Beta
day30report <- read_csv("/tank301/onedrive/OneDrive_Personal/job_stuff/portfolio/Data/day30report.csv")
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## TitleId = col_character(),
## Ts = col_datetime(format = ""),
## `Total Logins` = col_double(),
## `Unique Logins` = col_double(),
## UniquePayers = col_double(),
## Revenue = col_character(),
## Purchases = col_double(),
## `Total Calls` = col_number(),
## `Total Successful Calls` = col_number(),
## `Total Errors` = col_number(),
## Arpu = col_character(),
## Arppu = col_character(),
## `Average Purchase Price` = col_character(),
## `New Users` = col_double()
## )
glimpse(day30report)
## Rows: 12
## Columns: 14
## $ TitleId <chr> "9CEB3", "9CEB3", "9CEB3", "9CEB3", "9CEB3", …
## $ Ts <dttm> 2021-02-11, 2021-02-20, 2021-02-21, 2021-02-…
## $ `Total Logins` <dbl> 3, 25, 52, 88, 428, 453, 421, 548, 853, 687, …
## $ `Unique Logins` <dbl> 1, 3, 21, 55, 239, 264, 242, 322, 426, 348, 2…
## $ UniquePayers <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
## $ Revenue <chr> "$0.00", "$0.00", "$0.00", "$0.00", "$0.00", …
## $ Purchases <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
## $ `Total Calls` <dbl> 52, 529, 1533, 2778, 12017, 13071, 12011, 155…
## $ `Total Successful Calls` <dbl> 49, 523, 1465, 2636, 11160, 12004, 10822, 142…
## $ `Total Errors` <dbl> 3, 6, 68, 142, 857, 1067, 1189, 1296, 2182, 2…
## $ Arpu <chr> "$0.00", "$0.00", "$0.00", "$0.00", "$0.00", …
## $ Arppu <chr> "$0.00", "$0.00", "$0.00", "$0.00", "$0.00", …
## $ `Average Purchase Price` <chr> "$0.00", "$0.00", "$0.00", "$0.00", "$0.00", …
## $ `New Users` <dbl> 1, 3, 18, 49, 219, 230, 203, 266, 351, 269, 1…
untouched data
ggplot(day30report, aes(Ts, `New Users`))+
geom_line()+
theme_clean()+
labs(title = "New Players since 2/11") + xlab("Time Stamp")
removing crashed data
d30 <-
day30report[-c(10,11,12),]
str(d30)
## tibble [9 × 14] (S3: tbl_df/tbl/data.frame)
## $ TitleId : chr [1:9] "9CEB3" "9CEB3" "9CEB3" "9CEB3" ...
## $ Ts : POSIXct[1:9], format: "2021-02-11" "2021-02-20" ...
## $ Total Logins : num [1:9] 3 25 52 88 428 453 421 548 853
## $ Unique Logins : num [1:9] 1 3 21 55 239 264 242 322 426
## $ UniquePayers : num [1:9] 0 0 0 0 0 0 0 0 0
## $ Revenue : chr [1:9] "$0.00" "$0.00" "$0.00" "$0.00" ...
## $ Purchases : num [1:9] 0 0 0 0 0 0 0 0 0
## $ Total Calls : num [1:9] 52 529 1533 2778 12017 ...
## $ Total Successful Calls: num [1:9] 49 523 1465 2636 11160 ...
## $ Total Errors : num [1:9] 3 6 68 142 857 ...
## $ Arpu : chr [1:9] "$0.00" "$0.00" "$0.00" "$0.00" ...
## $ Arppu : chr [1:9] "$0.00" "$0.00" "$0.00" "$0.00" ...
## $ Average Purchase Price: chr [1:9] "$0.00" "$0.00" "$0.00" "$0.00" ...
## $ New Users : num [1:9] 1 3 18 49 219 230 203 266 351
Edited data graph
ggplot(d30, aes(Ts, `New Users`))+
geom_line()+
theme_clean()+
labs(title = "New Players since 2/11") + xlab("Time Stamp")
mutated Error function
d50 <- day30report %>%
mutate(errors = ave(`Total Errors`, FUN=function(x) c(0, diff(x))))
str(d50)
## spec_tbl_df [12 × 15] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ TitleId : chr [1:12] "9CEB3" "9CEB3" "9CEB3" "9CEB3" ...
