Impoting and cleaning data
options(repos = c(CRAN = "https://cran.rstudio.com/"))
install.packages("readxl")
##
## The downloaded binary packages are in
## /var/folders/qv/ynxnwkw51zz9p5kmfd4gz8bh0000gn/T//Rtmp5u2XxH/downloaded_packages
library(readxl)
mydata <- read_excel("~/Program R/Strategic Management/Anketa TS Strategic Management.xlsx") # Data was partialy cleaned in 1KA and Excel
mydata$ID <- seq(1,nrow(mydata)) # Adding variable ID for better understanding of data
head(mydata)
## # A tibble: 6 × 62
## Q2 Q3 Q4a Q4b Q4c Q4d Q4e Q4f Q4g Q6 Q7a_1 Q7b_1 Q7c_1
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 3 2 -2 -2 -2 -2 -2 -2 -2 3 4 2 3
## 2 4 1 0 1 1 1 1 0 0 3 3 2 4
## 3 4 2 -2 -2 -2 -2 -2 -2 -2 2 2 2 2
## 4 4 1 0 0 1 0 1 0 0 4 4 2 4
## 5 5 1 0 1 0 1 1 0 0 4 4 2 4
## 6 2 1 0 1 0 0 0 0 0 3 2 3 3
## # ℹ 49 more variables: Q7d_1 <dbl>, Q7e_1 <dbl>, Q7f_1 <dbl>, Q7g_1 <dbl>,
## # Q7h_1 <dbl>, Q8a_1 <dbl>, Q8b_1 <dbl>, Q8c_1 <dbl>, Q8d_1 <dbl>,
## # Q8e_1 <dbl>, Q8f_1 <dbl>, Q8g_1 <dbl>, Q8h_1 <dbl>, Q8i_1 <dbl>, Q9 <dbl>,
## # Q10 <dbl>, Q11_Q12a <dbl>, Q11_Q12b <dbl>, Q11_Q12c <dbl>, Q13a_1 <dbl>,
## # Q13b_1 <dbl>, Q13c_1 <dbl>, Q31_2a_1 <dbl>, Q31_2b_1 <dbl>, Q31_2c_1 <dbl>,
## # Q31_2d_1 <dbl>, Q31_2e_1 <dbl>, Q31_2f_1 <dbl>, Q15 <dbl>, Q16 <dbl>,
## # Q17 <dbl>, Q18 <dbl>, Q20 <dbl>, Q21 <dbl>, Q22 <dbl>, Q23 <dbl>, …
mydata$Q2 <- as.numeric(mydata$Q2)
mydata$Q6 <- as.numeric(mydata$Q6)
mydata$Q7a_1 <- as.numeric(mydata$Q7a_1)
mydata$Q7b_1 <- as.numeric(mydata$Q7b_1)
mydata$Q7c_1 <- as.numeric(mydata$Q7c_1)
mydata$Q7d_1 <- as.numeric(mydata$Q7d_1)
mydata$Q7e_1 <- as.numeric(mydata$Q7e_1)
mydata$Q7f_1 <- as.numeric(mydata$Q7f_1)
mydata$Q7g_1 <- as.numeric(mydata$Q7g_1)
mydata$Q7h_1 <- as.numeric(mydata$Q7h_1)
mydata$Q8a_1 <- as.numeric(mydata$Q8a_1)
mydata$Q8b_1 <- as.numeric(mydata$Q8b_1)
mydata$Q8c_1 <- as.numeric(mydata$Q8c_1)
mydata$Q8d_1 <- as.numeric(mydata$Q8d_1)
mydata$Q8e_1 <- as.numeric(mydata$Q8e_1)
mydata$Q8f_1 <- as.numeric(mydata$Q8f_1)
mydata$Q8g_1 <- as.numeric(mydata$Q8g_1)
mydata$Q8h_1 <- as.numeric(mydata$Q8h_1)
mydata$Q8i_1 <- as.numeric(mydata$Q8i_1)
mydata$Q10 <- as.numeric(mydata$Q10)
mydata$Q13a_1 <- as.numeric(mydata$Q13a_1)
mydata$Q13b_1 <- as.numeric(mydata$Q13b_1)
mydata$Q13c_1 <- as.numeric(mydata$Q13c_1)
mydata$Q31_2a_1 <- as.numeric(mydata$Q31_2a_1)
mydata$Q31_2b_1 <- as.numeric(mydata$Q31_2b_1)
mydata$Q31_2c_1 <- as.numeric(mydata$Q31_2c_1)
mydata$Q31_2d_1 <- as.numeric(mydata$Q31_2d_1)
mydata$Q31_2e_1 <- as.numeric(mydata$Q31_2e_1)
mydata$Q31_2f_1 <- as.numeric(mydata$Q31_2f_1)
mydata$Q16 <- as.numeric(mydata$Q16)
# Transforming variables to numeric
mydata$Q3 <- factor(mydata$Q3,
levels = c(1,2),
labels = c("Yes", "No"))
mydata$Q9 <- factor(mydata$Q9,
levels = c(1,2,3),
labels = c("Yes","No","Not sure"))
mydata$Q11_Q12a <- factor(mydata$Q11_Q12a,
levels = c(1,2,3),
labels = c("Yes","No","Not sure"))
mydata$Q11_Q12b <- factor(mydata$Q11_Q12b,
levels = c(1,2,3),
labels = c("Yes","No","Not sure"))
mydata$Q11_Q12c <- factor(mydata$Q11_Q12c,
levels = c(1,2,3),
labels = c("Yes","No","Not sure"))
mydata$Q15 <- factor(mydata$Q15,
levels = c(1,2,3,4,5,6,7,8,9,10),
labels = c("Reels","Posts","Podcast, Video","Reddit","Bank App","University","Books","Family","School","I don't like"))
mydata$Q17 <- factor(mydata$Q17,
