40560 10240842
## 287 2023 Q4 Galaxy Note10 No 35697 19462565
## 288 2023 Q4 Galaxy S20 No 47421 4828393
## 289 2023 Q4 Galaxy Note20 No 36328 33957659
## 290 2023 Q4 Galaxy S21 No 7177 12756980
## 291 2023 Q4 Galaxy A32 5G Yes 26543 22616883
## 292 2023 Q4 Galaxy A52 5G Yes 56191 12240588
## 293 2023 Q4 Galaxy A73 5G Yes 31159 41780959
## 294 2023 Q4 Galaxy Z Fold2 5G Yes 61064 65387195
## 295 2023 Q4 Galaxy Z Flip3 5G Yes 34138 54539173
## 296 2023 Q4 Galaxy S22 5G Yes 36216 29959374
## 297 2023 Q4 Galaxy Z Fold3 5G Yes 56470 29368073
## 298 2023 Q4 Galaxy A14 5G Yes 50544 43782286
## 299 2023 Q4 Galaxy S23 5G Yes 8752 38989375
## 300 2023 Q4 Galaxy Z Flip5 5G Yes 52161 28251610
## 301 2024 Q1 Galaxy S10 No 37131 45587128
## 302 2024 Q1 Galaxy Note10 No 22947 10581556
## 303 2024 Q1 Galaxy S20 No 32783 24954403
## 304 2024 Q1 Galaxy Note20 No 11825 26416167
## 305 2024 Q1 Galaxy S21 No 11094 32422221
## 306 2024 Q1 Galaxy A32 5G Yes 54883 29105715
## 307 2024 Q1 Galaxy A52 5G Yes 31220 36722606
## 308 2024 Q1 Galaxy A73 5G Yes 11255 40448790
## 309 2024 Q1 Galaxy Z Fold2 5G Yes 62968 36037742
## 310 2024 Q1 Galaxy Z Flip3 5G Yes 57073 27851666
## 311 2024 Q1 Galaxy S22 5G Yes 43911 45501963
## 312 2024 Q1 Galaxy Z Fold3 5G Yes 6628 36995469
## 313 2024 Q1 Galaxy A14 5G Yes 54366 58337180
## 314 2024 Q1 Galaxy S23 5G Yes 43162 43400861
## 315 2024 Q1 Galaxy Z Flip5 5G Yes 24624 36547058
## 316 2024 Q2 Galaxy S10 No 26237 30930456
## 317 2024 Q2 Galaxy Note10 No 22269 16275671
## 318 2024 Q2 Galaxy S20 No 27314 25415809
## 319 2024 Q2 Galaxy Note20 No 18938 16203613
## 320 2024 Q2 Galaxy S21 No 14478 46356728
## 321 2024 Q2 Galaxy A32 5G Yes 64381 8891741
## 322 2024 Q2 Galaxy A52 5G Yes 33515 35787764
## 323 2024 Q2 Galaxy A73 5G Yes 14587 28041205
## 324 2024 Q2 Galaxy Z Fold2 5G Yes 47712 18117011
## 325 2024 Q2 Galaxy Z Flip3 5G Yes 24276 82025137
## 326 2024 Q2 Galaxy S22 5G Yes 28349 29473665
## 327 2024 Q2 Galaxy Z Fold3 5G Yes 33169 35954900
## 328 2024 Q2 Galaxy A14 5G Yes 7463 54322195
## 329 2024 Q2 Galaxy S23 5G Yes 16723 10462399
## 330 2024 Q2 Galaxy Z Flip5 5G Yes 44327 51331263
## 331 2024 Q3 Galaxy S10 No 32671 47591497
## 332 2024 Q3 Galaxy Note10 No 40214 41136988
## 333 2024 Q3 Galaxy S20 No 5564 59801417
## 334 2024 Q3 Galaxy Note20 No 19903 17437927
## 335 2024 Q3 Galaxy S21 No 21386 36904340
## 336 2024 Q3 Galaxy A32 5G Yes 27969 70164648
## 337 2024 Q3 Galaxy A52 5G Yes 37433 18338586
## 338 2024 Q3 Galaxy A73 5G Yes 39286 60220617
## 339 2024 Q3 Galaxy Z Fold2 5G Yes 53528 21387152
## 340 2024 Q3 Galaxy Z Flip3 5G Yes 35291 49226395
## 341 2024 Q3 Galaxy S22 5G Yes 59709 24247149
## 342 2024 Q3 Galaxy Z Fold3 5G Yes 43568 7815666
## 343 2024 Q3 Galaxy A14 5G Yes 14266 34815442
## 344 2024 Q3 Galaxy S23 5G Yes 15763 7376209
## 345 2024 Q3 Galaxy Z Flip5 5G Yes 37683 25975312
## 346 2024 Q4 Galaxy S10 No 9805 4494602
## 347 2024 Q4 Galaxy Note10 No 39017 15031174
## 348 2024 Q4 Galaxy S20 No 7534 29771391
## 349 2024 Q4 Galaxy Note20 No 42266 23557755
## 350 2024 Q4 Galaxy S21 No 28852 14365866
## 351 2024 Q4 Galaxy A32 5G Yes 25724 57565237
## 352 2024 Q4 Galaxy A52 5G Yes 10554 21897426
## 353 2024 Q4 Galaxy A73 5G Yes 63966 35148376
## 354 2024 Q4 Galaxy Z Fold2 5G Yes 50336 24239867
## 355 2024 Q4 Galaxy Z Flip3 5G Yes 35834 8672653
## 356 2024 Q4 Galaxy S22 5G Yes 28368 17639256
## 357 2024 Q4 Galaxy Z Fold3 5G Yes 31313 33616387
## 358 2024 Q4 Galaxy A14 5G Yes 10175 31584424
## 359 2024 Q4 Galaxy S23 5G Yes 27459 48297091
## 360 2024 Q4 Galaxy Z Flip5 5G Yes 23576 7378715
## 361 2019 Q3 Galaxy A32 5G Yes 57404 22248957
## 362 2021 Q4 Galaxy Note10 No 39415 15774523
## 363 2022 Q3 Galaxy S22 5G Yes 16005 41833224
## 364 2022 Q3 Galaxy Note20 No 8330 26191928
## 365 2021 Q1 Galaxy S10 No 44456 14331276
## 366 2020 Q2 Galaxy A73 5G Yes 63974 66783552
## 367 2019 Q1 Galaxy S22 5G Yes 36255 12091674
## 368 2020 Q2 Galaxy A32 5G Yes 51214 22604101
## 369 2021 Q2 Galaxy S22 5G Yes 27446 66537701
## 370 2021 Q4 Galaxy S22 5G Yes 30537 33782555
## 371 2021 Q3 Galaxy A73 5G Yes 24758 51101598
## 372 2020 Q1 Galaxy Z Fold2 5G Yes 11438 56002244
## 373 2024 Q1 Galaxy Z Fold3 5G Yes 6628 36995469
## 374 2021 Q2 Galaxy A32 5G Yes 26634 17977115
## 375 2019 Q4 Galaxy A32 5G Yes 40164 21739756
## 376 2020 Q4 Galaxy Note10 No 35672 43822055
## 377 2024 Q2 Galaxy A73 5G Yes 14587 28041205
## 378 2023 Q2 Galaxy S20 No 24787 11158723
## 379 2021 Q2 Galaxy Z Flip3 5G Yes 31267 31015491
## 380 2021 Q2 Galaxy Note20 No 19360 21067766
## 381 2019 Q1 Galaxy Z Flip3 5G Yes 38119 49899541
## 382 2021 Q2 Galaxy Z Flip3 5G Yes 31267 31015491
## 383 2023 Q4 Galaxy S20 No 47421 4828393
## 384 2021 Q3 Galaxy S10 No 39031 18608941
## 385 2021 Q1 Galaxy S23 5G Yes 60093 79592293
## 386 2024 Q1 Galaxy Z Fold2 5G Yes 62968 36037742
## 387 2024 Q2 Galaxy Z Flip5 5G Yes 44327 51331263
## 388 2022 Q1 Galaxy Z Flip3 5G Yes 6800 6213700
## 389 2023 Q1 Galaxy Note20 No 41459 13687955
## 390 2020 Q4 Galaxy Note10 No 35672 43822055
## 391 2022 Q3 Galaxy S21 No 32243 28519800
## 392 2021 Q1 Galaxy A52 5G Yes 57898 19848318
## 393 2021 Q3 Galaxy Note20 No 42820 29532856
## 394 2021 Q2 Galaxy S10 No 33392 40200176
## 395 2022 Q3 Galaxy Z Fold2 5G Yes 64883 70601770
## 396 2021 Q1 Galaxy Note10 No 6548 54168173
## 397 2019 Q4 Galaxy A52 5G Yes 53367 69827344
## 398 2019 Q3 Galaxy S22 5G Yes 15756 19796586
## 399 2022 Q4 Galaxy A14 5G Yes 33785 47573855
## 400 2021 Q2 Galaxy Z Flip5 5G Yes 34710 17998845
## 401 2021 Q2 Galaxy A52 5G Yes 36653 38555751
## 402 2020 Q2 Galaxy Z Fold3 5G Yes 61462 55137531
## 403 2023 Q1 Galaxy A52 5G Yes 35503 31844795
## 404 2019 Q3 Galaxy Z Fold3 5G Yes 56418 53656998
## 405 2023 Q4 Galaxy Z Flip3 5G Yes 34138 54539173
## 406 2021 Q1 Galaxy S21 No 34090 7777936
## 407 2023 Q3 Galaxy Note10 No 14799 4874860
## 408 2020 Q3 Galaxy Z Fold2 5G Yes 33872 60050677
## 409 2019 Q1 Galaxy Note10 No 25671 7240266
## 410 2020 Q1 Galaxy Z Flip5 5G Yes 53118 71564620
## 411 2023 Q2 Galaxy Note20 No 40418 32030343
## 412 2024 Q3 Galaxy S22 5G Yes 59709 24247149
## 413 2021 Q2 Galaxy S21 No 41937 10442281
## 414 2023 Q3 Galaxy A32 5G Yes 18578 35842355
## 415 2021 Q4 Galaxy A32 5G Yes 7653 25848408
## 416 2019 Q1 Galaxy S10 No 26396 4212951
## 417 2022 Q2 Galaxy A52 5G Yes 15007 56176072
## 418 2021 Q2 Galaxy A14 5G Yes 33576 15758345
## 419 2023 Q2 Galaxy S20 No 24787 11158723
## 420 2019 Q1 Galaxy Note20 No 7177 21984416
## 421 2021 Q3 Galaxy Note10 No 34903 18518248
## 422 2023 Q1 Galaxy Note10 No 14664 15195951
## 423 2024 Q4 Galaxy S20 No 7534 29771391
## 424 2021 Q1 Galaxy Note20 No 36476 9769649
## 425 2021 Q3 Galaxy Z Fold2 5G Yes 50384 16798330
## 426 2020 Q2 Galaxy S21 No 8601 32922566
## 427 2023 Q2 Galaxy S10 No 28445 23846107
## 428 2023 Q4 Galaxy Z Flip3 5G Yes 34138 54539173
## 429 2021 Q2 Galaxy A14 5G Yes 33576 15758345
## 430 2022 Q3 Galaxy Z Fold3 5G Yes 55071 52340366
## 431 2024 Q4 Galaxy S23 5G Yes 27459 48297091
## 432 2022 Q2 Galaxy Note10 No 5465 14185150
## 433 2021 Q2 Galaxy A14 5G Yes 33576 15758345
## 434 2022 Q2 Galaxy A52 5G Yes 15007 56176072
## 435 2024 Q4 Galaxy Z Fold2 5G Yes 50336 24239867
## 436 2023 Q3 Galaxy Z Flip3 5G Yes 30009 27935762
## 437 2024 Q2 Galaxy A52 5G Yes 33515 35787764
