TidyVerse: Worked Example
Mubashira Qari
April 22, 2022
Objective: Create an Example:
I am using couple of TidyVerse packages here, and college ‘major_list’ dataset from fivethirtyeight.com , and creating a programming sample “vignette” that demonstrates how to use one or more of the capabilities of the selected TidyVerse package with my selected dataset.
Readr – Reading Files
The first package of TidyVerse to read the file is “Readr” It is a fast way to read rectangular data from delimited files, such as comma-separated values (CSV) and tab-separated values (TSV).
The easiest way to get readr is to install the whole tidyverse:
readr supports the following file formats with these read_*() functions:
read_csv(): comma-separated values (CSV) files read_tsv(): tab-separated values (TSV) files read_delim(): delimited files (CSV and TSV are important special cases) read_fwf(): fixed-width files read_table(): whitespace-separated files read_log(): web log files
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.1.3## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.2 v dplyr 1.0.7
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## Warning: package 'ggplot2' was built under R version 4.1.2## Warning: package 'stringr' was built under R version 4.1.2## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(readr)
library(dplyr)Fetching the majors-list dataset from the relational dataset on GitHub
college_major <- read_csv("https://raw.githubusercontent.com/uzmabb182/CUNY-SPS-Assignments/main/data_607/tidyverse_assignment/majors-list.csv", na="")##
## -- Column specification --------------------------------------------------------
## cols(
## FOD1P = col_character(),
## Major = col_character(),
## Major_Category = col_character()
## )head(college_major)## # A tibble: 6 x 3
## FOD1P Major Major_Category
## <chr> <chr> <chr>
## 1 1100 GENERAL AGRICULTURE Agriculture & Natural Resources
## 2 1101 AGRICULTURE PRODUCTION AND MANAGEMENT Agriculture & Natural Resources
## 3 1102 AGRICULTURAL ECONOMICS Agriculture & Natural Resources
## 4 1103 ANIMAL SCIENCES Agriculture & Natural Resources
## 5 1104 FOOD SCIENCE Agriculture & Natural Resources
## 6 1105 PLANT SCIENCE AND AGRONOMY Agriculture & Natural ResourcesUsing package ‘Deplyr’ – to Rename Columns
For rename():
college_major <- as_tibble(college_major) # so it prints a little nicercollege_major <- rename(college_major, Major_Id = FOD1P)
head(college_major)## # A tibble: 6 x 3
## Major_Id Major Major_Category
## <chr> <chr> <chr>
## 1 1100 GENERAL AGRICULTURE Agriculture & Natural Resources
## 2 1101 AGRICULTURE PRODUCTION AND MANAGEMENT Agriculture & Natural Resources
## 3 1102 AGRICULTURAL ECONOMICS Agriculture & Natural Resources
## 4 1103 ANIMAL SCIENCES Agriculture & Natural Resources
## 5 1104 FOOD SCIENCE Agriculture & Natural Resources
## 6 1105 PLANT SCIENCE AND AGRONOMY Agriculture & Natural ResourcesReading ‘all-ages’ dataset
all_ages <- read_csv("https://raw.githubusercontent.com/uzmabb182/CUNY-SPS-Assignments/main/data_607/tidyverse_assignment/all-ages.csv", na="")##
## -- Column specification --------------------------------------------------------
## cols(
## Major_code = col_double(),
## Major = col_character(),
## Major_category = col_character(),
## Total = col_double(),
## Employed = col_double(),
## Employed_full_time_year_round = col_double(),
## Unemployed = col_double(),
## Unemployment_rate = col_double(),
## Median = col_double(),
## P25th = col_double(),
## P75th = col_double()
## )head(all_ages)## # A tibble: 6 x 11
## Major_code Major Major_category Total Employed Employed_full_t~ Unemployed
## <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1100 GENERA~ Agriculture & ~ 128148 90245 74078 2423
## 2 1101 AGRICU~ Agriculture & ~ 95326 76865 64240 2266
## 3 1102 AGRICU~ Agriculture & ~ 33955 26321 22810 821
## 4 1103 ANIMAL~ Agriculture & ~ 103549 81177 64937 3619
## 5 1104 FOOD S~ Agriculture & ~ 24280 17281 12722 894
## 6 1105 PLANT ~ Agriculture & ~ 79409 63043 51077 2070
## # ... with 4 more variables: Unemployment_rate <dbl>, Median <dbl>,
## # P25th <dbl>, P75th <dbl>This ‘pipe operator’ replicate SQL query
summary <- all_ages %>%
group_by(Major_category) %>%
summarise(Employed = mean(Employed, na.rm = TRUE)) %>%
arrange(desc(Employed))
