library(tidyverse)
library(repurrrsive)
Exercise 1: Import and Combine Data
# Get all Month CSV files
file_list <- list.files("data/", pattern = "Month-.*\\.csv$", full.names = TRUE)
# Simple month mapping
month_names <- c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")
# Import files
df <- data.frame()
for (i in 1:length(file_list)) {
temp <- read_csv(file_list[i])
temp$Month <- month_names[i]
df <- bind_rows(df, temp)
}
## Rows: 54535 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): Factor_C, Factor_E, Transaction_Status, Month
## dbl (5): Account_ID, Factor_A, Factor_B, Factor_D, Response
## dttm (1): Transaction_Timestamp
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 44380 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): Factor_C, Factor_E, Transaction_Status, Month
## dbl (5): Account_ID, Factor_A, Factor_B, Factor_D, Response
## dttm (1): Transaction_Timestamp
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 53259 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): Factor_C, Factor_E, Transaction_Status, Month
## dbl (5): Account_ID, Factor_A, Factor_B, Factor_D, Response
## dttm (1): Transaction_Timestamp
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 51033 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): Factor_C, Factor_E, Transaction_Status, Month
## dbl (5): Account_ID, Factor_A, Factor_B, Factor_D, Response
## dttm (1): Transaction_Timestamp
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 55079 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): Factor_C, Factor_E, Transaction_Status, Month
## dbl (5): Account_ID, Factor_A, Factor_B, Factor_D, Response
## dttm (1): Transaction_Timestamp
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 59666 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): Factor_C, Factor_E, Transaction_Status, Month
## dbl (5): Account_ID, Factor_A, Factor_B, Factor_D, Response
## dttm (1): Transaction_Timestamp
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 64268 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): Factor_C, Factor_E, Transaction_Status, Month
## dbl (5): Account_ID, Factor_A, Factor_B, Factor_D, Response
## dttm (1): Transaction_Timestamp
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 69492 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): Factor_C, Factor_E, Transaction_Status, Month
## dbl (5): Account_ID, Factor_A, Factor_B, Factor_D, Response
## dttm (1): Transaction_Timestamp
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 71855 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): Factor_C, Factor_E, Transaction_Status, Month
## dbl (5): Account_ID, Factor_A, Factor_B, Factor_D, Response
## dttm (1): Transaction_Timestamp
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 80277 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): Factor_C, Factor_E, Transaction_Status, Month
## dbl (5): Account_ID, Factor_A, Factor_B, Factor_D, Response
## dttm (1): Transaction_Timestamp
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 94315 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): Factor_C, Factor_E, Transaction_Status, Month
