skimr
## Linking to GEOS 3.6.1, GDAL 2.2.3, PROJ 4.9.3| Name | iris |
| Number of rows | 150 |
| Number of columns | 5 |
| _______________________ | |
| Column type frequency: | |
| factor | 1 |
| numeric | 4 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| Species | 0 | 1 | FALSE | 3 | set: 50, ver: 50, vir: 50 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Sepal.Length | 0 | 1 | 5.84 | 0.83 | 4.3 | 5.1 | 5.80 | 6.4 | 7.9 | ▆▇▇▅▂ |
| Sepal.Width | 0 | 1 | 3.06 | 0.44 | 2.0 | 2.8 | 3.00 | 3.3 | 4.4 | ▁▆▇▂▁ |
| Petal.Length | 0 | 1 | 3.76 | 1.77 | 1.0 | 1.6 | 4.35 | 5.1 | 6.9 | ▇▁▆▇▂ |
| Petal.Width | 0 | 1 | 1.20 | 0.76 | 0.1 | 0.3 | 1.30 | 1.8 | 2.5 | ▇▁▇▅▃ |
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
## Registered S3 methods overwritten by 'broom.mixed':
## method from
## augment.lme broom
## augment.merMod broom
## glance.lme broom
## glance.merMod broom
## glance.stanreg broom
## tidy.brmsfit broom
## tidy.gamlss broom
## tidy.lme broom
## tidy.merMod broom
## tidy.rjags broom
## tidy.stanfit broom
## tidy.stanreg broom## Registered S3 methods overwritten by 'car':
## method from
## influence.merMod lme4
## cooks.distance.influence.merMod lme4
## dfbeta.influence.merMod lme4
## dfbetas.influence.merMod lme4## Loading required package: Matrixlibrary(ggstatsplot)
set.seed(123)
# to speed up the calculation, let's use only 10% of the data
movies_10 <- dplyr::sample_frac(tbl = ggstatsplot::movies_long, size = 0.1)
dim(movies_10)## [1] 158 8## # A tibble: 6 x 8
## title year length budget rating votes mpaa genre
## <chr> <int> <int> <dbl> <dbl> <int> <fct> <fct>
## 1 Shipping News, The 2001 111 35 6.9 7695 R Romance Drama
## 2 Rosewood 1997 140 31 6.8 1840 R Action Drama
## 3 Punch-Drunk Love 2002 95 25 7.5 18169 R RomCom
## 4 Get on the Bus 1996 120 2.4 6.6 1142 R Drama
## 5 Grosse Pointe Blank 1997 107 15 7.4 20400 R RomCom
## 6 Kansas City 1996 116 19 5.8 1077 R Drama# combining the two different plots
ggstatsplot::combine_plots(
# model 1: simple linear model
ggstatsplot::ggcoefstats(
x = stats::lm(
formula = scale(rating) ~ scale(budget),
data = movies_10
),
title = "linear model",
stats.label.color = "black",
exclude.intercept = FALSE # show the intercept
) +
ggplot2::labs(x = parse(text = "'standardized regression coefficient' ~italic(beta)")),
# model 2: linear mixed-effects model
ggstatsplot::ggcoefstats(
x = lme4::lmer(
formula = scale(rating) ~ scale(budget) + (budget | genre),
data = movies_10,
control = lme4::lmerControl(calc.derivs = FALSE)
),
title = "linear mixed-effects model",
stats.label.color = "black",
exclude.intercept = FALSE
) +
ggplot2::labs(
x = parse(text = "'standardized regression coefficient' ~italic(beta)"),
y = "fixed effects"
),
labels = c("(a)", "(b)"),
nrow = 2,
ncol = 1,
title.text = "Relationship between movie budget and its IMDB rating"
)
set.seed(123)
# to speed up the calculation, let's use only 10% of the data
movies_10 <- dplyr::sample_frac(tbl = ggstatsplot::movies_long, size = 0.1)
# tidy output
broom.mixed::tidy(
x = lme4::lmer(
formula = scale(rating) ~ scale(budget) + (budget | genre),
data = movies_10,
control = lme4::lmerControl(calc.derivs = FALSE)
),
conf.int = TRUE,
conf.level = 0.95
)## # A tibble: 6 x 8
## effect group term estimate std.error statistic conf.low conf.high
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 fixed <NA> (Intercept) -4.98e-2 0.147 -0.339 -0.338 0.238
## 2 fixed <NA> scale(budget) 4.28e-2 0.0806 0.531 -0.115 0.201
## 3 ran_pa~ genre sd__(Intercep~ 3.87e-1 NA NA NA NA
## 4 ran_pa~ genre sd__budget 8.43e-4 NA NA NA NA
## 5 ran_pa~ genre cor__(Interce~ -1.00e+0 NA NA NA NA
## 6 ran_pa~ Residu~ sd__Observati~ 9.49e-1 NA NA NA NAset.seed(123)
library(robustlmm)