summary## # A tibble: 16 x 2
## Major_category Employed
## <chr> <dbl>
## 1 Business 579219.
## 2 Communications & Journalism 355760.
## 3 Social Science 207978.
## 4 Health 182724.
## 5 Education 177075.
## 6 Humanities & Liberal Arts 166612.
## 7 Arts 163587.
## 8 Psychology & Social Work 156887
## 9 Law & Public Policy 143785.
## 10 Computers & Mathematics 128237
## 11 Industrial Arts & Consumer Services 107683.
## 12 Engineering 90413.
## 13 Physical Sciences 70713.
## 14 Biology & Life Science 67647
## 15 Agriculture & Natural Resources 48042.
## 16 Interdisciplinary 35706Using the ‘ggplot’ for visualizing which category has most most employed
ggplot(summary, aes(x = Employed, y = Major_category, fill = Major_category)) +
geom_col(position = "dodge")
Reading the ‘grad-students’ dataset
grad_students <- read_csv("https://raw.githubusercontent.com/uzmabb182/CUNY-SPS-Assignments/main/data_607/tidyverse_assignment/grad-students.csv", na="")##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Major = col_character(),
## Major_category = col_character()
## )
## i Use `spec()` for the full column specifications.head(grad_students)## # A tibble: 6 x 22
## Major_code Major Major_category Grad_total Grad_sample_size Grad_employed
## <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 5601 CONSTRUC~ Industrial Art~ 9173 200 7098
## 2 6004 COMMERCI~ Arts 53864 882 40492
## 3 6211 HOSPITAL~ Business 24417 437 18368
## 4 2201 COSMETOL~ Industrial Art~ 5411 72 3590
## 5 2001 COMMUNIC~ Computers & Ma~ 9109 171 7512
## 6 3201 COURT RE~ Law & Public P~ 1542 22 1008
## # ... with 16 more variables: Grad_full_time_year_round <dbl>,
## # Grad_unemployed <dbl>, Grad_unemployment_rate <dbl>, Grad_median <dbl>,
## # Grad_P25 <dbl>, Grad_P75 <dbl>, Nongrad_total <dbl>,
## # Nongrad_employed <dbl>, Nongrad_full_time_year_round <dbl>,
## # Nongrad_unemployed <dbl>, Nongrad_unemployment_rate <dbl>,
## # Nongrad_median <dbl>, Nongrad_P25 <dbl>, Nongrad_P75 <dbl>,
## # Grad_share <dbl>, Grad_premium <dbl>Mutating joins:
Mutating joins allow you to combine variables from multiple tables.
merge_df <- all_ages %>% left_join(grad_students, by = "Major_code")
merge_df## # A tibble: 173 x 32
## Major_code Major.x Major_category.x Total Employed Employed_full_tim~
## <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 1100 GENERAL AGR~ Agriculture & Nat~ 128148 90245 74078
## 2 1101 AGRICULTURE~ Agriculture & Nat~ 95326 76865 64240
## 3 1102 AGRICULTURA~ Agriculture & Nat~ 33955 26321 22810
## 4 1103 ANIMAL SCIE~ Agriculture & Nat~ 103549 81177 64937
## 5 1104 FOOD SCIENCE Agriculture & Nat~ 24280 17281 12722
## 6 1105 PLANT SCIEN~ Agriculture & Nat~ 79409 63043 51077
## 7 1106 SOIL SCIENCE Agriculture & Nat~ 6586 4926 4042
## 8 1199 MISCELLANEO~ Agriculture & Nat~ 8549 6392 5074
## 9 1301 ENVIRONMENT~ Biology & Life Sc~ 106106 87602 65238
## 10 1302 FORESTRY Agriculture & Nat~ 69447 48228 39613
## # ... with 163 more rows, and 26 more variables: Unemployed <dbl>,
## # Unemployment_rate <dbl>, Median <dbl>, P25th <dbl>, P75th <dbl>,
## # Major.y <chr>, Major_category.y <chr>, Grad_total <dbl>,
## # Grad_sample_size <dbl>, Grad_employed <dbl>,
## # Grad_full_time_year_round <dbl>, Grad_unemployed <dbl>,
## # Grad_unemployment_rate <dbl>, Grad_median <dbl>, Grad_P25 <dbl>,
## # Grad_P75 <dbl>, Nongrad_total <dbl>, Nongrad_employed <dbl>,
## # Nongrad_full_time_year_round <dbl>, Nongrad_unemployed <dbl>,
## # Nongrad_unemployment_rate <dbl>, Nongrad_median <dbl>, Nongrad_P25 <dbl>,
## # Nongrad_P75 <dbl>, Grad_share <dbl>, Grad_premium <dbl>Remove multiple Column in R using dplyr (by name and index) – TidyVerse
merge_df <- select(merge_df, -c(Major_category.y, Major.y))
head(merge_df)## # A tibble: 6 x 30
## Major_code Major.x Major_category.x Total Employed Employed_full_tim~
## <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 1100 GENERAL AGRI~ Agriculture & Nat~ 128148 90245 74078
## 2 1101 AGRICULTURE ~ Agriculture & Nat~ 95326 76865 64240
## 3 1102 AGRICULTURAL~ Agriculture & Nat~ 33955 26321 22810
## 4 1103 ANIMAL SCIEN~ Agriculture & Nat~ 103549 81177 64937
## 5 1104 FOOD SCIENCE Agriculture & Nat~ 24280 17281 12722
## 6 1105 PLANT SCIENC~ Agriculture & Nat~ 79409 63043 51077
## # ... with 24 more variables: Unemployed <dbl>, Unemployment_rate <dbl>,
## # Median <dbl>, P25th <dbl>, P75th <dbl>, Grad_total <dbl>,
## # Grad_sample_size <dbl>, Grad_employed <dbl>,
## # Grad_full_time_year_round <dbl>, Grad_unemployed <dbl>,
## # Grad_unemployment_rate <dbl>, Grad_median <dbl>, Grad_P25 <dbl>,
## # Grad_P75 <dbl>, Nongrad_total <dbl>, Nongrad_employed <dbl>,
## # Nongrad_full_time_year_round <dbl>, Nongrad_unemployed <dbl>,
## # Nongrad_unemployment_rate <dbl>, Nongrad_median <dbl>, Nongrad_P25 <dbl>,
## # Nongrad_P75 <dbl>, Grad_share <dbl>, Grad_premium <dbl>Change Name of Columns in R with dplyr rename()
merge_df <- merge_df %>%
rename(Major_category = Major_category.x,
Major = Major.x
)
head(merge_df)## # A tibble: 6 x 30
## Major_code Major Major_category Total Employed Employed_full_t~ Unemployed
## <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1100 GENERA~ Agriculture & ~ 128148 90245 74078 2423
## 2 1101 AGRICU~ Agriculture & ~ 95326 76865 64240 2266
## 3 1102 AGRICU~ Agriculture & ~ 33955 26321 22810 821
## 4 1103 ANIMAL~ Agriculture & ~ 103549 81177 64937 3619
## 5 1104 FOOD S~ Agriculture & ~ 24280 17281 12722 894
## 6 1105 PLANT ~ Agriculture & ~ 79409 63043 51077 2070
## # ... with 23 more variables: Unemployment_rate <dbl>, Median <dbl>,
## # P25th <dbl>, P75th <dbl>, Grad_total <dbl>, Grad_sample_size <dbl>,
## # Grad_employed <dbl>, Grad_full_time_year_round <dbl>,
## # Grad_unemployed <dbl>, Grad_unemployment_rate <dbl>, Grad_median <dbl>,
## # Grad_P25 <dbl>, Grad_P75 <dbl>, Nongrad_total <dbl>,
## # Nongrad_employed <dbl>, Nongrad_full_time_year_round <dbl>,
## # Nongrad_unemployed <dbl>, Nongrad_unemployment_rate <dbl>,
## # Nongrad_median <dbl>, Nongrad_P25 <dbl>, Nongrad_P75 <dbl>,
## # Grad_share <dbl>, Grad_premium <dbl>Reading the ‘grad-students’ dataset
women_stem <- read_csv("https://raw.githubusercontent.com/uzmabb182/CUNY-SPS-Assignments/main/data_607/tidyverse_assignment/grad-students.csv", na="")##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Major = col_character(),
