(IEA, Gender and Energy, IEA, Paris https://www.iea.org/data-and-statistics/data-product/gender-and-energy, Licence: CC BY 4.0)
In this post, I am going to analyze/paly around with IEA Gender and Energy Employment data. I downloaded CSV file from Gender and Energy - Data product - IEA .
The CSV file is like below.
I use R to analyze data.
I use tidyverse package.
Then, I use read_csv() function to load the CSV file into R.
I see the data frame has 22622 rows and 10 columns.
Let's use glimpse() function.
Let's use summary() function.
I see "update_at" has only one value, so I can remove it.
I will modify some variable names such as "Number of observatios" and convert character strings variables to factor type.
Let's use summary() to see how df looks like.
I see Topic has only one value, Employent, and Unit has only one value, Percent.
So, I remove the both.
Let's see numerica variables summary.
I found the data year is from 2010 to 2018. value, it is in percent is from -98 to 158, number of observations is from 11 to 1157008.
All right, I finished the first analys. Thank you!
Next post is below.
Below is R code of this post.
#
# load tidyverse package
library(tidyverse)
#
# Load the CSV file
df_raw <- read_csv("IEA_Gender_Energey_Employment.csv")
#
# glimpse
glimpse(df_raw)
#
# summary
summary(df_raw)
#
# make some changed to df_raw
df <- df_raw |>
select(-updated_at) |>
rename(sector = `Technology or Sector`,
category = `Indicator Categories`,
obs = `Number of observations`) |>
mutate(
across(where(is.character), as.factor)
)
#
# summary
summary(df)
#
# remove Topix and Unit.
df <- df |>
select(-Topic, -Unit)
#
# numeric variables summary
df |>
select(where(is.numeric)) |>
summary()
#
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