Dataset: Big5
Vist the website to know more details about the dataset: https://openpsychometrics.org/
Openness - People who like to learn new things and enjoy new experiences usually score high in openness. Openness includes traits like being insightful and imaginative and having a wide variety of interests.
Conscientiousness - People that have a high degree of conscientiousness are reliable and prompt. Traits include being organized, methodic, and thorough.
Extraversion - Extraverts get their energy from interacting with others, while introverts get their energy from within themselves. Extraversion includes the traits of energetic, talkative, and assertive.
Agreeableness - These individuals are friendly, cooperative, and compassionate. People with low agreeableness may be more distant. Traits include being kind, affectionate, and sympathetic.
Neuroticism - Neuroticism is also sometimes called Emotional Stability. This dimension relates to one’s emotional stability and degree of negative emotions. People that score high on neuroticism often experience emotional instability and negative emotions. Traits include being moody and tense.
Load the dataset
library(tidyverse)
df <- read_tsv("big5.csv")
Subset the dataset
# Here, I will focus on columns age, country of residence, and extraversion which labeled as E1, E2, …, E10.
df <- na.omit(df)
df <- select(df,'age','country', 'E1':'O10')
head(df)
## # A tibble: 6 x 52
## age country E1 E2 E3 E4 E5 E6 E7 E8 E9 E10
## <int> <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
## 1 53 US 4 2 5 2 5 1 4 3 5 1
## 2 46 US 2 2 3 3 3 3 1 5 1 5
## 3 14 PK 5 1 1 4 5 1 1 5 5 1
## 4 19 RO 2 5 2 4 3 4 3 4 4 5
## 5 25 US 3 1 3 3 3 1 3 1 3 5
## 6 31 US 1 5 2 4 1 3 2 4 1 5
## # ... with 40 more variables: N1 <int>, N2 <int>, N3 <int>, N4 <int>,
## # N5 <int>, N6 <int>, N7 <int>, N8 <int>, N9 <int>, N10 <int>, A1 <int>,
## # A2 <int>, A3 <int>, A4 <int>, A5 <int>, A6 <int>, A7 <int>, A8 <int>,
## # A9 <int>, A10 <int>, C1 <int>, C2 <int>, C3 <int>, C4 <int>, C5 <int>,
## # C6 <int>, C7 <int>, C8 <int>, C9 <int>, C10 <int>, O1 <int>, O2 <int>,
## # O3 <int>, O4 <int>, O5 <int>, O6 <int>, O7 <int>, O8 <int>, O9 <int>,
## # O10 <int>
Data transformation
# gather extraversion
df_e <- df %>%
select(1:17)%>%
gather('E1':'E10',
key = 'Extraversion',value = 'E_Value')
# gather neuroticism
df_n <- df %>%
select('N1':'N10')%>%
gather('N1':'N10',
key = 'Neuroticism',value = 'N_Value')
# gather agreeableness
df_a <- df %>%
select('A1':'A10')%>%
gather('A1':'A10',
key = 'Agreeableness',value = 'A_Value')
# gather conscientiousness
df_c <- df %>%
select('C1':'C10')%>%
gather('C1':'C10',
key = 'Conscientiousness',value = 'C_Value')
# gather openness
df_o <- df %>%
select('O1':'O10')%>%
gather('O1':'O10',
key = 'Openness',value = 'O_Value')
# combine dataset
l_df <- bind_cols(df_a,df_c,df_e,df_n,df_o)
head(l_df)
## # A tibble: 6 x 17
## Agreeableness A_Value Conscientiousne~ C_Value age country N1 N2
## <chr> <int> <chr> <int> <int> <chr> <int> <int>
## 1 A1 1 C1 4 53 US 1 5
## 2 A1 1 C1 4 46 US 2 3
## 3 A1 5 C1 4 14 PK 5 1
## 4 A1 2 C1 3 19 RO 5 4
## 5 A1 5 C1 3 25 US 3 3
## 6 A1 2 C1 2 31 US 1 5
## # ... with 9 more variables: N3 <int>, N4 <int>, N5 <int>,
## # Extraversion <chr>, E_Value <int>, Neuroticism <chr>, N_Value <int>,
## # Openness <chr>, O_Value <int>
## # A tibble: 6 x 17
## Agreeableness A_Value Conscientiousne~ C_Value age country N1 N2
## <chr> <int> <chr> <int> <int> <chr> <int> <int>
## 1 A10 3 C10 4 26 US 2 5
## 2 A10 5 C10 4 15 SG 5 3
## 3 A10 3 C10 3 37 US 2 4
## 4 A10 3 C10 5 16 US 5 1
## 5 A10 3 C10 5 16 NG 4 3
## 6 A10 4 C10 5 35 US 5 2
## # ... with 9 more variables: N3 <int>, N4 <int>, N5 <int>,
## # Extraversion <chr>, E_Value <int>, Neuroticism <chr>, N_Value <int>,
## # Openness <chr>, O_Value <int>
Then we can do further analysis of the impact of extraversion base on the long form dataset.
## # A tibble: 197,100 x 0
# One example is that we can plot the top 30 countries which have the largest mean value of the agreeableness
l_df %>%
group_by(country) %>%
summarise(avg=mean(A_Value)) %>%
head(30) %>%
ggplot(aes(x=reorder(country,avg),y=avg))+
geom_bar(stat = 'identity',aes(fill = country))+
labs(title = 'Country of the agreeableness')+
xlab("Country")+
ylab("Avg")+
coord_flip()+
theme(legend.position='none')
Dataset: Avocado Prices
http://www.hassavocadoboard.com/retail/volume-and-price-data
Load the dataset
avocado <- read_csv("avocado.csv")
Dataset: UNICEF dataset
The UNICEF dataset on under-five child mortality across 196 countries.
Vist the website to know more details about the dataset: http://www.childmortality.org/
Load the dataset
u5mr <- read_csv("unicef-u5mr.csv")