USA POWERLIFTING
USA Open Powerlifing Data
Research Question : What is the relationship between age and WILKS in powerlifting competitions in the United States?
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[38;5;246m# A tibble: 6 x 29[39m
MeetID LifterID Name Sex Event Equipment Age Division BodyweightKg
[3m[38;5;246m<int>[39m[23m [3m[38;5;246m<int>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<int>[39m[23m [3m[38;5;246m<chr>[39m[23m [3m[38;5;246m<dbl>[39m[23m
[38;5;250m1[39m 0 1 Angie Bel~ F SBD Wraps 47 Mst 45-49 59.6
[38;5;250m2[39m 0 2 Dawn Boga~ F SBD Single-p~ 42 Mst 40-44 58.5
[38;5;250m3[39m 0 2 Dawn Boga~ F SBD Single-p~ 42 Open Sen~ 58.5
[38;5;250m4[39m 0 2 Dawn Boga~ F B Raw 42 Open Sen~ 58.5
[38;5;250m5[39m 0 3 Destiny D~ F BD Raw 18 Teen 18-~ 63.7
[38;5;250m6[39m 0 4 Courtney ~ F SBD Wraps 28 Open Sen~ 62.4
[38;5;246m# ... with 20 more variables: WeightClassKg [3m[38;5;246m<chr>[38;5;246m[23m, Squat1Kg [3m[38;5;246m<dbl>[38;5;246m[23m,
# Squat2Kg [3m[38;5;246m<dbl>[38;5;246m[23m, Squat3Kg [3m[38;5;246m<dbl>[38;5;246m[23m, Squat4Kg [3m[38;5;246m<dbl>[38;5;246m[23m, BestSquatKg [3m[38;5;246m<dbl>[38;5;246m[23m,
# Bench1Kg [3m[38;5;246m<dbl>[38;5;246m[23m, Bench2Kg [3m[38;5;246m<dbl>[38;5;246m[23m, Bench3Kg [3m[38;5;246m<dbl>[38;5;246m[23m, Bench4Kg [3m[38;5;246m<dbl>[38;5;246m[23m,
# BestBenchKg [3m[38;5;246m<dbl>[38;5;246m[23m, Deadlift1Kg [3m[38;5;246m<dbl>[38;5;246m[23m, Deadlift2Kg [3m[38;5;246m<dbl>[38;5;246m[23m, Deadlift3Kg [3m[38;5;246m<dbl>[38;5;246m[23m,
# Deadlift4Kg [3m[38;5;246m<dbl>[38;5;246m[23m, BestDeadliftKg [3m[38;5;246m<dbl>[38;5;246m[23m, TotalKg [3m[38;5;246m<dbl>[38;5;246m[23m, Place [3m[38;5;246m<chr>[38;5;246m[23m,
# Wilks [3m[38;5;246m<dbl>[38;5;246m[23m, McCulloch [3m[38;5;246m<dbl>[38;5;246m[23m[39m
data frame with 0 columns and 0 rowsstr(usapl)Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 608400 obs. of 29 variables:
$ MeetID : int 0 0 0 0 0 0 0 0 0 0 ...
$ LifterID : int 1 2 2 2 3 4 5 5 6 6 ...
$ Name : chr "Angie Belk Terry" "Dawn Bogart" "Dawn Bogart" "Dawn Bogart" ...
$ Sex : chr "F" "F" "F" "F" ...
$ Event : chr "SBD" "SBD" "SBD" "B" ...
$ Equipment : chr "Wraps" "Single-ply" "Single-ply" "Raw" ...
$ Age : int 47 42 42 42 18 28 60 60 52 52 ...
$ Division : chr "Mst 45-49" "Mst 40-44" "Open Senior" "Open Senior" ...
$ BodyweightKg : num 59.6 58.5 58.5 58.5 63.7 ...
$ WeightClassKg : chr "60" "60" "60" "60" ...
$ Squat1Kg : num 38.6 120.2 120.2 NA NA ...
$ Squat2Kg : num 47.6 136.1 136.1 NA NA ...
$ Squat3Kg : num -54.4 142.9 142.9 NA NA ...
$ Squat4Kg : num NA NA NA NA NA ...