# model
roblmm.mod <-
robustlmm::rlmer(
formula = scale(Reaction) ~ scale(Days) + (Days | Subject),
data = sleepstudy,
rho.sigma.e = psi2propII(smoothPsi, k = 2.28),
rho.sigma.b = chgDefaults(smoothPsi, k = 5.11, s = 10)
)
# plot
ggstatsplot::ggcoefstats(
x = roblmm.mod,
title = "robust estimation of linear mixed-effects model",
conf.level = 0.90
)## Note: No model diagnostics information available, so skipping caption.## 
# data
library(lme4)
set.seed(123)
startvec <- c(Asym = 200, xmid = 725, scal = 350)
# model
nm1 <-
lme4::nlmer(
formula = circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym | Tree,
data = Orange,
start = startvec
)
# plot
ggstatsplot::ggcoefstats(
x = nm1,
title = "non-linear mixed-effects model"
)## Warning in vcov.merMod(object, use.hessian = use.hessian): variance-covariance matrix computed from finite-difference Hessian is
## not positive definite or contains NA values: falling back to var-cov estimated from RX## Warning in vcov.merMod(object, correlation = correlation, sigm = sig): variance-covariance matrix computed from finite-difference Hessian is
## not positive definite or contains NA values: falling back to var-cov estimated from RX## Warning in vcov.merMod(object): variance-covariance matrix computed from finite-difference Hessian is
## not positive definite or contains NA values: falling back to var-cov estimated from RX## Warning in vcov.merMod(object, use.hessian = use.hessian): variance-covariance matrix computed from finite-difference Hessian is
## not positive definite or contains NA values: falling back to var-cov estimated from RX## Warning in vcov.merMod(object, correlation = correlation, sigm = sig): variance-covariance matrix computed from finite-difference Hessian is
## not positive definite or contains NA values: falling back to var-cov estimated from RX
library(ggstatsplot)
# to speed up the calculation, let's use only 10% of the data
movies_10 <- dplyr::sample_frac(tbl = ggstatsplot::movies_long, size = 0.1)
# model
mod <-
stats::nls(
formula = rating ~ k / budget + c, # try toying around with the form of non-linear function
data = movies_10,
start = list(k = 1, c = 0)
)
# plot
ggstatsplot::ggcoefstats(
x = mod,
title = "non-linear least squares regression",
subtitle = "Non-linear relationship between budget and rating"
)
# setup
set.seed(123)
library(slm)
data("shan")
# model
mod <-
slm(
myformula = shan$PM_Xuhui ~ .,
data = shan,
method_cov_st = "fitAR",
model_selec = -1
)
# plot
ggstatsplot::ggcoefstats(
x = mod,
conf.level = 0.90,
title = "stationary linear models",
package = "rcartocolor",
palette = "Vivid"
)
#> Note: No model diagnostics information available, so skipping caption.
library(ggstatsplot)
# having a look at the Titanic dataset
df <- as.data.frame(x = Titanic)
str(df)## 'data.frame': 32 obs. of 5 variables:
## $ Class : Factor w/ 4 levels "1st","2nd","3rd",..: 1 2 3 4 1 2 3 4 1 2 ...
## $ Sex : Factor w/ 2 levels "Male","Female": 1 1 1 1 2 2 2 2 1 1 ...
## $ Age : Factor w/ 2 levels "Child","Adult": 1 1 1 1 1 1 1 1 2 2 ...
## $ Survived: Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ Freq : num 0 0 35 0 0 0 17 0 118 154 ...#> 'data.frame': 32 obs. of 5 variables:
#> $ Class : Factor w/ 4 levels "1st","2nd","3rd",..: 1 2 3 4 1 2 3 4 1 2 ...
#> $ Sex : Factor w/ 2 levels "Male","Female": 1 1 1 1 2 2 2 2 1 1 ...
#> $ Age : Factor w/ 2 levels "Child","Adult": 1 1 1 1 1 1 1 1 2 2 ...
#> $ Survived: Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
#> $ Freq : num 0 0 35 0 0 0 17 0 118 154 ...