levels = c(1,2,3),
labels = c("Yes","No","Not sure"))
mydata$Q18 <- factor(mydata$Q18,
levels = c(1,2,3,4,5,6,7),
labels = c("Friends","Family","Financial Experts","Influencers","School","My own research","Other"))
mydata$Q20 <- factor(mydata$Q20,
levels = c(1,2,3,4,5),
labels = c("A higher return always means it’s safer.","Investments with higher expected returns are usually more risky.","Risk and return are unrelated.","Government bonds always offer the highest returns.","I’m not sure."))
mydata$Q21 <- factor(mydata$Q21,
levels = c(1,2,3,4),
labels = c("Buy more","Buy same","Buy less","Don't know"))
mydata$Q22 <- factor(mydata$Q22,
levels = c(1,2,3,4),
labels = c("Earn more","Less risk","No tax","Not sure"))
mydata$Q23 <- factor(mydata$Q23,
levels = c(1,2,3,4),
labels = c("You’ll always earn more.","It helps reduce risk by spreading your money out.","You won’t have to pay taxes.","Not sure."))
mydata$Q24 <- factor(mydata$Q24,
levels = c(1,2,3,4),
labels = c("18-20","21-23","24-26","27-29"))
mydata$Q25 <- factor(mydata$Q25,
levels = c(1,2,3),
labels = c("Man","Woman","Dont want to answer"))
mydata$Q26 <- factor(mydata$Q26,
levels = c(1,2,3,4,5),
labels = c("High School","University","Employed","Unemployed","Other"))
mydata$Q27 <- factor(mydata$Q27,
levels = c(1,2,3),
labels = c("High School","Bachelor","Master"))
mydata$Q28 <- factor(mydata$Q28,
levels = c(1,2,3),
labels = c("Urban","Suburban","Rural"))
mydata$Q29 <- factor(mydata$Q29,
levels = c(1,2,3,4,5,6),
labels = c("Max 500","500-1000","1000-1500","1500-2000","Min 2000","Dont say"))
mydata$`Financialy literate` <- factor(mydata$`Financialy literate`,
levels = c("Yes","No"),
labels = c("Financially literate","Financially iliterate"))
summary(mydata)
## Q2 Q3 Q4a Q4b
## Min. :1.000 Yes:210 Min. :-2.0000 Min. :-2.0000
## 1st Qu.:2.000 No :116 1st Qu.:-2.0000 1st Qu.:-2.0000
## Median :4.000 Median : 0.0000 Median : 0.0000
## Mean :3.337 Mean :-0.5736 Mean :-0.3988
## 3rd Qu.:4.000 3rd Qu.: 0.0000 3rd Qu.: 1.0000
## Max. :5.000 Max. : 1.0000 Max. : 1.0000
##
## Q4c Q4d Q4e Q4f
## Min. :-2.0000 Min. :-2.0000 Min. :-2.0000 Min. :-2.0000
## 1st Qu.:-2.0000 1st Qu.:-2.0000 1st Qu.:-2.0000 1st Qu.:-2.0000
## Median : 0.0000 Median : 0.0000 Median : 0.0000 Median : 0.0000
## Mean :-0.5491 Mean :-0.3742 Mean :-0.2178 Mean :-0.6626
## 3rd Qu.: 0.0000 3rd Qu.: 1.0000 3rd Qu.: 1.0000 3rd Qu.: 0.0000
## Max. : 1.0000 Max. : 1.0000 Max. : 1.0000 Max. : 1.0000
##
## Q4g Q6 Q7a_1 Q7b_1
## Min. :-2.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:-2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median : 0.000 Median :2.500 Median :3.000 Median :3.000
## Mean :-0.635 Mean :2.758 Mean :3.166 Mean :3.215
## 3rd Qu.: 0.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. : 1.000 Max. :5.000 Max. :5.000 Max. :5.000
##
## Q7c_1 Q7d_1 Q7e_1 Q7f_1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :4.000 Median :3.000 Median :3.000 Median :3.000
## Mean :3.439 Mean :2.979 Mean :2.972 Mean :2.813
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
##
## Q7g_1 Q7h_1 Q8a_1 Q8b_1 Q8c_1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:4.000 1st Qu.:3.00
## Median :2.000 Median :3.000 Median :4.000 Median :4.000 Median :4.00
## Mean :2.417 Mean :2.942 Mean :3.304 Mean :4.058 Mean :3.85
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:5.00