## 438 2021 Q3 Galaxy S22 5G Yes 54330 37269680
## 439 2023 Q3 Galaxy A14 5G Yes 51399 42332669
## 440 2024 Q4 Galaxy A32 5G Yes 25724 57565237
## 441 2019 Q3 Galaxy Z Fold2 5G Yes 57951 41600297
## 442 2020 Q4 Galaxy A52 5G Yes 48805 32349455
## 443 2020 Q3 Galaxy A52 5G Yes 63137 23567298
## 444 2024 Q2 Galaxy A73 5G Yes 14587 28041205
## 445 2023 Q3 Galaxy A32 5G Yes 18578 35842355
## 446 2020 Q3 Galaxy Z Flip3 5G Yes 56598 9650778
## 447 2021 Q2 Galaxy Z Fold3 5G Yes 60479 80946999
## 448 2019 Q2 Galaxy S20 No 17404 14950209
## 449 2021 Q4 Galaxy Z Fold2 5G Yes 40197 47733242
## 450 2022 Q4 Galaxy Z Fold3 5G Yes 22829 33124655
## 451 2019 Q1 Galaxy Note10 No 25671 7240266
## 452 2021 Q3 Galaxy S10 No 39031 18608941
## 453 2022 Q3 Galaxy A73 5G Yes 43668 32615704
## 454 2020 Q1 Galaxy Note10 No 21472 34965645
## 455 2019 Q2 Galaxy S23 5G Yes 36613 63070768
## 456 2019 Q4 Galaxy Note20 No 8438 35324704
## 457 2020 Q4 Galaxy A14 5G Yes 36975 33358885
## 458 2022 Q2 Galaxy Note10 No 5465 14185150
## 459 2022 Q1 Galaxy A52 5G Yes 33814 62983633
## 460 2019 Q1 Galaxy Z Flip3 5G Yes 38119 49899541
## 461 2024 Q4 Galaxy Z Flip3 5G Yes 35834 8672653
## 462 2022 Q2 Galaxy Note10 No 5465 14185150
## 463 2019 Q3 Galaxy S23 5G Yes 18284 26788293
## 464 2020 Q3 Galaxy A52 5G Yes 63137 23567298
## 465 2019 Q2 Galaxy Z Flip3 5G Yes 31105 12818738
## 466 2023 Q2 Galaxy Note20 No 40418 32030343
## 467 2019 Q2 Galaxy S23 5G Yes 36613 63070768
## 468 2023 Q1 Galaxy A52 5G Yes 35503 31844795
## 469 2020 Q3 Galaxy S21 No 44767 6397386
## 470 2024 Q1 Galaxy S22 5G Yes 43911 45501963
## 471 2019 Q3 Galaxy Note20 No 12535 28466176
## 472 2021 Q1 Galaxy Note20 No 36476 9769649
## 473 2020 Q3 Galaxy A32 5G Yes 29628 50812407
## 474 2022 Q4 Galaxy S21 No 32799 30084812
## 475 2022 Q1 Galaxy S21 No 35233 10414206
## 476 2024 Q3 Galaxy Z Flip5 5G Yes 37683 25975312
## 477 2019 Q3 Galaxy S21 No 20683 15828499
## 478 2020 Q2 Galaxy Note10 No 8785 16339172
## 479 2024 Q2 Galaxy A52 5G Yes 33515 35787764
## 480 2023 Q3 Galaxy Z Fold3 5G Yes 35816 11652906
## 481 2022 Q1 Galaxy S21 No 35233 10414206
## 482 2024 Q4 Galaxy S22 5G Yes 28368 17639256
## 483 2022 Q2 Galaxy Z Flip3 5G Yes 54159 43416974
## 484 2019 Q3 Galaxy S10 No 27112 27051338
## 485 2023 Q1 Galaxy S22 5G Yes 25428 41184239
## 486 2024 Q3 Galaxy Note10 No 40214 41136988
## 487 2020 Q1 Galaxy A73 5G Yes 48484 10752134
## 488 2020 Q2 Galaxy S21 No 8601 32922566
## 489 2023 Q4 Galaxy Z Fold2 5G Yes 61064 65387195
## 490 2024 Q3 Galaxy S10 No 32671 47591497
## 491 2024 Q3 Galaxy Z Flip3 5G Yes 35291 49226395
## 492 2023 Q1 Galaxy Z Fold2 5G Yes 36500 11340926
## 493 2019 Q1 Galaxy Z Flip3 5G Yes 38119 49899541
## 494 2021 Q1 Galaxy Note20 No 36476 9769649
## 495 2023 Q3 Galaxy Z Fold3 5G Yes 35816 11652906
## 496 2020 Q2 Galaxy Z Flip5 5G Yes 47830 79207664
## 497 2022 Q1 Galaxy Z Flip3 5G Yes 6800 6213700
## 498 2020 Q1 Galaxy Z Fold2 5G Yes 11438 56002244
## 499 2023 Q1 Galaxy A32 5G Yes 42266 25950100
## 500 2022 Q1 Galaxy S21 No 35233 10414206
## 501 2022 Q2 Galaxy A14 5G Yes 58156 45419754
## 502 2022 Q3 Galaxy Note10 No 27006 5136020
## 503 2024 Q1 Galaxy Z Fold3 5G Yes 6628 36995469
## 504 2020 Q2 Galaxy Z Flip5 5G Yes 47830 79207664
## 505 2020 Q4 Galaxy Z Fold3 5G Yes 11163 12032305
## 506 2019 Q4 Galaxy S20 No 46113 23485210
## 507 2020 Q3 Galaxy Z Fold3 5G Yes 53108 8982958
## 508 2023 Q2 Galaxy Z Flip3 5G Yes 55851 19401460
## 509 2019 Q2 Galaxy Z Flip5 5G Yes 12030 33549566
## 510 2022 Q2 Galaxy Note10 No 5465 14185150
## 511 2021 Q1 Galaxy S21 No 34090 7777936
## 512 2022 Q3 Galaxy Note10 No 27006 5136020
## 513 2024 Q2 Galaxy Z Fold3 5G Yes 33169 35954900
## 514 2024 Q1 Galaxy Z Flip3 5G Yes 57073 27851666
## 515 2019 Q1 Galaxy Z Fold3 5G Yes 19643 36029499
## 516 2023 Q3 Galaxy A73 5G Yes 60353 10771601
## 517 2023 Q2 Galaxy S10 No 28445 23846107
## 518 2019 Q1 Galaxy Note10 No 25671 7240266
## 519 2021 Q4 Galaxy S22 5G Yes 30537 33782555
## 520 2019 Q1 Galaxy Z Fold2 5G Yes 40641 51959556
## 521 2020 Q1 Galaxy A32 5G Yes 42883 18588730
## 522 2024 Q2 Galaxy A14 5G Yes 7463 54322195
## 523 2021 Q3 Galaxy S22 5G Yes 54330 37269680
## 524 2022 Q1 Galaxy S22 5G Yes 6787 52571042
## 525 2023 Q4 Galaxy Z Flip5 5G Yes 52161 28251610
## 526 2019 Q4 Galaxy A32 5G Yes 40164 21739756
## 527 2023 Q4 Galaxy S22 5G Yes 36216 29959374
## 528 2020 Q4 Galaxy Note20 No 7473 19621771
## 529 2023 Q2 Galaxy Note20 No 40418 32030343
## 530 2020 Q3 Galaxy S21 No 44767 6397386
## 531 2023 Q4 Galaxy S22 5G Yes 36216 29959374
## 532 2020 Q4 Galaxy Note20 No 7473 19621771
## 533 2022 Q4 Galaxy Z Fold2 5G Yes 23653 24085328
## 534 2019 Q1 Galaxy A73 5G Yes 58711 70175779
## 535 2022 Q4 Galaxy Note10 No 16902 45919819
## 536 2023 Q2 Galaxy S22 5G Yes 43275 7555516
## 537 2020 Q3 Galaxy A73 5G Yes 56115 16158944
## 538 2020 Q3 Galaxy S20 No 7511 31385211
## 539 2019 Q2 Galaxy Z Flip3 5G Yes 31105 12818738
## 540 2021 Q4 Galaxy Z Fold3 5G Yes 16890 10789418
## 541 2023 Q3 Galaxy A32 5G Yes 18578 35842355
## 542 2021 Q3 Galaxy Z Fold2 5G Yes 50384 16798330
## 543 2022 Q2 Galaxy S23 5G Yes 33117 5432436
## 544 2020 Q3 Galaxy A73 5G Yes 56115 16158944
## 545 2024 Q4 Galaxy A73 5G Yes 63966 35148376
## 546 2024 Q3 Galaxy Z Flip5 5G Yes 37683 25975312
## 547 2019 Q1 Galaxy Z Fold3 5G Yes 19643 36029499
## 548 2021 Q2 Galaxy S21 No 41937 10442281
## 549 2023 Q3 Galaxy Z Flip5 5G Yes 19229 37535868
## 550 2019 Q1 Galaxy A73 5G Yes 58711 70175779
## 551 2023 Q4 Galaxy S20 No 47421 4828393
## 552 2024 Q1 Galaxy S10 No 37131 45587128
## 553 2021 Q2 Galaxy A32 5G Yes 26634 17977115
## 554 2023 Q3 Galaxy Z Fold3 5G Yes 35816 11652906
## 555 2019 Q3 Galaxy A14 5G Yes 30819 60274563
## 556 2021 Q4 Galaxy A14 5G Yes 50895 58673135
## 557 2021 Q1 Galaxy S20 No 36129 28657040
## 558 2019 Q3 Galaxy S10 No 27112 27051338
## 559 2022 Q3 Galaxy A32 5G Yes 49871 8902985
## 560 2020 Q2 Galaxy S23 5G Yes 53896 47982929
## 561 2022 Q1 Galaxy A32 5G Yes 41857 71129084
## 562 2022 Q2 Galaxy A52 5G Yes 15007 56176072
## 563 2019 Q3 Galaxy Note10 No 28788 10130231
## 564 2024 Q4 Galaxy Z Flip5 5G Yes 23576 7378715
## 565 2022 Q3 Galaxy Z Fold3 5G Yes 55071 52340366
## 566 2023 Q2 Galaxy S10 No 28445 23846107
## 567 2023 Q1 Galaxy Note20 No 41459 13687955
## 568 2019 Q2 Galaxy Z Fold3 5G Yes 8422 11292488
## 569 2019 Q3 Galaxy S21 No 20683 15828499
## 570 2019 Q1 Galaxy S20 No 16573 25608332
## 571 2020 Q4 Galaxy A52 5G Yes 48805 32349455
## 572 2019 Q2 Galaxy S22 5G Yes 24185 52192973
## 573 2021 Q4 Galaxy S22 5G Yes 30537 33782555
## 574 2022 Q4 Galaxy A73 5G Yes 15691 21823040
## 575 2021 Q2 Galaxy S10 No 33392 40200176
## 576 2020 Q1 Galaxy S21 No 18546 42315160
## 577 2022 Q3 Galaxy S20 No 35658 14607494
## 578 2020 Q2 Galaxy Note20 No 29178 41803911
## 579 2021 Q1 Galaxy A14 5G Yes 33689 37582038
## 580 2023 Q1 Galaxy Z Fold2 5G Yes 36500 11340926
## 581 2019 Q2 Galaxy S23 5G Yes 36613 63070768
## 582 2022 Q2 Galaxy A14 5G Yes 58156 45419754
## 583 2019 Q1 Galaxy S22 5G Yes 36255 12091674
## 584 2024 Q4 Galaxy Note20 No 42266 23557755
## 585 2020 Q1 Galaxy A52 5G Yes 49662 52134225
## 586 2024 Q4 Galaxy S10 No 9805 4494602
## 587 2022 Q1 Galaxy A52 5G Yes 33814 62983633
## 588 2021 Q1 Galaxy S22 5G Yes 42025 46339655
## 589 