## dbl (5): Account_ID, Factor_A, Factor_B, Factor_D, Response
## dttm (1): Transaction_Timestamp
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
glimpse(df)
## Rows: 698,159
## Columns: 10
## $ Account_ID <dbl> 5, 16, 28, 40, 62, 64, 69, 69, 70, 79, 88, 90, 9…
## $ Transaction_Timestamp <dttm> 2009-01-08 00:16:41, 2009-01-20 22:40:08, 2009-…
## $ Factor_A <dbl> 2, 2, 2, 2, 2, 7, 2, 2, 2, 7, 8, 10, 10, 2, 2, 2…
## $ Factor_B <dbl> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 18, 6, 6, 6, 6, 6,…
## $ Factor_C <chr> "VI", "VI", "VI", "VI", "VI", "MC", "VI", "VI", …
## $ Factor_D <dbl> 20, 20, 21, 20, 20, 20, 20, 20, 20, 20, 20, 20, …
## $ Factor_E <chr> "A", "H", "NULL", "H", "B", "NULL", "H", "H", "B…
## $ Response <dbl> 1020, 1020, 1020, 1020, 1020, 1020, 1020, 1020, …
## $ Transaction_Status <chr> "Approved", "Approved", "Approved", "Approved", …
## $ Month <chr> "Jan", "Jan", "Jan", "Jan", "Jan", "Jan", "Jan",…
Exercise 2: Check Column Classes
map(df, class)
## $Account_ID
## [1] "numeric"
##
## $Transaction_Timestamp
## [1] "POSIXct" "POSIXt"
##
## $Factor_A
## [1] "numeric"
##
## $Factor_B
## [1] "numeric"
##
## $Factor_C
## [1] "character"
##
## $Factor_D
## [1] "numeric"
##
## $Factor_E
## [1] "character"
##
## $Response
## [1] "numeric"
##
## $Transaction_Status
## [1] "character"
##
## $Month
## [1] "character"
Exercise 3: Count Unique Values
map_int(df, ~length(unique(.x)))
## Account_ID Transaction_Timestamp Factor_A
## 475413 686538 7
## Factor_B Factor_C Factor_D
## 6 4 15
## Factor_E Response Transaction_Status
## 63 42 2
## Month
## 11
Exercise 4: Recode Factor_D
df <- df %>% mutate(Factor_D = ifelse(Factor_D == 26, 25, Factor_D))
length(unique(df$Factor_D))
## [1] 14
table(df$Factor_D)
##
## 10 15 20 21 25 30 31 35 40 50 55
## 4595 1089 527882 68072 41021 7030 512 25298 2720 3709 15200
## 70 85 90
## 54 4 973
Exercise 5: Filter Out NULL Values
df <- df %>% filter_at(vars(starts_with("Factor_")), all_vars(. != "NULL"))
nrow(df)
## [1] 489537
Exercise 6: Convert to Factors
df <- df %>%
mutate_at(vars(-Transaction_Timestamp, -Month), as.factor) %>%
mutate(Month = factor(Month, levels = c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"), ordered = TRUE))
glimpse(df)
## Rows: 489,537
## Columns: 10
## $ Account_ID <fct> 5, 16, 40, 62, 69, 69, 70, 95, 101, 101, 102, 10…
## $ Transaction_Timestamp <dttm> 2009-01-08 00:16:41, 2009-01-20 22:40:08, 2009-…
## $ Factor_A <fct> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
## $ Factor_B <fct> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, …
## $ Factor_C <fct> VI, VI, VI, VI, VI, VI, VI, VI, VI, VI, VI, VI, …
## $ Factor_D <fct> 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, …
## $ Factor_E <fct> A, H, H, B, H, H, B, G, G2, G2, A, A, C, A, A, A…
## $ Response <fct> 1020, 1020, 1020, 1020, 1020, 1020, 1020, 1020, …
## $ Transaction_Status <fct> Approved, Approved, Approved, Approved, Approved…
## $ Month <ord> Jan, Jan, Jan, Jan, Jan, Jan, Jan, Jan, Jan, Jan…
levels(df$Month)
## [1] "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec"
Exercise 7: Summarize Unique Values