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.00
##
## Q8d_1 Q8e_1 Q8f_1 Q8g_1 Q8h_1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.00
## Median :4.000 Median :3.000 Median :4.000 Median :4.000 Median :4.00
## Mean :3.534 Mean :2.586 Mean :3.693 Mean :3.663 Mean :3.58
## 3rd Qu.:4.000 3rd Qu.:3.750 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:4.00
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.00
##
## Q8i_1 Q9 Q10 Q11_Q12a Q11_Q12b
## Min. :1.000 Yes :254 Min. :-2.000 Yes :267 Yes : 95
## 1st Qu.:4.000 No : 44 1st Qu.: 1.000 No : 43 No :202
## Median :4.000 Not sure: 28 Median : 2.000 Not sure: 16 Not sure: 29
## Mean :3.979 Mean : 1.463
## 3rd Qu.:5.000 3rd Qu.: 3.000
## Max. :5.000 Max. : 5.000
##
## Q11_Q12c Q13a_1 Q13b_1 Q13c_1 Q31_2a_1
## Yes :212 Min. :1.000 Min. :1.00 Min. :1.00 Min. :1.000
## No : 87 1st Qu.:1.000 1st Qu.:1.00 1st Qu.:1.00 1st Qu.:4.000
## Not sure: 27 Median :3.000 Median :2.00 Median :2.00 Median :4.000
## Mean :2.564 Mean :2.04 Mean :2.38 Mean :4.175
## 3rd Qu.:4.000 3rd Qu.:3.00 3rd Qu.:3.00 3rd Qu.:5.000
## Max. :5.000 Max. :5.00 Max. :5.00 Max. :5.000
##
## Q31_2b_1 Q31_2c_1 Q31_2d_1 Q31_2e_1
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.681 Mean :3.525 Mean :4.012 Mean :4.101
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
##
## Q31_2f_1 Q15 Q16 Q17
## Min. :1.000 Podcast, Video:60 Min. :1.000 Yes : 99
## 1st Qu.:2.000 Reels :53 1st Qu.:2.000 No :166
## Median :4.000 Bank App :53 Median :3.000 Not sure: 61
## Mean :3.261 School :35 Mean :3.126
## 3rd Qu.:4.000 Books :27 3rd Qu.:4.000
## Max. :5.000 Posts :26 Max. :5.000
## (Other) :72
## Q18
## Friends : 40
## Family : 54
## Financial Experts:122
## Influencers : 3
## School : 2
## My own research :105
## Other : 0
## Q20
## A higher return always means it’s safer. : 11
## Investments with higher expected returns are usually more risky.:280
## Risk and return are unrelated. : 10
## Government bonds always offer the highest returns. : 4
## I’m not sure. : 21
##
##
## Q21 Q22
## Buy more : 9 Earn more: 9
## Buy same : 4 Less risk:280
## Buy less :300 No tax : 4
## Don't know: 13 Not sure : 33
##
##
##
## Q23 Q24
## You’ll always earn more. : 21 18-20: 33
## It helps reduce risk by spreading your money out.:265 21-23:144
## You won’t have to pay taxes. : 0 24-26: 94
## Not sure. : 40 27-29: 55
##
##
##
## Q25 Q26 Q27 Q28
## Man :190 High School: 8 High School:118 Urban :229
## Woman :136 University :221 Bachelor :160 Suburban: 68
## Dont want to answer: 0 Employed : 91 Master : 48 Rural : 29
## Unemployed : 6
## Other : 0
##
##
## Q29 Q20 correct Q21 correct Q22 correcrt
## Max 500 :111 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 500-1000 : 61 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:1.0000
## 1000-1500: 47 Median :1.0000 Median :1.0000 Median :1.0000
## 1500-2000: 51 Mean :0.8589 Mean :0.9202 Mean :0.8589
## Min 2000 : 38 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Dont say : 18 Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## Q23 correct 3 correct Financialy literate
## Min. :0.0000 Min. :0.000 Financially literate :278
## 1st Qu.:1.0000 1st Qu.:3.250 Financially iliterate: 48
## Median :1.0000 Median :4.000
## Mean :0.8129 Mean :3.451
## 3rd Qu.:1.0000 3rd Qu.:4.000
## Max. :1.0000 Max. :4.000
##
## ID
## Min. : 1.00
## 1st Qu.: 82.25
## Median :163.50
## Mean :163.50
## 3rd Qu.:244.75
## Max. :326.00
##
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