2019 Q1 Galaxy Z Flip3 5G Yes 38119 49899541
## 590 2022 Q2 Galaxy A73 5G Yes 10585 29685804
## 591 2021 Q2 Galaxy Z Flip5 5G Yes 34710 17998845
## 592 2020 Q3 Galaxy Note10 No 38772 27841227
## 593 2020 Q1 Galaxy S22 5G Yes 40807 28409469
## 594 2023 Q4 Galaxy A73 5G Yes 31159 41780959
## 595 2024 Q1 Galaxy S20 No 32783 24954403
## 596 2022 Q4 Galaxy S22 5G Yes 47629 40258148
## 597 2024 Q1 Galaxy S22 5G Yes 43911 45501963
## 598 2019 Q1 Galaxy S22 5G Yes 36255 12091674
## 599 2022 Q2 Galaxy S22 5G Yes 17217 31929960
## 600 2019 Q4 Galaxy S10 No 30040 34166065
## 601 2024 Q1 Galaxy Z Flip3 5G Yes 57073 27851666
## 602 2022 Q3 Galaxy S22 5G Yes 16005 41833224
## 603 2023 Q2 Galaxy Z Fold3 5G Yes 14970 18515549
## 604 2019 Q2 Galaxy Note10 No 38253 53195937
## 605 2022 Q2 Galaxy A32 5G Yes 46798 34010965
## 606 2021 Q4 Galaxy A52 5G Yes 10112 41338292
## 607 2022 Q1 Galaxy Z Fold3 5G Yes 34677 15661457
## 608 2019 Q1 Galaxy A14 5G Yes 12612 57161552
## 609 2021 Q3 Galaxy S10 No 39031 18608941
## 610 2019 Q3 Galaxy Z Flip5 5G Yes 48617 6134803
## 611 2020 Q1 Galaxy A73 5G Yes 48484 10752134
## 612 2020 Q4 Galaxy Z Fold2 5G Yes 52951 13524138
## 613 2019 Q4 Galaxy A52 5G Yes 53367 69827344
## 614 2020 Q2 Galaxy S23 5G Yes 53896 47982929
## 615 2024 Q4 Galaxy S21 No 28852 14365866
## 616 2024 Q2 Galaxy S10 No 26237 30930456
## 617 2021 Q3 Galaxy S22 5G Yes 54330 37269680
## 618 2024 Q1 Galaxy A52 5G Yes 31220 36722606
## 619 2022 Q4 Galaxy Note10 No 16902 45919819
## 620 2022 Q4 Galaxy Note10 No 16902 45919819
## 621 2022 Q2 Galaxy A73 5G Yes 10585 29685804
## 622 2022 Q2 Galaxy A32 5G Yes 46798 34010965
## 623 2021 Q4 Galaxy S21 No 39509 57709592
## 624 2024 Q3 Galaxy S23 5G Yes 15763 7376209
## 625 2020 Q4 Galaxy Z Fold3 5G Yes 11163 12032305
## 626 2021 Q2 Galaxy A32 5G Yes 26634 17977115
## 627 2019 Q3 Galaxy A73 5G Yes 6542 13222481
## 628 2023 Q1 Galaxy A14 5G Yes 39916 30964782
## 629 2024 Q3 Galaxy A32 5G Yes 27969 70164648
## 630 2019 Q1 Galaxy S22 5G Yes 36255 12091674
## 631 2020 Q1 Galaxy A32 5G Yes 42883 18588730
## 632 2023 Q1 Galaxy Z Flip5 5G Yes 11658 14130689
## 633 2020 Q1 Galaxy S22 5G Yes 40807 28409469
## 634 2019 Q4 Galaxy Z Flip3 5G Yes 34541 23308776
## 635 2019 Q4 Galaxy S22 5G Yes 6719 38311286
## 636 2023 Q4 Galaxy S22 5G Yes 36216 29959374
## 637 2022 Q2 Galaxy S21 No 33806 23699379
## 638 2022 Q1 Galaxy S10 No 23678 23302026
## 639 2023 Q4 Galaxy Note10 No 35697 19462565
## 640 2020 Q4 Galaxy Note20 No 7473 19621771
## Market.Share.... Regional.5G.Coverage.... X5G.Subscribers..millions.
## 1 1.04 57.36 39.55
## 2 2.82 85.80 42.58
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## 638 0.58 45.61 43.79
## 639 2.49 36.55 36.44
## 640 3.88 74.66 27.55
## Avg.5G.Speed..Mbps. Preference.for.5G.... Region
## 1 293.10 55.87 Asia-Pacific
## 2 67.46 37.26 Latin America
## 3 77.25 84.66 Middle East & Africa
## 4 105.27 40.03 North America
## 5 206.17 76.88 Latin America
## 6 179.15 80.79 Middle East & Africa
## 7 259.16 77.55 Latin America
## 8 191.42 74.83 Europe
## 9 151.18 66.51 North America
## 10 284.33 87.01 Latin America
## 11 81.43 85.88 Middle East & Africa
## 12 220.82 88.16 Asia-Pacific
## 13 161.49 85.67 Middle East & Africa
## 14 233.87 45.66 Europe
## 15 130.87 53.81 Latin America
## 16 156.93 64.04 Middle East & Africa
## 17 273.85 38.28 Middle East & Africa
## 18 105.21 67.78 Middle East & Africa
## 19 283.25 44.96 Middle East & Africa
## 20 112.81 85.33 Middle East & Africa
## 21 212.78 63.69 Europe
## 22 263.32 64.63 Asia-Pacific
## 23 59.84 53.20 North America
## 24 251.51 88.81 North America
## 25 110.55 73.07 North America
## 26 200.47 83.06 Middle East & Africa
## 27 170.31 78.44 Latin America
## 28 167.75 55.21 North America
## 29 280.73 75.51 North America
## 30 227.63 91.79 Latin America
## 31 152.00 50.23 North America
## 32 129.04 46.41 Middle East & Africa
## 33 132.81 77.31 Asia-Pacific
## 34 111.28 47.14 Latin America
## 35 239.67 46.13 Europe
## 36 211.08 58.89 Europe
## 37 83.18 94.70 Latin America
## 38 115.63 54.75 Asia-Pacific
## 39 184.77 67.90 Asia-Pacific
## 40 124.19 54.15 Europe
## 41 206.39 88.16 Middle East & Africa
## 42 61.49 87.97 Middle East & Africa
## 43 199.68 61.60 North America
## 44 137.73 81.32 Latin America
## 45 88.28 82.55 Latin America
## 46 243.47 58.24 North America
## 47 130.25 45.44 Europe
## 48 167.31 45.48 Europe
## 49 80.15 75.72 Middle East & Africa
## 50 73.31 70.74 Europe
## 51 210.05 58.83 Latin America
## 52 185.70 93.45 North America
## 53 120.07 87.24 Middle East & Africa
## 54 183.03 72.21 Asia-Pacific
## 55 138.67 80.35 Latin America
## 56 110.97 87.80 Asia-Pacific
## 57 295.62 58.71 North America
## 58 265.71 90.81 Europe
## 59 156.88 53.55 Asia-Pacific
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## 61 245.48 75.23 Europe
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## 63 196.59 78.94 North America
## 64 157.10 53.68 Latin America
## 65 110.90 49.63 Latin America
## 66 273.59 52.90 Asia-Pacific
## 67 187.23 76.50 Middle East & Africa
## 68 87.88 79.09 Middle East & Africa
## 69 168.24 63.04 Asia-Pacific
## 70 151.66 52.29 Latin America
## 71 95.38 57.91 Latin America
## 72 228.07 69.24 North America
## 73 54.56 66.45 Middle East & Africa
## 74 187.76 51.45 North America
## 75 120.41 76.81 North America
## 76 193.23 38.50 Latin America
## 77 96.06 70.49 Europe
## 78 282.82 64.08 Europe
## 79 134.32 74.93 Latin America
## 80 139.42 52.96 Asia-Pacific
## 81 153.45 54.94 North America
## 82 130.68 54.46 Europe
## 83 264.15 79.22 Middle East & Africa
## 84 175.90 45.25 North America
## 85 210.86 88.02 Europe
## 86 130.24 86.46 North America
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## 88 105.02 75.93 Latin America
## 89 92.93 86.89 Latin America
## 90 236.28 50.08 Asia-Pacific
## 91 57.25 41.31 Middle East & Africa
## 92 136.07 84.47 Europe
## 93 52.52 85.45 North America
## 94 59.98 45.43 Middle East & Africa
## 95 94.90 53.63 Middle East & Africa
## 96 66.69 63.04 Asia-Pacific
## 97 94.47 82.60 North America
## 98 193.54 89.31 Europe
## 99 186.09 61.17 Middle East & Africa
## 100 69.04 88.70 Latin America
## 101 289.41 93.02 Europe
## 102 100.39 59.44 Latin America
## 103 169.67 79.56 Europe
## 104 94.48 55.85 Europe
## 105 236.45 87.73 North America
## 106 221.54 67.28 North America
## 107 285.05 50.09 Asia-Pacific
## 108 225.49 56.78 Middle East & Africa
## 109 177.22 72.36 North America
## 110 155.16 44.89 Latin America
## 111 189.60 62.18 Asia-Pacific
## 112 96.26 57.17 North America
## 113 295.85 48.71 Asia-Pacific
## 114 52.85 70.84 Europe
## 115 194.98 54.61 Middle East & Africa
## 116 248.52 85.87 North America
## 117 122.22 83.63 North America
## 118 72.54 57.77 North America
## 119 154.42 78.77 Latin America
## 120 137.42 71.50 Middle East & Africa
## 121 129.50 66.93 North America
## 122 279.58 73.97 Europe
## 123 209.38 62.41 Middle East & Africa
## 124 138.12 52.54 Europe
## 125 171.59 50.17 Middle East & Africa
## 126 184.80 61.75 Middle East & Africa
## 127 277.65 55.07 Latin America
## 128 97.53 53.50 Europe
## 129 127.45 85.11 Asia-Pacific
## 130 102.66 60.11 Europe
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## 132 288.87 72.55 Asia-Pacific