## Major_category = col_character()
## )
## i Use `spec()` for the full column specifications.head(women_stem)## # A tibble: 6 x 22
## Major_code Major Major_category Grad_total Grad_sample_size Grad_employed
## <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 5601 CONSTRUC~ Industrial Art~ 9173 200 7098
## 2 6004 COMMERCI~ Arts 53864 882 40492
## 3 6211 HOSPITAL~ Business 24417 437 18368
## 4 2201 COSMETOL~ Industrial Art~ 5411 72 3590
## 5 2001 COMMUNIC~ Computers & Ma~ 9109 171 7512
## 6 3201 COURT RE~ Law & Public P~ 1542 22 1008
## # ... with 16 more variables: Grad_full_time_year_round <dbl>,
## # Grad_unemployed <dbl>, Grad_unemployment_rate <dbl>, Grad_median <dbl>,
## # Grad_P25 <dbl>, Grad_P75 <dbl>, Nongrad_total <dbl>,
## # Nongrad_employed <dbl>, Nongrad_full_time_year_round <dbl>,
## # Nongrad_unemployed <dbl>, Nongrad_unemployment_rate <dbl>,
## # Nongrad_median <dbl>, Nongrad_P25 <dbl>, Nongrad_P75 <dbl>,
## # Grad_share <dbl>, Grad_premium <dbl>Reading the ‘recent-grads’ dataset
recent_grads <- read_csv("https://raw.githubusercontent.com/uzmabb182/CUNY-SPS-Assignments/main/data_607/tidyverse_assignment/recent-grads.csv", na="")##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## Major = col_character(),
## Major_category = col_character()
## )
## i Use `spec()` for the full column specifications.head(recent_grads)## # A tibble: 6 x 21
## Rank Major_code Major Total Men Women Major_category ShareWomen Sample_size
## <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 1 2419 PETR~ 2339 2057 282 Engineering 0.121 36
## 2 2 2416 MINI~ 756 679 77 Engineering 0.102 7
## 3 3 2415 META~ 856 725 131 Engineering 0.153 3
## 4 4 2417 NAVA~ 1258 1123 135 Engineering 0.107 16
## 5 5 2405 CHEM~ 32260 21239 11021 Engineering 0.342 289
## 6 6 2418 NUCL~ 2573 2200 373 Engineering 0.145 17
## # ... with 12 more variables: Employed <dbl>, Full_time <dbl>, Part_time <dbl>,
## # Full_time_year_round <dbl>, Unemployed <dbl>, Unemployment_rate <dbl>,
## # Median <dbl>, P25th <dbl>, P75th <dbl>, College_jobs <dbl>,
## # Non_college_jobs <dbl>, Low_wage_jobs <dbl>Merging merge_df and recent_grads
merge_df <- merge_df %>% left_join(recent_grads, by = "Major_code")
merge_df## # A tibble: 173 x 50
## Major_code Major.x Major_category.x Total.x Employed.x Employed_full_ti~
## <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 1100 GENERAL AG~ Agriculture & Na~ 128148 90245 74078
## 2 1101 AGRICULTUR~ Agriculture & Na~ 95326 76865 64240
## 3 1102 AGRICULTUR~ Agriculture & Na~ 33955 26321 22810
## 4 1103 ANIMAL SCI~ Agriculture & Na~ 103549 81177 64937
## 5 1104 FOOD SCIEN~ Agriculture & Na~ 24280 17281 12722
## 6 1105 PLANT SCIE~ Agriculture & Na~ 79409 63043 51077
## 7 1106 SOIL SCIEN~ Agriculture & Na~ 6586 4926 4042
## 8 1199 MISCELLANE~ Agriculture & Na~ 8549 6392 5074
## 9 1301 ENVIRONMEN~ Biology & Life S~ 106106 87602 65238
## 10 1302 FORESTRY Agriculture & Na~ 69447 48228 39613
## # ... with 163 more rows, and 44 more variables: Unemployed.x <dbl>,
## # Unemployment_rate.x <dbl>, Median.x <dbl>, P25th.x <dbl>, P75th.x <dbl>,
## # Grad_total <dbl>, Grad_sample_size <dbl>, Grad_employed <dbl>,
## # Grad_full_time_year_round <dbl>, Grad_unemployed <dbl>,
## # Grad_unemployment_rate <dbl>, Grad_median <dbl>, Grad_P25 <dbl>,
## # Grad_P75 <dbl>, Nongrad_total <dbl>, Nongrad_employed <dbl>,
## # Nongrad_full_time_year_round <dbl>, Nongrad_unemployed <dbl>,
## # Nongrad_unemployment_rate <dbl>, Nongrad_median <dbl>, Nongrad_P25 <dbl>,
## # Nongrad_P75 <dbl>, Grad_share <dbl>, Grad_premium <dbl>, Rank <dbl>,
## # Major.y <chr>, Total.y <dbl>, Men <dbl>, Women <dbl>,
## # Major_category.y <chr>, ShareWomen <dbl>, Sample_size <dbl>,
## # Employed.y <dbl>, Full_time <dbl>, Part_time <dbl>,
## # Full_time_year_round <dbl>, Unemployed.y <dbl>, Unemployment_rate.y <dbl>,
## # Median.y <dbl>, P25th.y <dbl>, P75th.y <dbl>, College_jobs <dbl>,
## # Non_college_jobs <dbl>, Low_wage_jobs <dbl>Remove multiple Column in R using dplyr (by name and index) – TidyVerse
merge_df <- select(merge_df, -c(Major_category.y, Major.y))
merge_df## # A tibble: 173 x 48
## Major_code Major.x Major_category.x Total.x Employed.x Employed_full_ti~
## <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 1100 GENERAL AG~ Agriculture & Na~ 128148 90245 74078
## 2 1101 AGRICULTUR~ Agriculture & Na~ 95326 76865 64240
## 3 1102 AGRICULTUR~ Agriculture & Na~ 33955 26321 22810
## 4 1103 ANIMAL SCI~ Agriculture & Na~ 103549 81177 64937
## 5 1104 FOOD SCIEN~ Agriculture & Na~ 24280 17281 12722
## 6 1105 PLANT SCIE~ Agriculture & Na~ 79409 63043 51077
## 7 1106 SOIL SCIEN~ Agriculture & Na~ 6586 4926 4042
## 8 1199 MISCELLANE~ Agriculture & Na~ 8549 6392 5074
## 9 1301 ENVIRONMEN~ Biology & Life S~ 106106 87602 65238
## 10 1302 FORESTRY Agriculture & Na~ 69447 48228 39613
## # ... with 163 more rows, and 42 more variables: Unemployed.x <dbl>,
## # Unemployment_rate.x <dbl>, Median.x <dbl>, P25th.x <dbl>, P75th.x <dbl>,
## # Grad_total <dbl>, Grad_sample_size <dbl>, Grad_employed <dbl>,
## # Grad_full_time_year_round <dbl>, Grad_unemployed <dbl>,
## # Grad_unemployment_rate <dbl>, Grad_median <dbl>, Grad_P25 <dbl>,
## # Grad_P75 <dbl>, Nongrad_total <dbl>, Nongrad_employed <dbl>,
## # Nongrad_full_time_year_round <dbl>, Nongrad_unemployed <dbl>,
## # Nongrad_unemployment_rate <dbl>, Nongrad_median <dbl>, Nongrad_P25 <dbl>,
## # Nongrad_P75 <dbl>, Grad_share <dbl>, Grad_premium <dbl>, Rank <dbl>,
## # Total.y <dbl>, Men <dbl>, Women <dbl>, ShareWomen <dbl>, Sample_size <dbl>,
## # Employed.y <dbl>, Full_time <dbl>, Part_time <dbl>,
## # Full_time_year_round <dbl>, Unemployed.y <dbl>, Unemployment_rate.y <dbl>,
## # Median.y <dbl>, P25th.y <dbl>, P75th.y <dbl>, College_jobs <dbl>,
## # Non_college_jobs <dbl>, Low_wage_jobs <dbl>To remove a common suffix from column names in an R data frame
colnames(merge_df)<-gsub(".x","",colnames(merge_df))
merge_df## # A tibble: 173 x 48