## $ Ts : POSIXct[1:12], format: "2021-02-11" "2021-02-20" ...
## $ Total Logins : num [1:12] 3 25 52 88 428 453 421 548 853 687 ...
## $ Unique Logins : num [1:12] 1 3 21 55 239 264 242 322 426 348 ...
## $ UniquePayers : num [1:12] 0 0 0 0 0 0 0 0 0 0 ...
## $ Revenue : chr [1:12] "$0.00" "$0.00" "$0.00" "$0.00" ...
## $ Purchases : num [1:12] 0 0 0 0 0 0 0 0 0 0 ...
## $ Total Calls : num [1:12] 52 529 1533 2778 12017 ...
## $ Total Successful Calls: num [1:12] 49 523 1465 2636 11160 ...
## $ Total Errors : num [1:12] 3 6 68 142 857 ...
## $ Arpu : chr [1:12] "$0.00" "$0.00" "$0.00" "$0.00" ...
## $ Arppu : chr [1:12] "$0.00" "$0.00" "$0.00" "$0.00" ...
## $ Average Purchase Price: chr [1:12] "$0.00" "$0.00" "$0.00" "$0.00" ...
## $ New Users : num [1:12] 1 3 18 49 219 230 203 266 351 269 ...
## $ errors : num [1:12] 0 3 62 74 715 210 122 107 886 297 ...
## - attr(*, "spec")=
## .. cols(
## .. TitleId = col_character(),
## .. Ts = col_datetime(format = ""),
## .. `Total Logins` = col_double(),
## .. `Unique Logins` = col_double(),
## .. UniquePayers = col_double(),
## .. Revenue = col_character(),
## .. Purchases = col_double(),
## .. `Total Calls` = col_number(),
## .. `Total Successful Calls` = col_number(),
## .. `Total Errors` = col_number(),
## .. Arpu = col_character(),
## .. Arppu = col_character(),
## .. `Average Purchase Price` = col_character(),
## .. `New Users` = col_double()
## .. )
d50 plot
ggplot(d50, aes(Ts, `errors`))+
geom_line()+
theme_clean()+
labs(title = "number of errors since last time stamp 2/11") + xlab("Time Stamp")
view(d30)
Alpha
alpha30 <- read_csv("/tank301/onedrive/OneDrive_Personal/job_stuff/portfolio/Data/alphadata30.csv")
alpha30
## # A tibble: 30 x 14
## TitleId Ts `Total Logins` `Unique Logins` UniquePayers
## <chr> <dttm> <dbl> <dbl> <dbl>
## 1 D8AB8 2021-01-20 00:00:00 25 13 0
## 2 D8AB8 2021-01-21 00:00:00 53 15 0
## 3 D8AB8 2021-01-22 00:00:00 18 7 0
## 4 D8AB8 2021-01-23 00:00:00 33 9 0
## 5 D8AB8 2021-01-24 00:00:00 56 14 0
## 6 D8AB8 2021-01-25 00:00:00 37 9 0
## 7 D8AB8 2021-01-26 00:00:00 42 9 0
## 8 D8AB8 2021-01-27 00:00:00 45 5 0
## 9 D8AB8 2021-01-28 00:00:00 58 13 0
## 10 D8AB8 2021-01-29 00:00:00 131 14 0
## # … with 20 more rows, and 9 more variables: Revenue <chr>, Purchases <dbl>,
## # Total Calls <dbl>, Total Successful Calls <dbl>, Total Errors <dbl>,
## # Arpu <chr>, Arppu <chr>, Average Purchase Price <chr>, New Users <dbl>
untouched data
ggplot(alpha30, aes(Ts, `New Users`))+
geom_line()+
theme_clean()+
labs(title = "New Players since 2/11") + xlab("Time Stamp")
removing crashed data
a30 <-
alpha30[-c(10,11,12),]
str(d30)