# model
mod <-
stats::glm(
formula = Survived ~ Sex + Age,
data = df,
weights = df$Freq,
family = stats::binomial(link = "logit")
)
# plot
ggstatsplot::ggcoefstats(
x = mod,
exponentiate = TRUE,
ggtheme = ggthemes::theme_economist_white(),
title = "generalized linear model (glm)",
vline.color = "red",
vline.linetype = "solid",
label.segment.color = "red",
stats.label.color = c("orangered", "dodgerblue")
)
## -- Attaching packages -------------------------------------------------------------------------------- tidyverse 1.3.0 --## v tibble 2.1.3 v dplyr 0.8.4
## v tidyr 1.0.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0
## v purrr 0.3.3## -- Conflicts ----------------------------------------------------------------------------------- tidyverse_conflicts() --
## x tidyr::expand() masks Matrix::expand()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x tidyr::pack() masks Matrix::pack()
## x tidyr::unpack() masks Matrix::unpack()## To enable
## caching of data, set `options(tigris_use_cache = TRUE)` in your R script or .Rprofile.##
## Attaching package: 'tigris'## The following object is masked from 'package:graphics':
##
## plot## Loading required package: viridisLite## To install your API key for use in future sessions, run this function with `install = TRUE`.## Getting data from the 1990 decennial Census## # A tibble: 6 x 4
## GEOID NAME variable value
## <chr> <chr> <chr> <dbl>
## 1 01 Alabama H043A001 325
## 2 02 Alaska H043A001 559
## 3 04 Arizona H043A001 438
## 4 05 Arkansas H043A001 328
## 5 06 California H043A001 620
## 6 08 Colorado H043A001 418
## # A tibble: 6 x 3
## name label concept
## <chr> <chr> <chr>
## 1 B00001_001 Estimate!!Total UNWEIGHTED SAMPLE COUNT OF THE POP~
## 2 B00002_001 Estimate!!Total UNWEIGHTED SAMPLE HOUSING UNITS
## 3 B01001_001 Estimate!!Total SEX BY AGE
## 4 B01001_002 Estimate!!Total!!Male SEX BY AGE
## 5 B01001_003 Estimate!!Total!!Male!!Under 5~ SEX BY AGE
## 6 B01001_004 Estimate!!Total!!Male!!5 to 9 ~ SEX BY AGEga1 <- get_acs(geography = "tract", year = 2017,
variables = c(medincome = "B19013_001"),
state = "GA",
county = "Fulton")## Getting data from the 2013-2017 5-year ACS## # A tibble: 6 x 5
## GEOID NAME variable estimate moe
## <chr> <chr> <chr> <dbl> <dbl>
## 1 13121000100 Census Tract 1, Fulton County, Georgia medincome 149450 23192
## 2 13121000200 Census Tract 2, Fulton County, Georgia medincome 116094 18910
## 3 13121000400 Census Tract 4, Fulton County, Georgia medincome 91857 21546
## 4 13121000500 Census Tract 5, Fulton County, Georgia medincome 85750 23402
## 5 13121000600 Census Tract 6, Fulton County, Georgia medincome 50969 8609
## 6 13121000700 Census Tract 7, Fulton County, Georgia medincome 71214 10372ga2 <- get_acs(geography = "tract", year = 2017,
variables = c(medincome = "B19013_001",
medhomevalue = "B25077_001"),
state = "GA",
county = "Fulton", output = "wide")## Getting data from the 2013-2017 5-year ACS## # A tibble: 6 x 6
## GEOID NAME medincomeE medincomeM medhomevalueE medhomevalueM
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1312100~ Census Tract 18, F~ 42159 9769 136500 20463
## 2 1312100~ Census Tract 32, F~ 84350 23612 228700 20385
## 3 1312100~ Census Tract 36, F~ 41372 8278 175900 25191
## 4 1312100~ Census Tract 68.01~ NA NA NA NA
## 5 1312100~ Census Tract 88, F~ 95524 17032 285100 32455
## 6 1312101~ Census Tract 105.1~ 30819 5281 92800 39929ga1 %>%
mutate(NAME = gsub(", Fulton County, Georgia", "", NAME), # clean the name field
rank = min_rank(desc(estimate))) %>% # create a rank variable in descending order
filter(rank<=10) %>% # keep top 10 wealthiest tracts
ggplot(aes(x = estimate, y = reorder(NAME, estimate))) +
geom_errorbarh(aes(xmin = estimate - moe, xmax = estimate + moe)) +
geom_point(color = "red", size = 3) +
labs(title = "Household income by tract in Fulton County, Georgia",
subtitle = "2013-2017 American Community Survey",
y = "",
x = "ACS estimate (bars represent margin of error)")
ga1 %>%
mutate(NAME = gsub(", Fulton County, Georgia", "", NAME),