$ BestSquatKg : num 47.6 142.9 142.9 NA NA ...
$ Bench1Kg : num 15.9 88.5 88.5 88.5 29.5 ...
$ Bench2Kg : num 20.4 95.2 95.2 95.2 31.8 ...
$ Bench3Kg : num -24.9 -97.5 -97.5 -97.5 -34 ...
$ Bench4Kg : num NA NA NA NA NA NA NA NA NA NA ...
$ BestBenchKg : num 20.4 95.2 95.2 95.2 31.8 ...
$ Deadlift1Kg : num 61.2 136.1 136.1 NA 90.7 ...
$ Deadlift2Kg : num 70.3 149.7 149.7 NA -97.5 ...
$ Deadlift3Kg : num -77.1 163.3 163.3 NA NA ...
$ Deadlift4Kg : num NA NA NA NA NA NA NA NA NA NA ...
$ BestDeadliftKg: num 70.3 163.3 163.3 NA 90.7 ...
$ TotalKg : num 138.3 401.4 401.4 95.2 122.5 ...
$ Place : chr "1" "1" "1" "1" ...
$ Wilks : num 155 456 456 108 130 ...
$ McCulloch : num 168 466 466 110 138 ...
- attr(*, "problems")=Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 175332 obs. of 5 variables:
..$ row : int 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 ...
..$ col : chr "Age" "Age" "Age" "Age" ...
..$ expected: chr "no trailing characters" "no trailing characters" "no trailing characters" "no trailing characters" ...
..$ actual : chr ".5" ".5" ".5" ".5" ...
..$ file : chr "'/Users/racruz/Desktop/usapl.csv'" "'/Users/racruz/Desktop/usapl.csv'" "'/Users/racruz/Desktop/usapl.csv'" "'/Users/racruz/Desktop/usapl.csv'" ...
- attr(*, "spec")=List of 2
..$ cols :List of 29
.. ..$ MeetID : list()
.. .. ..- attr(*, "class")= chr "collector_integer" "collector"
.. ..$ LifterID : list()
.. .. ..- attr(*, "class")= chr "collector_integer" "collector"
.. ..$ Name : list()
.. .. ..- attr(*, "class")= chr "collector_character" "collector"
.. ..$ Sex : list()
.. .. ..- attr(*, "class")= chr "collector_character" "collector"
.. ..$ Event : list()
.. .. ..- attr(*, "class")= chr "collector_character" "collector"
.. ..$ Equipment : list()
.. .. ..- attr(*, "class")= chr "collector_character" "collector"
.. ..$ Age : list()
.. .. ..- attr(*, "class")= chr "collector_integer" "collector"
.. ..$ Division : list()
.. .. ..- attr(*, "class")= chr "collector_character" "collector"
.. ..$ BodyweightKg : list()
.. .. ..- attr(*, "class")= chr "collector_double" "collector"
.. ..$ WeightClassKg : list()
.. .. ..- attr(*, "class")= chr "collector_character" "collector"
.. ..$ Squat1Kg : list()
.. .. ..- attr(*, "class")= chr "collector_double" "collector"
.. ..$ Squat2Kg : list()
.. .. ..- attr(*, "class")= chr "collector_double" "collector"
.. ..$ Squat3Kg : list()
.. .. ..- attr(*, "class")= chr "collector_double" "collector"
.. ..$ Squat4Kg : list()
.. .. ..- attr(*, "class")= chr "collector_double" "collector"
.. ..$ BestSquatKg : list()
.. .. ..- attr(*, "class")= chr "collector_double" "collector"
.. ..$ Bench1Kg : list()
.. .. ..- attr(*, "class")= chr "collector_double" "collector"
.. ..$ Bench2Kg : list()
.. .. ..- attr(*, "class")= chr "collector_double" "collector"
.. ..$ Bench3Kg : list()
.. .. ..- attr(*, "class")= chr "collector_double" "collector"
.. ..$ Bench4Kg : list()
.. .. ..- attr(*, "class")= chr "collector_double" "collector"
.. ..$ BestBenchKg : list()
.. .. ..- attr(*, "class")= chr "collector_double" "collector"
.. ..$ Deadlift1Kg : list()
.. .. ..- attr(*, "class")= chr "collector_double" "collector"
.. ..$ Deadlift2Kg : list()
.. .. ..- attr(*, "class")= chr "collector_double" "collector"
.. ..$ Deadlift3Kg : list()
.. .. ..- attr(*, "class")= chr "collector_double" "collector"
.. ..$ Deadlift4Kg : list()
.. .. ..- attr(*, "class")= chr "collector_double" "collector"