mydata_summary1 <- mydata %>%
group_by(mydata$Q25) %>%
summarise(
'Smaller minimal investment' = mean(Q8a_1, na.rm = TRUE),
'Smaller management fees' = mean(Q8b_1, na.rm = TRUE),
'Clearer information about risk and return' = mean(Q8c_1, na.rm = TRUE),
'Better user experience' = mean(Q8d_1, na.rm = TRUE),
'ESG focus' = mean(Q8e_1, na.rm = TRUE),
'Transparent investment strategy' = mean(Q8f_1, na.rm = TRUE),
'Good customer support' = mean(Q8g_1, na.rm = TRUE),
'Peer recommendations' = mean(Q8h_1, na.rm = TRUE),
'Easy to understand content' = mean(Q8i_1, na.rm = TRUE)
)
print(mydata_summary1)
## # A tibble: 2 × 10
## `mydata$Q25` `Smaller minimal investment` `Smaller management fees`
## <fct> <dbl> <dbl>
## 1 Man 3.06 4.13
## 2 Woman 3.64 3.96
## # ℹ 7 more variables: `Clearer information about risk and return` <dbl>,
## # `Better user experience` <dbl>, `ESG focus` <dbl>,
## # `Transparent investment strategy` <dbl>, `Good customer support` <dbl>,
## # `Peer recommendations` <dbl>, `Easy to understand content` <dbl>
library(tidyr)
library(ggplot2)
library(dplyr)
# Rename the grouping column to "Group" for clarity
mydata_summary1 <- mydata_summary1 %>%
rename(Group = `mydata$Q25`) # This is crucial
# Pivot longer for ggplot
mydata_long1 <- mydata_summary1 %>%
pivot_longer(cols = -Group, names_to = "variable", values_to = "mean_score")
# Plot
ggplot(mydata_long1, aes(x = variable, y = mean_score, fill = Group)) +
geom_col(position = "dodge") +
theme_minimal() +
labs(
title = "Likelyhood of investments into mutual funds based on features by gender",
x = "Perception Attribute",
y = "Average Score"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("~/Program R/Strategic Management/Gender.png", width = 12, height = 6, dpi = 300)
library(dplyr)
mydata_summary2 <- mydata %>%
group_by(mydata$'Financialy literate') %>%
summarise(
'Smaller minimal investment' = mean(Q8a_1, na.rm = TRUE),
'Smaller management fees' = mean(Q8b_1, na.rm = TRUE),
'Clearer information about risk and return' = mean(Q8c_1, na.rm = TRUE),
'Better user experience' = mean(Q8d_1, na.rm = TRUE),
'ESG focus' = mean(Q8e_1, na.rm = TRUE),
'Transparent investment strategy' = mean(Q8f_1, na.rm = TRUE),
'Good customer support' = mean(Q8g_1, na.rm = TRUE),
'Peer recommendations' = mean(Q8h_1, na.rm = TRUE),
'Easy to understand content' = mean(Q8i_1, na.rm = TRUE)
)
print(mydata_summary2)
## # A tibble: 2 × 10
## `mydata$"Financialy literate"` Smaller minimal invest…¹ Smaller management f…²
## <fct> <dbl> <dbl>
## 1 Financially literate 3.25 4.13
## 2 Financially iliterate 3.60 3.65
## # ℹ abbreviated names: ¹`Smaller minimal investment`,
## # ²`Smaller management fees`
## # ℹ 7 more variables: `Clearer information about risk and return` <dbl>,
## # `Better user experience` <dbl>, `ESG focus` <dbl>,
## # `Transparent investment strategy` <dbl>, `Good customer support` <dbl>,
## # `Peer recommendations` <dbl>, `Easy to understand content` <dbl>
library(tidyr)
library(ggplot2)
library(dplyr)
# Rename the grouping column to "Group" for clarity
mydata_summary2 <- mydata_summary2 %>%
