Publications by Job Boonstoppel
DAT 3000 Code Along 10
Chapter 15 Creating factors General Social Survey gss_cat ## # A tibble: 21,483 × 9 ## year marital age race rincome partyid relig denom tvhours ## <int> <fct> <int> <fct> <fct> <fct> <fct> <fct> <int> ## 1 2000 Never married 26 White $8000 to 9999 Ind,near … Prot… Sout… 12 ## 2...
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DAT 3000 - Apply 9
Import your data # excel file data <- read_excel("myData_charts.xlsx") data ## # A tibble: 45,090 × 10 ## stock_symbol date open high low close adj_close volume ## <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 AAPL 2010-01-04 00:00:00 7.62 7.66 7.58 7.64 6.52 4937296...
227 sym
DAT 3000 Code Along 9 Chapter 14
Introduction String basics chac_data <- "I'm 'very' hungry." stringr::str_c(c("I", " am"), collapse = "") ## [1] "I am" stringr::str_c("I", " am", sep = " ;") ## [1] "I ; am" str_sort(c("John", "Mary", "Aaron")) ## [1] "Aaron" "John" "Mary" Matching patterns with regular expressions flights %>% glimpse() ## Rows: 336,776 ## Columns: 19 ## $ yea...
360 sym
DAT 3000 - Apply 8
Import your data # excel file data <- read_excel("myData_charts.xlsx") data ## # A tibble: 45,090 × 10 ## stock_symbol date open high low close adj_close volume ## <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 AAPL 2010-01-04 00:00:00 7.62 7.66 7.58 7.64 6.52 4937296...
612 sym
DAT 3000 - Apply 7
Import your data # excel file data <- read_excel("myData_charts.xlsx") data ## # A tibble: 45,090 × 10 ## stock_symbol date open high low close adj_close volume ## <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 AAPL 2010-01-04 00:00:00 7.62 7.66 7.58 7.64 6.52 4937296...
171 sym Python (5999 sym/16 pcs)
DAT 3000 Code Along 8
Introduction nycflights13 Keys Mutating joins Inner join x <- tribble( ~key, ~val_x, 1, "x1", 2, "x2", 3, "x3" ) y <- tribble( ~key, ~val_y, 1, "y1", 2, "y2", 4, "y3" ) inner_join(x, y) ## Joining with `by = join_by(key)` ## # A tibble: 2 × 3 ## key val_x val_y ## <dbl> <chr> <chr> ## 1 1 x1 y1 ...
204 sym Python (5274 sym/43 pcs)
DAT 3000 Code Along 7
Tidy data Pivoting long to wide form table4a_long <- table4a %>% pivot_longer(cols = c('1999', '2000'), names_to = "year", values_to = "cases") table4a_long ## # A tibble: 6 × 3 ## country year cases ## <chr> <chr> <dbl> ## 1 Afghanistan 1999 745 ## 2 Afghanistan 2000 2666 ## 3 Br...
181 sym Python (2903 sym/17 pcs)
DAT 3000 - Apply 6
Import data ## # A tibble: 45,090 × 10 ## stock_symbol date open high low close adj_close volume ## <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 AAPL 2010-01-04 00:00:00 7.62 7.66 7.58 7.64 6.52 493729600 ## 2 AAPL 2010-01-05 00:00:00 7.66 7.70 7.62 7.6...
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DAT 3000 Code Along 6
Introduction Question 2 Variation Visualizing distributions diamonds %>% ggplot(aes(x = cut)) + geom_bar() diamonds %>% ggplot(mapping = aes(x = carat)) + geom_histogram(binwidth = 0.5) diamonds %>% filter(carat < 3) %>% ggplot(aes(x = carat)) + geom_histogram(binwidth = 0.5) diamonds %>% ggplot(aes(x = ca...
287 sym R (1856 sym/19 pcs) 15 img
DAT 3000 - Apply 5
Import data Shows my whole dataset ## tibble [45,090 × 10] (S3: tbl_df/tbl/data.frame) ## $ stock_symbol: chr [1:45090] "AAPL" "AAPL" "AAPL" "AAPL" ... ## $ date : POSIXct[1:45090], format: "2010-01-04" "2010-01-05" ... ## $ open : num [1:45090] 7.62 7.66 7.66 7.56 7.51 ... ## $ high : num [1:45090] 7.66 7.7 7.69 7.57 7.57...
1152 sym 2 img