.. ..$ BestDeadliftKg: list()
.. .. ..- attr(*, "class")= chr "collector_double" "collector"
.. ..$ TotalKg : list()
.. .. ..- attr(*, "class")= chr "collector_double" "collector"
.. ..$ Place : list()
.. .. ..- attr(*, "class")= chr "collector_character" "collector"
.. ..$ Wilks : list()
.. .. ..- attr(*, "class")= chr "collector_double" "collector"
.. ..$ McCulloch : list()
.. .. ..- attr(*, "class")= chr "collector_double" "collector"
..$ default: list()
.. ..- attr(*, "class")= chr "collector_guess" "collector"
..- attr(*, "class")= chr "col_spec"missmap(usapl, main = "Missing values vs observed")the condition has length > 1 and only the first element will be usedUnknown or uninitialised column: 'arguments'.Unknown or uninitialised column: 'arguments'.Unknown or uninitialised column: 'imputations'.
library(VIM)
aggr_plot <- aggr(usapl, col=c('salmon','light green'), numbers=TRUE, sortVars=TRUE, labels=names(usapl), cex.axis=.7, gap=3, ylab=c("Histogram of missing data","Pattern"))
Variables sorted by number of missings: 
ggplot(usapl,
aes(x=Age,y=Wilks,color=BodyweightKg)) +
geom_point(cex=.7) +
facet_wrap(~Sex) +
scale_color_gradient2(guide="colourbar",low = "cyan", mid ="cyan1", high = "yellow") +
ggtitle("How age impacts WILKS by Gender")+
labs(y="Wilks",x="Age")+
theme(axis.title=element_text(size=16),
axis.text=element_text(size=10),
plot.title=element_text(size=15))
ggplot(usapl,
aes(x=Age,y=Wilks,color=BodyweightKg)) +
geom_point(cex=.7) +
facet_wrap(~Equipment) +
scale_color_gradient2(guide="colourbar",low = "yellow", mid = "gold", high ="green") +
ggtitle("How age impacts WILKS by Equipment Use")+
labs(y="Wilks",x="Age")+
theme(axis.title=element_text(size=16),
axis.text=element_text(size=10),
plot.title=element_text(size=15))
library(plyr)------------------------------------------------------------------------------
You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)
------------------------------------------------------------------------------
Attaching package: <U+393C><U+3E31>plyr<U+393C><U+3E32>
The following objects are masked from <U+393C><U+3E31>package:plotly<U+393C><U+3E32>:
arrange, mutate, rename, summarise
The following object is masked from <U+393C><U+3E31>package:lubridate<U+393C><U+3E32>:
here
The following objects are masked from <U+393C><U+3E31>package:dplyr<U+393C><U+3E32>:
arrange, count, desc, failwith, id, mutate, rename, summarise,
summarize
The following object is masked from <U+393C><U+3E31>package:purrr<U+393C><U+3E32>:
compact# The following two lines are equivalent:
usapl$Sex <- revalue(usapl$Sex, c("F"="0", "M"="1"))##Correlation MATRIX
usapl <- na.omit(usapl)library(corrplot)
numeric.var <- sapply(usapl, is.numeric)
corr.matrix <- cor(usapl[,numeric.var])
corrplot(corr.matrix, main="\n\nCorrelation Plot for Numerical Variables", method="number")
usapl <- usapl %>%
mutate(Sex = as.factor(Sex),
Wilks = as.numeric(Wilks),
McCulloch = as.numeric(McCulloch),
Equipment = as.factor(Equipment))library(nlme)
lm1 <- lm(Wilks ~ Age + Sex + Equipment, data = usapl)
plot(lm1, col = "purple")
summary(lm1, digits = 7)
Call:
lm(formula = Wilks ~ Age + Sex + Equipment, data = usapl)
Residuals:
Min 1Q Median 3Q Max
-200.38 -68.46 -15.10 88.43 150.50
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 485.9924 68.5727 7.087 0.00000000141 ***
Age -2.4620 0.7318 -3.364 0.00131 **
Sex1 11.6731 24.4575 0.477 0.63481
EquipmentRaw -99.7750 58.4252 -1.708 0.09261 .