df %>% summarize_if(is.factor, n_distinct)
## Account_ID Factor_A Factor_B Factor_C Factor_D Factor_E Response
## 1 324174 2 3 2 12 62 30
## Transaction_Status Month
## 1 2 11
df %>%
group_by(Transaction_Status) %>%
summarize_if(is.factor, n_distinct)
## # A tibble: 2 × 9
## Transaction_Status Account_ID Factor_A Factor_B Factor_C Factor_D Factor_E
## <fct> <int> <int> <int> <int> <int> <int>
## 1 Approved 316172 2 3 2 11 59
## 2 Declined 14066 2 3 2 11 57
## # ℹ 2 more variables: Response <int>, Month <int>
Exercise 8: Quarter Conversion
convert_to_qtr <- function(months) {
case_when(
months %in% c("Jan", "Feb", "Mar") ~ "Q1",
months %in% c("Apr", "May", "Jun") ~ "Q2",
months %in% c("Jul", "Aug", "Sep") ~ "Q3",
months %in% c("Oct", "Nov", "Dec") ~ "Q4"
)
}
example_months <- c("Jan", "Mar", "May", "May", "Aug", "Nov", "Nov", "Dec")
convert_to_qtr(example_months)
## [1] "Q1" "Q1" "Q2" "Q2" "Q3" "Q4" "Q4" "Q4"
df <- df %>% mutate(Qtr = convert_to_qtr(Month))
df %>% count(Qtr)
## Qtr n
## 1 Q1 85588
## 2 Q2 100227
## 3 Q3 161071
## 4 Q4 142651
Exercise 9: Star Wars Character Names
sw_people %>% map_chr("name")
## [1] "Luke Skywalker" "C-3PO" "R2-D2"
## [4] "Darth Vader" "Leia Organa" "Owen Lars"
## [7] "Beru Whitesun lars" "R5-D4" "Biggs Darklighter"
## [10] "Obi-Wan Kenobi" "Anakin Skywalker" "Wilhuff Tarkin"
## [13] "Chewbacca" "Han Solo" "Greedo"
## [16] "Jabba Desilijic Tiure" "Wedge Antilles" "Jek Tono Porkins"
## [19] "Yoda" "Palpatine" "Boba Fett"
## [22] "IG-88" "Bossk" "Lando Calrissian"
## [25] "Lobot" "Ackbar" "Mon Mothma"
## [28] "Arvel Crynyd" "Wicket Systri Warrick" "Nien Nunb"
## [31] "Qui-Gon Jinn" "Nute Gunray" "Finis Valorum"
## [34] "Jar Jar Binks" "Roos Tarpals" "Rugor Nass"
## [37] "Ric Olié" "Watto" "Sebulba"
## [40] "Quarsh Panaka" "Shmi Skywalker" "Darth Maul"
## [43] "Bib Fortuna" "Ayla Secura" "Dud Bolt"
## [46] "Gasgano" "Ben Quadinaros" "Mace Windu"
## [49] "Ki-Adi-Mundi" "Kit Fisto" "Eeth Koth"
## [52] "Adi Gallia" "Saesee Tiin" "Yarael Poof"
## [55] "Plo Koon" "Mas Amedda" "Gregar Typho"
## [58] "Cordé" "Cliegg Lars" "Poggle the Lesser"
## [61] "Luminara Unduli" "Barriss Offee" "Dormé"
## [64] "Dooku" "Bail Prestor Organa" "Jango Fett"
## [67] "Zam Wesell" "Dexter Jettster" "Lama Su"
## [70] "Taun We" "Jocasta Nu" "Ratts Tyerell"
## [73] "R4-P17" "Wat Tambor" "San Hill"
## [76] "Shaak Ti" "Grievous" "Tarfful"
## [79] "Raymus Antilles" "Sly Moore" "Tion Medon"
## [82] "Finn" "Rey" "Poe Dameron"
## [85] "BB8" "Captain Phasma" "Padmé Amidala"
Exercise 10: Count Films per Character
sw_people %>% map_int(~length(.x$films))
## [1] 5 6 7 4 5 3 3 1 1 6 3 2 5 4 1 3 3 1 5 5 3 1 1 2 1 2 1 1 1 1 1 3 1 2 1 1 1 2
## [39] 1 1 2 1 1 3 1 1 1 3 3 3 2 2 2 1 3 2 1 1 1 2 2 1 1 2 2 1 1 1 1 1 1 1 2 1 1 2
## [77] 1 1 2 2 1 1 1 1 1 1 3
Exercise 11: Plot Films per Character
sw_people %>%
map_chr("name") %>%
set_names(sw_people, nm = .) %>%
map_df(~tibble(Films = length(.x$films)), .id = "Character") %>%
pivot_longer(
cols = Films,
names_to = "Variable",
values_to = "Films"
) %>% ggplot(aes(Films, reorder(Character, Films))) +
geom_point()