## 133 133.77 92.93 Europe
## 134 130.83 75.69 Latin America
## 135 58.21 60.85 Asia-Pacific
## 136 255.14 39.33 North America
## 137 285.13 73.56 North America
## 138 291.06 65.82 North America
## 139 119.68 71.10 Latin America
## 140 106.63 42.60 Asia-Pacific
## 141 238.86 74.08 Middle East & Africa
## 142 280.30 66.00 Middle East & Africa
## 143 62.63 92.83 Asia-Pacific
## 144 93.60 93.25 Latin America
## 145 75.66 48.08 Europe
## 146 199.61 89.51 North America
## 147 215.46 49.82 Latin America
## 148 69.65 58.52 Europe
## 149 160.45 71.77 Latin America
## 150 237.56 76.80 Middle East & Africa
## 151 107.98 58.30 Asia-Pacific
## 152 217.97 54.81 Europe
## 153 291.12 85.21 Latin America
## 154 188.16 46.50 Latin America
## 155 269.38 59.06 Middle East & Africa
## 156 196.85 72.96 Middle East & Africa
## 157 202.32 88.65 Europe
## 158 259.73 81.14 Middle East & Africa
## 159 132.01 80.99 Asia-Pacific
## 160 297.81 77.33 North America
## 161 212.64 88.87 Middle East & Africa
## 162 99.20 71.55 Europe
## 163 271.18 90.26 Asia-Pacific
## 164 274.80 87.00 Europe
## 165 166.12 82.49 Asia-Pacific
## 166 297.30 66.82 Asia-Pacific
## 167 242.80 86.48 North America
## 168 122.97 82.42 Latin America
## 169 97.80 53.34 Europe
## 170 214.47 84.68 Middle East & Africa
## 171 183.48 54.28 Middle East & Africa
## 172 93.60 72.67 Asia-Pacific
## 173 296.12 93.14 North America
## 174 70.52 74.59 Europe
## 175 240.77 51.48 Asia-Pacific
## 176 162.28 64.74 Latin America
## 177 202.39 52.08 Europe
## 178 226.07 94.36 Asia-Pacific
## 179 139.82 81.79 Asia-Pacific
## 180 184.61 68.86 Europe
## 181 156.56 72.06 Europe
## 182 202.74 65.05 Latin America
## 183 137.77 75.23 North America
## 184 128.61 41.67 North America
## 185 92.35 51.97 North America
## 186 173.38 53.59 Europe
## 187 170.67 72.61 Europe
## 188 157.75 57.04 Europe
## 189 272.72 57.82 Europe
## 190 81.93 52.88 Middle East & Africa
## 191 274.81 47.70 Latin America
## 192 165.76 55.31 Latin America
## 193 118.36 80.72 North America
## 194 174.15 73.56 Asia-Pacific
## 195 195.16 74.46 Latin America
## 196 79.96 66.24 Latin America
## 197 292.18 37.14 North America
## 198 298.70 43.46 Asia-Pacific
## 199 92.32 39.57 Europe
## 200 129.70 78.41 North America
## 201 287.88 87.65 Middle East & Africa
## 202 258.98 59.60 Latin America
## 203 171.64 87.93 Europe
## 204 160.24 51.79 Asia-Pacific
## 205 232.71 51.01 Latin America
## 206 291.72 45.78 Middle East & Africa
## 207 286.49 48.06 North America
## 208 232.59 86.74 Middle East & Africa
## 209 282.72 86.09 Asia-Pacific
## 210 284.12 72.56 Latin America
## 211 249.36 77.13 Asia-Pacific
## 212 179.88 63.62 Middle East & Africa
## 213 232.38 66.96 Asia-Pacific
## 214 169.49 53.29 North America
## 215 82.24 53.90 North America
## 216 240.70 53.91 North America
## 217 215.78 74.82 North America
## 218 164.53 62.10 Middle East & Africa
## 219 154.51 92.80 Latin America
## 220 246.75 65.46 Middle East & Africa
## 221 281.70 86.82 Middle East & Africa
## 222 279.27 73.87 Europe
## 223 63.15 69.85 North America
## 224 222.44 74.49 Asia-Pacific
## 225 182.47 50.98 Asia-Pacific
## 226 213.49 61.07 North America
## 227 66.34 47.23 Asia-Pacific
## 228 137.24 49.71 Asia-Pacific
## 229 63.87 78.05 Asia-Pacific
## 230 253.23 61.38 Europe
## 231 265.78 86.45 North America
## 232 281.31 81.51 Middle East & Africa
## 233 135.63 72.27 North America
## 234 170.64 46.15 Europe
## 235 68.70 94.84 Europe
## 236 174.55 88.86 Latin America
## 237 125.31 93.20 North America
## 238 109.94 93.28 Latin America
## 239 213.39 76.20 Asia-Pacific
## 240 253.73 45.63 Latin America
## 241 272.01 80.79 Europe
## 242 171.98 74.11 North America
## 243 175.27 73.89 Europe
## 244 287.50 41.54 Latin America
## 245 108.49 65.46 North America
## 246 262.25 45.63 Asia-Pacific
## 247 262.84 68.00 Asia-Pacific
## 248 227.77 87.07 Europe
## 249 177.90 65.83 Asia-Pacific
## 250 256.03 54.70 Asia-Pacific
## 251 262.01 54.80 North America
## 252 231.91 69.15 Middle East & Africa
## 253 260.80 82.88 Middle East & Africa
## 254 240.21 75.96 Asia-Pacific
## 255 117.92 62.56 Europe
## 256 175.60 66.60 Europe
## 257 106.03 70.52 Europe
## 258 138.00 68.94 Europe
## 259 160.98 45.26 North America
## 260 75.69 57.66 Middle East & Africa
## 261 95.71 54.69 Latin America
## 262 269.75 52.92 Asia-Pacific
## 263 63.50 81.51 Latin America
## 264 249.43 45.62 North America
## 265 187.41 45.43 Latin America
## 266 214.90 48.30 Latin America
## 267 57.27 51.12 Latin America
## 268 210.17 70.25 Middle East & Africa
## 269 51.12 86.82 Latin America
## 270 178.32 45.09 Latin America
## 271 280.41 64.86 Asia-Pacific
## 272 220.38 51.94 Middle East & Africa
## 273 87.57 83.22 Europe
## 274 238.14 47.82 Europe
## 275 77.87 49.98 Europe
## 276 192.01 73.64 North America
## 277 168.21 88.53 North America
## 278 79.34 58.28 North America
## 279 50.37 45.65 Asia-Pacific
## 280 157.00 94.16 Europe
## 281 246.52 46.97 North America
## 282 85.44 54.57 North America
## 283 212.73 87.94 Latin America
## 284 52.28 75.87 Asia-Pacific
## 285 122.87 91.72 Latin America
## 286 154.81 68.24 North America
## 287 236.39 47.11 North America
## 288 174.21 73.78 Latin America
## 289 248.93 80.82 Middle East & Africa
## 290 244.75 58.59 Latin America
## 291 142.99 65.49 Europe
## 292 152.26 87.72 Asia-Pacific
## 293 294.91 53.57 Middle East & Africa
## 294 170.74 59.27 Asia-Pacific
## 295 195.17 79.73 Asia-Pacific
## 296 177.43 63.86 Latin America
## 297 192.30 46.87 North America
## 298 184.26 51.56 North America
## 299 286.82 62.63 Europe
## 300 255.02 48.38 Middle East & Africa
## 301 274.20 42.93 North America
## 302 62.01 43.39 Europe
## 303 165.62 84.08 Asia-Pacific
## 304 267.68 82.48 Asia-Pacific
## 305 259.15 73.14 Middle East & Africa
## 306 117.64 58.15 North America
## 307 130.35 80.87 Latin America
## 308 211.00 67.67 North America
## 309 236.61 93.38 Middle East & Africa
## 310 196.33 89.62 Latin America
## 311 293.87 46.55 Europe
## 312 157.51 45.53 Europe
## 313 83.75 56.88 Asia-Pacific
## 314 195.74 49.58 Middle East & Africa
## 315 120.07 50.72 North America
## 316 205.74 48.09 Middle East & Africa
## 317 216.59 73.24 North America
## 318 84.12 40.12 Europe
## 319 201.33 75.69 Europe
## 320 190.40 78.16 Latin America
## 321 147.48 52.94 North America
## 322 256.14 51.86 Latin America
## 323 126.75 94.43 Asia-Pacific
## 324 172.94 72.78 North America
## 325 177.35 73.86 Latin America
## 326 103.77 65.41 Asia-Pacific
## 327 294.86 54.66 Europe
## 328 82.85 88.18 North America
## 329 162.51 72.81 Middle East & Africa
## 330 198.84 46.44 Middle East & Africa
## 331 71.21 82.95 Asia-Pacific
## 332 259.95 42.51 Asia-Pacific
## 333 213.04 46.39 Europe
## 334 289.86 52.20 Middle East & Africa
## 335 296.75 75.59 Latin America
## 336 103.25 63.21 Middle East & Africa
## 337 101.77 50.02 Middle East & Africa
## 338 284.67 72.09 Europe
## 339 130.55 85.26 Middle East & Africa
## 340 220.65 89.72 North America
## 341 284.37 67.07 Middle East & Africa
## 342 255.35 72.28 North America
## 343 190.78 88.08 Latin America
## 344 173.23 52.39 North America
## 345 270.57 63.33 Europe
## 346 203.22 57.32 Middle East & Africa
## 347 268.60 38.07 Asia-Pacific
## 348 51.73 74.44 Europe
## 349 294.81 51.79 Latin America
## 350 194.32 86.51 Middle East & Africa