## Major_code Major Major_category Total Employed Employed_full_t~ Unemployed
## <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1100 GENERA~ Agriculture &~ 128148 90245 74078 2423
## 2 1101 AGRICU~ Agriculture &~ 95326 76865 64240 2266
## 3 1102 AGRICU~ Agriculture &~ 33955 26321 22810 821
## 4 1103 ANIMAL~ Agriculture &~ 103549 81177 64937 3619
## 5 1104 FOOD S~ Agriculture &~ 24280 17281 12722 894
## 6 1105 PLANT ~ Agriculture &~ 79409 63043 51077 2070
## 7 1106 SOIL S~ Agriculture &~ 6586 4926 4042 264
## 8 1199 MISCEL~ Agriculture &~ 8549 6392 5074 261
## 9 1301 ENVIRO~ Biology & Lif~ 106106 87602 65238 4736
## 10 1302 FOREST~ Agriculture &~ 69447 48228 39613 2144
## # ... with 163 more rows, and 41 more variables: Unemployment_rate <dbl>,
## # Median <dbl>, P25th <dbl>, P75th <dbl>, Grad_total <dbl>,
## # Grad_sample_size <dbl>, Grad_employed <dbl>,
## # Grad_full_time_year_round <dbl>, Grad_unemployed <dbl>,
## # Grad_unemployment_rate <dbl>, Grad_median <dbl>, Grad_P25 <dbl>,
## # Grad_P75 <dbl>, Nongrad_total <dbl>, Nongrad_employed <dbl>,
## # Nongrad_full_time_year_round <dbl>, Nongrad_unemployed <dbl>,
## # Nongrad_unemployment_rate <dbl>, Nongrad_median <dbl>, Nongrad_P25 <dbl>,
## # Nongrad_P75 <dbl>, Grad_share <dbl>, Grad_premium <dbl>, Rank <dbl>,
## # Total.y <dbl>, Men <dbl>, Women <dbl>, ShareWomen <dbl>, Sample_size <dbl>,
## # Employed.y <dbl>, Full_time <dbl>, Part_time <dbl>,
## # Full_time_year_round <dbl>, Unemployed.y <dbl>, Unemployment_rate.y <dbl>,
## # Median.y <dbl>, P25th.y <dbl>, P75th.y <dbl>, College_jobs <dbl>,
## # Non_college_jobs <dbl>, Low_wage_jobs <dbl>Quantiles
To demonstrate this new flexibility in a more useful situation, let’s take a look at quantile(). quantile() was hard to use previously because it returns multiple values. Now it’s straightforward
merge_df %>%
group_by(Major_category) %>%
summarise(x = quantile(Grad_unemployment_rate, c(0.25, 0.5, 0.75)), q = c(0.25, 0.5, 0.75))## `summarise()` has grouped output by 'Major_category'. You can override using the `.groups` argument.## # A tibble: 48 x 3
## # Groups: Major_category [16]
## Major_category x q
## <chr> <dbl> <dbl>
## 1 Agriculture & Natural Resources 0.0223 0.25
## 2 Agriculture & Natural Resources 0.0304 0.5
## 3 Agriculture & Natural Resources 0.0344 0.75
## 4 Arts 0.0407 0.25
## 5 Arts 0.0541 0.5
## 6 Arts 0.0627 0.75
## 7 Biology & Life Science 0.0210 0.25
## 8 Biology & Life Science 0.0250 0.5
## 9 Biology & Life Science 0.0329 0.75
## 10 Business 0.0419 0.25
## # ... with 38 more rowsSummerizing the merge_df using ‘summarise’ function on ‘Grad_unemployed’ column
summary1 <- merge_df %>%
group_by(Major_category) %>%
summarise(Grad_unemployed = mean(Grad_unemployed, na.rm = TRUE)) %>%
arrange(desc(Grad_unemployed))
summary1## # A tibble: 16 x 2
## Major_category Grad_unemployed
## <chr> <dbl>
## 1 Business 7846.
## 2 Social Science 6725.
## 3 Humanities & Liberal Arts 5669.
## 4 Psychology & Social Work 5492
## 5 Communications & Journalism 4433.
## 6 Education 4184.
## 7 Arts 3070.
## 8 Computers & Mathematics 2642
## 9 Physical Sciences 2403
## 10 Biology & Life Science 2287.
## 11 Engineering 2244.
## 12 Health 2164.
## 13 Law & Public Policy 2002.
## 14 Industrial Arts & Consumer Services 1283.
## 15 Agriculture & Natural Resources 500.
## 16 Interdisciplinary 261Visualizing the trend of Grad_unemployed vs Major_category
ggplot(data = summary1) +
geom_count(mapping = aes(x = Grad_unemployed, y = Major_category, color = Major_category) )