Examining common themed variables Emily Robinson Data Scientist - - PowerPoint PPT Presentation

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examining common themed variables

Examining common themed variables Emily Robinson Data Scientist - - PowerPoint PPT Presentation

Dec 21, 2022 207 likes •410 views

DataCamp Categorical Data in the Tidyverse CATEGORICAL DATA IN THE TIDYVERSE Examining common themed variables Emily Robinson Data Scientist DataCamp Categorical Data in the Tidyverse Tidying data WorkChallengeFrequencyExplaining

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DataCamp Categorical Data in the Tidyverse

Examining common themed variables

CATEGORICAL DATA IN THE TIDYVERSE

Emily Robinson

Data Scientist

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SLIDE 2

DataCamp Categorical Data in the Tidyverse

Tidying data

WorkChallengeFrequencyExplaining WorkChallengeFrequencyIntegration <chr> <chr> 1 Often Often 2 Most of the time Most of the time work_challenge frequency <chr> <chr> 1 Explaining Often 2 Explaining Most of the time 3 Integration Often 4 Integration Most of the time

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DataCamp Categorical Data in the Tidyverse

Selecting and gathering data

multipleChoiceResponses %>% select(contains("WorkChallengeFrequency")) %>% gather(work_challenge, frequency) # A tibble: 367,752 x 2 work_challenge frequency <chr> <chr> 1 WorkChallengeFrequencyPolitics Rarely 2 WorkChallengeFrequencyPolitics NA 3 WorkChallengeFrequencyPolitics NA 4 WorkChallengeFrequencyPolitics Often 5 WorkChallengeFrequencyPolitics Often 6 WorkChallengeFrequencyPolitics NA 7 WorkChallengeFrequencyPolitics NA 8 WorkChallengeFrequencyPolitics NA

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DataCamp Categorical Data in the Tidyverse

Changing strings

work_challenges <- multipleChoiceResponses %>% select(contains("WorkChallengeFrequency")) %>% gather(work_challenge, frequency) %>% mutate(work_challenge = str_remove(work_challenge, "WorkChallengeFrequency")) # A tibble: 367,752 x 2 work_challenge frequency <chr> <chr> 1 Politics Rarely 2 Politics NA 3 Politics NA 4 Politics Often 5 Politics Often 6 Politics NA 7 Politics NA

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DataCamp Categorical Data in the Tidyverse

if_else() and summarizing

work_challenges %>% filter(!is.na(frequency)) %>% mutate(frequency = if_else( frequency %in% c("Most of the time", "Often"), 1, 0) ) %>% group_by(work_challenge) %>% summarise(perc_problem = mean(frequency)) # A tibble: 22 x 2 work_challenge perc_problem <chr> <dbl> 1 Clarity 0.0930 2 DataAccess 0.0923 3 DataFunds 0.0367 4 Deployment 0.0265 5 DirtyData 0.176 6 DomainExpertise 0.0573

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DataCamp Categorical Data in the Tidyverse

Let's practice!

CATEGORICAL DATA IN THE TIDYVERSE

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DataCamp Categorical Data in the Tidyverse

Tricks of ggplot2

CATEGORICAL DATA IN THE TIDYVERSE

Emily Robinson

Instructor

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DataCamp Categorical Data in the Tidyverse

Job title data

job_titles_by_perc # A tibble: 16 x 2 CurrentJobTitleSelect perc_w_title <chr> <dbl> 1 Business Analyst 0.0673 2 Computer Scientist 0.0283 3 Data Analyst 0.103 4 Data Miner 0.00997 5 Data Scientist 0.206 6 DBA/Database Engineer 0.0158

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DataCamp Categorical Data in the Tidyverse

Initial plot

ggplot(job_titles_by_perc, aes(x = CurrentJobTitleSelect,, y = perc_w_title)) + geom_point()

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DataCamp Categorical Data in the Tidyverse

Changing tick labels angle

ggplot(job_titles_by_perc, aes(x = CurrentJobTitleSelect, y = perc_w_title)) + geom_point() + theme(axis.text.x = element_text(angle = 90, hjust = 1))

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DataCamp Categorical Data in the Tidyverse

Using fct_reorder()

ggplot(job_titles_by_perc, aes(x = fct_reorder(CurrentJobTitleSelect, perc_w_title), y = perc_w_title)) + geom_point() + theme(axis.text.x = element_text(angle = 90, hjust = 1))

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DataCamp Categorical Data in the Tidyverse

Adding fct_rev()

ggplot(job_titles_by_perc, aes(x = fct_rev(fct_reorder(CurrentJobTitleSelect, perc_w_title)), y = perc_w_title)) + geom_point() + theme(axis.text.x = element_text(angle = 90, hjust = 1))

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DataCamp Categorical Data in the Tidyverse

Using labs()

ggplot(job_titles_by_perc, aes(x = fct_rev(fct_reorder(CurrentJobTitleSelect, perc_w_title)), y = perc_w_title)) + geom_point() + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(x = "Job Title", y = "Percent with title")

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DataCamp Categorical Data in the Tidyverse

Changing to % scales

ggplot(job_titles_by_perc, aes(x = fct_rev(fct_reorder(CurrentJobTitleSelect, perc_w_title)), y = perc_w_title)) + geom_point() + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(x = "Job Title", y = "Percent with title") + scale_y_continuous(labels = scales::percent_format())

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DataCamp Categorical Data in the Tidyverse

Let's practice!

CATEGORICAL DATA IN THE TIDYVERSE

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DataCamp Categorical Data in the Tidyverse

Changing and creating variables with case_when()

CATEGORICAL DATA IN THE TIDYVERSE

Emily Robinson

Data Scientist

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DataCamp Categorical Data in the Tidyverse

case_when()

x <- 1:20 x [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 [19] 19 20 case_when(x %% 15 == 0 ~ "fizz buzz", x %% 3 == 0 ~ "fizz", x %% 5 == 0 ~ "buzz", TRUE ~ as.character(x) ) [1] "1" "2" "fizz" "4" [5] "buzz" "fizz" "7" "8" [9] "fizz" "buzz" "11" "fizz" [13] "13" "14" "fizz buzz" "16" [17] "17" "fizz" "19" "buzz"

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DataCamp Categorical Data in the Tidyverse

Order matters

case_when(x %% 3 == 0 ~ "fizz buzz", x %% 5 == 0 ~ "buzz", x %% 3 == 0 ~ "fuzzy buzz", TRUE ~ as.character(x) ) [1] "1" "2" "fizz buzz" "4" [5] "buzz" "fizz buzz" "7" "8" [9] "fizz buzz" "buzz" "11" "fizz buzz" [13] "13" "14" "fizz buzz" "16" [17] "17" "fizz buzz" "19" "buzz"

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DataCamp Categorical Data in the Tidyverse

case_when() with multiple variables

> moods # A tibble: 4 x 2 mood status <chr> <chr> 1 happy know it 2 happy do not know it 3 sad know it 4 happy know it moods %>% mutate(action = case_when( mood == "happy" & status == "know it" ~ "clap your hands", mood == "happy" & status == "do not know it" ~ "stomp your feet", mood == "sad" ~ "look at puppies", TRUE ~ "jump around")

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DataCamp Categorical Data in the Tidyverse

Let's practice!

CATEGORICAL DATA IN THE TIDYVERSE