## 351 231.50 84.08 Latin America
## 352 187.87 87.11 Asia-Pacific
## 353 171.35 81.11 North America
## 354 205.63 73.46 Middle East & Africa
## 355 97.86 49.00 Middle East & Africa
## 356 99.55 92.85 Europe
## 357 229.94 66.10 North America
## 358 170.92 87.54 Asia-Pacific
## 359 186.37 92.94 North America
## 360 261.80 75.18 Asia-Pacific
## 361 211.08 58.89 Europe
## 362 242.80 86.48 North America
## 363 281.70 86.82 Middle East & Africa
## 364 169.49 53.29 North America
## 365 129.50 66.93 North America
## 366 264.15 79.22 Middle East & Africa
## 367 81.43 85.88 Middle East & Africa
## 368 153.45 54.94 North America
## 369 199.61 89.51 North America
## 370 162.28 64.74 Latin America
## 371 259.73 81.14 Middle East & Africa
## 372 168.24 63.04 Asia-Pacific
## 373 157.51 45.53 Europe
## 374 238.86 74.08 Middle East & Africa
## 375 210.05 58.83 Latin America
## 376 285.05 50.09 Asia-Pacific
## 377 126.75 94.43 Asia-Pacific
## 378 138.00 68.94 Europe
## 379 75.66 48.08 Europe
## 380 119.68 71.10 Latin America
## 381 284.33 87.01 Latin America
## 382 75.66 48.08 Europe
## 383 174.21 73.78 Latin America
## 384 107.98 58.30 Asia-Pacific
## 385 130.83 75.69 Latin America
## 386 236.61 93.38 Middle East & Africa
## 387 198.84 46.44 Middle East & Africa
## 388 81.93 52.88 Middle East & Africa
## 389 287.50 41.54 Latin America
## 390 285.05 50.09 Asia-Pacific
## 391 82.24 53.90 North America
## 392 277.65 55.07 Latin America
## 393 188.16 46.50 Latin America
## 394 255.14 39.33 North America
## 395 154.51 92.80 Latin America
## 396 279.58 73.97 Europe
## 397 185.70 93.45 North America
## 398 206.39 88.16 Middle East & Africa
## 399 109.94 93.28 Latin America
## 400 237.56 76.80 Middle East & Africa
## 401 280.30 66.00 Middle East & Africa
## 402 280.82 85.04 Latin America
## 403 262.84 68.00 Asia-Pacific
## 404 61.49 87.97 Middle East & Africa
## 405 195.17 79.73 Asia-Pacific
## 406 171.59 50.17 Middle East & Africa
## 407 220.38 51.94 Middle East & Africa
## 408 186.09 61.17 Middle East & Africa
## 409 67.46 37.26 Latin America
## 410 120.41 76.81 North America
## 411 160.98 45.26 North America
## 412 284.37 67.07 Middle East & Africa
## 413 106.63 42.60 Asia-Pacific
## 414 192.01 73.64 North America
## 415 183.48 54.28 Middle East & Africa
## 416 293.10 55.87 Asia-Pacific
## 417 258.98 59.60 Latin America
## 418 69.65 58.52 Europe
## 419 138.00 68.94 Europe
## 420 105.27 40.03 North America
## 421 217.97 54.81 Europe
## 422 171.98 74.11 North America
## 423 51.73 74.44 Europe
## 424 138.12 52.54 Europe
## 425 132.01 80.99 Asia-Pacific
## 426 139.42 52.96 Asia-Pacific
## 427 175.60 66.60 Europe
## 428 195.17 79.73 Asia-Pacific
## 429 69.65 58.52 Europe
## 430 279.27 73.87 Europe
## 431 186.37 92.94 North America
## 432 292.18 37.14 North America
## 433 69.65 58.52 Europe
## 434 258.98 59.60 Latin America
## 435 205.63 73.46 Middle East & Africa
## 436 157.00 94.16 Europe
## 437 256.14 51.86 Latin America
## 438 212.64 88.87 Middle East & Africa
## 439 212.73 87.94 Latin America
## 440 231.50 84.08 Latin America
## 441 184.77 67.90 Asia-Pacific
## 442 96.26 57.17 North America
## 443 94.47 82.60 North America
## 444 126.75 94.43 Asia-Pacific
## 445 192.01 73.64 North America
## 446 69.04 88.70 Latin America
## 447 215.46 49.82 Latin America
## 448 105.21 67.78 Middle East & Africa
## 449 70.52 74.59 Europe
## 450 125.31 93.20 North America
## 451 67.46 37.26 Latin America
## 452 107.98 58.30 Asia-Pacific
## 453 164.53 62.10 Middle East & Africa
## 454 247.01 80.17 Latin America
## 455 280.73 75.51 North America
## 456 80.15 75.72 Middle East & Africa
## 457 72.54 57.77 North America
## 458 292.18 37.14 North America
## 459 170.67 72.61 Europe
## 460 284.33 87.01 Latin America
## 461 97.86 49.00 Middle East & Africa
## 462 292.18 37.14 North America
## 463 137.73 81.32 Latin America
## 464 94.47 82.60 North America
## 465 110.55 73.07 North America
## 466 160.98 45.26 North America
## 467 280.73 75.51 North America
## 468 262.84 68.00 Asia-Pacific
## 469 94.90 53.63 Middle East & Africa
## 470 293.87 46.55 Europe
## 471 111.28 47.14 Latin America
## 472 138.12 52.54 Europe
## 473 66.69 63.04 Asia-Pacific
## 474 253.23 61.38 Europe
## 475 92.35 51.97 North America
## 476 270.57 63.33 Europe
## 477 239.67 46.13 Europe
## 478 96.06 70.49 Europe
## 479 256.14 51.86 Latin America
## 480 85.44 54.57 North America
## 481 92.35 51.97 North America
## 482 99.55 92.85 Europe
## 483 232.71 51.01 Latin America
## 484 152.00 50.23 North America
## 485 262.01 54.80 North America
## 486 259.95 42.51 Asia-Pacific
## 487 87.88 79.09 Middle East & Africa
## 488 139.42 52.96 Asia-Pacific
## 489 170.74 59.27 Asia-Pacific
## 490 71.21 82.95 Asia-Pacific
## 491 220.65 89.72 North America
## 492 177.90 65.83 Asia-Pacific
## 493 284.33 87.01 Latin America
## 494 138.12 52.54 Europe
## 495 85.44 54.57 North America
## 496 236.28 50.08 Asia-Pacific
## 497 81.93 52.88 Middle East & Africa
## 498 168.24 63.04 Asia-Pacific
## 499 262.25 45.63 Asia-Pacific
## 500 92.35 51.97 North America
## 501 232.59 86.74 Middle East & Africa
## 502 179.88 63.62 Middle East & Africa
## 503 157.51 45.53 Europe
## 504 236.28 50.08 Asia-Pacific
## 505 122.22 83.63 North America
## 506 167.31 45.48 Europe
## 507 100.39 59.44 Latin America
## 508 187.41 45.43 Latin America
## 509 227.63 91.79 Latin America
## 510 292.18 37.14 North America
## 511 171.59 50.17 Middle East & Africa
## 512 179.88 63.62 Middle East & Africa
## 513 294.86 54.66 Europe
## 514 196.33 89.62 Latin America
## 515 220.82 88.16 Asia-Pacific
## 516 79.34 58.28 North America
## 517 175.60 66.60 Europe
## 518 67.46 37.26 Latin America
## 519 162.28 64.74 Latin America
## 520 151.18 66.51 North America
## 521 273.59 52.90 Asia-Pacific
## 522 82.85 88.18 North America
## 523 212.64 88.87 Middle East & Africa
## 524 274.81 47.70 Latin America
## 525 255.02 48.38 Middle East & Africa
## 526 210.05 58.83 Latin America
## 527 177.43 63.86 Latin America
## 528 177.22 72.36 North America
## 529 160.98 45.26 North America
## 530 94.90 53.63 Middle East & Africa
## 531 177.43 63.86 Latin America
## 532 177.22 72.36 North America
## 533 170.64 46.15 Europe
## 534 191.42 74.83 Europe
## 535 66.34 47.23 Asia-Pacific
## 536 214.90 48.30 Latin America
## 537 193.54 89.31 Europe
## 538 52.52 85.45 North America
## 539 110.55 73.07 North America
## 540 202.39 52.08 Europe
## 541 192.01 73.64 North America
## 542 132.01 80.99 Asia-Pacific
## 543 282.72 86.09 Asia-Pacific
## 544 193.54 89.31 Europe
## 545 171.35 81.11 North America
## 546 270.57 63.33 Europe
## 547 220.82 88.16 Asia-Pacific
## 548 106.63 42.60 Asia-Pacific
## 549 122.87 91.72 Latin America
## 550 191.42 74.83 Europe
## 551 174.21 73.78 Latin America
## 552 274.20 42.93 North America
## 553 238.86 74.08 Middle East & Africa
## 554 85.44 54.57 North America
## 555 199.68 61.60 North America
## 556 226.07 94.36 Asia-Pacific
## 557 209.38 62.41 Middle East & Africa
## 558 152.00 50.23 North America
## 559 240.70 53.91 North America
## 560 92.93 86.89 Latin America
## 561 173.38 53.59 Europe
## 562 258.98 59.60 Latin America
## 563 129.04 46.41 Middle East & Africa
## 564 261.80 75.18 Asia-Pacific
## 565 279.27 73.87 Europe
## 566 175.60 66.60 Europe
## 567 287.50 