rename(Group = `mydata$"Financialy literate"`) # This is crucial
# Pivot longer for ggplot
mydata_long2 <- mydata_summary2 %>%
pivot_longer(cols = -Group, names_to = "variable", values_to = "mean_score")
# Plot
ggplot(mydata_long2, aes(x = variable, y = mean_score, fill = Group)) +
geom_col(position = "dodge") +
theme_minimal() +
labs(
title = "Likelyhood of investments into mutual funds based on features by financial literacy",
x = "Perception Attribute",
y = "Average Score"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("~/Program R/Strategic Management/FinancialLiteracy.png", width = 12, height = 6, dpi = 300)
library(dplyr)
mydata_summary3 <- mydata %>%
group_by(mydata$Q24) %>%
summarise(
'Smaller minimal investment' = mean(Q8a_1, na.rm = TRUE),
'Smaller management fees' = mean(Q8b_1, na.rm = TRUE),
'Clearer information about risk and return' = mean(Q8c_1, na.rm = TRUE),
'Better user experience' = mean(Q8d_1, na.rm = TRUE),
'ESG focus' = mean(Q8e_1, na.rm = TRUE),
'Transparent investment strategy' = mean(Q8f_1, na.rm = TRUE),
'Good customer support' = mean(Q8g_1, na.rm = TRUE),
'Peer recommendations' = mean(Q8h_1, na.rm = TRUE),
'Easy to understand content' = mean(Q8i_1, na.rm = TRUE)
)
print(mydata_summary3)
## # A tibble: 4 × 10
## `mydata$Q24` `Smaller minimal investment` `Smaller management fees`
## <fct> <dbl> <dbl>
## 1 18-20 3.58 4.09
## 2 21-23 3.26 4.22
## 3 24-26 3.33 3.66
## 4 27-29 3.22 4.29
## # ℹ 7 more variables: `Clearer information about risk and return` <dbl>,
## # `Better user experience` <dbl>, `ESG focus` <dbl>,
## # `Transparent investment strategy` <dbl>, `Good customer support` <dbl>,
## # `Peer recommendations` <dbl>, `Easy to understand content` <dbl>
library(tidyr)
library(ggplot2)
library(dplyr)
# Rename the grouping column to "Group" for clarity
mydata_summary3 <- mydata_summary3 %>%
rename(Group = `mydata$Q24`) # This is crucial
# Pivot longer for ggplot
mydata_long3 <- mydata_summary3 %>%
pivot_longer(cols = -Group, names_to = "variable", values_to = "mean_score")
# Plot
ggplot(mydata_long3, aes(x = variable, y = mean_score, fill = Group)) +
geom_col(position = "dodge") +
theme_minimal() +
labs(
title = "Likelyhood of investments into mutual funds based on features by age group",
x = "Perception Attribute",
y = "Average Score"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("~/Program R/Strategic Management/AgeGroup.png", width = 12, height = 6, dpi = 300)
library(dplyr)
mydata_summary4 <- mydata %>%
group_by(mydata$Q27) %>%
summarise(
'Smaller minimal investment' = mean(Q8a_1, na.rm = TRUE),
'Smaller management fees' = mean(Q8b_1, na.rm = TRUE),
'Clearer information about risk and return' = mean(Q8c_1, na.rm = TRUE),
'Better user experience' = mean(Q8d_1, na.rm = TRUE),
'ESG focus' = mean(Q8e_1, na.rm = TRUE),
'Transparent investment strategy' = mean(Q8f_1, na.rm = TRUE),
'Good customer support' = mean(Q8g_1, na.rm = TRUE),
'Peer recommendations' = mean(Q8h_1, na.rm = TRUE),
'Easy to understand content' = mean(Q8i_1, na.rm = TRUE)
)
print(mydata_summary3)