## tibble [9 × 14] (S3: tbl_df/tbl/data.frame)
## $ TitleId : chr [1:9] "9CEB3" "9CEB3" "9CEB3" "9CEB3" ...
## $ Ts : POSIXct[1:9], format: "2021-02-11" "2021-02-20" ...
## $ Total Logins : num [1:9] 3 25 52 88 428 453 421 548 853
## $ Unique Logins : num [1:9] 1 3 21 55 239 264 242 322 426
## $ UniquePayers : num [1:9] 0 0 0 0 0 0 0 0 0
## $ Revenue : chr [1:9] "$0.00" "$0.00" "$0.00" "$0.00" ...
## $ Purchases : num [1:9] 0 0 0 0 0 0 0 0 0
## $ Total Calls : num [1:9] 52 529 1533 2778 12017 ...
## $ Total Successful Calls: num [1:9] 49 523 1465 2636 11160 ...
## $ Total Errors : num [1:9] 3 6 68 142 857 ...
## $ Arpu : chr [1:9] "$0.00" "$0.00" "$0.00" "$0.00" ...
## $ Arppu : chr [1:9] "$0.00" "$0.00" "$0.00" "$0.00" ...
## $ Average Purchase Price: chr [1:9] "$0.00" "$0.00" "$0.00" "$0.00" ...
## $ New Users : num [1:9] 1 3 18 49 219 230 203 266 351
Edited data graph
ggplot(a30, aes(Ts, `New Users`))+
geom_line()+
theme_clean()+
labs(title = "New Players since 2/11") + xlab("Time Stamp")
mutated Error function
a50 <- alpha30 %>%
mutate(errors = ave(`Total Errors`, FUN=function(x) c(0, diff(x))))
str(a50)
## spec_tbl_df [30 × 15] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ TitleId : chr [1:30] "D8AB8" "D8AB8" "D8AB8" "D8AB8" ...
## $ Ts : POSIXct[1:30], format: "2021-01-20" "2021-01-21" ...
## $ Total Logins : num [1:30] 25 53 18 33 56 37 42 45 58 131 ...
## $ Unique Logins : num [1:30] 13 15 7 9 14 9 9 5 13 14 ...
## $ UniquePayers : num [1:30] 0 0 0 0 0 0 0 0 0 0 ...
## $ Revenue : chr [1:30] "$0.00" "$0.00" "$0.00" "$0.00" ...
## $ Purchases : num [1:30] 0 0 0 0 0 0 0 0 0 0 ...
## $ Total Calls : num [1:30] 951 1938 1050 671 1442 ...
## $ Total Successful Calls: num [1:30] 947 1851 1050 663 1426 ...
## $ Total Errors : num [1:30] 4 87 0 8 16 24 97 0 38 141 ...
## $ Arpu : chr [1:30] "$0.00" "$0.00" "$0.00" "$0.00" ...
## $ Arppu : chr [1:30] "$0.00" "$0.00" "$0.00" "$0.00" ...
## $ Average Purchase Price: chr [1:30] "$0.00" "$0.00" "$0.00" "$0.00" ...
## $ New Users : num [1:30] 1 3 0 1 3 2 4 0 3 5 ...
## $ errors : num [1:30] 0 83 -87 8 8 8 73 -97 38 103 ...
## - attr(*, "spec")=
## .. cols(
## .. TitleId = col_character(),
## .. Ts = col_datetime(format = ""),
## .. `Total Logins` = col_double(),
## .. `Unique Logins` = col_double(),
## .. UniquePayers = col_double(),
## .. Revenue = col_character(),
## .. Purchases = col_double(),
## .. `Total Calls` = col_number(),
## .. `Total Successful Calls` = col_number(),
## .. `Total Errors` = col_double(),
## .. Arpu = col_character(),
## .. Arppu = col_character(),
## .. `Average Purchase Price` = col_character(),
## .. `New Users` = col_double()
## .. )
d50 plot
ggplot(a50, aes(Ts, `errors`))+
geom_line()+
theme_clean()+
labs(title = "number of errors since last time stamp 2/11") + xlab("Time Stamp")
view(a30)