41.54 Latin America
## 568 170.31 78.44 Latin America
## 569 239.67 46.13 Europe
## 570 77.25 84.66 Middle East & Africa
## 571 96.26 57.17 North America
## 572 200.47 83.06 Middle East & Africa
## 573 162.28 64.74 Latin America
## 574 135.63 72.27 North America
## 575 255.14 39.33 North America
## 576 110.90 49.63 Latin America
## 577 232.38 66.96 Asia-Pacific
## 578 134.32 74.93 Latin America
## 579 133.77 92.93 Europe
## 580 177.90 65.83 Asia-Pacific
## 581 280.73 75.51 North America
## 582 232.59 86.74 Middle East & Africa
## 583 81.43 85.88 Middle East & Africa
## 584 294.81 51.79 Latin America
## 585 187.23 76.50 Middle East & Africa
## 586 203.22 57.32 Middle East & Africa
## 587 170.67 72.61 Europe
## 588 176.96 71.11 North America
## 589 284.33 87.01 Latin America
## 590 171.64 87.93 Europe
## 591 237.56 76.80 Middle East & Africa
## 592 136.07 84.47 Europe
## 593 95.38 57.91 Latin America
## 594 294.91 53.57 Middle East & Africa
## 595 165.62 84.08 Asia-Pacific
## 596 174.55 88.86 Latin America
## 597 293.87 46.55 Europe
## 598 81.43 85.88 Middle East & Africa
## 599 291.72 45.78 Middle East & Africa
## 600 243.47 58.24 North America
## 601 196.33 89.62 Latin America
## 602 281.70 86.82 Middle East & Africa
## 603 57.27 51.12 Latin America
## 604 273.85 38.28 Middle East & Africa
## 605 287.88 87.65 Middle East & Africa
## 606 93.60 72.67 Asia-Pacific
## 607 165.76 55.31 Latin America
## 608 161.49 85.67 Middle East & Africa
## 609 107.98 58.30 Asia-Pacific
## 610 88.28 82.55 Latin America
## 611 87.88 79.09 Middle East & Africa
## 612 52.85 70.84 Europe
## 613 185.70 93.45 North America
## 614 92.93 86.89 Latin America
## 615 194.32 86.51 Middle East & Africa
## 616 205.74 48.09 Middle East & Africa
## 617 212.64 88.87 Middle East & Africa
## 618 130.35 80.87 Latin America
## 619 66.34 47.23 Asia-Pacific
## 620 66.34 47.23 Asia-Pacific
## 621 171.64 87.93 Europe
## 622 287.88 87.65 Middle East & Africa
## 623 214.47 84.68 Middle East & Africa
## 624 173.23 52.39 North America
## 625 122.22 83.63 North America
## 626 238.86 74.08 Middle East & Africa
## 627 115.63 54.75 Asia-Pacific
## 628 260.80 82.88 Middle East & Africa
## 629 103.25 63.21 Middle East & Africa
## 630 81.43 85.88 Middle East & Africa
## 631 273.59 52.90 Asia-Pacific
## 632 117.92 62.56 Europe
## 633 95.38 57.91 Latin America
## 634 138.67 80.35 Latin America
## 635 110.97 87.80 Asia-Pacific
## 636 177.43 63.86 Latin America
## 637 129.70 78.41 North America
## 638 156.56 72.06 Europe
## 639 236.39 47.11 North America
## 640 177.22 72.36 North Americasamsung_sales_clean <- samsung_sales %>%
distinct()
head(samsung_sales_clean)## Year Quarter Product.Model X5G.Capability Units.Sold Revenue....
## 1 2019 Q1 Galaxy S10 No 26396 4212951
## 2 2019 Q1 Galaxy Note10 No 25671 7240266
## 3 2019 Q1 Galaxy S20 No 16573 25608332
## 4 2019 Q1 Galaxy Note20 No 7177 21984416
## 5 2019 Q1 Galaxy S21 No 45633 16342438
## 6 2019 Q1 Galaxy A32 5G Yes 15912 17178327
## Market.Share.... Regional.5G.Coverage.... X5G.Subscribers..millions.
## 1 1.04 57.36 39.55
## 2 2.82 85.80 42.58
## 3 -0.03 47.02 3.78
## 4 0.84 25.70 23.41
## 5 2.36 89.13 44.43
## 6 5.41 59.12 12.14
## Avg.5G.Speed..Mbps. Preference.for.5G.... Region
## 1 293.10 55.87 Asia-Pacific
## 2 67.46 37.26 Latin America
## 3 77.25 84.66 Middle East & Africa
## 4 105.27 40.03 North America
## 5 206.17 76.88 Latin America
## 6 179.15 80.79 Middle East & Africa3. Checking and cleaning outliers
3.1 Checking
For outliers, we detect it first using boxplot (in all numerical values). We found a small outlier only in Revenue distribution.
box1 <-
ggplot(samsung_sales_clean, aes(y = Units.Sold)) +
geom_boxplot(outlier.colour = 'blue',fill='#B4BFC8') +
labs(title = "Units Sold Distribution",
y = "Count of Units Sold")
box2 <-
ggplot(samsung_sales_clean, aes(y = Revenue....)) +
geom_boxplot(outlier.colour = 'blue',fill='#B4BFC8') +
labs(title = "Revenue Distribution",
y = "Count of Revenue")
box3 <-
ggplot(samsung_sales_clean, aes(y = Market.Share....)) +
geom_boxplot(outlier.colour = 'blue',fill='#B4BFC8') +
labs(title = "Market Share Distribution",
y = "Count of Market Share")
box4 <-
ggplot(samsung_sales_clean, aes(y = Regional.5G.Coverage....)) +
geom_boxplot(outlier.colour = 'blue',fill='#B4BFC8') +
labs(title = "Regional 5G Coverage Distribution",
y = "Count of Regional 5G Coverage")
box5 <-
ggplot(samsung_sales_clean, aes(y = Avg.5G.Speed..Mbps.)) +
geom_boxplot(outlier.colour = 'blue',fill='#B4BFC8') +
labs(title = "Average 5G Speed Distribution",
y = "Average 5G Speed")
box6 <-
ggplot(samsung_sales_clean, aes(y = X5G.Subscribers..millions.)) +
geom_boxplot(outlier.colour = 'blue',fill='#B4BFC8') +
labs(title = "5G Subscriber Distribution",
y = "Count of Subscriber(millions)")
box7 <-
ggplot(samsung_sales_clean, aes(y = Preference.for.5G....)) +
geom_boxplot(outlier.colour = 'blue',fill='#B4BFC8') +
labs(title = "Preference for 5G Distribution",
y = "Preference (Scale)")
grid.arrange (box1, box2, box3, box4, box5, box6, box7, ncol=3)
3.2 Cleaning
We’re using IQR method to erase outliers, and with the same command, filter, we want to see the outliers first before removing it. The outliers can be checked in samsung_sales_outlier. Lastly, we’re using reverse code of samsung_sales_outlier to filter the rows which is not duplicate and place it in new variable called samsung_sales_veryclean.
Q1 <- quantile(samsung_sales_clean$Revenue...., 0.25, na.rm = TRUE)
Q3 <- quantile(samsung_sales_clean$Revenue...., 0.75, na.rm = TRUE)
IQR <- Q3 - Q1
samsung_sales_outlier <- samsung_sales_clean %>%
filter(Revenue.... < (Q1 - 1.5 * IQR) | Revenue.... > (Q3 + 1.5 * IQR))
samsung_sales_outlier## Year Quarter Product.Model X5G.Capability Units.Sold Revenue....
## 1 2021 Q4 Galaxy S23 5G Yes 14293 84264944
## Market.Share.... Regional.5G.Coverage.... X5G.Subscribers..millions.
## 1 3.84 95.98 19.39
## Avg.5G.Speed..Mbps. Preference.for.5G.... Region
## 1 139.82 81.79 Asia-Pacificsamsung_sales_veryclean <- samsung_sales_clean %>%
filter(Revenue.... > (Q1 - 1.5 * IQR) | Revenue.... < (Q3 + 1.5 * IQR))
head(samsung_sales_veryclean)## Year Quarter Product.Model X5G.Capability Units.Sold Revenue....
## 1 2019 Q1 Galaxy S10 No 26396 4212951
## 2 2019 Q1 Galaxy Note10 No 25671 7240266
## 3 2019 Q1 Galaxy S20 No 16573 25608332
## 4 2019 Q1 Galaxy Note20 No 7177 21984416
## 5 2019 Q1 Galaxy S21 No 45633 16342438
## 6 2019 Q1 Galaxy A32 5G Yes 15912 17178327
## Market.Share.... Regional.5G.Coverage.... X5G.Subscribers..millions.
## 1 1.04 57.36 39.55
## 2 2.82 85.80 42.58
## 3 -0.03 47.02 3.78
## 4 0.84 25.70 23.41
## 5 2.36 89.13 44.43
## 6 5.41 59.12 12.14
## Avg.5G.Speed..Mbps. Preference.for.5G.... Region
## 1 293.10 55.87 Asia-Pacific
## 2 67.46 37.26 Latin America
## 3 77.25 84.66 Middle East & Africa
## 4 105.27 40.03 North America
## 5 206.17 76.88 Latin America
## 6 179.15 80.79 Middle East & Africa4. Checking and changing datatype
4.1 Checking datatype
To make sure the data type of each variable in the dataset is correct, we check it first with the summarise-across-everything by class command.
samsung_sales_veryclean %>%
summarise(across(everything(), class))## Year Quarter Product.Model X5G.Capability Units.Sold Revenue....