EquipmentSingle-ply -170.1382 79.3741 -2.143 0.03594 *
EquipmentWraps -5.4093 53.8616 -0.100 0.92032
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 96.02 on 63 degrees of freedom
Multiple R-squared: 0.2703, Adjusted R-squared: 0.2123
F-statistic: 4.666 on 5 and 63 DF, p-value: 0.001095visreg::visreg(lm1)
library(car)Loading required package: carData
Attaching package: <U+393C><U+3E31>car<U+393C><U+3E32>
The following object is masked from <U+393C><U+3E31>package:gtools<U+393C><U+3E32>:
logit
The following object is masked from <U+393C><U+3E31>package:dplyr<U+393C><U+3E32>:
recode
The following object is masked from <U+393C><U+3E31>package:purrr<U+393C><U+3E32>:
some#Multicollinearity As a rule of thumb, a VIF value that exceeds 5 or 10 indicates a problematic amount of collinearity. In our example, there is no collinearity: all variables have a value of VIF well below 5.
car::vif(lm1) GVIF Df GVIF^(1/(2*Df))
Age 1.200587 1 1.095713
Sex 1.117007 1 1.056885
Equipment 1.322602 3 1.047703library(GGally)
Attaching package: <U+393C><U+3E31>GGally<U+393C><U+3E32>
The following object is masked from <U+393C><U+3E31>package:dplyr<U+393C><U+3E32>:
nasalibrary(broom)
lmg <- GGally::ggcoef(lm1, exponentiate = TRUE, color = "light green", size = 5, shape = 18, alpha = 0.9) Exponentiating coefficients, but model did not use a log or logit link functionlmg
lm2 <- lm(McCulloch ~ Age + Sex + Equipment, data = usapl)
plot(lm2)
summary(lm2, digits = 7)
Call:
lm(formula = McCulloch ~ Age + Sex + Equipment, data = usapl)
Residuals:
Min 1Q Median 3Q Max
-164.315 -64.038 -9.775 75.642 160.189
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 463.63022 65.41785 7.087 0.00000000141 ***
Age -0.08685 0.69815 -0.124 0.9014
Sex1 32.46746 23.33227 1.392 0.1690
EquipmentRaw -129.76304 55.73723 -2.328 0.0231 *
EquipmentSingle-ply -186.00654 75.72237 -2.456 0.0168 *
EquipmentWraps -47.70193 51.38357 -0.928 0.3568
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 91.6 on 63 degrees of freedom
Multiple R-squared: 0.1901, Adjusted R-squared: 0.1258
F-statistic: 2.957 on 5 and 63 DF, p-value: 0.01845visreg::visreg(lm2)
lmc <- GGally::ggcoef(lm2, color = "salmon", size = 5, shape = 18, alpha = 0.9)
lmc
pl <- lm(TotalKg ~ Age + BodyweightKg + Sex + Equipment, data = usapl)
summary(pl)
Call:
lm(formula = TotalKg ~ Age + BodyweightKg + Sex + Equipment,
data = usapl)
Residuals:
Min 1Q Median 3Q Max
-241.43 -95.92 1.48 73.22 291.36
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 254.9459 94.5597 2.696 0.00902 **
Age -2.4239 0.9281 -2.612 0.01129 *
BodyweightKg 2.7147 0.6081 4.464 0.0000347 ***
Sex1 159.8322 33.2914 4.801 0.0000104 ***
EquipmentRaw -100.6469 73.0684 -1.377 0.17333
EquipmentSingle-ply -207.7093 100.2608 -2.072 0.04246 *
EquipmentWraps 25.8905 68.1693 0.380 0.70539
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 120 on 62 degrees of freedom
Multiple R-squared: 0.6154, Adjusted R-squared: 0.5782