## # A tibble: 4 × 10
## Group Smaller minimal investme…¹ Smaller management f…² Clearer information …³
## <fct> <dbl> <dbl> <dbl>
## 1 18-20 3.58 4.09 4.03
## 2 21-23 3.26 4.22 4.09
## 3 24-26 3.33 3.66 3.63
## 4 27-29 3.22 4.29 3.49
## # ℹ abbreviated names: ¹`Smaller minimal investment`,
## # ²`Smaller management fees`, ³`Clearer information about risk and return`
## # ℹ 6 more variables: `Better user experience` <dbl>, `ESG focus` <dbl>,
## # `Transparent investment strategy` <dbl>, `Good customer support` <dbl>,
## # `Peer recommendations` <dbl>, `Easy to understand content` <dbl>
library(tidyr)
library(ggplot2)
library(dplyr)
# Rename the grouping column to "Group" for clarity
mydata_summary4 <- mydata_summary4 %>%
rename(Group = `mydata$Q27`) # This is crucial
# Pivot longer for ggplot
mydata_long4 <- mydata_summary4 %>%
pivot_longer(cols = -Group, names_to = "variable", values_to = "mean_score")
# Plot
ggplot(mydata_long4, aes(x = variable, y = mean_score, fill = Group)) +
geom_col(position = "dodge") +
theme_minimal() +
labs(
title = "Likelyhood of investments into mutual funds based on features by highest education attained",
x = "Perception Attribute",
y = "Average Score"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("~/Program R/Strategic Management/Highest education.png", width = 12, height = 6, dpi = 300)
library(dplyr)
mydata_summary5 <- mydata %>%
group_by(mydata$Q26) %>%
summarise(
'Smaller minimal investment' = mean(Q8a_1, na.rm = TRUE),
'Smaller management fees' = mean(Q8b_1, na.rm = TRUE),
'Clearer information about risk and return' = mean(Q8c_1, na.rm = TRUE),
'Better user experience' = mean(Q8d_1, na.rm = TRUE),
'ESG focus' = mean(Q8e_1, na.rm = TRUE),
'Transparent investment strategy' = mean(Q8f_1, na.rm = TRUE),
'Good customer support' = mean(Q8g_1, na.rm = TRUE),
'Peer recommendations' = mean(Q8h_1, na.rm = TRUE),
'Easy to understand content' = mean(Q8i_1, na.rm = TRUE)
)
print(mydata_summary5)
## # A tibble: 4 × 10
## `mydata$Q26` `Smaller minimal investment` `Smaller management fees`
## <fct> <dbl> <dbl>
## 1 High School 4 4
## 2 University 3.28 4.04
## 3 Employed 3.32 4.13
## 4 Unemployed 3 3.67
## # ℹ 7 more variables: `Clearer information about risk and return` <dbl>,
## # `Better user experience` <dbl>, `ESG focus` <dbl>,
## # `Transparent investment strategy` <dbl>, `Good customer support` <dbl>,
## # `Peer recommendations` <dbl>, `Easy to understand content` <dbl>
library(tidyr)
library(ggplot2)
library(dplyr)
# Rename the grouping column to "Group" for clarity
mydata_summary5 <- mydata_summary5 %>%
rename(Group = `mydata$Q26`) # This is crucial
# Pivot longer for ggplot
mydata_long5 <- mydata_summary5 %>%
pivot_longer(cols = -Group, names_to = "variable", values_to = "mean_score")
# Plot
ggplot(mydata_long5, aes(x = variable, y = mean_score, fill = Group)) +
geom_col(position = "dodge") +
theme_minimal() +
labs(
title = "Likelyhood of investments into mutual funds based on features by current status",
x = "Perception Attribute",
y = "Average Score"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("~/Program R/Strategic Management/CurrentStatus.png", width = 12, height = 6, dpi = 300)
library(dplyr)
mydata_summary6 <- mydata %>%
group_by(mydata$Q28) %>%
summarise(
'Smaller minimal investment' = mean(Q8a_1, na.rm = TRUE),
'Smaller management fees' = mean(Q8b_1, na.rm = TRUE),
'Clearer information about risk and return' = mean(Q8c_1, na.rm = TRUE),
'Better user experience' = mean(Q8d_1, na.rm = TRUE),
'ESG focus' = mean(Q8e_1, na.rm = TRUE),
'Transparent investment strategy' = mean(Q8f_1, na.rm = TRUE),