## 1 integer character character character integer numeric
## Market.Share.... Regional.5G.Coverage.... X5G.Subscribers..millions.
## 1 numeric numeric numeric
## Avg.5G.Speed..Mbps. Preference.for.5G.... Region
## 1 numeric numeric character4.2 Changing datatype
With the information above, we decided to change the data type of the 5G.Capability variable from char to factor because the values are boolean. This will be very helpful for us to visualize the data later.
samsung_sales_veryclean$X5G.Capability <-as.factor(samsung_sales_veryclean$X5G.Capability)
samsung_sales_veryclean %>%
summarise(across(everything(), class))## Year Quarter Product.Model X5G.Capability Units.Sold Revenue....
## 1 integer character character factor integer numeric
## Market.Share.... Regional.5G.Coverage.... X5G.Subscribers..millions.
## 1 numeric numeric numeric
## Avg.5G.Speed..Mbps. Preference.for.5G.... Region
## 1 numeric numeric character5. Checking Inconsistent Formatting
To check if there is inconsistent formatting, we want to see all unique values in variables of type char. And luckily we found nothing wrong.
format_checks <- list(
"Region" = unique(samsung_sales_veryclean$Region),
"Quarter" = unique(samsung_sales_veryclean$Quarter),
"Product Model" = unique(samsung_sales_veryclean$Product.Model)
)
cat("Unique Values Check")## Unique Values Checkfor (i in seq_along(format_checks))
{
cat("\n**", names(format_checks)[i], "**:\n")
print(format_checks[[i]])
}##
## ** Region **:
## [1] "Asia-Pacific" "Latin America" "Middle East & Africa"
## [4] "North America" "Europe"
##
## ** Quarter **:
## [1] "Q1" "Q2" "Q3" "Q4"
##
## ** Product Model **:
## [1] "Galaxy S10" "Galaxy Note10" "Galaxy S20"
## [4] "Galaxy Note20" "Galaxy S21" "Galaxy A32 5G"
## [7] "Galaxy A52 5G" "Galaxy A73 5G" "Galaxy Z Fold2 5G"
## [10] "Galaxy Z Flip3 5G" "Galaxy S22 5G" "Galaxy Z Fold3 5G"
## [13] "Galaxy A14 5G" "Galaxy S23 5G" "Galaxy Z Flip5 5G"6. Checking and fixing logical inconsistency
6.1 Checking
We check the logical inconsistency of numerical data by seeing if there are values that are less than 0 and more than 100 for percentage-based variables. While for numerical data based on amount and income, we want to see if there are any negative values.
- Units Sold
inconsistent_units <- samsung_sales_veryclean[samsung_sales_veryclean$Units.Sold < 0, ]
inconsistent_units## [1] Year Quarter
## [3] Product.Model X5G.Capability
## [5] Units.Sold Revenue....
## [7] Market.Share.... Regional.5G.Coverage....
## [9] X5G.Subscribers..millions. Avg.5G.Speed..Mbps.
## [11] Preference.for.5G.... Region
## <0 rows> (or 0-length row.names)- Revenue
inconsistent_revenue <- samsung_sales_veryclean[samsung_sales_veryclean$Revenue.... < 0, ]
inconsistent_revenue## [1] Year Quarter
## [3] Product.Model X5G.Capability
## [5] Units.Sold Revenue....
## [7] Market.Share.... Regional.5G.Coverage....
## [9] X5G.Subscribers..millions. Avg.5G.Speed..Mbps.
## [11] Preference.for.5G.... Region
## <0 rows> (or 0-length row.names)- Market Share
inconsistent_market_share <- samsung_sales_veryclean[samsung_sales_veryclean$Market.Share.... < 0 | samsung_sales_veryclean$Market.Share.... > 100, ]
inconsistent_market_share## Year Quarter Product.Model X5G.Capability Units.Sold Revenue....
## 3 2019 Q1 Galaxy S20 No 16573 25608332
## 17 2019 Q2 Galaxy Note10 No 38253 53195937
## 77 2020 Q2 Galaxy Note10 No 8785 16339172
## 91 2020 Q3 Galaxy S10 No 27328 31739576
## 108 2020 Q4 Galaxy S20 No 23896 18747121
## 110 2020 Q4 Galaxy S21 No 11913 41627181
## 136 2021 Q2 Galaxy S10 No 33392 40200176
## 138 2021 Q2 Galaxy S20 No 9689 18237818
## 140 2021 Q2 Galaxy S21 No 41937 10442281
## 154 2021 Q3 Galaxy Note20 No 42820 29532856
## 166 2021 Q4 Galaxy S10 No 5309 30375440
## 183 2022 Q1 Galaxy S20 No 49869 25015562
## 200 2022 Q2 Galaxy S21 No 33806 23699379
## 227 2022 Q4 Galaxy Note10 No 16902 45919819
## 229 2022 Q4 Galaxy Note20 No 20904 41945285
## 244 2023 Q1 Galaxy Note20 No 41459 13687955
## 303 2024 Q1 Galaxy S20 No 32783 24954403
## Market.Share.... Regional.5G.Coverage.... X5G.Subscribers..millions.
## 3 -0.03 47.02 3.78
## 17 -0.05 76.73 31.96
## 77 -0.36 50.50 16.16
## 91 -0.42 68.61 19.37
## 108 -0.49 32.09 10.83
## 110 -0.35 64.46 0.84
## 136 -0.14 69.32 24.76
## 138 -0.39 45.74 39.58
## 140 -0.20 67.00 23.33
## 154 -0.47 68.24 19.11
## 166 -0.17 38.35 33.49
## 183 -0.10 66.77 25.65
## 200 -0.23 77.31 47.51
## 227 -0.25 68.67 2.53
## 229 -0.06 53.51 35.45
## 244 -0.11 62.18 11.77
## 303 -0.12 68.38 18.52
## Avg.5G.Speed..Mbps. Preference.for.5G.... Region
## 3 77.25 84.66 Middle East & Africa
## 17 273.85 38.28 Middle East & Africa
## 77 96.06 70.49 Europe
## 91 57.25 41.31 Middle East & Africa
## 108 225.49 56.78 Middle East & Africa
## 110 155.16 44.89 Latin America
## 136 255.14 39.33 North America
## 138 291.06 65.82 North America
## 140 106.63 42.60 Asia-Pacific
## 154 188.16 46.50 Latin America
## 166 297.30 66.82 Asia-Pacific
## 183 137.77 75.23 North America
## 200 129.70 78.41 North America
## 227 66.34 47.23 Asia-Pacific
## 229 63.87 78.05 Asia-Pacific
## 244 287.50 41.54 Latin America
## 303 165.62 84.08 Asia-Pacific- Regional 5G Coverage
inconsistent_regcoverage <- samsung_sales_veryclean[samsung_sales_veryclean$Regional.5G.Coverage.... < 0 | samsung_sales_veryclean$Regional.5G.Coverage.... > 100, ]
inconsistent_regcoverage## Year Quarter Product.Model X5G.Capability Units.Sold Revenue....
## 23 2019 Q2 Galaxy A73 5G Yes 39191 19631909
## 37 2019 Q3 Galaxy A52 5G Yes 56869 78970161
## 75 2020 Q1 Galaxy Z Flip5 5G Yes 53118 71564620
## 101 2020 Q3 Galaxy S22 5G Yes 8082 53518254
## 191 2022 Q1 Galaxy S22 5G Yes 6787 52571042
## 192 2022 Q1 Galaxy Z Fold3 5G Yes 34677 15661457
## 205 2022 Q2 Galaxy Z Flip3 5G Yes 54159 43416974
## 224 2022 Q3 Galaxy S23 5G Yes 47291 19328485
## 250 2023 Q1 Galaxy Z Flip3 5G Yes 31535 75369161
## 284 2023 Q3 Galaxy S23 5G Yes 25724 4967037
## 339 2024 Q3 Galaxy Z Fold2 5G Yes 53528 21387152
## 341 2024 Q3 Galaxy S22 5G Yes 59709 24247149
## Market.Share.... Regional.5G.Coverage.... X5G.Subscribers..millions.
## 23 3.35 100.83 49.18
## 37 6.12 102.54 21.09
## 75 4.21 103.57 52.17
## 101 3.35 102.49 49.53
## 191 4.93 103.73 29.49
## 192 6.11 101.07 48.06
## 205 5.91 101.80 45.27
## 224 6.51 102.80 36.28
## 250 4.85 103.92 6.42
## 284 2.80 102.38 45.67
## 339 6.42 103.82 51.01
## 341 6.30 103.78 12.99
## Avg.5G.Speed..Mbps. Preference.for.5G.... Region
## 23 59.84 53.20 North America
## 37 83.18 94.70 Latin America
## 75 120.41 76.81 North America
## 101 289.41 93.02 Europe
## 191 274.81 47.70 Latin America
## 192 165.76 55.31 Latin America
## 205 232.71 51.01 Latin America
## 224 222.44 74.49 Asia-Pacific
## 250 256.03 54.70 Asia-Pacific
## 284 52.28 75.87 Asia-Pacific
## 339 130.55 85.26 Middle East & Africa
## 341 284.37 67.07 Middle East & Africa- 5G Subscriber (millions)
inconsistent_subsformat <- samsung_sales_veryclean[samsung_sales_veryclean$X5G.Subscribers..millions. < 0,]
inconsistent_subsformat## Year Quarter Product.Model X5G.Capability Units.Sold Revenue....