F-statistic: 16.54 on 6 and 62 DF, p-value: 0.00000000002833plot(pl)
visreg::visreg(pl)
lmp <- GGally::ggcoef(pl, color = "gold", size = 5, shape = 18, alpha = 0.9)
lmp
anova(lm1, test = "f")Analysis of Variance Table
Response: Wilks
Df Sum Sq Mean Sq F value Pr(>F)
Age 1 74261 74261 8.0542 0.006101 **
Sex 1 2613 2613 0.2834 0.596369
Equipment 3 138249 46083 4.9981 0.003578 **
Residuals 63 580866 9220
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1anova(lm2, test = "f")Analysis of Variance Table
Response: McCulloch
Df Sum Sq Mean Sq F value Pr(>F)
Age 1 6074 6074 0.7239 0.398097
Sex 1 3762 3762 0.4483 0.505578
Equipment 3 114216 38072 4.5371 0.006077 **
Residuals 63 528647 8391
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1anova(pl ,test = "f")Analysis of Variance Table
Response: TotalKg
Df Sum Sq Mean Sq F value Pr(>F)
Age 1 171914 171914 11.9480 0.0009935 ***
BodyweightKg 1 829146 829146 57.6252 0.0000000002029 ***
Sex 1 196751 196751 13.6741 0.0004630 ***
Equipment 3 229792 76597 5.3235 0.0024984 **
Residuals 62 892093 14389
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1model1 <- zelig(Wilks ~ Age + Sex + Equipment, model = "ls", data = usapl, cite = F)
summary(model1)Model:
Call:
z5$zelig(formula = Wilks ~ Age + Sex + Equipment, data = usapl)
Residuals:
Min 1Q Median 3Q Max
-200.38 -68.46 -15.10 88.43 150.50
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 485.9924 68.5727 7.087 0.00000000141
Age -2.4620 0.7318 -3.364 0.00131
Sex1 11.6731 24.4575 0.477 0.63481
EquipmentRaw -99.7750 58.4252 -1.708 0.09261
EquipmentSingle-ply -170.1382 79.3741 -2.143 0.03594
EquipmentWraps -5.4093 53.8616 -0.100 0.92032
Residual standard error: 96.02 on 63 degrees of freedom
Multiple R-squared: 0.2703, Adjusted R-squared: 0.2123
F-statistic: 4.666 on 5 and 63 DF, p-value: 0.001095
Next step: Use 'setx' methodx.male <- setx(model1, Sex = 1)
x.female<- setx(model1, Sex = 0)s.multi <- sim(model1, x = x.female, x1 = x.male)
summary(s.multi)
sim x :
-----
ev
mean sd 50% 2.5% 97.5%
1 392.6624 17.30496 392.4917 360.2961 429.1421
pv
mean sd 50% 2.5% 97.5%
[1,] 389.667 99.50367 386.401 201.0891 578.3677
sim x1 :
-----
ev
mean sd 50% 2.5% 97.5%
1 403.8979 18.60749 404.1677 366.9595 439.5772
pv
mean sd 50% 2.5% 97.5%
[1,] 405.0983 94.43364 406.873 215.6658 592.7281
fd
mean sd 50% 2.5% 97.5%
1 11.23553 23.58493 11.42046 -36.37625 57.06361graphics.off()
par("mar")[1] 5.1 4.1 2.1 2.1 par(mar=c(1,1,1,1))
plot(s.multi)
fd <- s.multi$get_qi(xvalue="x1", qi="fd")
summary(fd) 1
Min. :-58.682
1st Qu.: -3.918
Median : 11.420
Mean : 11.236
3rd Qu.: 27.435
Max. : 76.445 dfd <- as.data.frame(cbind(fd))
head(dfd)library(tidyr)
tidd <- dfd %>%
gather(Sex, simv, 1:1)
head(tidd)tidd %>%
group_by(Sex) %>%
summarise(mean = mean(simv), sd = sd(simv))ggplot(tidd, aes(simv)) + geom_histogram(color = "grey", fill = "gold", alpha = .9) + theme_dark() + ggtitle("Estimated First Diffrences of Male WILKS in Powerlifting")
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