Video Games and Sliced
March 16, 2021
Last updated: 2021-09-29
Checks: 7 0
Knit directory: myTidyTuesday/
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The #TidyTuesday data this week comes from Steam by way of Kaggle and originally came from SteamCharts. The data was scraped and uploaded to Kaggle.
suppressPackageStartupMessages({
library(tidyverse)
library(tidymodels)
library(lubridate)
library(hrbrthemes)
library(systemfonts)
})
source(here::here("code","_common.R"),
verbose = FALSE,
local = knitr::knit_global())
ggplot2::theme_set(theme_jim(base_size = 12))tt <- tidytuesdayR::tt_load("2021-03-16")
Downloading file 1 of 1: `games.csv`ttgames <- tt$games %>%
mutate(date = ym(paste0(as.character(year), month))) %>%
complete(nesting(date, gamename),
fill = list(avg = 0, peak = 0)
)Let’s have a look at the games themselves
Common games
Plot
games %>%
filter(avg > 0) %>%
count(gamename, sort = TRUE) %>%
ggplot(aes(n)) +
geom_histogram(bins = 30) +
labs(
y = "Count of Months in dataset",
x = "Number of Titles"
)
Discoveries:
There are some non-UTF characters in the game names.
There are 1248 distinct game names.
About 200 of the games appear in all 104 months of the study(??)
games %>%
group_by(date) %>%
summarise(engagement = sum(avg)) %>%
ggplot(aes(date, engagement)) +
geom_line() +
scale_y_continuous(labels = scales::comma) +
scale_x_date(
date_breaks = "year",
date_labels = "%Y"
) +
labs(
x = NULL, y = NULL, title = "Steam Engagement, Sum of Monthly Avg",
caption = paste0("Source: Kaggle")
)
The Sid Meier series:
games %>%
filter(str_detect(gamename, "Sid Meier")) %>%
mutate(
name = str_remove(gamename, "Sid Meier's Civilization"),
name = str_remove(name, "\\: ")
) %>%
ggplot(aes(date, peak, color = name)) +
geom_line() +
scale_y_continuous(labels = scales::comma) +
scale_x_date(
date_breaks = "year",
date_labels = "%Y"
) +
labs(
x = NULL,
y = NULL,
title = "Sid Meier's Civilization Peak Engagement on Steam",
caption = paste0("Source: Kaggle")
) +
theme(
legend.position = c(0.8, 0.75),
legend.background = element_rect(color = "white")
)
games %>%
filter(str_detect(gamename, "Sid Meier")) %>%
mutate(
name = str_remove(gamename, "Sid Meier's Civilization"),
name = str_remove(name, "\\: ")
) %>%
ggplot(aes(date, peak, color = name)) +
geom_line(show.legend = FALSE) +
scale_y_continuous(labels = scales::comma) +
scale_x_date(
date_breaks = "3 years",
date_labels = "%Y"
) +
facet_wrap(~name, scales = "free_y") +
labs(
x = NULL,
y = NULL,
title = "Sid Meier's Civilization Peak Engagement on Steam",
caption = paste0("Source: Kaggle")
)
I wonder if the all of the game engagement has a seasonal peak, like at a given month?