## 35 2019 Q3 Galaxy S21 No 20683 15828499
## 198 2022 Q2 Galaxy S20 No 22036 14156371
## 212 2022 Q3 Galaxy Note10 No 27006 5136020
## Market.Share.... Regional.5G.Coverage.... X5G.Subscribers..millions.
## 35 0.34 30.32 -0.65
## 198 2.12 60.31 -0.89
## 212 0.36 70.18 -0.05
## Avg.5G.Speed..Mbps. Preference.for.5G.... Region
## 35 239.67 46.13 Europe
## 198 298.70 43.46 Asia-Pacific
## 212 179.88 63.62 Middle East & Africa- 5G Preference
inconsistent_preference <- samsung_sales_veryclean[samsung_sales_veryclean$Preference.for.5G.... < 0 | samsung_sales_veryclean$Preference.for.5G.... > 100, ]
inconsistent_preference## [1] Year Quarter
## [3] Product.Model X5G.Capability
## [5] Units.Sold Revenue....
## [7] Market.Share.... Regional.5G.Coverage....
## [9] X5G.Subscribers..millions. Avg.5G.Speed..Mbps.
## [11] Preference.for.5G.... Region
## <0 rows> (or 0-length row.names)- Average 5G Speed (Mbps)
inconsistent_avspeed <- samsung_sales_veryclean[samsung_sales_veryclean$Avg.5G.Speed..Mbps. < 0, ]
inconsistent_avspeed## [1] Year Quarter
## [3] Product.Model X5G.Capability
## [5] Units.Sold Revenue....
## [7] Market.Share.... Regional.5G.Coverage....
## [9] X5G.Subscribers..millions. Avg.5G.Speed..Mbps.
## [11] Preference.for.5G.... Region
## <0 rows> (or 0-length row.names)- Checking logical inconsistency relationship between revenue and units sold
inconsistent_revenuegrowth <- samsung_sales_veryclean %>%
filter(Revenue.... <= 0 | (Units.Sold > 0 & Revenue.... == 0))
inconsistent_revenuegrowth## [1] Year Quarter
## [3] Product.Model X5G.Capability
## [5] Units.Sold Revenue....
## [7] Market.Share.... Regional.5G.Coverage....
## [9] X5G.Subscribers..millions. Avg.5G.Speed..Mbps.
## [11] Preference.for.5G.... Region
## <0 rows> (or 0-length row.names)Logical inconsistency is found in Market Share, Regional Coverage, and 5G Subscriber.
6.2 Fixing
From the previous check, we found that there are logical inconsistencies in the Market Share, Regional Coverage, and 5G Subscriber. Then we replace those values with their median values.
- 5G SUbscriber
samsung_sales_veryclean$`X5G.Subscribers..millions.`[samsung_sales_veryclean$`X5G.Subscribers..millions.` < 0] <- median(samsung_sales_veryclean$X5G.Subscribers..millions.)- Regional Coverage
samsung_sales_veryclean$`Regional.5G.Coverage....`[samsung_sales_veryclean$`Regional.5G.Coverage....` < 0] <- median(samsung_sales_veryclean$Regional.5G.Coverage....)
samsung_sales_veryclean$`Regional.5G.Coverage....`[samsung_sales_veryclean$`Regional.5G.Coverage....` > 100] <- median(samsung_sales_veryclean$Regional.5G.Coverage....)- Market Share
samsung_sales_veryclean$`Market.Share....`[samsung_sales_veryclean$`Market.Share....` < 0] <- median(samsung_sales_veryclean$Market.Share....)
samsung_sales_veryclean$`Market.Share....`[samsung_sales_veryclean$`Market.Share....` > 100] <- median(samsung_sales_veryclean$Market.Share....)Summary of the post-cleaned dataset
Here is the summary of the cleaned dataset.
summary(samsung_sales_veryclean)## Year Quarter Product.Model X5G.Capability
## Min. :2019 Length:360 Length:360 No :120
## 1st Qu.:2020 Class :character Class :character Yes:240
## Median :2022 Mode :character Mode :character
## Mean :2022
## 3rd Qu.:2023
## Max. :2024
## Units.Sold Revenue.... Market.Share.... Regional.5G.Coverage....
## Min. : 5309 Min. : 2987436 Min. :0.000 Min. :25.34
## 1st Qu.:18955 1st Qu.:14453054 1st Qu.:2.820 1st Qu.:50.33
## Median :33018 Median :27846446 Median :3.715 Median :67.00
## Mean :32646 Mean :29907300 Mean :3.902 Mean :65.69
## 3rd Qu.:44534 3rd Qu.:41607018 3rd Qu.:5.280 3rd Qu.:80.36
## Max. :64883 Max. :84264944 Max. :6.950 Max. :99.95
## X5G.Subscribers..millions. Avg.5G.Speed..Mbps. Preference.for.5G....
## Min. : 0.34 Min. : 50.37 Min. :37.14
## 1st Qu.:18.95 1st Qu.:122.71 1st Qu.:53.54
## Median :29.94 Median :178.74 Median :67.47
## Mean :30.39 Mean :179.97 Mean :67.29
## 3rd Qu.:44.27 3rd Qu.:239.81 3rd Qu.:80.83
## Max. :54.94 Max. :298.70 Max. :94.84
## Region
## Length:360
## Class :character
## Mode :character
##
##
## Data Analysis
How do Samsung 5G and non-5G phone sales compare since the launch of the 5G network?
To answer our first question, we use line chart to see the trends over the years.
# Grouping by Year, Quarter, and 5G Capability
trend <- samsung_sales_veryclean %>%
group_by(Year, Quarter, X5G.Capability) %>%
summarise(Units_Sold = sum(Units.Sold), .groups = 'drop') %>%
mutate(Year_Quarter = paste(Year, paste0("Q", Quarter)))
# Plotting Line Chart
ggplot(trend, aes(x = Year_Quarter, y = Units_Sold, color = X5G.Capability, group = X5G.Capability)) +
geom_line(size = 1) +
geom_point(size = 2) +
theme_minimal() +
labs(title = "Sales Trend 5G vs Non-5G",
x = "Year & Quarter",
y = "Units Sold",
color = "5G Support") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
scale_color_manual(values = c("Yes" = "#5AB2FF", "No" = "#fee391")) +
scale_x_discrete(limits = unique(trend$Year_Quarter))+
scale_y_continuous(labels = scales::comma)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
From this line chart, it could be seen that:
- Samsung’s mobile phone with 5G-feature sales are consistently higher compared to non-5G
- The biggest increase in 5G units sold occurred in the Q1 and Q2 range in 2020
- The biggest decrease in 5G units sold occurred in the Q2 and Q3 range in 2023
Here is some strategies for Samsung according to the insights that we got:
- Prioritize 5G product launches in the first semester (Q1–Q2)
- Strengthen promotions and discounts in the second semester (Q3–Q4)
Do Regional 5G Coverage affects Sales?
To analyze how regional 5G coverage affects sales, we use clustered bar chart to visualize.
# 1. Grouping the regional 5G coverage into 5 group
samsung_sales_veryclean <- samsung_sales_veryclean %>%
mutate(Coverage_Group = cut(Regional.5G.Coverage....,
breaks = c(0, 20, 40, 60, 80, 100),
labels = c("0–20%", "21–40%", "41–60%", "61–80%", "81–100%")))
# 2. Agregate Units Sold by Region and Coverage Group
coverage_region_sales <- samsung_sales_veryclean %>%
group_by(Region, Coverage_Group) %>%
summarise(Total_Units_Sold = sum(Units.Sold), .groups = "drop")
# 3. Plot Clustered Bar Chart
ggplot(coverage_region_sales, aes(x = Region, y = Total_Units_Sold, fill = Coverage_Group)) +
geom_col(position = "dodge") +
theme_minimal() +
labs(title = "Total Units Sold in each Region by 5G Coverage",
x = "Region",
y = "Total Units Sold",
fill = "Coverage 5G (%)") +
scale_fill_brewer(palette = "Blues")+
scale_y_continuous(labels = scales::comma) #Formats the Y-axis values as thousands with comma as the separator
Insights from this clustered bar chart:
- North America is the region with the highest average sales.
- Asia-Pacific is the region with the lowest average sales.
- Sales tend to be higher in areas with high 5G Coverage.
Furthering our analysis to give strategies for future references, we use box plot to see 5G preferences in each regions.
#Plot Box Plot
ggplot(samsung_sales_veryclean, aes(x = Region, y = Preference.for.5G...., fill = Region)) +
geom_boxplot(fill="#6baed6",alpha = 0.7) +
theme_minimal() +
labs(title = "5G Preference by Region",
x = "Region",
y = "Preferensi 5G (%)") +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "none") +
scale_y_continuous(
labels = scales::percent_format(scale = 1), #Formats the Y-axis values as percentages.
limits = c(0, 100), #Y-axis start at 0 and end at 100.
expand = c(0, 0) #Removing white space
)
After analyzing both clustered bar chart and the box plot, here is our strategies recommendation:
- Increase promotion in regions with the highest median 5G preference, which is Latin America.
- Double down in regions with high 5G coverage (e.g., free storage upgrade, upgrade to Galaxy, product differentiation).
- Maintain 4G dominance in regions with limited 5G coverage, while slowly introducing 5G.
Do models that have 5g generate more revenue than models that don’t?
To see if models that have 5g get more revenue than models that don’t, we are using horizontal bar chart that contains top 3 5G models with the highest revenue and top 3 non-5G models with the highest revenue.
# Total revenue for each model
revenue_model <- samsung_sales_veryclean %>%
group_by(Product.Model) %>%
summarise(Total_Revenue = sum(Revenue....),FiveG = first(X5G.Capability), .groups = 'drop')
# Filtering top 3 for model with 5G
top3_5g <- revenue_model %>%
filter(FiveG == "Yes") %>%
arrange(desc(Total_Revenue)) %>%
slice_head(n = 3)
# Filtering top 3 for model without 5G
top3_non5g <- revenue_model %>%
filter(FiveG == "No") %>%
arrange(desc(Total_Revenue)) %>%
slice_head(n = 3)
# Binding the top threes
top3_both <- bind_rows(top3_5g, top3_non5g) %>%
mutate(Product.Model = reorder(Product.Model, Total_Revenue))
# Bar chart
ggplot(top3_both, aes(x = Product.Model, y = Total_Revenue, fill = FiveG)) +
geom_col() +
coord_flip() +
theme_minimal() +
geom_text(aes(label = Total_Revenue), vjust = -0.5, size = 3)+
labs(title = "Model's Revenue by 5G vs Non-5G",
x = "Model",
y = "Total Revenue",
fill = "5G Support") +
scale_fill_manual(values = c("No" = "#fee391", "Yes" = "skyblue"))+
scale_y_continuous(labels = scales::comma)
Some insights that we got from this bar chart:
- 5G models generate the highest revenue.
- Revenue difference between 5G and non-5G models is significant.
- 5G has a positive impact on mid-range HP sales, shown by A14 5G.
In the future, Samsung could produce more products with 5G-featured especially for the mid-range market to increase their revenue since the mid-range market has shown potential as well.
Conclusion
Based on the analysis, the adoption of 5G features in Samsung smartphones has shown a clear positive impact on both sales and revenue performance. Regions with higher 5G coverage tend to generate greater sales volumes, indicating that good network access plays a role in buying decisions. Additionally, Samsung models that support 5G consistently outperform non-5G models in terms of revenue, highlighting a market preference for 5G. Overall, 5G technology significantly contributes to the success of Samsung’s mobile products, influencing not only sales but also the overall financial return.