Week 8: Apply it to your data 7

Import your data

library(tidyverse)

data <- read_csv("../00_data/Mydata.csv")

## New names:
## Rows: 41152 Columns: 17
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (11): Name, Sex, Event, Equiptment, Age Class, Division, Weight Class KG... dbl
## (5): Age, Bodyweight, Best Sqaut KG, Best Bench KG, Best Deadlifting lgl (1):
## ...17
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...17`

skimr::skim(data)

Data summary

Name	data
Number of rows	41152
Number of columns	17
_______________________
Column type frequency:
character	11
logical	1
numeric	5
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
Name	0	1.00	2	36	0	17805	0
Sex	0	1.00	1	1	0	2	0
Event	0	1.00	1	3	0	3	0
Equiptment	0	1.00	3	10	0	3	0
Age Class	2884	0.93	5	6	0	16	0
Division	627	0.98	4	11	0	12	0
Weight Class KG	1	1.00	2	5	0	38	0
Place	0	1.00	1	2	0	34	0
Date	0	1.00	6	8	0	224	0
Federation	0	1.00	3	3	0	1	0
Meet Name	0	1.00	11	54	0	32	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
…17	41152	0	NaN	:

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Age	2906	0.93	34.77	14.62	0.50	22.5	31.50	45.0	93.5	▂▇▅▂▁
Bodyweight	187	1.00	81.15	24.93	37.29	60.0	75.55	97.3	240.0	▇▆▂▁▁
Best Sqaut KG	13698	0.67	217.55	74.61	-210.00	160.0	215.00	270.0	490.0	▁▁▇▇▁
Best Bench KG	2462	0.94	144.68	60.03	-160.00	97.5	140.00	185.0	415.0	▁▁▇▃▁
Best Deadlifting	14028	0.66	221.84	63.72	-215.00	170.0	222.50	270.0	420.0	▁▁▃▇▂

data_small <- data %>%
    select(Sex, Event, `Best Bench KG`) %>%
    sample_n(10)

Pivoting

long to wide form

?pivot_wider

data_wide <- data_small%>%
    pivot_wider(names_from = Event, values_from = Sex, values_fill = "0")
data_wide

## # A tibble: 9 × 3
##   `Best Bench KG` B     SBD  
##             <dbl> <chr> <chr>
## 1           100   M     F    
## 2            90   F     0    
## 3            65   0     F    
## 4           195   0     M    
## 5           140   0     M    
## 6            82.5 F     0    
## 7           158.  0     M    
## 8           230   0     M    
## 9            NA   0     F

wide to long form

?pivot_longer

data_wide %>%
    pivot_longer(cols = B: SBD)

## # A tibble: 18 × 3
##    `Best Bench KG` name  value
##              <dbl> <chr> <chr>
##  1           100   B     M    
##  2           100   SBD   F    
##  3            90   B     F    
##  4            90   SBD   0    
##  5            65   B     0    
##  6            65   SBD   F    
##  7           195   B     0    
##  8           195   SBD   M    
##  9           140   B     0    
## 10           140   SBD   M    
## 11            82.5 B     F    
## 12            82.5 SBD   0    
## 13           158.  B     0    
## 14           158.  SBD   M    
## 15           230   B     0    
## 16           230   SBD   M    
## 17            NA   B     0    
## 18            NA   SBD   F

Separating and Uniting

Unite two columns

?unite

data_unite <- data_small %>%
    unite(col = "Sex_Event", Event, Sex, sep = "_")

data_unite[10,1] <- "SBD_M_event"
data_unite

## # A tibble: 10 × 2
##    Sex_Event   `Best Bench KG`
##    <chr>                 <dbl>
##  1 B_M                   100  
##  2 B_F                    90  
##  3 SBD_F                  65  
##  4 SBD_M                 195  
##  5 SBD_M                 140  
##  6 B_F                    82.5
##  7 SBD_M                 158. 
##  8 SBD_M                 230  
##  9 SBD_F                  NA  
## 10 SBD_M_event           100

Separate a column

data_unite %>%
    separate(col = Sex_Event, into = c("Sex", "event"), sep = "_")

## Warning: Expected 2 pieces. Additional pieces discarded in 1 rows [10].

## # A tibble: 10 × 3
##    Sex   event `Best Bench KG`
##    <chr> <chr>           <dbl>
##  1 B     M               100  
##  2 B     F                90  
##  3 SBD   F                65  
##  4 SBD   M               195  
##  5 SBD   M               140  
##  6 B     F                82.5
##  7 SBD   M               158. 
##  8 SBD   M               230  
##  9 SBD   F                NA  
## 10 SBD   M               100