'Good customer support' = mean(Q8g_1, na.rm = TRUE),
'Peer recommendations' = mean(Q8h_1, na.rm = TRUE),
'Easy to understand content' = mean(Q8i_1, na.rm = TRUE)
)
print(mydata_summary6)
## # A tibble: 3 × 10
## `mydata$Q28` `Smaller minimal investment` `Smaller management fees`
## <fct> <dbl> <dbl>
## 1 Urban 3.36 4.07
## 2 Suburban 2.91 4
## 3 Rural 3.79 4.07
## # ℹ 7 more variables: `Clearer information about risk and return` <dbl>,
## # `Better user experience` <dbl>, `ESG focus` <dbl>,
## # `Transparent investment strategy` <dbl>, `Good customer support` <dbl>,
## # `Peer recommendations` <dbl>, `Easy to understand content` <dbl>
library(tidyr)
library(ggplot2)
library(dplyr)
# Rename the grouping column to "Group" for clarity
mydata_summary6 <- mydata_summary6 %>%
rename(Group = `mydata$Q28`) # This is crucial
# Pivot longer for ggplot
mydata_long6 <- mydata_summary6 %>%
pivot_longer(cols = -Group, names_to = "variable", values_to = "mean_score")
# Plot
ggplot(mydata_long6, aes(x = variable, y = mean_score, fill = Group)) +
geom_col(position = "dodge") +
theme_minimal() +
labs(
title = "Likelyhood of investments into mutual funds based on features by area of living",
x = "Perception Attribute",
y = "Average Score"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("~/Program R/Strategic Management/AreaOfLiving.png", width = 12, height = 6, dpi = 300)
library(dplyr)
mydata_summary7 <- mydata %>%
group_by(mydata$Q29) %>%
summarise(
'Smaller minimal investment' = mean(Q8a_1, na.rm = TRUE),
'Smaller management fees' = mean(Q8b_1, na.rm = TRUE),
'Clearer information about risk and return' = mean(Q8c_1, na.rm = TRUE),
'Better user experience' = mean(Q8d_1, na.rm = TRUE),
'ESG focus' = mean(Q8e_1, na.rm = TRUE),
'Transparent investment strategy' = mean(Q8f_1, na.rm = TRUE),
'Good customer support' = mean(Q8g_1, na.rm = TRUE),
'Peer recommendations' = mean(Q8h_1, na.rm = TRUE),
'Easy to understand content' = mean(Q8i_1, na.rm = TRUE)
)
print(mydata_summary7)
## # A tibble: 6 × 10
## `mydata$Q29` `Smaller minimal investment` `Smaller management fees`
## <fct> <dbl> <dbl>
## 1 Max 500 3.38 4.11
## 2 500-1000 3.10 4.15
## 3 1000-1500 3.47 3.98
## 4 1500-2000 3.04 4.02
## 5 Min 2000 3.45 4
## 6 Dont say 3.56 3.89
## # ℹ 7 more variables: `Clearer information about risk and return` <dbl>,
## # `Better user experience` <dbl>, `ESG focus` <dbl>,
## # `Transparent investment strategy` <dbl>, `Good customer support` <dbl>,
## # `Peer recommendations` <dbl>, `Easy to understand content` <dbl>
library(tidyr)
library(ggplot2)
library(dplyr)
# Rename the grouping column to "Group" for clarity
mydata_summary7 <- mydata_summary7 %>%
rename(Group = `mydata$Q29`) # This is crucial
# Pivot longer for ggplot
mydata_long7 <- mydata_summary7 %>%
pivot_longer(cols = -Group, names_to = "variable", values_to = "mean_score")
# Plot
ggplot(mydata_long7, aes(x = variable, y = mean_score, fill = Group)) +
geom_col(position = "dodge") +
theme_minimal() +
labs(
title = "Likelyhood of investments into mutual funds based on features by average net monthly income",
x = "Perception Attribute",
y = "Average Score"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
ggsave("~/Program R/Strategic Management/NetIncome.png", width = 12, height = 6, dpi = 300)