games %>%
mutate(
month = factor(month, levels = month.name),
month = fct_relabel(month, ~month.abb)
) %>%
group_by(year, month) %>%
summarise(
avg = median(avg),
.groups = "drop"
) %>%
filter(row_number() != first(row_number())) %>%
mutate(pandemic = case_when(
year %in% 2020:2021 ~ "pandemic",
TRUE ~ "the before times"
)) %>%
ggplot(aes(month, avg, group = year, color = pandemic)) +
geom_line(size = 1.4, alpha = 0.7) +
labs(
x = NULL,
color = NULL,
y = "Median concurrent players for all games",
title = "Video games on Steam and the pandemic",
subtitle = "The overall median number of concurrent players is higher during the pandemic",
caption = "Source: Kaggle"
) +
theme(legend.position = "top")
sessionInfo()R version 4.1.1 (2021-08-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 22000)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] systemfonts_1.0.2 hrbrthemes_0.8.0 lubridate_1.7.10 yardstick_0.0.8
[5] workflowsets_0.1.0 workflows_0.2.3 tune_0.1.6 rsample_0.1.0
[9] recipes_0.1.17 parsnip_0.1.7.900 modeldata_0.1.1 infer_1.0.0
[13] dials_0.0.10 scales_1.1.1 broom_0.7.9 tidymodels_0.1.3
[17] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
[21] readr_2.0.2 tidyr_1.1.4 tibble_3.1.4 ggplot2_3.3.5
[25] tidyverse_1.3.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.1 selectr_0.4-2
[4] plyr_1.8.6 tidytuesdayR_1.0.1 splines_4.1.1
[7] listenv_0.8.0 usethis_2.0.1 digest_0.6.27
[10] foreach_1.5.1 htmltools_0.5.2 viridis_0.6.1
[13] fansi_0.5.0 magrittr_2.0.1 tzdb_0.1.2
[16] globals_0.14.0 modelr_0.1.8 gower_0.2.2
[19] extrafont_0.17 vroom_1.5.5 R.utils_2.11.0
[22] extrafontdb_1.0 hardhat_0.1.6 colorspace_2.0-2
[25] rvest_1.0.1 textshaping_0.3.5 haven_2.4.3
[28] xfun_0.26 crayon_1.4.1 jsonlite_1.7.2
[31] survival_3.2-11 iterators_1.0.13 glue_1.4.2
[34] gtable_0.3.0 ipred_0.9-12 R.cache_0.15.0
[37] Rttf2pt1_1.3.9 future.apply_1.8.1 DBI_1.1.1
[40] Rcpp_1.0.7 viridisLite_0.4.0 bit_4.0.4
[43] GPfit_1.0-8 lava_1.6.10 prodlim_2019.11.13
[46] httr_1.4.2 ellipsis_0.3.2 farver_2.1.0
[49] pkgconfig_2.0.3 R.methodsS3_1.8.1 nnet_7.3-16
[52] sass_0.4.0 dbplyr_2.1.1 utf8_1.2.2
[55] here_1.0.1 labeling_0.4.2 tidyselect_1.1.1
[58] rlang_0.4.11 DiceDesign_1.9 later_1.3.0
[61] munsell_0.5.0 cellranger_1.1.0 tools_4.1.1
[64] cachem_1.0.6 cli_3.0.1 generics_0.1.0
[67] evaluate_0.14 fastmap_1.1.0 yaml_2.2.1
[70] ragg_1.1.3 bit64_4.0.5 knitr_1.36
[73] fs_1.5.0 future_1.22.1 whisker_0.4
[76] R.oo_1.24.0 xml2_1.3.2 compiler_4.1.1
[79] rstudioapi_0.13 curl_4.3.2 reprex_2.0.1
[82] lhs_1.1.3 bslib_0.3.0 stringi_1.7.4
[85] highr_0.9 gdtools_0.2.3 lattice_0.20-44
[88] Matrix_1.3-4 styler_1.6.2 conflicted_1.0.4
[91] vctrs_0.3.8 pillar_1.6.3 lifecycle_1.0.1
[94] furrr_0.2.3 jquerylib_0.1.4 httpuv_1.6.3
[97] R6_2.5.1 promises_1.2.0.1 gridExtra_2.3
[100] parallelly_1.28.1 codetools_0.2-18 MASS_7.3-54
[103] assertthat_0.2.1 rprojroot_2.0.2 withr_2.4.2
[106] parallel_4.1.1 hms_1.1.1 grid_4.1.1
[109] rpart_4.1-15 timeDate_3043.102 class_7.3-19
[112] rmarkdown_2.11 git2r_0.28.0 pROC_1.18.0