rank = min_rank(estimate)) %>% # create a rank variable in descending order
filter(rank<=10) %>% # keep bottom 10 least-wealthy tracts
ggplot(aes(x = estimate, y = reorder(NAME, estimate))) +
geom_errorbarh(aes(xmin = estimate - moe, xmax = estimate + moe)) +
geom_point(color = "red", size = 3) +
labs(title = "Household income by tract in Fulton County, Georgia",
subtitle = "2013-2017 American Community Survey",
y = "",
x = "ACS estimate (bars represent margin of error)")
options(tigris_use_cache = TRUE) # to cache downloaded data for future use
# to setp up your own cache driver, run the following line and restart R
# tigris_cache_dir("PATH TO YOUR CUSTOM CACHE DIRECTORY")
fulton <- get_acs(state = "GA", county = "Fulton", geography = "tract",
variables = "B19013_001", year = 2017, geometry = TRUE) # median household income ## Getting data from the 2013-2017 5-year ACS##
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Downloading: 1.8 MB## Simple feature collection with 6 features and 5 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -84.43234 ymin: 33.77138 xmax: -84.34816 ymax: 33.81313
## epsg (SRID): 4269
## proj4string: +proj=longlat +datum=NAD83 +no_defs
## GEOID NAME variable estimate moe
## 1 13121000100 Census Tract 1, Fulton County, Georgia B19013_001 149450 23192
## 2 13121000200 Census Tract 2, Fulton County, Georgia B19013_001 116094 18910
## 3 13121000400 Census Tract 4, Fulton County, Georgia B19013_001 91857 21546
## 4 13121000500 Census Tract 5, Fulton County, Georgia B19013_001 85750 23402
## 5 13121000600 Census Tract 6, Fulton County, Georgia B19013_001 50969 8609
## 6 13121000700 Census Tract 7, Fulton County, Georgia B19013_001 71214 10372
## geometry
## 1 MULTIPOLYGON (((-84.36737 3...
## 2 MULTIPOLYGON (((-84.37421 3...
## 3 MULTIPOLYGON (((-84.38757 3...
## 4 MULTIPOLYGON (((-84.40086 3...
## 5 MULTIPOLYGON (((-84.41522 3...
## 6 MULTIPOLYGON (((-84.43234 3...# for now, this is "un"projected
fulton %>%
ggplot(aes(fill = estimate, color = estimate)) +
geom_sf() + # geom_sf helps plot sf objects on a 2D plane.
#coord_sf(crs = 26911) + # coordinate reference system, more details next week
scale_fill_viridis(option = "magma", direction = -1) +
scale_color_viridis(option = "magma", direction = -1)
racevars <- c(White = "P005003",
Black = "P005004",
Asian = "P005006",
Hispanic = "P004003")
gwinnett <- get_decennial(geography = "tract", variables = racevars, # By default, 2010 dicennial census
state = "GA", county = "Gwinnett County", geometry = TRUE,
summary_var = "P001001") # this helps calculate percent ## Getting data from the 2010 decennial Census##
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|======================================================================| 100%## Simple feature collection with 6 features and 5 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -84.11389 ymin: 34.0433 xmax: -83.94986 ymax: 34.16787
## epsg (SRID): 4269
## proj4string: +proj=longlat +datum=NAD83 +no_defs
## # A tibble: 6 x 6
## GEOID NAME variable value summary_value geometry
## <chr> <chr> <chr> <dbl> <dbl> <MULTIPOLYGON [°]>
## 1 13135~ Census Tr~ White 6021 9882 (((-84.04025 34.11132, -84.042~
## 2 13135~ Census Tr~ White 1725 3651 (((-84.02758 34.10506, -84.027~
## 3 13135~ Census Tr~ White 5477 9513 (((-83.97996 34.08514, -83.980~
## 4 13135~ Census Tr~ White 4543 6798 (((-84.0585 34.12659, -84.0601~
## 5 13135~ Census Tr~ White 6809 9660 (((-84.04206 34.11135, -84.040~
## 6 13135~ Census Tr~ White 3873 7056 (((-84.02768 34.10508, -84.027~gwinnett %>%
mutate(pct = 100 * (value / summary_value)) %>% # compute the percent of individual racial groups
ggplot(aes(fill = pct)) +
facet_wrap(~variable) + # create faceted plots by race
geom_sf(color = "white", size = 0.1) +
#coord_sf(crs = 26915) +
scale_fill_viridis(direction = -1) +theme_bw()
ny <- get_acs(geography = "tract",
variables = "B19013_001",
state = "NY",
county = "New York",
geometry = TRUE)## Getting data from the 2014-2018 5-year ACS##
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