A04 - Data Wrangling

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Revised

19 Jun 2023

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Programming Environment

library(tidyverse)  # includes lots of data verbs like `group_by()` and `summarise()`
library(mosaicData) # includes the `HELPmiss` data set

sessionInfo()

── Attaching core tidyverse packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.2     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ ggplot2   3.4.3     ✔ tibble    3.2.1
✔ lubridate 1.9.2     ✔ tidyr     1.3.0
✔ purrr     1.0.2     

── Conflicts ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

R version 4.3.0 (2023-04-21)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 14.4.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] mosaicData_0.20.3 lubridate_1.9.2   forcats_1.0.0     stringr_1.5.0    
 [5] dplyr_1.1.2       purrr_1.0.2       readr_2.1.4       tidyr_1.3.0      
 [9] tibble_3.2.1      ggplot2_3.4.3     tidyverse_2.0.0  

loaded via a namespace (and not attached):
 [1] gtable_0.3.3     jsonlite_1.8.5   compiler_4.3.0   crayon_1.5.2    
 [5] tidyselect_1.2.0 IRdisplay_1.1    scales_1.2.1     uuid_1.1-0      
 [9] fastmap_1.1.1    IRkernel_1.3.2   R6_2.5.1         generics_0.1.3  
[13] munsell_0.5.0    pillar_1.9.0     tzdb_0.4.0       rlang_1.1.1     
[17] utf8_1.2.3       stringi_1.7.12   repr_1.1.6       timechange_0.2.0
[21] cli_3.6.1        withr_2.5.0      magrittr_2.0.3   digest_0.6.31   
[25] grid_4.3.0       base64enc_0.1-3  hms_1.1.3        pbdZMQ_0.3-9    
[29] lifecycle_1.0.3  vctrs_0.6.3      evaluate_0.21    glue_1.6.2      
[33] fansi_1.0.4      colorspace_2.1-0 tools_4.3.0      pkgconfig_2.0.3 
[37] htmltools_0.5.5 

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# Load the `HELPmiss` data set into our RStudio environment
#   data("HELPmiss", package = "mosaicData")

?mosaicData::HELPmiss

head(HELPmiss)

A data.frame: 6 × 28

	age	anysub	cesd	d1	daysanysub	dayslink	drugrisk	e2b	female	sex	⋯	pcs	pss_fr	racegrp	satreat	sexrisk	substance	treat	avg_drinks	max_drinks	hospitalizations
	<int>	<fct>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<fct>	⋯	<dbl>	<int>	<fct>	<fct>	<int>	<fct>	<fct>	<int>	<int>	<int>
1	37	yes	49	3	177	225	0	NA	0	male	⋯	58.41369	0	black	no	4	cocaine	yes	13	26	3
2	37	yes	30	22	2	NA	0	NA	0	male	⋯	36.03694	1	white	no	7	alcohol	yes	56	62	22
3	26	yes	39	0	3	365	20	NA	0	male	⋯	74.80633	13	black	no	2	heroin	no	0	0	0
4	39	yes	15	2	189	343	0	1	1	female	⋯	61.93168	11	white	yes	4	heroin	no	5	5	2
5	32	yes	39	12	2	57	0	1	0	male	⋯	37.34558	10	black	no	6	cocaine	no	10	13	12
6	47	yes	6	1	31	365	0	NA	1	female	⋯	46.47521	5	black	no	5	cocaine	yes	4	4	1

# number of cases
HELPmiss %>%
  summarize(count = n())

A data.frame: 1 × 1

count
<int>
470

# total e2b
HELPmiss %>%
  summarize(
    total_e2b = sum(e2b, na.rm = TRUE)
  )

A data.frame: 1 × 1

total_e2b
<int>
549

# mean daysanysub
HELPmiss %>%
  summarize(
    mean_daysanysub = mean(daysanysub, na.rm = TRUE)
  )

A data.frame: 1 × 1

mean_daysanysub
<dbl>
75.13095

HELPmiss %>%
  group_by(sex) %>%
  summarize(
    count           = n(),
    total_e2b       = sum(e2b,         na.rm = TRUE),
    mean_daysanysub = mean(daysanysub, na.rm = TRUE)
  )

A tibble: 2 × 4

sex	count	total_e2b	mean_daysanysub
<fct>	<int>	<int>	<dbl>
male	359	457	72.60513
female	111	92	83.77193

HELPmiss %>%
  group_by(homeless) %>%
  summarize(
    count           = n(),
    total_e2b       = sum(e2b,         na.rm = TRUE),
    mean_daysanysub = mean(daysanysub, na.rm = TRUE)
  )

A tibble: 2 × 4

homeless	count	total_e2b	mean_daysanysub
<fct>	<int>	<int>	<dbl>
housed	251	160	77.10078
homeless	219	389	73.06504

HELPmiss %>%
  group_by(substance) %>%
  summarize(
    count           = n(),
    total_e2b       = sum(e2b,         na.rm = TRUE),
    mean_daysanysub = mean(daysanysub, na.rm = TRUE)
  )

A tibble: 4 × 4

substance	count	total_e2b	mean_daysanysub
<fct>	<int>	<int>	<dbl>
alcohol	185	242	78.49495
cocaine	156	98	86.67033
heroin	128	208	52.82258
missing	1	1	NaN

HELPmiss %>%
  group_by(homeless, sex) %>%
  summarize(
    count           = n(),
    total_e2b       = sum(e2b,         na.rm = TRUE),
    mean_daysanysub = mean(daysanysub, na.rm = TRUE)
  )

`summarise()` has grouped output by 'homeless'. You can override using the `.groups` argument.

A grouped_df: 4 × 5

homeless	sex	count	total_e2b	mean_daysanysub
<fct>	<fct>	<int>	<int>	<dbl>
housed	male	182	110	74.30851
housed	female	69	50	84.60000
homeless	male	177	347	71.01980
homeless	female	42	42	82.45455

HELPmiss %>%
  group_by(homeless, substance) %>%
  summarize(
    count           = n(),
    total_e2b       = sum(e2b,         na.rm = TRUE),
    mean_daysanysub = mean(daysanysub, na.rm = TRUE)
  )

`summarise()` has grouped output by 'homeless'. You can override using the `.groups` argument.

A grouped_df: 7 × 5

homeless	substance	count	total_e2b	mean_daysanysub
<fct>	<fct>	<int>	<int>	<dbl>
housed	alcohol	76	34	104.79487
housed	cocaine	96	37	81.05556
housed	heroin	79	89	41.16667
homeless	alcohol	109	208	61.40000
homeless	cocaine	60	61	94.86486
homeless	heroin	49	119	68.96154
homeless	missing	1	1	NaN

HELPmiss %>%
  count(substance) %>%
  arrange(desc(n))

A data.frame: 4 × 2

substance	n
<fct>	<int>
alcohol	185
cocaine	156
heroin	128
missing	1

ggplot(data = HELPmiss) +
  geom_boxplot(mapping = aes(x = age, fill = substance))

options(repr.plot.width = 20, repr.plot.height = 10)

ggplot(data = HELPmiss) +
  geom_bar(
    mapping     = aes(x    = age,
                      fill = substance),
    position    = 'stack',
    show.legend = TRUE,
    stat        = 'count'
  ) +
  facet_grid(sex ~ racegrp) +
  theme(text = element_text(size = 20))

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